## Technical Support Document for the TRIM

- Based Multipathway Tiered Screening Methodology for RTR

October 201 4

Prepared For:

U.S. Environmental Protection Agency Office of Air Quality Planning and Standards Research Triangle Park, NC 27711

Prepared By:

ICF International 2635 Meridian Parkway Suite 200 Durham, NC 27713[This page intentionally left blank.]TRIM

- Based T iered Screening Methodology for RTR



## C ONTENTS

Technical Support Document for the TRIM

- Based Multipathway Tiered Screening Methodology for RTR

................................ ................................ ................................ ........... i

1. Introduction and Background

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

1.1 Tier 1

................................ ................................ ................................ ......................... 2 1.1.1 Chemicals of Concern

................................ ................................ ................ 2 1.1.2 Development of Emission Thresholds

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

1.2 Tier 2

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

1.3 Tier 3

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

1.4 Site

- Specific Assessment

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

2. Tier 1 Methodology

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

2.1 Introduction

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

2.2 Summary of Approach

................................ ................................ ............................. 12 2.2.1 Overvie w

................................ ................................ ................................ ... 12 2.2.2 Chemicals of Potential Concern

................................ ................................ . 15 2.2.3 Conceptual Exposure Scenario

................................ ................................ . 16 2.2.4 Approach to Development of the Tier 1 Scenario

................................ ....... 18 2.2.5 Fate and Transport Modeling (TRIM.FaTE)

................................ ............... 20 2.2.6 Exposure Modeling and Risk Characterization (MIRC)

.............................. 21 2.2.7 Implementation of Risk

- based Emission Scaling Factors for POM and Dioxin Emissions

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

2.3 Description of Environmental Modeling Scenario

................................ ..................... 26 2.3.1 Chemical Properties

................................ ................................ .................. 26 2.3.2 S patial Layout

................................ ................................ ............................ 27 2.3.3 Watershed and Water Body Parameterization

................................ ........... 28 2.3.4 Meteorology

................................ ................................ ............................... 30 2.3.5 Aquatic Food Web

................................ ................................ ..................... 32 2.3.6 Using TRIM.FaTE Media Concentrations

................................ .................. 33

2.4 Description of Ex posure and Risk Modeling Scenario

................................ .............. 34 2.4.1 Calculating Concentrations in Farm Food Chain Media

............................. 35 2.4.2 Ingestion Exposure

................................ ................................ .................... 35 2.4.3 Calculating Risk

................................ ................................ ......................... 38 2.4.4 Summary of Tier 1 Assumptions

................................ ................................ 39

2.5 Evaluation of Screening Scenario

................................ ................................ ............ 42 2.5.1 Introduction

................................ ................................ ................................ 42 2.5.2 Cadmium Compounds

................................ ................................ ............... 42 2.5.3 Mercury Compounds

................................ ................................ ................. 44 2.5.4 Dioxins

................................ ................................ ................................ ...... 46 2.5.5 Polycyclic Aromatic Hydrocarbons

................................ ............................. 48 2.5.6 Summary

................................ ................................ ................................ ... 50

3. Tier 2 Methodology

................................ ................................ ................................ ........... 52

3.1 Overview o f Approach

................................ ................................ ............................. 52

3.2 Selection of Site

- Specific Characteristics for the Tier 2 Assessment

........................ 55TRIM

- Based T iered Screening Methodology for RTR

3.3 Estimation of Adjustment Facto rs for Selected Site

- Specific Parameters

................................ ................................ ................................ .............. 55 3.3.1 Selection Values for Variables of Interest

................................ .................. 56 3.3.2 Estimation of Adjustme nt Factors

................................ .............................. 59

3.4 Preparing National Databases of Lake and Meteorological Data

............................. 64 3.4.1 Processing Lake Data for Tier 2 Asse ssment

................................ ............ 64 3.4.2 Processing Meteorological Data for Tier 2 Assessment

............................. 68

3.5 Implementation of Tier 2 Assessment

................................ ................................ ...... 71

4. Tier 3 Methodology

................................ ................................ ................................ ........... 74

4.1 Overview of Approach

................................ ................................ ............................. 74

4.2 Lake Ass essment

................................ ................................ ................................ .... 74

4.3 Plume

- rise Assessment

................................ ................................ ........................... 76

4.4 Assessment of Time

- series Meteorology and Effective Release Heights

................................ ................................ ................................ .................... 76

5. References

................................ ................................ ................................ ....................... 78

Attachment A. TRIM.FaTE Inputs

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

- 1

Description of Multimedia Ingestion Risk Calculator (MIRC) Attachment B. Used for RTR Exposure and Risk Estimates

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

- 1
Dermal Risk Screening

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

- 1 Attachment C.

Summary of TRIM.FaTE Parameters Considered for Inclusion in Attachment D. Tier 2 Assessment

................................ ................................ ........................ D

- 1

Analysis of Lake and Sustainable Fish Population

............................... E

- 1 Attachment E. SizeTRIM

- Based T iered Screening Methodology for RTR

Introduction and Background 1 October 2014 

## 1. Introduction and Background

Section 112 of t he Clean Air Act (CAA) directs the U.S. Environmental Protection Agency (EPA) to assess the risk remaining (i.e., residual risk) from emissions of hazardous air pollutants (HAPs) following the implementation of maximum achievable control technology (MACT) standards for emission sources. This risk assessment is a major component of EPA’s Risk and Technology Review (RTR) program . As part of this program , EPA must consider additional emission sta ndards for a source category if the current emission standards

— with MACT regulations in place

— do not provide an “ample margin of safety” for human health. One aspect of human health that EPA must consider under RTR is the potential for health effects resu lting from exposures to persistent and bioaccumulative HAPs (PB

- HAPs) via non

- inhalation pathways , namely ingestion and dermal exposure

. EPA’s a ssessment for RTR focuses on specific PB

- HAPs that the Office of Air Quality Planning and Standards (OAQPS) has identifi ed as candidates for multipathway risk assessments (selection of the PB

- HAPs is discussed in Section 2.2.2 ). These non

- inhalation human health risks are considered in combination with estimated inhalation human health risks, potential ecological impacts, and other factors to support decisions about residual risk for RTR source categories. For PB

- HAPs, exposures via ingestion are anticipated to be significantly higher than any dermal exposures that might occur

as a resu lt of the same emissions (see Attachment C ). Consequently, a methodology has been developed to evaluate ingestion exposure and risk for PB

- HAPs efficiently in the context of EPA’s RTR program.

T o evaluate ingestion exposures a nd human health risks for RTR on a source category basis

, an iterative approach was developed that enables EPA to confidently screen out PB

- HAP emissions unlikely to pose health risks above levels of concern (i.e., a cancer risk of 1 in 1 million or a nonc ancer hazard of 1.0 ) and to focus additional resources on sources of greater

concern within the category

. To estimate exposure and risk, two models are used : the Fate, Transport, and Ecological Exposure module of EPA’s Total Risk Integrated Methodology ( TRIM.FaTE) to model the fate and transport of pollutants released to the environment and the Multimedia Ingestion Risk Calculator (MIRC) to estimate transfer and uptake into the food chain and exposure to receptors consuming contaminated food products and soil. This approach is divided into three tiers of increasing refinement, as follows.

• Tier 1 of the approach begins by identifying the facility

- level emissions of PB

- HAPs within a source category and comparing them to risk

- based emi ssion thresholds. The risk

- based thresholds are derived using the aforementioned models applied for a hypothetical environmental and exposure scenario , assuming ingestion of locally caught fish, locally grown produce and livestock, and local soil . This hypothetical “screening scenario ” is intended to represent a situation in which the ingestion exposure is unlikely to be exceeded at any actual facility evaluated through the RTR program . The thresholds for Tier 1 are derived by estimating t he emission rate that corresponds to a lifetime cancer risk of 1 in 1 million or a chronic non

- cancer hazard quotient (HQ) of 1 for an individual exposed according to the characteristics associated with the screening scenario

. For a facility, if the emission rate of each PB

- HAP is less than th e Tier 1 threshold emission rate, risks are assumed to be low and no additional multipathway screening is done

. If, however, the emission rate of any PB

- HAP exceeds the Tier 1 threshold emission rate, the facility c an be evaluated further in Tier 2.

• In T ier 2, the actual location of the facility is used to refine some assumptions associated with the environmental scenario while maintaining the Tier 1 ingestion exposure scenario assumptions . Th e environmental scenario assumptions are refined by incorporat ing binned site

- specific meteorological data and and location s of fishable lakes near the facility (see below)

. T he risk

- based threshold for each PB

- HAP is then adjusted for thatTRIM

- Based T iered Screening Methodology for RTR

Introduction and Background 2 October 2014 facility based on an understanding of how exposure concentrations estimated for the screening scenario change with meteorology and lake location. PB

- HAP emissions that do not exceed the adjusted threshold are assumed to pose risks below levels of concern and no additional multipathway assessment is required. F acilities having em issions that exceed the adjusted thresholds for Tier 2 may require additional analysis

.

• A Tier 3 screening assessment can be conducted on facilities that do not screen out with the Tier 2 assessment, at the discretion of the risk assessor. The Tier 3 scr eening approach consists of three individual assessments that further refine the Tier 2 screening scenario based on additional site

- specific data and evaluations. One of the Tier 3 assessments (i.e., the lake assessment) results in the rescreening of the facility’s emissions using the Tier 2 methods and using a revised lake database. The other two assessments (i.e., the plume

- rise and time

- series assessments) each result in an adjustment factor to be applied to the screening result reflecting the Tier 3 la ke assessment. F acilities having emissions that exceed the adjusted thresholds for Tier 3 may require additional analysis

.

• For facilities emitting PB

- HAPs at levels that cannot be ruled out as being above levels of concern based on the screening assessment s , a refined

, site

- specific, multipathway risk assessment can be conducted . Such an assessment would incorporate location

- or facility

- specific characteristics regarding the environment to which PB

- HAPs are emitted, relevant exposure pathways, ingestion r ates or other exposure factors, and other parameters

. A range of exposure scenarios could be evaluated as part of a site

- specific assessment, resulting in a range of risk estimates.

The key processes and decisions that make up t his approach are summarized in Exhibit 1

. In the remainder of this document , each of the tiers in the multipathway assessment approach is described in additional detail. The a ttachments provide a comprehensive record of t he characteristics and methods ass ociated with Tier 1 and Tier 2 assessments

. If a site

- specific assessment is conducted

, a separate report detailing that assessment will be prepared.



### 1.1 Tier 1

The methods used in Tier 1 are intended to enable EPA to evaluate PB

- HAP emissions from multiple f acilities in a source category quickly and efficiently and to remove from consideration those that are unlikely to pose risks above levels of concern , while also minimizing the possibility of EPA’s failing to identify risks that exceed levels of concern

. The hypothetical scenario used to estimate Tier 1 thresholds is designed to be health

- protective in estimating exposures and risks ; specifically, it is intended to avoid underestimating exposures to PB

- HAPS that might be encountered for any location throug hout the United States. The scenario also is intended to avoid grossly overestimating risk to the point where no emissions screen out (i.e., overprotective , resulting in too many “false positives”).

1.1.1 Chemicals of Concern

The assessment of risk from multi pathway exposures begins with a review of data for sources in a particular category to determine if emissions of any of the following PB

- HAPs are reported:

• Cadmium compounds

,

• Chlorinated dibenzodioxins and furans (dioxins)

,

• Mercury compounds

, and

• Polycycl ic organic matter (POM).TRIM

- Based Tiered Screening Methodology for RTR

Introduction and Background 3 October 2014 Exhibit 1

. Conceptual Decision Tree for Evaluati ng Non

- Inhalation Exposures for PB

- HAPs



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( o.u- )TRIM

- Based T iered Screening Methodology for RTR

Introduction and Background 4 October 2014 Based on current emissions , bioaccumulation potential, and toxicity considerations, emissions of these four PB

- HAPs are expected to pose the majority of the non

- inhalation risks to humans from air emissions at sources subject to residual risk provisions of the CAA 1 Thus, although EPA has identified nine other PB

- HAPs that should be evaluated as part of residual ri sk assessments, the methods for multipathway assessment described here encompass only these four at this time

. If emissions of any of the four PB

- HAPs listed above are reported for a facility, the emission rate for that PB

- HAP is compared to the threshol d emissions rate derived for that chemical using the hypothetical TRIM

- based screening scenario. This threshold is the emission rate that, when input to the models used in evaluating multipathway risk for RTR, results in a specified cancer risk or non

- can cer HQ threshold level of concern. For the screening scenario, threshold emission rate s were calculated for a cancer risk of 1 in 1 million or an HQ of 1.0, depending on the more sensitive health effect of the PB

- HAP. However, due to the hypothetical nat ure of the screening scenario, exceeding the threshold emissions rate by 60 times, for dioxins for example, does not imply a resulting cancer risk of 60 in 1 million. Rather, exceeding the threshold emissions rate by 60 times for dioxin implies that it is highly unlikely that the actual risk would exceed 60 in 1 million

.
I mportant to note for dioxins and POM is that the screening methodology assesses individual congeners , taking into account differences in both the fate and transport and the toxicity amo ng the various congeners

. The details of the methods for assessing dioxins and POM are provided in Section 2

.
1.1.2 Development of Emission Thresholds

Generally, t he approach used to assess ingestion exposure s and resulting risk s fo r RTR has four components ( Exhibit 2 ) :

1. F ate and transport modeling of PB

- HAPs emitted to air that partition into soil, water, and other environmental media (including fish uptake );

2. M odeling of uptake of PB

- HAPs by farm food cha in media f rom soil and air;

3. E stimating ingestion exposures in terms of average daily dose for consumption of farm food items by a hypothetical exposed human; and

4. C alculating lifetime cancer risk estimates or chronic non

- cancer HQs for each HAP and corres ponding screening threshold emission rates

. The TRIM.FaTE model is used in the first component, and the MIRC model is used to conduct calculations for the other three components. 2 To derive the emission thresholds used in Tier 1, these models are used to estimate the emission rate corresponding to a cancer risk of 1 in

1 Potential impacts on human health from non

- inhalation expos ures to lead are evaluated for RTR using the National Ambient Air Quality Standard for lead, which takes into account multipathway risks.

