### 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: D ocket EPA

- HQ

- OAR

- 2009

- 0234

DATE: March 1 6 , 2011

The attached document consist s 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

sel ecting 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 depositio n 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 form s , 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 depositi on, 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 su pport 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 ). A nother critical li mitation 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

mult iple 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 Toxic s Rule (specif ically 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 dep osition from other sources

within and outside the US

. Specifically, air quality mode ling completed for these analyse s has shown that there are regions of the country (e.g., the Ohio River Valley) where US EGU

- sourced mercury deposition can be substantiall y 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 r elatively 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 re gulate 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

R isks Associated with Mercury E missions 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

### [This page intentionally left blank.]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 Ex posure 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 Values2/17/2011 iv DRAFT 

### [This page intentionally left blank.]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 Modele d 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 Compartments a

................................ ............. 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. 90 th Percentile and Mean Fish Ingestion Rates Representative of Individuals in U.S. Population Consuming Fish

................................ ................................ ................................ ... 43

Table 3

- 15. 90 th 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

................................ ................................ ........... 572/17/2011 vi DRAFT 

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### LIST OF FIGURE S

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 In gestion 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

Figu re 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 Di et 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 R ate

................................ ................................ ........... 502/17/2011 viii DRAFT 

### [This page intentionally left blank.]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 (EGU s ). 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) exposure s 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 wer e 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 subsist ence fishing activity

at those lakes.

T here 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 st udy 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 re gion, 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 Oil

fired 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 combin ed 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

co nsidered 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 assessment s

. 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 estimat e 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,

o nly EGU source

- attribu table exposures and the associated risks were evaluated. E xposures to mercury from any other sources, including background mercu ry 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 R isk 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 . C onsequently, the contamination of ecosystems in the vicinity of major

s ources 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 met hyl mercury via biologically

- mediated processes. Methyl mercury, which has been demonstrated to cause adverse effects in humans, readily bioa ccumulates 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 trans port 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 omni vorous) 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 , T N (NEI facility ID NEI 837 3 ); and2/17/2011 3 DRAFT 

### • Santee Cooper Jefferies, near Moncks Corner, SC ( NEI383 27)

.

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 . T he assessment usually focuses on those scenarios that are

assumed a priori to lead to the highest in dividual ex posure 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 th e mercury case stud ies

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

individual s 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)

. Th is 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 the2/17/2011 4 DRAFT 

### U.S. ingestion s cenario (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 dispo sed to higher rates of fish consumption , including African American, f emale, 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 t hree

- 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. M odeling 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 HQ s using MIRC

.

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

and HQ s for the scenarios included in the EGU case studies. The overall modeling approach is based on the risk assessment methodology applied to evaluate multipathwa y 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 secti on. 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 Methodology2/17/2011 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 compartmen ts. 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 fl exibility 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

a llows 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 characte rize 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 de veloped 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 overvie w 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 environm ental 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 e ach 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 , t o determine

model set

- up and application ( 40 U. S. Code of Federal Regulations [CFR] Part 51. Appendix W, Guid e line on Air Quality Mode ls. [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 ca se 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 

### D eposition fluxes were calculated for a grid of receptors around the facilities that encompass ed nearby water bodies of interest

. N ested square grid s 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) a nd for all areas within 5 km of the TRIM.FaTE source parcel surrounding the EGU facilities. This ensured that even the smallest surface parcel s 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, a t 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 Mari on 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 n ot 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 f acility (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 Parcel s for TVA

G allatin2/17/2011 8 DRAFT 

### Figure 3

- 4

. AERMOD Receptor Grid and TRIM.FaTE Air and Surface Parcel s 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, a n 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 deposit ion 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 d eposition 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 represen t 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 pseudo

source e mission 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 proce sses of air2/17/2011 9 DRAFT 

### transport and deposition were replaced by “emissions” directly into surface soil, plant, and surface water compartments.

P seudo

- sources were assigned to placeholder volume elements and linked to transfer mass to the appropriate surface compartm ents. 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 a lgorithms for air

- soil and air

- plant
transfers. The placeholder volume elements were designed to prevent transfer to any compartm ent 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 t o 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 pl aceholder 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 t hat 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) we re 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 deposi tion 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, ch aracteristics 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 documen ted in Appendix A to this report.

3.2.1 **Facility and Site Descriptions** 

T he 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,

resp ectively [U.S. Census Bureau 2010] ), about 9 km southeast of Gallatin and about 38 km2/17/2011 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, t wo large water bodies are located near the facility , including Old Hickory Lake (which meanders east t o 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 G allatin area is not arid, and the terrain nearby the facility ranges from about 1 40 m to
3 50 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 dow nwind 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

- 6 a ). 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 Facility a

a Shading indicates land use t ype in 2001 (wit h simplified c ategories) ; data from

U SGS 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

.



### T he area map for the Santee Cooper Jeff eries 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] ) ap proximately 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). Figur e 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

- 8 a ). (b) Times with No Precipitation (c) Times With Precipitation (a) All Times2/17/2011 12 DRAFT 

### Figure 3

- 7

. Site Vicinity Map: Santee Cooper Jefferies Facility a

a Shading indicates land use t ype in 2001 (with simplified c ategories) ; data from

U SGS 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 infor mational purposes.

Table 3

- 1

. Mercury E missions for Modeled EGUs

Facility S tack 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

Jefferies a 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 Times2/17/2011 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 p rovide a point of comparison. T otal 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**

C hemical/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 chemi cal

- 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, t hese processes determine the long

term 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.

A n initial step in developing the TRIM.FaTE application s for these case studies was the

collect ion of meteorological data for the model ed region s . The long

- term trends in these data
were used to infor m the development of the modeling layout s that were the bas e s 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

- 200 9 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 AE RMOD analyses used five years of meteorological data, but

because the data must be repeated to create a 50

- year record for modeling, and because leap2/17/2011 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 13 880 ; 38 km south of the facility ) (NOAA 2009a, 2009b).

