MEMORANDUM

SUBJECT: Case Study Analyses of Potential Local-scale Human Health Risks Associated 

with Mercury Emissions from Electric Utility Steam-generating Units

FROM: Dave Guinnup, Air Toxics Assessment Group (C539-02)

TO: Docket EPA-HQ-OAR-2009-0234

DATE: March 16, 2011

The attached document consists of two site-specific case study risk assessments that were 

conducted to assess the potential near-field (i.e., local) exposures and health risks associated with 

mercury emissions from an individual electric utility steam generating unit (EGU) facility as a 

result of consuming fish caught in a nearby lake. Ingestion of fish is the exposure pathway likely

to pose the highest near-field health risks associated with mercury emissions from individual 

facilities. We selected the case study facilities based on data the Agency had collected as the 

result of an information collection request (ICR). In the ICR, we collected mercury emissions 

data from about 330 of the 1,100 units. Because the ICR data were collected for the purposes of 

developing maximum achievable control technology (MACT) standards, the ICR was targeted 

toward better performing sources, with a small set of other randomly selected sources. In 

selecting the case study locations, we considered proximity of the facilities to fishable lakes, 

magnitude of mercury emissions, and other criteria. The local-scale assessments captured 

mercury deposition impacts within 50 km of each of the case study facilities.

Results from the two case studies suggest that for these two facilities, risks associated 

with local mercury exposures may be relatively low, and there may be several reasons for this. 

Because elemental mercury does not readily deposit, local deposition of elemental mercury is 

low. Divalent and particle-bound mercury more readily deposit locally. Since not all of the 

mercury emitted by EGUs is in the divalent and particle bound forms, not all is expected to 

deposit locally. Further, the emissions from EGUs are generally released from very tall stacks 

and are buoyant because of the high temperatures of the releases. This can result in significant 

dispersion of pollutants and low local deposition, even for the divalent and particle bound 

mercury (i.e., much of the deposition likely occurs outside the 50 km modeling domain). As a 

result, we are not certain that the near-field deposition captures the majority of the risk associated 

with the case-study facilities. Further consideration of the design of the case-studies has also 

called into question our ability to draw any conclusions regarding the mercury risks posed by 

these facilities, and as a result, these case studies are not being used to support any aspect of 

rulemaking.

Specifically, there is the potential that the two case studies do not actually capture 

facilities with some of the highest near- field impacts (this issue is already discussed in the 

attached document). Another critical limitation is the fact that each case study only considered 

emissions and impacts from that particular facility (i.e., single-facility near-field impacts). The 

case studies do not cover the potential for combined deposition over specific watersheds from 

multiple facilities located in the same region, or the combination of mercury deposition from 
U.S. EGU sources with mercury deposition from domestic non-EGU sources and non-US 

sources. Analysis completed in support of other elements of the Toxics Rule (specifically the 

national-scale mercury risk assessment completed in support of the appropriate and necessary 

determination USEPA, 2011), have demonstrated the importance of mercury deposition from 

multiple US EGUs acting in concert with each other, and with deposition from other sources 

within and outside the US. Specifically, air quality modeling completed for these analyses has 

shown that there are regions of the country (e.g., the Ohio River Valley) where US EGU-sourced 

mercury deposition can be substantially elevated over general national trends (see Section 2.3 of 

the Mercury Risk TSD, USEPA, 2011). This increased deposition occurs due to a number of 

factors including meteorology and topography, but it also reflects the combined impacts of 

multiple US EGUs. The potentially important role of multiple facility impacts on driving risk 

needs to be considered when interpreting the risk estimates generated for these two case studies. 

Although risk estimates for each facility when considered in isolation may be relatively low, the 

combined impact of multiple facilities on regional watersheds can be substantially greater, as 

shown in our national-scale analyses. 

Although the attached document was not used to determine whether it is appropriate and 

necessary to regulate coal- and oil-fired EGUs under CAA section 112, it is being included in the 

docket for completeness to present all the recent analyses that were performed for EGUs.

Attachment
Technical Support Document:

Case Study Analyses of Potential Local-scale Human Health 

Risks Associated with Mercury Emissions from Electric 

Utility Steam-generating Units

DRAFT

March 16, 2011

Prepared for:

Terri Hollingsworth, Work Assignment Manager

Office of Air Quality Planning and Standards

U.S. Environmental Protection Agency

Research Triangle Park, NC 27711

Contract No. EP-D-06-115

Work Assignment No. 4-02

Prepared by:

ICF International

2222 East NC-54 

Beta Building, Suite 480

Durham, NC 27713
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2/17/2011 iii DRAFT

TABLE OF CONTENTS

1 Introduction.......................................................................................................................... 1

2 Overall Approach and Scope of the Assessment .............................................................. 2

2.1 Conceptual Approach...................................................................................................... 2

2.2 Exposure Scenarios ........................................................................................................ 3

3 Methodology......................................................................................................................... 4

3.1 Air Modeling (AERMOD) ................................................................................................. 6

3.1.1 AERMOD Deposition Modeling........................................................................... 6

3.1.2 Incorporating AERMOD Results into TRIM.FaTE................................................ 8

3.2 Fate and Transport Modeling (TRIM.FaTE)..................................................................... 9

3.2.1 Facility and Site Descriptions .............................................................................. 9

3.2.2 Chemical Properties...........................................................................................14

3.2.3 Meteorological Data...........................................................................................14

3.2.4 Extent and Dimensions of Modeled Environment...............................................16

3.2.5 TRIM.FaTE Parcel Design .................................................................................19

3.2.5.1 Modeling Spatial Extent and Modeled Water Bodies ..............................19

3.2.5.2 Source Parcel.........................................................................................22

3.2.5.3 Layout of Watershed Surface Parcels and Outer Surface Parcels ..........24

3.2.5.4 Surface Parcel Vegetation Types ...........................................................26

3.2.5.5 Air Parcel Layout ....................................................................................30

3.2.6 Abiotic Environment...........................................................................................33

3.2.6.1 Soil Properties........................................................................................33

3.2.6.2 Erosion...................................................................................................34

3.2.6.3 Runoff.....................................................................................................35

3.2.6.4 Surface Water and Sediment Properties.................................................35

3.2.6.5 Water Transfers......................................................................................36

3.2.6.6 Sediment................................................................................................37

3.2.7 Biotic Environment.............................................................................................37

3.2.7.1 Terrestrial Plants ....................................................................................37

3.2.7.2 Aquatic Ecosystems ...............................................................................38

3.3 Exposure and Risk Calculations.....................................................................................40

3.3.1 Ingestion Exposure Assessment........................................................................40

3.3.1.1 Exposure Scenarios and Corresponding Inputs......................................41

3.3.1.2 Calculating Average Daily Doses............................................................43

3.3.2 Calculation of the Hazard Quotient ....................................................................44

4 Modeling Results and Risk Characterization ....................................................................45

4.1 Estimated Media Concentrations....................................................................................45

4.2 Human Health Risk Assessment Results .......................................................................49

4.3 Discussion of Uncertainties and Limitations ...................................................................51

4.3.1 Uncertainties Related to Fate and Transport Modeling (AERMOD and 

TRIM.FaTE)......................................................................................................53

4.3.2 Uncertainties Related to Exposure Modeling and Risk Calculation ....................56

5 References...........................................................................................................................58

Appendix A. Detailed TRIM.FaTE Parameter Values
2/17/2011 iv DRAFT

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2/17/2011 v DRAFT

LIST OF TABLES

Table 3-1. Mercury Emissions for Modeled EGUs..................................................................................13

Table 3-2. Divalent Mercury Emissions Compared with RTR PB-HAP Screening Thresholds ..............14

Table 3-3. Average Meteorological Conditions Near TVA Gallatin and Santee Cooper Jefferies.........15

Table 3-4. Water Bodies Included in the Modeled Regions of the TVA Gallatin and Santee 

Cooper Jefferies Facilities......................................................................................................22

Table 3-5. TRIM.FaTE Properties that Vary by Vegetation Type ...........................................................27

Table 3-6. Soil and Groundwater Compartment Depths.........................................................................34

Table 3-7. Selected Properties of Soil and Groundwater Compartmentsa .............................................34

Table 3-8. Selected Surface Water and Sediment Properties ................................................................35

Table 3-9. Lake Parameter Values for Modeled Water Bodies ..............................................................36

Table 3-10. Biomass Parameters for Old Hickory Lake near TVA Gallatin Facility..................................39

Table 3-11. Biomass Parameters for Lake Moultrie near Santee Cooper Jefferies ..................................39

Table 3-12. Overview of Exposure Factors used for Fish Ingestion Exposure Scenarios ........................42

Table 3-13. Exposure Duration and Mean Body Weight Estimates for Adults and Children.....................42

Table 3-14. 90th Percentile and Mean Fish Ingestion Rates Representative of Individuals in U.S. 

Population Consuming Fish...................................................................................................43

Table 3-15. 90th Percentile and Mean Fish Ingestion Rates for Additional Fish Consumers 

Evaluated ...............................................................................................................................43

Table 4-1. Modeled Mercury Concentrations and Speciation for Lake Moultrie, SC, near 

Santee Cooper Jefferies ........................................................................................................46

Table 4-2. Modeled Mercury Concentrations and Speciation for Eastern Branch of Old 

Hickory Lake, TN, near TVA Gallatin.....................................................................................46

Table 4-3. Hazard Quotients for Exposure to Methyl Mercury via Fish Ingestion ..................................51

Table 4-4. Uncertainty Factors in the Multipathway Residual Risk Assessment for EGUs ....................52

Table 4-5. Sensitivity Analysis for Fish Ingestion Rate...........................................................................57
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2/17/2011 vii DRAFT

LIST OF FIGURES

Figure 2-1. Conceptual Exposure Model for Fisher Scenario................................................................. 3

Figure 3-1. Overview of Case Study Modeling Methodology.................................................................. 4

Figure 3-2. Overview of the Multimedia Ingestion Risk Calculator .........................................................6

Figure 3-3. AERMOD Receptor Grid and TRIM.FaTE Air and Surface Parcels for TVA Gallatin ..........7

Figure 3-4. AERMOD Receptor Grid and TRIM.FaTE Air and Surface Parcels for Santee 

Cooper Jefferies ................................................................................................................... 8

Figure 3-5. Site Vicinity Map: TVA Gallatin Facility..............................................................................10

Figure 3-6. Wind Roses Representing Wind Flow Conditions Near the TVA Gallatin Facility .............11

Figure 3-7. Site Vicinity Map: Santee Cooper Jefferies Facility...........................................................12

Figure 3-8. Wind Roses Representing Wind Flow Conditions Near the Santee Cooper Jefferies 

Facility.................................................................................................................................13

Figure 3-9. Surface Water Bodies and Watersheds near TVA Gallatin................................................17

Figure 3-10. Land Use and Watersheds Near TVA Gallatin...................................................................17

Figure 3-11. Surface Water Bodies and Watersheds near Santee Cooper Jefferies .............................18

Figure 3-12. Land Use and Watersheds near Santee Cooper Jefferies.................................................19

Figure 3-13. Modeled Water Bodies and Modeling Extent for TVA Gallatin...........................................20

Figure 3-14. Modeled Water Bodies and Modeling Extent for Santee Cooper Jefferies ........................21

Figure 3-15. Location of Source Parcel for TVA Gallatin........................................................................23

Figure 3-16. Location of Source Parcel for Santee Cooper Jefferies .....................................................24

Figure 3-17. Surface Parcels for TVA Gallatin........................................................................................25

Figure 3-18. Surface Parcels for Santee Cooper Jefferies .....................................................................26

Figure 3-19. TRIM Vegetation Types for the TVA Gallatin Facility .........................................................29

Figure 3-20. TRIM Vegetation Types for the Santee Cooper Jefferies Facility ......................................30

Figure 3-21. Air Parcels for the TVA Gallatin Facility..............................................................................31

Figure 3-22. Air Parcels for the TVA Gallatin Facility, Overlaid with the Surface Parcels ......................32

Figure 3-23. Air Parcels for the Santee Cooper Jefferies Facility...........................................................32

Figure 3-24. Air Parcels for the Santee Cooper Jefferies Facility, Overlaid with the Surface 

Parcels................................................................................................................................33

Figure 3-25. Aquatic Food Web and Diet Fractions................................................................................40

Figure 4-1. Comparison of Calculated Bioaccumulation Factors Based on Model Outputs 

to Literature.........................................................................................................................48

Figure 4-2. Source-attributable Hazard Quotients for Exposure to Methyl Mercury for 

Recreational Angler Scenarios, RME Ingestion Rate.........................................................50

Figure 4-3. Source-attributable Hazard Quotients for Exposure to Methyl Mercury for Other 

Angler Populations, RME Ingestion Rate ...........................................................................50
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2/17/2011 1 DRAFT

1 Introduction

This technical report documents the approach and results of two case study analyses 

conducted for EPA by ICF to evaluate the potential for near-field human health risks resulting 

from mercury emissions from electric utility steam-generating units (EGUs). Section 112(a)(8) 

of the Clean Air Act defines an EGU as: (1) any fossil-fuel-fired combustion unit of more than 

25 megawatts electric (MWe) that serves a generator that produces electricity for sale; or (2) a

unit that cogenerates steam and electricity and supplies more than one-third of its potential 

electric output capacity and more than 25 MWe output to any utility power distribution system for 

sale.

The analysis consisted of two site-specific case-study risk assessments that assessed the 

potential near-field (i.e., local) exposures to mercury emissions from an EGU source via 

ingestion of fish caught in a nearby lake. This exposure pathway is believed a priori to pose the 

highest near-field health risks associated with mercury emissions from such a facility. The 

facilities subject to evaluation in this analysis were selected by EPA based on (a) relatively high 

divalent Hg emissions from facilities with available ICR test data, (b) presence of waterbodies 

(preferably moderate-sized stationary lakes) and, (c) the potential for subsistence fishing activity 

at those lakes. 

There is the potential that the two case studies, selected for this analysis, do not actually 

capture facilities with some of the highest near- field impacts. Another critical limitation is the 

fact that each case study only considered emissions and impacts from that particular facility 

(i.e., single-facility near-field impacts). The case studies do not cover the potential for combined 

deposition over specific watersheds from multiple facilities located in the same region, or the 

combination of mercury deposition from U.S. EGU sources with mercury deposition from 

domestic non-EGU sources and non-US sources. Analysis completed in support of other 

elements of the Toxics Rule (specifically the Technical Support Document: National-scale 

Mercury Risk Assessment Supporting the Appropriate and Necessary Finding for Coal- and Oilfired Electric Generating Units – DRAFT (USEPA, 2011)) have demonstrated the importance of 

mercury deposition from multiple US EGUs acting in concert with each other, and with 

deposition from other sources within and outside the US. Specifically, air quality modeling 

completed for these analyses has shown that there are regions of the country (e.g., the Ohio 

River Valley) where US EGU-sourced mercury deposition can be substantially elevated over 

general national trends (see Section 2.3 of the Mercury Risk TSD, USEPA, 2011). This 

increased deposition occurs due to a number of factors including meteorology and topography, 

but it also reflects the combined impacts of multiple US EGUs. The potentially important role of 

multiple facility impacts on driving risk needs to be considered when interpreting the risk 

estimates generated for these two case studies. Although risk estimates for each facility when 

considered in isolation may be relatively low, the combined impact of multiple facilities on 

regional watersheds can be substantially greater, as shown in our national-scale analyses

(USEPA, 2011).

This report presents the approach, assumptions, model inputs, and results of the assessments. 

Section 2 describes the conceptual approach, defines the exposure scenario evaluated, and 

provides an overview of the modeling approach. Section 3 provides a detailed description of the 

assessment methodology, including the modeling approach used to simulate the fate and 

transport of emitted mercury in the environment and estimate human exposure levels and the 

associated heath risks. Results of the assessment are presented in Section 4, along with a 

discussion of sources of uncertainty in the data and methods. Appendix A provides parameter 

values input to TRIM.FaTE for the model applications.
2/17/2011 2 DRAFT

2 Overall Approach and Scope of the Assessment

These case studies evaluated the incremental risks from near-field human exposure to methyl 

mercury via consumption of fish contaminated with mercury emitted from EGUs. In other words, 

only EGU source-attributable exposures and the associated risks were evaluated. Exposures to 

mercury from any other sources, including background mercury present in the environment, 

were not included in the assessment. The chemical and exposure modeling approach used for 

these risk assessments was generally consistent with the methods EPA uses to evaluate 

residual risks for source categories under the Risk and Technology Review (RTR) program, with 

the notable exception of using AERMOD to estimate air-to-surface deposition of emitted 

mercury (see Section 3.1.1). 

The remainder of this section provides a general overview of the conceptual approach and the

exposure scenarios that were evaluated. Detailed discussions of the modeling efforts are 

provided in Section 3. 

2.1 Conceptual Approach

Because mercury has a relatively long half-life in the atmosphere, the majority of mercury 

emitted to the air by EGUs and other sources contributes primarily to the regional/global burden. 

Some forms of mercury, however, can readily deposit to land areas and surface water bodies 

located near a source. Consequently, the contamination of ecosystems in the vicinity of major 

sources of mercury emissions is a concern. In particular, the accumulation of mercury in water 

bodies derived from near-field sources is a concern because inorganic mercury deposited to a 

water body (and the surrounding watershed) can be transformed to methyl mercury via 

biologically-mediated processes. Methyl mercury, which has been demonstrated to cause 

adverse effects in humans, readily bioaccumulates in aquatic organisms. As a consequence, 

elevated methyl mercury concentrations can result in game fish residing at the top of the food 

chain. Consumption of fish contaminated with methyl mercury is believed to be a primary route 

of exposure to methyl mercury for humans and can be a particular concern for individuals that 

consume large quantities of fish.

The objective of these case studies was to estimate the potential for near-field adverse impacts 

on human health via this exposure pathway (i.e., through the consumption of fish that have 

accumulated methyl mercury derived from local EGU emissions). This was accomplished by 

conducting scenario-based exposure assessments that estimated potential exposures and the 

resulting hazard quotients (HQs) for individuals consuming fish caught in a lake in the vicinity of 

an EGU source. The modeling approach for the analyses used AERMOD to model the 

dispersion and transport of mercury emissions in the atmosphere and subsequent deposition to 

land and water surfaces. These deposition rates were input into TRIM.FaTE, which was used to 

model the subsequent fate and transport within the environment, including transformation to 

methyl mercury and bioaccumulation in an aquatic food web. Exposures for individuals 

consuming fish were estimated based on assumptions regarding quantity and type of fish (i.e., 

carnivorous or omnivorous) caught locally and consumed. The resulting individual exposure 

estimates were used to calculate incremental HQs for exposure to methyl mercury.

Two facilities were selected by EPA for evaluation in the near-field case studies conducted by 

ICF:

• TVA Gallatin, near Gallatin, TN (NEI facility ID NEI8373); and
2/17/2011 3 DRAFT

• Santee Cooper Jefferies, near Moncks Corner, SC (NEI38327). 

Both of these facilities have water bodies located nearby that are of sufficient size to support 

populations of upper-trophic-level fish. 

2.2 Exposure Scenarios

In a scenario-based exposure assessment, one or more plausible scenarios are evaluated for 

the exposure situation of interest. The assessment usually focuses on those scenarios that are 

assumed a priori to lead to the highest individual exposure and risks. Risk metrics, such as 

incremental lifetime cancer risk and chronic non-cancer hazard quotient, are then calculated for 

potentially exposed individuals for each scenario. As appropriate, information regarding the 

likelihood of a specific exposure scenario actually occurring can be used to develop estimates of 

uncertainty for each scenario and the variations thereof. For the mercury case studies 

described in this report, exposure estimates and risks were calculated for scenarios involving 

individuals who regularly consume fish caught in freshwater lakes in the vicinity of the source of 

interest. Figure 2-1 presents the conceptual exposure model for this scenario. The exposed 

individual consumes fish from a water body impacted by a near-field source of mercury 

emissions (i.e., one of the two EGUs evaluated). This scenario is expected to cover the highest 

possible individual exposures and human health risks associated with mercury emissions from 

an EGU. 

Figure 2-1. Conceptual Exposure Model for Fisher Scenario

Variations of this basic fish consumption scenario were evaluated using different assumptions 

about the water body from which fish were caught, the age of the individual exposed, and the 

fish ingestion rate. Exposures were calculated for five age groups for a fish consumer in the 
2/17/2011 4 DRAFT

U.S. ingestion scenario (identified as a “recreational angler”), with fish consumption rates 

representing members of the U.S. population who consume some fish. Additionally, exposures 

were calculated for five populations of adults who are culturally or economically disposed to 

higher rates of fish consumption, including African American, female, and individuals of 

Hispanic, Laotian, and Vietnamese descent. HQs were then calculated for each of the 

populations evaluated.

3 Methodology

Figure 3-1 provides an overview of the three-step methodology used to assess multipathway 

risks from EGU emissions of mercury. 

1. Modeling of transport in air of emitted elemental and divalent mercury and 

subsequent dry and wet deposition to soil and water using AERMOD;

2. Modeling of mercury fate and transport in the physical environment and biological 

ecosystem subsequent to deposition, including chemical transformations (e.g., 

methylation) and uptake and cycling in the aquatic food web using TRIM.FaTE; and

3. Estimating the resulting source-attributable ingestion exposures for individuals 

consuming fish from a local lake and calculating the associated HQs using MIRC.