2 EPA’s TRIM methodology was conceived as a comprehensive modeling framework for evaluating risks from air toxics . It w as designed to address each of the four steps in screening ingestion risk ; however, only the fate and transport module currently is available for use . For the RTR screening scenario, the Multimedia Ingestion Risk Calculator (MIRC) was constructed to com plete the calculations required for estimating PB

- HAP concentrations in farm food chain media, average daily ingestion doses, and cancer risks and chronic non

- cancer HQs. The framework is conceptually identical to the ingestion exposure and risk analyses that TRIM intended to cover. Information about the current status of TRIM modules and documentation of modules developed thus far can be accessed on EPA’s Technology Transfer Network (TTN) on the Fate, Exposure, and Risk Analysis website (http://www.epa.go v/ttn/fera/).TRIM

- Based T iered Screening Methodology for RTR

Introduction and Background 5 October 2014 1 million or a noncancer HQ of 1 (depending on the PB

- HAP) for each PB

- HAP within the hypothetical environmental scenario, as described in more detail in the following sections.

Exhibit 2

. Overview of Ingest ion Exposure and Risk Screening Evaluation Method

1.1.2.1 Modeling Fate and Transport

To model chemical fate and transport in the environment when deriving emission thresholds for Tier 1, the TRIM.FaTE module of the TRIM system was used

. 3 The two main components of the fate and transport modeling are (1) the modeled domain, including the meteorological data and (2) the environmental and chemical

- specific properties associated with fate and transport through the environment. The hypothetical modeled domain includes a farm homestead and a fishable lake near (i.e., 2 km) an emissions source, which are assumed to be the primary food sources for exposed individuals. The spatial layout and other physical aspects of t he modeled domain configuration are designed to be health

- protective, which results in an ingestion exposure situation that is unlikely to be exceeded at any actual facility evaluated under the RTR program. The environmental and chemical

- specific properti es governing fate and transport of PB

- HAPs are parameterized with either conservative (i.e., health protective) values or central

tendency values. The mix of health protective and central

- tendency assumptions and parameterization is expected to result in a scenario configuration that, on average, is likely to overpredict environmental concentrations of PB

- HAPs in media of interest for this evaluation. T he inclusion of central

- tendency values where warranted is intended to minimize the number of false posit ives

. Based on sensitivity analyses and model testing it is g enerally recognized that the spatial layout of the modeled domain (e.g, distance to a fishable lake) and the meteorological data used (or a combination of these two factors) are more influential than physical/chemical parameters in dictating the resulting chemical concentrations in air, soil, water, sediment, and fish within TRIM.FaTE . The Tier 1 assumptions about these two components of fate and transport modeling are refined with relatively mo re site

- specific data in subsequent tiers. The spatial layout used to develop the threshold emission levels in Tier 1 and other details of the Tier 1 methodology are presented in Section 2

.

3 http://www.epa.gov/ttn/fera/trim_fate.html Chemical Emissions to Air

Cancer Risk Hazard Quotient Chemical fate and transport: Physical environment and aquatic ecosystem Uptake and transfer:

Produce and livestock Human Ingestion Exposure Risk and Hazard EstimationTRIM

- Based T iered Screening Methodology for RTR

Introduction and Background 6 October 2014 1.1.2.2 Modeling Transfer and Uptake

MIRC w as developed to conduct the required calculations involving farm food chain transfer, ingestion exposure, and risk. TRIM.FaTE outputs that are used as inputs to MIRC include :

• PB

- HAP concentrations in air

,

• A ir

- to

- surface deposition rates for PB

- HAPs in bot h particle and vapor phases

,

• PB

- HAP concentrations in fish tissue for fish consumed

, and

• PB

- HAP concentrations in surface soil and root zone soil.

From these inputs

, MIRC calculates t he transfer and uptake of PB

- HAPs through the farm food chain using algo rithms based on those included in EPA’s Human Health Risk Assessment Protocol for Hazardous Waste Combust ion Facilities (EPA 2005) and biotransfer factors (e.g., soil

- to

- plant factors, which are the ratios of the concentrations in plants to concentrations in soil). The outputs of MIRC are PB

- HAP concentrations in contaminated food items.

1.1.2.3 Estimating Ingestion Exposure

MIRC is also used to estimate exposure in terms of average daily doses (ADDs)

, normalized to body weight for the following e xposure pathways :

• Incidental ingestion of soil

• Ingestion of homegrown produce

• Ingestion of homegrown beef

• Ingestion of milk from homegrown

cows • Ingestion of homegrown poultry/eggs

• Ingestion of homegrown pork

• Ingestion of fish

• Ingestion of breast milk (children <1 year ol d; dioxins only ) 4

Chemicals are modeled separately to evaluate the potential for risks, with exposures (in terms of ADD ) for each PB

- HAP summed across all ingestion exposure pathways

. For the screening scenario used in Tiers 1 and 2 , exposure characteris tics were selected that result in a highly health protective estimate of total exposure

. The ingestion rate for each exposure pathway listed above was set (as feasible) equal to an upper percentile value (99 th percentile for fish and 90 th percentile for a ll other food types) based on EPA’s Exposure Factors Handbook (EPA 2011 a ) or other sources as appropriate

. All media were assumed to be obtained from a location impacted by the modeled source

. T his approach results in an overestimate of total chemical

ex posure for a hypothetical exposure scenario

. F or example, the resulting total food ingestion rate is extremely h igh for a hypothetical consumer, with ingestion rates at the 99 th percentile for fish and the 90 th percentile for every other farm food type

. These health protective exposure assumptions can be replaced in a site

- specific assessment as appropriate (e.g., with distributions of the data for key exposure factors).

D ermal absorption of chemicals that are originally airborne is generally relatively m inor and this pathway was not included in the scenario used to calculate Tier 1 emission thresholds (this topic is discussed further in Section 2 and Attachment C )

.

4 Breast milk ingestion is an important exposure pathway for lipophilic compounds like dioxins and has been shown not to contribute meaningfully for exposures to mercury, cadmium, and POM. See Sec tion 2.4.2.3 and Section 3.4 of Attachment B for full discussions of infant exposures via breast milk ingestion.TRIM

- Based T iered Screening Methodology for RTR

Introduction and Background 7 October 2014 1.1.2.4 Calculating Lifetime Cancer Risk and Non

- Cancer HQs

Lifetime cancer risks and the potential for chronic non

- cancer effects are estimated using chemical

- specific oral cancer slope factors and oral reference doses. Lifetime c ancer risk estimates are calculated separately for each PB

- HAP. As provided in Section 5.1 of Attachment B

, age

- group specific cancer risk estimates are calculated and the estimated lifetime cancer risk equals the sum of the se age

- group specific risks. Similarly , HQ s are calculated separately for each PB

- HAP and for each age group . However, as detailed in Section 5.2 of Attachment B

, the HQ for the most sensitive age group is used to determine the screenin g threshold emission rate.

1.1.2.5 Determining Threshold Emission Rates

Tier 1 emission threshold s were calculated by conducting iterative model simulations in TRIM.FaTE and MIRC using the screening scenario described above to determine emission rates for cadmium , mercury, dioxins , and POM that correspond to a cancer risk of 1 in 1 million

or a chronic non

- cancer HQ of 1

. Given the generally health protective nature of the scenario inputs, these thresholds are assumed to be appropriate for screening facilities e mi tting these PB

- HAPs.



### 1.2 Tier 2

The Tier 1 screening approach is, by design, generic and health protective

. It was constructed for quick application to a large number of facilities in a source category with the least chance of returning false negatives for ri sk potential

. O nce the Tier 1 screen is complete, however, facilities whose emissions exceed the emission screening threshold for any PB

- HAP can be scrutinized further . Based on screening assessments conducted for RTR to date, many facilities might not s creen out of the Tier 1 assessment for some source cat egories. However, conducting a full site

- specific assessment of all facilities that cannot be screened out in Tier 1 would not be practical

.
S ite

- specific values for some influential variables , howev er, can be determined without intensive effort during the assessment

. The use of some site

- specific characteristics instead of the generic characteristics used in Tier 1 ca n justify adjusting the threshold emission rate for a given PB

- HAP at that facility, potentially screening out the facility while maintaining a high degree of confidence that risks above levels of concern have not been overlooked

. In addition, using a scenario in which a fisher fishes from multiple nearby lakes , catching an amount of fish from each lake that is limited by the lake’s theoretical fish productivity, creates a more site

- specific approach while also maintaining health protectiveness.

In selecting the scenario characteristics to modify in Tier 2, a balance was struck between th e degree of impact on the risk estimate, the ease of implementation, and the ease of obtaining relatively certain site

- specific values for all facilities that might be evaluated under the RTR program. As discussed in the remainder of this section, some of t hese characteristics affect the PB

- HAP concentrations in environmental media estimated by TRIM.FaTE

. Other characteristics affect the sources of ingested fish and , potentially, the fish ingestion rates

.TRIM

- Based T iered Screening Methodology for RTR

Introduction and Background 8 October 2014 1.2.1.1 Site Specific Considerations

F or Tier 2, location

- s pecific data on two types of variables are taken into account :

• M eteorological characteristics, including the fraction of time the wind blows toward each farm and lake ( using wind direction), wind speed, precipitation rate, and mixing height ; and

• Location s of fishable lake(s) relative to the facility 5 (including the absence of a fishable lake)

. For efficient Tier 2 evaluation of the impacts these parameters could have on specific facilities, a series of TRIM.FaTE simulations was performed that systematical ly varied the values used in the screening scenario for four of the five selected variables (lake location, wind speed, precipitation rate, and mixing height). Wind direction affects only whether the chemical mass advects toward the farm s and lake s , so the effect of site

- specific wind directions can be evaluated outside of the TRIM.FaTE simulations. The values of each of the four variables were changed, independent of any other changes. The values (four to six for each variable, including the original , Tie r 1 scenario values) were selected using statistics on U.S. meteorological data or professional judgment to capture the expected range in the data. Four to six values were selected to result in a total number of runs that was reasonable.

Based on the TRI M.FaTE results of these simulations (and the subsequent exposure and risk characterization, conducted using MIRC)

, threshold adjustment factors were calculated for each unique combination of the five parameters, for each PB

- HAP. These adjustment factors

r epresent the ratio between the risk metric (i.e., cancer risk or HQ) obtained using the Tier 1 screening scenario and the risk metric obtained from the Tier 2 run. For a given facility , for each PB

- HAP it emits and each nearby lake evaluated , an adjusted Tier 2 emission threshold can be estimated by dividing the Tier 1 emission threshold by the adjustment factor that best corresponds to the meteorological conditions present at the site and the location of the lake (hypothetical farms are assessed for every facility, at a constant distance from the facility)

. The contribution of each exposure media toward each scenario’s Tier 2 risk metric (i.e., the individual contributions of fish ingestion, soil ingestion, beef ingestion, etc. toward the total Tier 2 risk metric) was included in the matrix because the Tier 2 assessment separates chemical exposure from fish ingestion from exposure from farm food chain ingestion.

To facilitate the implementation of this approach without requiring facility

- specific data searc hes for each new source category evaluated, databases of the relevant U.S. meteorological and lake data were created that could be accessed readily during a Tier 2 evaluation. These databases are described in more detail in Section 3.4

. The meteorological database is based on the same hourly meteorology data used for RTR inhalation assessments. The meteorology database includes annual summary statistics on wind direction, wind speed, precipitation, and mixing heights for more t han 800 surface stations

, paired with their closest upper

- air station with available data

, located throughout the United States (data available from the National Oceanic and Atmospheric Administration). The lake database, based on U.S. Geological Survey ( USGS) data and including location and size information, consist s of hundreds of thousands of water bodies classified as “Lake/Pond” or “Reservoir” but not designated for disposal, evaporation, or treatment. To focus on lakes that can support fish ing of upp er trophic level fish, a minimum lake surface area of 25 acres is recommended. Very large lakes and bays (i.e., those larger than 100,000 acres) are not included because their watersheds are too large and

5 The lake size also was changed for each lake distance allowing for a constant ratio between watershed and erosion area compared with lake area within the TRIM modeling structure.TRIM

- Based T iered Screening Methodology for RTR

Introduction and Background 9 October 2014 their lake dynamics are too complex to realisticall y model in the TRIM.FaTE system. Lakes and bays larger than 100,000 acres include the Great Lakes, the Great Salt Lake, Lake Okeechobee, Lake Pontchartrain, Lake Champlain, Green Bay, and Galveston Bay.

When the Tier 2 screening is conducted, three addit ional processing steps are completed for each facility and PB

- HAP that will be analyzed in Tier 2 before looking up the appropriate adjustment factors. First, a proximity analysis for each lake is conducted, whereby each relevant lake within 50 km of the facility is identified and matched to its respective directional “octant” relative to the facility. For the purposes of Tier 2, a “relevant” lake meets the size and designation criteria discussed in the previous paragraph. Second, the lakes are manually

e valuated to remove lakes whose names suggest uses related to disposal, evaporation, or treatment (sometimes the name indicates one of these uses while the USGS designations do not ; f or example, the Gavin Fly Ash Impooundment would not be included in the sc reening process ). Third, the lakes around the facility that remain after the first two processing steps are ranked in order of highest to lowest PB

- HAP concentrations in fish . The se rankings are then used to refine the Tier 2 risk metric for the fisher , as discussed in the next section

.
To perform Tier 2 assessment s , a Microsoft® AccessTM tool was created that merges Tier 1 screening results with the Tier 2 adjustment factors and the lake and meteorology information relevant to a specific facility. In the to ol, each facility is matched with the same meteorology station used in RTR inhalation assessments, and the values for the four relevant meteorological parameters at that station are recorded. The distances from the facility to relevant lakes are computed i n the tool as well. These five parameter values become the set of facility

- specific inputs in Tier 2 . Then, the Tier 2 adjustment factors are determined for each PB

- HAP for the combination of these five variables. As described above, the Tier 1 screening e mission threshold is then divided by the appropriate adjustment factor to obtain a revised , Tier 2 emission threshold for that PB

- HAP. More information about Tier 2 assessment methods can be found in Section 3

.
1.2.1.2 Refined Tier 2 F isher A ssessment

The refined fisher scenario is based on the idea that an adult fisher might travel to multiple lakes if the first lake (i.e., the lake with the highest concentrations in fish for a given PB

- HAP) is unable to provide him an adequate catch t o satisfy the assumed ingestion rate ( i.e.