T hese meteorological data deviate d 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 area s can have the effect of de creasing modeled wet deposition. De creased rainfall may also in crease c hemical concentrations in water bodies by

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

de creased 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 M eteorological C onditions N ear 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 Averages b 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 No rma ls (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 Figure s 3

- 6 b and 3

- 6 c for the TVA Gallatin facility , wind speeds during mod eled precipitation events tended to be larger than during times without precipitation. T he frequency
distribution of wind directions was similar between times with precipitation and times without precipitation

. Southerly winds dominate d during modeled times with out and with precipitation ( 37 and 46 percent of the time, respectively), so areas north of the facility should experience the2/17/2011 16 DRAFT 

### greatest dry and wet deposition. Irrespective of precipitation status, easterly winds occur red least frequently (14 pe rcent of the time overall), so areas west of the facility should experience the least total deposition.

As shown in Figure s 3

- 8 b and 3

- 8 c for the Santee Cooper Jefferies facility , wind speeds during

modeled precipitation events tend ed to be larger than d uring 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 occur red 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, north easterly winds occur red most frequently

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

areas south west 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 a nd 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 featu res 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 the se basins drain northwestward or westward into the Ohio and Mississippi

Rivers

. Large and small lakes and reservoirs are located throughout the se basins , including Old Hickory Lake ( 79.4 km 2 

### 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 km 2 

### 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 cropla nd), and commercial and residential uses, with small amounts of water (see Figure s 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 nor thern Green Basin.2/17/2011 17 DRAFT 

### Figure 3

- 9

. Surface Water Bodies and Watersheds near TVA Gallatin a

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 U se and Watersheds Near TVA Gallatin a

a Shading indicates land use t ype in 2001 (with simplified c ategories) ; data from

U SGS 2001.2/17/2011 18 DRAFT 

### As illustrated in Figure 3

- 11 , the Santee Cooper Jefferies facility is located within the Edisto

South 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 rese rvoirs are located throughout the basins, including the Upper and Lower Reservoirs nearby the facility (0.801 km 2



### , 20 km southwest of the facility) as well as Lake Moultrie (229.5 km 2 

### , extends to 17 km west and north of the facility) and Lake Marion ( 356.1 km 2 

### , 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 Jefferies a

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 prov ided 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) , l and 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 Figure s 3

- 7 and 3

- 1 2 ). Land use becomes more urban proceeding southward from the facility towards Charleston. The Charleston metropolitan area is the lies2/17/2011 19 DRAFT 

### about 55 km south of the facility within the Edisto

- South Carolina Coas tal 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

- 1 2 . Land Use and Watersheds near Santee Cooper Jefferies a

a Shading indicates land use t ype in 2001 (with simplified c ategories) ; data from

U SGS 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

. T he air parcel l ayout is then superimposed over the surface layout . These layouts provide the spatial reference s 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 TRI M.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 exten ded out as far as

35 km This p roximity is derived from an internal study using the ISCST3 model ( Summary of ISC Analysis at the NY TRIM Sit e , 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 the2/17/2011 20 DRAFT 

### proximity somewhat (e.g., to 25 or 35 km) in at least one di rection 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 ass essment. Figure 3

- 1 3 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 f or the wind roses). Figure 3

- 1 3 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 pre determined extent irrespective of the site (see Section 3. 2

. 5 .5 for more detail), it extend ed 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 t he air parcels ) so that every air parcel ha d a surface parcel(s) underneath it. As such, Figure 3

- 1 3 also shows the resulting full modeling extent. Any watersheds or water bodies in these outer surface parcels were not specifically modeled (i.e., outer s urface parcels were not drawn specific to the watersheds or water bodies).

Figure 3

- 1 3 . Modeled Water Bodies and Modeling Extent for TVA Gallatin a

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

- 1 4 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

- 1 4 also shows the extent of the surface parcels that were based on watershed data (the surface parcels the mselves 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 exten ded beyond the extent of the watershed surface p arcels. “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 h ad surface parcel(s) underneath it. As such, Figure 3

- 1 4 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

- 1 4 . Modeled Water Bodies and Modeling Extent for Santee Cooper Jefferies a

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

- s cale 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 two2/17/2011 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 Jeffer ies 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 L ake 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 layou t (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 parce ls 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 Mari on 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 (km 2 ) a Surface Parcel Name Modeled
Surface Area

of TRIM.FaTE Parcel (km 2 )

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 c alibrated to2/17/2011 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, (Figure s 3

- 1 5 and 3

- 1 6 , respective ly).

Figure 3

- 1 5 . Location of S ource P arcel for TVA Gallatin2/17/2011 24 DRAFT 

### Figure 3

- 1 6 . Location of S ource P arcel 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 g enerally drawn following subwatershed boundaries while also considering topographic data (USGS 2006) and wind flow data. Some surface parcels contain ed multiple subwatersheds. The shapes of surface parcels were kept as simple as possible to reduce comple xity 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 share d at least one side or corner with another parcel.

T he air parcel layout was s quare with a n extent that extend ed 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 pa rcels, out to the extent of the air parcels, so that every air parcel ha d 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 a nd 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

- 1 7 for TVA Gallatin and Figure 3

- 1 8 for Santee Cooper Jefferies . T he full

TRIM.FaTE modeling extent of each facility analysis was about 79 km square. For the TVA Gallatin facility , the watershed surface parcels extend ed as close as about 1 8 km from the facility (towards the north

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

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

exten ded as close as about 1 7 km from the facility (towards the north) and as far away as about

38 km from the facility (towards the nor th west ).

Figure 3

- 1 7 . Surface Parcels for TVA Gallatin a

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

### Fig ure 3

- 18 . Surface Parcels for Santee Cooper Jefferies a

a Elevation data from USGS National Elevation Dat aset (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 result s 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 summa rizes 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 ar e 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, relativel y 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 T illed 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 methodolog y 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 P roperties that V ary by V egetation T ype

Vegetation Type Litter f all Allow Exchange Coverage Management

Factor a 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 for est was more common than2/17/2011 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.