This specific combination of models was used per EPA recommendation to estimate exposures 

and HQs for the scenarios included in the EGU case studies. The overall modeling approach is 

based on the risk assessment methodology applied to evaluate multipathway exposures for 

EPA’s residual risk assessments of emissions of air toxics, but with the use of AERMOD (rather 

than TRIM.FaTE) to estimate air-to-surface deposition. A brief description of each of these 

three models is presented in this introductory section. The configuration and application of 

these models for the EGU case studies are then described in the remainder of Section 3.

Figure 3-1. Overview of Case Study Modeling Methodology
2/17/2011 5 DRAFT

AERMOD

The deposition fluxes from air to surfaces were estimated by EPA using the AMS/EPA 

Regulatory Model, or AERMOD (Version 09292) (U.S. EPA 2010a), for a selected modeling 

zone around each facility. In 2005, AERMOD became the preferred model for near-field (less 

than 50 km) dispersion. Because these case studies were designed to evaluate near-field 

impacts associated with facility emissions of mercury, AERMOD was the appropriate model for 

these studies. 

TRIM.FaTE

ICF then used the AERMOD outputs to develop chemical source terms for input into the Fate, 

Transport, and Ecological Exposure module of the TRIM modeling system (TRIM.FaTE, Version 

3.6.2). TRIM.FaTE is a spatially explicit, compartmental mass balance model that describes the 

movement and transformation of pollutants over time, through a user-defined, bounded system 

that includes both biotic and abiotic compartments. Outputs include pollutant concentrations in 

multiple environmental media and biota, which provide exposure estimates for ecological 

receptors (i.e., plants and animals). Significant features of TRIM.FaTE include: (1) a fully 

coupled multimedia model; (2) user flexibility in defining scenarios in terms of the links among 

compartments and number and types of compartments, as appropriate, for the application 

spatial and temporal scale; (3) a transparent, user-accessible algorithm and input library that 

allows the user to review and modify how environmental transfer and transformation processes 

are modeled; (4) a full accounting of emitted pollutant mass as it moves among environmental 

compartments during a simulation; (5) an embedded procedure to characterize uncertainty and 

variability; and (6) the capability to provide exposure estimates for ecological receptors. 

Additional information about TRIM.FaTE, including support documentation, software, and the 

TRIM.FaTE public reference library, is available on EPA’s Technology Transfer Network (TTN)

(U.S. EPA 2010b). The fate and transport modeling conducted for the EGU case studies is 

discussed in Section 3.2.

MIRC

The Multimedia Ingestion Risk Calculator (MIRC), a Microsoft Access-based computer 

framework developed by ICF primarily for use in EPA’s residual risk assessments, was used to 

complete the calculations required for estimating mercury concentrations in aquatic media, 

average daily ingestion doses, and chronic non-cancer HQs. The algorithms included in MIRC 

are based largely on the exposure equations presented by EPA in the Human Health Risk 

Assessment Protocol for Hazardous Waste Combustion Facilities (HHRAP; EPA 2005) (U.S. 

EPA 2005a). The implementation of these algorithms in MIRC is described in detail in Appendix 

C (Attachment C-2) to EPA’s report to SAB on the risk assessment methods used in the RTR 

program (U.S. EPA 2009). These algorithms, and the required exposure factors and other 

parameter values, were compiled into a database. An overview of the computational processes 

this tool carries out and the types of input data it requires is presented in Figure 3-2. This figure 

demonstrates the general relationships between the relevant TRIM.FaTE outputs (i.e., chemical 

concentrations in environmental media and fish) and the ingestion exposure and risk 

calculations carried out using MIRC. A description of the exposure and risk calculations for this 

screening scenario is presented in Section 3.3. 
2/17/2011 6 DRAFT

Figure 3-2. Overview of the Multimedia Ingestion Risk Calculator

3.1 Air Modeling (AERMOD)

The AERMOD steady-state plume model was used to model deposition for this analysis. This 

section describes the AERMOD modeling conducted and the method used to incorporate 

AERMOD results into site applications of TRIM.FaTE.

3.1.1 AERMOD Deposition Modeling

For each of the two facilities selected for the case study analyses, EPA used AERMOD to 

estimate deposition fluxes for input to TRIM.FaTE. EPA relied on guidance included in the 

Guideline on Air Quality Models, also published as Appendix W of 40 CFR Part 51, to determine 

model set-up and application (40 U.S. Code of Federal Regulations [CFR] Part 51. Appendix 

W, Guideline on Air Quality Models. [Current through 2/11/2011]). Appendix W is the primary 

source of information on the regulatory application of air quality models for State Implementation 

Plan (SIP) revisions for existing sources and to new source reviews (NSR), including prevention 

of significant deterioration (PSD). 

EPA conducted the AERMOD deposition modeling with input from ICF. Although the case 

studies’ modeling are not regulatory applications of AERMOD, much of the guidance in the 

Guideline are still applicable and were applied to this modeling, including: (1) use of five years 

of representative meteorological data (years 2005 through 2009), (2) inclusion of terrain 

elevations for sources and receptors, (3) use of urban dispersion where appropriate, and (4) 

inclusion of building downwash where applicable. AERMOD emissions inputs were hourly 

emissions based on information collection request (ICR) emissions data provided for each boiler 

for elemental mercury, divalent particulate mercury (Santee Cooper Jefferies only), and 

gaseous divalent mercury. 
2/17/2011 7 DRAFT

Deposition fluxes were calculated for a grid of receptors around the facilities that encompassed 

nearby water bodies of interest. Nested square grids with resolutions of 250 m and 1000 m

between points were used. The 250 m resolution grid encompassed the water bodies 

parameterized for TRIM.FaTE modeling (and small buffer areas surrounding them) and for all 

areas within 5 km of the TRIM.FaTE source parcel surrounding the EGU facilities. This ensured 

that even the smallest surface parcels of approximately 500 m by 500 m had at least two

receptors. The 1000 m resolution was used for all other areas within the TRIM.FaTE modeling 

extent. The receptor grids for the TVA Gallatin and the Santee Cooper Jefferies facilities are 

shown in Figures 3-3 and 3-4, respectively.

There were two exceptions to this receptor grid setup. First, at the time that the AERMOD 

receptor grid was designed, Lake Marion had not been selected to be included in the 

TRIM.FaTE model scenario, and therefore the grid spacing over Lake Marion was 1000 m 

rather than the 250 m used for other water bodies. Lake Marion contained dozens of receptors, 

however, and this resolution was deemed acceptable for the modeling application. Second, 

because the receptor setup was designed before the TRIM.FaTE modeling extent was finalized, 

some areas near the outside border did not have receptors. However, the outer-extent 

TRIM.FaTE surface parcels still contained dozens to hundreds of receptor grid points. This 

second exception was also considered acceptable because the deposition fluxes were small at 

these distances from the facility (compared to areas nearby the facility) and because it was 

expected to lead to very small (and likely health conservative) impacts on multipathway 

modeling results. 

Figure 3-3. AERMOD Receptor Grid and TRIM.FaTE Air and Surface Parcels for TVA 

Gallatin
2/17/2011 8 DRAFT

Figure 3-4. AERMOD Receptor Grid and TRIM.FaTE Air and Surface Parcels for Santee 

Cooper Jefferies 

3.1.2 Incorporating AERMOD Results into TRIM.FaTE

Using the receptor grid described in Section 3.1.1, AERMOD produced hundreds of deposition 

estimates for each TRIM.FaTE surface compartment. To aggregate these outputs for input to 

TRIM.FaTE, an average deposition flux for each TRIM.FaTE surface parcel was computed from 

the point estimates within each parcel (where each receptor represented an area dictated by its 

receptor grid spacing). Area-weighted averages were used to ensure that varying receptor 

densities between and within parcels were properly accounted for. For a surface parcel that 

had an area without receptors, the parcel’s area-weighted deposition flux was calculated by 

ignoring the area without receptors; this is expected to result in a small overestimation of parcel 

deposition flux, compared to if receptors had covered the whole parcel. 

AERMOD deposition fluxes were calculated on an hourly basis by the model. These outputs 

were averaged across the entire five-year simulation period to produce a long-term overall 

average deposition for each modeled grid point. 

In order to insert AERMOD average deposition values in place of TRIM.FaTE-modeled 

deposition fluxes, several modifications to the model scenarios and libraries were required. A 

pseudo-source was created to represent the AERMOD-derived deposition rate for each parcel, 

with an emission rate equal to the product of the parcel surface area and the spatially averaged 

deposition flux. Because the deposition fluxes are mass per unit area per unit time, the pseudosource emission rates were set equal to the total chemical mass deposited onto the surface of 

each parcel each time period. Separate pseudo-sources were created for each deposition type: 

wet and dry, vapor phase and particulate. In this way, the TRIM.FaTE processes of air 
2/17/2011 9 DRAFT

transport and deposition were replaced by “emissions” directly into surface soil, plant, and 

surface water compartments.

Pseudo-sources were assigned to placeholder volume elements and linked to transfer mass to 

the appropriate surface compartments. For water parcels, the entire mass is transferred to the 

surface water compartment. For land parcels, algorithms apportioning mass between surface 

soil and leaves were derived from the existing FaTE algorithms for air-soil and air-plant 

transfers. The placeholder volume elements were designed to prevent transfer to any 

compartment other than those prescribed, and each contained only one pseudo-source.

Once the pseudo-source set-up was designed and tested, the process of making necessary 

revisions to the FaTE scenarios and libraries was automated to reduce the likelihood of error. 

With four pseudo-sources (representing dry/wet and vapor/particle deposition) for each parcel, 

along with the corresponding placeholder compartments, links, and algorithms, each scenario 

required input files several thousand lines long. Automation allowed the following process to be 

consistently applied across multiple scenarios:

1. Create a volume elements file by evaluating the scenario layout and defining the 

coordinates of four placeholder volume elements for each surface parcel, with one each 

for dry particle, dry vapor, wet particle, and wet vapor deposition.

2. Create a library import file with supplemental compartment types, property types, and 

algorithms used in linking placeholder volume elements to targeted surface 

compartments. This file also includes the definitions and locations of pseudo-sources 

with emission rate formulas accounting for parcel surface area.

3. Create a properties import file defining the actual links that connect the placeholder 

compartments to their surface targets. This includes determining which water, soil, 

and/or plant compartments are present on the surface of each parcel.

To complete the process, deposition rates (averaged as described above) were specified for 

each pseudo-source. At each time step of the TRIM.FaTE simulation, this system multiplies 

deposition by area and transfers the resulting mass to the appropriate surface compartments. 

In this assessment, constant, long-term average deposition rates were used. The mass transfer 

rates for output from pseudo-sources were time-varying because they rely on factors such as 

daylight and leaf coverage.

3.2 Fate and Transport Modeling (TRIM.FaTE)

This section describes the TRIM.FaTE modeling conducted for this assessment. Most of the 

material presented here describes the assumptions and data sources used to set TRIM.FaTE 

inputs and settings related to meteorological inputs used by the model, the spatial aspects of 

the modeled regions for each site, characteristics of abiotic environmental compartments and 

plants included in the analyses, and the aquatic ecosystems setup in each water body of 

interest. All of the user-supplied parameter values input to TRIM.FaTE for these model 

applications are documented in Appendix A to this report.

3.2.1 Facility and Site Descriptions

The TVA facility near Gallatin, TN (hereafter referred to as the TVA Gallatin facility) is located on 

the border of Wilson and Sumner Counties (est. 2009 populations 112,377 and 158,759, 

respectively [U.S. Census Bureau 2010]), about 9 km southeast of Gallatin and about 38 km 
2/17/2011 10 DRAFT

northeast of Nashville-Davidson (est. 2009 populations 30,504 and 605,473, respectively [U.S. 

Census Bureau 2010]). As illustrated in Figure 3-5, two large water bodies are located near the 

facility, including Old Hickory Lake (which meanders east to west past the facility, from 42 km 

east of the facility to 22 km west of the facility) and J. Percy Priest Reservoir (which is about 28 

km southwest of the facility). 

The Gallatin area is not arid, and the terrain nearby the facility ranges from about 140 m to 

350 m above sea level. Based on the representative meteorological data used in the 

TRIM.FaTE modeling, the portion of Old Hickory Lake adjacent to the facility is downwind from 

the facility close to 50 percent of the modeled hours, while the J. Percy Priest Reservoir is 

downwind from the facility about 27 percent of the modeled hours (see Figure 3-6a). The parts 

of Old Hickory Lake further west and east of the facility are downwind from the facility about 14 

and 20 percent of the time, respectively.

Figure 3-5. Site Vicinity Map: TVA Gallatin Facilitya

a Shading indicates land use type in 2001 (with simplified categories); data from 

USGS 2001.
2/17/2011 11 DRAFT

Figure 3-6. Wind Roses Representing Wind Flow Conditions Near the TVA Gallatin

Facility

Wind roses reflect surface hourly meteorological data used for AERMOD modeling 

from the Nashville International Airport (WBAN ID 13897) for years 2005-2008.

The area map for the Santee Cooper Jefferies facility near Moncks Corner, SC, is presented in 

Figure 3-7. The facility is located in Berkeley County (est. 2009 population 173,498 [U.S. 

Census Bureau 2010]) approximately 5 km north of the town of Moncks Corner (est. 2009 

population 7,266 [U.S. Census Bureau 2010]). Two large water bodies are located near the 

facility. Lake Moultrie is directly adjacent to the facility to the west and extends to 17 km west 

and north of the facility. Lake Marion is further to the northwest of Lake Moultrie and extends 

out to approximately 75 km northwest of the facility. The Upper and Lower Reservoirs are also 

nearby the facility (about 20 km southwest of the facility). Figure 3-7 shows these water bodies.

The Moncks Corner area is not arid. The terrain near the facility ranges from 0 m to 50 m above 

sea level. Lake Moultrie was downwind from the facility about 35 percent of modeled hours, 

while Lake Marion and the Upper and Lower Reservoirs were each downwind from the facility 

about 9 percent of the time (see Figure 3-8a). 

(b) Times with No Precipitation (c) Times With Precipitation

(a) All Times

 

 

 

 

 

 

 

 
2/17/2011 12 DRAFT

Figure 3-7. Site Vicinity Map: Santee Cooper Jefferies Facilitya

a Shading indicates land use type in 2001 (with simplified categories); data from 

USGS 2001.
2/17/2011 13 DRAFT

Figure 3-8. Wind Roses Representing Wind Flow Conditions Near the Santee Cooper 

Jefferies Facility

a From the surface hourly meteorological data from the Charleston International

Airport (WBAN ID 13880) for years 2005-2008.

Facility emissions for the two facilities included in the case studies are presented in Table 3-1. 

These emissions were not used as inputs to TRIM.FaTE (emissions to air and subsequent 

deposition were modeled using AERMOD) but are presented here for informational purposes.

Table 3-1. Mercury Emissions for Modeled EGUs

Facility Stack ID Emissions of Divalent 

Mercury (tons/yr)

Emissions of Elemental 

Mercury (tons/yr)

TVA Gallatin STK1 3.45E-02 5.38E-02

STK2 1.38E-02 3.88E-02

Santee Cooper 

Jefferiesa

STK1 1.42E-02 4.93E-03

STK2 1.43E-02 4.93E-03 a Particulate and vapor-phase divalent mercury emissions were modeled separately for this facility by EPA in the 

AERMOD portion of modeling. The divalent mercury emissions included in this table represent total emissions of 

divalent mercury (particulate+vapor).

(b) Times with No Precipitation (c) Times With Precipitation

(a) All Times

 

 

 

 

 

 

 

 
2/17/2011 14 DRAFT

Emission screening thresholds have been developed for divalent mercury for use in conducting 

multipathway residual risk assessments for EPA; these thresholds were compared to facility 

emissions to provide a point of comparison. Total reported emissions of divalent mercury used 

in AERMOD modeling for this facility exceed EPA’s current RTR divalent mercury screening

level by a modest margin, as illustrated in Table 3-2.

Table 3-2. Divalent Mercury Emissions Compared with RTR PB-HAP Screening 

Thresholds

Facility

Total Divalent Mercury 

Emissions from EGU

(tons/yr)

RTR Screening 

Level

(tons/yr)

Screening 

Results

TVA Gallatin 4.83E-02 1.946E-03 Exceeds threshold 

by ~25x

Santee Cooper Jefferies 2.85E-02 1.946E-03 Exceeds threshold 

by ~15x

3.2.2 Chemical Properties

Chemical/physical properties required by TRIM.FaTE, such as Henry’s law constant and 

molecular weight, were obtained from peer-reviewed and standard reference sources. 

Numerous other chemical-specific properties that are related to a particular abiotic or biotic 

compartment type are discussed in later sections.

3.2.3 Meteorological Data

TRIM.FaTE uses several meteorological parameters to determine chemical transfers among the 

air compartments in a modeling scenario via advective transport (i.e., wind-driven physical 

movement through the atmosphere) and from air to underlying soil or water surfaces via 

deposition transfers. In a typical TRIM.FaTE application, these processes determine the longterm spatial patterns of chemical distribution in air within the modeling analysis, and modeled 

concentrations are highly sensitive to the meteorological inputs used in TRIM.FaTE. For the 

current analyses, however, AERMOD was used to model air transport and subsequent 

deposition to soil and water surfaces. As a result, the algorithms that determine transport and 

fate in air (which were kept on in the current applications) only affected mercury re-emitted to air 

from soil, water, and plant surfaces. 

An initial step in developing the TRIM.FaTE applications for these case studies was the 

collection of meteorological data for the modeled regions. The long-term trends in these data 

were used to inform the development of the modeling layouts that were the bases of these 

modeling analyses. To maintain consistency with the development of the TRIM.FaTE 

application, we present in this section the specifications of the meteorological data and a 

summary of the long-term temporal trends. 

The meteorological inputs that TRIM.FaTE requires include wind speed, wind direction, 

precipitation, ambient air temperature, and mixing height. EPA modeled mercury deposition 

near these two sites using AERMOD with 2005-2009 meteorology data. Because the AERMOD 

deposition amounts were incorporated into this multipathway analysis (see Section 3.1), this 

multipathway analysis also used the same meteorology data as the AERMOD modeling. There 

was one exception to this – the AERMOD analyses used five years of meteorological data, but 

because the data must be repeated to create a 50-year record for modeling, and because leap 
2/17/2011 15 DRAFT

years occur every four years, the meteorological data were reduced to four years (2005-2008) 

to make it simpler to repeat. The meteorological data for the TVA Gallatin facility were the 

2005-2008 hourly surface data and twice-daily upper-air data from the Nashville International

Airport (WBAN ID 13897; 34 km southwest of the facility) (NOAA 2009a, 2009b). The 

meteorological data for the Santee Cooper Jefferies facility were the 2005-2008 hourly surface 

data and twice-daily upper-air data from the Charleston International Airport (WBAN ID 13880; 

38 km south of the facility) (NOAA 2009a, 2009b).

These meteorological data deviated from the 1971-2000 historical 30-year normal values (Table 

3-3) in ways that are similar to or smaller than those of the Ravena Portland Cement analysis 

for the 2009 EPA Science Advisory Board (SAB) review (U.S. EPA 2009). In the natural 

environment, rainfall amounts that are smaller than climatologically typical quantities in the TVA 

Gallatin and Santee Cooper Jefferies areas can have the effect of decreasing modeled wet 

deposition. Decreased rainfall may also increase chemical concentrations in water bodies by 

decreasing their volumes and flush rates, although the water bodies would also collect 

decreased amounts of chemicals from their tributaries. The relatively small deviations from 

normal (warmer and slightly drier for Nashville, drier and slightly warmer for Charleston) intrinsic 

in the data used for modeling were assumed to be acceptable for this assessment.

Table 3-3. Average Meteorological Conditions Near TVA Gallatin and Santee Cooper 

Jefferies

TVA Gallatin Santee Cooper Jefferies

Nashville International Airport 

(WBAN 13897)

Charleston International Airport 

(WBAN ID 13880)

Daily 

Max. 

Temp. 

(

◦

C)

Daily 

Min. 

Temp. 

(

◦

C)

Daily 

Mean 

Temp. 

(

◦

C)a

Total 

Precip. 

(mm)

Daily 

Max. 

Temp. 

(

◦

C)

Daily 

Min. 

Temp. 

(

◦

C)

Daily 

Mean 

Temp. 

(

◦

C)a

Total 

Precip. 

(mm)

Annual 1971-

2000 Climate 

Normal 

Averagesb

20.6 9.3 14.9 1222 24.4 12.6 18.5 1,309

Annual 

Averages from 

Data Used in 

This Analysis

21.5 10.6 16.1 1058 24.5 13.7 19.1 871

Deviation from 

Normal 

Averages

+0.9

(+4.4%)

+1.3

(+14.0%)

+1.2

(+8.1%)

-164

(-13.4%)

+0.1

(+0.4%)

+1.1

(+8.7%)

+0.6

(+3.2%)

-438

(-33.5%)

a The daily mean temperature is calculated as the average of the daily maximum and minimum temperatures. b 1971-2000 Climate Normals (NOAA 2005).

Calm winds occurred for about 15 percent of the modeled hours for the TVA Gallatin facility and 

about 13 percent of the modeled hours for the Santee Cooper Jefferies facility. In TRIM.FaTE 

modeling, calm winds cause chemical mass to build up in the source compartment and stagnate 

over the rest of the modeling region until the next positive wind speed. As such, all calm wind 

values were set to 0.75 m s-1 for modeling. 