, 373 g/d for adults )

. This refined fisher assessment uses the assumption that the biological productivity limitation of each lake is 1 gram of fish (wet weight) per acre of lake; meaning that in order to fulfill t he adult ingestion rate, the fisher will need to fish from 373 total acres of lakes

.
In the refined fisher scenario, a fisher travels to each relevant lake in order of highest to lowest chemical concentration in fish (of a given PB

- HAP) and catches fish u p to the lake’s assumed biological productivity limitation. A maximum travel radius of 50 km relative to the facility is used to maintain a realistic scenario. The final Tier 2 scr e ening result for the fisher can be expressed as the sum of the screening re sult from each lake that is fished ( which is based on the amount of fish ingested for each lake multiplied by the PB

- HAP concentration in fish )

. If the highest

concentration lake is at least 373 acres, the ingestion rate is not altered (i.e., remains 373 g /d for adults). If the cumulative size of multiple visited lakes exceeds 373 acres, the fisher catches from the final lake only the amount of fish necessary to satisfy the ingestion rate (i.e., to reach the 373 g/d)

. If there are not 373 total a cres of lak e s , the risk reflects a reduced ingestion rate based on the cumulative lake acreage.TRIM

- Based T iered Screening Methodology for RTR

Introduction and Background 10 October 2014 

### 1.3 Tier 3

A Tier 3 screening assessment can be conducted on facilities that do not screen out with the Tier 2 assessment , at the discretion of the risk assessor

. The Tier 3 screening approach consists of three individual assessments that further refine the screening scenario (beyond the refinements in Tier 2) based on additional site

- specific data and evaluations. Because t he Tier 3 assessment introduces additional site

- spec ificity to the screening scenario, it requires a potentially higher level of effort than the Tier 2 assessment, but still a much lower level of effort than the full site

- specific assessment ( discussed in Section 1.4 )

. One of th e Tier 3 assessments (i.e., the lake assessment) potentially results in the rescreening of the facility’s emissions using the Tier 2 methods described in Section 1.2 and using a revised lake dataset

. The other two assessments ( i.e., the plume

- rise and time

- series assessment s ) each result in an adjustment factor to be applied to the screening result reflecting the Tier 3 lake assessment . The adjustment factors of the plume

- rise and time

- series assessments cannot be compounded (i. e., the time

- series assessment, if conducted, supplants the plume

- rise assessment already conducted)

. One component of the Tier 3 assessment is evaluating the fishability of the lakes used in Tier 2. The USGS dataset occasionally includes lakes that appear to be misclassified, no longer exist, or are estuarian by nature. These lakes are removed from the Tier 3 a ssessment after evaluat ing the ir validity using aerial imagery and other available data

. These additional data sources are not used in the Tier 2 sc reening. Examples of lakes removed from Tier 3 source category assessments are provided in Section 4.2

. If one or more lakes are removed from a facility’s assessment, the facility’s emissions are rescreened usin g the revised lake database and the Tier 2 methods described in Section 1.2 . If removing a lake(s) causes the originally

fished lakes to sum to less than 373 total acres, then in the rescreening

, the fisher will catch and

consu me fish from an additional lake(s) if available. In this situation, the Tier 3 lake assessment is conducted on the newly added lake(s), and another rescreening is conducted, and so on until no further lakes are removed or added to the assessment

. The Tier 3 lake assessment is more thoroughly described in Section 4.2

.
If

, after the lake assessment , the Tier 3 screening result is still above a level of concern, the risk assessor may choose to conduct a plume

- rise a ssessment. Atmospheric conditions coupled with the physical parameters of the chemical release point can cause the chemical plume to rise substantially beyond the physical release height. This process is not explicitly modeled by TRIM.FaTE but can substant ially reduce ground

- level chemical exposure if the plume frequently rises above the mixing height. This assessment uses a scenario in which the chemical release height varies over time due to hourly meteorological conditions and the parameters associated

w ith the chemical release point (i.e.

, physical release height and diameter, exit velocity, and gas temperature). If the resulting “effective release height” is above the mixing height for a given hour, then in the TRIM.FaTE modeling system there is no chem ical deposition or exposure for that hour. I f this occurs across many hours, it will substantially reduce total PB

- HAP exposure and reduce the screening result. The plume

- rise adjustment factor

— the number of hours when the effective release height remains below the mixing height, divided by the number of total modeled hours

— is multiplied by the Tier 2 screening result, thus lowering the screening result. A more thorough description is described in Section 4.3

.
If the Tier 3 screening result after the lake and plume

- rise assessments still is above a level of concern, the risk assessor may choose to conduct a time

- series assessment. This assessment utilizes hourly effective release heights (computed in the plume

- ris e assessment above) along with the hour ly meteorology data associated with the facility (i.e., the meteorology data that was summarized and used in Tier 2)

. Using hourly meteorology data adds additional site

- specificity compared to the summarized, binned m eteorology statistics used in Tier 2

. U sing th e se data inTRIM

- Based T iered Screening Methodology for RTR

Introduction and Background 11 October 2014 combination with hourly effective release heights is a more complete evaluation of hourly chemical losses due to plume rise compared with the Tier 3 plume

- rise assessment described above. These time

- varying release height and meteorology files are used in a run of TRIM.FaTE that also uses the facility’s PB

- HAP emissions and the Tier 2 spatial scenario associated with the lake being assessed. The TRIM.FaTE modeling, and subsequent exposure and risk ch aracterization conducted using MIRC, leads directly to a screening

- level cancer risk or HQ (i.e., a revised screening result)

. For simplicity in the software implementation of the Tiers 2 and 3 screening assessments, the result of this Tier 3 time

- series a ssessment is converted to a time

- series adjustment factor

— the revised screening result divided by the screening result after the Tier 3 lake assessment

. T his ratio can then be multiplied by the screening result after the Tier 3 lake assessment, yielding th e revised screening result accounting for the time

- series assessment.



### 1.4 Site

- Specific Assessment

If , based on results of the screening assessments , a risk assessor concludes that there is a reasonable probability that individuals could be adversely affected by the facility emissions, a refined site

- specific multipathway assessment might be performed. Examples of recent refined multipathway assessments include residual risk assessments of a ferroalloys production facility (EPA 201 4a )

, petroleum re finery faci lity (EPA 2014b )

, two secondary lead smelting facilities (EPA 2011b), assessments of two coal

- fired electric utility units conducted in support of EPA’s utility rule (EPA 2011c), and a case study evaluation of a Portland cement facility included with other RTR materials presented to the Science Advisory Board for review (Appendix I of EPA 2009).

Whereas Tier 2 and Tier 3 assessments incorporate some site

- specific and regional information on meteorology and water bodies , a refined multipathway assessment u ses detailed site

specific data to parameterize more accurately (to the extent possible) each important parameter that affects pollutant fate and transport. These site

- spec ific properties are incorporated into model scenarios configured in TRIM.FaTE and M IRC. Important site

- specific data likely would include emission release height and plume buoyancy, hourly meteorology (e.g., wind flow, temperature, mixing height, and precipitation)

, surface compartments based on watershed and terrain data, local farms a nd water bodies, land use, soil properties , erosion and runoff rates

, surface water and sediment properties , water transfer rates , and aquatic ecosystem information

. The outputs from the site

- specific run of TRIM.FaTE (i.e., chemical concentrations in

env ironmental media and fish) are used in MIRC to produce estimates of exposure and health risk (i.e., risk and/or HQ values). Additional analyses of t he media concentrations, exposure estimates, and risk estimates for the various ingested media using a rang e of ingestion rates for each modeled PB

- HAP allows the risk assessor to understand, based on TRIM.FaTE and MIRC, the sources and pathways of possible human health risk from emissions of PB

- HAPs.TRIM

- Based T iered Screening Me thodology for RTR

Tier 1 Methodology 12 October 2014 

## 2. Tier 1 Methodology

2.1 Introduction

As discussed in Section 1 , the U.S. Environmental Protection Agency (EPA) implement s a tiered approach to evaluate multipathway exposures and human health risks for the Risk and Technology Review (RTR) program

. EPA’s a ssessment for RTR focuses on per sistent and bioaccumulative hazardous air pollutants ( PB

- HAPs ) that the Office of Air Quality Planning and Standards (OAQPS) has identifi ed as candidates for multipathway risk assessments (selection of the PB

- HAPs is discussed in Section 2.2.2 ). In the first tier, a screen is conducted that focuses on the identity and magnitude of emissions of PB

- HAPs from a given facility to determine whether a facility passes certain human health risk

- based criteria. Sources that are “screene d out” in the Tier 1 assessment are assumed to pose no risks to human health above levels of concern and and are not considered in further assessments . For sources that do not pass the Tier 1 screen, more refined assessments, up to and including site

- spec ific multipathway assessments, can be conducted as appropriate.

This section describes the technical basis for the first screening

- level tier of EPA’s multipathway human health evaluation of PB

- HAP emissions from RTR sources. Specifically, the scenari os, models, configurations, and inputs used to derive screening threshold emission rates in the first tier of the approach are described in detail in the following sections.

• Section 2.2 presents an overview of how screening is conducted in Tier 1, the chemicals and exposure scenario evaluated in Tier 1, and the models and methods used to conduct the screen.

• Sections 2.3 and 2.4 present technical descriptions of the hypo thetical environmental setting and the exposure modeling scenario used in Tier 1 as well as the models used in the screen.

• Section 2.5 provides a brief discussion of the screening threshold emissions for each of the chemicals a ssessed.

The Tier 2 and Tier 3 s creen ing methods are discussed in Section s 3 and 4

, respectively, and references cited are provided in Section 5

.


### 2.2 Summary of Approach

2.2.1 Overview

The Tier 1 approach for evaluating non

- inhalation, multipathway exposures to PB

- HAPs for RTR is diagrammed in Exhibit 3 . Air toxics emitted by a source under consideration are reviewed to determine, first, whether emissions are reported for any of the four PB

- HAPs of concern for non

inhalation pathways. If such emissions are reported, the emission rates are compared to Tier 1 threshold

- screening emission rates that have been derived using the TRIM

- based Tier 1 scenario described in this document (s ee Exhibit 4 for threshold screening emission rates )

.TRIM

- Based T iered Screening Me thodology for RTR

Tier 1 Methodology 13 October 2014 Exhibit 3

. Conceptual Decision Tree for Tier 1 Evaluation of Non

- Inhalation Exposures of PB

- HAPs

Facility screens out

Non

- inhalation risks not likely

, but evaluate on a case

- by

- case basis

Facility screens out ; no concern for multipathway risk ( check emissions of PB

- HAPs other than Cd

, dioxins

, Hg

, POM

, Pb on a case

- by

case basis ) Are any PB

- HAPs emitted ?

Are PB

- HAPs of primary concern for RTR emitted ? ( Cd

, dioxins

, Hg

, POM ) ? Evaluate HAP emissions by facility

YES YES

NO NO NO

Does the emission

rate for any PB

HAP exceed Tier

1 threshold ? Diagram Key

Outcome Process Decision

PointTIER1EVALUATION

Proceed to Tier 2 YESTRIM

- Based T iered Screening Me thodology for RTR

Tier 1 Methodology 14 October 2014 The TRIM

- based multipathway modeling configura tion, referred to in this document as the Tier 1 scenario, forms the technical basis for determining the Tier 1 emission thresholds. The term

, Tier 1 scenario

, is used to refer collectively to the specific TRIM.FaTE and exposure modeling configuration des cribed here in , including the set of assumptions and input values associated with a hypothetical watershed and the exposure and risk scenarios evaluated for this watershed

. The Tier 1 scenario is a static configuration , and its primary purpose is as a mode ling tool to calculate the Tier 1 emission rate thresholds for PB

- HAPs of concern

. The two potential outcomes of the Tier 1 evaluation are :

• Non

- inhalation exposures are unlikely to pose a human health problem (i.e., the emissions evaluated “pass” the s creen); or

• Risks above the levels of concern from n on

- inhalation exposure s cannot be ruled out

. An ideal screening approach strikes a balance between being health

- protective

— to ensure that risks above levels of concern are identified

, and being accurate

— to minimize results suggesting that additional analysis is required when in fact the actual risk is low . Typically, gains in accuracy in environmental modeling are accompanied by additional resource requirements. Stated another way, a suitable approach minimizes both false negatives and false positives. False negatives (i.e., results that suggest that the risk is acceptable when in fact the actual risk is high) can lead to inappropriate and non

- protective health or environmental policy decisions. False positives (i.e., results that suggest more assessment is required when in fact the actual risk is low) can result in wasted resources by leading to additional, unnecessary analysis. For the evaluation of multipathway human health exposures to PB

- HAPs, th e methods for screening described in this document are intended to achieve this balance.

Because the Tier 1 evaluation enables EPA to confidently eliminate from consideration those facilities where risks from non

- inhalation exposures are projected to be mi nimal

, resources can be target ed toward th os e facilities that do not pass the screening test . For facilities that do not pass the Tier 1 screening, in additional tiers of analysis, some of the Tier 1 parameters are reassessed, and if appropriate, are chan ged to more accurately reflect site

- specific characteristics. With each successive tier of the assessment, additional Tier 1 assumptions are evaluated and refined to better reflect site

- specific characteristics of the facility being modeled.

The Tier 1 screening evaluation for RTR compares reported air emission rates of PB

- HAPs (summed by PB

- HAP for each facility) to screening threshold emission rates derived using the Tier 1 scenario. A threshold emission rate is the level that, when input to a risk mo del us ing emissions as a parameter, corresponds to a specified cancer risk or non

- cancer hazard quotient ( HQ ) that, for the purposes of the evaluation being conducted, is assumed to be below a level of

concern. Tier 1 t hreshold emission rates were calcula ted for a cancer risk of 1 in 1 million or an HQ of 1.0 and are presented in Exhibit 4

. 6 Conceptually, a threshold level for the RTR multipathway screening evaluation could be obtained by back

- calculating the emission rate that

r esults in the specified cancer risk or HQ level, taking into account the exposure and fate and transport calculations included in the model. Because the models used in this assessment are not designed to run “backwards,” the rates instead were derived fro m regression equations established following a series of TRIM.FaTE and exposure/risk model runs spanning a wide

6 For chemicals known to cause both cancer and chronic non

- cancer impacts, and for which acceptable quantitative dose

- response value s are available for both cancer and non

- cancer endpoints, the endpoint that results in the lower threshold emission level is used for screening (i.e., the threshold will be based on the effect that occurs at the lower exposure level). For the set of PB

- HA Ps for which screening threshold levels have been derived, only chlorinated dibenzo

- dioxins and

- furans meet both criteria. Because the cancer dose

- response value is lower than that for

non

- cancer effects, the screening threshold value is based on the can cer endpoint.TRIM

- Based T iered Screening Me thodology for RTR

Tier 1 Methodology 15 October 2014 range of emission rates for each chemical. The estimated screening

- level emission rates are verified by performing model runs using the estimat ed threshold emission rate to confirm that the emission rates result in a cancer risk of 1 in 1 million or an HQ of 1.0.