**•** 

### “Gr asses/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 type s :

o The untilled and tilled TRIM land use type s 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 de void of vegetation, a surface parcel c ould not be described as “no vegetation” unless at least a majority of the parcel qualifie d as “no vegetation

. ” If “no vegetation” w as 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 proper ties 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 w etlands” 2001 NLCD types together could have be en 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

spec ific 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

- 1 9 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 v egetation 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 most2/17/2011 29 DRAFT 

### fre quent 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

- 1 9 . TRIM Vegetation Types for the TVA Gallatin Facility2/17/2011 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 d istance 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 dis tance 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

anal ysis 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 a ir 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. F our 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 for2/17/2011 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 Coop er Jefferies analysis. Figure 3

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

- 2 2 shows the air parcel and surface parcel layouts overlaid. Likewise, Figure 3

- 2 3 shows the air parcel layout for the Santee Cooper
Jefferies analysis, and Figure 3

- 2 4 shows the air parcel and surface parcel layouts overlaid.

Figure 3

- 21 . Air Parcels for the TVA Gallatin Facility

The s ource p arcel at the c enter is not l abeled

.2/17/2011 32 DRAFT 

### Figure 3

- 2 2 . Air Parcels for the TVA Gallatin Facility, Overlaid with the Surface Parcels

Figure 3

- 2 3 . Air Parcels for the Santee Cooper Jefferies Facility

The s ource p arcel at the c enter is not l abeled

.2/17/2011 33 DRAFT 

### Figure 3

- 2 4

. 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, so il porosity and water content, erosion and runoff rates for surface soil compartments , and suspended sediment concentration s in surface water

. S ite

- specific inputs were used for this assessment where data supporting such a value were readily available. R egional or national
defaults identified by ICF were used in some instances, especially for those parameters that are not expected to strongly influence chemical concentration s . 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. T he 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 C ompartment D epths

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)

. S elected soil and groundwater properties are shown in Table 3

- 7

.

Table 3

- 7

. Selected P roperties of S oil and G roundwater Compartments a

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 o f 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.

R epre sentative site

- specific values were used in the USLE and SD equations to estimate
erosion for th e case study application s . 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 Arc Info (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 to2/17/2011 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 

### f or 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 applicatio n to a single slope or field, rather than to an entire watershed. Using average values across a watershed parcel introduce s 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 t o 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 subtract ing 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)

. Th e resulting total r unoff values include interflow and ground water recharge in addition to surface runoff. T o estimate surface runoff only, total runoff was reduced by the amount expected to infiltrate the groundwater based on information compiled by
USGS (USGS 1985)

. Tota l 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 mo deled

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 S urface W ater and S ediment P roperties

Property TVA Gallatin a Santee Cooper
Jefferies b

Temperature (K) 298 298

Suspended sediment concentration (kg [ sediment ]/ kg [ water ] ) 0.05 0.052/17/2011 36 DRAFT 

### Table 3

- 8

. Selected S urface W ater and S ediment P roperties

Property TVA Gallatin a Santee Cooper

Jefferies b

Water column and sediment organic carbon content (kg [ organic carbon ]/ kg [ solid wet weight ] ) 0.02 0.02

Water column pH 7.9 7.6 c

S ediment 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, Tennessee

Transfer 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, includ ing 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 i nputs 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 ave rage 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 b ody. 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 sta te 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 (km 2 ) Average Depth (m ) Surface

Runoff

from

Land (km 3 /yr) Discharge

Rate (m 3 / s) Turnover

Rate (flushes/ yr) Sediment Re

susp ension

Rate (m[sediment]/ day)

TVA Gallatin

Old Hickory Lake West 31.96 5.69 a 0.16 35.96 6.24 9.46E

- 5

Old Hickory Lake East 11.56 5.69 a 0.50 27.04 12.96 9.91E

- 52/17/2011 37 DRAFT 

### Table 3

- 9. Lake Parameter Values for Modeled Water Bodies

Water Body Name Area (km 2 ) Average Depth (m ) Surface

Runoff

from

Land (km 3 /yr) Discharge

Rate (m 3 / s) Turnover

Rate (flushes/ yr) Sediment Re

susp ension

Rate (m[sediment]/ day)

Priest Reservoir 59.96 8.74 a 0.19 10.66 0.64 9.56E

- 5

Santee Cooper Jefferies

Lake Moultrie 229.6 5.7 b 0.02 8.37 0.20 9.62E

- 5

Upper and Lower

Reservoirs 0.82 5.7 b 0.01 0.57 3.83 9.87E

- 5

Lake Marion 97.0 4 b 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

- spec ific 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

ca lculated 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 laye r, 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 wat er bodies was assumed to be 0.05 kg[sediment]/ m 3 

### [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 growin g

, 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 fo r 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 frost2/17/2011 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 terre strial 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 becau se 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 prima ry 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 inverteb rate consumers (second trophic level) in the benthic

environment and zooplankton to represent the primary invertebrate consumers in the water

column. T he benthic and water column fish compartments represent the higher trop h ic 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 a nd the distribution of biomass among the trophic levels must be a s 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 biot ic 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, s treams) and low

- flow (ponds, lakes, reservoir s) 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 encompa ssed 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. Literat ure 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 wer e 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 a re presented in Table 3

- 10.2/17/2011 39 DRAFT 

### Table 3

- 10. Biomass P arameters for Old Hickory Lake near TVA Gallatin Facility

Biotic Compartment Biomass Density Units

Macrophytes 0.5 kg[ww ]/ m 2

Algae 0.007038 g[ww] / L

Zooplankton 0.02811 kg[ww ]/ m 2

Benthic Invertebrates 0.009723 kg[ww ]/ m 2

Benthic Omnivores 0.00249 kg[ww ]/ m 2

Benthic Carnivores 0.00124 kg[ww ]/ m 2

Water Column Planktivores 0.00249 kg[ww ]/ m 2

Water Column Omnivores 0.000623 kg[ww ]/ m 2

Water Column Carnivores 0.000248 kg[ww ]/ m 2

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 densi ties 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 P arameters for L ake Moultrie near Santee Cooper Jefferies

Biotic Compartment Biomass Value Units

Macrophytes 0.5 kg[ww ]/ m 2

Algae 0.0095 g[ww] / L

Zooplankton 0.0304 kg[ww ]/ m 2

Benthic Invertebrates 0.0115 kg[ww ]/ m 2

Benthic Omnivores 0.00294 kg[ww ]/ m 2

Benthic Carnivores 0.00147 kg[ww ]/ m 2

Water Column Planktivores 0.00294 kg[ww ]/ m 2

Water Column Omnivores 0.000737 kg[ww ]/ m 2

Water Column Carnivores 0.000292 kg[ww ]/ m 2

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 pr ey from the trophic level directly below it, thus favoring higher bioaccumulation by upper trophic levels of2/17/2011 40 DRAFT 

### bioaccumulative contaminants. Figure 3

- 2 5 illustrates the structure and diet fractions of the aquatic food web modeled for both facilities.

