# Appendix 9

– Draft Protocol for Site

Specific Multipathway Risk AssessmentDRAFT Protocol for Developing a TRIM.FaTE Model Scenario to Support a Site

- Specific Risk Assessment in the RTR Program

Working Draft 1

- 2 July 2013 

## Protocol for Developing a TRIM.FaTE Model Scenario to Support a Site

- Specific Risk Assessment in the RTR Program

WORKING DRAFT

Februar y 2014

Prepared For:

U.S. Environmental Protection Agency Office of Air Quality Planning and Standards Research Triangle Park, NC 27711 Contract No. EP

- W

- 12

- 010

Prepared By:

ICF International 2635 Meridian Parkway Suite 20 0 Durham, NC 27713[This page intentionally left blank.]Working Draft i February 2014



## CONTENTS

1. Introduction

................................ ................................ ................................ ......................... 1

1.1 Regulatory Context and Approach to Ri sk Assessment for PB

- HAPs

........................ 1

1.2 Purpose of this Protocol

................................ ................................ ............................. 2

1.3 Scope and Limitations

................................ ................................ ............................... 2

1.4 Caveats

................................ ................................ ................................ ..................... 2

1.5 Protocol Road Map

................................ ................................ ................................ .... 3

2. A Brief Introduction to TRIM.FaTE Input Requirements

................................ ....................... 3

2.1 TRIM.FaTE Input Files and Contents

................................ ................................ ......... 3

2.2 Recommended Sequence of Activities for TRIM.FaTE Set Up

................................ ... 4

3. Meteorological Data Development

................................ ................................ ...................... 5

3.1 The Role of Meteorological Data in TRIM.FaTE

................................ ......................... 5

3.2 Summary of Meteorological Parameteri zation in TRIM.FaTE

................................ ..... 5

3.3 Required Meteorological Parameters and Data Time Steps

................................ ....... 6

3.4 Selecting Appropriate Surface and Upper

- air Dat a

................................ .................... 7

3.5 Replacing Missing Data

................................ ................................ ............................. 8

3.6 Aggregate and Duplicate

................................ ................................ ......................... 10

3.7 Plume Rise

................................ ................................ ................................ .............. 10

4. Air and Surface Parcel Design

................................ ................................ .......................... 11

4.1 The Role of Spatial Layouts in TRIM.FaTE

................................ .............................. 11

4.2 Theoretical Considerations Influencing Parcel Design

................................ ............. 11 4.2.1 Cross

- Wind Dispersion

................................ ................................ .............. 11 4.2.2 Numerical Diffusi on

................................ ................................ ................... 11 4.2.3 Shape of the Deposition Profile with Distance from Source

....................... 12

4.3 Empirical Evidence Relating to TRIM.FaTE Parcel Design

................................ ...... 12

4.4 Recommended Best Practices for Air and Surface Parcel Design

........................... 12

5. Surface Hydrology and Erosion Property Definitions

................................ ......................... 15

5.1 Surface Parcel Chemical Transfer Dynamics in TRIM.FaTE

................................ .... 15

5.2 Estimating Runoff and Erosion Fractions without Sophisticated GIS Software

................................ ................................ ................................ .................. 16

5.3 Estimating Runoff and Erosion Fractions with Sophisticated GIS Software

................................ ................................ ................................ .................. 17

6. Compartment Properties Recommended for Site

- Specif ic Parameterization

..................... 18

6.1 The Role of Properties in TRIM.FaTE

................................ ................................ ...... 18DRAFT Protocol for Developing a TRIM.FaTE Model Scenario to Support a Site

- Specific Risk Assessment in the RTR Program

Working Draft i i February 2014 

### 6.2 Approach to Prioritizing Properties for Site

- Specific Parameteri zation

..................... 18

6.3 Elimination of Properties from Site

- Specific Parameterization

................................ .. 19 6.3.1 Process

- based Elimination of Parameters

................................ ................. 19 6.3.2 Data Availability

- based Elimination of Parameters

................................ ..... 20 6.3.3 Combination of Data

- and Sensitivity

- based Elimination of

Parameters

................................ ................................ ............................... 20 6.3.4 Elimination of Physical and Chemical Characteristics

................................ 21

6.4 Properties Recommended for Site

- Specific Parameterization

................................ .. 21

7. Properties Recommended for Land Use

- Based Values

................................ .................... 22

8. Properties Recommended for National Values

................................ ................................ .. 23

9. The FaTEmaster Scenario Builder

................................ ................................ .................... 24

10. Potential Future Improvements

................................ ................................ ......................... 24

References

................................ ................................ ................................ ............................... 25

Appendix A. Documentation of Empirical Analyses Used to Prioritize TRIM.FaTE Properties

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

- 1

Appendix B. TRIM.FaTE National Property Values

................................ ................................ . B

- 1

Appendix C. The FaTEmaster Scenario Builder Tool

................................ .............................. C

- 1DRAFT Protocol for Developing a TRIM.FaTE Model Scenario to Support a Site

- Specific Risk Assessment in the RTR Program

Working Draft iii February 2014 

## T ABLES

Table 3

- 1. Meteorological Parameters Required for Meteorology Input File for

TRIM.FaTE

................................ ................................ ................................ ........... 6

Table 3

- 2. Potential Sources of Hourly Surface Meteorological Data (not all

inclusive)

................................ ................................ ................................ ............... 9

Table 6

- 1. TRIM.Fa TE Properties Recommended for Site

- Specific

Parameterization

................................ ................................ ................................ . 22

Table 7

- 1. TRIM.FaTE Properties Recommended for Land use

- Specific

Parameterization

................................ ................................ ................................ . 23[ This page intentionally left blank.]DRAFT Protocol for Developing a TRIM.FaTE Model Scenario to Support a Site

- Specific Risk Assessment in the RTR Program

Working Draft 1 February 2014 

## 1. Introduction

This document presents a protocol for developing TRIM.FaTE scenarios in support of site

- specific risk assessments conducted within the RTR program using the TRIM.FaTE environmental fate and transport model.

This sect ion describes the regulatory context, intended purpose of the protocol, the scope and limitations of the protocol, and some caveats to its use. It also presents a road

- map to the content and structure of this document.



### 1.1 Regulatory Context and Approach to Ri sk Assessment for PB

- HAPs

Section 112 of the Clean Air Act (CAA) directs the U.S. Environmental Protection Agency (EPA) to assess the risk remaining (i.e., residual risk) from emissions of persistent and bioaccumulative hazardous air pollutants (PB

- HAPs) f ollowing the implementation of maximum achievable control technology (MACT) standards for emission sources. Such risk assessments for various emission source categories are a major component of EPA’s Risk and Technology Review (RTR) program

.

To evaluate in gestion exposures and human health risks for RTR on a source category basis, an iterative approach i s currently employed

. The approach enable s EPA to confidently screen out PB

- HAP emissions unlikely to pose health risks above levels of concern (i.e., a can cer risk exceeding 1

- in

- one million or a
non

- cancer hazard quotient exceeding 1 1 ) and to focus additional resources on sources of greater concern within the category.

T wo models are used t o estimate ingestion exposure and ingestion risk in the RTR program :

 T he Fate, Transport, and Ecological Exposure module of EPA’s Total Risk Integrated Methodology (TRIM.FaTE) is used to model the fate and transport of pollutants released to the environment ; and

 T he Multimedia Ingestion Risk Calculator (MIRC) is used to es timate transfer and uptake into the food chain and exposure to receptors consuming contaminated food products and soil. A subset of media concentration estimates from TRIM.FaTE serve as inputs to MIRC, which also depends on other exposure and biotransfer

- r elated input parameters.

The RTR approach is divided into three steps of in creasing refinement:

1. Tier 1 of the approach identif ies facility

- level emissions of PB

- HAPs within a source category and compar es them to risk

- based emission thresholds.

2. Tier 2 uses the actual location of the facility emitting PB

- HAPs to refine a subset of the assumptions associated with the modeled Tier 1 environmental scenario while maintaining the Tier 1 ingestion exposure scenario assumptions ; and

3. The final step, f or facilities th at cannot be ruled out based on the Tier 1 and Tier 2 screening process, is to conduct a more refined assessment, up to and sometimes including site

- specific multipathway risk assessment. A site

- specific risk assessment is intended to incorporate location

- or facility

- specific characteristics regarding the environment to which PB

- HAPs are emitted, relevant exposure pathways, ingestion rates or other exposure factors, and other parameters. Site

- specific risk assessments undertaken in the past as part of the RTR process have involved extensive literature search es for model parameters and require d more time and resources to complete than the Tier 1 and Tier 2 screening analyses.

1 EPA considers “cancer risks exceeding 1

- in

- one million” to refer to risks of at least 1.5

- in

- one million, and “non

cancer hazard quotients exceed ing 1” to refer to hazard quotients of at least 1.5.DRAFT Protocol for Developing a TRIM.FaTE Model Scenario to Support a Site

- Specific Risk Assessment in the RTR Program

Working Draft 2 February 2014 

### 1.2 Purpose of this Protocol

The site

- specific protocol presented in this document is i ntended to serve as a guiding framework to set up and parameterize scenarios in TRIM.FaTE that support accurate and cost

- effective site

- specific risk assessments as part of the RTR framework.

The purpose of the protocol is to develop a standard set of guid elines and recommendations for conducting site

- specific assessments

, provid ing a streamlined and replicable framework for configuring and parameterizing the TRIM.FaTE model. The protocol aims to balance modeling accuracy with cost

effectiveness in implemen tation, and to facilitate consistency and transparency across diverse assessments. This protocol is also intended to function as part of the technical documentation for future site

- specific residual risk assessments by providing a clear and transparent des cription of the approach to parameterization and some of the relevant sources

. Deviations from this protocol would need to be documented on a case

- by

- case basis. This document also provides guidance on ICF’s Fa TE m aster Scenario Builder tool and discusses h ow that tool can be used to support site

- specific risk assessments using the TRIM.FaTE model.



### 1.3 Scope and Limitations

The site

- specific protocol presented in this document focuses on the fundamental aspects of setting up a scenario in TRIM.FaTE from an RTR p erspective. While the TRIM.FaTE User’s Guide ( U.S. EPA 2005) provides guidance on the mechanistic aspects of designing a simulation, the protocol focuses on identifying best practices that optimize model set

- up efficiency while maintaining a high level of model precision in the RTR context.

These best practices have been developed with a focus on the impact of alternative model configuration and parameterization approaches on ingestion risk in the RTR process. Thus, if two alternative model configuration ap proaches are estimated to have similar impacts on risk estimates in the RTR process, the protocol will recommend the less effort

- intensive approach where appropriate. For instance, the protocol identifies only a limited set of TRIM.FaTE model properties as requiring site

- specific parameterization, while proposing land use

- specific or nationally representative or health protective values for others based on the finding that relatively few model parameters substantially influence risk in the RTR context.

Howe ver, the protocol is not driven exclusively by considerations of cost

- effectiveness. In some instances, the protocol aims to provide superior methods of model configuration based on model accuracy and scientific considerations that were previously not clea rly articulated in available TRIM.FaTE guidance, and that have a focus on the RTR program

. This protocol is not intended to serve as a substitute for the TRIM.FaTE User’s Guide ( U.S. EPA 2005) or the TRIM.FaTE Technical Support Document ( U.S. EPA 2002). It is not step

- by

- step guide to running the model. It is recommended that the protocol be read in conjunction with the U ser ’s G uide and the T echnical S upport D ocumentation to provide a holistic perspective on how the model should be used in site

- specific RTR applications.



### 1.4 Caveats

The findings and recommendations presented in this document are subject to several caveats:

 Some of the conclusions presented in this protocol are based on a combination of available empirical evidence, theoretical considerations, an d expert judgment. A “brute

- force” empirical approach to test an extensive range of scenarios and parameters was not feasible

.

 For some model parameters, ICF relied on sensitivity analyses performed on previous configurations of the model. It is possible t hat the results of previous sensitivity analyses differ slightly from the current Tier 1 model configuration.

 ICF did not test the sensitivity of model parameters in alternative model configurations ; andDRAFT Protocol for Developing a TRIM.FaTE Model Scenario to Support a Site

- Specific Risk Assessment in the RTR Program

Working Draft 3 February 2014  ICF did not research and identify land use

- specific parameter values for soil properties values as part of this protocol, although it recommends their use.

Despite these limitations, the current recommendations are expected to meet the objectives of providing a cost

- effective and accurate approach to site

- specific risk assessment in the RTR program. However, users are encouraged to extend site

- specific model design and parameterization beyond the levels proposed here as circumstances permit.



### 1.5 Protocol Road Map

This protocol contains the following types of i nformation:

 b est practices for TRIM.FaTE model configuration for use in site

- specific RTR applications;

 d ocumentation of the rationale for best practice recommendations;

 n ationally representative or health protective model parameter values for site

- specifi c applications of TRIM.FaTE; and

 a guide to the FaTE m aster Scenario Builder tool to create input files for TRIM.FaTE

. These distinct elements are woven together in the following structure:

 Section 2 sets the context with a summ ary of TRIM.FaTE input files and their content;

 Section 3 discusses the model’s meteorological data requirements, potential data sources, approaches to address missing data, data processing requirements, and the issue of plume rise;

 Section 4 presents recommendations and rationale for best practices for designing air and surface parcels in TRIM.FaTE;

 Section 5 presents recommendations and rationale for best practices for defining surface hydrology and erosion parameters required by TRIM.FaTE;

 Section 6 identifies parameters recommended for site

- specific parameterization;

 Section 7 identifies parameters recommended f or land

- use specific parameterization;

 Section 8 identifies parameters recommended for national default parameterization;

 Section 9 presents a guide to using ICF’s Fa TEm aster Scenario Builder tool i n site

- specific RTR applications; and

 Section 1 0 discusses potential future improvements and enhancements to the protocol.