Exhibit 4

. Emission Thresholds for Screening of Multipathway Exposures

Chemical Screening Threshold Emissi on Rate (TPY) Basis of Threshold (Type of Health Endpoint)

POM (as b enzo(a)pyrene equivalents) a 2. 58 E

- 03 Cancer Dioxins (as 2,3,7,8

- TCDD equivalents) a 2. 81 E

- 09 Cancer

Cadmium 1.18E

- 02 Non

- c ancer
M ercury (as divalent mercury emissions) 3.16E

- 04 Non

- c an cer

T PY = U.S. short tons per year

a See Section 2.2.7 for a discussion on the derivation of equivalent emissions.

The more probable risk for each emission rate would be lower than the level corresponding to the screening thre shold risk quantities in nearly all circumstances

, given the health protective and hypothetical nature of the Tier 1 sc reening configuration

. It is considered very unlikely that the estimated risk at a real site would be greater than the estimated risk fo r the simulated Tier 1 scenario at equivalent emission rates. This is because the Tier 1 scenario assumes

, for many of the most risk influential parameter s in the model, parameter values that result in high

- end risk estimates

. I n the real world, the proba bility of such risk

- maximizing conditions prevailing across multiple parameters is very low. For example, the Tier 1 scenario assumes a fishable lake approximately 2 km from any given facility, when in reality, a lake may be more than 50 km away. Additio nal conservative assumptions used in the Tier 1 screen are described in Section 2.4.4

.
Tier 1 emission thresholds were developed individually for elemental and divalent mercury. Both were based on the lower of the thresholds a ssociated with multipathway exposures to divalent mercury and methyl mercury. 7 Only speciated emissions of divalent mercury are screened because the sum of elemental mercury emissions across all National Emission Inventory (NEI) facilities is less than th e elemental mercury screening threshold level. See Section 2.5.3 for a detailed discussion of mercury.

2.2.2 Chemicals of Potential Concern

EPA’ s a ssessment of multipathway human exposures for RTR focuses on PB

- HAPs 8 that the Offi ce of Air Quality Planning and Standards (OAQPS) has identifi ed as candidates for multipathway risk assessments

. OAQPS developed a list of 14 chemicals and chemical groups that are PB

- HAPs based on a two

- step process taking into account the following ( EPA 2004a):

7 Note that TRIM.FaTE models the transformation of mercury within the environment; thus, emissions of both divalent and elemental mercury will result in multipathway exposures to elemental mercury, methyl mercury, and divalent mercury.
8 Al though POM (polycyclic organic matter) is the name of the HAP listed in the Clean Air Act, the term “polycyclic aromatic hydrocarbons” or PAHs is used in many cases. Much of the literature regarding toxicity and fate and transport of this chemical group r efers to PAHs rather than POM. In addition, the individual POM species that are of concern with respect to health risk for RTR evaluations are all PAHs (i.e., there are no POM species explicitly evaluated for RTR that do not include an aromatic ring). Th e terms are used interchangeably throughout this text.TRIM

- Based T iered Screening Me thodology for RTR

Tier 1 Methodology 16 October 2014 • their presence on three existing EPA lists of persistent, bioaccumulative, and toxic substances, and

• a semiquantitative ranking of toxicity and bioaccumulation potential of the entire list of HAPs.

The list’s development and utility in hazard i dentification for multipathway risk assessment are explained further in Chapter 14 and Appendix D of Volume I of EPA’s Air Toxics Risk Assessment (ATRA) Reference Library ( EPA 2004a )

. Exhibit 5 presents t he 14 chemicals and groups that are PB

- HAPs

.
Exhibit 5 . OAQPS PB

- HAP Compounds

PB

- HAP Compound a Addressed by Screening Scenario? Cadmium compounds Yes
Chlordane No
Chlorinated dibenzodioxins and furans Yes DDE (1,1

- dichloro

- 2,2

- bis(p

- chlorophenyl) eth ylene) No Heptachlor No
Hexachlorobenzene No Hexachlorocyclohexane (all isomers) No Lead compounds No Mercury compounds Yes Methoxychlor No Polychlorinated biphenyls No Polycyclic organic matter (POM) Yes Toxaphene No
Trifluralin No

a Source of list: EPA (2004a).

The screening scenario described in this document is not configured for evaluating the risk potential for all 14 PB

- HAPs on the list

. Currently, the scenario can only be used to estimate exposures and risks quantitatively for 4 of the 14 PB

- HAPs ( as indicated in Exhibit 5 )

. These four PB

- HAPs are the focus of the current scenario because, based on current emissions

, bioaccumulation potential, and toxicity considerations, they are expected to pose the vast majority of the non

- inhalation risks to humans from air emissions at sources subject to residual risk provisions of the Clean Air Act

. 9

2.2.3 Conceptual Exposure Scenario

A conceptual exposure scenario was developed that encompasses the specific exposure routes and pathways of interest for the four PB

- HAPs that are assessed in the Tier 1 assessment

. Exposure routes and pathways describe the movement of air toxics from the point of release to the point where exposure occurs and generally consist of the following elements:

• Release to the environment (i.e., emissions);

9 Potential impacts on human health from non

- inhalation exposures to lead are evaluated for RTR using the National Ambient Air Quality Standard for lead, which takes into account multipathway risks. Non

- inhalation exposures to the other nine PB

- HAPs not addressed by the modeling scenario discussed in this report will be evaluated on an individual facility or source category basis as appropriate

.TRIM

- Based T iered Screening Me thodology for RTR

Tier 1 Methodology 17 October 2014 • A retention medium, or a transport mechanism and subsequent retention medium in cases involving media transfer of chemicals;

• A point of potential human contact with the contaminated medium; and

• An e xposure route.

An exposure route is the particular means of entry into the body. Receptors are exposed to chemicals emitted to the atmosphere via two primary routes: either directly via inhalation, or indirectly via ingestion or dermal contact with vario us media that have been contaminated with the emitted PB

- HAPs. (Inhalation pathways are assessed separately and are not considered in the Tier 1 assessment presented here.)

PB

- HAPs can persist in the environment for long periods of time and also build up in soil and in the food chain, including fish, fruits and vegetables, and animal products (e.g., meat, dairy, eggs). For this reason, ingestion of foods grown within an area impacted by RTR sources can be an important source of exposure to PB

- HAPs.

To as sess risks from hazardous waste combustion facilities, EPA identifie d several hypothetical receptor scenarios, noting that these scenarios are considered appropriate for a broad range of situations, rather than to represent any actual scenario. These scen arios are described in EPA’s Human Health Risk Assessment Protocol for Hazardous Waste Combustion Facilities, or HHRAP ( EPA 2005a) . In this document, EPA recommends assessment of the following hypothetical receptors: a Farmer, Farmer Child, Resident, Resi dent Child, Fisher

, Fisher Child, Acute Receptor, and Nursing Infant. These receptors are distinguished by their pathways of exposures. EPA further notes in HHRAP that some exposure settings might warrant including additional exposure pathways; such as i ncluding exposure through fish ingestion for the farmer receptor. For the RTR screening scenario, risks are assessed for a single hypothetical receptor. Based on the guidance provided in HHRAP, a health protective exposure scenario was developed whereby the hypothetical receptor receives ingestion exposure via both the farm food chain and the fish ingestion pathways. The exposure scenario for the RTR Tier 1 assessment includes the following ingestion pathways:

• Incidental ingestion of soil,

• Ingestion of h omegrown fruits and vegetables,

• Ingestion of homegrown beef,

• Ingestion of dairy products from homegrown cows,

• Ingestion of homegrown poultry and eggs,

• Ingestion of homegrown pork,

• Ingestion of locally caught fish, and

• Ingestion of breast milk (for childre n less than 1 year old and for dioxins only ). 10

As discussed in detail in Section 2.4.2 , exposure via these pathways is assessed for adults, various age categories for children, and nursing infants (for dioxins only).

10 Breast milk ingestion is an important exposure pathway fo r lipophilic compounds like dioxins and has been shown not to contribute meaningfully for exposures to mercury, cadmium, and POM. See Section 2.4.2.3 below and Section 3.4 of Attachment B for full discussions of infant exposures via breast milk ingestion.TRIM

- Based T iered Screening Me thodology for RTR

Tier 1 Methodology 18 October 2014 Other n on

- inhalation exposure routes of possible concern for PB

- HAPs discussed in HHRAP include the use of surface waters as a drinking water source and dermal exposure. These exposure routes, however, are not evaluated in the current assessment

. The drinking wate r exposure pathway is not likely for the modeling scenario developed for th is assessment because the likelihood that humans would use a lake as a drinking water source was assumed to be low. 11 D ermal absorption of chemicals that are originally airborne has been shown to a relatively minor pathway of exposure compared to other exposure pathways ( EPA 2006

, Cal / EPA 2000) . Preliminary calculations of estimated dermal exposure and risk of PB

- HAP s , presented in Attachment C

, showed t hat the dermal exposure route is not a significant risk pathway relative to ingestion exposures.

2.2.4 Approach to Development of the Tier 1 Scenario

The TRIM

- based Tier 1 scenario described in this document is used to provide a means to qualitatively estimate t he potential for non

- inhalation risks above the levels of concern for PB

HAPs emissions from facilities in the context of residual risk assessment s conducted as part of RTR. The Tier 1 scenario used to derive the threshold emission rates is not intended t o be representative of any particular situation

. Rather, it was developed for the purpose of RTR to portray a hypothetical exposure scenario that will generate emissions screening levels that are health protective for any potential exposure situation that might plausibly be encountered in the United States

. A range of conditions was assessed when conceptualizing and developing the screening scenario. The final configuration was chosen so that for a given individual , any potential long

- term exposure level s for a given geographic region would be reasonably unlikely to exceed those of the Tier 1 configuration

. These criteria were met by constructing a hypothetical scenario that would be health

- protective in key aspects, including spatial orientation, meteor ology, types of exposures, and ingestion rates

. The overall result is a scenario that is unlike ly to occur at any one location but has a high likelihood of representing the upper end of all potential exposures

. This latter aspect accomplishes the goal of striking a balance between health protectiveness and the level of accuracy called for in the ideal screening approach previously discussed

.
The develop ment and application of the Tier 1 scenario for residual risk evaluations considered EPA’s technical and policy guidelines presented in the R esidual Risk Report to Congress ( EPA 1999) ; Volumes I and II of the Air Toxics Risk Assessment Reference Library ( EPA 2004 a, 2005 a ) ; and other EPA publications

. The scenario described herin is the culmination of

assess ments completed since 2005; it provides the basis for a n efficient and scientifically defensible method for screening multipathway human health risk and provides a solid baseline from which to perform Tier 2 and Tier 3 assessments , as described in Section s 3 and 4

, respectively

. Nevertheless, this scenario should not be considered “final” but rather a product that can continue to evolve based on feedback from the scientific community and Agency revi ewers, lessons learned as the scenario is further applied for RTR, variations in EPA’s needs and requirements, and other factors.

2.2.4.1 Modeling Framework

The approach for multipathway risk screening and evaluation for RTR can be divided into four steps :

11 An exception to this generality would be reservoirs used for drinking water supplies. This situation might be worthy of additional analysis, if warranted by the chara cteristics of a given assessment (e.g., to estimate PB

- HAP concentrations in treated drinking water derived from reservoirs).TRIM

- Based T iered Screening Me thodology for RTR

Tier 1 Methodology 19 October 2014 1. Fate an d transport modeling of PB

- HAPs emitted to air by the source that partition into soil, water, and other environmental media (including fish 12 );

2. Modeling of transfer and uptake of PB

- HAPs into farm food chain media (e.g., produce, livestock, dairy products) from soil and air , and calculation of FFC media concentrations ;
3. Estimating exposures from ingestion of selected media and estimating average daily ingestion doses for a hypothetical human receptor; and

4. Calculating lifetime cancer risk estimates or chroni c non

- cancer HQs

, as appropriate

, for each PB

- HAP and comparing these to selected evaluation criteria

.
The relationship among these four processes is shown in Exhibit 6

.

As shown in Exhibit 6 , two models are used to evaluate the four steps outlined above. EPA’s TRIM methodology was conceived as a comprehensive modeling framework for evaluating risks from air toxics, and the TRIM system was designed to address each of the four steps involved in screening ingestion risk. 13 Currently , however, only one component corresponding to the first step included in Exhibit 6

— the fa te and transport module

— is available for application in an ingestion risk assessment

. EPA has completed some development activities for TRIM.Expo

- Ingestion and TRIM.Risk

- Human Health, two additional modules that cover the other three steps. M odeling soft ware , however, is not currently available for these modules

. For the RTR screening scenario, the Multimedia Ingestion Risk Calculator (MIRC), a Microsoft ® Excel TM

- based comput ing framework

, was constructed to complete the calculations required for estimati ng PB

- HAP concentrations in farm food chain media, average daily ingestion doses, and

12 As discussed below, concentrations in fish calculated by the TRIM.FaTE model were used to estimate ingestion exposures for humans consuming fish. Modeling of fish concentrations is therefore discussed herein as part of the fate and transport modeling. Up take of PB

- HAPs into all other biotic media assumed to be ingested is modeled in the second step of the modeling framework.

13 Information about the current status of TRIM modules and comprehensive documentation of modules developed thus far can be accessed on EPA’s Technology Transfer Network (TTN) on the Fate, Exposure, and Risk Analysis website ( http://www.epa.gov/ttn/fera/ ). Exhibit 6 . Overview of Ingestion Exposure and Risk Screening Evaluation Method

Chemical fate and transport : Physical environment and aquatic ecosystem

TRIM

. FaTE Uptake & transfer into produce and livestock

Multimedia Ingestion Risk Calculator ( MIRC ) Human ingestion exposure Risk & hazard estimation 

## Chemical Emissions to Air

Cancer Risk Hazard QuotientTRIM

- Based T iered Screening Me thodology for RTR

Tier 1 Methodology 20 October 2014 cancer risks and chronic non

- cancer HQs

. This framework is conceptually identical to the ingestion exposure and risk assessments that TRIM is intended to cover.