Figure 3

- 2 5 . 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 init iatives.

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

. T he following two subsections describe the parameterization of the ingestion exposure scenario and the exposure estimate s 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, s pecific in dividual scenarios we re 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 characte ristics 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 a mount 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

. I n keeping with nomencl ature and precedent set by risk analyses performed under the purview of the Superfund programs , t hese two variants are referenced as follows :

**•** 

### a c entral 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 “r easonable maximum exposure ” (RME), represented in this assessment by the 90 th 

### percentile value from a distribution of ingestion rates.

The r ange 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 remain ed the same. An overview of the exposure factors
selected for the fish ingestion scenario is summarized in Table 3

- 1 2

. Specific exposure factors are presented in more detail in Tables 3

- 1 3 through 3

- 1 5

.2/17/2011 42 DRAFT 

### Table 3

- 1 2 . Overview of E xposure F actors 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: 90 th percentile of distribution of population of

interest a

CTE Scenarios: mean of distribution of population of interest a

Cooking conversion factor (CCF) 1.5 b

Exposure frequency (EF) 365 days/year

Exposure duration (ED) Varies (see Table 3

- 1 3 )

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 re duce the overall weight of fish, and volatilization of mercury is unlikely to occur during cooking, thereby i ncreasing the concentration of mercury by a factor of roughly 1.5 (Morgan et al. 1997)

.



### Default b ody weight s (BW s ) in MIRC are the mean values for adults and the four children’s age groups. The BW values are listed in Table 3

- 1 3

. 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 provid ed in EPA’s Child

- Specific Exposure Factors

Handbook ( U.S. EPA 2008) (see below).

Table 3

- 1 3

. Exposure Duration and Mean Body Weight Estimates for Adults and
Children

Life stage (years) Duration (years) Mean Body Weight (kg)

Adult a (20

- 70) 50 71.4

Child 1

- 2 b 2 12.6

Child 3

- 5 c 3 18.6

Child 6

- 11 d 6 31.8

Child 12

- 19 e 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 adul ts 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 obtain ed 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 rate s (IR s ). All default IRs in MIRC are 90 th


### percentile values. In addition to the default 90 th 

### percentile ingestion rates, MIRC includes
values for the mean and the 95 t h 

### and 99 th 

### 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

- 1 4 presents the age

- specific mean and 90th percentile fi sh ingestion rates.

Table 3

- 1 4 . 90 th 

### 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

90 th 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

- 1 5 presents the mean and 90 th 

### percentile fish ingestion rates for these additional high

- end fish consumption populations.

Table 3

- 1 5 . 90 th 

### Percentile and Mean Fish Ingestion Rates for Additional Fish Co nsumers Evaluated

Product Population

Units African American Angler a Female Angler a Hispanic Angler b Laotian Angler b Vietnamese Angler b

Mean g/day 171 39.1 25.8 47.2 27.1

90 th 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



###  ** **  ** **  ** **  ** **  ** **  ** **  ** **  ****

**× ×**  **× ×** 

**=** days 365 (y)

(y) (y) (y) (i) (y,i) (i) (y,i) 

### EF

AT BW ED FC IR C ADD

w here :

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 (m g 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 (u nitless )

ED (y) 

### = Exposure duration for receptor y (years)

BW (y) 

### = Body weight for receptor y (k g )

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

ac count)

. 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 ( AT y 

### ) is equal to the exposure duration for that group ( ED y 

### ); therefore these variables drop out of Equation 3

- 1

.

3.3.2 **Calculation of th e 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

- da y 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 use d for risk
assessments of hazardous air pollutants (more information on these values is included on EPA’s Fate, Exposure, and Risk Assessment (FERA) web site (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 t hat there is no app reciable 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 presente d here , however, comparing the highest ADD from among the child age categorie s 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 foun d 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 50 th 

### (i.e., final) year of the modeling period. Methyl mercury concentrations in fish from this year were used to estimate human exposures via fish

consu mption for this assessment.

Table 4

- 1 presents media concentrations of divalent, elemental, and methyl mercury for the 50 th 

### year of the modeling period for the Santee Cooper Jefferies site

. The aquatic media concentrations are from Lake Moultrie, which w as 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 Hg 0 Hg 2 MeHg % Hg 0 % Hg 2 % 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 Hick ory 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 Hg 0 Hg 2 MeHg % Hg 0 Hg 2 % % 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 Hg 0 Hg 2 MeHg % Hg 0 Hg 2 % % 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 be tween 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

a nd 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 a re somewhat low compared to background deposition in remote locations. The mean deposition in the entire modeling
domain was 0. 1 9 7 ug/m 2



### - yr, while the mean for Lake Moultrie was 0.64 ug/m 2



### - yr. By comparison, background deposition in remote locations ranged from <5

- 30 ug/m 2



### - yr (Miller et al. 2005).

**•** The percentage of divalent mercury emissions that are estimated to deposit in the 3 0

- 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 CarnivoreBioaccumulation Factor

Water Column Aquatic Biota Driscoll, 2007

Raymond, 2009

Handbook of Ecotoxicology, 2002

Estimated in TRIM, SC

Estimated in TRIM, TN2/17/2011 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 (0.0 34 ng [tot al Hg]/L ) appeared low relative to levels ( 0. 3 to 8 ng [tot al 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 rate s, based on previous TRIM modeling. Partitioning between benthic sediment and surface

water also seemed reasonable.