## 2. A Brief Introduction to TRIM.FaTE Input Requirements

TRIM.FaTE is a spatially and temporally explicit mu ltimedia environmental fate and transport model that estimates the concentrations of emitted chemicals in biotic and abiotic environmental media. The model uses a compartmental box model approach to track the movement of chemicals in environmental media. T he model is based on representing environmental media as compartments, moving chemical mass between interacting compartments consistent with a set of governing mathematical algorithms that describe environmental physical and chemical processes, and assumin g instantaneous mixing within each compartment.



### 2.1 TRIM.FaTE Input Files and Contents

TRIM.FaTE requires a variety of inputs from users to define the modeled environment and to quantify the various environmental mass transfer processes. These inputs are prov ided to the model in the form of the following files:DRAFT Protocol for Developing a TRIM.FaTE Model Scenario to Support a Site

- Specific Risk Assessment in the RTR Program

Working Draft 4 February 2014  A “volume elements” file defines the spatial layout of the modeled domain in terms of three

dimensional abiotic compartments. Each volume element provides a frame of reference for one or more biotic com partments within it.

 A “compartments” file places biotic and abiotic compartments (modeling unit containing chemical mass) within the volume elements.

 A library file contains all the model algorithms, properties, and emission source information. Examples

o f the kinds of properties that are defined in the library file include:

– s cenario characteristics (e.g., start/stop time, modeling time parameters, output options);

– s ource characteristics (e.g., chemicals emitted, location, emission rate);

– c hemical

- specific properties, including physical

- chemical (e.g., molecular weight, K ow ) and abiotic chemical

- specific (e.g., degradation half

- life) ;

– n on

- chemical

- specific characteristics of biota (e.g., body weight, food intake rate);

– s ite

- specific ecological setting data and characteristics of biota (e.g., type of species present, population and density information, food web relationships); and

– a biotic environmental setting data such as abiotic media characteristics (e.g., air/water content of soil, pH of surface water, su spended sediment density), runoff/erosion fractions for adjacent surface soil compartments, and water flow between connected surface water compartments.

 A properties file typically contains : (i) simulation

- and site

- specific property values that are used t o overwrite default library values

, and (ii) the location of time

- varying input files for parameters such as meteorological and vegetation parameters.

These input files must be developed using syntax that is consistent with TRIM.FaTE requirements. ICF’s Fa TE m aster Scenario Builder tool, discussed further in Section 9 , provides a spreadsheet

- based interface that facilitates the automated generation of syntactically accurate TRIM.FaTE input files from

user

- specified inputs. Furthe r detail on the required syntax of the input files, and the process of setting up and running the model using these input files, is available in the TRIM.FaTE User’s Guide (U.S. EPA, 2005)

. Much of the challenge in a site

- specific TRIM.FaTE application lie s in designing a spatial layout that is consistent with the nature of the governing algorithms and that reflect s the environmental dynamics of the modeled domain, researching and estimating numerous environmental properties that serve as inputs into the mo del, finding and preparing appropriate meteorological and climate

- related data, and setting up the input files. The following sections will discuss the optimal methods of performing these tasks from the perspective of a site

- specific RTR application.



### 2.2 Recom mended S equence of A ctivities for TRIM

. F a TE S et U p

The following sections of this document focus on various aspects of TRIM.FaTE set up as discrete elements in the model configuration process. There are, however, interconnections between the research requ ired to guide various components of the set up process. Although there are no firm rules governing the order in which the model’s input files must be developed, this protocol recommends a sequence of activities as a means to enhance efficiency and accuracy in the model configuration process:

1. Perform qualitative spatial analys e s of topography, hydrography (boundaries of watersheds, flow lines ), and land cover around the site. This will aid in identifying meteorology data , identifying modeled lakes and farms or potential farmland , and shaping the model domain;

2. Identify meteorological data based on RTR considerations (e.g., what meteorology did the RTR inhalation risk assessment use for the site?), data availability, data quality, and the representativeness of the data and instrument siting with respect to the modeled facility . Further,DRAFT Protocol for Developing a TRIM.FaTE Model Scenario to Support a Site

- Specific Risk Assessment in the RTR Program

Working Draft 5 February 2014 c reate the meteorological file and use the meteorological data and emission source parameters to estimate plume rise where necessary;

3. Identify lakes to model based on lake size a nd a preliminary assessment of risk potential and data availability ;

4. Identify farms or potential farmland to model based on a preliminary assessment of risk potential and data availability ;

5. Create spatial layout ;

6. Estimate and define surface hydrology and e rosion dynamics within the layout ;

7. Gather data on site

- specific properties per the protocol ;

8. Enter input property data into FaTE m aster Scenario Builder tool ;

9. Generate TRIM.FaTE input files ; and

10. Run TRIM.Fa TE

. 

## 3. Meteorological Data Development

3.1 The Role of Me teorological Data in TRIM.FaTE

The algorithms that simulate the advection of chemicals between the air compartments and the simultaneous deposition of chemicals to the underlying surface compartments depend on numerous meteorological parameters such as win d speed, wind direction, mixing height, and rainfall rate, amongst others. Sensitivity analyses have indicated that meteorological parameters are among the most risk influential parameters in the TRIM.FaTE model ( U.S. EPA 2009). Previous evaluations have a lso suggested that the effects of these parameters are important in TRIM.FaTE, favoring the use of time

varying meteorological data at the temporal resolution at which the measurements are reported, rather than data averaged over longer time periods.

For t hese reasons, it is recommended that all meteorological parameters be site

- specific. This accounts for the potential interactive effects between meteorological parameters. The meteorology data should also be hourly, where possible

— averaging to coarser time steps can obscure real trends in the data

. This section discusses best practices in incorporating site

- specific meteorological parameters in TRIM.FaTE applications.



### 3.2 Summary of Meteorological Parameterization in TRIM.FaTE

The following steps

, discussed in greater detail in subsequent sections, serve as a general guide to generat ing TRIM.FaTE meteorology input data in RTR site

- specific applications :

1. Determine the meteorology time step to be used (e.g., hourly, n hours per day, daily, etc.). Surface meteorolo gy data available from federal agencies is typically hourly. Longer time steps reduce model run time but can obscure real and significant trends in the data. Hourly values are recommended ;

2. If it is appropriate to use the same meteorology data used in RTR i nhalation risk assessments, obtain those data. Otherwise, i dentify the source(s) of meteorology data that meet the following criteria :DRAFT Protocol for Developing a TRIM.FaTE Model Scenario to Support a Site

- Specific Risk Assessment in the RTR Program

Working Draft 6 February 2014 The data contain all the meteorology fields required by TRIM.FaTE . Note that u pper

- air radiosonde data do not contain mix ing height values. Mixing height values must be calculated, typically using a combination of surface and upper

- air data (see Bullet 3 below);

.

a. The data have no more than 10 percent missing data for any of the meteorology fields required by TRIM.FaTE ; and

b. T he data are as representative as possible of the area being modeled.

3. If necessary, use a meteorological preprocessor or other method of estimating mixing heights at the desired time step (upper

- air data is typically measured twice daily with a radiosonde a nd does not include mixing heights );

4. Replace all missing data with values that are as reasonable as possible ;

5. If desired (although not recommended), aggregate the meteorology data to larger time steps. Then, replicate the data as needed to create a meteoro logy input file that covers the entire period of modeling ; and

6. The combination of emission source characteristics (e.g., exit gas temperatures and velocities) and meteorological characteristics (e.g., stability, ambient temperature) might lead to significa nt emission plume rise during some time steps. In these cases, the user may use the emission source characteristics and meteorology data to estimate plume rise values.



### 3.3 Required Meteorological Parameters and Data Time Steps

TRIM.FaTE meteorology data must i nclude the fields in Table 3

- 1

. Mixing heights are not part of observed surface meteorology data. Mixing heights can be manually estimated using a vertical profile plot of upper

air data from radiosondes, though the typical method of calculating mixing heights relies on using surface and upper

- air observed data in a meteorological processor such as AERMOD’s meteorological processor (AERMET) or the U.S. EPA mixing height program. These meteorological processors, sources of surface

a nd upper

- air data, and sources of pre

- calculated mixing heights are discussed in Section 3.4

. Surface meteorology data are typically available in hourly time steps, with some wind data available at smaller time steps. Upper

- air data from radiosondes are typically available twice per day. These data time steps (hourly for surface data, twice

- daily for upper

- air data) are typically required by meteorological processors to estimate hourly mixing height values. The TRIM.FaTE meteoro logy data file does not require hourly time steps

, al though hourly data are typically used in site

- specific assessments. Larger time steps will shorten model run time. The aggregation of data into larger time steps (although not recommended) should take pl ace after mixing height values are determined.

Table 3

- 1

. Meteorological Parameters Required for Meteorology Input File for TRIM.FaTE



### Parameter Format Units Further Description and Notes

Date M/D/YYYY NA NA

Hour Numeric NA NA

Time Zone e.g., “ EST ” NA NA

Horizontal Wind Speed Numeric m/s NADRAFT Protocol for Developing a TRIM.FaTE Model Scenario to Support a Site

- Specific Risk Assessment in the RTR Program

Working Draft 7 February 2014 

### Parameter Format Units Further Description and Notes

Wind Direction Numeric degrees degrees clockwise from north ; blowing from e.g., from north is 360 degrees; from east is 90 degrees; from south is 180 degrees; and so on . 0 degrees is reserved for calm winds (e.g., wind speed = 0 m/s)
Air Temperature Numeric K NA
Mixing Height Numeric m Not an observed parameter. Must be calculated, typically using a meteorology processor with surface and upper

- air data inputs.

Rain Ra te Numeric m/day NA

Cumulative Rain Numeric m Total precipitation in a precipitation event. A multi

- hour event will have equal cumulative rainfall value s for each hour.
Is Day Boolean (i.e., 1 or 0) NA Daytime (value of 1; after sunrise) or nighttime (va lue of 0; after sunset). Calculated using U.S. EPA’s SR

SS.exe program, available with TRIM.FaTE.



### 3.4 Selecting Appropriate Surface and Upper

- air Data

RTR inhalation risk assessments match a facility with the Automated Surface Observing Station that is close st to the facility and that has relatively complete data for one recent year (that year is currently 2011). That surface station is coupled with its closest, regularly reporting upper

- air station. Unless meteorology data were measured at the facility itsel f, it is usually appropriate to use those same meteorology stations for RTR ingestion risk assessments, subject to EPA discretion and the availability of good quality data for multiple recent years.

S urface and upper

- air radiosonde data can be obtained fr om the National Climatic Data Center (NCDC), regional climate centers, and third party vendors. Some sources of hourly surface meteorological data across the United States are shown in Table 3

- 2 . These data sources are shown with generally the mo re current and more spatially dense data (i.e., the large and freely available Integrated Surface Hourly data) listed first. Some state agencies also maintain their own station networks and might be good sources of meteorology data.

Twice

- d aily upper

- air radiosonde data from stations operated by federal agencies can be obtained for free from the National Oceanic and Atmospheric Administration ( NOAA ) Earth System Research Laboratory ( ESRL ) in Forecast Systems Laboratory ( FSL ) format for over 100 U.S. stations and for 1994 through present , where available (ESRL 2011). ESRL also has archived FSL

- formatted upper

- air data going back to 1946, available for purchase on disc. M ixing heights are not part of observational data. Hourly mixing heights ca n be manually estimated or they can be calculated using a combination of surface and upper

air data. Typically, these hourly mixing height calculations are performed in a meteorology data preprocessor such as AERMET (U.S. EPA 2012a). AERMET accept s some of the surface meteorology data formats presented in Table 3

- 2 and FSL

- formatted upper

- air data. Pre

- calculated, twice

- daily mixing height data also are available from U.S. EPA for over 70 U.S. stations for 1984 through 1991 (U.S. E PA 2010a) and can be used in the computer version of the meteorological processor for the Regulatory Air Model (PCRAMMET; U.S. EPA 2012c)

.DRAFT Protocol for Developing a TRIM.FaTE Model Scenario to Support a Site

- Specific Risk Assessment in the RTR Program

Working Draft 8 February 2014 The user should select appropriate data based on proximity of the station to the modeling site and on the station sit e’s representativeness of the modeling site (e.g., elevation, land use, and wind flow). Evaluating the representativeness of the site and data is subjective. The surface and upper

- air stations closest to the modeling site often will be the best choice s . Be cause the upper

- air station network is much sparser than the surface network, proximity might be a less restrictive criterion in choosing upper

- air stations compared to choosing surface stations. Instead, it might be more important to choose an upper

- air s ite that experiences boundary layer characteristics that are similar to those of the modeling site. For example, if the modeling site is well inland, it might not be appropriate to use an upper

- air station on the coast.

The availability and quality of the data must also be considered. It is typically recommended that no more than 10 percent of the data be missing, and the data values should be within expected bounds. The user should also note that some meteorological data are archived in UTC, or Zulu time, which is functionally the same as Greenwich Mean Time and equivalent to Eastern Standard Time plus five hours. The data should be converted to local standard time for use in TRIM.FaTE , and standard time must be used throughout the year (most archived meteo rology data are in standard time).

One year of data might be sufficient, as long as the data on average are representative of recent climatological averages at the site. NCDC 30

- year climate normals are recommended for comparison (NCDC 2011). When data fro m a single year are found to be significantly different from typical conditions, the user can use several consecutive years that, together on average, are closer to typical conditions.