As shown in Figures 3-6b and 3-6c for the TVA Gallatin facility, wind speeds during modeled 

precipitation events tended to be larger than during times without precipitation. The frequency 

distribution of wind directions was similar between times with precipitation and times without 

precipitation. Southerly winds dominated during modeled times without and with precipitation 

(37 and 46 percent of the time, respectively), so areas north of the facility should experience the 
2/17/2011 16 DRAFT

greatest dry and wet deposition. Irrespective of precipitation status, easterly winds occurred

least frequently (14 percent of the time overall), so areas west of the facility should experience 

the least total deposition. 

As shown in Figures 3-8b and 3-8c for the Santee Cooper Jefferies facility, wind speeds during 

modeled precipitation events tended to be larger than during times without precipitation. The 

frequency distribution of wind directions was somewhat different depending on modeled 

precipitation status. During times with no precipitation, winds from the north and northeast and 

from the south and west occurred with about the same frequency (between 13 and 18 percent

of the time), so areas northwest and southeast of the facility should experience the least dry 

deposition. During modeled precipitation events, northeasterly winds occurred most frequently 

and northwesterly winds occur least frequently (23 and 6 percent of the time, respectively), so 

areas southwest of the facility should experience the greatest wet deposition and areas 

southeast of the facility should experience the least wet deposition. 

3.2.4 Extent and Dimensions of Modeled Environment

This section describes the environment for which media concentrations were estimated using 

TRIM.FaTE and the geographic characteristics of the modeled environment (e.g., layout of the 

modeled domain and geometry of the constituents included). The design of the modeling layout 

was developed based primarily on the physical and geographic characteristics of the 

watersheds in the area and the land use data for the region. This section provides a brief 

overview of the features present in the vicinity of the modeled facilities.

As illustrated in Figure 3-9, the TVA Gallatin facility is located within the Lower Cumberland

Basin (HUC-051302), although the TRIM.FaTE modeling analysis for this facility also extends 

into the Upper Cumberland Basin (HUC-051301) and the Green Basin (HUC-051100). Rivers 

and streams in these basins drain northwestward or westward into the Ohio and Mississippi 

Rivers. Large and small lakes and reservoirs are located throughout these basins, including Old 

Hickory Lake (79.4 km2 surface area; meanders east-to-west past the facility, from 42 km east 

of the facility to 22 km west of the facility) and the J. Percy Priest Reservoir (57.6 km2 surface 

area; about 28 km southwest of the facility).

Based on the 2001 National Land Cover Database (NLCD) (USGS 2001) from the US 

Geological Survey (USGS), land use in the immediate vicinity of the TVA Gallatin facility is 

classified as a mix of forest (mostly deciduous), land in agricultural use (for pasture and 

cropland), and commercial and residential uses, with small amounts of water (see Figures 3-5

and 3-10). Land use becomes more urban proceeding southwest from the facility towards the 

Nashville-Davidson County metropolitan area. Nashville-Davidson County is the only major 

metropolitan area in the basins, but otherwise the land use patterns in the basins are similar to 

the area immediately around the facility, with more forestland towards the western Lower 

Cumberland Basin, eastern Upper Cumberland Basin, and northern Green Basin.
2/17/2011 17 DRAFT

Figure 3-9. Surface Water Bodies and Watersheds near TVA Gallatina

a Green, Lower Cumberland, and Upper Cumberland Basin data were obtained 

from the US Geological Survey (USGS) National Hydrography Dataset (NHD)

(USGS 2011). These data are based on the content of USGS 1:100,000-scale 

data. 

Figure 3-10. Land Use and Watersheds Near TVA Gallatina

a Shading indicates land use type in 2001 (with simplified categories); data from 

USGS 2001.
2/17/2011 18 DRAFT

As illustrated in Figure 3-11, the Santee Cooper Jefferies facility is located within the EdistoSouth Carolina Coastal Basin (HUC-030502), though the TRIM.FaTE modeling analysis for this 

facility also extends into the Lower Pee Dee (HUC-030402) and Santee (HUC-030501) Basins. 

Rivers and streams within the Santee Basin drain into Lake Marion (and eventually into the 

Atlantic Ocean), while rivers and streams within the Edisto-South Carolina Coastal and Lower 

Pee Dee Basins drain into the Atlantic Ocean. Relatively small lakes and reservoirs are located 

throughout the basins, including the Upper and Lower Reservoirs nearby the facility (0.801 km2

, 

20 km southwest of the facility) as well as Lake Moultrie (229.5 km2

, extends to 17 km west and 

north of the facility) and Lake Marion (356.1 km2

, extends 23 to 75 km northwest of the facility). 

Lake Marion is the largest water body among these basins.

Figure 3-11. Surface Water Bodies and Watersheds near Santee Cooper Jefferiesa

a Edisto-South Carolina Coastal, Santee, and Lower Pee Dee Basin data were 

obtained from the US Geological Survey (USGS) National Hydrography Dataset 

(NHD) (USGS 2011). These data are based on the content of USGS 

1:100,000-scale data. The Pee Dee River is highlighted in the figure using a 

water bodies file provided by Environmental Systems Research Institute, Inc. 

(ESRI 2011) and based on the US National Atlas Water Feature Areas (U.S. 

National Atlas 2011).

Based on the 2001 National Land Cover Database (NLCD) from the US Geological Survey 

(USGS) (USGS 2001), land use in the immediate vicinity of the Santee Cooper Jefferies facility 

is classified as a mix of forest (mostly coniferous), land in agricultural use (for pasture and 

cropland), water (including wetlands), and commercial and residential uses, and small amounts 

of shrub/scrub (see Figures 3-7 and 3-12). Land use becomes more urban proceeding 

southward from the facility towards Charleston. The Charleston metropolitan area is the lies 
2/17/2011 19 DRAFT

about 55 km south of the facility within the Edisto-South Carolina Coastal Basin, but otherwise 

the Edisto-South Carolina Coastal Basin’s land use patterns are similar to the area immediately 

around the facility, with fewer wetlands to the west. 

Figure 3-12. Land Use and Watersheds near Santee Cooper Jefferiesa

a Shading indicates land use type in 2001 (with simplified categories); data from 

USGS 2001.

3.2.5 TRIM.FaTE Parcel Design

The TRIM.FaTE surface parcel layout is the two-dimensional configuration of soil and water 

regions included in the modeled domain. The air parcel layout is then superimposed over the 

surface layout. These layouts provide the spatial references for three-dimensional 

compartments that contain the modeled chemical mass.

3.2.5.1 Modeling Spatial Extent and Modeled Water Bodies

The spatial extent of previous TRIM.FaTE modeling analyses, such as those conducted for 

evaluative purposes and initial modeling runs performed in support of RTR risk assessments, 

has typically covered at least a 15-km radius around the facility and has extended out as far as 

35 km This proximity is derived from an internal study using the ISCST3 model (Summary of 

ISC Analysis at the NY TRIM Site, prepared by ICF International, Draft, May 6, 2002). The 

results of this study suggest that increasing the modeling domain beyond a 15-km radius of the 

facility generally does not capture enough additional chemical deposition to counterbalance the 

added computational resources needed to model the larger analysis. However, increasing the 
2/17/2011 20 DRAFT

proximity somewhat (e.g., to 25 or 35 km) in at least one direction may be desired in order to 

model a certain water body and its watershed or to accommodate location-specific wind 

patterns. 

The modeling extent has typically been square or rectangular in shape and is constructed to 

encompass water bodies of interest for the assessment. For the TVA Gallatin facility, Old 

Hickory Lake and J. Percy Priest Reservoir were included in the modeled area for the purposes 

of modeling fish concentrations for the risk assessment. Figure 3-13 shows these lakes and the 

surface parcels created to represent them. Both lakes are large enough to support large 

populations of fish. J. Percy Priest Reservoir and the part of Old Hickory Lake nearby the TVA 

Gallatin facility were downwind from the facility about 27 and 50 percent of modeled hours, 

respectively. The parts of Old Hickory Lake further west and east of the facility were downwind 

from the facility about 14 and 20 percent of modeled hours, respectively (see Figure 3-6 for the 

wind roses). Figure 3-13 also shows the extent of the surface parcels that were based on 

watershed data (the surface parcels themselves are not shown here; see Section 3.2.5.3 for 

more detail). Because the air parcel layout is square and has a predetermined extent 

irrespective of the site (see Section 3.2.5.5 for more detail), it extended beyond the extent of the 

watershed surface parcels. “Outer” surface parcels were added beyond the extent of the 

watershed surface parcels (out to the extent of the air parcels) so that every air parcel had a 

surface parcel(s) underneath it. As such, Figure 3-13 also shows the resulting full modeling 

extent. Any watersheds or water bodies in these outer surface parcels were not specifically 

modeled (i.e., outer surface parcels were not drawn specific to the watersheds or water bodies). 

Figure 3-13. Modeled Water Bodies and Modeling Extent for TVA Gallatina

a Green, Lower Cumberland, and Upper Cumberland Basin data were obtained 

from the US Geological Survey (USGS) National Hydrography Dataset (NHD)

(USGS 2011).
2/17/2011 21 DRAFT

For the Santee Cooper Jefferies facility, three water bodies were included in the modeled area: 

Lake Moultrie and Lake Marion to the northwest, and Upper and Lower Reservoirs to the 

southeast. Figure 3-14 shows these lakes and the surface parcels created to represent them. 

These water bodies are large enough to support a fish population of reasonable size for 

sustenance fishing. They were not often downwind from the facility during modeled hours, but 

Lake Moultrie is directly adjacent to the source (see Figure 3-8 for the wind roses). Figure 3-14

also shows the extent of the surface parcels that were based on watershed data (the surface 

parcels themselves are not shown here; see Section 3.2.5.3 for more detail). Since the air 

parcel layout is square and has a predetermined extent irrespective of the site (see Section 

3.2.5.5 for more detail), it extended beyond the extent of the watershed surface parcels. “Outer” 

surface parcels were added beyond the extent of the watershed surface parcels, out to the 

extent of the air parcels, so that every air parcel had surface parcel(s) underneath it. As such, 

Figure 3-14 also shows the resulting full modeling extent. Any watersheds or water bodies in 

these outer surface parcels were not specifically modeled (i.e., outer surface parcels were not 

drawn specific to the watersheds or water bodies). 

Figure 3-14. Modeled Water Bodies and Modeling Extent for Santee Cooper Jefferiesa

a Edisto-South Carolina Coastal, Santee, and Lower Pee Dee Basin data were 

obtained from the US Geological Survey (USGS) National Hydrography Dataset 

(NHD) (USGS 2011). These data are based on the content of USGS 

1:100,000-scale data.

The surface parcels created to represent the water bodies were developed as simplified shapes 

with the goal of representing the actual water body surface areas as accurately as possible. For 

the TVA Gallatin facility, the actual extent of Old Hickory Lake is significantly longer than the two 
2/17/2011 22 DRAFT

surface parcels that represent it. Old Hickory Lake is widest (about 1,500 m wide) at the Old 

Hickory Lock and Dam in the west, and its width decreases going eastward to about 375 m wide 

near the facility and about 150 m wide a few kilometers upstream of the facility. Because the 

width of the lake is rather small just east of the facility, the parcels used to represent it were 

extended from the dam to about 8.5 km upstream from the facility. This portion of Old Hickory 

Lake was divided into two adjacent surface parcels, with a division located where Spencer and 

Station Camp Creeks feed into the lake (roughly where the width of the lake becomes smaller 

than about 500 m). Also, for the Santee Cooper Jefferies facility, note that Lake Marion extends 

westward far beyond the extent of modeling. Because Lake Marion feeds into Lake Moultrie 

(which was the primary reason why Lake Marion was included in the modeling scenario), the 

section of Lake Marion nearest Lake Moultrie was included in the modeling. This approach was 

assumed to be a reasonable compromise between including the entirety of Lake Marion (which 

would have required the modeling domain to be extremely large) and excluding Lake Marion 

from the layout (which would fail to account for chemical mass deposited to Lake Marion and 

subsequently transported to Lake Moultrie via the connecting canal).

Table 3-4 indicates the actual surface areas of these water bodies compared to the surface 

areas of the parcels representing them (USGS 2011). Note that the “actual surface areas” 

shown for Old Hickory Lake and Lake Marion are only for the portions of the lakes that were 

parameterized for modeling. The two Old Hickory Lake surface parcels together were about 26

percent smaller than the actual surface area because many of the small appendages of the lake 

were not captured by the parcel design. The surface area of J. Percy Priest Reservoir was 

within 4 percent of the actual water body surface area. The Lake Marion surface parcel was 

about 15 percent smaller than the actual surface area because some of the small appendages 

of the lake were not captured by the parcel design. The surface areas of Lake Moultrie and the 

Upper and Lower Reservoirs were within about 3 percent of the actual surface areas.

Table 3-4. Water Bodies Included in the Modeled Regions of the TVA Gallatin and 

Santee Cooper Jefferies Facilities

Facility Water Body 

Name

Actual 

Surface 

Area 

(km2)

a

Surface Parcel Name Modeled 

Surface Area 

of TRIM.FaTE 

Parcel (km2)

TVA Gallatin Old Hickory Lake 

(portion)

58.665 OldHickoryLake_W 31.983

OldHickoryLake_E 11.568

(Total) (43.551)

J. Percy Priest 

Reservoir

57.604 PercyReservoir 60.000

Santee Cooper 

Jefferies 

Lake Marion 

(portion)

113.877 LakeMarion 97.064

Lake Moultrie 229.5 LakeMoultrie 229.747

Upper and Lower 

Reservoirs

0.801 Upper&LowerReservoirs 0.821

a Actual surface areas are from the USGS NHD (USGS 2011).

3.2.5.2 Source Parcel

Although mercury emissions from the sources were not included in the TRIM.FaTE modeling, 

source parcels were included in each of the site applications for consistency with a typical 

TRIM.FaTE application. The source parcel for TRIM.FaTE applications was calibrated to 
2/17/2011 23 DRAFT

roughly capture the actual locations of all emissions sources considered in the application. The 

source parcels for the TVA Gallatin and Santee Cooper Jefferies facilities were approximately

500 m on a side, (Figures 3-15 and 3-16, respectively). 

Figure 3-15. Location of Source Parcel for TVA Gallatin
2/17/2011 24 DRAFT

Figure 3-16. Location of Source Parcel for Santee Cooper Jefferies

3.2.5.3 Layout of Watershed Surface Parcels and Outer Surface Parcels

The chief goal in designing the surface parcel layout was to accurately capture the watersheds 

surrounding the water bodies selected for modeling (i.e., those that contain fish that people are 

assumed to eat). In pursuing this goal, surface parcels were generally drawn following 

subwatershed boundaries while also considering topographic data (USGS 2006) and wind flow 

data. Some surface parcels contained multiple subwatersheds. The shapes of surface parcels 

were kept as simple as possible to reduce complexity in the layout and corresponding model run 

time. As required by TRIM.FaTE, no surface parcel was fully contained within any other surface 

parcel; all surface parcels shared at least one side or corner with another parcel. 

The air parcel layout was square with an extent that extended beyond the extent of the surface 

parcels that were drawn to subwatershed boundaries (hereafter referred to as “watershed 

surface parcels”). “Outer” surface parcels were added beyond the extent of the watershed 

surface parcels, out to the extent of the air parcels, so that every air parcel had a surface parcel 

underneath it. These outer surface parcels were not drawn to watershed boundaries; instead, 

their inside boundaries were shared with the watershed surface parcels and their outside 

boundaries were shared with the outer extent of the air parcels.
2/17/2011 25 DRAFT

The watershed surface parcels are shown as the polygons inside the full modeling extent 

square in Figure 3-17 for TVA Gallatin and Figure 3-18 for Santee Cooper Jefferies. The full 

TRIM.FaTE modeling extent of each facility analysis was about 79 km square. For the TVA 

Gallatin facility, the watershed surface parcels extended as close as about 18 km from the 

facility (towards the north-northwest) and as far away as about 42 km from the facility (towards 

the southwest). For the Santee Cooper Jefferies facility, the watershed surface parcels 

extended as close as about 17 km from the facility (towards the north) and as far away as about 

38 km from the facility (towards the northwest).

Figure 3-17. Surface Parcels for TVA Gallatina

a Elevation data from USGS National Elevation Dataset (USGS 2006).
2/17/2011 26 DRAFT

Figure 3-18. Surface Parcels for Santee Cooper Jefferiesa

a Elevation data from USGS National Elevation Dataset (USGS 2006).

3.2.5.4 Surface Parcel Vegetation Types

Calculations of the areal coverage of each land use type (using the 2001 NLCD [USGS 2001]) 

within each surface soil (i.e., not water body) parcel were used to set each parcel’s dominant 

vegetation type. This strategy results in some simplification because most parcels were at least 

several square kilometers in area and contained a variety of land use types. 

The vegetation type determines which algorithms and inputs will be used to model terrestrial 

plants. Table 3-5 summarizes the vegetation types used in this analysis and the corresponding 

inputs and algorithms that vary by land use type. In the current TRIM.FaTE library, only one 

vegetation type can be assigned per surface soil parcel. The four vegetation types that are in 

the current TRIM.FaTE library are:

• Deciduous Forest: Deciduous tree plant cover (leaf and leaf particle), “regular” 

surface soil depth (i.e., 0.01 m surface soil depth, 0.79 m root zone depth), standard 

site-specific soil erosion parameters, relatively small coverage management (i.e., 

0.039); 

• Coniferous Forest: Same as “Deciduous Forest” except with coniferous tree plant 

cover and a larger coverage management factor (i.e., 0.1);
2/17/2011 27 DRAFT

• Grasses/Herbs (and Untilled Farmland): Same as “Deciduous Forest” except with 

grasses/herbs plant cover (leaf, leaf particle, root, and stem) and a smaller coverage 

management factor (i.e., 0.003); and

• Agriculture: Currently not used.

There are also two surface parcel types that do not have vegetation during TRIM.FaTE 

modeling:

• Untilled Soil (and No Vegetation): Same as “Deciduous Forest” except with no 

plants. “No vegetation” is used in situations where the area is highly 

developed/paved or where the user wants to omit terrestrial plants for a model run; 

and

• Tilled Soil (and Tilled Farmland): Same “Untilled Soil” and “No Vegetation” except 

with thicker surface soil (i.e., 0.2 m) and thinner root zone depth (i.e., 0.6 m). If “tilled 

farmland” is used, the tilled soil concentrations are used in the exposure assessment 

methodology to approximate concentrations in produce and animal feed, and the 

untilled soil concentrations are used for where animals graze and people live. 

Table 3-5. TRIM.FaTE Properties that Vary by Vegetation Type

Vegetation 

Type Litterfall Allow Exchange

Coverage 

Management 

Factora

Plants 

Included in 

Parcel

Surface 

Soil Depth 

(m)

Root Zone 

Depth (m)

Deciduous 

Forest

15 percent of 

leaves fall 

each day for 

30 days after 

first autumn 

freeze

Off after 

first autumn 

freeze; On 

after last 

spring 

freeze

0.039 Leaf and Leaf 

Particle: 

Deciduous 

Forest

0.01 0.79

Coniferous 

Forest

99 percent of 

leaves fall 

over 6 years

Always On 0.1 Leaf and Leaf 

Particle: 

Coniferous 

Forest

0.01 0.79

Grasses/Herbs 

and Untilled 

Farmland

15 percent of 

leaves fall 

each day for 

30 days after 

first autumn 

freeze 

Off after 

first autumn 

freeze; On 

after last 

spring 

freeze

0.003 Leaf, Leaf 

Particle, Root, 

and Stem: 

Grasses/Herbs

0.01 0.79

Tilled Soil and 

Tilled Farmland

N/A N/A 0.2 N/A 0.01 0.79

Untilled Soil and 

No Vegetation

N/A N/A 0.2 N/A 0.20 0.60

The vegetation type for each surface parcel was determined by calculating the most common 

land use type (using the 2001 NLCD [USGS 2001]) in each parcel. Below are further details on 

these processes:

• “Deciduous Forest” and “Coniferous Forest” TRIM vegetation types:

o The 2001 NLCD contains a “mixed forest” category (in addition to the coniferous 

and deciduous categories), so if deciduous forest was more common than 
2/17/2011 28 DRAFT

coniferous forest in a given parcel, then mixed forest was considered to be 

entirely deciduous; likewise, if coniferous forest was more common than 

deciduous forest, then mixed forest was considered to be entirely coniferous. 

• “Grasses/Herbs” TRIM vegetation type:

o This was defined as a combination of the “grassland/herbaceous” and 

“shrub/scrub” 2001 NLCD types. 

• “Untilled Soil,“ “Untilled Farmland,” “Tilled Soil,” and “Tilled Farmland” TRIM 

vegetation types:

o The untilled and tilled TRIM land use types were defined as the “pasture/hay” 

and “cultivated crops” 2001 NLCD types, respectively. 

• “No Vegetation” TRIM vegetation type:

o The “no vegetation” TRIM vegetation type was defined as a combination of the 

four developed and the “barren land” 2001 NLCD types.

o Because it was rare for a surface parcel to be truly devoid of vegetation, a 

surface parcel could not be described as “no vegetation” unless at least a 

majority of the parcel qualified as “no vegetation.” If “no vegetation” was the 

most frequent land use type of a parcel but it did not make up a majority, then the 

next most frequent vegetation type was assigned to the parcel.