**•** Modeled methyl mercury concentration in carnivorous game fish (the upper most 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 bio accumulation 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 Sec tion 4.2.2 provides further details about the risk estimates for methyl mercury exposure.

The annually

- averaged concentrations estimated by TRIM.FaTE for the 50 th 

### year of the

model ing period were used to calculate chroni c non

- cancer hazard quotients (HQs) for methyl mercury exposures estimated to be attributed to source emissions. As described in Section 3, for methyl mercury, a n EPA

- recommended cooking conversion factor (CCF) of 1.5 was utilized
to account for the fact that cooking fish reduces the overal l 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 evaluat ed 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. E xposures were also calculated for five addition al adult populations of anglers ( African American and female anglers and anglers of Hispanic, Laotia n, and Vietnamese descent) who are culturally or economically disposed to hig her rates of fish consumption.

HQs were below one for all scenarios modeled at both facilities

. N on

- 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 rate s representative of the upper end (i.e., 90 th 

### 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 HQ s. HQs for all populations at both CTE and RME ingestion rates are p resented 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. 3 2 was estimated

. This result reflect s 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 Rat e 1.0E

- 03 1.0E

- 02 1.0E

- 01 1.0E+00

Lake Moultrie, Santee Cooper

Jefferies East Old Hickory Lake, TVA

GallatinNoncancer 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 represen tation 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 in puts 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 pr ocesses 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 (s uch 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 anal ysis). 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 resul ts of this assessment. The scope of t his 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 t h e modeling zones was estimated, and n o 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 considerat ion

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 att ributable 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 d etail on the uncertainty r elated to e xposure m odeling and r isk 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 o n 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 species

specific 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. Medium2/17/2011 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 o n 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 high

end 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 va lidate 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. Defaul t 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 env ironmental models and may be most accurate in specific conditions that meet restrictive assumptions. The2/17/2011 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 processe s in terms of first

- order differential equations ; h owever, 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 the se process es, 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 th e 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

. M ethylation 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 infl uence on sensitivity of an aquatic ecosystem to mercury inputs and specifically influences the amount of methyl mercury available for bio accumulation. Some water bodies that are efficient at methylating inorganic mercury can show significant methyl mercur y 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 red ox potential

, pH, sulfite con centration

, 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 diss olved 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

- specifi c data are limited and the regional default rate constants used may not represent conditions at the modeled lakes.

Aquatic Food Web Parameters. E stimates of methyl mercury concentrations in fish consumed by people are extremely sensitive to aquatic food w eb parameters. Limited site

- and species

specific data were available on a range of aquatic food web parameters

, including biomass, food web structure, assimilation efficiencies, elimination rates, ingestio n rates, and gill absorption rates. S ite

- specifi c 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. S ite

specific

, average depth data were used for all lakes

, but there were limited data avail able 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 co mpartments. As noted earlier, some algorithms like methylation do not consider all the factors known to affect the process. While2/17/2011 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. R etention 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 ) c ould result in an underestimate of pollutant

concentration s 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 dep th. 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 t he 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. Fo r 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 loca tions

, 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, erosio n 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 th at local erosion rates may differ from the USLE estimate.

Precipitation Rate. T he 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 whic h 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 co ncentration 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 i nto the water body. In the TRIM.FaTE model, these processes are simplistically represented by default sediment deposition rates and suspended solids2/17/2011 56 DRAFT 

### concentrations, which do not account for hydro dynamic sediment cycling processes. These parameters were n ot 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. HQ s were calculated using EPA ’s recommended RfD for methyl

mercury. Reference doses are typically estimated after building in uncertainty factors for

p harmacokinetic variability and uncertainty, pharmacodynamic variability and uncertainty, inter

or intra

- species variability, and potentially other factors. An awareness of the values and ranges of these uncertainty fa ctors (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 percentil e 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, 90 th 

### , 95 th 

### , and, where available, the 99 th


### 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 proporti onate 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 population s 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, part icularly 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

95 th 

### percentile is f airly 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 99 th 

### percentile ingestion rate (from 245% to 1,614%). Larger sample sizes would reduce some of the

unc ertainty 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 r ates

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 i n 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

Rate a (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

90 th percentile Ingestion 

### 17 1.2E

- 02 1.0E

- 02 146%

95 th percentile Ingestion 

### 25 1.8E

- 02 1.5E

- 02 262%

99 th percentile Ingestion 

### 118.3 8.6E

- 02 7.3E

- 02 1

, 614%

African American Adult

Mean 

### 171 1.2E

- 01 1.1E

- 01 NA

90 th percentile Ingestion 446 

### 3.2E

- 01 2.8E

- 01 161%

95 th percentile Ingestion 

### 557 4.0E

- 01 3.4E

- 01 226%

99 th percentile Ingestion 

### 590 4.3E

- 01 3.6E

- 01 245%

Female Adult

Mean 

### 39.1 2.8E

- 02 2.4E

- 02 NA

90 th percentile Ingestion 

### 123 8.9E

- 02 7.6E

- 02 215%

95 th percentile Ingestion 

### 173 1.3E

- 01 1.1E

- 01 342%

99 th percentile Ingestion 

### 373 2.7E

- 01 2.3E

- 01 854%

Hispanic Adult

Mean 

### 25.8 2.2E

- 03 1.6E

- 02 NA

90 th percentile Ingestion 

### 98 8.3E

- 03 6.1E

- 02 280%

95 th percentile Ingestion 

### 155.9 1.1E

- 01 9.6E

- 02 504%

99 th percentile Ingestion NA NA NA NA

Laotian Adult

Mean 

### 47.2 4.0E

- 03 1.7E

- 02 NA

90 th percentile Ingestion 

### 144.8 1.2E

- 02 8.9E

- 02 207%

95 th percentile Ingestion 

### 265.8 1.9E

- 01 1.6E

- 01 463%

99 th percentile Ingestion NA NA NA NA

Vietnamese Adult

Mean 

### 27.1 2.3E

- 03 1.7E

- 02 NA

90 th percentile Ingestion 

### 99.1 8.4E

- 03 6.1E

- 02 266%

95 th percentile Ingestion 

### 152.4 1.1E

- 01 9.4E

- 02 462%

99 th 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

### [This page intentionally left blank.]2/17/2011 A

- i DRAFT 2/17/2011 

### LIST OF TABLES

Table A

- 1. TRIM.FaTE Simulation Para meters

................................ ................................ ......................... 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 G allatin