### 3.5 Replacing Missing Data

TRIM.FaTE requires that there be no missing data in its meteorology fields. U.S. EPA’s recommended guidance for replacing missing meteorology data (U.S. EPA 1992) has a series of objective data replacement steps as a first pass, but those steps might not fill in all missing data. The guidance suggests

s ome subjective procedures for filling in remaining missing data; however, these are manual steps and do not cover all possible cases of missing data (e.g., if more than a few contiguous hours of data are missing). A meteorologist or experienced air quality modeler should perform these subjective data fill procedures. The user should expect that the quality of substituted values will be worse for longer contiguous periods of missing data versus only a few contiguous hours of missing data. However, as long

as the amount of data originally missing is no more than 10 percent, and as long as the substituted values are not out of normal bounds, then substituted data will have only a small impact on modeling results

— especially for TRIM.FaTE assessments where the de sired outputs are final accumulated media concentrations after several decades of modeling.

ICF developed a tool (AERMET2TRIM), based in Microsoft Access, that fills in missing data in all meteorology fields needed by TRIM.FaTE. The procedures are based on those provided in U.S. EPA (1992). AERMET2TRIM requires first that AERMET be run on the surface and upper

- air data in order to estimate hourly mixing heights. AERMET2TRIM then reformats the data into the format required by TRIM.FaTE.DRAFT Protocol for Developing a TRIM.FaTE Model Scenario to Support a Site

- Specific Risk Assessment in the RTR Program

Working Draft 9 February 2104 Table 3

- 2 . Potential Sources of Hourly Surface Meteorological Data (not all

- inclusive)



### Name Notes on Measured Data Years Available Spatial Coverage Source Other Notes

Integrated Surface Hourly (ISH) Data NA Over 100 years to toda y, where available Thousands of

stations worldwide NCDC (2008) Available in two formats

-- abbreviated and full. AERMET requires the full format. T he availability and structure of the formats ha ve changed through the years and through the different availab le data storage media. Free via online download. Quality Controlled Local Climatological Data (QCLCD) NA 2005 to today Approx. 1,600 U.S. stations NCDC (2013) Free via online download.

U.S. EPA AERMOD

formatted d ata for the Human Exposure Model ( HEM

- 3 ) N ot strictly observational data

– processed observational data through AERMOD's preprocessors to produce AERMOD

- ready format

. 2011 only Over 800 U.S. stations U.S. EPA

(2013) Free from EPA

.

Solar and Meteorological Surface Observation Network (SAMSON) NA 1 961 through 1990, where available Approx. 237

U.S. stations NCDC (2012) File formats changed after approx. 2007

. Available for purchase on disc.

U.S. EPA Support Center for Regulatory Atmospheric Modeling (SCRAM) No precipitation data (request DSI

- 3240 fo rmat precipitation data from NCDC) 1984 through 1992, where available Approx. 250

U.S. stations U.S. EPA

(2010 b ) DSI

- 1440 format

.

Free via online download.DRAFT Protocol for Developing a TRIM.FaTE Model Scenario to Support a Site

- Specific Risk Assessment in the RTR Program

Working Draft 10 February 2014 

### 3.6 Aggregate and Duplicate

If desired (though not recommended), t he user should aggregate the data to conform to the modeling time step being used. Values of wind speed, air temperature, mixing height, and rain rates should be averaged. For wind direction, the hourly values of wind speed and wind direction should be used to calculate the vector components of the wind (i.e., u and v values), those vector components should be averaged, and the averaged vectors should be used to calculate the average wind direction. Calculate new cumulative rain values after averaging the rain rates. Use professional judgment to determine appropriate values for the “Is Day” parameter.

Previous site

- specific assessments conducted using TRIM.FaTE typically have used 50

- year modeling periods. The meteorology data in TRIM.FaTE format should be duplicated as necessary to produce a 50

- year data period (or other appropriate time period that matches the facility lifetime/emissions period selected during the initial assessment planning stage). For example, if the meteorology data represent years 200 8 through 20 11 , the user should copy

- a nd

- paste that four

- year period to create 50 years of data ( e.g. , 2000 through 2049). The user should make sure that the year values are changed appropriately, and that leap years contain data for February 29. If the user’s original meteorology data do not contain a leap day, simply duplicate data from February 28 and label as February 29.



### 3.7 Plume Rise

Emission plumes in nature usually have a vertical component to their dispersion pattern (i.e., plume rise). Multiple forces can affect vertical dispersion, incl uding the temperature of the plume compared to the ambient air temperature, the wind speed at the release point, the ambient vertical temperature gradient, atmospheric stability, the diameter of the release point, and the temperature and velocity of the em issions as they exit the release point.

TRIM.FaTE does not calculate plume rise. Instead, emitted chemicals are advected horizontally through fully mixed air compartments whose dimensions are defined by the user. This advection occurs in one of two air vol ume compartments

— the mixing layer compartment (where people, plants, animals, soil, and water are exposed to the chemicals) or the upper

- air compartment above the mixing layer (where the chemicals are removed from ground

- level exposure; i.e., a sink). Beca use an emission source’s release height is static, the mixing height for a given modeling time step determines which air layer the chemicals are emitted into in that time step

.

AERMET2TRIM has a function that estimates hourly plume rise given the physical parameters of an emission source (i.e., latitude

- longitude coordinates, release point height and diameter, and exit gas temperature and velocity), the horizontal distance from the source at which to estimate the plume rise (i.e., the radius of the modeled source parcel) , and the meteorology data being used for the TRIM.FaTE analysis. Because AERMET output data do not contain cloud ceiling heights, the AERMET2TRIM plume rise calculation s require a supplementary file contain ing those data (such ceiling data a re available in ISH and SAMSON data, for example). These plume rise estimations are based on methods summarized by Seinfeld and Pandis (1998). The output from this function is a text file with hourly values for effective release height, which is the actual release height plus the hourly plume rise value. If desired, the user can then aggregate the data to the time step being modeled.

Including hourly effective release heights will increase model runtime. In previous TRIM.FaTE site

- specific assessments, a 5% rule has been used to judge whether or not to use hourly heights ; that is

, if the effective release height was above the mixing height less than 5% of the time, hourly heights were not used ( i.e., the static, physical stack height was used). The user also has the option to estimate an average effective release height for the entire modeling period, and simply use that average height as the release height for all modeling times.DRAFT Protocol for Developing a TRIM.FaTE Model Scenario to Support a Site

- Specific Risk Assessment in the RTR Program

Working Draft 11 February 2014 

## 4. Air and Surface Parcel Design

4.1 The Role of Spatial Layouts in TRIM.FaTE

One of the primary inputs required by the TRIM.FaTE model is the specification of a spatial layout using Cartesian coordinates to define the vertices of surface and air parcels and volume elements. This information is input into the model via the “volume elements ” input file

. To construct the volume elements input file, users are required to divide the modeled domain into two

- dimensional air parcels and surface parcels. Air and surface parcels need not line up in all cases. Each parcel is also associated with a he ight, which may vary in time. The parcel coordinates and height are combined to define three

dimensional abiotic volume elements that contain biotic and abiotic compartments used to model the movement of chemical mass in TRIM.FaTE.

In a site

- specific asses sment, the spatial layout should capture the features of interest (e.g., farms or lakes) at the surface level and also specify how the overlying air domain is to be divided to produce accurate and informative estimates. Although the TRIM.FaTE User’s Guide (U.S. EPA 2005) provides useful mechanistic guidelines and rules of thumb on the design of air and surface parcels, those recommendations are not specific to the RTR context and are not based on an ingestion risk perspective. The following guidelines, as n oted in the introduction to this document, are intended to support site

specific risk assessments in the RTR program and should be considered in addition to the instructions and recommendations provided in the User’s Guide and Technical Support Document (U .S. EPA 2002)

.


### 4.2 Theoretical Considerations Influencing Parcel Design

Discussed below are the theoretical considerations related to the TRIM.FaTE model that were used to inform the air and surface parcel design recommendations presented later in this sectio n.

4.2.1 Cross

- Wind Dispersion

TRIM.FaTE models the movement of chemical mass based on first

- order differential equations. Consequently, the model does not account for dispersive processes in air that carry mass in a cross

- wind direction. Air parcel design must be conscious of, and attempt to compensate for, this limitation of the TRIM.FaTE model. For instance, using a square grid for air parcels may result in “pipeline flow” with the bulk of the emitted chemical concentrated in a compartment directly downwind. T he concentration estimates based on such a design would not align well with empirical evidence or with theoretical Gaussian plume models that are based on the second

- order advection dispersion equation.

Although a compartmental model with instantaneous mix ing (such as TRIM.FaTE) could never produce identical spatial results to a Gaussian plume model, air parcel design in TRIM.FaTE must attempt to ensure that an appropriate amount of lateral dispersion is permitted to occur. The Pa squill

- Gifford estimates of cross

- wind dispersion are commonly applied as parameters in air dispersion models for alternative atmospheric conditions ( U.S. EPA 1995). These cross

- wind dispersion estimates are in the form of graphs which plot cross

- wind dispersion as a function of dow nwind distance. The Turner equations are mathematical representations of these plots

. A plot of the Turner equations, which numerically approximate the Pasquill

- Gifford estimates of cross

- wind dispersion, suggest s that 99% of the emitted mass in neutral at mospheric conditions is likely to be contained within a cone subtending an angle of 20 degrees from the source, assuming a constant wind direction through the centerline. This provides a guideline about the ideal breadth of air parcels

, especially those do wnwind of features of interest.

4.2.2 Numerical Diffusion

All compartmental models potentially experience the issue of “numerical diffusion,” which refers, in the context of an air dispersion model, to the propagation of mass further downwind at a given point in time than is physically possible given the wind speed. Maintaining a fixed relationship between the size of theDRAFT Protocol for Developing a TRIM.FaTE Model Scenario to Support a Site

- Specific Risk Assessment in the RTR Program

Working Draft 12 February 2014 time step used in the simulation, the length of compartments

, and the wind speed helps prevent issues of numerical diffusion. Users interested in strict time accuracy are advised to ensure that compartment lengths are shorter than the wind speed mult iplied by the model time

- step. For RTR purposes, however, average annual values in the 50 th year are of primary interest

, and strict restraints on pa rcel length for a given time

- step are considered less important but still good practice to implement

.

4.2.3 Shape of the Deposition Profile with Distance from Source

The TRIM.FaTE model produces a deposition profile that has the largest values in the source comp artment and decreases with increasing distance in a pattern similar to an exponential curve. Theoretical and empirical evidence suggests, however, that the maximum deposition rate would not be experienced at the point of emission

, but further downwind. At a wind speed of 5 m/s , the point of maximum deposition for fine particulates and gases was estimated to be between 1 and 5 km (Overcamp 1976). Users of the TRIM.FaTE model should therefore be conscious of potential overestimation of deposition in surface p arcels adjacent to the source and a potential for bias in the deposition profile.



### 4.3 Empirical Evidence Relating to TRIM.FaTE Parcel Design

A limited number of TRIM.FaTE scenarios were modeled to provide empirical data about the impact on risks (in the RTR co ntext) from air and surface parcel layout design . Most of these scenarios were based on simple variations of the Tier 1 screening scenario layout. The main findings are summarized here :

 Extending an air parcel beyond the outer boundary of the underlying su rface parcel (i.e., having an air parcel “overhang” the surface parcel beneath it) reduces deposition over the surface parcel compared to air parcels that are co

- terminus with surface parcels (i.e., having the air parcel directly overlay the surface parcel );

 The number of air parcels that precede the air parcel overlying the surface feature of interest (i.e., the number of air parcels between the source parcel and an air parcel of interest) does not materially impact deposition over that surface parcel ;

 Soi l concentrations in the surface compartment adjacent to the source are considerably higher than if the same surface compartment is set back by a short distance of approximately 400m (i.e., a 400

- m buffer parcel between the source and the parcel of interest ) . This is consistent with TRIM.FaTE’s exponential deposition gradient ; and

 Increasing the length and/or breadth of a surface compartment m ight substantially reduce surface soil concentrations

More detailed results from these scenarios are documented in Appendix A

.


### 4.4 Recommended Best Practices for Air and Surface Parcel Design

The following recommendations for air and surface parcel design are intended to maintain a high degree of modeling accuracy while reducing design effort and potentially optimizing computer run time. While these guidelines are intended to facilitate optimal parcel design, every scenario is unique and m ight require site

- specific adjustments beyond the suggested approach provide here. Any adjustments or improvem ents to the proposed approach should aim to be consistent with the governing principles and empirical evidence discussed earlier in Section 4

. 

### Step 1: Identify Features of Interest
Several steps are recommended for identifying features of interest:

 Use geospatial data (e.g., aerial imagery, data on watersheds and water bodies, and remotely

sensed land cover and crop growth) to identify features of potential interest from the RTR perspective, such as farms and lakes ;DRAFT Protocol for Developing a TRIM.FaTE Model Scenario to Support a Site

- Specific Risk Assessment in the RTR Program

Working Draft 13 February 2014 – Geospatial d ata can include Google Earth (Google 2013), the National Hydrography Dataset (USGS 2013), the National Land Cover Database (MRLC 2013), and the Cropland Data Layer (USDA 2013)

.