• Special “Wetlands” TRIM vegetation type:

o TRIM.FaTE is not currently set up to model the various properties of wetlands, 

marshes, and swamps. 

o For a parcel that was not intended to be modeled as a water body, it was

possible for its most frequent land use type to be water-related. For example, the 

“open water,” “woody wetlands,” and “emergent herbaceous wetlands” 2001 

NLCD types together could have been the most frequent land use type. If these 

three types together made up a majority of the parcel, then the TRIM vegetation 

type of the parcel was set as the next most frequent vegetation type, but some 

specific parcel properties were modified to reflect the fact that the soil was often 

saturated. For example, the run-off should be larger than for non-saturated soil, 

and the erosion should be smaller. The parameters in the Universal Soil Loss 

Equation were selected specifically to account for a wetlands floor (see Section 

3.2.6.1 for more information on soil properties).

Figure 3-19 shows the TRIM vegetation type assigned to each surface parcel for the TVA 

Gallatin facility. Most surface parcels were set as either untilled soil (not meant to be treated as 

farm/pasture for exposure purposes) or deciduous forest. Because of the heavier urbanization 

to the south and southwest of the facility, some parcels were set as no vegetation.

Figure 3-20 shows the TRIM vegetation type assigned to each surface parcel for the Santee 

Cooper Jefferies facility. The majority of surface parcels were set as coniferous forest. For two 

parcels (i.e., parcels NW0 and SE0), the majority land use type was wetlands, and the most 
2/17/2011 29 DRAFT

frequent vegetation type was coniferous forest, so they were set as wetlands/coniferous forest. 

Specific parameters in the Universal Soil Loss Equation were modified to account for the 

coniferous forest canopy and wetlands floor. 

Figure 3-19. TRIM Vegetation Types for the TVA Gallatin Facility
2/17/2011 30 DRAFT

Figure 3-20. TRIM Vegetation Types for the Santee Cooper Jefferies Facility

3.2.5.5 Air Parcel Layout

The design of the air parcel layout for the TRIM.FaTE analysis was simpler to construct than for 

the surface parcel layout because watersheds, terrain, and land use are not taken into account 

in the design. The air parcel layout consists of concentric squares around the source overlaid 

onto lines emanating radially in a regular 45-degree pattern. The distance from the side of a 

given square to the side of the next outward square increases with increasing distance from the 

source, in much the same way that modeled pollutant air concentrations and depositions 

generally decrease rapidly with increasing distance from the source. Radial lines divide each 

concentric square such that eight parcels of equal area can be formed. This radial layout 

minimizes the TRIM.FaTE bias for over-accumulation of mass along the axes of the grid (refer 

to the TRIM.FaTE User's Guide (U.S. EPA 2005b) for additional discussion of this design).

The sizes of the concentric air parcel squares were derived from an internal ICF case study of 

the deposition of mercury and PAHs using the ISCST3 model (U.S. EPA 2005c). Part of that 

analysis involved examining the cumulative sum of the modeled deposition of divalent mercury 

(as a ratio of divalent mercury emissions) with increasing distance from the source. Results 

from this analysis were used as a guide to determining the sizes of the air parcel squares, 

keeping in mind the predetermined criteria that (1) the concentric squares should increase 

exponentially in size going outward from the facility, (2) the number of concentric squares 

should be "reasonable" (i.e., less than about 6), and (3) the outward-most square should fully 

encompass all of the watershed surface parcels. Four concentric air parcel squares were 

constructed at 2 km from the side of the source (containing 4 percent deposition of emitted 

mercury), at 6 km (8 percent), at 15 km (12 percent), and at 39 km (16 percent). The 39-km 

square should be large enough to encompass all reasonable surface parcel layouts for 
2/17/2011 31 DRAFT

TRIM.FaTE analyses. If a surface parcel layout was fully contained within one of the smaller air 

parcel squares (e.g., the 15-km square), then the larger air parcel square(s) (e.g., the 39-km 

square) should not be used. 

Overall, 33 air parcels, including the source parcel, were included in the air parcel layout for the 

TVA Gallatin analysis and for the Santee Cooper Jefferies analysis. Figure 3-21 shows the air 

parcel layout for the TVA Gallatin analysis, and Figure 3-22 shows the air parcel and surface 

parcel layouts overlaid. Likewise, Figure 3-23 shows the air parcel layout for the Santee Cooper 

Jefferies analysis, and Figure 3-24 shows the air parcel and surface parcel layouts overlaid.

Figure 3-21. Air Parcels for the TVA Gallatin Facility

The source parcel at the center is not labeled.
2/17/2011 32 DRAFT

Figure 3-22. Air Parcels for the TVA Gallatin Facility, Overlaid with the Surface Parcels

Figure 3-23. Air Parcels for the Santee Cooper Jefferies Facility

The source parcel at the center is not labeled.
2/17/2011 33 DRAFT

Figure 3-24. Air Parcels for the Santee Cooper Jefferies Facility, Overlaid with the 

Surface Parcels

3.2.6 Abiotic Environment

TRIM.FaTE requires various environmental properties for each abiotic compartment included in 

a scenario. Examples of abiotic environmental properties include the depth of surface soil, soil 

porosity and water content, erosion and runoff rates for surface soil compartments, and 

suspended sediment concentrations in surface water. Site-specific inputs were used for this 

assessment where data supporting such a value were readily available. Regional or national 

defaults identified by ICF were used in some instances, especially for those parameters that are 

not expected to strongly influence chemical concentrations. This section lists some of the 

important values used for this application. A complete list of all TRIM.FaTE inputs used for 

abiotic compartments is provided in Appendix A.

3.2.6.1 Soil Properties

For both sites, soils were modeled as three stacked soil layers (surface, root zone, and vadose 

zone soil) over ground water. The surface soil layer that receives deposited mercury was 

assumed to be 1 centimeter (cm) deep. Depths for subsurface soil layers were based on 

“typical” state values for multimedia modeling and are presented in Table 3-6 (McKone et al. 

2001).
2/17/2011 34 DRAFT

Table 3-6. Soil and Groundwater Compartment Depths

Zone Compartment Depth (m from surface)

TVA Gallatin Santee Cooper Jefferies

Root soil 0.01 – 0.80 0.01 – 0.80

Vadose soil 0.80 – 2.30 0.80 – 2.50

Groundwater 2.30 – 5.30 2.50 – 5.50 

For most of the basic surface soil properties, values also were defined using typical regional or 

state values compiled by McKone et al. (2001). Selected soil and groundwater properties are

shown in Table 3-7. 

Table 3-7. Selected Properties of Soil and Groundwater Compartmentsa

Property Surface Soil Root Zone 

Soil Vadose Soil Groundwater

TVA Gallatin

pH 6.8 6.8 6.8 6.8

Organic carbon content 0.005 0.005 0.002 0.004

Volume fraction, vapor (air content) 0.26 0.23 0.19 NA

Volume fraction, liquid (water content) 0.2 0.22 0.25 NA

Average downwind vertical velocity of 

water infiltrating the soil (m day-1) 5.75E-4 5.75E-4 5.75E-4 NA

Santee Cooper Jefferies

pH 6.8 6.8 6.8 6.8

Organic carbon content 0.006 0.006 0.003 0.004

Volume fraction, vapor (air content) 0.29 0.24 0.22 NA

Volume fraction, liquid (water content) 0.15 0.2 0.21 NA

Average downwind vertical velocity of 

water infiltrating the soil (m day-1) 7.17E-4 7.17E-4 7.17E-4 NA

a All values from McKone et al. (2001) NA: Not applicable.

3.2.6.2 Erosion

Erosion rates for each surface parcel for all facilities were estimated using a modified Universal 

Soil Loss Equation (USLE), with a sediment delivery (SD) ratio adjustment. The USLE was

developed to predict the long-term average soil losses from individual field areas (Wischmeier 

and Smith 1978) and represents the sheet and rill erosion from a small plot or agricultural field. 

Application of the USLE to an entire watershed requires modification of the equation to account 

for subsequent re-deposition of eroded soil before it reaches the water body. The SD ratio was 

developed for this purpose: it estimates the fraction of sediment that reaches a water body 

based on the size of the watershed.

Representative site-specific values were used in the USLE and SD equations to estimate 

erosion for the case study applications. Rainfall/erosivity values were estimated from the 

isoerodent gradient map of the continental U.S. developed by Wischmeier and Smith (1978). In 

cases where the facility location fell between lines of equal erosivity, linear interpolation was 

used. Soils data were obtained from the Soil Survey Geographic (SSURGO 2.2) database for 

the counties of interest (obtained from the USDA Natural Resources Conservation Service) to 

calculate site-specific soil erodibility factors. ESRI ArcInfo (ESRI 2011b) was used to generate 

soil erodility factors (K values) for each parcel with the same vegetation type (see Section 

3.2.5.4 for vegetation types). Different cover management factors were used for natural forests 

and wetlands. Calculated erosion rates for modeled surface soil parcel ranged from 7.39E-5 to 
2/17/2011 35 DRAFT

8.59E-4 kg m-2 day-1 for the TVA Gallatin facility and from 6.78E-6 to 2.99E-5 kg m-2 day-1 for the 

Santee Cooper Jefferies facility.

The USLE is an empirical model, and therefore modeled conditions should be similar to 

conditions for which the model has been calibrated to yield useful results. In particular, the 

USLE was developed for application to a single slope or field, rather than to an entire 

watershed. Using average values across a watershed parcel introduces uncertainty in the 

prediction; predictions are improved when individual analyses of the slopes within the watershed 

are conducted. EPA’s HHRAP documentation states that using the USLE to calculate sediment 

load to a lake from the surrounding watershed can sometimes lead to overestimates (U.S. EPA 

2005a). The use of area-weighted averages for some of the USLE variables helps to avoid

under- or over-estimating by assuming uniformity across the watershed. The area-weighted soil 

erodibility factor (K) and cover management factor (C) are not expected to contribute 

significantly to inaccurate soil erosion estimates. 

Estimating the length-slope (LS) factor is more challenging than any other factor for the USLE

(Moore and Wilson 1992), especially for complex watersheds. Values for LS were estimated 

following the approach developed by Moore and Wilson (1992). In actual watersheds, the entire 

watershed has neither uniform slope length nor uniform slope steepness. Also, due to 

nonlinearities in the equation to calculate the LS factor, the assumption of uniformity can result 

in underestimates or overestimates of the LS factor. The use of average slope likely would 

underpredict the LS factor. A representative slope-length was selected for each scenario using 

Google Terrain software (Google Inc. 2010). Finally, uncertainty was introduced when using the 

SD ratio to account for the re-deposition of soil before it reaches the water body. The degree to 

which the SD ratio underpredicts or overpredicts actual sediment delivery is unknown. 

3.2.6.3 Runoff

Runoff from surface parcels into water bodies was calculated by subtracting the annual 

evaporation (0.9855 and 1.314 m/yr for TVA Gallatin and Santee Cooper Jefferies, respectively)

from the annual precipitation (1.05 and 1.31 m/yr for TVA Gallatin and Santee Cooper Jefferies, 

respectively) (NOAA 2009a). The resulting total runoff values include interflow and ground 

water recharge in addition to surface runoff. To estimate surface runoff only, total runoff was 

reduced by the amount expected to infiltrate the groundwater based on information compiled by 

USGS (USGS 1985). Total runoff for all surface parcels into each lake is reported below in 

Table 3-9 (source parcel excluded). The source parcels were not included in runoff because the 

facilities are assumed to have different containment configurations than the rest of the modeled 

areas. 

3.2.6.4 Surface Water and Sediment Properties

Selected surface water and sediment properties for all lakes are shown in Table 3-8 (all other 

TRIM.FaTE surface water and sediment properties are listed in Appendix A).

Table 3-8. Selected Surface Water and Sediment Properties

Property TVA Gallatina Santee Cooper 

Jefferiesb

Temperature (K) 298 298

Suspended sediment concentration (kg[sediment]/kg[water]) 0.05 0.05
2/17/2011 36 DRAFT

Table 3-8. Selected Surface Water and Sediment Properties

Property TVA Gallatina Santee Cooper 

Jefferiesb

Water column and sediment organic carbon content 

(kg[organic carbon]/kg[solid wet weight]) 0.02 0.02

Water column pH 7.9 7.6c

Sediment pH 7.3 7.3

Chlorophyll concentration (mg/L) 0.0029 0.0029

Chloride concentration (mg/L) 8 8

Algae density in water column (g/L) 0.007038 0.0095 a Data for lakes near TVA Gallatin from Tennessee Valley Authority (2002). Environmental Assessment and 

Finding of No Significant Impact: Hartsville Nuclear Plant Site Trousdale and Smith Counties, TennesseeTransfer of TVA Property for Industrial Park.

b Data for Santee Cooper Jefferies from Santee Cooper (2004). c Multiple sources were available for pH in the lakes near Santee Cooper Jefferies, including Santee Cooper 

(2004) and Bowers, J.A. (1992). Value for modeling was selected based on the most reliable and, when 

available, site-specific sources.

3.2.6.5 Water Transfers

To estimate annual flush (turnover) rates and other properties for each modeled lake, a 

volumetric water balance was assumed. Estimated water inputs to each water body included 

runoff from the surrounding watershed and direct precipitation to the lake. Outputs (i.e., water 

removal) from each water body included outflow through the lake outlet and evaporation from 

the lake surface. 

Long-term average precipitation rates used to calculate water balances for each lake were 

obtained from nearby meteorological stations identified in Section 3.2.3. For each water body, 

this precipitation was added as a water input, based on surface area of the water body. Runoff 

from the watershed was calculated by subtracting annual average evapotranspiration and 

groundwater infiltration from annual average precipitation and multiplying the difference by the 

total watershed area. Evapotranspiration data for each state were obtained from the USGS 

Water Summary Table (USGS 1985).

Evaporation from each water body was subtracted from the water inputs to estimate the 

volumetric flow of water leaving the water bodies. Using surface areas and mean depths to 

calculate lake volumes, turnover rates in flushes per year were calculated. The values of these 

turnover rates are presented in Table 3-9. For the TVA Gallatin facility, Old Hickory Lake was 

split into an eastern and western section. Table 3-9 presents the results for each of these 

sections.

Table 3-9. Lake Parameter Values for Modeled Water Bodies

Water Body Name Area (km2) Average 

Depth (m)

Surface 

Runoff 

from 

Land 

(km3/yr)

Discharge 

Rate 

(m3/s)

Turnover 

Rate

(flushes/

yr)

Sediment Resuspension 

Rate 

(m[sediment]/

day)

TVA Gallatin

Old Hickory Lake West 31.96 5.69a 0.16 35.96 6.24 9.46E-5

Old Hickory Lake East 11.56 5.69a 0.50 27.04 12.96 9.91E-5
2/17/2011 37 DRAFT

Table 3-9. Lake Parameter Values for Modeled Water Bodies

Water Body Name Area (km2) Average 

Depth (m)

Surface 

Runoff 

from 

Land 

(km3/yr)

Discharge 

Rate 

(m3/s)

Turnover 

Rate

(flushes/

yr)

Sediment Resuspension 

Rate 

(m[sediment]/

day)

Priest Reservoir 59.96 8.74a 0.19 10.66 0.64 9.56E-5

Santee Cooper Jefferies

Lake Moultrie 229.6 5.7b 0.02 8.37 0.20 9.62E-5

Upper and Lower 

Reservoirs 0.82 5.7b 0.01 0.57 3.83 9.87E-5

Lake Marion 97.0 4b 0.15 7.34 0.60 9.64E-5

a Depth data for lakes near TVA Gallatin from Tennessee Valley Authority (2002). b Multiple sources were available for lake depth in the lakes near Santee Cooper Jefferies, including Santee Cooper 

(2004), Bowers (1992), Tufford and McKellar (1999). Value for modeling was selected based on the most reliable 

and, when available, site-specific sources.

3.2.6.6 Sediment

The sediment mass balance of each watershed/water body system modeled was estimated by 

accounting for sediment inputs to the water body based on the erosion calculations and the 

removal of sediment from the modeled system via benthic burial and outflow of suspended 

sediment in the water column. In this scenario, assumptions about the physical environment 

were used to calculate sediment input through erosion and sediment removal through 

suspended sediment flushing. All sediment inputs to a lake are estimated based on the 

calculated erosion inputs from the surrounding watershed. 

For this assessment, all sediment that was not flushed out as suspended sediment was

assumed to be buried (i.e., removed from the modeled system by transfer to the consolidated 

benthic sediment layer, where it was assumed to no longer interact with the overlying water 

column). Suspended sediment depositional velocity was used to calculate total deposition to 

the lake bottom, and the difference between deposition and burial was then used to calculate

the sediment that was resuspended. The suspended sediment concentration for all water 

bodies was assumed to be 0.05 kg[sediment]/m3

[water].

3.2.7 Biotic Environment

3.2.7.1 Terrestrial Plants

For most plant compartment properties required by TRIM.FaTE, the default values included in 

the TRIM.FaTE public reference library were used for the EGU case studies. In addition, two 

site-specific, seasonally-varying plant properties were set: (1) an “allow exchange” property that 

dictates whether plants are actively growing, and thus able to exchange chemical mass to and 

from the ambient air and take up chemical mass from soil; and (2) a litterfall rate property that 

dictates when and how fast chemical mass accumulated by a leaf is transferred to underlying 

surface soil (to account for chemical transfers to soil from leaves dropped by deciduous trees 

and plants in the autumn). For this assessment, the dates at which these seasonal events 

occur were based on the dates of the first and last frosts reported for the regions in which the 

two facilities are located (Koss et al. 1988). The “allow exchange” property was set to enable 

chemical exchange between plants and air/soil compartments in the period between these frost 
2/17/2011 38 DRAFT

dates (i.e., the spring, summer, and autumn). Litterfall was assumed to begin on the first day of 

frost and to end 30 days later, with a litterfall rate of 15 percent of the remaining detritus falling 

per day (this rate assures that essentially all mass is transferred to the soil within 30 days).

Modeling terrestrial plants also requires an assigned vegetation type for each surface parcel. 

See Section 3.2.5.4 for details on how each parcel was assigned a vegetation type.

3.2.7.2 Aquatic Ecosystems

The aquatic food web was an important part of the EGU case studies because the chemical 

concentrations modeled by TRIM.FaTE in fish are used to calculate human ingestion exposure 

and risks associated with eating contaminated fish. A biokinetic approach to modeling 

bioaccumulation in fish was used in the assessment. The primary producers (first trophic level) 

in the TRIM.FaTE aquatic ecosystems for the lakes at these two sites are algae. The scenario 

used for all aquatic food webs in this assessment includes a benthic invertebrate compartment 

to represent the primary invertebrate consumers (second trophic level) in the benthic 

environment and zooplankton to represent the primary invertebrate consumers in the water 

column. The benthic and water column fish compartments represent the higher trophic levels in 

the aquatic system. For TRIM.FaTE to provide reasonable predictions of the distribution of a 

chemical across biotic and abiotic compartments in aquatic systems, the biomass of the aquatic 

biotic compartments and the distribution of biomass among the trophic levels must be as 

realistic as possible.

To support the development of a representative freshwater aquatic ecosystem in which to model 

bioaccumulation in fish, a literature search, review, and analysis was conducted in support of 

developing and parameterizing aquatic biotic compartments for TRIM.FaTE. This research 

demonstrated that the diversity of species and food webs across U.S. aquatic ecosystems is 

substantial, reflecting the wide range of sizes, locations, and physical/chemical attributes of both 

flowing (rivers, streams) and low-flow (ponds, lakes, reservoirs) water bodies. In general, lentic

bodies of water (lakes and ponds) are at a higher risk of accumulating contaminants in both 

sediments and biota than are lotic systems (rivers, streams). Also, the literature suggested that 

a lake of 60 hectares (ha) or larger would likely be sufficient to support higher trophic level 

predatory fish, with some fraction of their diet comprising smaller fish.

For the TVA Gallatin facility, the modeling zone encompassed Old Hickory Lake and J. Percy 

Priest Reservoir. The exposure and risk assessment focused on Old Hickory Lake because it 

was closer to the emission source and was therefore assumed to have higher concentrations of 

source-attributable mercury. Literature confirmed that OId Hickory Lake supported a range of 

biotic components consistent with the default aquatic biota compartments in the TRIM.FaTE 

model (Tennessee Valley Authority 1995). These components included macrophytes, algae, 

zooplankton, benthic invertebrates, benthic omnivores, benthic carnivores, water column 

planktivores, water column omnivores, and water column carnivores. However, site-specific 

data on the precise biomass densities of these biotic components in the Old Hickory Lake were

not located. Empirical equations were used to predict the biomass densities of the modeled 

biotic compartments on the basis of the annual average total phosphorus levels in the lake

(Peters 1986). The relative biomass densities of different types of fish were calibrated within the 

observed empirical ranges to reflect the appropriate level of bioaccumulation and 

biomagnification in the food chain. The biomass densities for the various aquatic biota 

compartments included in TRIM.FaTE for the Old Hickory Lake are presented in Table 3-10.
2/17/2011 39 DRAFT

Table 3-10. Biomass Parameters for Old Hickory Lake near 

TVA Gallatin Facility

Biotic Compartment Biomass Density Units

Macrophytes 0.5 kg[ww]/m2

Algae 0.007038 g[ww]/L

Zooplankton 0.02811 kg[ww]/m2

Benthic Invertebrates 0.009723 kg[ww]/m2

Benthic Omnivores 0.00249 kg[ww]/m2

Benthic Carnivores 0.00124 kg[ww]/m2

Water Column Planktivores 0.00249 kg[ww]/m2

Water Column Omnivores 0.000623 kg[ww]/m2

Water Column Carnivores 0.000248 kg[ww]/m2

Annual Average Total Phosphorus Level = 33.13 μg/L 

(Tennessee Department of Environment and Conservation 2004)

For the Santee Cooper Jefferies facility, the modeling zone encompassed Lake Moultrie, Lake 

Marion, and the Upper and Lower Reservoirs. The exposure and risk assessment focused on 

Lake Moultrie because it was closest to the emission source. A literature search indicated that 

Lake Moultrie supported a healthy aquatic ecosystem consistent with the default aquatic biota 

compartments in the TRIM.FaTE model (Santee Cooper Green 2007). An online search of 

fishing reports confirmed the occurrence of higher trophic level game fish in Lake Moultrie

(South Carolina Fishing Report 2009). In the absence of site-specific data on biomass densities 

and total phosphorus levels in Lake Moultrie, total phosphorus data from Lake Marion were 

used to predict biotic densities in Lake Moultrie using empirical relationships (Tufford and 

McKellar 1999). The biomass densities for the various aquatic biota compartments in 

TRIM.FaTE for Lake Moultrie are presented in Table 3-11.