................................ ................................ ........ 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 Properti es

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

- 20

Table A

- 14. Mercury Chemical

- Specific Properties for Abiotic Compartments

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

- 21

Table A

- 15. Mercury Chemical

- Specific Pro perties for Plant Compartments

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

- 25

Table A

- 16. Mercury Chemical

- Specific Properties for Aquatic Species

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

- 272/17/2011 A

- ii DRAFT 2/17/2011 

### This page intentionally left blank.]2/17/2011 A

- 1 DRAFT 

### Table A

- 1. TRIM.FaTE S imulation P arameters

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

- y ear modeling period.

Simulation time step hr 1 Selected value.

Output time step a 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. Meteorolog ical I nputs

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 sp eed 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

Win d

direction degrees
clockwise from N (blowing from) Varies by

hour NCDC Hourly Surface Hourly Meteorological

Data (NCDC 2010)

Rainfall

Rate m 3 [rain]/m 2 [surf ace area] / day Varies by

hour NCDC Hourly Surface Hourly Meteorological

Data (NCDC 2010) Mixing

hei ght (used

to set air VE property
named “top”) m 800 5 th percentile annual average mixing heights (calculated from daily morning and afternoon values), for all stations i n 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 P arameters

Parameter Name Units Value Used Reference
Atmospheric dust load kg[dust]/m 3 [air] 6.15E

- 08 Bidleman 1988

Density of air g/cm 3 0.0012 U.S. EPA 1997

Dust density kg[dust]/m 3 [dust] 1,400 Bidleman 1988

Fraction organic matter on particulates unitless 0.2 Harner and Bidleman 1998

Height m 800 5 th percentile annual average mixing heights (calculated from daily morning and afternoon values), for all

stations i n SCRAM Surface

Archived Data (40 state, 70

stations ; U.S. EPA 2010)

. Calculated by met data. This

v alue is not used in TRIM.FaTE,2/17/2011 A

- 4 DRAFT 

### Table A

- 4. Soil and G roundwater P arameter s

Parameter Name Units TVA
Gallatin Santee Cooper

Jefferies Reference



### Surface Soil Compartment Type

Air content volume[air]/volume[com partment] 0.2 6 0. 29 McKone et al. 2001. ( Table A

- 2 ) Average vertical velocity of water (percolation) m/day 5.75 E

- 04 7.17 E

- 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]/m 3 [soil] 2600 2600 McKone et al. 2001 ( Default in Table 3)

Thickness

- untilled a m 0.01 0.01 McKone et al. 2001 (p. 30).

Thickness

- tilled a m 0.20 0.20 U

. S

. EPA 2005.

Erosion fraction unitless varies b varie s b See Erosion and Runoff

Fraction table.

Fraction of area

available for erosion m 2 [area available]/m 2 [total] 1 1 Professional judgment;

area assumed rural.

Fraction of area

available for runoff m 2 [area available]/m 2 [total] 1 1 Professional judgment;

are a assumed rural.

Fraction of area
available for vertical

diffusion m 2 [area available]/m 2 [total] 1 1 Professional judgment;

area assumed rural.

Fraction s and unitless 0.25 0.25 Professional judgment.

Organic carbon

fraction unitless 0.00 5 0.0 06 McKone et al. 2001 (U.S. average in Table

A

- 3). pH unitless 6.8 6.8 Professional judgment.

Runoff fraction unitless varies b varies b See Erosion and Runoff

Fraction table.

Total erosion rate kg[soil]/m 2 /day varies b varies b See Total Erosion

Rates table.

Total ru noff rate m 3 [water]/m 2 /day 8.05 E

- 04 7.17 E

- 0 4 Calculated using

scenario

- specific precipitation rate and assumptions associated

with water balance.

Water content volume[water]/volume[c ompartment] 0.2 0.1 5 McKone et al 2001 (Table A

- 2). 

### Root Zone Soil Compa rtment 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 5.75 E

- 04 7.17 E

- 04 Assumed to be 0.2

times the average precipitation for the site. Density of soil solids (dry weight) kg[soil]/m 3 [soil] 2,600 2,600 McKone et al. 2001 (Table 3).

### Table A

- 4 (cont.)

. Soil and G roundwater p arameters

2/17/2011 A

- 5 DRAFT Parameter Name Units TVA
Gallatin Santee Cooper

Jefferies Reference

Fraction s and unitless 0.25 0.25 Professional judgment.

Thickness

- untilled a m 0.79 0.79 McKone et al. 2001 ( U.S. average in Tabl e 16).

Thickness

- tilled a m 0.6 0.6 McKone et al. 2001 ( Adjusted from Table 16). Organic carbon

fraction unitless 0.00 5 0.0 06 McKone et al. 2001 (Table A

- 3). pH unitless 6.8 6.8 Professional judgment.

Water content volume[water]/volume[c ompartment] 0.2 2 0. 2 McKone et al. 2001 (Table A

- 3).



### Vado se Zone Soil Compartment Type

Air content volume[air]/volume[com partment] 0.1 9 0.2 2 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]/m 3 [soil] 2,600 2,600 McKone et al. 2001 (Default in Table A

- 3).

Fraction s and unitless 0.35 0.35 P r ofessional judgment.

Thickness a m 1. 5 1. 7 McKone et al. 2001 (Table A

- 4). Organic carbon

fraction unitless 0.00 2 0.00 3 M cKone et al. 2001 (Table A

- 4). pH unitless 6.8 6.8 Professional judgment.