 Consider only lakes that are at least as large as 25 acres (approx. 10 hectares ) 2 and that research suggests are fishable ; and

 Use the following guidance to finalize the selection of lakes and farms f or modeling:

– Limit farms to those within roughly 5 km of the source ;

– Do not select multiple farms in the same direction from the source ;

– Do not select farms adjacent to the source parcel. Choose a buffer distance of at least 400 m ;

– Prefer farms and lakes closer to the emission source versus those farther away ;

– Prefer lakes for which preliminary research suggests good availability of model ing data (e.g., flush rates, depth, pH, total phosphorus levels, suspended sediment concentration);

– Prefer lakes and farms that are frequently downwind from the emission source, if they exist

, based on the meteorology data selected for modeling; and

– Prefer the lake(s) selected in the Tier 2 screening analysis

. 

### Step 2: Breadth of Air Parcels (Air Parcel Radials)
Several steps are recommended for defining the breath of air parcels:

1. Emissions center point : Identify the center point of what will later become t he emission source parcel. It should roughly be the centroid of all the actual emission sources of concern at the facility;

2. Draw 20

- degree radials : Manually or using geospatial software, overlay radials that intersect at the source center point, are 20 deg rees apart. These radials roughly create triangles and represent preliminary breadth boundaries of air parcels;

3. Adjust the radial grid : Rotate the radial grid so that the maximum number of surface features of interest (e.g., farms and lakes) falls within s ingle triangles;

4. Smaller features of interest : For features that fall within a single triangle, use the boundaries of the triangle to define the breadth dimension of the air parcel overlying the surface feature;

5. Larger features of interest : For features th at fall within multiple triangles, merge and adjust the multiple triangles into a single triangle that fully contains the feature. For instance, if a lake subtends an angle of 50 degrees with respect to the source, three 20 degree triangles should be merge d and adjusted to form a 50 degree triangle to define the breadth of the overlying air parcel. This process will distort the shape of the surrounding triangles (i.e., they will be greater than the suggested 20 degrees), but that is acceptable; and

6. Areas no t overlying features of interest : For air parcels not overlying any features of interest, less resolution is required. Merge three 20 degree triangles to create a 60 degree triangle to define the breadth dimension of the overlying air parcel.

2 Based on available data, for RTR multipathway emission screening analyses, EPA defines potentially fishable lakes as those larger than 100 acres

, without exceeding 100,000 acres. Even a 100

- acre lake i s unlikely to be large enough to sustain harvesting the number of piscivorous fish required for the current screening ingestion rate (i.e., 373 g ww fillet/day )

. This is discussed in Section B.3.1 of Attachment B in Appendix 4 to the Risk Report

. However, EPA includes smaller lakes (as small as 25 acres) in site

- specific RTR multipathway analyses to be health protective and to ensure that small lakes that might be more highly contaminated than estimated by the screening analyses are not eliminated.DRAFT Protocol for Developing a TRIM.FaTE Model Scenario to Support a Site

- Specific Risk Assessment in the RTR Program

Working Draft 14 February 2014 

### Step 3 : Defin e the Outer Boundaries of the Domain
Draw the outer boundaries of the domain approximately 5 km beyond the farthest feature of interest (in relation to the emission source) , using the remaining parcel radials as the design template . This 5

- km buffer is fle xible depending on the characteristics of the nearby watersheds and how they might impact the runoff and erosion of chemical into the features of interest. Truncate the outer boundary of the domain in areas without features of interest, provided that these areas contribute much less significantly than other areas toward chemical runoff or erosion towards the features of interest.



### Step 4 : Complete Air and Surface Parcels for Features of Interest
Within the triangle overlying the feature (defined in Step 2 ) , encase the feature within a trapezoid ( ) by drawing straight lines at an angle to the sides of the triangle to define the inside boundary (the side closest to the source) and outside boundary of the feature. The trapezoid should be perpendicular to an imaginary radial originating at the emission source center point; put another way, t hese new lines should be parallel to each other and perpendicular to an imaginary radial bisecting the triangle, thus creating a trapezoid.

Where possible, the surfac e parcel representing the feature of interest should coincide with the air parcel trapezoid described above. This might require slightly distorting the dimensions of the surface feature of interest, but that is a permissible approach when the shape of the feature of interest is not very different from the overlying air parcel trapezoid

. For irregular surface features (i.e., those whose shapes are very different from the overlying air parcel trapezoid ) , create additional surface parcels as required to fill t he space between the actual boundaries of the feature of interest and the boundaries of the overlying air parcel trapezoid

. The additional surface parcels adjacent to the features of interest should be constructed subject to the consistency of land cover, terrain patterns, and/or hydrography

. A single adjacent parcel should never surround another parcel entirely. Instead, two adjacent parcels should bound the irregularly shaped feature on either side.



### Step 5 : Draw Air and Surface Parcels for the Emission So urce The air and surface parcels for the emission source should line up. The source parcel should be centered on the center point identified in Step 2 above and should accurately reflect where chemicals of concern are actually being emitted at the facility (i.e., it should fully contain all of the actual emission sources of concern). It should be a polygon where each side connects the sides of each air parcel triangle, and each side is perpendicular to an imaginary radial bisecting the triangle. For example , if the air parcel triangles each subtend exactly 60 degrees, and if the real emission sources span a 500

- m distance, then the source parcel would be a perfect hexagon that is centered on the emission source center point and has a diameter of 500 m

. 

### Step 6 : Complete Air and Surface Parcels Upwind of the Features of Interest
Within the two radials that bound a feature of interest, create a single air parcel between the feature and the source parcel (i.e., upwind of the feature of interest) . Like the air pa rcel above the feature itself, this upwind air parcel will be a trapezoid bounded by two lines that define the inside and outside boundaries (parallel to the inside and outside lines of the feature, relative to the emission source) and by the radials.

Ther e should be a corresponding surface parcel that lines up with the upwind air parcel. If the upwind region contains several different regimes of land cover, terrain, and/or hydrography that are large relative to the region

, then divide the surface parcel in to separate parcels for each of those different, major regimes. For example, for an upwind surface parcel oriented north

- south, if the northern half contains urban land cover and the southern half is a deciduous forest, then it might be reasonable to divid e the parcel into a northern parcel (with no vegetation) and a southern parcel (with deciduous forest). On the other hand, if the region is a scattered mix of forested and pasture/hay land cover, it would not be reasonable or efficient to create many very small parcels for each small area of forest or pasture/hay.



### Step 7 : Air and Surface Parcels Downwind of the Features of InterestDRAFT Protocol for Developing a TRIM.FaTE Model Scenario to Support a Site

- Specific Risk Assessment in the RTR Program

Working Draft 15 February 2014 Create an air parcel downwind of the feature of interest (i.e., on the side of the feature opposite the emission source) with a length dimension no larger than 5 km. The relative small size of this downwind parcel increase s the accuracy of chemical transfer via runoff and erosion.

There should be a corresponding surface parcel that lines up with the downwind air parcel. If the do wnwind region contains several different regimes of land cover, terrain, and/or hydrography that are large relative to the region

, then divide the surface parcel into separate parcels for each of those different, major regimes.


### Step 8 : Complete Air and Su rface Parcels Crosswind of the Features of Interest
Several considerations are recommended for defining air parcels crosswind of features of interest (i.e., parcels to the “left” and “right” of the feature, relative to the emission source):

 The out side bo undary of an air parcel immediately crosswind of a feature of interest should not extend beyond the out side boundary of the feature itself (i.e., the outside boundaries of the feature parcel and the crosswind parcel, relative to the emission source, should connect) . This is to increase the accuracy of chemical transfer via runoff and erosion ;

 The breadth dimensions of a crosswind air parcel should be defined by the radial grid. That is, the “left” and “right” sides of the crosswind parcels (relative to the emission source) should be defined by the radial grid developed in Step 2 above ;

 The in side boundary is the source parcel, unless there is already a parcel drawn between the source and the crosswind parcel (i.e., a parcel related to a different feature of interest ); and

 There should be a corresponding surface parcel that lines up with the crosswind air parcel. If the crosswind region contains several different regimes of land cover, terrain, and/or hydrography that are large relative to the region

, divide the surface parcel into separate parcels for each of those different, major regimes.



### Step 9 : All Other Air and Surface Parcels
All other air and surface parcels should be fitted within the boundaries defined by : (i) the air parcel radials (Step 2 ); (ii) t he boundaries of the features of interest and their upwind, downwind, and crosswind neighbors (Steps 6

- 8); and (iii) the outer boundaries of the domain (Step 3 ) . These other air and surface parcels are subject to continuity of land cover, terrain, and /or h ydrography, as discussed in the above steps.



## 5. Surface Hydrology and Erosion Property Definitions

5.1 Surface Parcel Chemical Transfer Dynamics in TRIM.FaTE

The TRIM.FaTE model incorporates the ability to account for chemical transfers between adjacent surface parcels via runoff and erosion. The algorithms that model surface runoff and erosion in TRIM.FaTE simulate the advective chemical transfer dynamics between surface parcels without requiring spatial elevation information or land

- cover details as inputs. Ins tead, the algorithms depend on inputs explicitly specifying the destination of erosion and runoff from a specific parcel. In other words, for each surface parcel, users must specify the proportion of the erosion and runoff originating in that parcel that

r eaches specific adjacent parcels. These inputs are known as link properties in TRIM.FaTE and are typically specified in the TRIM.FaTE “properties” file discussed in Section 2

. Users must also separately specify the average runoff a nd erosion rate for each surface parcel. These inputs are combined internally with estimates of the chemical concentration in surface soil and soil water to estimate mass transfers that occur in conjunction with erosion and runoff processes.

The inter

- parc el runoff and erosion parameter inputs in TRIM.FaTE are inherently site

- specific because there is no logical default value for the percentage of runoff and erosion from one parcel that reaches an adjacent parcel. Simulations indicate that ingestion risk in the RTR process is sensitive to the choice ofDRAFT Protocol for Developing a TRIM.FaTE Model Scenario to Support a Site

- Specific Risk Assessment in the RTR Program

Working Draft 16 February 2014 these values (refer to Appendix A ). This section discusses options for parameterizing these inputs in site

specific TRIM.FaTE applications for RTR.

For users not having access to ( or expertise in using) geographical information systems (GIS) software with features to quantitatively analyze surface hydrology and erosion, the recommended method of estimating parcel

- to

- parcel runoff/erosion fractions is summarized in Section 5.2

. If sophisticated GIS software with features to analyze surface hydrology and erosion based on elevation is to be used, the recommended method is summarized in Section 5.3

.


### 5.2 Estimating Runoff and Erosion F ractions without Sophisticated GIS Software

Without a license for sophisticated GIS software, the user can still obtain free GIS viewing tools that allow the user to display multiple layers of geospatial data and that have limited interaction with the data

, including querying the data and measuring distances. With such viewing software, the method for estimating runoff/erosion fractions provided in Module 11 of the TRIM.FaTE User’s Guide (U.S. EPA 2005) is appropriate. This method is summarized briefly here , with some additional tips not provided in the U ser’s G uide.


### Step 1: Assemble Hydrological and Elevation Data

T he user should obtain geospatial data indicating boundaries of hydrological units relevant to the modeling domain. These hydrological data are a vailable from the U.S. Geological Survey (USGS) National Hydrography Dataset (NHD; USGS 2013). These hydrography data should already have been obtained and used to inform the design of the modeling parcels. The NHD offers several levels of hydrological uni ts, typically from regions (the most spatially coarse) to subwatersheds (typically the highest spatial resolution). Considering that the typical site

- specific TRIM.FaTE modeling domain has a radius less than 50 km and is divided into several surface parcel s, subbasins or watersheds will usually offer the most appropriate resolution for use in configuring parcels and estimating runoff/erosion fractions

. The NHD also offers directional flow lines of streams, rivers, and other hydrographic features

.

The user s hould also obtain elevation data for the modeling domain. High resolution data are available from the USGS National Elevation Dataset (NED; USGS 2006). These elevation data should already have been obtained and used to help construct the modeling parcels. The data with the highest spatial resolution are not necessary; 30

- m resolution usually is appropriate.


### Step 2: Relate Surface Modeling Parcels to Each Other and to Hydrological Units
The user should display the modeling surface parcels along with the appr opriate hydrologic unit boundaries from the NHD. For each parcel (“sending parcel”), do the following:

 For each hydrologic unit that occupies at least part of the sending parcel, e stimate (or calculate, if able) the ratio [surface area of the part of the h ydrologic unit that is inside the sending parcel] to [surface area of the sending parcel];

 Identify each neighboring parcel (“receiving parcel”), including sinks where appropriate for the sides of the sending parcel that lie along the outer boundary of the modeling;

 For each hydrologic unit that occupies at least part of the sending parcel, estimate or calculate the length of each interface between the hydrologic unit and each receiving parcel (not discussed in the TRIM.FaTE User’s Guide ); and

 Estimate or c alculate the fraction of the sending parcel’s perimeter that interfaces with each receiving parcel (not discussed in the TRIM.FaTE User’s Guide).



### Step 3: Estimate Fraction of Runoff and Erosion
For each hydrologic unit that occupies at least part of a send ing parcel, one should use NED elevation data and NHD flow lines to estimate the fraction of runoff that will flow from the hydrologic unit into eachDRAFT Protocol for Developing a TRIM.FaTE Model Scenario to Support a Site

- Specific Risk Assessment in the RTR Program

Working Draft 17 February 2014 receiving parcel and, where appropriate, into sinks outside the modeling domain

. A fraction might be 0 if the elevation and flow lines suggest that all water in the hydrologic unit flows away from a receiving parcel.