Table 3-11. Biomass Parameters for Lake Moultrie near 

Santee Cooper Jefferies

Biotic Compartment Biomass Value Units

Macrophytes 0.5 kg[ww]/m2

Algae 0.0095 g[ww]/L

Zooplankton 0.0304 kg[ww]/m2

Benthic Invertebrates 0.0115 kg[ww]/m2

Benthic Omnivores 0.00294 kg[ww]/m2

Benthic Carnivores 0.00147 kg[ww]/m2

Water Column Planktivores 0.00294 kg[ww]/m2

Water Column Omnivores 0.000737 kg[ww]/m2

Water Column Carnivores 0.000292 kg[ww]/m2

Annual Average Total Phosphorus Level = 41.92 ug/L

For both facilities, the aquatic food web (predator-prey) relationships between the biotic 

components were conservatively structured to simulate elevated exposure for human 

consumers of fish. In the water column, each trophic level exclusively consumed prey from the 

trophic level directly below it, thus favoring higher bioaccumulation by upper trophic levels of 
2/17/2011 40 DRAFT

bioaccumulative contaminants. Figure 3-25 illustrates the structure and diet fractions of the 

aquatic food web modeled for both facilities.

Figure 3-25. Aquatic Food Web and Diet Fractions

3.3 Exposure and Risk Calculations

This section describes the approach for estimating human exposures and risks associated with 

consumption of fish. All exposure and risk calculations conducted for the EGU case studies 

were performed using the Multimedia Ingestion Risk Calculator (MIRC). MIRC is a 

computational, computer-based framework developed by ICF for conducting multipathway risk 

assessments for EPA’s residual risk program and similar initiatives. 

3.3.1 Ingestion Exposure Assessment 

MIRC was used to estimate body weight-normalized long-term average daily doses (ADDs) for 

methyl mercury via the fish ingestion pathway. The following two subsections describe the 

parameterization of the ingestion exposure scenario and the exposure estimates for the 

individual scenarios of interest. 
2/17/2011 41 DRAFT

3.3.1.1 Exposure Scenarios and Corresponding Inputs

To assess exposure from fish ingestion, specific individual scenarios were developed for a 

range of ingestion patterns (i.e., how much fish was consumed) and the associated 

characteristics of the hypothetical exposed human receptor (e.g., age and body weight). Data 

related to exposure factors and characteristics of the receptor were obtained primarily from 

EPA’s Exposure Factors Handbook and Child-Specific Exposure Factors Handbook (U.S. EPA 

1997a, 2008).

For this assessment, ICF evaluated individuals who were assumed to eat fresh-water game fish 

caught in a local lake within the modeled domain. It was assumed that fish intake consisted of 

50% omnivorous fish and 50% carnivorous fish. Exposures were estimated for U.S. populations 

that have been identified as eating a large amount of self-caught fish. Specifically, for these 

case studies, exposures were estimated for recreational anglers as a whole (for an adult and 

child receptor), and for adult African American and female anglers and adult anglers of 

Hispanic, Laotian, and Vietnamese descent who are culturally or economically disposed to 

higher rates of fish consumption. 

Two variants of each of the individual exposure scenarios were modeled to provide information 

on the range of possible exposures. In keeping with nomenclature and precedent set by risk 

analyses performed under the purview of the Superfund programs, these two variants are 

referenced as follows:

• a central tendency estimate (CTE), representative of a “typical” member of the 

population of interest, represented in this assessment by the mean value from a 

distribution of ingestion rates; and

• an estimate representative of a “reasonable maximum exposure” (RME), represented 

in this assessment by the 90th percentile value from a distribution of ingestion rates.

The range of exposures for these two variants for the specific populations evaluated was 

estimated by varying only the individual fish ingestion rates; other exposure values, including 

body weight and exposure frequency remained the same. An overview of the exposure factors

selected for the fish ingestion scenario is summarized in Table 3-12. Specific exposure factors 

are presented in more detail in Tables 3-13 through 3-15.
2/17/2011 42 DRAFT

Table 3-12. Overview of Exposure Factors used for Fish Ingestion Exposure Scenarios

Exposure Factor Selection for Screening Assessment

Age group evaluated 

Children 1 to 2 years of age

Children 3 to 5 years of age

Children 6 to 11 years of age

Children 12 to 19 years of age

Adult (20 to 70 years)

Body weight (BW; varies by age) Weighted mean of national distribution

Ingestion rate for fish (IR; varies by age and 

subpopulation)

RME Scenarios: 90th percentile of distribution of population of 

interesta

CTE Scenarios: mean of distribution of population of interesta

Cooking conversion factor (CCF) 1.5b

Exposure frequency (EF) 365 days/year

Exposure duration (ED) Varies (see Table 3-13) a U.S. EPA (2002), Burger (2010), Shilling et al. (2010). b EPA-recommended CCF which accounts for the difference between ingestion rates for cooked fish and calculated 

mercury concentrations in whole fish. Cooking fish tends to reduce the overall weight of fish, and volatilization of 

mercury is unlikely to occur during cooking, thereby increasing the concentration of mercury by a factor of roughly 

1.5 (Morgan et al. 1997).

Default body weights (BWs) in MIRC are the mean values for adults and the four children’s age 

groups. The BW values are listed in Table 3-13. For adults, BW represents the weighted 

average of male and female mean body-weights for all races, ages 18-74 years, from EPA’s 

EFH (U.S. EPA 1997a,b). In general, BW values for the five children’s age groups were 

calculated from the summary data provided in EPA’s Child-Specific Exposure Factors 

Handbook (U.S. EPA 2008) (see below).

Table 3-13. Exposure Duration and Mean Body Weight Estimates for Adults and 

Children

Life stage (years) Duration (years) Mean Body Weight (kg)

Adulta (20-70) 50 71.4

Child 1-2b 2 12.6

Child 3-5c 3 18.6

Child 6-11d 6 31.8

Child 12-19e 8 64.2 a BW represents the sample-size weighted average of male and female mean body weights (all races, 18-74 

years) from EPA (Tables 7-4 for males and 7-5 for females). Note that these weights include the weight of 

clothing, estimated to range from 0.09 to 0.28 kg. Although the 18 to 74 year age category in EPA’s EFH does not 

match exactly the age 20 to 70 year categorization of adults in MIRC, the magnitude of error in the mean and 

percentile body weights is likely to be very small (i.e., less than 1%).

b Each BW represents a time-weighted average of body weights for age groups 1 to <2 years and 2 to <3 years 

from Table 8-3 of the 2008 CSEFH (U.S. EPA 2008). Original sample sizes for each of these age groups can also 

be found in Table 8-3. c BWs obtained directly from Table 8-3 of the 2008 CSEFH (U.S. EPA 2008) (age group 3 to <6 years). d BWs obtained directly from Table 8-3 of the 2008 CSEFH (U.S. EPA 2008) (age group 6 to <11 years). This 

value represents a conservative (i.e., slightly low) estimate of BW for ages 6 through 11 years since 11-year olds 

are not included in this CSEFH age group.

e Mean BW estimated using Table 8-22 of the 2008 CSEFH (U.S. EPA 2008), which is based on NHANES IV data 

as presented in Portier et al. (2007). This estimate was calculated as the average of the 8 single-year age groups 

from 12 to 13 years through 19 to 20 years.
2/17/2011 43 DRAFT

MIRC includes built-in, age-specific, fish ingestion rates (IRs). All default IRs in MIRC are 90th

percentile values. In addition to the default 90th percentile ingestion rates, MIRC includes 

values for the mean and the 95th and 99th percentile fish ingestion rates (freshwater and 

estuarine fish only) based on EPA’s analysis of 1994-96 and 1998 Continuing Survey of Food 

Intakes by Individuals (CSFII) (U.S. EPA 2002, 2008), part of USDA’s Nationwide Food 

Consumption Survey (NFCS) (USDA 1994). Due to limitations of the available data, ICF 

analyzed the survey data to estimate child fish ingestion rates. The details of ICF’s approach 

are presented in U.S. EPA (2009). Table 3-14 presents the age-specific mean and 90th 

percentile fish ingestion rates. 

Table 3-14. 90th Percentile and Mean Fish Ingestion Rates Representative of Individuals in 

U.S. Population Consuming Fish

Product Age Group

Units Child 1-2 

Years

Child 3-5 

Years

Child 6-11 

Years

Child 12-

19 Years

Adult 20 -

70 years

Mean g/day 1.37 2.03 2.71 3.9 6.9

90th Percentile g/day 3.24 4.79 6.9 8.95 17

Source: U.S. EPA (2002)

MIRC also includes values for the mean and the 90th percentile fish ingestion rates for select 

populations that exhibit higher rates of fish ingestion than the general population. Table 3-15

presents the mean and 90th percentile fish ingestion rates for these additional high-end fish 

consumption populations.

Table 3-15. 90th Percentile and Mean Fish Ingestion Rates for Additional Fish Consumers 

Evaluated

Product Population

Units African American 

Anglera Female Anglera Hispanic Anglerb

Laotian 

Anglerb

Vietnamese 

Anglerb

Mean g/day 171 39.1 25.8 47.2 27.1

90th Percentile g/day 446 123 98 144.8 99.1

a Burger et al. (2010) b Shilling et al. (2010)

3.3.1.2 Calculating Average Daily Doses 

MIRC calculates human chemical intake rates from the ingestion of locally caught fish as 

average daily doses (ADDs) normalized to body weight for each receptor. ADDs, calculated 

using Equation 3-1, are expressed in milligrams of chemical per kilogram of body weight per day 

(mg/kg-day). The ADD accounts for ingestion of both carnivorous and omnivorous fish. 
2/17/2011 44 DRAFT

Equation 3-1. Average Daily Dose 

































×

× × × = 365 days

(y)

(y) (y)

(i) (y,i) (i) (y)

(y,i)

EF

BW AT

C IR FC ED

ADD

where: 

ADD(y,i) = Average daily dose for receptor y from fish type i (mg chemical/kg body 

weight-day)

C(i) = Concentration of chemical in prepared fish type i harvested from the 

contaminated area (mg chemical/kg food)

IR(y,i) = Ingestion rate for receptor y of fish type i (kg/day)

FC(i) = Fraction of fish type i that was caught in contaminated area (unitless)

ED(y) = Exposure duration for receptor y (years)

BW(y) = Body weight for receptor y (kg)

AT(y) = Averaging time for calculation of daily dose (years) for receptor y, set equal to 

ED in MIRC

EF(y) = Annual exposure frequency for receptor y (days)

ADD values, expressed as intakes, not absorbed doses, are appropriate for comparison with 

oral reference dose (RfD) values to estimate risk, as discussed in Section 3.3.2. All 

components of this equation are assumed to remain constant for consumers in a given 

population over time (e.g., seasonal and annual variations in diet are not explicitly taken into 

account). To calculate an ADD(y,i) from the contaminated area for food group i (in this case, fish) 

over an entire lifetime of exposure, age-group-specific ingestion rates and body weights are 

used. In MIRC, the averaging time used to calculate the daily dose for an age group (ATy) is 

equal to the exposure duration for that group (EDy); therefore these variables drop out of 

Equation 3-1. 

3.3.2 Calculation of the Hazard Quotient

MIRC was used to calculate a non-cancer hazard quotient (HQ) for each individual exposure 

scenario using the ratio of the calculated ADDs to the ingestion dose-response value for methyl 

mercury. Specifically, EPA’s RfD of 1x10-4 mg/kg-day for chronic exposure to methyl mercury 

was used. This value was obtained from EPA’s Integrated Risk Information System (IRIS), and 

it is also the value included by OAQPS in tabulated dose-response values typically used for risk 

assessments of hazardous air pollutants (more information on these values is included on 

EPA’s Fate, Exposure, and Risk Assessment (FERA) website (U.S. EPA 2007). The critical 

effect on which this RfD is based is developmental neuropsychological impairment, although 

data suggest that other adverse effects, including cardiovascular, persistent and delayed 

neurotoxicity, and reproductive effects, might also be associated with exposures to mercury.

If the HQ for a chemical is equal to or less than 1, EPA believes that there is no appreciable risk 

that non-cancer health effects will occur. If the HQ is greater than 1, however, EPA cautions 

that adverse health effects are possible, although an HQ above 1 does not indicate an effect will 

definitely occur. This is because of the margin of safety inherent in the derivation of all RfD 

values. The larger the HQ value, the more likely it is that an adverse effect may occur.
2/17/2011 45 DRAFT

Equation 3-2. Hazard Quotient for Chemicals with a Chronic RfD

RfD

ADD HQ =

where:

HQ = Hazard quotient for chemical (unitless)

ADD = Average daily ingested dose of chemical (mg/kg-day) 

RfD = Chronic oral reference dose for chemical (mg/kg-day)

For chemicals for which the toxicity reference value is an RfD based on developmental effects in 

infants, children, or young animals, a shorter exposure duration (ED) and averaging time (AT) 

may be required. For methyl mercury, the appropriate ED/AT and sensitive lifestage for 

exposure may need to be estimated from the information provided in the critical developmental 

study(ies) from which the RfD was derived (e.g., in consultation with the RfD documentation in 

EPA’s IRIS or in a toxicological profile developed for the chemical). For the type of assessment

presented here, however, comparing the highest ADD from among the child age categories 

provided in MIRC to the RfD is considered a conservative approach. 

4 Modeling Results and Risk Characterization

The results presented and discussed in this section include estimated concentrations of mercury 

in environmental media and biota (Section 4.1) and estimates of mercury exposures and 

associated non-cancer human health risks (Section 4.2). The presentation of results is followed 

by a discussion of the major sources of uncertainty (Section 4.3). 

Results are presented for one lake at each of the two facilities included in the assessment. As 

previously noted the results do not consider risks associated with any other source of mercury, 

(i.e., long-range transport from other utilities) just the subject facilities in isolation. A more 

detailed analysis and discussion of media concentration trends for the Santee Cooper Jefferies 

facility is included to provide some additional insight on how mercury concentrations and 

exposures were estimated using the models applied for these case studies. This discussion 

includes a brief comparison of the modeled biota-surface water mercury partitioning behavior 

with data found in literature. 

4.1 Estimated Media Concentrations 

For each of the abiotic and biotic compartments described in Section 3.2, TRIM.FaTE was used 

to estimate annual average mercury concentrations for each year in the 50-year modeling 

period. To simplify the presentation of the estimated media concentrations, this section focuses 

primarily on results for the 50th (i.e., final) year of the modeling period. Methyl mercury 

concentrations in fish from this year were used to estimate human exposures via fish 

consumption for this assessment. 

Table 4-1 presents media concentrations of divalent, elemental, and methyl mercury for the 50th

year of the modeling period for the Santee Cooper Jefferies site. The aquatic media 

concentrations are from Lake Moultrie, which was the closest water body to the emission 

source. The surface soil concentrations are from a surface soil compartment representative of 

the immediate watershed of Lake Moultrie. 
2/17/2011 46 DRAFT

Table 4-1. Modeled Mercury Concentrations and Speciation for Lake Moultrie, SC, 

near Santee Cooper Jefferies

TRIM.FaTE 

Compartment

Concentration Speciation

Units Total 

Hg Hg0 Hg2 MeHg % Hg0 % Hg2 % MeHg

Surface water mg/L 3.4E-08 1.0E-08 2.3E-08 1.0E-09 29.98% 67.03% 3.00%

Algae mg/kg wet 

weight 4.4E-05 0 3.3E-05 1.2E-05 <0.01% 73.72% 26.28%

Macrophyte mg/kg wet 

weight 1.2E-08 1.4E-18 1.2E-08 6.7E-10 <0.01% 94.66% 5.34%

Zooplankton mg/kg wet 

weight 8.5E-05 0 2.4E-05 6.0E-05 <0.01% 28.66% 71.34%

Water Column 

Carnivore

mg/kg wet 

weight 5.8E-03 0 2.1E-06 5.8E-03 <0.01% 0.04% 99.96%

Water Column 

Herbivore

mg/kg wet 

weight 2.3E-04 0 9.6E-06 2.2E-04 <0.01% 4.14% 95.86%

Water Column 

Omnivore

mg/kg wet 

weight 1.1E-03 0 4.0E-06 1.1E-03 <0.01% 0.36% 99.64%

Sediment ug/g dry 

weight 3.9E-04 9.7E-06 3.7E-04 1.5E-05 2.47% 93.78% 3.75%

Benthic 

Invertebrate

mg/kg wet 

weight 6.3E-05 5.0E-07 1.9E-05 4.4E-05 0.80% 30.27% 68.93%

Benthic 

Omnivore

mg/kg wet 

weight 1.7E-04 3.7E-15 8.6E-06 1.6E-04 <0.01% 5.03% 94.97%

Benthic 

Carnivore

mg/kg wet 

weight 1.4E-03 1.3E-15 7.5E-06 1.4E-03 <0.01% 0.55% 99.45%

Surface Soil ug/g dry 

weight 4.5E-04 1.1E-07 4.4E-04 7.4E-06 0.02% 98.34% 1.64%

Table 4-2 presents the analogous TRIM.FaTE results for the TVA Gallatin site. The aquatic 

media concentrations are from the eastern branch of Old Hickory Lake, which was the closest 

water body to the emission source. The surface soil concentrations are from a surface soil 

compartment representative of part of watershed of Old Hickory Lake.

Table 4-2. Modeled Mercury Concentrations and Speciation for Eastern Branch of 

Old Hickory Lake, TN, near TVA Gallatin

TRIM.FaTE 

Compartment

Concentration Speciation

Units Total 

Hg Hg0 Hg2 MeHg % Hg0 Hg2

% % MeHg

Surface water mg/L 2.3E-08 2.8E-09 2.0E-08 8.5E-10 12.02% 84.35% 3.63%

Algae mg/kg wet 

weight 3.9E-05 0 2.8E-05 1.0E-05 <0.01% 73.99% 26.01%

Macrophyte mg/kg wet 

weight 5.8E-09 3.9E-19 5.2E-09 5.7E-10 <0.01% 90.15% 9.85%
2/17/2011 47 DRAFT

Table 4-2. Modeled Mercury Concentrations and Speciation for Eastern Branch of 

Old Hickory Lake, TN, near TVA Gallatin

TRIM.FaTE 

Compartment

Concentration Speciation

Units Total 

Hg Hg0 Hg2 MeHg % Hg0 Hg2

% % MeHg

Zooplankton mg/kg wet 

weight 7.3E-05 0 2.1E-05 5.2E-05 <0.01% 28.83% 71.17%

Water Column 

Carnivore

mg/kg wet 

weight 5.0E-03 0 1.8E-06 5.0E-03 <0.01% 0.04% 99.96%

Water Column 

Herbivore

mg/kg wet 

weight 2.0E-04 0 8.4E-06 1.9E-04 <0.01% 4.17% 95.83%

Water Column 

Omnivore

mg/kg wet 

weight 9.6E-04 0 3.5E-06 9.6E-04 <0.01% 0.37% 99.63%

Sediment ug/g dry 

weight 3.2E-04 2.7E-06 3.1E-04 1.2E-05 0.85% 95.34% 3.81%

Benthic 

Invertebrate

mg/kg wet 

weight 5.2E-05 1.4E-07 1.6E-05 3.6E-05 0.27% 30.43% 69.30%

Benthic 

Omnivore

mg/kg wet 

weight 1.4E-04 1.0E-15 7.1E-06 1.4E-04 <0.01% 4.93% 95.07%

Benthic 

Carnivore

mg/kg wet 

weight 1.1E-03 3.8E-16 6.1E-06 1.1E-03 <0.01% 0.54% 99.46%

Surface Soil ug/g dry 

weight 2.5E-06 3.6E-09 2.4E-06 4.0E-08 0.15% 98.22% 1.64%

For both facilities, the model results suggest divalent mercury is the dominant species in surface 

water, sediment, and surface soil. Methyl mercury is the dominant species in the higher trophic 

levels of the aquatic biota, progressively bioaccumulating up the food chain until it represents 

over 95 percent of total mercury in game fish. These speciation trends are broadly consistent 

with the literature (see, for example, Driscoll et al. 2007).