2.2.4.2 Model Co nfiguration and Parameterization

Th e Tier 1 scenario is intended to reduce the possibility that EPA would underestimate potential multipathway human health risks

. Although the health protective approach likely overestimates risk, EPA determined that this approach is appropriate for the purposes of an initial multipathway screening assessment

. As was done with the preliminary multipathway screening for RTR conducted in 2006 ( EPA 2006) , exposures were modeled for a hypothetical farm homestead and fishable l ake located adjacent to an emissions source

. The hypothetical individual for which exposures were calculated was assumed to derive all potentially contaminated foodstuffs from adjacent locations, and many of the exposure/activity assumptions (e.g., amount of food consumed per day) were selected from the upper ends of representative exposure parameter distributions

. The physical/chemical environment represented in the screening scenario was parameterized with two types of values. One type is typical value s, such as national averages. The second type is health

- protective values, or values that would tend to overestimate concentrations in media driving ingestion exposures for humans, based on knowledge of exposure patterns. In general, the spatial and temp oral aspects of the scenario and the components of the scenario that influence air concentrations and deposition rates (which in turn affect all other exposures) were defined to be health protective. Chemical

- specific and non

- chemical

- specific properties of the environmental media were parameterized with either typical or health protective values; properties having greater uncertainty were assigned a greater level of health protective bias. The spatial layout of the Tier 1 scenario and the meteorological d ata (or a combination of these two factors) are generally more influential than physical/chemical parameters in dictating the screening model outcomes , taking into account the potential range of variation in possible values

. For example, where and how the layout is spatially oriented relative to the dominant wind direction can dramatically affect the concentrations in air, thereby driving estimated concentrations of PB

- HAPs in soil, water, and biota . In contrast, relatively large change s in soil character istics within the range of possible values (e.g., organic carbon content, water content) typically result in relatively small changes in media concentrations.

The mix of health protective and central

- tendency assumptions and parameterization is expected to result in a scenario configuration that, on average, is likely to overpredict environmental concentrations of PB

- HAPs in media of interest for this evaluation

. Given the intended application of this scenario as a screening tool, this health protective bias was deliberate, because of the desire to ensure that risks above levels of potential concern are not overlooked (i.e., to minimize false negatives) . Although t he inclusion of central

- tendency values where warranted is intended to minimize the number of false positives, some false positives are to be expected from a screening scenario . False positives are addressed in subsequent tiers of the screening evaluation for a particular source

.
2.2.5 Fate and Transport Modeling (TRIM.FaTE)

The fate and transport mo deling step depicted in the first box in Exhibit 6 is implemented for RTR using t he Fate, Transport, and Ecological Exposure module of the T RIM modeling system (TRIM.FaTE )

. 14 In developing the Tier 1 scenario, Version 3.6.2 of TRI M.FaTE was used to

14 TRIM.FaTE is a spatially explicit, compartmental mass balance model that describes m ovement and transformation of pollutants over time, through a user

- defined, bounded system that includes both biotic and abiotic compartments

. Outputs include pollutant concentrations in multiple environmental media and biota

.TRIM

- Based T iered Screening Me thodology for RTR

Tier 1 Methodology 21 October 2014 model the fate and transport of emitted PB

- HAPs and to estimate concentrations in relevant media

. Additional information about TRIM.FaTE, including support documentation, software, and the TRIM.FaTE public reference library, is availabl e at http://www.epa.gov/ttn/fera/

. The algorithms used to model mercury species and polyaromatic hydrocarbons (PAHs) are described in Volume II of the TRIM.FaTE Technical Support Document ( EPA 2002a). A compreh ensive evaluation of the performance of TRIM.FaTE for modeling mercury was documented in Volumes I and II of the T RIM.FaTE Evaluation Report ( EPA 2002b, 2005b). Algorithms specific to the fate and transport of chlorinated dibenzo

- dioxin and

- furan cong eners were added following the addition of those for mercury and PAHs. Documentation of the application of TRIM.FaTE for dioxin emissions is contained in the third volume of the TRIM.FaTE Evaluation Report ( EPA 2004). More recently

, the TRIM.FaTE public reference library was updated to include information on modeling for cadmium

. In general, many of the algorithms and properties included in the public reference library that are used to model mercury ( except for the mercury transformation algorithms) are also applicable to cadmium

. Comprehensive t echnical documentation of TRIM.FaTE algorithms specific to cadmium has not yet been compiled ; however, all chemical

- specific properties used by TRIM.FaTE to model cadmium (as well as PAHs, mercury, and dioxins) a re documented in Attachment A . Based on a thorough 2011 evaluation of TRIM.FaTE performance in modeling mercury’s fate, transport, and transformation in the aquatic food web, a zooplankton compartment was added to TRIM.FaTE’s aquatic compartment to increase the resolution and accuracy of the aquatic food web modeling. Parameterization of the TRIM.FaTE scenario used for RTR screening is described in more detail in Section 2.3

.
2.2.6 Exposure Modeling and Risk Characterization (MIRC)

The algorithms included in MIRC that calculate chemical concentrations in farm food chain media and ingestion exposures for hypothetical individuals were obtained from EPA’s Human Health Risk Assessment Protocol for Hazardous W aste Combustion Facilities, or HHRAP ( EPA 2005a). 15 These algorithms, and the required exposure factors and other parameter values, were compiled into a database. An overview of the computational processes this tool carrie s out and the types of input data it require s is presented in Exhibit 7

. This exhibit demonstrates the general relationship s between the relevant TRIM.FaTE outputs (i.e., chemical concentrations in environmental media and fish) and the ingestion exposure and ris k calculations carried out using MIRC. Additional discussion of exposure and risk calculations for the Tier 1 scenario is presented in Section 2.4 and Attachment B , and all inputs required by these calculations are documented in Attachment B

.

15 The farm food chain calcula tions and ingestion exposure equations to be included in the TRIM.Expo software are expected to be very similar to those included in HHRAP.TRIM

- Based T iered Screening Me thodology for RTR

Tier 1 Methodology 22 October 2014 Exhibit 7

. Overview of Process Carried Out in the Multimedia Ingestion Risk Calculator

2.2.7 Implementation of Risk

- based Emission Scaling Factors for POM and Dioxin Emissions

Two of the four PB

- HAPs for which screening emission thresholds have been developed for RTR

— POM and dioxins

— are chemical groups comprising numerous individual entities. The members of these categories reported in NEI include both specific chemicals and groups containing multiple chemicals. For example, for POM, emissions reported in NEI include various species, such as benz[a]anthracene, 2

- methylnaphthalene, and chrysene, as well as

non

- specific entries, such as “PAH, total.” The constituents included in the POM and dioxin PB

- HAP categories are grouped together not only because they are types of the “same” HAP, but also because members of these groups are assumed to have similar characteristics with respect to toxicity and behavio r in the environment.

To facilitate a practical application of the multipathway screening methods for RTR, reported emissions of POM and dioxins are normalized or scaled to a single reference chemical for each group. The reference chemicals used in RTR fo r POM and dioxins are benzo[a]pyrene and 2,3,7,8

- TCDD, respectively . T hese compounds were selected because they are relatively well

studied among the members of the two groups and are also among the most toxic species within each group.

Derivation of app ropriate scaling factors begins with an evaluation of the basic relationship used to characterize health risk:

Risk 

# ∝ ( Exposure Concentration) × (Toxicity)

For a given air pollutant, the incremental exposure concentration is directly proportional to the

e missions of that substance. That is, as the emissions increase, so too does the exposure to Farm Food Chain

Concentration

Calculator Ingestion Exposure Calculator Risk and Hazard Calculator TRIM

. FaTE

outputs

Human activity / exposure factors Plant

- and animal

- specific

parameters Chemical

specific uptake / transfer factors Ingestion dose

- response values **FFC media concentrations**  **Average daily doses**  **Cancer risks and hazard quotients** 

Access db

process **Tool output**  Input data Key to symbols :TRIM

- Based T iered Screening Me thodology for RTR

Tier 1 Methodology 23 October 2014 that substance. Furthermore, toxicity is assumed to increase linearly with concentration. Consequently, emissions of one substance (e.g., chrysene) can be scaled proportional to a reference compound (e.g., benzo(a)pyrene or BaP ) by applying weighting factors corresponding to the relative differences in exposure behavior and toxicity. Using the POM group as an example and BaP as the reference compound, this scalin g can be expressed through an equation as follows:

Emiss PAHi:BaP = Emiss PAHi × EEF PAHi:BaP × TEF PAHi :BaP

w here :

Emiss PAHi : BaP = Risk

- weighted emissions of PAH i (weighted according to cancer risk relative to BaP for oral exposures)

Emiss PAHi = Emission r ate of PAH i

EEF PAHi:BaP = Exposure equivalency (weighting) factor accounting for difference in relative oral exposure between PAH i and BaP

TEF PAHi:BaP = Toxicity equivalency (weighting) factor accounting for difference in relative toxicity via oral rout e between PAH i and BaP

In combination, the product of the EEF and TEF for a given substance is considered to be a “risk equivalency factor” for the purposes of RTR evaluations that enables scaling of emissions of a given substance for a given exposure sce nario.

The TEF for each PAH and dioxin species can be calculated on the basis of relative toxicities. Toxicities were not evaluated separately for RTR but are based on analyses conducted by EPA elsewhere. For PAHs, oral toxicity values for individual spe cies have been derived following the same approach used to develop inhalation toxicity values. For dioxins, TEFs are based on the relative toxicities developed by EPA recently and are ultimately based on the values developed by the World Health Organizati on (van de n Berg et al. 2006). Refer to Section 4 of Attachment B for more information on these values.

The EEFs can be calculated directly for each individual chemical that can be modeled in TR IM.FaTE and MIRC. TRIM.FaTE is configured for 14 POM congener s and 17 dioxin/furan congeners . For these substances, EEFs were calculated directly using the modeling approach and parameterization scheme for the Tier 1 scenario described in this document

. S everal other POM and dioxin emissions , however, are reported in the NEI

. For these, exposure surrogates must be assigned after evaluating the correlation between chemical properties of the POM or dioxin congener and exposure quantified as lifetime avera ge daily dose. The specific calculations for EEFs and exposure surrogates for each chemical group are discussed in the sections that follow.

2.2.7.1 Calculation of Scaling Factors for POM C ongeners

The calculated EEFs , TEFs, and total REFs for the 14 POM congener s that are configured in TRIM.FaTE , plus 15 others not configured in TRIM.FaTE, are shown in Exhibit 8

. To determine appropriate exposure surrogates for chemicals not parameterized in TRIM.FaTE, EPA evaluated the relationships be tween chemical

- specific properties (e.g., K ow and Henry’s law constant, k h ) and intermediate modeled values (e.g., deposition , soil concentration ) and exposure in terms of lifetime average daily dose (LADDs) where the average daily doses (ADDs) for the you ngest two age groups were adjusted by the age

- dependent adjustment factors (ADAFs) to account for the mutagenic mode of action of PAHs. The correlation between K ow and exposure is stronger than for any other chemical

- specific property. ExposureTRIM

- Based T iered Screening Me thodology for RTR

Tier 1 Methodology 24 October 2014 surrogate s were thus identified for each congener by calculating Total Lifetime Average Daily Dose (Age Adjusted) for each based on the congener’s K ow and the power regression of the modeled PAHs. Exhibit 9 shows that as K ow increases, so too does exposure.

Exhibit 8 . Exposure, Toxicity, and Risk Equi valency Factors Relative to BaP for POM Congeners Currently Evaluated in Tiers 1 and 2 Assessments

Chemical Fully Parameterized in TRIM.FaTE? Exposure Equivalency Fa ctor (EEF) Toxicity Equivalency Factor (TEF) a Risk Equivalency Factor (REF) Dibenzo(a,i)pyrene n 27.5 16.4 452 7,12

- Dimethylbenz(a)anthracene Y 5.4 34.2 186
3

- Methylcholanthrene n 4.3 3.0 12.9 Dibenz(a,h)anthracene Y 8.0 0.6 4.5 Benzo(k)fluoranthene Y 18.0 0.2 3.0 Benzo(a)pyrene Y 1.0 1.00 1.00 Benzo(b)flouranthene Y 11.4 0.2 1.9 Indeno(1,2,3

- cd)pyrene Y 4.5 0.2 0.7 PAH, total n 5.1 0.07 0.3 Polycyclic Organic Matter n 5.1 0.07 0.3 Benzo(g,h,i)perylene Y 4.3 0.07 0.3 Benzo(e)pyrene n 4.5 0.0 7 0.3 Retene n 3.7 0.07 0.3 Dibenzo(a,j)acridine n 0.78 0.2 0.1 Perylene n 1.2 0.07 0.08 Benzo(a)anthracene Y 0.09 0.16 0.01 Chrysene Y 0.25 0.02 0.004
2

- Acetylaminofluorene n 0.005 1.4 0.01 Fluoranthene Y 0.04 0.07 0.003 Acenaphthylene Y 0.04 0.07 0.003
2

- Chloronaphthalene n 0.03 0.07 0.002 Fluorene Y 0.03 0.07 0.002 Acenaphthene Y 0.02 0.07 0.002
1

- Methylnaphthalene n 0.02 0.07 0.001
2

- Methylnaphthalene Y 0.02 0.07 0.001 Carbazole n 0.01 0.003 0.00003 Anthracene n 0.06 0 b 0 Phenanthrene n 0.06 0 b 0 Pyrene n 0.15 0 b 0

a TEFs are calculated as the ratio of the cancer slope factor ( CSF) for each specific POM congener to the CSF for benzo(a)pyrene. Dose response values, including CSFs, that are used in the screening assessment are discussed in Section 4 of Attachment B

. b Weight of evidence evaluations indicated that the available data were adequate to determine that three PAHs (anthracene, phenanthrene, and pyrene) were not carcinogenic (EPA 2010 a ),

For POMs reported as unspeciated groups (i.e., “PAH, total” and “Polycyclic Organic Matter”) EPA assigned surrogates with K ow values near the upper end of the range of all of the K ow values, corresponding to an exposure near the upper end of the range (log K ow = 6.5). This assignment is assumed to be health protective and likely will not under predict exposure

.TRIM

- Based T iered Screening Me thodology for RTR

Tier 1 Methodology 25 October 2014 Exhibit 9 . Relationship between Exposure and K ow for POM Congeners

It is worthy of noting that naphthalene is not included in the POM category for the RTR multipathway (i.e., non

- inhalation) analyses. Naphthalene is listed individually as a HAP under Section 112(b) of the Clean Air Act. POM also is listed as a HAP under Section 112(b) and is defined as organic compounds with more than one benzene ring and a boiling point greater than or equal to 100° C (see http://www.epa.gov/ttn/atw/orig189.html). Under this definition, naphthalene could be considered as part of the POM listing. However, naphthalene is short

lived in environmental media due to its tendency to volatilize and biodegrade and, consequently, will not build up in environmental media over time (ATSDR 2005). Additionally, based on a log Kow of 3.29, it has a mod erate affinity for lipids and will undergo short

- term bioaccumulation in tissues; however, biochemical processes lead to its biodegradation and elimination. Because it is neither persistent nor bioaccumulative, it is not considered a PB

- HAP, and EPA believ es that its inclusion in the POM category would result in artificially inflated media concentrations and multipathway risk estimates for POM

.