Water content volume[water]/volume[c ompartment] 0.2 5 0. 21 McKone et al. 2001 (Table A

- 4). 

### Ground Water Compartment Type

Thickness a m 3 3 McKone et al. 2001 (Table 3).

Fractio n s and 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 Ta ble 3).

Density of Solid material in aquifer kg[soil]/m 3 [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 

### Table A

- 5.1. Runoff F ractions

– TVA Gallatin a

Sending Compartment Receiving CompartmentOld Hickory Lake WOldHickory Lake EPriest ReservoirSourceS1N2N3NWWSW4ES3SW1S2NESW2N1SW3SW0SE0S0NE0NW0

Old Hickory

Lake West 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 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 0

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 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

S1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

N2 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

N3 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

NW 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

W 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

SW4 0 0 0 0.7

5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.25 0 0 0

E 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0

S3 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

SW1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

S2 0 0 0.4 0 0 0 0 0 0 0 0 0 0 0 0 0.6 0 0 0 0 0 0 0

NE 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

### Table A

- 5.1 (cont.). Runoff Fractions

– TVA Gallatin a

2/17/2011 A

- 7 DRAFT Sending Compartment Receiving CompartmentOld Hickory Lake WOldHickory 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. Ru noff f ractions

– Santee Cooper Jefferies a

Sending Compartment Receiving CompartmentOld Hickory Lake WOldHickory 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 F ractions

– Santee Cooper Jefferies a

2/17/2011 A

- 9 DRAFT Sending Compartment Receiving CompartmentOld Hickory Lake WOldHickory 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. USL E P arameters a

Land Use Type Rainfall / Erosivity

Index Soil Erodibility

Index Length

Slope

Factor Cover

M anagement
Factor Supporting

Practices

Factor Unit Soil Loss

R (100 ft

ton/ac) K (ton/ac/(100

ft

- ton/acre)) LS (USCS) C (USCS) P A (ton/ac/yr) A (kg/ m 2 /d)



### TVA Gallatin

No Vegetation 225 0. 17 0. 47 0.45 1 8.09 4.97 E

- 0 3

Untilled
Soil 225 0.3 4 0. 47 0.082 1 2.95 1.81 E

- 03

Deciduous
forest 225 0. 26 0. 47 0.1 1 2.75 1. 69 E

- 03



### Santee Cooper Jefferies

Untilled
Soil 325 0.14 0.06 0.082 1 0.22 1.4E

- 04

Evergree n 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 parameters2/17/2011 A

- 11 DRAFT 

### Table A

- 7 .1

. Terrest r ial P lant P lacement

– TVA Gallatin

Surface Soil

Volume

Element Surface S oil 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 x2/17/2011 A

- 12 DRAFT 

### Table A

- 7.2. Terrest r ial P lant P lacement

– 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 x2/17/2011 A

- 13 DRAFT 

### Table A

- 8. Terrestrial P lant P arameter s

Parameter Name Units Deciduous Forest a Grass/Herb a

V alue

Used Reference Value

Used Reference



### Leaf Compartment Type

Allow exchange 1=yes, 0=no seasonal b Begins on day of last freeze in the spring and ends on day of

first freeze in the fall.

- seasonal b Begins on day of last freeze in the spring and ends on day of

first freeze in the fall.

-- Average leaf area index m 2 [leaf]/

m 2 [area] 3.4 CDIAC 2010 ( Harvard For est, 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 (stomata l 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/m 3 820 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 f or different crops and elements) 3.00E

- 04 Muller and Prohl 1993 ( 1E

- 04 to
6E

- 04 fo r 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 seasonal c value assumes 1 st

- order relationship and that 15% of leaves fall each day for 30 d ays after the first freeze seasonal c

-

Stomatal area normalized effective diffusion path length 1/m 200 Wilmer and Fricker 1996. 200 Wilmer and Fricker 1996.

Vegetation attenuation
factor m 2 /kg 2.9 Baes et al. 1984 (Grass/hay) 2.9 Baes et al. 1984 (Grass /hay)

### Table A

- 8 (cont.)

. Terrestrial P lant P arameters

2/17/2011 A

- 14 DRAFT Parameter Name Units Deciduous Forest a Grass/Herb a

V alue
Used Reference Value
Used Reference

Water content unitless 0.8 Paterson et al. 1991. 0.8 Paterson et al. 1991.

Wet dep osition 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 Calculat ed based on the meteorology data used within

TRIM.FaTE

Wet mass of leaf per soil

area kg[fresh leaf]/m 2 [area] 0 .6 Simonich and Hites 1994 ( Calculated from l eaf area index, leaf thickness, density of wet foliage ) 0.6 Calculated from leaf area index

and Lei th 1975.



### Particle on Leaf Compartment Type

Allow exchange 1=yes, 0=no seasonal c

- seasonal b

- Volume particle per area
leaf m 3 [leaf particles]/m 2 [lea f] 1.00E

- 09 Coe and Lindberg 1987 ( Based on particle density and size distribution for atmospheric

p articles 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

- - seas onal b

- 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/m 3

- - 820 Paterson et al. 1991 (Soybean)

Wet mass per soil area kg/m 2

- - 1.4 Jackson et al. 1996 (Temperate grassland) 

### Stem Compartment Type

- Nonwoody Only

Allow exchange 1=yes, 0=no

- - seasonal b

- Correction exponent, octanol to lipid unitless

- - 0.76 Trapp 1995

De nsity of phloem fluid kg/m 3

- - 1,000 Professional judgment.

### Table A

- 8 (cont.)

. Terrestrial P lant P arameters

2/17/2011 A

- 15 DRAFT Parameter Name Units Deciduous Forest a Grass/Herb a

V alue
Used Reference Value
Used Reference

Density of xylem fluid kg/cm 3

- - 900 Professional judgment.

Flow rate of transpired water per leaf area m 3 [water]/m 2 [lea f] - - 0.0048 Crank et al. 1981.

Fraction of transpiration

flow rate th at 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/m 3

- - 830 Professi onal judgment.