The User’s Guide (U.S. EPA 2005) Section A.5 discusses runoff/erosion fractions. Although not discussed there, the NHD flow lines can help estima te the relative distribution of runoff from a sending parcel to its receiving parcel s , or from a hydrologic unit in the sending parcel to a receiving parcel . One can examine the flow lines along each sending

- receiving boundary to get a sense how much of th e boundary has flows from the sending area to the receiving area. This information can be combined with information on how much of the sending area’s perimeter interfaces with the receiving area in question, aid ing the user in developing runoff/erosion fra ctions.

As discussed in the User’s Guide Sections A.5 and A.6

— separately for each hydrologic unit in a sending parcel, multiply [the fraction of sending parcel ’s area covered by the hydrologic unit] by [the runoff/erosion fraction from the hydrologic unit to the receiving parcel] for each of the sending parcel’s receiving parcel s

. Then, for each of the se receiving parcels, sum this product across the hydrologic units. This sum provides the final fraction of runoff/erosion from each sending parcel to each r eceiving parcel. For each sending parcel, the fractions will sum to 1 when sinks are included as appropriate

. Another option is to estimate the runoff and erosion fractions based on visual inspection. This approach does not explicitly relate the area of e ach hydrologic unit to each sending parcel . T herefore, it does not explicitly assume that water cannot cross the boundaries of hydrologic units. Like the methods described above, this option uses flow lines and the interfacial length between adjacent parce ls. In this option, for

e ach sending parcel

, the user visually examines the NHD flow lines to see where (if at all) flow lines cross each interfacial boundary and into the receiving parcels. For each sending

- receiving pair of parcels, the user should estim ate (or measure, if possible) the length of the part of the interfacial boundary that has flow lines crossing into the receiving parcel. Then, divide that length by the total perimeter length of the sending parcel. This ratio provides the fraction of runof f/erosion from the sending parcel into the receiving parcel. Some professional judgment is required to subjectively adjust these fractions based on the relative magnitude of runoff across the various interfacial boundaries. These relative magnitudes can co nsider the overall terrain and flow patterns throughout the sending parcel (i.e., a flow into the receiving parcel with a relatively small fetch will likely carry less chemical into the receiving parcel than a flow with a relatively long fetch)

.


### 5.3 Estimatin g Runoff and Erosion Fractions with Sophisticated GIS Software

The method discussed in this section requires the use of ESRI ® ArcGIS TM software. The software license must enable the “Spatial Analyst” extension. ICF is currently developing an ArcGISTM model, coupled with a Microsoft® ExcelTM post

- processing tool, that largely automates the below procedures.

In ArcGIS, select the “Flow Dire ction” tool of the “Spatial Analyst” extension. Given a raster elevation dataset (such as the NED), this tool will determine the flow direction of each raster cell to the steepest downhill neighboring raster cell. The output of this tool will be a raster, where the value of each raster cell will indicate the flow direction.

Then, select the “Flow Accumulation” tool of the “Spatial Analyst” extension. The input to the “Flow Accumulation” tool is the output of the “Flow Direction” tool described above. Separa tely for each input raster cell, the “Flow Accumulation” tool will follow the flow direction into the appropriate neighboring cell, and continue follow ing the flow direction of that cell into a third cell, and so on, “connecting the dots” of the flow vecto rs until an endpoint is reached. This creates flow line s across the raster . Then, the tool calculates the number of these flow lines that cross each raster cell. This is the “flow accumulation” number produced by this tool. The flow accumulation is unitles s, as it does not represent an actual amount of water or chemical flowing from one place to another; the accumulation values should be viewed relative to each other.DRAFT Protocol for Developing a TRIM.FaTE Model Scenario to Support a Site

- Specific Risk Assessment in the RTR Program

Working Draft 18 February 2014 For each sending parcel, the user would use the combination of flow direction and flow acc umulation data from the above tools to calculate the total flow (unitless) from the sending parcel to each receiving parcel. The runoff/erosion fraction from the sending parcel into receiving parcel “A” would be the accumulated flow from the sending parcel to receiving parcel “A” divided by the total accumulated flow from the sending parcel to all its receiving parcels.



## 6. Compartment Properties Recommended for Site

- Specific Parameterization

6.1 The Role of Properties in TRIM.FaTE

The TRIM.FaTE model is dependen t on hundreds of user

- specified properties that describe the biotic and abiotic environments being modeled. Properties in TRIM.FaTE can be broadly divided into the following types:

 non

- chemical specific properties that define biotic compartments ( e.g., bio mass of game fish in a lake, the length of a leaf on a deciduous plant),

 non

- chemical specific properties that define abiotic compartments ( e.g., porosity of surface soil, the total suspended solids concentration in a lake),

 chemical

- specific properties (i ncluding system

- wide chemical properties such as the Henry’s Law constant, the octanol

- water partition coefficient, and compartment

- specific chemical properties such as reaction and degradation rate constants in various environmental media), and

 simulation

- specific properties ( e.g., model run

- time, model time step).

All user

- defined (e.g., non

- formula) properties in a TRIM.FaTE scenario can be assigned simulation

- or site

- specific values. In theory, the more properties that are assigned site

- specific values , the more accurately the simulation will represent chemical fate and transport at that location. Following this logic, the user should try to find site

- specific values for as many properties as possible. However, although each model property is potentiall y important in defining a particular environmental fate and transport process , it is apparent based on theoretical considerations and empirical evidence (i.e., analysis of model results and model evaluations) that there is a subset of model properties that more significantly influences the environmental concentrations that drive the risks of importance in the exposure scenarios evaluated in RTR assessments. The fact that some parameters are more influential on results is true for complex models in general. This is the focus of sensitivity analyses.

In previous site

- specific risk assessments using TRIM.FaTE, which were conducted for RTR and in other regulatory applications, a substantial portion of the level of effort required to perform the assessments

was d irected towards site

- specific property parameterization. One of the specific objectives of this protocol is to take advantage of the results of sensitivity analyses and model evaluations conducted of TRIM.FaTE. Based on these results, we have identified th ose compartment properties that are a high priority for site

- specific parameterization, those that can be adequately represented by regional or land

use

- specific default values, and those for which nationally representative or health

- protective values are adequate. This classification scheme is intended to reduce the level of effort required to adequately parameterize site

- specific assessments while maintaining a high level of accuracy in risk estimates for RTR.



### 6.2 Approach to Prioritizing Properties for Site

- Specific Parameterization

ICF relied on a combination of theoretical reasoning and empirical evidence to prioritize TRIM.FaTE properties for the purposes of this protocol. In this way, ICF was able to limit the need for “brute

- force” empirical evaluations (e.g., comprehensive sensitivity analyses that systematically vary all or most of the

user

- defined inputs, such as those conducted prior to the 2009 SAB review (U.S. EPA 2009) ) andDRAFT Protocol for Developing a TRIM.FaTE Model Scenario to Support a Site

- Specific Risk Assessment in the RTR Program

Working Draft 19 February 2014 additional resource

- intensive literature searches. ICF’s justification for determining that properties were not high priority was based on three lines of evidence:

1. ICF followed a “process”

- based approach to rule out a large subset of TRIM.FaTE properties from the need for site

- specific parameterization. This approach was founded on the idea that the TRIM.FaTE model produces greater than necessary resolution (in terms of the number of concentrations that are calculated for different environmental media types) when viewed from the RTR perspective. The individual human ingestion exp osure scenarios evaluated for RTR rely most directly on results from TRIM.FaTE for surface soil compartments at the location of a farm and fish compartments in a lake of interest. All fate and transport processes

— and the properties that exclusively define those processes

— that do not strongly influence these concentrations can reasonably be ruled out from requiring site

- specific parameterization. The implications of this approach will be discussed in greater detail below.

2. ICF also used practical consideratio ns regarding data availability to rule out certain properties from site

- specific parameterization. Over the course of numerous site

- specific assessments and the parameterization of the screening scenarios, ICF has conducted literature searches on numerous TRIM.FaTE properties. ICF used the insight gained from these exercises to identify certain sets of parameters as being too data

- scarce to parameterize on a site

- specific basis at this time without expending a substantial amount of time and money (for possi bly uncertain results).

3. Physical constants and physico chemical properties of the modeled PB

- HAPs were also ruled out from site

- specific parameterization based on their largely unchanging nature in the environment for the chemicals considered for RTR.

ICF e valuated the parameters not eliminated by the above considerations to determine which properties should be the focus of data collection efforts during site

- specific TRIM.FaTE modeling for RTR. ICF conducted a limited number of evaluations and used the resu lts of previous sensitivity analyses to decide which of these shortlisted parameters should be prioritized for site

- specific parameterization, for land

- use

based parameterization, or for regional parameterization.

Other scenario properties such as emission period and the model’s numerical integration time

- step are typically not varied between site

- specific assessments. These properties are not discussed further in this protocol, but are documented in Appendix B

.


### 6.3 Elimination of P roperties from Site

- Specific Parameterization

6.3.1 Process

- based Elimination of Parameters

The operative principle in the process

- based elimination of parameters is that fate and transport processes that do not substantially influence concentrations of interest from an RTR perspective are less important to parameterize. ICF used theoretical considerations based on the evaluation of the underlying TRIM.FaTE algorithms

, combined with empirical evidence from TRIM.FaTE simulations

, to identify the less important fat e and transport processes and eliminate the need to parameterize those processes on a site

- specific basis. The specific processes identified as being of less importance in the RTR context and the underlying justification for ruling them out from site

- speci fic consideration are listed below:

 Chemical transport via water percolation through the sub

- surface soil layers (not including surface soil) does not affect farm soil or lake water concentrations . T heoretical considerations suggest that chemical

, once tra nsported into the lower soil layers

, will not substantially make its way back to the surface compartments of interest ;

 Chemical transport via sub

- surface soil diffusive processes, although having the potential to transfer mass upwards, are not sizeable in comparison to advective transfer processes. An evaluation of relative mass flux in the Tier 1 screening scenario supports this assertion for all the chemicals evaluated ; andDRAFT Protocol for Developing a TRIM.FaTE Model Scenario to Support a Site

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Working Draft 20 February 2014  Chemical transport to the lake via horizontal groundwater flow and recharge is neg ligibly small compared to other advective chemical inputs into the lake. The relative mass flux for this process compared to other advective transfer processes carrying chemical into the lake in the Tier 1 screening scenario supports this assertion for all chemicals evaluated

.

Because the RTR user has no int rinsic interest in the concentrations prevailing in the lower soil layers, all of the above processes have be en ruled out from consideration for site

- specific parameterization

. As a consequence, it is po ssible to rule out all sub

- surface soil compartment properties from requiring site

specific parameterization.

6.3.2 Data Availability

- based Elimination of Parameters

Chemical

- Specific Aquatic Biota Properties : The aquatic biota compartments in TRIM.FaTE

— currentl y including benthic invertebrates and five types of fish

— are characterized by several potentially site

- specific properties that control algorithms influencing the uptake, degradation

, and elimination of chemicals in the aquatic organisms. These chemical

- sp ecific properties include the absorption rates of chemical into each type of fish from surface water, elimination rates from fish digestive systems, degradation rates within the fish, and other parameters. In the course of parameterizing TRIM.FaTE for the screening scenario and conducting extensive evaluations of parameter sensitivity, it has become apparent that only a limited number of studies are available for several of these properties for most combinations of chemicals and organisms.

Although these pr operties may potentially differ in alternative climates and conditions, it appears unlikely that additional literature searches and evaluations would yield better, more appropriate site

- specific values than the current defaults. Until such time as more stu dies on these properties are available, practical considerations suggest that these chemical

- specific aquatic biota properties be ruled out from site

- specific parameterization.

Chemical

- Specific Abiotic Compartment Propertie s : TRIM.FaTE algorithms model ch emical reaction and degradation processes in several abiotic compartments (e.g., surface soil ) . These algorithms depend on chemical

- specific parameters such as degradation rates (or half

- lives), transformation rates, and other properties

. Literature search es conducted during previous site

- specific assessments in the RTR process and other regulatory applications using TRIM.FaTE have suggested that data are limited for these properties.

These chemical properties ( with the exception of oxidation, reduction, an d methylation and demethylation rates influencing mercury ) therefore are currently ruled out from site

- specific consideratio n. The mercury transformation p roperties have been shown to be highly risk

- influential as well as variable across different ecosyste m types and conditions, and these properties are reserved for site

- or land

- use

- specific parameterization in the future, subject to greater data availability and the results of additional evaluations.

6.3.3 Combination of Data

- and Sensitivity

- based Elimination of Parameters

Terrestrial Vegetation : The terrestrial vegetation compartments in TRIM.FaTE

— currently including grass, coniferous forest, deciduous forest, wetland grass, and wetland forest

— are not directly part of the RTR risk assessment calculations (i.e. , chemical concentrations in these compartments are not used as inputs to the MIRC ingestion exposure model). However, these compartments act as sinks for chemicals and also transfer chemicals from air to soil via litterfall. In this way, the choice of ter restrial vegetation influences surface soil concentrations and, ultimately, risk.

The terrestrial vegetation compartments depend on properties such as the lipid content of leaves, wet density of leaves , area indices of leaves, etc. Although it is possible that these properties differ on a site

specific basis

— for instance, the characteristics of coniferous tree s in Oregon are different from those of coniferous tree s in North Carolina

— these differences are not expected to have a substantial influence on risk. ICF’s simulations indicate that the impact on risk of alternative vegetation scenarios is limited afterDRAFT Protocol for Developing a TRIM.FaTE Model Scenario to Support a Site

- Specific Risk Assessment in the RTR Program

Working Draft 21 February 2014 accounting for differences in erosion regimes specific to land

- use type (see Appendix A ). It is expected that site

- specifi c differences within a single vegetation type would be even lower.