2.2.7.2 Calculation of Scaling Factors for Dioxin Congeners

The calculated EEFs, TEFs, and REFs for the 17 dioxin congene rs that are configured in TRIM.FaTE are presented in Exhibit 10

. Although there are many dioxins reported in the NEI other than the 17 configured for TRIM.FaTE, to date, none of them have been included in emissions datasets that have been screened. Therefore, no surrogate EEF’s have been developed for dioxins. In future screening assessments, if surrogate EEFs are needed, an approach similar to that used for POM will be used to develop surrogate EEFs for dioxins.

Many facilities report dioxins as “ Dioxins, Total, without Indiv idual Isomers R e p or t e d ,” “Dioxins,” or as “2,3,7,8

- TCDD TEQ,” and in these cases, we do not adjust or scale the emissions. That is, we assume that they behave like and possess the toxic characteristics of TC DD. This approach could be improved by obtaining information on the speciation of dioxin emissions for each facility or an average speciation profile that could be assumed to apply to all facilities in a source category.TRIM

- Based T iered Screening Me thodology for RTR

Tier 1 Methodology 26 October 2014 Exhibit 10 . Exposure and Toxicity Equivalency Factors Relative to TCDD for Modeled Dioxin Congeners

Chemical Exposure Equivalency Factor (EEF) Toxicity Equivalency Factor (TEF) a Risk Equivalency Factor (REF) PentaCDD, 1,2,3,7,8

- 3.8 1 3.8
TetraCDD, 2,3,7,8

- 1 1 1
Dioxins, Total, w/o Indiv. Isomers Rptd. 1 1 1

Dioxins 1 1 1
2,3,7,8

- TCDD TEQ 1 1 1
HexaCDD, 1,2,3,4,7,8

- 1.6 0.1 0.2
PentaCDF, 2,3,4,7,8

- 0.4 0.3 0.1
HexaCDD, 1,2,3,6,7,8

- 1.0 0.1 0.1
HexaCDF, 1,2,3,7,8,9

- 0.5 0.1 0.05
HexaCDF, 1,2,3,6,7,8

- 0. 5 0.1 0.05
HexaCDD, 1,2,3,7,8,9

- 1.0 0.04 0.04
HexaCDF, 1,2,3,4,7,8

- 0.3 0.1 0.03
HexaCDF, 2,3,4,6,7,8

- 0.2 0.1 0.02
PentaCDF, 1,2,3,7,8

- 0.4 0.03 0.01
TetraCDF, 2,3,7,8

- 0.1 0.1 0.01
HeptaCDD, 1,2,3,4,6,7,8

- 1.0 0.01 0.01
HeptaCDF, 1,2,3,4,6,7,8

- 0.2 0.01 0.002
HeptaCDF, 1,2,3,4,7,8,9

- 0.2 0.01 0.002
OctaCDD, 1,2,3,4,6,7,8,9

- 1.1 0.0003 0.0003
OctaCDF, 1,2,3,4,6,7,8,9

- 0.2 0.0003 0.0001

a Values from Van den Berg et al. (2006), except for 1,2,3,7,8,9

- hexaCDD, which is calculated based on the ra tio of the IRIS

based CSF for 1,2,3,7,8,9

- hexaCDD to the IRIS

- based CSF for 2,3,7,8

- TCDD. Dose response values, including CSFs, that are used in the screening assessment are discussed in Section 4 of Attachment B

.


### 2.3 Description of Environmental Modeling Scenario

As described in Section 2.2.4.2 , the physical configuration of the RTR Screening Scenario was designed to encompass the upper end of potential lo ng

- term PB

- HAP exposures, and the environmental and chemical

- specific properties were parameterized with either health protective or central

- tendency values

. Information regarding the scenario configuration and important aspects of the parameterization pr ocess, justifications for selecting particular property values, and model uncertainties is presented in the sections that follow

. Comprehensive documentation of TRIM.FaTE property values for this scenario is provided in Attachm ent A

.
2.3.1 Chemical Properties

The general c hemical/physical properties that TRIM.FaTE require s , such as Henry’s law constant, molecular weight, and melting point , were obtained from peer

- reviewed and standard reference sources . N umerous other chemical

- speci fic properties are related more specifically toTRIM

- Based T iered Screening Me thodology for RTR

Tier 1 Methodology 27 October 2014 a particular abiotic or biotic compartment type; these properties are discussed generally in the sections that follow and are documented in Attachment A

.
2.3.2 Spatial Layout

For the p urpose of estimating media concentrations, the TRIM.FaTE scenario is intended to represent a farm homestead and a fishable lake (and its surrounding watershed) located near the emissions source of interest

. A diagram of the surface parcel layout is presen ted in Exhibit 11

. The source parcel is parameterized as a square with sides of 250 m, which is assumed to be a fair estimation for the si z e of a relatively small

- to

- medium facility at the fence line . With a predominant wind dir ection toward the east, the modeled layout is generally symmetric about an east

- west line and is wedge

- shaped to reflect Gaussian dispersion of the emission plume.

A lateral, downwind distance of 10 km was established for the watershed included in the

scen ario. Based on the results of dispersion modeling

, the location of the maximum air concentration and deposition rate is expected to occur relatively close to the facility (probably within a few hundred meters, with the exact location varying with stack he ight and other parameters) and well within a 10

- km radius. Additionally, deposition rates for the PB

- HAPs for which this screening scenario is applicable is expected to decrease by about two orders of magnitude relative to the predicted maximum rate withi n a 10

- km radius. Extending the modeling layout beyond a 10

- km downwind distance would increase the amount of deposition “captured” by the modeled watershed, but the incremental chemical mass expected to accumulate in the watershed diminishes rapidly with distance. In addition, the impact of this additional deposited mass on ingestion exposures is expected to be negligible. 16 Given these conditions, a downwind length of 10 km was determined to be appropriate for the screening scenario.

Exhibit 11

. TRIM.FaTE Surface Parcel Layout

16 M ass deposited at the outer edge of the watershed is expected to result in only a very small increase in estimated exposure via fish consumption by increasing the chemical mass transported to the lake through erosion and runoff. The distance from these more distant locations to the lake would attenuate transport of chemical mass by erosion and runoff, dampening the ef fect of including additional deposition beyond 10 km. (Other exposure pathways largely would be unaffected; the soil concentrations used to calculate exposures for the farm food scenario are derived from soil parcels located close to the source and unaffe cted by deposition to the far reaches of the watershed.)TRIM

- Based T iered Screening Me thodology for RTR

Tier 1 Methodology 28 October 2014 The north

- south width of the wedge

- shaped watershed was set based on the observed behavior of chemicals emitted to the ambient air. If meteorological stability is known or can be assumed, the latera l spread of the plume (σ y , measured from the centerline) at a certain distance from the source can be estimated using the Pasquill

- Gifford curves. Turner (1970) derived the equations for these curves, which can be found in the Industrial Source Complex 3 Dispersion Model Manual (among other sources). 17 For a relatively neutral atmosphere (stability class D), σ at 10km is about 550 m using this estimation. In a Gaussian distribution, about 99.6 percent of the plume spread area is contained within 3σ of the median line. Therefore, the plume σ was set at 3 times 550 m, or approximately 1.75 km from the centerline at a distance of 10 km. The plume width for these conditions is expected to be about twice this distance, or 3.5 km. These dimensions were used t o define the dimensions of the overall air and surface parcel layouts for the screening scenario.

The surface (land and surface water) modeling area was initially divided into five pairs of parcels the areas of which increase with distance from the source, which approximately corresponds to the spatial gradient that is expected in the downwind direction from the source . The second north parcel from the source was divided further into two parts; one of them tilled soil (Parcel N6) to represent agricultural conditions and the other (Parcel N7) untilled to represent pasture.

The depth of the surface soil compartments was set to 1 cm , except for Parcel N6, for which the depth was set to 20 cm to simulate the effect of tillage

. Characteristics of the soil lay ers (e.g., organic carbon content, air and water content , and subsoil depth ) generally were set to represent typical or national averages as summarized by McKone et al. (2001)

. The overall shape and boundaries of the air parcel layout mirror th os e of the surface parcel layout . A single air parcel (N2) overlies s urface Parcel s N6 and N7, and the air over the lake is divided into air Parcel s S2 and S3 (mirroring the analogous parcels on the north side of the lake).

2.3.3 Watershed and Water Body Parameterization

Properties associated with the watershed soil and lake determine how pollutants in the system are transported through and accumulate in various media compartments

. These properties describe the physical characteristics of the environmental media included in the modeled region, as well as the assumed connections and relationships between media types and modeled spatial components th at in turn affect chemical transport via water runoff, ground infiltration

, deposition of suspended sediments in the water col umn, and other processes

. This section presents the justification for setting the key properties of the soil, water, and sediment compartments

. Also discussed are some of the chemical properties related to watershed and waterbody processes (chemical

- spec ific compartment properties in TRIM.FaTE) and the configuration of terrestrial plants included in the scenario.

2.3.3.1 Water Balance

Water

- related properties of the lake and related watershed characteristics (e.g., runoff rates from each surface soil compartment) were set s o that a simplified water balance is achieved

. Although TRIM.FaTE maintains a chemical mass balance

, the model does not calculate or maintain media mass balance s (e.g., for water) except where specified in certain formula s. For the Tier 1 scen ario, the parameter s were set to satisfy two equations relating water volume

. The first equation maintains a balance of water entering and leaving the terrestrial portion of the scenario:

17 http://www.epa.gov/scram001/userg/regmod/isc3v2.pdfTRIM

- Based T iered Screening Me thodology for RTR

Tier 1 Methodology 29 October 2014 [ total precipitation] = [evapotranspiration] + [total runoff]

In th is equation, t otal runoff is equal to the sum of overland runoff to the lake and seepage to the lake via groundwater

. The second equation describes the volumetric balance of transfers of water to and from the lake:

[total runoff] + [direct precipitation to the lake] = [evaporation from the lake surface] + [outflow from the lake]

Note that TRIM.FaTE actually uses only some of these properties (e.g., precipitation rate and surface runoff , but not evapotranspiration )

. The water characteristics assumed for th e Tier 1 scenario are meant to represent a relatively wet and moderately warm location in the United States (USGS 1987) . F ollowing are the assumptions for this scenario:

• 35 percent of the total precipitation leaves the scenario through evapotranspiration.

• 25 percent of total precipitation infiltrates into the groundwater and eventually flows into the lake.

• 40 percent of total precipitation contributes to overland runoff.

For these calculations, the source parcel was considered to be outside the watershed and therefore was not included in the water balance

. The evaporation rate from the lake was assumed to be 700 mm/yr based on data reported by Morton (1986) for various lakes

. The runoff rate was defined to be spatially constant and temporally constant ( i.e., it is not linked to precipitation events) throughout the modeled domain . Based on these assumptions, the outflow of water from the lake is about 18 million m 3 /yr, which translates to a volumetric turnover rate of about 12.2 lake volumes per year.

Other quantitative water body and watershed characteristics TRIM.FaTE uses are listed in Attachment A

.
2.3.3.2 Sediment Balance

A simplified balance of sediment transfers between the watershed and the lake was also maintained for the screening scenario via the parameterization of sediment

- related properties

. As with water, the model does not internally balance sediment mass; these calculations were performed externally for the purposes of setting parameter values. The sediment balan ce maintained is described by the following equation, where terms represent mass of sediment:

[total surface soil transfers to the lake via erosion] = [removal of sediment from the water column via outflow] + [sediment burial]

where the second term (remo val of sediment from the water column via outflow) is accounted for in TRIM.FaTE by lake flushing rate and the third term (sediment burial) is the transfer of sediment from the unconsolidated benthic sediment compartment to the consolidated sediment layer.

To maintain the sediment balance, erosion rates were calculated for each surface soil compartment using the universal soil loss equation (USLE, Wischmeier and Smith 1978)

, assuming a relatively high rate of erosion

. The total suspended sediment concent ration isTRIM

- Based T iered Screening Me thodology for RTR

Tier 1 Methodology 30 October 2014 assumed to remain constant in TRIM.FaTE

, and the flush ing rate of the lake (calculated via the water balance approach described above) w as then used to estimate the removal of sediment from the modeling domain via lake water outflow

. The differe nce between these sediment fluxes was taken to be the sediment burial rate

. The sediment burial rate is the rate at which sediment particles in the unconsolidated benthic sediment layer are transported to the consolidated sediment, where the particles can no longer freely interact with the water column

. In TRIM.FaTE, the consolidated sediment layer is represented with a sediment sink; as with all sinks in TRIM.FaTE, chemical mass sorbed to buried sediment that is transported to the sink cannot be returne d to the modeling domain

. The burial rate is a formula property calculated by the model according to the difference between user

- specified values for sediment deposition velocity (from the water column to the benthic sediment) and sediment resuspension ve locity (back into the water column from the benthic sediments)

. These formula properties assume a constant volume of particles in the sediment layer (because the densities for benthic and suspended sediment particles were defined to the same value, the ma ss of particles in the sediment is also constant)

.
For the Tier 1 scenario described here, the average sediment delivery rate (i.e., transfer of sediment mass from watershed surface soil to the lake due to erosion ) for the entire watershed was estimated to be about 0.0036 kg/m 2

- day , based on calculations using the USLE. T he HHRAP documentation notes that using the USLE to calculate sediment load to a lake from the surrounding watershed sometimes lead s to overestimates ( EPA 2005a)

. For the Tier 1 scenari o, however, this approach was considered to be appropriate in that health protective assumptions are a goal of the screening scenario. 18 S urface soil compartments adjacent to the lake are linked directly to the lake for the purposes of estimating erosion a nd runoff transfers (see layout in Exhibit 11 )

. Erosion and runoff from the source parcel are linked directly to a sink and do not enter the Tier 1 scenario lake. The transport of sediment to the lake via overland is thus assume d to be efficient

. Note that erosion from parcels not directly adjacent to the lake is assumed to be somewhat attenuated, effected by using a lower sediment delivery ratio in the USLE.