Mercury partitioning between surface water and sediment, surface water and the various biotic 

compartments, and between the biota themselves are also consistent with a range of empirical 

data (Raymond and Rossman 2009). To evaluate TRIM.FaTE’s performance with respect to 

methyl mercury, modeled concentrations in aquatic biota were divided by modeled 

concentrations in surface water to derive a bioaccumulation factor based on outputs of the 

bioenergetic food web included in TRIM.FaTE. Figure 4-1 presents the results of these 

calculations and comparisons for Lake Moultrie in SC and Old Hickory Lake in TN to each other 

and to values found in literature (Hoffman et al. 2002, Driscoll et al. 2007, Raymond and 

Rossman 2009).
2/17/2011 48 DRAFT

Figure 4-1. Comparison of Calculated Bioaccumulation Factors Based on Model Outputs 

to Literature

A detailed analysis of deposition and media concentration trends was performed for the Santee 

Cooper Jefferies facility in SC because it presented the higher risk of the two facilities. The 

highlights of this analysis are presented below:

• The average mercury deposition rates (dry and wet, gaseous and particulate, divalent 

and elemental) based on the 5 years of AERMOD output are somewhat low compared to 

background deposition in remote locations. The mean deposition in the entire modeling 

domain was 0.197 ug/m2

-yr, while the mean for Lake Moultrie was 0.64 ug/m2-yr. By 

comparison, background deposition in remote locations ranged from <5-30 ug/m2

-yr

(Miller et al. 2005).

• The percentage of divalent mercury emissions that are estimated to deposit in the 30-km 

radius modeling zone was about 5 percent. This is a somewhat smaller percentage than 

has been observed in previous mercury modeling exercises. (Emissions at the Santee 

Cooper Jefferies facility were 74 percent divalent mercury, however, compared to 100

percent divalent mercury in the ISC study.)

• Dry deposition was substantially larger than wet deposition and made up 99.9 percent of 

total deposition (aggregate of all mercury species). Gaseous deposition was larger than 

particulate deposition and made up about 93.9 percent of total deposition (aggregate of 

1.00E+00

1.00E+01

1.00E+02

1.00E+03

1.00E+04

1.00E+05

1.00E+06

1.00E+07

1.00E+08

Algae Zooplankton Planktivore Omnivore Carnivore

Bioaccumulation Factor

Water Column Aquatic Biota

Driscoll, 2007

Raymond, 2009

Handbook of Ecotoxicology, 2002

Estimated in TRIM, SC 

Estimated in TRIM, TN
2/17/2011 49 DRAFT

all mercury species). Gaseous divalent mercury deposition made up 85 percent of total 

deposition of all mercury species.

• Modeled surface water concentrations of total mercury in Lake Moultrie (0.034 ng [total

Hg]/L) appeared low relative to levels (0.3 to 8 ng [total Hg]/L) in lakes and streams 

lacking substantive on-site anthropogenic or geologic sources (Hoffman, 2002). 

However, surface water concentrations were consistent with lake deposition rates, 

based on previous TRIM modeling. Partitioning between benthic sediment and surface 

water also seemed reasonable.

• Modeled methyl mercury concentration in carnivorous game fish (the uppermost trophic 

level in the lake, representative of the water column carnivore compartment) at 0.006

mg/kg ww was at the low end of the range of mean concentrations (0.001-8.94 mg/kg 

ww) found in game fish in lakes in a national study (U.S. EPA 1999). As illustrated 

above in Figure 4-1, the modeled biomagnification and bioaccumulation estimates 

reflected well-established literature trends.

4.2 Human Health Risk Assessment Results 

This section presents the human health risk assessment results of the case study analysis. 

Section 4.2.1 provides an overview of the results and Section 4.2.2 provides further details 

about the risk estimates for methyl mercury exposure.

The annually-averaged concentrations estimated by TRIM.FaTE for the 50th year of the 

modeling period were used to calculate chronic non-cancer hazard quotients (HQs) for methyl 

mercury exposures estimated to be attributed to source emissions. As described in Section 3, 

for methyl mercury, an EPA-recommended cooking conversion factor (CCF) of 1.5 was utilized 

to account for the fact that cooking fish reduces the overall mass of the fish without a reduction 

in the mercury level, which effectively increases the mercury concentration in the cooked fish

(Morgan et al. 1997). Separate HQs were then calculated for different individual variations of the 

fisher scenario evaluated by ICF. As described in Section 3.3, for the recreational angler, these 

variations were modeled using fish ingestion rates that varied by age for individuals in the 

general U.S. population who eat fish. Exposures were also calculated for five additional adult 

populations of anglers (African American and female anglers and anglers of Hispanic, Laotian, 

and Vietnamese descent) who are culturally or economically disposed to higher rates of fish 

consumption.

HQs were below one for all scenarios modeled at both facilities. Non-cancer HQs for methyl 

mercury are presented in Figures 4-2 and 4-3 for the fisher scenarios evaluated. Results are 

presented for the RME ingestion rates representative of the upper end (i.e., 90th percentile) of 

the distribution of ingestion rates for each type of individual fish consumer evaluated. The HQs 

calculated using CTE ingestion rates are approximately half of the RME HQs. HQs for all 

populations at both CTE and RME ingestion rates are presented in Table 4-3. The majority of 

HQs are well below 1.0. The highest HQ was calculated for African American anglers, where an 

HQ of 0.32 was estimated. This result reflects the higher rate of fish ingestion assumed for this 

population. 
2/17/2011 50 DRAFT

Figure 4-2. Source-attributable Hazard Quotients for Exposure to Methyl Mercury for 

Recreational Angler Scenarios, RME Ingestion Rate

Figure 4-3. Source-attributable Hazard Quotients for Exposure to Methyl Mercury for 

Other Angler Populations, RME Ingestion Rate

1.0E-03

1.0E-02

1.0E-01

1.0E+00

Lake Moultrie, Santee Cooper 

Jefferies

East Old Hickory Lake, TVA 

Gallatin

Noncancer Hazard Quotient

HQ Child (1-2)

HQ Child (3-5)

HQ Child (6-11)

HQ Child (12-19)

HQ Adult (20-70)
2/17/2011 51 DRAFT

Table 4-3. Hazard Quotients for Exposure to Methyl Mercury via Fish Ingestion

Non-Cancer Hazard Quotients

Lake Moultrie (Santee Cooper 

Jefferies)

East Old Hickory Lake (TV 

Gallatin)

CTE Ingestion RME Ingestion CTE Ingestion RME Ingestion

Recreational Angler

Child 1-2 5.63E-03 1.33E-02 4.79E-03 1.13E-02

Child 3-5 5.65E-03 1.33E-02 4.81E-03 1.14E-02

Child 6-11 4.41E-03 1.12E-02 3.76E-03 9.57E-03

Child 12-19 3.14E-03 7.22E-03 2.68E-03 6.15E-03

Adult 5.00E-03 1.23E-02 4.26E-03 1.05E-02

African American Adult 1.24E-01 3.23E-01 1.06E-01 2.75E-01

Female Adult 2.83E-02 8.92E-02 2.41E-02 7.59E-02

Hispanic Adult 1.87E-02 7.10E-02 1.59E-02 6.05E-02

Laotian Adult 1.96E-02 1.05E-01 1.67E-02 8.94E-02

Vietnamese Adult 1.96E-02 7.18E-02 1.67E-02 6.12E-02

4.3 Discussion of Uncertainties and Limitations

The exposure and risk modeling process attempts to describe naturally occurring physical, 

chemical and biological processes in terms of mathematical algorithms. In a risk assessment, 

the modeling process generally involves a relatively simplistic representation of highly complex 

processes for computational tractability. This simplification introduces uncertainty that must be 

considered when evaluating the risk assessment results and the intermediate values calculated 

by models over the course of the risk assessment. Furthermore, algorithms that describe the 

movement of pollutants in the natural environment depend on a large number of environmental 

parameters whose values may be inherently variable and whose variability may not be well 

characterized in the literature for the specific sites being modeled. While these unavoidable 

issues do not invalidate the integrity of the modeling process, the evaluation of model results 

must be informed by consideration of model uncertainties, parameter variability, and related 

issues. 

The media concentration and risk results presented above must be interpreted in light of the 

uncertainties associated with both the model inputs and the modeling results. The behavior of 

mercury in the environment is highly complex, and a range of natural processes are represented 

in a relatively simplistic manner by AERMOD and TRIM.FaTE, including, but not limited to: 

• gaseous and particulate deposition from air; 

• mercury biogeochemical cycling in the aquatic environment, and especially mercury 

transformation through methylation and demethylation at the sediment-surface interface; 

• mixing processes and suspended and benthic sediment dynamics in lakes; and 

• biotic processes such as growth, reproduction, and predation. 
2/17/2011 52 DRAFT

In addition, the toxicology of methyl mercury is complex and uncertain, as are the empirical 

estimates of human exposure from which the health benchmark (RfD) is based.

Examples of parameters and model inputs for which there is variability and uncertainty include 

aquatic food web parameters (such as assimilation efficiencies and elimination rates), 

topographic parameters (such as lake depth, runoff rates, and erosion rates), meteorological 

parameters (such as evaporation and precipitation rates), chemical transformation parameters 

(such as methylation and demethylation rates), human exposure parameters (especially fish 

consumption rates), and the reference dose used to assess potential adverse effects. 

Given restrictions inherent to the modeling approach and the parameters of this evaluation, this 

assessment did not attempt to quantitatively estimate the full range of uncertainty associated 

with results (such as might be obtained in a probabilistic analysis). Instead, this analysis 

depended on central tendency and reasonable maximum exposures to bound risk estimates. 

The omission of any background mercury from the estimation of potential health impacts must 

also be considered when interpreting the results of this assessment. The scope of this analysis 

encompassed only facility-attributable exposures and the resulting incremental HQ as a risk 

assessment endpoint. No quantitative health impact of the number or fraction of people 

potentially affected in the modeling zones was estimated, and no estimates of specific impacts 

like IQ decrements or cardio-vascular effects were made. This does not represent a source of 

uncertainty affecting the modeling results presented here, but it is an important consideration 

that must be taken into account when interpreting the results of this analysis.

This section describes the key uncertainties in the modeling process for risks from EGUs. Table 

4-4 presents major sources of uncertainty affecting model and risk results; describes the 

sensitivity of model results, in general, to the uncertainty factor; offers a brief comment on the 

treatment of the issue in the current assessment; and provides a qualitative assessment of the 

level of uncertainty in the current results attributable to the various factors. Each of the 

uncertainty factors is also discussed further below: Section 4.3.1 provides additional information 

on the major sources of uncertainty in the fate and transport modeling and Section 4.3.2 

provides additional detail on the uncertainty related to exposure modeling and risk calculations.

Table 4-4. Uncertainty Factors in the Multipathway Residual Risk Assessment for EGUs

Uncertainty Factor Sensitivity of 

Risk Results Comment

Estimated 

Impact of 

Uncertainty on 

Results

Methylation and 

demethylation rates in 

sediment and surface 

water

High

Model uses fixed rate constants for 

methylation and demethylation in abiotic 

media. Model does not capture complex 

dependence of rate constants on 

environmental conditions.

Medium-High

Aquatic food web 

parameters High

Limited data on chemical and speciesspecific parameters such as assimilation 

efficiency, elimination rates, etc. 

Conservative assumptions used.

Medium

Depth of lakes High

Based on limited data. Averaged over lake 

area and time. Perfect mixing assumed in 

estimating concentrations.

Medium
2/17/2011 53 DRAFT

Table 4-4. Uncertainty Factors in the Multipathway Residual Risk Assessment for EGUs

Uncertainty Factor Sensitivity of 

Risk Results Comment

Estimated 

Impact of 

Uncertainty on 

Results

Toxicity reference 

value (RfD) for methyl 

mercury

High

Used EPA recommended value, but estimate 

includes inherent variability and uncertainty. Low-Medium

Fate and transport 

modeling process High

TRIM.FaTE model may not capture all natural 

processes or describe them precisely for 

particular sites. 

Low-Medium

Ingestion exposure 

parameters High

Used EPA recommended values, but highend ingestion rates may be based on 

extrapolation from survey data with small 

sample sizes.

Low-Medium

Retention time/flush 

rate Medium-High

Calculated from depth. Site-specific data 

would help validate inflows and runoff 

fractions.

Medium

Modeling resolution 

and lay out Medium

Resolution of compartments in modeling 

zone is relatively coarse. Higher area 

averaging may dilute exposure point 

concentrations.

Low-Medium

Runoff rates Medium

Based on judgment in absence of scientific 

method. Could not validate owing to lack of 

data.

Low-Medium

Erosion rates Medium Based on USLEs. State-specific erosion data 

may differ. Low-Medium

Precipitation rate Medium

Data for four year period may not represent 

true average. Affects deposition quantity and 

type.

Low-Medium

Evaporation rate Medium Limited site specific data. Impacts lake flush 

rates and concentrations. Low-Medium

Wind speed and 

direction Medium

Data for four year period may not represent 

average conditions. Excessive dispersion 

may underestimate risk.

Low-Medium

Sediment dynamics Medium Model uses simplistic algorithms to describe 

deposition and resuspension of sediment. Low-Medium

Other soil, surface 

water, air & vegetation 

physico-chemical 

parameters

Medium

Limited site-specific data. Default 

assumptions often used. Low-Medium

4.3.1 Uncertainties Related to Fate and Transport Modeling (AERMOD and 

TRIM.FaTE)

The algorithms representing the transport and eventual fate of mercury in air, surface water, 

sediment and biotic media are simplified representations of complex natural processes. 

Estimated deposition rates and concentrations may vary across different environmental models 

and may be most accurate in specific conditions that meet restrictive assumptions. The 
2/17/2011 54 DRAFT

AERMOD model assumes steady-state conditions, which may not exist at the modeled sites. 

The TRIM.FaTE model represents all fate and transport processes in terms of first-order 

differential equations; however, some processes like chemical diffusion are known to follow 

second-order dynamics. Other algorithms, like those dealing with methylation and 

demethylation, do not consider all the factors known to affect these processes, or the processes 

may not be well understood (as is the case with mercury methylation and demethylation). As 

noted in Section 4.1, the deposition rates and media concentrations predicted in the current 

model are at the low end of the range found at remote sites; these results should be viewed in 

light of model uncertainty.

This section expands on some of the major uncertainties included in the previous table that are 

specific to the fate and transport modeling conducted for the case studies.

Methylation and Demethylation Rates. Methylation and demethylation in sediment and 

surface water are key processes governing biogeochemical cycling of mercury in the aquatic 

environment. The rate of occurrence of these processes has a great influence on sensitivity of 

an aquatic ecosystem to mercury inputs and specifically influences the amount of methyl 

mercury available for bioaccumulation. Some water bodies that are efficient at methylating 

inorganic mercury can show significant methyl mercury concentrations in biota despite a 

relatively small mercury input into the system. The representation of these processes in the 

TRIM.FaTE model does not explicitly account for known dependencies of transformation rates 

on redox potential, pH, sulfite concentration, dissolved organic carbon content, and 

hydrodynamics at the sediment-surface water interface. Both methylation and demethylation 

can occur either biotically or abiotically. Certain conditions, like specific ranges of chloride, 

sulfide and dissolved organic matter concentrations, can increase the bioavailability of divalent 

mercury for methylation. Redox conditions can influence the rate of abiotic demethylation. 

These process mechanics, potentially antagonistic interactions with heavy metals like selenium, 

and the potential for photodegradation of methyl mercury are not captured in the TRIM.FaTE 

model. Instead, user-supplied first-order rate constants are used to model methylation and 

demethylation in surface water and sediment. Site-specific data are limited and the regional 

default rate constants used may not represent conditions at the modeled lakes.

Aquatic Food Web Parameters. Estimates of methyl mercury concentrations in fish consumed 

by people are extremely sensitive to aquatic food web parameters. Limited site- and speciesspecific data were available on a range of aquatic food web parameters, including biomass, 

food web structure, assimilation efficiencies, elimination rates, ingestion rates, and gill 

absorption rates. Site-specific data were used when feasible, but conservative assumptions 

based on literature review and professional judgment were used in the absence of specific data.

Depth of Lakes. The concentration of pollutants transported into lakes (and thereafter up the 

aquatic food chain) has a sharp dependence on the depth of lake assumed in the model. Sitespecific, average depth data were used for all lakes, but there were limited data available for 

cross-verification. Averaging concentrations over space and time implies perfect mixing in the 

water body and may not account for local pockets or periods of high concentration.

Fate and Transport Modeling Process. The TRIM.FaTE model represents all fate and 

transport processes in terms of first-order differential equations. However, some processes like 

diffusion are known to follow second-order dynamics. TRIM.FaTE also does not explicitly 

account for lateral or vertical dispersion in the air compartments. As noted earlier, some 

algorithms like methylation do not consider all the factors known to affect the process. While 
2/17/2011 55 DRAFT

the model’s algorithms have been validated and are based on professional judgment, some 

level of uncertainty may result from such simplifications.

Surface Water Retention Time/Flush Rate. Retention time, which is inversely proportional to 

flush rate, determines how quickly pollutants are passed out of a lake. A flush rate that is too 

high (or retention time that is too low) could result in an underestimate of pollutant 

concentrations in surface water and in the aquatic food chain. For the current case study, 

retention time was calculated based on information and assumptions regarding inflow into the 

lake, evaporation, and depth. Lack of data prevented cross-verification of the computed 

retention time/flush rates.

Modeling Resolution and Lay Out. In a compartmental box model like TRIM.FaTE, if the size 

of the compartments is large relative to the rate at which concentrations change with distance 

from the emission source, high concentration areas would be averaged with low concentration 

areas resulting in a potential risk dilution for some areas. Furthermore, lateral diffusion in air 

compartments in TRIM.FaTE is sensitive to the size and shape of air compartments. We 

attempted to minimize the potential for underestimation of risk by selecting smaller compartment 

areas close to the source and gradually increasing the compartment dimensions away from the 

source.

Runoff Rates. For pollutants whose risks are transmitted chiefly by the fish consumption 

pathway, the amount of pollutant entering lakes is a significant variable. Because runoff can 

account for a significant portion of the pollutant transported into the lake in some locations, 

runoff rate from the watershed is a potentially sensitive parameter in the model. ICF estimated 

these runoff rates based on information about the surrounding topography and local 

precipitation data. 

Erosion Rates. Similar to runoff rates, erosion rates can affect the quantity of pollutants 

transported into a water body. ICF estimated erosion rates using the universal soil loss 

equation, which is a generalized estimate that is dependent on local topography, land use and 

climate. It is possible that local erosion rates may differ from the USLE estimate.

Precipitation Rate. The precipitation rate in the model potentially affects the rate at which 

pollutants are transported between surface soil compartments and water bodies and also the 

rate at which pollutants are flushed out of water bodies. ICF used rainfall data for a four-year 

period, which may not represent average or future conditions.

Evaporation Rate. The evaporation rate affects flush rate computation in the model, and 

consequently lake concentration and aquatic biota concentration estimates. Limited site-specific 

data were available and regional estimates had to be used in the model.

Wind Speed and Direction. Wind speed and direction affect dispersion of the pollutant in the 

model. Because these data were derived from a single-four period, they may not be 

representative of average or future conditions.

Sediment Dynamics. The suspension and burial of sediment can have a significant impact on 

surface water concentration and speciation by influencing the methylation and demethylation 

process. Suspended solid concentrations also affect the amount of mercury transported out of 

the water body during flushing. Resuspension of buried sediment could remobilize previously 

deposited mercury into the water body. In the TRIM.FaTE model, these processes are 

simplistically represented by default sediment deposition rates and suspended solids 
2/17/2011 56 DRAFT

concentrations, which do not account for hydrodynamic sediment cycling processes. These 

parameters were not based on site-specific data.

Other Soil, Surface Water, Air & Vegetation Physico-Chemical Parameters. Because of a 

lack of easy accessible site-specific data, default or national-average values were used for a 

number of other soil, surface water, air, and vegetation physical and chemical parameters. 

4.3.2 Uncertainties Related to Exposure Modeling and Risk Calculation

Toxicity Reference Value. HQs were calculated using EPA’s recommended RfD for methyl 

mercury. Reference doses are typically estimated after building in uncertainty factors for 

pharmacokinetic variability and uncertainty, pharmacodynamic variability and uncertainty, interor intra-species variability, and potentially other factors. An awareness of the values and ranges 

of these uncertainty factors (documented in EPA’s IRIS data base) can help inform risk 

management decisions. For methyl mercury, EPA has reported a high confidence in the oral 

reference dose assessment. 

Ingestion Exposure Parameters. Although the fish ingestion rates used in this analysis for 

recreational anglers were obtained from recommended EPA exposure factors and additional 

data that were provided by EPA for high-exposure populations, there is associated uncertainty. 

This is especially true for the upper percentile estimates that were derived using statistical 

inferences from limited survey data. To evaluate the sensitivity of risk results to the selected 

fish ingestion rates, HQs were calculated for the mean, 90th, 95th, and, where available, the 99th

percentile ingestion rates for each of the populations assessed. These results, as well as the 

ingestion rates and the relative percentage increases from the mean for each of the ingestion 

rates, are provided in Table 4-5. (Note that because HQs are linearly proportionate with the 

ingestion rate, the relative percentage increase from the mean for the ingestion rate is equal to 

the relative percentage increase from the mean for the associated HQ.) 

As can be seen in Table 4-5, the sample size for many of the populations is relatively low. The 

African American population has the highest ingestion rates and associated HQs with a 

maximum HQ of 4.3E-1. However, the ingestion rates for this population are based on only 39 

data points, which leads to high uncertainties, particularly near the edges of the distribution. The 

Vietnamese population is the only group whose ingestion rates are based on a smaller sample 

size (33). Overall, the increase in ingestion rate, and subsequently, HQ, from the mean to the 

95th percentile is fairly small across populations (from 226% to 504%). However, much greater 

variability can be seen when looking at the increase in HQ from the mean to the 99th percentile 

ingestion rate (from 245% to 1,614%). Larger sample sizes would reduce some of the 

uncertainty related to ingestion rates, especially for higher-end rates.