Literature search es during previous site

- specific assessments in the RTR process have indicated that highly intensive literature search would be required to parameterize the full range of t errestrial vegetation parameters required by the TRIM.FaTE algorithms. Based on the limited risk impact of terrestrial vegetation properties, and limited data availability at the site

- specific level, these properties are currently ruled out from site

- speci fic parameterization.

6.3.4 Elimination of Physical and Chemical Characteristics

Algorithms in the TRIM.FaTE model frequently depend on physical and chemical parameters, such as the Henry’s Law constant and the octanol

- water partition coefficient, to partition c hemicals between phases within a compartment. These properties are, by their nature, relatively unchanging across most standard environmental conditions for the non

- ionic organic compounds currently evaluated (i.e., dioxins and PAHs) 3 . These properties are thus ruled out from requiring site

- specific parameterization for the time being.



### 6.4 Properties Recommended for Site

- Specific Parameterization

Following the elimination process described above, ICF identified a set of parameters for further evaluation based t heoretical considerations as well as higher sensitivity potential displayed in previous sensitivity analyses ( e.g., U.S. EPA 2009). To estimate the risk influence of these parameters, ICF performed a limited set of additional sensitivity analyses. The eval uated parameters are listed bel ow, grouped by compartment type :

 Air : d ust load, fraction of organic matter

.

 Surface Soil : u nit soil loss

, inter

- compartment drainage and erosion fractions

, soil particle density

, soil air fraction

, soil organic content

, soil pH

, soil water content

.

 Surface Water and Sediment : s uspended solids concentration

, bed sediment density

, suspended solids density

, bed sediment porosity

.

 Aquatic Biota : b iomass of various aquatic biota compartments

.

 Terrestrial Vegetation : “Allow exchang e” and “litterfall” file inputs

. Unlike previous analyses, these sensitivity analyses were not based on fixed perturbations from the default values but instead used reasonable high and/or low bounds approximately corresponding to the range found in the env ironment. The impacts on risk were computed with respect to the Tier 1 screening scenario results at equivalent emission rates.

ICF extended the scope of the current analyses by also us ing the results of TRIM.FaTE sensitivity analyses conducted in previous regulatory applications and pertaining to air, surface soil, and surface water and sediment

. Although these analyses were performed on a different version of the Tier 1 screening scenario set up, the results are considered informative

. The specific detail s of the analyses conducted as part of this protocol development are reported in Appendix A

, while other supporting evidence has been drawn from previous reports ( e.g., U.S. EPA 2009)

. Based on the results of these analyses, Table 6

- 1 contains TRIM.FaTE properties recommended for site

- specific parameterization in the RTR process. These properties have been further classified as high, medium

, and low priority to facilitate an appropriate allocation of av ailable resources in the parameterization process.

3 For me rcury, some analogous properties, such as the partition coefficient for mercury in the aqueous phase, do vary according to pH; these relationships are incorporated into the model as formula properties.DRAFT Protocol for Developing a TRIM.FaTE Model Scenario to Support a Site

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Working Draft 22 February 2014 Table 6

- 1

. TRIM.FaTE Properties Recommended for Site

- Specific Parameterization



### Compartment Property Priority Remark

Surface Water Depth High Having depth a s well as flush rate helps serve as a check on surface hydrology assumptions. Flush Rate High Suspended Solids Concentration High Attempt to find a column

- averaged value

.

pH Moderate Important for metals

. Algae Density Moderate May be estimated fr om total phosphorus concentrations in the absence of measured values

. Organic Carbon Fraction Moderate Important for TCDD (U.S. EPA 2009). Data availability may be limited.

Water Temperature Moderate Sensitive but unlikely to manifest wide range

.

Aqua tic Biota Biomass Moderate May be estimated from total phosphorus concentrations in absence of measured values.

Surface Soil pH Moderate Important for metals.

Terrestrial Biota “Allow Exchange”

and “Litterfall” data files Low These files govern how long leaves remain open for stomatal exchange during different times of the year and also when the leaves fall off the trees onto the surface soil. Although the impact of these properties ha s not been empirically tested, theoretical considerations suggest they will have a low impact when estimating average annual risks.

In addition to these values, meteorology parameters, surface hydrology and erosion

- related parameters, and the spatial layout are fundamentally site

- specific elements of a TRIM.FaTE simulation, as noted in the previous sections.



## 7. Properties Recommended for Land Use

- Based Values

In addition to the properties identified in Section 6 as desirable for site

- specific parameterization, we identified properties that also inf luence risk substantially but for which the impacts on risk are expected to be largely captured by land use

- specific parameters. In other words, for these properties, accounting for variations that correspond to land use is expected to adequately account f or any variation in these parameters (to the extent that they influence risk). Additional variation in parameter values resulting from site

- specific variations within a particular land use category is not expected to be significant . For example, difference s in surface soil erosion (as expressed by the unit soil loss rate property in TRIM.FaTE) are expected to be larger between the average deciduous forest and the average parcel of tilled soil than between different types of deciduous forest or between diffe rent types of tilled soil. The use of land use

specific values for such properties is expected, therefore, to adequately capture their impact on risk estimates in the RTR process.

The rationale for identifying properties as land use

- based in this protocol is a combination of risk sensitivity analys is ( Appendix A and U.S. EPA 2009 ) , professional judgment about the range exhibited inDRAFT Protocol for Developing a TRIM.FaTE Model Scenario to Support a Site

- Specific Risk Assessment in the RTR Program

Working Draft 23 February 2014 the environment, and expected data availability at the site

- specific level. For land use

- specific properties, users performing a site

- specific assessment should identify the land use type of each surface parcel in the Fa TEm aster Scenario Builder tool. The tool would then automatically assign the appropriate land use

specific property values

. It should be noted, however, that the tool is not currently parameterized with land

- use specific property values.

Tab le 7

- 1 lists the TRIM.FaTE parameters that are recommended for land use

- specific parameterization. These parameters are all related to the surface soil parcel and assume distinct values for each of the land use types modeled in TRIM.FaTE. These land use types currently include deciduous forest, coniferous forest, grass, agricultural soil, untilled soil, forested wetlands

, and grassy wetlands. Other land use types may also be defined using the Fa TEm aster Scenario Builder tool. Land use type is not an explicit input in TRIM.FaTE but is implicitly reflected in the TRIM.FaTE property values corresponding to each surface parcel.

Tab le 7

- 1

. TRIM.FaTE Properties Recommended for Land use

- Specific Parameterization



### Property Remark

Organic carbon fraction Fraction of dry soil solids that is organic in origin.

Water content The sum of the water and air content fractions of a soil determines its porosity. Air content
Particle density Refers to the dry density of the average soil particle.
Rainfall/erosivity index Universal Soil Loss Equation (USLE) propert ies used in FaTE m aster Scenario Builder Tool to compute each surface soil compartment’s average erosion rate

. Soil erodibility index

Topographical (LS) factor

Cover/management factor

Supporting practices factor

Fraction of precipitation that evapotranspires Water balance

- re lated property used in FaTE m aster Scenario Builder Tool to compute each surface soil compartment’s average runoff rate

. Fraction of precipitation subject to overland runoff



## 8. Properties Recommended for National Values

Nationally

- representative or health

- protective values are recommended for all TRIM.FaTE properties that are not identified for site

- specific or land use

- based parameterization in Sections 6 and 7 above. These properties are expected either to (1) not substantially influence risk in the RTR process

, (2) not have adequate data to support site

- specific parameterization, or (3) be relatively constant in the environment, as discussed in greater detail in the approach described in Section 6

. These properties have been previously characterized in the RTR Tier 1 and Tier 2 screening threshold derivation analyses by eitherDRAFT Protocol for Developing a TRIM.FaTE Model Scenario to Support a Site

- Specific Risk Assessment in the RTR Program

Working Draft 24 February 2014 nationally

- representative values or health

- protective values. The same values are recommended for these properties in site

- specific analyses and are listed with references in Appendix B

. 

## 9. The FaTE m aster Scenario Builder

All user inputs can be provided to TRIM.FaTE via text

- based or delimited data input files. The files must be defined in syntax specific to TRIM.FaTE; a full description of syntax requirements is provided in the TRIM.FaTE User’s Guide (U.S. EPA 2005)

ICF’s FaTE m aster Scenario Builder tool provides a Microsoft® Excel TM

- based environment that facilitates translat ion of user inputs into appropriately formatted TRIM.FaTE input files that can be used to set up and run site

- specific scenarios. The FaTE m aster Scenario Builder tool does not, however, create the TRIM.FaTE Master Library file, which contains library prope rties and can be used to set property values that are not expected to vary between scenarios. The recommendations for the use of site

- specific properties made earlier in this document are not envisaged to require changes to the TRIM.FaTE Master Library fil e. The FaTE m aster Scenario Builder tool is further documented in Appendix C

. 

## 10. Potential Future Improvements

This protocol represents a first attempt at documenting the current state of knowledge related to conducting site

- spec ific environmental modeling in support of RTR multipathway risk assessments. The protocol could be enhanced in the future by documenting best practices and developing recommendations regarding the following issues (among others):

 Identification of land use

- specific parameters for the identified soil properties based on literature review ;

 Application of enhanced technical approaches, such as the use of a sensitivity score approach

, to identify the most influential model properties ;

 Potential development of r egional parameters for a subset of model properties based on the results of further sensitivity analysis and data availability assessments ;

 Greater use of graphics and figures to illustrate model set

- up concepts ;

 Enhanced technical editing to help the prot ocol be more self

- explanatory and independent of other TRIM.FaTE support documents in its scope ;

 Researching the potential for geographically variable biotransfer factors and other parameters in MIRC ; and

 Further research and development of GIS

- based appro aches to surface hydrology and erosion property parameterization

.DRAFT Protocol for Developing a TRIM.FaTE Model Scenario to Support a Site

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Working Draft 25 February 2014 

## References

ESRL (Earth System Research Laboratory). 2011. NOAA/ESRL Radiosonde Database Access. Available online at: http://www.esrl.noaa.gov/r aobs/ . Accessed March 3, 2011.

Google. 2013. Google Earth. Available at http://www.google.com/earth/index.html . Accessed July 24, 2013.

Multi

- resolution Land Characterization (MRLC) Consortium

. 2013. Na tional Land Cover Database. Available online at http://www.mrlc.gov/index.php . Web page last updated February 7, 2013.

NCDC ( National Climatic Data Center). 2008. Integrated Surface Database. Available at: http://www.ncdc.noaa.gov/oa/climate/isd/index.php . Web page last updated August 20, 2008.

NCDC. 2011 . NCDC 1981

- 2010 Climate Normal. Available at: http://www.ncdc.noaa.gov/oa/climate/normals/usnormals.html . Web page last updated January 5, 2011.

NCDC. 2012 . NCDC CD

- ROM and DVD Climate Products. Available at: http://ols.nndc.noaa.gov/plolstore/plsql/olstore.prodlist?category=C&subcatc=01&groupi n=CDV

.
NCDC. 2013 . Quality Controlled Local Climatological Data (QCLCD). Available at: http://www.ncdc.noaa.gov/data

- access/land

- based

- station

- data/land

- based

datasets/quality

- controlled

- local

- climatological

- data

- qclcd

. Overcamp, TJ. 1976. A General Gaussian Diffusion

- Deposition Model for Elevated Point Sources. J ournal of Applied Meteor ology 15: 1167

- 1171.

Seinfeld, JH; Pandis, SN. 1998. Atmospheric Chemistry and Physics: From Air Pollution to Climate Change. Wiley

- Interscience, New York, pp. 931

– 933.

U. S. Department of Agriculture (USDA)

. 2013. CropScape

– Cropland Data Layer. Available online at http://nassgeodata.gmu.edu/CropScape/

. U.S. EPA (U.S. Environmental Protection Agency). 1992. Pro cedures for Substituting Values for Missing NWS M eteorological Data for Use in Regulatory Air Quality Models (Dennis Atkinson and Russell F. Lee). July 7, 1992. Available online at http://www.e pa.gov/ttn/scram/surface/missdata.txt . Last accessed March 07, 2011.

U.S. EPA. 1995. User's Guide for the Industrial Source Complex (ISC3) Dispersion Models Volume II

- Description of Model Algorithms. September 1995 . ( EPA

- 454/B

- 95

- 003b )

U.S. EPA. 2002. T RIM.FaTE Technical Support Document. U.S. EPA Office of Air Quality Planning and Standards. Available at: http://www.epa.gov/ttn/fera/trim_fate.html#current_user

U.S. EPA. 2005. TR IM.FaTE User’s Guide. Office of Air Quality Planning and Standards. September, 2005. Available at: http://www.epa.gov/ttn/fera/trim_fate.html#current_user

.DRAFT Protocol for Developing a TRIM.FaTE Model Scenario to Support a Site

- Specific Risk Assessment in the RTR Program

Working Draft 26 February 2014 

### U.S. EPA. 2009. Risk and Technology Review (RTR) Risk Assessment Methodologies: For Review by the EPA’s Science Advisory Board. Attachment C

- 3 in Appendix C. ( EPA

452/R

- 09

- 006 )

U.S. EPA. 2010a. SCRAM (Support Center for Regulatory Atmospheric Modeling) Mixing Height Data. Availabl e at: http://www.epa.gov/ttn/scram/mixingheightdata.htm . Web page last updated May 13, 2010.

U.S. EPA. 2010b. SCRAM Surface Meteorological Archived Data: 1984

- 1992. Available at: http://www.epa.gov/ttn/scram/surfacemetdata.htm . Web page last updated May 13, 2010.