Using the calculated surface soil erosion rates for the scenario, th e total average daily sediment load to the lake from the watershed is about 16,60 0 kg/day

. A bout 15 percent of this load is removed from the lake via outflow of suspended sediments (based on a calculated flush rate of 12.2 volume turnovers per year) with the remainder of the sediment input to the lake transferred to the sediment burial sink.

2.3.4 Meteorology

Meteorological properties used in TRIM.FaTE algorithms include air temperature, mixing height, wind speed and direction, and precipitation rate

. These pro perties, which can vary significantly among geographic locations

, and seasonally and hourly for a single location, greatly influence the chemical concentrations predicted in media of interest

. Because the screening scenario is intended to be generally app licable for any U.S

. location, and to minimize the frequency of false negatives, a health protective configuration was used

. The meteorology of the screening scenario was defined to ensure that (when used in combination with the selected spatial layout) t he maximum exposures that might be encountered for the scenarios of interest would be encompassed (i.e., consumption of home

- grown farm foodstuffs and self

- caught fish, with all farm foods and fish obtained from locations impacted by chemicals emitted from the local

18 Based on sensitivity analysis, a higher erosion rate will both increase surface water concentrations and decrease surface soil concentrations; the relative impact on resulting concentrations, however, will be proportionally greater in the waterbody.TRIM

- Based T iered Screening Me thodology for RTR

Tier 1 Methodology 31 October 2014 source). Ensuring t hat the meteorological parameters were not overly protective of health , such

as always having the wind blow toward the location of interest, however, was also important to avoid too many false positives

. The meteorological data for the screening scenario are intended to be representative of a location with a low wind speed, a wind direction that strongly favors the watershed, and a relatively high amount of total precipitation falling on the watershed

. The values used were based on actual data trends for U.S

. locations as specified in Exhibit 12 but an artificial data set was compiled for this assessment (for example, temporally variable meteorological parameters were made to vary only on a daily ba sis). This simplified approach allowed for greater control (relative to selecting a data set for an actual location) so that desired trends or outcomes could be specified. Also, using a meteorological data set with values varying on a daily basis rather than a shorter period (such as hourly, which is the typical temporal interval for meteorological measurements) reduced required model run time. Meteorological inputs are summarized in Exhibit 12

. The sensitivity of modeled PB

- H APs to changes in these meteorological variables was tested. Lower wind speeds and mixing heights affected concentrations the most. This sensitivity is not unexpected because lower wind speeds should increase pollutant deposition onto the soil and lower mixing heights should reduce the volume through which pollutants disperse. The wind speed used for the screening scenario was 2.8 m/s, the 5 th percentile of the annual averages among 239 stations; by comparison, the mean annual average wind speed is appro ximately 4 m/s in the contiguous United States). The mixing height (mean heights from 4 states) used was 710 m (the 5 th percentile of annual averages among all 40 states in the SCRAM database).

Exhibit 12 . Summary of Key Meteoro logical Inputs
Meteorological Property Selected Value Justification

Air temperature Constant at 298 Kelvin Recommended default value listed in HHRAP ( EPA 2005a). Value is similar to the mean daily June temperature in much of the U.S. Southern Plains and Southeast. a Mixing height Constant at 710 m Value is 5 th percentile of annual average mixing heights for 75 U.S. locations, using data obtained from EPA’s SCRAM Web site. b Value is the approximate U.S. median for periods without precipitation, based on data compiled by Holzworth (1972). Value is conservative compared to the 1

- to 2

- km typical mid

- latitude daytime value (Stull 1988).
Wind direction Blows from source parcel into scenario domain (west to east) 3 days per week; during other times does not blow into domain A wind direction that favors the location of interest (for example, a watershed downwind of a source of concern) will tend to result in more emitted mass accumulating in the location of interest. For much of the U.S. mid

- Atlantic and we stern regions, the wind tends to favor the eastward direction. a Among the NCDC 1981

– 2010 normalized wind vector data, the average wind direction had a strong eastward component at over one

- third of the stations. c For the hypothetical RTR scenario, a more extreme example of this pattern is represented by conditions in Yakima, Washington, where the wind blows eastward approximately 40 percent of the time based on a review of wind direction data compiled by the National Weather Service (NCDC 1995). This pat tern was approximated in the RTR scenario with a configuration in which the modeled domain is downwind of the source 3 out of 7 days

. Horizontal wind speed Constant at 2.8 m/sec Set to 5 th percentile of annual average speed for 239 stations across the con tiguous United States (about 50 years of data per station). Value is similar to the annual average wind speeds ofTRIM

- Based T iered Screening Me thodology for RTR

Tier 1 Methodology 32 October 2014 Meteorological Property Selected Value Justification
many areas of the U.S. east coast and west coast. a Precipitation frequency Precipitation occurs 3 days per week; wind direction blows into d omain 2 of these days This value was selected so that two

- thirds of the total precipitation occurs when the domain is downwind of the modeled source. This pattern approximates that for rainy U.S. locations, where precipitation occurs 35

– 40% of the time (H olzworth 1972). These locations include parts of the U.S. Northeast and Northwest. a

Total Precipitation 1.5 m/yr Assumed to represent rainy conditions for the United States. This annual precipitation amount is experienced in parts of the U.S. Deep South and parts of the U.S. northwest coast. a Conditional precipitation rate (rainfall rate when precipitation is occurring) is 9.59 mm/d, which is similar to conditions in many areas along the U.S. east coast and in the Midwest and Plains. a

a National Climati c Data Center CliMaps (NCDC

- CliMaps) (2007). http://cdo.ncdc.noaa.gov/cgi

- bin/climaps/climaps.pl

. b Support Center for Regulatory Atmospheric Modeling; http://www.epa.gov/ttn/scram/

.
c National Climatic Data Center 1981

- 2010 Climate Normals; http://www.ncdc.noaa.gov/oa/climate/normals/usnormals.html

2.3.5 Aquatic Food Web

The aquatic food web is an important part of the screening scenario because the chemical concentrations modeled in fish are used to calculate human ingestion exposure and risks associated with eating contaminated local fish

. A biokinetic approach to modeli ng bioaccumulation in fish is used in the RTR screening scenario. The primary producers (first trophic level) in the TRIM.FaTE aquatic ecosystems are algae and macrophytes in the water column and detritus in the sediments (the latter sim ulated as sediment particles). Zooplankton feed on algae in the water column, while benthic invertebrate s, represented as a single compartment , consume detritus that settles to the sediment compartment. In the water column, small young

- of

- the

- year fish and minnows feed on zo oplankton and phytopolankton. The small fish are in turn consumed by larger or “pan” fish (e.g., bluegills, white perch), which are in turn consumed by the top consumers (e.g., gar, pickerel). The invertebrates in the sediments of the benthic environment s upport small bottom

- feeding fish (young

- of

- the

- year fish for many species), which in turn are consumed by larger bottom

- feeding fish (e.g., catfish). For TRIM.FaTE to provide reasonable predictions of the distribution of a chemical across biotic and abioti c compartments in aquatic systems, the biomass of the aquatic biotic compartments must represent all biota in the system and the distribution of biomass among the trophic levels and groups must be as realistic as possible

. To support the development of a relatively generic freshwater aquatic ecosystem in which to model bioaccumulation in fish, a literature search, review, and analysis was conducted ( ICF 2005). This research demonstrated that the diversity of sp ecies and food webs across U.S. aquatic ecos ystems is substantial, reflecting the wide range of sizes, locations, and physical/chemical attributes of both flowing (rivers, streams) and low

- flow water bodi es (ponds, lakes, reservoirs). In general, lentic bodies of water (lakes and ponds) can accumula te higher levels of contaminants in both sediments and biota than lotic systems (rivers, streams). Also, that initial research (ICF 2005) suggest s that a lake of at least 60 hectares (ha) or 150 acres could support higher trophic level predatory fish, wit h some fraction of the ir diet comprising smaller fish

.
The RTR Tier 1 scenario includes a generic aquatic ecosystem with a 47

- h ectare (116

- acre) lake

. Although slightly smaller than the size suggested by the previous review (ICF 2005)

, a 47

- ha lake is l arge enough to support higher trophic level fish given appropriate conditions (e.g., high productivity given a sufficient nutrient base and average temperature and growing season ).TRIM

- Based T iered Screening Me thodology for RTR

Tier 1 Methodology 33 October 2014 Also, t his size was compatible with the overall size of the defined waters hed in the screening scenario. The fish types, biomass, diet fractions, and body weights recommended for fish compartments for the Tier 1 scenario are listed in Exhibit 13

. B iomass is based on an assumption that the total fish b iomass (wet

- weight) for the aquatic ecosystem is 5.7 g rams per square meter (g w /m 2

, ICF 2005). That assumption yields health protective (i.e., higher) estimates of chemical concentrations in fish than would the assumption of higher standing biomass and fis h productivity.

In general, the food web implemented in the Tier 1 scenario is consistent with aquatic food webs that support trophic level 4 fish (to maximize bioaccumulation), and is intended to be generally health protective.

Exhibit 13 . Aquatic Biota Parameters for the TRIM.FaTE Screening Scenario

TRIM.FaTE Compartment Type Organisms Represented by

Compartment Biomass

Diet Average Body Weight (kg) Areal density (g w /m 2 ) Fraction of Total Fish Biomass

Algae green algae, diatoms, blue

green algae 7.95

– Autotrophic

–

Zooplankton crustaceans, rotifers, protozoans 6.36

– 100% algae a 5.7 E−8

Macrophyte hydrilla, milfoil 500

– – – Water column planktivore young

- of

- the

year, minnows 2.0 35.1% 100% zooplankton 0.025

Water column omnivore bluegill, white perch 0.5 8.8% 100% water column planktivore 0.25

Water column carnivore largemo uth bass, walleye 0.2 3.5% 100% water column omnivore 2.0

Benthic invertebrate aquatic insect larvae, crustaceans, mollusks b 20

– detritus in sediments 0.000255

Benthic omnivore small catfish, rock bass 2.0 35.1% 100% benthic invert. 0.25

Benthic carni vore large catfish, sculpins 1.0 17.5% 50% benthic invert. 50% benthic omniv. 2.0

Total Fish Biomass c 5.7

a Algae is modeled as a phase of surface water in TRIM.FaTE.

b Benthic invertebrates include aquatic insects (e.g., nymphs of mayflies, caddisfli es, dragonflies, and other species that emerge from the water when they become adults), crustaceans (e.g., amphipods, crayfish), and mollusks (e.g., snails, mussels).

c Total fish biomass does not include algae, macrophytes, zooplankton, or benthic inverteb rates.

2.3.6 Using TRIM.FaTE Media Concentrations

The Tier 1 scenario outputs include average PB

- HAP concentrations and deposition rates for each year and for each parcel of the model scenario

. In each surface parcel, deposition rates to the soil are provided as are soil concentrations for the surface, root, and vadose zones and grass or leaf concentrations as appropriate for the plants

. For each air parcel, air concentrations are provided

. For the lake, surface water concentrations and concentrations in the various levels of the aquatic food chain are provided. For the ingestion exposureTRIM

- Based T iered Screening Me thodology for RTR

Tier 1 Methodology 34 October 2014 calculations, some concentrations are used to calculate direct exposure (e.g., soil ingestion)

, and some are used to perform the farm food chain concentration calculations in the various media that humans can ingest

. The locations that determine exposures were selected to be health protective. Decisions regarding which TRIM.FaTE outputs to use in calculating exposures for the Tier 1 scenario assume exposure at locations v ery close to the modeled source. These locations are predicted to have amongst the highest media concentrations consistent with the specified spatial layout, thereby resulting in higher exposures to the emitted chemicals. These assumptions are summarize d in Exhibit 14

.

Exhibit 14 . Spatial Considerations

– TRIM.FaTE Results Selected for Calculating Farm Food Chain Media Concentrations and Receptor Exposures

TRIM.FaTE Output Used in Exposure Calculatio ns Representative Compartment
Concentration in air, for uptake by plants via vapor transfer Air compartment in air Parcel N2 (air over tilled soil)

Deposition rates, for uptake by farm produce Deposition to surface soil compartment in surface Parcel N6 (tilled soil) Concentration in surface soil, for incidental ingestion by humans and farm animals Surface soil compartment in surface Parcel N1 (untilled soil, closest to facility)
C oncentration in soil , for uptake by farm produce and animal feed Surface soil compartment in surface Parcel N6 (tilled soil) Concentration in fish consumed by fisher Water column carnivore compartment in lake (50% of fish consumed) and benthic carnivore in lake (50% of fish consumed)

TRIM.FaTE can output instantaneous chemic al concentrations for a user

- specified time step and also can be configured to calculate temporal averages (e.g., annual averages). For the Tier 1 scenario, the model is set up to output results on a daily basis, largely because daily is the smallest time step over which input data change (i.e., wind direction and precipitation rate). Daily concentration results were averaged to obtain annual average concentrations. The default assumption is annual average concentrations for media during the fiftieth yea r of emissions.

For the chemicals modeled in this scenario, long

- term concentrations in environmental media will be relatively constant at 50 years

. A side from mercury, chemicals modeled for RTR approach steady state before 50 years . And, a lthough m ercu ry concentrations do not achieve steady state after 50 years in the modeled screening scenario configuration, the rate of change in mercury concentrations shows a decreasing trend

.


### 2.4 Description of Exposure and Risk Modeling Scenario

This section describes the approach for modeling chemical concentrations in farm food chain (FFC) media (Section 2.4.1 ); estimating human exposures associated with ingestion of FFC media, incidental ingestion of soil, ingestion of fish, and infant c onsumption of breast milk (Section 2.4.2 ); and calculating human health screening risk metrics associated with these exposure pathways (Section 2.4.3 ). All of these calculations are conducted using MIRC. For this multipathway screening evaluation

, partitioning of PB

- HAPs into FFC media is modeled with MIRC

, not as a part of the TRIM.FaTE modeling

. Consequently, processes and inputs related to estimating chemical levels in FFC media are summarized in this section and discussed in detail in Attachment B

.TRIM

- Based T iered Screening Me thodology for RTR

Tier 1 Methodology 35 October 2014 2.4.1 Calculating Concentrations in Farm Food Chain Media

As was shown in Exhibit 7

, MIRC was compiled to calculate concentrations of PB

- HAPs in foods tuffs that are part of the farm food chain . The FFC media included in this screening scenario include :

• exposed and protected fruit,

• exposed and protected vegetables,

• root vegetables,

• beef,

• dairy products,

• pork, and

• poultry and eggs.