Wet mass per soil area kg/m 2

- - 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/d ay during this time (value assumes 9 9 percent of leaves fall in 30 days).2/17/2011 A

- 16 DRAFT 

### Table A

- 9. Surface W ater P arameters

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[w ater] 0.0 0703 0.0 095 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]/m 3 [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) Depth a m Varies by

lake b Varies by

lake c TVA Assessment, 2002 ; Bowers, 1992; Tufford & McKellar, 1999

Dimensionless viscous sublayer

thickness unitl ess 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

lake d Varies by

lake d Calculated based on pond
dimensions and flow

calculations.

Fraction Sand unitless 0.25 0.25 Profes sional 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

c oncentration kg[sediment]/m 3 [ 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 Re servoir 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 D RAFT 

### Table A

- 10. Sediment P arameters

Parameter Name Units Value Used Reference

Depth a m 0.05 McKon e et al. 2001 (Table 3).

Fraction s and 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]/m 3 [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 D RAFT 

### Table A

- 11. Aquatic P lants

Parameter Name Units Value
Used Reference

Macrophyte biomass p er water area kg/m 2 0.5 Professional judgment.
Density of macrophytes kg/L 1 Professional judgment.2/17/2011 A

- 19 DRAFT 

### Table A

- 1 2

. Aquatic F ood W eb

Aquatic Biota (Consuming

Organism) Fraction DietAlgaeMacrophyteZooplanktonBethicInvertebrateWater ColumnHerbivoreBenthicOmnivoreWater ColumnOmnivoreBenthicCarnivoreWater ColumnCarnivore Biomass (kg/m 2 ) 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 Profes sional 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 P rofessional 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

- 1 3

. Mercury C hemical

- S pecific P roperties

Parameter Name Units Value Reference Hg(0) b Hg(2) b M e Hg b

CAS number unitless 7439

- 97

- 6 14302

- 87

- 5 22967

- 92

- 6

-

Diffusion coefficient in pure

air m 2 [air]/day 0.478 0.478 0 .456 U

. S

. EPA 1997.

Diffusion coefficient in pure

water m 2 [water]/day 5.54E

- 05 5.54E

- 05 5.28E

- 05 U

. S

. EPA 1997.

Henry's Law

constant Pa

- m 3 /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 m 3 [air]/m 3 [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

- 1 4

. Mercury C hemical

- S pecific P roperties for A biotic C ompartments

Parameter Name Units Value R eference Hg(0) Hg(2) M e Hg 

### 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

- lif e range (6 months to 2 years))

Reduction rate 1/day 0 0 0 Professional judgment.

Washout Ratio m 3 [air]/m 3 [rain] 200,000 200,000 200,000 Professional judgment. 

### Surface Soil Compartment Ty pe 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 w t] 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

. EP A, 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 sur face 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

- 1 4 (cont.)

. Mercury C hemical

- S pecific P roperties for A biotic C ompartments

2/17/2011 A

- 22 DRAFT Parameter Name Units Value R eference Hg(0) Hg(2) M e Hg 

### Root Zone Soil Compartment Type
Input characteristic de pth (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 1 997

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 de pth (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

- 1 4 (cont.)

. Mercury C hemical

- S pecific P roperties for A biotic C ompartments

2/17/2011 A

- 23 DRAFT Parameter Name Units Value R eference Hg(0) Hg(2) M e Hg

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 C ompartment 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 demet hylation 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/d ay 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 Compartm ent Type Algal surface area

- specific uptake rate

constant nmol/[μm 2

- day

nmol] 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

- 1 4 (cont.)

. Mercury C hemical

- S pecific P roperties for A biotic C ompartments

2/17/2011 A

- 24 DRAFT Parameter Name Units Value R eference Hg(0) Hg(2) M e Hg

Reduction rate 1/day 0 0.0075 0 Value use d 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 C hemical

- S pecific P roperties for P lant C ompartments

Parameter Name Unit s Value Reference Hg(0) Hg(2) M e Hg 

### 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 Co mpartment 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 r ate 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 m 3 [bulk root soil]/

m 3 [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 ra te 1/day 0 0 0 Professional judgment.

### Table A

- 15 (cont.)

. Mercury C hemical

- S pecific P roperties for P lant C ompartments

2/17/2011 A

- 26 DRAFT Parameter Name Unit s Value Reference Hg(0) Hg(2) M e Hg

Oxidation rate 1/day 0 0 0 Professional judgment.

Reduction rate 1/day 0 0 0 Professional judgment.



### Stem Compartment Type

- Grasses and Herbs a

Transpiration stream concentration factor (TSCF) m 3 [soil pore water ]/

m 3 [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 Professi onal judgment.

Reduction rate 1/day 0 0 0 Professional judgment.



### Macrophyte Compartment Type

Water Column Dissolved Partition

Alpha of Equilibrium unitless 0.95 0.95 0.95 Selected value.

Water Column Dissolved Partition

Partition Coefficient L[wate r]/ kg[macrophyte wet wt] 0.883 0.883 4.4 Ribeyre and Boudou 1994 ( Elodea

densa )

Water Column Dissolved Partition

Time to Reach Equilibrium unitless 0.95 0.95 0.95 Selected value.

Oxidation rate 1/day 1.00E+09 0 0 Professional judgment.

t

- alpha day 18 1 8 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 C hemical

- S pecific P roperties for A quatic S pecies

Parameter Name Units V alue Reference Hg(0) Hg(2) M e Hg 

### 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 w et wt] 0.0824 0.0824 5.04 Saouter et al. 1991 ( Hg(0)

- assumed based on Hg(2) value; Hg(2) and

M e Hg )

t

- alpha for equilibrium for sediment partitioning day 14 14 14 Experiment duration from Saouter et al.

1991.



### All Fish Compartments Types a

Eliminat ion adjustment factor unitless 3 3 1 Trudel and Rasmussen 1997. Assimilation efficiency from food unitless 0.0 6 0.0 6 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 sc enario 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 o ccurring direct diffusion processes in the model.

c W ater 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 

## A

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