For the populations other than the recreational angler scenario, taken from the EFH (U.S. EPA 

1997a), data was collected at one geographical location. Regional differences in ingestion rates 

may not be captured with the data that were used in these analyses. There also could be highly 

exposed populations that have not been identified that could reside within the modeled area. It 

also is possible that not all of the populations assessed in this analysis are present in the 

modeling zone of the two facilities considered. All of these issues provide some level of 

uncertainty to the results of this analysis.
2/17/2011 57 DRAFT

Table 4-5. Sensitivity Analysis for Fish Ingestion Rate

Ingestion 

Ratea (g/day)

HQ

Lake Moultrie

HQ

East Old 

Hickory

Relative % 

Increase from 

Mean

Recreational Angler Adult

Mean 6.9 5.0E-03 4.3E-03 NA

90th percentile Ingestion 17 1.2E-02 1.0E-02 146%

95th percentile Ingestion 25 1.8E-02 1.5E-02 262%

99th percentile Ingestion 118.3 8.6E-02 7.3E-02 1,614%

African American Adult

Mean 171 1.2E-01 1.1E-01 NA

90th percentile Ingestion 446 3.2E-01 2.8E-01 161%

95th percentile Ingestion 557 4.0E-01 3.4E-01 226%

99th percentile Ingestion 590 4.3E-01 3.6E-01 245%

Female Adult

Mean 39.1 2.8E-02 2.4E-02 NA

90th percentile Ingestion 123 8.9E-02 7.6E-02 215%

95th percentile Ingestion 173 1.3E-01 1.1E-01 342%

99th percentile Ingestion 373 2.7E-01 2.3E-01 854%

Hispanic Adult

Mean 25.8 2.2E-03 1.6E-02 NA

90th percentile Ingestion 98 8.3E-03 6.1E-02 280%

95th percentile Ingestion 155.9 1.1E-01 9.6E-02 504%

99th percentile Ingestion NA NA NA NA

Laotian Adult

Mean 47.2 4.0E-03 1.7E-02 NA

90th percentile Ingestion 144.8 1.2E-02 8.9E-02 207%

95th percentile Ingestion 265.8 1.9E-01 1.6E-01 463%

99th percentile Ingestion NA NA NA NA

Vietnamese Adult

Mean 27.1 2.3E-03 1.7E-02 NA

90th percentile Ingestion 99.1 8.4E-03 6.1E-02 266%

95th percentile Ingestion 152.4 1.1E-01 9.4E-02 462%

99th percentile Ingestion NA NA NA NA a Sample size = 1,633 for recreational angler, 39 for African American, 149 for female, 45 for Hispanic, for 54 for 

Laotian, and for 33 for Vietnamese.
2/17/2011 58 DRAFT

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Appendix A. TRIM.FaTE Inputs for Utilities Scenario

TVA Gallatin and Santee Cooper Jefferies
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LIST OF TABLES

Table A-1. TRIM.FaTE Simulation Parameters ......................................................................................... A-1

Table A-2. Meteorological Inputs............................................................................................................. A-2

Table A-3. Air Parameters........................................................................................................................ A-3

Table A-4. Soil and Groundwater Parameters......................................................................................... A-4

Table A-5.1. Runoff Fractions – TVA Gallatin........................................................................................... A-6

Table A-5.2. Runoff fractions – Santee Cooper Jefferies......................................................................... A-8

Table A-6. USLE Parameters................................................................................................................... A-10

Table A-7.1. Terrestrial Plant Placement – TVA Gallatin........................................................................ A-11

Table A-7.2. Terrestrial Plant Placement – Santee Cooper Jefferies..................................................... A-12

Table A-8. Terrestrial Plant Parameters................................................................................................. A-13

Table A-9. Surface Water Parameters.................................................................................................... A-16

Table A-10. Sediment Parameters......................................................................................................... A-17

Table A-11. Aquatic Plants..................................................................................................................... A-18

Table A-12. Aquatic Food Web .............................................................................................................. A-19

Table A-13. Mercury Chemical-Specific Properties................................................................................ A-20

Table A-14. Mercury Chemical-Specific Properties for Abiotic Compartments..................................... A-21

Table A-15. Mercury Chemical-Specific Properties for Plant Compartments........................................ A-25

Table A-16. Mercury Chemical-Specific Properties for Aquatic Species................................................ A-27
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2/17/2011 A-1 DRAFT

Table A-1. TRIM.FaTE Simulation Parameters

Parameter 

Name

Units Value Used Reference

Start of 

simulation

date/time 1/1/1990, 

midnight

Consistent with met data.

End of 

simulation

date/time 1/1/2040, 

midnight

Consistent with met data set; 

selected to provide a 50-year 

modeling period.

Simulation 

time step

hr 1 Selected value.

Output time 

stepa

hr 4 Selected value.

a Output time step is set in TRIM.FaTE using the scenario properties 

"simulationStepsPerOutputStep" and "simulationTimeStep."
2/17/2011 A-2 DRAFT

Table A-2. Meteorological Inputs

Parameter 

Name

Units Value 

Used

Reference

Meteorological Inputs (all TRIM.FaTE scenario properties, except mixing height)

Air 

temperature

degrees K Varies by 

hour

NCDC Hourly Surface Hourly Meteorological 

Data (NCDC 2010)

Horizontal 

wind speed

m/s Varies by 

hour

NCDC Hourly Surface Hourly Meteorological 

Data (NCDC 2010)

Vertical 

wind speed

m/s 0.0 Professional judgment; vertical wind speed not 

used by any of the algorithms in the version of 

the TRIM.FaTE library used for secondary lead

Wind 

direction

degrees 

clockwise from 

N (blowing 

from)

Varies by 

hour

NCDC Hourly Surface Hourly Meteorological 

Data (NCDC 2010)

Rainfall 

Rate

m3[rain]/m2[surf

ace area]/day

Varies by 

hour

NCDC Hourly Surface Hourly Meteorological 

Data (NCDC 2010)

Mixing 

height (used 

to set air VE 

property 

named 

“top”)

m 800 5th percentile annual average mixing heights 

(calculated from daily morning and afternoon 

values), for all stations in SCRAM Surface 

Archived Data (40 state, 70 stations; U.S. EPA 

2010). Calculated by met data. This value is not 

used in TRIM.FaTE,

isDay_Stea

dyState_for

Air

unitless -- Value not used in current dynamic runs (would 

need to be reevaluated if steady-state runs are 

needed).

isDay_Stea

dyState_for

Other

unitless --
2/17/2011 A-3 DRAFT

Table A-3. Air Parameters

Parameter Name Units Value Used Reference

Atmospheric dust load kg[dust]/m3[air] 6.15E-08 Bidleman 1988

Density of air g/cm3 0.0012 U.S. EPA 1997

Dust density kg[dust]/m3[dust] 1,400 Bidleman 1988

Fraction organic matter 

on particulates

unitless 0.2 Harner and Bidleman 1998

Height m 800 5th percentile annual average 

mixing heights (calculated 

from daily morning and 

afternoon values), for all 

stations in SCRAM Surface 

Archived Data (40 state, 70 

stations; U.S. EPA 2010). 

Calculated by met data. This 

value is not used in 

TRIM.FaTE,
2/17/2011 A-4 DRAFT

Table A-4. Soil and Groundwater Parameters

Parameter Name Units TVA 

Gallatin

Santee 

Cooper 

Jefferies

Reference

Surface Soil Compartment Type

Air content volume[air]/volume[com

partment]

0.26 0.29 McKone et al. 2001. 

(Table A-2)

Average vertical 

velocity of water 

(percolation)

m/day 5.75E-04 7.17E-04 Assumed to be 0.2 

times the average 

precipitation for the site.

Boundary layer 

thickness above 

surface soil

m 0.005 0.005 Thibodeaux 1996; 

McKone et al. 2001 

(Table 3).

Density of soil solids 

(dry weight)

kg[soil]/m3[soil] 2600 2600 McKone et al. 2001 

(Default in Table 3)

Thickness - untilleda m 0.01 0.01 McKone et al. 2001 (p. 

30).

Thickness - tilleda m 0.20 0.20 U.S. EPA 2005.

Erosion fraction unitless variesb variesb See Erosion and Runoff 

Fraction table.

Fraction of area 

available for erosion

m2[area 

available]/m2[total]

1 1 Professional judgment; 

area assumed rural.

Fraction of area 

available for runoff

m2[area 

available]/m2[total]

1 1 Professional judgment; 

area assumed rural.

Fraction of area 

available for vertical 

diffusion

m2[area 

available]/m2[total]

1 1 Professional judgment; 

area assumed rural.

Fraction sand unitless 0.25 0.25 Professional judgment.

Organic carbon 

fraction

unitless 0.005 0.006 McKone et al. 2001 

(U.S. average in Table 

A-3).

pH unitless 6.8 6.8 Professional judgment.

Runoff fraction unitless variesb variesb See Erosion and Runoff 

Fraction table.

Total erosion rate kg[soil]/m2/day variesb variesb See Total Erosion 

Rates table.

Total runoff rate m3[water]/m2/day 8.05E-04 7.17E-04 Calculated using 

scenario-specific 

precipitation rate and 

assumptions associated 

with water balance.

Water content volume[water]/volume[c

ompartment]

0.2 0.15 McKone et al 2001 

(Table A-2).

Root Zone Soil Compartment Type

Air content volume[air]/volume[com

partment]

0.23 0.24 McKone et al 2001 

(Table A-3).

Average vertical 

velocity of water 

(percolation)

m/day 5.75E-04 7.17E-04 Assumed to be 0.2 

times the average 

precipitation for the site.

Density of soil solids 

(dry weight)

kg[soil]/m3[soil] 2,600 2,600 McKone et al. 2001 

(Table 3).
Table A-4 (cont.). Soil and Groundwater parameters

2/17/2011 A-5 DRAFT

Parameter Name Units TVA 

Gallatin

Santee 

Cooper 

Jefferies

Reference

Fraction sand unitless 0.25 0.25 Professional judgment.

Thickness - untilleda m 0.79 0.79 McKone et al. 2001 

(U.S. average in Table

16).

Thickness - tilleda m 0.6 0.6 McKone et al. 2001 

(Adjusted from Table 

16).

Organic carbon 

fraction

unitless 0.005 0.006 McKone et al. 2001 

(Table A-3).

pH unitless 6.8 6.8 Professional judgment.

Water content volume[water]/volume[c

ompartment]

0.22 0.2 McKone et al. 2001 

(Table A-3).

Vadose Zone Soil Compartment Type

Air content volume[air]/volume[com

partment]

0.19 0.22 McKone et al. 2001 

(Table A-4).

Average vertical 

velocity of water 

(percolation)

m/day 6.8 6.8 Assumed as 0.2 times 

the average 

precipitation for the site

Density of soil solids 

(dry weight)

kg[soil]/m3[soil] 2,600 2,600 McKone et al. 2001 

(Default in Table A-3).

Fraction sand unitless 0.35 0.35 Professional judgment.

Thicknessa m 1.5 1.7 McKone et al. 2001 

(Table A-4).

Organic carbon 

fraction

unitless 0.002 0.003 McKone et al. 2001 

(Table A-4).

pH unitless 6.8 6.8 Professional judgment.

Water content volume[water]/volume[c

ompartment]

0.25 0.21 McKone et al. 2001 

(Table A-4).

Ground Water Compartment Type

Thicknessa m 3 3 McKone et al. 2001 

(Table 3).

Fraction sand unitless 0.4 0.4 Professional judgment.

Organic carbon 

fraction

unitless 0.004 0.004 Professional judgment.

pH unitless 6.8 6.8 Professional judgment.

Porosity volume[total pore 

space]/volume[compart

ment]

0.2 0.2 McKone et al. 2001 

(Default in Table 3).

Density of Solid 

material in aquifer

kg[soil]/m3[soil] 2,600 2,600 McKone et al. 2001 

(Default in Table 3). a Set using the volume element properties file

b See separate tables for erosion/runoff fractions and total erosion rates.
2/17/2011

A-6 DRAFT

TableA-5.1. Runoff Fractions– TVA Gallatin

a

Sending 

Compartment

Receiving Compartment

Old Hickory Lake WOld Hickory Lake EPriest ReservoirSourceS1N2N3NWWSW4ES3SW1S2NESW2N1SW3SW0SE0S0NE0NW0

Old Hickory 

Lake West

01000000000000000000000

OldHickory 

Lake East

00100000000000000000000

Priest Reservoir

00010000000000000000000

Source

10000000000000000000000

S1

00100000000000000000000

N2

01000000000000000000000

N3

00100000000000000000000

NW

01000000000000000000000

W

01000000000000000000000

SW4

000

0.75

000000000000000 0.25000

E

00000000000000010000000

S3

00010000000000000000000

SW1

01000000000000000000000

S2

00 0.4000000000000 0.60000000

NE

00100000000000000000000
Table A-5.1 (cont.). Runoff Fractions – TVA Gallatina

2/17/2011 A-7 DRAFT

Sending 

Compartment

Receiving Compartment

Old Hickory Lake WOld Hickory Lake EPriest ReservoirSourceS1N2N3NWWSW4ES3SW1S2NESW2N1SW3SW0SE0S0NE0NW0

SW2

0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0

N1

0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

SW3

0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

SW0

1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

SE0

0.6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.4

S0

0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0

NE0

0.6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.4 0 0 0 0 0 0 0

NW0

0.9 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.1 0 0 0

a Values estimated from analysis of the National Elevation Data (USGS 2010)
2/17/2011 A-8 DRAFT

Table A-5.2. Runoff fractions – Santee Cooper Jefferiesa

Sending 

Compartment

Receiving Compartment

Old Hickory Lake WOld Hickory Lake EPriest ReservoirSourceS1N2N3NWWSW4ES3SW1S2NESW2N1SW3SW0SE0S0NE0NW0

Old Hickory 

Lake West

1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0

OldHickory 

Lake East

0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1

Priest Reservoir

0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

Source

0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1

S1

0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

N2

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0

N3

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0

NW

0 0 0 0 0 0 0 0.25 0

0.2

5 0 0 0 0 0 0 0 0 0.5 0 0 0 0

W

0 0 0

0.

1 0 0 0 0 0 0 0.9 0 0 0 0 0 0 0 0 0 0 0 0

SW4

0 0 0

0.

1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.9 0 0 0 0

E

0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0

S3

0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0

SW1

0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0

S2

0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0
Table A-5.2 (cont.). Runoff Fractions –Santee Cooper Jefferiesa

2/17/2011 A-9 DRAFT

Sending 

Compartment

Receiving Compartment

Old Hickory Lake WOld Hickory Lake EPriest ReservoirSourceS1N2N3NWWSW4ES3SW1S2NESW2N1SW3SW0SE0S0NE0NW0

NE

0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1

SW2

0 0 0 0 0 0 0 0 0 0 0 0 0 0.5 0 0 0 0.5 0 0 0 0 0

N1

1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0

SW3

1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0

SW0

1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0

SE0

1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0

S0

0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1

NE0

0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

NW0

0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1

a Values estimated from analysis of the National Elevation Data (USGS 2010)
2/17/2011 A-10 DRAFT

Table A-6. USLE Parametersa

Land Use 

Type

Rainfall /

Erosivity 

Index

Soil 

Erodibility 

Index

LengthSlope 

Factor

Cover 

Management

Factor

Supporting 

Practices 

Factor

Unit Soil Loss

R (100 ftton/ac)K 

(ton/ac/(100 

ft-ton/acre))

LS 

(USCS)

C

(USCS)

P A 

(ton/ac/yr)

A 

(kg/m2/d)

TVA Gallatin

No 

Vegetation

225 0.17 0.47 0.45 1 8.09 4.97E-03

Untilled 

Soil

225 0.34 0.47 0.082 1 2.95 1.81E-03

Deciduous

forest

225 0.26 0.47 0.1 1 2.75 1.69E-03

Santee Cooper Jefferies

Untilled 

Soil

325 0.14 0.06 0.082 1 0.22 1.4E-04

Evergreen 

forest

325 0.13 0.06 0.1 1 0.25 1.6E-04

Evergreen 

Wetlands/

Wetlands

325 0.13 0.06 0.14 1 0.35 2.2E-04

a See section 3.2.5 for details on specific parameters
2/17/2011 A-11 DRAFT

Table A-7.1. Terrestrial Plant Placement – TVA Gallatin

Surface Soil 

Volume 

Element

Surface Soil 

Depth (m)

Deciduous 

Forest

Untilled 

Soil

No 

Vegetat

ion

S1 0.01 x

N2 0.01 x

N3 0.01 x

NW 0.01 x

W 0.01 x

SW4 0.01 x

E 0.01 x

S3 0.01 x

SW1 0.01 x

S2 0.01 x

NE 0.01 x

SW2 0.01 x

N1 0.01 x

SW3 0.01 x

SW0 0.01 x

SE0 0.01 x

S0 0.01 x

NE0 0.01 x

NW0 0.01 x
2/17/2011 A-12 DRAFT

Table A-7.2. Terrestrial Plant Placement – Santee Cooper 

Jefferies

Surface Soil 

Volume 

Element

Surface Soil 

Depth (m)

Evergreen 

Forest

Evergreen Forest/ 

Wetland

NW1 0.01 x

W 0.01 x

SW2 0.01 x

S2 0.01 x

S1 0.01 x

SE1 0.01 x

SE2 0.01 x

E 0.01 x

NE 0.01 x

NW2 0.01 x

SW1 0.01 x

NW0 0.01 x

NE0 0.01 x

SE0 0.01 x

SW0 0.01 x
2/17/2011 A-13 DRAFT

Table A-8. Terrestrial Plant Parameters

Parameter Name Units Deciduous Foresta Grass/Herba

Value 

Used

Reference Value 

Used

Reference

Leaf Compartment Type

Allow exchange 1=yes, 0=no seasonalb Begins on day of last freeze in 

the spring and ends on day of 

first freeze in the fall.-

seasonalb Begins on day of last freeze in 

the spring and ends on day of 

first freeze in the fall.--

Average leaf area index m2[leaf]/ 

m2[area]

3.4 CDIAC 2010 (Harvard Forest, 

dom. red oak and red maple)

5.0 Mid-range of 4-6 for old fields, 

R.J. Luxmoore, ORNL.

Calculate wet dep 

interception fraction 

(Boolean)

1=yes, 0=no 0 Professional judgment. 0 Professional judgment.

Correction exponent, 

octanal to lipid

unitless 0.76 Trapp 1995 (From roots) 0.76 Trapp 1995 (From roots)

Degree stomatal opening unitless 1 Set to 1 for daytime based on 

professional judgment (stomatal 

diffusion is turned off at night 

using a different property, 

IsDay).

1 Set to 1 for daytime based on 

professional judgment (stomatal 

diffusion is turned off at night 

using a different property, 

IsDay).

Density of wet leaf kg/m3 820 Paterson et al. 1991. 820 Paterson et al. 1991.

Leaf wetting factor m 3.00E-04 Muller and Prohl 1993 (1E-04 to 

6E-04 for different crops and 

elements)

3.00E-04 Muller and Prohl 1993 (1E-04 to 

6E-04 for different crops and 

elements)

Length of leaf m 0.1 Professional judgment. 0.05 Professional judgment.

Lipid content kg/kg[wet 

weight]

0.00224 Riederer 1995 (European beech) 0.00224 Riederer 1995 (European beech)

Litter fall rate 1/day seasonalc value assumes 1st-order 

relationship and that 15% of 

leaves fall each day for 30 days 

after the first freeze

seasonalc -

Stomatal area normalized 

effective diffusion path 

length

1/m 200 Wilmer and Fricker 1996. 200 Wilmer and Fricker 1996.

Vegetation attenuation 

factor

m2/kg 2.9 Baes et al. 1984 (Grass/hay) 2.9 Baes et al. 1984 (Grass/hay)
Table A-8 (cont.). Terrestrial Plant Parameters

2/17/2011 A-14 DRAFT

Parameter Name Units Deciduous Foresta Grass/Herba

Value 

Used

Reference Value 

Used

Reference

Water content unitless 0.8 Paterson et al. 1991. 0.8 Paterson et al. 1991.

Wet deposition interception 

fraction

unitless Calculate

d within 

TRIM.FaT

E

Calculated based on the 

meteorology data used within 

TRIM.FaTE

Calculate

d within 

TRIM.FaT

E

Calculated based on the 

meteorology data used within 

TRIM.FaTE

Wet mass of leaf per soil 

area

kg[fresh 

leaf]/m2

[area]

0.6 Simonich and Hites 1994

(Calculated from leaf area index, 

leaf thickness, density of wet 

foliage)

0.6 Calculated from leaf area index 

and Leith 1975.

Particle on Leaf Compartment Type

Allow exchange 1=yes, 0=no seasonalc - seasonalb -

Volume particle per area 

leaf

m3[leaf 

particles]/m2[lea

f]

1.00E-09 Coe and Lindberg 1987 (Based 

on particle density and size 

distribution for atmospheric 

particles measured on an 

adhesive surface)

1.00E-09 Coe and Lindberg 1987 (Based 

on particle density and size 

distribution for atmospheric 

particles measured on an 

adhesive surface)

Root Compartment Type - Nonwoody Only

Allow exchange 1=yes, 0=no - - seasonalb -

Correction exponent, 

octanol to lipid

unitless - - 0.76 Trapp 1995.

Lipid content of root kg/kg[wet 

weight]

- - 0.011 Calculated.