U.S. EPA. 2012a. AERMET Meteorological Preprocessor. Available at: http://www.epa.gov/ttn/scram/metobsdata_procaccprogs.htm#aermet . Web page last updated December 18, 2012.

U.S. EPA. 2012b . Mixing Height Program. Available at: http://www.epa.gov/ttn/scram/metobsdata_procaccprogs.htm#mixing . Web page last updated December 18, 2012.

U.S. EPA. 2012c. PCRAMMET M eteorological Processor. Available at: http://www.epa.gov/ttn/scram/metobsdata_procaccprogs.htm#pcrammet . Web page last updated December 18, 2012.

U.S. EPA. 2013. HEM D ownload Page. Available at: http://www.epa.gov/ttn/f era/hem_download.html . Web page last updated June 19, 2013.

USGS (U.S. Geological Survey) . 2006. National Elevation Dataset. Available at: http://ned.usgs.gov/

. Web page last updated August, 2006.

USGS. 2013. National Hy drography Dataset. Available at http://nhd.usgs.gov/ . Web p age last updated July 19, 2013.DRAFT Protocol for Developing a TRIM.FaTE Model Scenario to Support a Site

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Working Draft A

- 1 February 2014 

## Appendix A. Documentation of Empirical Analyses Used to Prioritize TRIM.FaTE Properties

A.1. Introduction ICF performed a series of empirical analy ses to prioritize TRIM.FaTE model properties for site

- specific parameterization. These analyses were based on changing the value of one or more model properties relative to the Tier 1 screening scenario and measuring the relative impact on risk. Unlike in a traditional sensitivity analysis, this analysis changed property values to approximate high and low

- end values within the environmental range of the property of interest, instead of using a fixed perturbation. The measured impacts on risk, the expected r ange in the environment, and data availability were considered in prioritizing model properties for site

- specific parameterization, as discussed in Sections 6 and 7

.

Table A

- 1 summarizes the various empirical an a lyses that were conducted, the risk impact of the scenario modifications, and conclusions from the analyses.DRAFT Protocol for Developing a TRIM.FaTE Model Scenario to Support a Site

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Working Draft A

- 2 February 2014 Table A

- 1

. Results and Conclusions from Empirical Analyses U sed to Prioritize TRIM.FaTE Properties

Scenario Name Scenario Descripti on

(with respect to Tier 1 Screening Scenario) Normalized Risk Relative to Tier 1 Screening Scenario Risk Impact of Scenario Modification Conclusions TetraCDD, 2,3,7,8

- Benzo(A)

pyrene Cadmium Methyl Mercury

Tier 1 SS Tier 1 Screening

Scenario

. 1.00 1.00 1.00 1.00 Designed to produce most conservative risk estimate. All relative risks for modified scenarios are measured relative to the Tier 1 screening scenario.

WF1 Reduce watershed

flows (erosion and runoff) to half screening scenario levels. Redir ect remainder to sink. Maintain same flow directions as screening scenario. 0.56 0.69 0.21 0.38 Reducing the quantity of runoff and erosion reaching receiving compartments reduces chemical inputs into those compartments, including the lake, and reduces ris k. Surface hydrology and erosion flows (where and how much of the erosion and runoff from a compartment reaches) are potentially highly sensitive properties in the model (influencing risk by up to a factor of 10) and are recommended for site

specific param eterization. WF2 Reduce watershed

flows (erosion and runoff) to 1/10 screening scenario levels. Redirect remainder to sink. Maintain same flow directions as screening scenario. 0.34 0.68 0.11 0.25

ER0 Switch off erosion

. 0.39 1.02 1.10 0.43 Turning off erosion reduces chemical inputs into the lake and reduces chemical removal off the farm. Although erosion is a relatively important process, its maximum impact on risk is less than a factor of 3, even whenDRAFT Protocol for Developing a TRIM.FaTE Model Scenario to Support a Site

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Working Draft A

- 3 February 2014 Scenario Name Scenario Descripti on

(with respect to Tier 1 Screening Scenario) Normalized Risk Relative to Tier 1 Screening Scenario Risk Impact of Scenario Modification Conclusions TetraCDD, 2,3,7,8

- Benzo(A)

pyrene Cadmium Methyl Mercury

ER1 Double erosion rates

. 1.09 0.99 0.86 0.86 Increasing erosion produces competing effects: while it increases chemical inputs into the lake, it also increases the burial rate of sediment and increases chemical removal from the farm. accounting for variable runoff rates. A land

- use specific parameterization approach is recommended for the average erosion rates of surface soil compartments.

ER

- RUN1 Double erosion and

runoff rates (same flush rate; higher lake depth)

. 0.94 0.96 0.74 0.89 Increasing the runoff rate increases the input of soluble chemical s into the lake and decreases the removal of those chemicals from the farm.

ER

- RUN2 Double erosion and

runoff rates (higher flush rate; same lake depth)

. 1.09 0.99 0.74 0.85 Increased runoff rates can be accommodated by means of increased lake depths or increased flush rates.

RUN1 Switch off runoff;

maintain flush rate and depth

. 0.99 1.00 0.70 0.96 Nullifying chemical transfer through runoff reduces chemical input into the lake and reduces chemical removal from the farm

. Runoff rates have a limited imp act on risk. A land

- use specific parameterization approach is recommended for average runoff rates from surface soil compartments. RUN2 Implement cumulative

runoff regime

. 1.02 1.00 1.14 1.10 Assumes runoff from one compartment does not evaporate but cont ributes to runoff from the receiving compartment.DRAFT Protocol for Developing a TRIM.FaTE Model Scenario to Support a Site

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Working Draft A

- 4 February 2014 Scenario Name Scenario Descripti on

(with respect to Tier 1 Screening Scenario) Normalized Risk Relative to Tier 1 Screening Scenario Risk Impact of Scenario Modification Conclusions TetraCDD, 2,3,7,8

- Benzo(A)

pyrene Cadmium Methyl Mercury

FR1 Double lake depth,

half flush rate, same rainfall, and same runoff fraction. 0.71 0.96 1.00 1.15 Doubling depth reduces concentrations but halving the flush rate reduces chemical output from the lake. Lake depth and flush rate have a modest impact on risk. However, knowledge of both these parameters can help guide the surface hydrology and erosion direction flows in the watershed which can more substantially influence risk. S ite

- specific parameterizati on is recommended for lake depth and flush rate. FR2 Half lake depth,

double flush rate, same rainfall, and same runoff fraction. 1.28 1.07 1.00 0.92 Halving depth increases concentrations but doubling the flush rate increases chemical output from the lak e.

FR3 Double depth, same

flush rate, same rainfall, same runoff fraction (violate water balance in screening scenario)

. 0.69 0.96 0.58 1.02 Doubling depth reduces lake concentrations for most chemicals.

FR4 Double flush rate,

same depth, same rainfall

, and same runoff fraction (violate water balance in screening scenario)

. 0.95 1.00 0.58 0.89 Doubling flush rate reduces lake concentrations.

PERC1 Implement balanced

percolation regime

. 0.99 1.00 0.62 0.99 Assumes runoff from one compartment does not

e vaporate but percolates in the receiving compartment. Percolation rate (the fraction of rainfall that is subject to percolation into the sub

- surface) has a modest impact on risk. Land use

- based parameterization is recommended for this property.DRAFT Protocol for Developing a TRIM.FaTE Model Scenario to Support a Site

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Working Draft A

- 5 February 2014 Scenario Name Scenario Descripti on

(with respect to Tier 1 Screening Scenario) Normalized Risk Relative to Tier 1 Screening Scenario Risk Impact of Scenario Modification Conclusions TetraCDD, 2,3,7,8

- Benzo(A)

pyrene Cadmium Methyl Mercury

R1 Reduce rainfall down

to 1/3rd SS value; same lake depth; runoff rates and flush rate down to 1/3 rd

. 0.64 0.59 0.92 0.58 Reducing rainfall reduces chemical washout from air. This run, when combined with earlier runs focusing on the impacts of flush rate, suggests that the chemical washout impact of rainfall has more influence on risk than the impact of rainfall levels on hydrological properties like flush rate. This r einforces the argument for site

specific meteorological parameters.

V_C Set all surface

compartmen ts except farm to coniferous forests

. 0.79 0.75 0.40 0.87

The choice of vegetation in surface soil compartments impacts risk by absorbing chemicals from air and soil and then redepositing them onto the surface soil via litterfall. Land use

- type has a limit ed impact on risk. Based on these results, terrestrial vegetation parameters are recommended for land

- use specific parameterization. In interpreting these results, it is important to note that these runs have not been normalized for erosion rates. Therefor e, the impacts on risk presented here are from a combination of impacts from differential erosion rates and vegetation types. V_D Set all surface

compartments except farm to deciduous forests

. 0.34 0.92 0.49 0.39

V_G Set all surface

compartments except farm to grassland

. 0.88 0.82 0.45 0.92

V_U Set all surface

compartments except farm to untilled soil

. 0.42 0.73 0.37 0.81

V_WW Set all surface

compartments except farm to forested wetlands

. 0.36 0.92 0.49 0.47DRAFT Protocol for Developing a TRIM.FaTE Model Scenario to Support a Site

- Specific Risk Assessment in the RTR Program

Working Draft A

- 6 February 2014 Scenario Name Scenario Descripti on

(with respect to Tier 1 Screening Scenario) Normalized Risk Relative to Tier 1 Screening Scenario Risk Impact of Scenario Modification Conclusions TetraCDD, 2,3,7,8

- Benzo(A)

pyrene Cadmium Methyl Mercury

V_WG Set all surface

compartments ex cept farm to grassy wetlands

. 0.86 0.83 0.46 0.94

BM1 Increase aquatic

biomass uniformly by a factor of 10

. 0.84 0.39 0.90 0.99 Increasing aquatic biomass reduces chemical concentration in biomass as the same amount of chemical is distributed in a highe r amount of biomass. Risk is sensitive to the aquatic biomass levels. These properties are therefore recommended for site

- specific parameterization. In interpreting the results of these runs, it may be noted that all biomass levels were uniformly raised. I n real applications, the biomass levels of the upper trophic levels may constitute a lower percentage of the total biomass as total biomass increases, suggesting slightly lower risk sensitivity than apparent here. BM2 Increase aquatic

biomass uniformly by a factor of 100

. 0.35 0.32 0.29 0.79

Air_DL1 Increase air dust load

by a factor of 10

. 2.34 2.31 0.50 0.98 Increasing the dust load in air increases Although these runs indicate that air dust loadDRAFT Protocol for Developing a TRIM.FaTE Model Scenario to Support a Site

- Specific Risk Assessment in the RTR Program

Working Draft A

- 7 February 2014 Scenario Name Scenario Descripti on

(with respect to Tier 1 Screening Scenario) Normalized Risk Relative to Tier 1 Screening Scenario Risk Impact of Scenario Modification Conclusions TetraCDD, 2,3,7,8

- Benzo(A)

pyrene Cadmium Methyl Mercury

Air_DL2 Increase air dust load

by a factor of 100

. 4.14 2.71 0.50 0.90 particulate deposition to the surface. moderately influ ences risk, literature search indicated that the range manifested by this property is relatively small and the default value used is already in the high end of the observed range in the U.S. Therefore, this property is not recommended for site

specific par ameterization.

Air_FOM1 Halve the fraction of

organic matter in air solids

. 0.87 0.66 0.50 1.00 The organic content of air solids can differentially influence chemical adherence to the solid phase. Although these runs indicate that the fraction of organic matter in air solids moderately influences risk, literature search indicated that site

- specific data may be difficult to obtain. This property is not recommended for site

specific p arameterization. Air_FOM2 Double the fraction of

organic matter in air solids

. 1.23 1.43 0.50 1.00

Soil_Air Double the soil air

content

. 1.21 1.29 0.50 1.14 Increasing the soil air fraction reduces soil solids, which distributes the same amount of chem ical over a lower solids content, thereby increasing soil concentrations. Although these runs indicate that air dust load moderately influences risk, literature search indicated that the range manifested by this property is relatively small and the default value used is already in the high end of the observed range in the U.S. Therefore, this property is not recommended for site

Soil_FOC Increase the soil

organic fraction content by a factor of 10

. 1.04 1.01 0.60 1.00 Increasing soil organic content increases chemical adherence to soil for some chemicals.DRAFT Protocol for Developing a TRIM.FaTE Model Scenario to Support a Site

- Specific Risk Assessment in the RTR Program

Working Draft A

- 8 February 2014 Scenario Name Scenario Descripti on

(with respect to Tier 1 Screening Scenario) Normalized Risk Relative to Tier 1 Screening Scenario Risk Impact of Scenario Modification Conclusions TetraCDD, 2,3,7,8

- Benzo(A)

pyrene Cadmium Methyl Mercury

Soil_pH1 Set soil pH at 4

. 1.00 1.00 0.39 1.00 Soil pH can influence chemical adherence to soil solids for some chemicals. specific parameterization.

Soil_pH2 Set soil pH at 10

. 1.00 1.00 0.66 1.00

Soil_Rho Set soil solids density

at 1000 kg/m 3

. 1.41 1.43 0.50 1.16 Decreasing soil particle density increases soil concentrations when normalized by soil weight.

Soil_Water Double the soil water

content

. 1.00 1.00 0.50 1.00 Increasing soil water content increases chemical removal by percolation for some chemicals.

SusSed_TS S1 Increase lake

suspended solids concentration by a factor of 2

. 0.73 0.98 1.01 0.74 Increasing suspended solids in water causes more chemical to be deposited to sediment. Suspended solids concentration in lakes has a moderate influence on risk. Due to the wide range potentially exhibited by this property, it has been recommended for site

specific parameterization. SusSed_TS S2 Increase lake

suspended solids concentration by a factor of 10

. 0.33 0.98 0.46 0.38

Sed_Bur Halve sediment burial

rate; same erosion rate (violate solids balance in screening scenario)

. 1.11 1.00 1.12 1.31 Decreasing the burial rate reduces the removal of chemicals from the sediment layer. Sediment propertie s have a moderate impact on risk, given the limited range of values assumed by them in the environment. Additionally, data limitations would limit the potential for site

- specific parameterization. These properties are therefore not recommended for site

spe cific parameterization.DRAFT Protocol for Developing a TRIM.FaTE Model Scenario to Support a Site

- Specific Risk Assessment in the RTR Program

Working Draft A

- 9 February 2014 Scenario Name Scenario Descripti on

(with respect to Tier 1 Screening Scenario) Normalized Risk Relative to Tier 1 Screening Scenario Risk Impact of Scenario Modification Conclusions TetraCDD, 2,3,7,8

- Benzo(A)

pyrene Cadmium Methyl Mercury

Sed_Rho Decrease bed

sediment particle density to 1000 kg/m 3

. 1.36 1.00 0.63 2.74 The lower the sediment particle density, the lower the volumetric resuspension rate from sediment and the higher the volumetric burial rate.

Sed_Po r Halve sediment bed

porosity

. 0.85 1.00 0.42 0.78 The lower the sediment porosity, the lower the volumetric resuspension rate from sediment and the lower the volumetric burial rate.[This page intentionally left blank.]Working Draft B

- 1 February 2014 

## Appendix B. TRIM.FaTE National Property Va lues

B.1. Introduction This protocol recommends the use of nationally representative or health protective values (referred to as national values) for TRIM.FaTE model properties that have not been identified for site

- specific or land

use

- based parameterization.

These national values are readily accessible in the FaTE m aster Scenario Builder tool

, with references. This tool is included with the protocol and is documented in Appendix C

. The national values are also documented in Appendix 4 of the Risk Report (see its Attachment A, Addendum 1).[This page intentionally left blank.]Working Draft C

- 1 February 2014



## Appendix C. The FaTE m aster Scenario Builder Tool

C.1. Introduction

A TRIM. FaTE scenario requires a variety of inputs from users to define the modeled environment and to para meterize the algorithms for physical, chemical, and biological processes that transfer and transform chemical mass between and within environmental media . These inputs are provided to the TRIM.FaTE model in the form of the following files:

 A “volume elemen ts” file, which defines the spatial layout of the modeled domain in terms of three

dimensional abiotic compartments. Each volume element provides a frame of reference for one or more biotic compartments within it.

 A “compartments” file, which places biotic and abiotic compartments (modeling unit s containing chemical mass) within the volume elements.

 A Master L ibrary file, which contains all the model algorithms, properties, and emission source information.

 A properties file, which typically contains (i) si mulation

- and scenario

- specific properties that overwrite default values specified in the Master Library and (ii) the location of time

- varying input files for parameters such as meteorological and vegetation parameters.

These input files must be construct ed in syntax consistent with TRIM. FaTE requirements , as described in Module 3 of the TRIM.FaTE User’s Guide

.
ICF’s FaTE m aster Scenario Builder tool provides an MS

- Excel spreadsheet

- based interface that facilitates the automated generation of syntactically accurate TRIM. FaTE input files derived from user

specified inputs. The tool does not output the TRIM.FaTE Master Library file.

This appendix describes how the FaTE m aster Scenario Builder tool can be used to construct input files for the TRIM.FaTE model fo r use in site

- specific risk assessments in the RTR process. While this document discusses the principal steps required to operate the tool, it does not provide line

- by

- line guidance for each of the hundreds of inputs contained within it. The tool already i ncorporates comments and guidance for each input cell and is largely self

- explanatory. This overview of the tool is intended to facilitate an enhanced understanding of the scope of the tool and its operational structure. This document is not intended to se rve as a guide to running the TRIM.FaTE model itself.



### C.1.1. Output Files from the FaTE m aster Scenario Builder Tool

The FaTE m aster Scenario Builder Tool produces five text output files , which serve in turn as input files to the TRIM.FaTE model. The filenames, con tents

, and purpose of the output files generated by the tool are described in Table C

- 1

.


### C.1.2. Summary Worksheets in the FaTE m aster Scenario Builder Tool

The FaTE m aster Scenario Builder Tool consists of 15 worksheets. The names, contents, and purpose of these wo rksheets are described in Table C

- 2, which has been color

- coded in an identical manner to the worksheet “tabs” in the tool.Working Draft C

- 2 February 2014 Table C

- 1. Output Files Generated by the FaTE m aster Scenario Builder Tool



### File Name Contents Purpose

Sources.txt Coordinates spe cifying the location of the emission sources and the chemical emission rates. This file facilitates the creation of a unique scenario

- specific TRIM. FaTE Sources Library. This file must be manually imported through the TRIM.FaTE graphical interface and save d as a library. This Sources Library must then be loaded to the site

- specific TRIM.FaTE scenario together with the TRIM.FaTE Master Library.

Volume Elements.txt Coordinates specifying the spatial dimensions of the volume elements and specification of the primary abiotic compartment within each volume element. This file serves as a TRIM.FaTE input file to define the spatial layout of the modeled domain in terms of three

- dimensional abiotic compartments , each of which can contain other biotic and abiotic

co mpartments.

Compartments.txt The names of biotic and non

primary abiotic compartments located within each primary abiotic compartment defined by the Volume Elements file. This file serves as a TRIM.FaTE input file to situate user

- specified biotic and abio tic compartments within each volume element. These compartments are the discrete units that contain chemical mass in TRIM.FaTE.

Properties.txt Values defining site

- specific properties relating to the scenario, biotic and abiotic compartments, and user

- spe cified link properties. This files serve as a TRIM.FaTE input file to define the values of various properties that define the scenario. It overwrites default values for these properties specified in the TRIM.FaTE Master Library file.

Other Properties.txt Values defining properties that are not included amongst the standard sections of the tool for

user

- specification. This is a discretionary or optional file that may be useful when overwriting library properties that are not redefined by the standard elemen ts of the tool. This file serves as a TRIM.FaTE input file to define the values of any of the various properties that define the scenario. It overwrites default values for these properties specified in the TRIM.FaTE Master Library file.Working Draft C

- 3 February 2014

Table C

- 2. Summ ary of Worksheets Generated by the FaTE m aster Scenario Builder Tool



### Worksheet Name Contents Purpose

Tracking  Command button

 Documentation of version changes. Contains “Export All” button that generates TRIM.FaTE input files based on values specified in ot her worksheets. Also serves as a documentation sheet for tool software developers only.

Parameters  User

- specified property values for several TRIM.FaTE model properties. Provides a user

- friendly interface for the definition of site

- specific model properti es. Adds the properties defined here to the Properties file.

Layout  Coordinates of the vertices of the parcels that define the spatial layout.

 Coordinates of the location of the emission sources.

 Emission rates of each chemical species from each source. Serves as a basis for the creation of the Volume Elements and the Sources output files.

Land  User

- specified parameter values for the USLE equation for different land use types. Computes average erosion rates based on the USLE equation for surface soil co mpartments and adds those estimates to the Properties file. Also contains elements used to construct the Compartments file.

Soil  User

- specified property values for all soil layers differentiated by land use types. Computes average runoff and percolation r ates, and adds these estimates and other soil

- related properties to the Properties file. Also contains elements used to construct the Compartments file.

Plants  User

- specified vegetation types and vegetation components for each land use category. Places th e appropriate vegetation composite compartments within soil compartments based on user

specified land use. This is used to construct the Compartments file.

Watersheds  User

- specified inter

- compartment erosion and runoff directions and percentages. Defines inter

- compartmental link properties for runoff and erosion and adds them to the Properties file.

Lakes  User

- specified lake and sediment properties.

 Equations to compute lake flush rates/depth and sediment resuspension velocity based on watershed flows. A dds lake and sediment related properties, and computed lake and sediment hydrodynamic parameters, to the Properties file.

Fish  User

- specified aquatic food web and biomass levels. Adds aquatic food web and biomass properties to the Properties file. These elements are also used to construct the Compartments file.Working Draft C

- 4 February 2014



### Worksheet Name Contents Purpose

Soil Data  User

- specified soil properties differentiated by land use type. Serves as an input sheet for soil properties that is called by the “Soil” worksheet. May potentially be parameterized on a regional basis. Sources No user inputs are required on these worksheets. They are constructed from the inputs specified in the previous tabs and contain the content of the output files that will later be generated by the tool as text files. Volume Cm pts Props

OtherProps User may enter supplementary properties into this worksheet consistent with TRIM.FaTE syntax. This worksheet can be used to overwrite properties that are defined in the Master Library file or to define properties that are not previ ously defined either in the library file or other input files.



### C.1.3. Additional Salient Features of Worksheets in the FaTE m aster Scenario Builder Tool This section provides a limited description of the features of the various worksheets within the FaTE m aster Scenario Builder, with a focus on the most salient operational aspects from the user perspective.

A general rule when working with the tool, also mentioned clearly at the top of each worksheet, is that only cells color

- coded green may be modified by users . Blue and white color

- coded cells are not to be modified by users.



### (i) The Tracking Worksheet

 Users should click the “Export All” command button after they have made all the necessary input modifications to the other worksheets. This will be the final step in operating the tool.

 The remainder of the worksheet is intended for software developers only to document version changes in the tool.


### (ii) The Parameters Worksheet

 Specify a “set up” file directory in cell D9.

– Model output will be directed to this directory i nto a sub

- folder named “Output”.

– This directory will also be the destination to which the tool will write its five output files.

– This directory should contain the time

- varying meteorological values file, leaf “allow exchange” file, and litterfall data fil e later referenced in the worksheet.

 In cell D48, enter the average annual precipitation based on the average from the time

- varying site

specific meteorological file. This averaging must be performed manually offline.

 In cell D49, update the formula refere nce with the name of the applicable meteorological values file if they have changed.

 In cells D80, D92 and D103 update the formula with the names of the leaf allow exchange data files if they have changed.

 In cells D84, D96 and D107 update the formula wit h the names of the litterfall data files if they have changed.

 Note: Cells commented as “Reported Value” will be reported in the tables for documentation purposes but will not be used in TRIM.FaTE input files.Working Draft C

- 5 February 2014

 Note: All other input cells may be updated as required. Cell comments have been provided for guidance.


### (iii) The Layout Worksheet

 Based on the spatial layout for air and surface parcels developed in GIS or through manual mapping, enter the Cartesian coordinates of each vertex point in columns C and D. The c enter of the emissions source (or facility) parcel should be defined as the origin of the system.

 Specify offset coordinates in cells E9 and F9 to situate the layout spatially, consistent with the specified map projection system in cell D4. (This is simil ar to a latitude and longitude specification. It does not affect TRIM.FaTE results, however.)

 Specify the name of each parcel in the spatial layout and the vertex points that define the parcel in a clockwise or anticlockwise direction around the perimeter of the parcel in columns H and K.

 Specify the parcel category from the available options in column I.

 Specify the land use corresponding to each parcel in column J.

 Specify the source name, source parcel location

, and source elevation in column U.

 Specify the chemical emission rates in column U.


### (iv) The Land Worksheet

 In columns E through L, enter the USLE parameters that are used to compute average erosion rates for each land use type.

 Enter the vegetation type within each land use type.


### (v) The Soil Worksheet

 Th is worksheet requires no user inputs. It draws inputs from other worksheets and calculates average runoff and percolation rates based on those values.


### (vi) The P lan ts Worksheet

 Specify the vegetation components corresponding to each land use type.

 Note: If new types of vegetation components are being defined, their corresponding properties and algorithms should be defined in the Master Library or separately within property files.


### (vii) The Watershed s Worksheet

 For each parcel, specify which of its adjoining parcels r eceive the runoff and erosion originating from that parcel and in what amounts (specified in percentages).

 Click on the “Refresh” button after updating the worksheet.


### (viii) The Lake s Worksheet

 Specify lake and sediment properties in column K.

 Specify one of eit her lake depth or flush rate in cells K18 and K19.

 Note: The worksheet will compute the unspecified property (either lake depth or flush rate) using a water balance. The water balance assumes that runoff entering the lake is the sum of runoff entering the lake from adjacent soil compartments and cumulative runoff from the larger watershed.

 Note: The worksheet also computes sediment resuspension velocity and sediment burial rate using principles discussed in the TRIM.FaTE Technical Support Document (Section 4.2.2).Working Draft C

- 6 February 2014



### (ix) The Fish Worksheet

 Define the aquatic food web by specifying the diet fraction for each aquatic organism.

 Specify the total biomass of each organism type and the single body weight of each organism.


### (x) The Soil Data Worksheet

 For each land

- use type, s pecify properties for all soil layers.

 (Note: The tool has the capacity to accommodate region

- specific definitions of soil parameters too but this functionality has not currently been parameterized.)



### C.1.4. Generating Output Files from the FaTE m aster Scenario Bu ilder Tool After entering site

- specific inputs into the various worksheets within the tool as required, the following steps should be used to complete the generation of output files:

 Navigate to the “Layout” tab, and click “Refresh”.

 Navigate to the “Water sheds” tab, and click “Refresh”.

 Navigate to the “Tracking” tab, and click “Export All”.

The five output files generated by the tool can then be used as input files to set up the site

- specific TRIM.FaTE scenario. (It is reiterated that the tool does not ge nerate the TRIM.FaTE Master Library file, the contents of which are largely invariable between applications.