Water content of root kg/kg[wet 

weight]

- - 0.8 Professional judgment.

Wet density of root kg/m3 - - 820 Paterson et al. 1991 (Soybean)

Wet mass per soil area kg/m2 - - 1.4 Jackson et al. 1996 (Temperate 

grassland)

Stem Compartment Type - Nonwoody Only

Allow exchange 1=yes, 0=no - - seasonalb -

Correction exponent, 

octanol to lipid

unitless - - 0.76 Trapp 1995

Density of phloem fluid kg/m3 - - 1,000 Professional judgment.
Table A-8 (cont.). Terrestrial Plant Parameters

2/17/2011 A-15 DRAFT

Parameter Name Units Deciduous Foresta Grass/Herba

Value 

Used

Reference Value 

Used

Reference

Density of xylem fluid kg/cm3 - - 900 Professional judgment.

Flow rate of transpired 

water per leaf area

m3[water]/m2[lea

f]

- - 0.0048 Crank et al. 1981.

Fraction of transpiration 

flow rate that is phloem 

rate

unitless - - 0.05 Paterson et al. 1991.

Lipid content of stem kg/kg[wet 

weight]

- - 0.00224 Riederer 1995 (Leaves of 

European beech)

Water content of stem unitless - - 0.8 Paterson et al. 1991

Wet density of stem kg/m3 - - 830 Professional judgment.

Wet mass per soil area kg/m2 - - 0.24 Calculated from leaf and root 

biomass density based on 

professional judgment. a See separate table for assignment of plant types to surface soil compartments.

b TVA Gallatin begins March 9 (set to 1), ends November 7; Santee Cooper Jefferies begins March 21 ends November 11 (set to 0). 

c TVA Gallatin begins November 7, ends December 7; Santee Cooper Jefferies begins November 11, ends December 11; rate = 0.15/day during this time (value assumes 99 

percent of leaves fall in 30 days).
2/17/2011 A-16 DRAFT

Table A-9. Surface Water Parameters

Parameter Name Units TVA 

Gallatin

Santee 

Cooper 

Jefferies

Reference

Algae carbon 

content (fraction)

unitless 0.465 0.465 APHA 1995.

Algae density in 

water column

g[algae]/L[water] 0.00703 0.0095 Based on Roberts, 1986. See 

section 3.1.6 for methodology.

Algae growth rate 1/day 0.7 0.7 Hudson et al. 1994 as cited in 

Mason et al. 1995b

Algae radius um 2.5 2.5 Mason et al. 1995b.

Algae water content 

(fraction)

unitless 0.9 0.9 APHA 1995.

Average algae cell 

density (per vol cell, 

not water)

g[algae]/m3[algae

]

1,000,000 1,000,000 Mason et al. 1995b, Mason et 

al. 1996.

Boundary layer 

thickness above 

sediment

m 0.02 0.02 Cal EPA 1993.

Chloride 

concentration

mg/L 8.0 8.0 Kaushal et al. 2005.

Chlorophyll 

concentration

mg/L 0.0029 0.0029 ICF Estimate, additional details 

in ICF (2005)

Deptha m Varies by 

lakeb Varies by lakec TVA Assessment, 2002; Bowers, 1992; Tufford & 

McKellar, 1999

Dimensionless 

viscous sublayer 

thickness

unitless 4 4 Ambrose et al. 1995.

Drag coefficient for 

water body

unitless 0.0011 0.0011 Ambrose et al. 1995.

Flush rate 1/year Varies by 

laked Varies by laked Calculated based on pond dimensions and flow 

calculations.

Fraction Sand unitless 0.25 0.25 Professional judgment.

Organic carbon 

fraction in 

suspended 

sediments

unitless 0.02 0.02 Professional judgment.

pH unitless 7.9 7.6 TVA Assessment, 2002; 

Bowers, 1992

Suspended 

sediment deposition 

velocity 

m/day 2 2 U.S. EPA 1997.

Total suspended 

sediment 

concentration

kg[sediment]/m3[

water column]

0.05 0.05 U.S. EPA 2005.

Water temperature degrees K 298 298 U.S. EPA 2005.

a Set using the volume element properties named "top" and "bottom."

b Average depth for Old Hickory (East and West) = 5.69 m, Priest Reservoir 8.74 m c Average depth for Lake Moultrie and the Upper and Lower Reservoirs = 5.7 m, Lake Marion = 4 m d See section 3.2.5 for Lake specific values.
2/17/2011 A-17 DRAFT

Table A-10. Sediment Parameters

Parameter Name Units Value Used Reference

Deptha m 0.05 McKone et al. 2001 (Table 3).

Fraction sand unitless 0.25 Professional judgment.

Organic carbon fraction unitless 0.02 McKone et al. 2001 (Table 3).

Porosity of the sediment 

zone

volume[total pore 

space]/volume[sediment 

compartment]

0.6 U.S. EPA 1998.

Solid material density in 

sediment

kg[sediment]/m3[sediment] 2,600 McKone et al. 2001 (Table 3).

a Set using the volume element properties named "top" and "bottom."
2/17/2011 A-18 DRAFT

Table A-11. Aquatic Plants

Parameter Name Units Value 

Used

Reference

Macrophyte biomass per water area kg/m2 0.5 Professional judgment.

Density of macrophytes kg/L 1 Professional judgment.
2/17/2011 A-19 DRAFT

Table A-12. Aquatic Food Web

Aquatic Biota 

(Consuming 

Organism)

Fraction Diet

AlgaeMacrophyteZooplanktonBethic InvertebrateWater Column HerbivoreBenthic OmnivoreWater Column OmnivoreBenthic CarnivoreWater Column Carnivore

Biomass

(kg/m2)

Body

Weight

(kg)

Reference

Macrophyte 0% 0% 0% 0% 0% 0% 0% 0% 0% 0.5 - Professional 

judgment.

Zooplankton 100% 0% 0% 0% 0% 0% 0% 0% 0% 0.0379 5.70E-08 Professional 

judgment.

Benthic 

Invertebrate

0% 0% 0% 0% 0% 0% 0% 0% 0% 0.0182 2.55E-04 Professional 

judgment.

Water Column 

Herbivore

0% 0% 100% 0% 0% 0% 0% 0% 0% 0.00466 0.025 Professional 

judgment.

Benthic 

Omnivore

0% 0% 0% 100% 0% 0% 0% 0% 0% 0.00466 0.25 Professional 

judgment.

Water Column 

Omnivore

0% 0% 0% 0% 100% 0% 0% 0% 0% 0.001169 0.25 Professional 

judgment.

Benthic 

Carnivore

0% 0% 0% 50% 0% 50% 0% 0% 0% 0.00233 2.0 Professional 

judgment.

Water Column 

Carnivore

0% 0% 0% 0% 0% 0% 100% 0% 0% 0.00047 2.0 Professional 

judgment.
2/17/2011 A-20 DRAFT

Table A-13. Mercury Chemical-Specific Properties

Parameter Name Units Value Reference

Hg(0)b Hg(2)b MeHgb

CAS number unitless 7439-97-

6

14302-87-5 22967-92-6 -

Diffusion 

coefficient in pure 

air

m2[air]/day 0.478 0.478 0.456 U.S. EPA 1997.

Diffusion 

coefficient in pure 

water

m2[water]/day 5.54E-05 5.54E-05 5.28E-05 U.S. EPA 1997.

Henry's Law 

constant

Pa-m3/mol 719 7.19E-05 0.0477 U.S. EPA 1997.

Melting Point degrees K 234 550 443 CARB 1994.

Molecular weight g/mol 201 201 216 U.S. EPA 1997.

Octanol-water 

partition 

coefficient (Kow)

L[water]/kg[octanol] 4.15 3.33 1.7 Mason et al. 1996.

Vapor washout 

ratio

m3[air]/m3[rain] 1,200 1.6E+06 0 U.S. EPA 1997, 

based on 

Petersen et al. 

1995. a All parameters in this table are TRIM.FaTE chemical properties.

b On this and all following tables, Hg(0) = elemental mercury, Hg(2) = divalent mercury, and MHg = methyl mercury.
2/17/2011 A-21 DRAFT

Table A-14. Mercury Chemical-Specific Properties for Abiotic Compartments 

Parameter Name Units Value Reference

Hg(0) Hg(2) MeHg

Air Compartment Type

Particle dry deposition velocity m/day 500 500 500 McKone et al. 2001 (CalTOX value)

Demethylation rate 1/day N/A N/A 0 Professional judgment.

Methylation rate 1/day 0 0 0 Professional judgment.

Oxidation Rate 1/day 0.00385 0 0 U.S. EPA 1997 (Low end of half-life range (6 

months to 2 years))

Reduction rate 1/day 0 0 0 Professional judgment.

Washout Ratio m3[air]/m3[rain] 200,000 200,000 200,000 Professional judgment.

Surface Soil Compartment Type

Input characteristic depth (user 

supplied)

m 0.08 0.08 0.08 Not used (model set to calculate value).

Use input characteristic depth (boolean) 0 = no, Else = 

yes

0 0 0 Professional judgment.

Soil-water partition coefficient L[water]/kg[soil 

wet wt]

1,000 58,000 7,000 U.S. EPA 1997.

Vapor dry deposition velocity m/day 50 2500 0 Hg(0) - from Lindberg et al. 1992; Hg(2) -

estimate by U.S. EPA using the Industrial 

Source Complex (ISC) Model - [See Vol. III, 

App. A of the Mercury Study Report (U.S. EPA, 

1997)].

Demethylation rate 1/day N/A N/A 0.06 Range reported in Porvari and Verta 1995 is 

3E-2 to 6E-2 /day; value is average maximum 

potential demethylation rate constant under 

anaerobic conditions.

Methylation rate 1/day 0 0.001 0 Range reported in Porvari and Verta 1995 is 

2E-4 to 1E-3 /day; value is average maximum 

potential methylation rate constant under 

anaerobic conditions.

Oxidation rate 1/day 0 0 0 Value assumed in U.S. EPA 1997.

Reduction rate 1/day 0 1.25E-05 0 Value used for untilled surface soil (2cm), 10% 

moisture content, in U.S. EPA 1997; general 

range is (0.0013/day)*moisture content to 

(0.0001/day)*moisture content for forested 

region (Lindberg 1996; Carpi and Lindberg 

1997).
Table A-14 (cont.). Mercury Chemical-Specific Properties for Abiotic Compartments 

2/17/2011 A-22 DRAFT

Parameter Name Units Value Reference

Hg(0) Hg(2) MeHg

Root Zone Soil Compartment Type

Input characteristic depth (user 

supplied)

m 0.08 0.08 0.08 Not used (model set to calculate value).

Use input characteristic depth (Boolean) 0 = no, Else = 

yes

0 0 0 Professional judgment.

Soil-water partition coefficient L[water]/kg[soil 

wet wt]

1,000 58,000 7,000 U.S. EPA 1997

Demethylation rate 1/day N/A N/A 0.06 Range reported in Porvari and Verta 1995 is 

3E-2 to 6E-2 /day; value is average maximum 

potential demethylation rate constant under 

anaerobic conditions.

Methylation rate 1/day 0 0.001 0 Range reported in Porvari and Verta 1995 is 

2E-4 to 1E-3 /day; value is average maximum 

potential methylation rate constant under 

anaerobic conditions.

Oxidation rate 1/day 0 0 0 Value assumed in U.S. EPA 1997.

Reduction rate 1/day 0 3.25E-06 0 Value used for tilled surface soil (20 cm), 10% 

moisture content, in U.S. EPA 1997 (Lindberg 

1996; Carpi and Lindberg 1997).

Vadose Zone Soil Compartment Type

Input characteristic depth (user 

supplied)

m 0.08 0.08 0.08 Not used (model set to calculate value),

Use input characteristic depth (Boolean) 0 = no, Else = 

yes

0 0 0 Professional judgment.

Soil-water partition coefficient L[water]/kg[soil 

wet wt]

1,000 58,000 7,000 U.S. EPA 1997.

Demethylation rate 1/day N/A N/A 0.06 Range reported in Porvari and Verta 1995 is 

3E-2 to 6E-2 /day; value is average maximum 

potential demethylation rate constant under 

anaerobic conditions.

Methylation rate 1/day 0 0.001 0 Range reported in Porvari and Verta 1995 is 

2E-4 to 1E-3 /day; value is average maximum 

potential methylation rate constant under 

anaerobic conditions.

Oxidation rate 1/day 0 0 0 Value assumed in U.S. EPA 1997.
Table A-14 (cont.). Mercury Chemical-Specific Properties for Abiotic Compartments 

2/17/2011 A-23 DRAFT

Parameter Name Units Value Reference

Hg(0) Hg(2) MeHg

Reduction rate 1/day 0 3.25E-06 0 Value used for tilled surface soil (20cm), 10% 

moisture content, in U.S. EPA 1997 (Lindberg 

1996; Carpi and Lindberg 1997).

Ground Water Compartment Type

Soil-water partition coefficient L[water]/kg[soil 

wet wt]

1,000 58,000 7,000 U.S. EPA 1997.

Demethylation rate 1/day N/A N/A 0.06 Range reported in Porvari and Verta 1995 is 

3E-2 to 6E-2 /day; value is average maximum 

potential demethylation rate constant under 

anaerobic conditions.

Methylation rate 1/day 0 0.001 0 Range reported in Porvari and Verta 1995 is 

2E-4 to 1E-3 /day; value is average maximum 

potential methylation rate constant under 

anaerobic conditions.

Oxidation rate 1/day 1.00E-08 0 0 Small default nonzero value (0 assumed in U.S. 

EPA 1997).

Reduction rate 1/day 0 3.25E-06 0 Value used for tilled surface soil (20 cm), 10% 

moisture content, in U.S.EPA 1997 (Lindberg 

1996; Carpi and Lindberg 1997).

Surface Water Compartment Type

Algal surface area-specific uptake rate 

constant

nmol/[μm2-daynmol]0 2.04E-10 3.60E-10 Assumes radius = 2.5 mm, Mason et al. 1995b, 

Mason et al. 1996; Hg(0) assumed same as 

Hg(2).

Dow ("overall Kow") L[water]/kg[oct

anol]

0 -

a -b Mason et al. 1996.

Solids-water partition coefficient L[water]/kg[solids 

wet wt]

1,000 100,000 100,000 U.S. EPA 1997.

Vapor dry deposition velocity m/day N/A 2500 U.S. EPA 1997 (Vol. III, App. A).

Demethylation rate 1/day N/A N/A 0.013 Average of range of 1E-3 to 2.5E-2/day from 

Gilmour and Henry 1991.

Methylation rate 1/day 0 0.001 0 Value used in U.S. EPA 1997; range is from 

1E-4 to 3E-4/day (Gilmour and Henry 1991).

Oxidation rate 1/day 0 0 0 Professional judgment.
Table A-14 (cont.). Mercury Chemical-Specific Properties for Abiotic Compartments 

2/17/2011 A-24 DRAFT

Parameter Name Units Value Reference

Hg(0) Hg(2) MeHg

Reduction rate 1/day 0 0.0075 0 Value used in U.S. EPA 1997; reported values 

range from less than 5E-3/day for depths 

greater than 17m, up to 3.5/day (Xiao et al. 

1995; Vandal et al. 1995; Mason et al. 1995a; 

Amyot et al. 1997).

Sediment Compartment Type

Solids-water partition coefficient L[water]/kg[solids 

wet wt]

3,000 50,000 3,000 U.S. EPA 1997.

Demethylation rate 1/day N/A N/A 0.5 Value representative of Lake Waccamaw, NC. 

(Knightes, et al. 2009.)

Methylation rate 1/day 0 0.02 N/A Value representative of Lake Waccamaw, NC. 

(Knightes, et al. 2009.)

Oxidation rate 1/day 0 0 0 Professional judgment.

Reduction rate 1/day 0 1.00E-06 0 Inferred value based on presence of Hg(0) in 

sediment porewater (U.S. EPA 1997; Vandal et 

al. 1995). a TRIM.FaTE Formula Property, which varies from 0.025 to 1.625 depending on pH and chloride concentration. b TRIM.FaTE Formula Property, which varies from 0.075 to 1.7 depending on pH and chloride concentration.
2/17/2011 A-25 DRAFT

Table A-15. Mercury Chemical-Specific Properties for Plant Compartments

Parameter Name Units Value Reference

Hg(0) Hg(2) MeHg

Leaf Compartment Type

Transfer factor to leaf particle 1/day 0.002 0.002 0.002 Professional judgment (assumed 1% of 

transfer factor from leaf particle to leaf).

Demethylation rate 1/day N/A N/A 0.03 Calculated from Bache et al. 1973.

Methylation rate 1/day 0 0 0 Assumed from Gay 1975, Bache et al. 

1973.

Oxidation rate 1/day 1.0E+06 0 0 Professional judgment; assumed close to 

instantaneous

Reduction rate 1/day 0 0 0 Professional judgment.

Particle on Leaf Compartment Type

Transfer factor to leaf 1/day 0.2 0.2 0.2 Professional judgment.

Demethylation rate 1/day N/A N/A 0 Professional judgment.

Methylation rate 1/day 0 0 0 Professional judgment.

Oxidation rate 1/day 0 0 0 Professional judgment.

Reduction rate 1/day 0 0 0 Professional judgment.

Root Compartment Type - Grasses and Herbs a

Alpha for root-root zone bulk soil unitless 0.95 0.95 0.95 Selected value.

Root/root-zone-soil-water partition 

coefficient

m3[bulk root soil]/ 

m3[root]

0 0.18 1.2 Hg2- geometric mean Leonard et al. 

1998, John 1972, Hogg et al. 1978; 

MHg- assumed, based on Hogg et al. 

1978.

t-alpha for root-root zone bulk soil day 21 21 21 Professional judgment.

Demethylation rate 1/day N/A N/A 0 Professional judgment.

Methylation rate 1/day 0 0 0 Professional judgment.
Table A-15 (cont.). Mercury Chemical-Specific Properties for Plant Compartments

2/17/2011 A-26 DRAFT

Parameter Name Units Value Reference

Hg(0) Hg(2) MeHg

Oxidation rate 1/day 0 0 0 Professional judgment.

Reduction rate 1/day 0 0 0 Professional judgment.

Stem Compartment Type - Grasses and Herbsa

Transpiration stream concentration 

factor (TSCF)

m3[soil pore water]/ 

m3[xylem fluid]

0 0.5 0.2 Calculation from Norway spruce, Scots 

pine, Bishop et al. 1998.

Demethylation rate 1/day N/A N/A 0.03 Calculated from Bache et al. 1973.

Methylation rate 1/day 0 0 0 Professional judgment.

Oxidation rate 1/day 0 0 0 Professional judgment.

Reduction rate 1/day 0 0 0 Professional judgment.

Macrophyte Compartment Type

Water Column Dissolved PartitionAlpha of Equilibriumunitless 0.95 0.95 0.95 Selected value.

Water Column Dissolved PartitionPartition CoefficientL[water]/ 

kg[macrophyte wet 

wt]

0.883 0.883 4.4 Ribeyre and Boudou 1994 (Elodea 

densa)

Water Column Dissolved PartitionTime to Reach Equilibriumunitless 0.95 0.95 0.95 Selected value.

Oxidation rate 1/day 1.00E+09 0 0 Professional judgment.

t-alpha day 18 18 18 Ribeyre and Boudou 1994 (Experiment 

duration) a Roots and stems are not modeled for deciduous or coniferous forest in the current version of TRIM.FaTE.
2/17/2011 A-27 DRAFT

Table A-16. Mercury Chemical-Specific Properties for Aquatic Species

Parameter Name Units Value Reference

Hg(0) Hg(2) MeHg

Benthic Invertebrate Compartment Type

Alpha of equilibrium for sediment 

partitioning

unitless 0.95 0.95 0.95 Selected value.

Benthic invertebrate-bulk sediment 

partition coefficient

kg[bulk 

sediment]/kg[invert

ebrate wet wt]

0.0824 0.0824 5.04 Saouter et al. 1991 (Hg(0) - assumed 

based on Hg(2) value; Hg(2) and 

MeHg)

t-alpha for equilibrium for sediment 

partitioning

day 14 14 14 Experiment duration from Saouter et al. 

1991.

All Fish Compartments Types a

Elimination adjustment factor unitless 3 3 1 Trudel and Rasmussen 1997.

Assimilation efficiency from food unitless 0.06 0.06 b 0.5 c Professional judgment based on Trudel 

and Rasmussen 2001, Muir, 1986.

Demethylation rate 1/day N/A N/A 0 Professional judgment.

Methylation rate 1/day 0 0 0 Professional judgment.

Oxidation rate 1/day 1.0E+06 0 0 Professional judgment.

Reduction rate 1/day 0 0 0 Professional judgment.

Water-column Herbivore Compartment Type

Assimilation efficiency from plants unitless 1 1 1 Phillips and Gregory 1979. a The modeling scenario includes the following aquatic biota: Algae, Macrophyte, Zooplankton, Benthic Invertebrates, Water-column Herbivore, Benthic Omnivore, Water-column 

Omnivore, Benthic Carnivore, and Water-column Carnivore. b Zooplankton were assigned an assimilation efficiency of 0.2 for divalent mercury to compensate for the absence of naturally occurring direct diffusion processes in the model. c Water Column Carnivores were assigned an assimilation efficiency of 0.2 for methyl mercury to compensate for the absence of naturally occurring death , growth, reproduction 

and predation processes in the scenario/model. 
2/17/2011 A-28 DRAFT

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