The algorithms used i n MIRC were obtained from EPA’s Human Health Risk Assessment Protocol for Hazardous Waste Combustion Facilities (HHRAP; EPA 2005a ). These algorithms model the transfer of concentrations of PB

- HAP s in FFC media using biotransfer factors. Environmental med ia concentrations (i.e., the chemical source terms in these algorithms) are obtained from TRIM.FaTE . As noted in Section 1.1.2.2 , the TRIM.FaTE outputs included as inputs to MIRC are the following:

• PB

- HAP conce ntrations in air;

• air

- to

- surface deposition rates for PB

- HAPs in both particle and vapor phases;

• PB

- HAP concentrations in fish tissue for water column carnivores and benthic carnivores; and

• PB

- HAP concentrations in surface soil and root zone soil

. In ge neral, p lant

- and animal

- specific parameter values, including chemical

- specific transfer factors for FFC media, were obtained from the Hazardous Waste Companion Database included in HHRAP ( EPA 2005a)

. Attachment B provides par ameter values used in MIRC for the Tier 1 assessment.

2.4.2 Ingestion Exposure

MIRC was used to estimate ingestion rates as ADDs

, normalized to body weight for a range of exposure pathways

. Exposure pathways included are incidental ingestion of soil

, consumpti on of fish, produce, farm animals and related products , and ingestion of breast milk by infants

. The ingestion exposure pathways included in the screening evaluation and the environmental media through which these exposures occur are summarize d in Exhibit 15

.
2.4.2.1 Exposure Scenarios and Corresponding Inputs

Specific exposure scenarios are developed by defining the ingestion activity patterns (i.e., estimating how much of each medium is consumed and the fraction of the consumed medium t hat is grown in or obtained from contaminated areas) and the characteristics of the hypothetical human exposed (e.g., age and body weight). MIRC computes exposure doses and risks for each ingestion pathway separately, enabling the pathway(s) of interest f or each PB

- HAP to be determined. Data related to exposure factors and receptor characteristics were obtained primarily from EPA’s Exposure Factors Handbook ( EPA 2011).TRIM

- Based T iered Screening Me thodology for RTR

Tier 1 Methodology 36 October 2014 Exhibit 15 . Summary of Ingestion Exposure Pathways and Route s of Uptake

Ingestion Exposure Pathway Medium Ingested Intermediate Exposure Pathway

– Farm Animals a Environmental Uptake Route

Medium Process b

Incidental ingestion of soil Untilled surface soil N/A Surface soil Deposition; transfer via erosion and ru noff c

Consumption of fish Fish from local water body N/A Fish tissue Direct uptake from water and consumption of food compartments modeled in TRIM.FaTE c

Consumption of breast milk d Breast milk N/A Breast milk Contaminant i ngested by mother partition s t o breast milk

Consumption of produce Aboveground produce, exposed fruits and vegetables N/A Air Air Soil Deposition to leaves/plants Vapor transfer Root uptake Aboveground produce, protected fruits and vegetables N/A Soil Root uptake

Belowground pr oduce N/A Soil Root uptake

Consumption of farm animals and related food products Beef Ingestion of forage Air Air Soil Direct deposition to plant Vapor transfer to plant Root uptake Ingestion of silage
Ingestion of grain Soil Root uptake Ingestion of soil Soil Ingestion from surface

Dairy (milk) Ingestion of forage Air Air Soil Direct deposition to plant Vapor transfer to plant Root uptake Ingestion of silage
Ingestion of grain Soil Root uptake Ingestion of soil Soil Inges tion from surface

Pork Ingestion of silage Air Air Soil Direct deposition to plant Vapor transfer to plant Root uptake Ingestion of grain Soil Root uptake Ingestion of soil Soil Ingestion from surface

Poultry Ingestion of grain Soil Root uptak e Ingestion of soil Soil Ingestion from surface

Eggs Ingestion of grain Soil Root uptake Ingestion of soil Soil Ingestion from surface

a Calculation of intermediate exposure concentrations were required only for the farm animal/animal product inges tion pathways.

b Process by which HAP enters medium ingested by humans.

c Modeled in TRIM.FaTE.

d The consumption of breast milk exposure scenario is discussed in Section 2.4.2.3

. For the Tier 1 scenario described here, exposure characteristics that would result in a highly health protective estimate of total exposure were selected. The ingestion rate for each medium was set at high

- end values (equal to the 90 th percentile values for all food types except for fish, which was set at 99 th percentile value s )

. All media were assumed to be obtained from locations impacted by the modeled source

. Although this approach could result in an overestimate ofTRIM

- Based T iered Screening Me thodology for RTR

Tier 1 Methodology 37 October 2014 total chemical exposure for a hypothetical exposure scenario (for example, note th at the total food ingestion rate that results is extremely high for a hypothetical consumer with ingestion rates in the upper percentile for every food type), it was selected to avoid underestimating exposure for any single farm food type

. The exposure ch aracteristics selected for the Tier 1 scenario are summarized in Exhibit 16

.
Exhibit 16 . Overview of Exposure Factors Used for RTR Multipathway Screening a,b

Exposure Factor Selection for Screening Asses sment Age group evaluated Infants under 1 year (breast milk only) Children 1

– 2 years of age Children 3

– 5 years of age Children 6

– 11 years of age Children 12

– 19 years of age Adult (20

– 70 years) Body weight (BW; varies by age) Weighted mean of national di stribution or recommended value
I ngestion rate (IR) for farm produce and animal products other than fish (varies by age and medium ) 90 th percentile of distribution of consumers who produce own food Ingestion rate for fish For adults, 99 th percentile as

- p repared ingestion rate representative of subsistence fisher woman

. For children, based on 99 th percentile

, as

prepared

, consumer

- only

, national ingestion rates

– adjusted. Exposure frequency (EF) 365 days/year Exposure duration Lifetime, for estimating cancer risk; varies by chemical for chronic non

- cancer evaluation Fraction contaminated (FC) (varies by media consumed) c 1

Cooking loss d Assumed to be “typical” ; varies depending on food product (see Attachme nt B ) . Cooking losses were not considered for fish consumption because intake rates represent “as prepared” values.
Food preparation/cooking adjustment factor for fish e Mercury = 1.5 Cadmium = 1.5 Dioxin = 0.7 PAH = 1.0

a Data for exposure characteristi cs are presented in Attachment B

. Exposure parameter values were based on data obtained primarily from the Exposure Factors Handbook ( EPA 2011 )

. See Attachment B fo r details.

b Exposure factor inputs are used in calculating ADD estimates for each exposure pathway

. ADD equations for each pathway evaluated in this screening assessment are provided in Attachment B

. c Fraction contaminated represents the fraction of food product that is derived from the environment included in the screening scenario (e.g., produce grown on soil impacted by PB

- HAPs). This parameter is defined separately for each FFC medium; however, for the Tier 1 scenario, all ingested media are assumed to be impacted.

d Cooking loss inputs were included to simulate the amount of a food product that is not ingested due to loss during preparatio n or cooking, or after cooking.

e Because “as consumed,” fish consump tion rates are used with whole

- fish concentrations, adjust ments might be appropriate to adjust the fish tissue concentrations to reflect concentrations after food preparation. See Attachment B , Section 6.4.4 for additional discussion

.
2.4.2.2 Calculating Average Daily Doses

MIRC calculates chemical

- specific ADDs normalized to body weight (mg PB

- HAP per kg of body weight per day)

. Equations used to calculate ADDs were adapted from the algo rithms provided in the technical documentation of EPA’s Multimedia, Multipathway, and Multireceptor Risk Assessment (3MRA) Modeling System ( EPA 2003), which derived much of its input dataTRIM

- Based T iered Screening Me thodology for RTR

Tier 1 Methodology 38 October 2014 from the Exposure Factors Handbook ( EPA 2011 )

. The ingestion exposu re modeling approach embodied by 3MRA is conceptually similar to that presented in HHRAP

. A discussion of exposure dose estimation and the equations to calculate ADDs for each ingestion pathway are provided in Attachment B

.
2.4.2.3 In fant Ingestion of Breast Milk

A nursing mother exposed to contaminants by any ingestion pathway described above can pass the contaminants to her infant through breast milk (ATSDR 1998). The nursing infant’s exposure can be estimated from the levels of che mical concentrations in the breast milk, which in turn can be estimated based on the mother’s chemical intake.

Reports of bioaccumulation of lipophilic compounds such as polychlorinated biphenyls (PCBs), polychlorinated dibenzofurans (PCDFs), and dioxins are prevalent in the scientific literature. Due to their high lipophilicity, these compounds partition almost exclusively to the milk fat of breast milk rather than the aqueous phase ( EPA 1998). PCBs, PCDFs, and PCDDs are the most documented groups of c ontaminants found in breast milk. Other compounds with lower octanol

- water partition coefficients, such as phenol, benzene, halobenzenes, and PAHs, are found in both the milk fat and the aqueous phase of breast milk. Heavy metals such as arsenic, lead, c admium, and mercury have been found in the aqueous phase of the breast milk. However, given their chemical and physical characteristics (and the impact such qualities have on partitioning within the body and pharmacokinetics), substances that do not parti tion as strongly to the lipophilic phase of breast milk tend to be of lower concern with regard to exposures to nursing infants. Because of the greater concern with regard to dioxins for this exposure pathway, it is the only PB

- HAP included in the breast m ilk exposure pathway for RTR at this time. This approach is consistent with the risk assessment procedures included in EPA’s Human Health Risk Assessment Protocol ( EPA 2005 a ).

Exposure via the breast milk consumption pathway is estimated in MIRC for dioxi ns only. This pathway is included in computing total exposure for developing the screening threshold for dioxins. In the absence of congener

- specific data, dioxin congeners were assumed to manifest the same tendency to accumulate in breast milk as 2,3,7,8

- TCDD

.
2.4.3 Calculating Risk

MIRC was used to calculate excess lifetime cancer risk and non

- cancer hazard ( expressed as the hazard quotient or HQ) using the calculated ADDs and ingestion dose

- response values

. Chemical dose

- response data include cancer slope factors ( C SFs ) for ingestion and non

- cancer oral RfDs . The CSFs and RfDs for the PB

- HAPs included in the Tier 1 scenario are presented in Exhibit 17 and are discussed in more detail i n Attachment B

. Equations used to estimate cancer risk and non

- cancer hazard also are provided in Attachment B

.TRIM

- Based T iered Screening Me thodology for RTR

Tier 1 Methodology 39 October 2014 Exhibit 17 . Dose

- response Values for PB

- HAPs Addressed by the Screening Scenario

PB

- HAP CSF ([mg/kg

- day] - 1 ) Source RfD (mg/kg

- day) Source

**Inorganics**
Cadmium c ompounds (as Cd) not available 1E−3 IRIS Elemental mercury not available not available Divalent mercury not available 3E−4 IRIS Methyl m ercury not available 1E−4 IRIS **Organics**
Benzo [a] pyren e a 7.3 IRIS not available 2,3,7,8

- TCDD 1

. 5 E+5 EPA ORD 7E−10 IRIS

Source: EPA ( 2007 ). CSF = cancer slope factor; RfD = reference dose; IRIS = EPA’s Integrated Risk Information System; Cal/EPA = California Environmental Protection Agency; EPA ORD = EPA’s Office of Research and Development

a For consistency with the overall approach for dose

- response assessment of PAHs, the CSF listed in IRIS for benzo[a]pyrene ([7.3 mg/kg

- day] - 1 ) was adjusted due to its mutagenic mode of action as discussed below (see also Attachment B )

. Estimated individual cancer risks for the PAHs included in the screening scenario were adjusted upward to account for the mutagenic cancer potency of these compounds during childhood, as specified by EPA in sup plemental guidance for cancer risk assessment (EPA 2005c). Specifically, cancer potency for PAHs is assumed to be tenfold greater for the first 2 years of life and threefold greater for the next 14 years. These factors were incorporated into a time

weigh ted total increase in potency over a lifetime of 70 years. The cancer potency adjustment for chemicals with a mutagenic mode of action is discussed in Attachment B

.
2.4.4 Summary of Tier 1 Assumptions

As emphasized previously, the s creening scenario created for evaluating PB

- HAP emissions from RTR facilities is intended to be health protective to prevent underestimating risk

. The scenario also is intended to avoid grossly overestimating risk to the point where no emissions

screen. The overall degree to which the scenario is health protective is the sum of the multiple assumptions that affect the outputs of the fate and transport, exposure, and risk modeling. Exhibit 18 summarizes important characteristics that influence exposure and risk estimates for this scenario and indicates the general degree of health protectiveness associated with the values for each assumption. Although this summary does not provide a quantitative estimate of the output uncertainty or the degree to which exposures and risks estimated using the scenario would be overestimated, it does demonstrate qualitatively that the scenario generally overestimates exposure and thus favors a health

- protective risk output.

Exhibit 18 . Summary of RTR Tier 1 Screening Scenario Assumptions

Characteristic Value Neutral or Health Protective? Comments on Assumptions

**General Spatial Attributes**
Farm location 375 m from source; generally downwind Health Protective Location dicta tes soil and air concentrations and deposition rates used to calculate chemical levels in farm produce.
Lake location 375 m from source; generally downwind Health Protective Location dictates where impacted fish population is located.TRIM

- Based T iered Screening Me thodology for RTR

Tier 1 Methodology 40 October 2014 Characteristic Value Neutral or Health Protective? Comments on Assumptions

Surface soil proper ties Typical values or national averages Neutral Based on existing EPA documentation and other references. Size of farm parcel About 4 ha Health Protective Relatively small parcel size results in higher chemical concentration.
Size of lake 47 ha; about 3 m average depth Health Protective Lake is large enough to support an aquatic ecosystem with higher trophic

- level fish, but is relatively small and shallow (thus increasing surface area

- to

- volume ratio). **Meteorological Inputs** Total precipitation 1.5 m/yr Health Protective Intended to represent rainy U.S

. location; set to highest state

- wide average for the contiguous United States

. Precipitation frequency (with respect to impacted farm/lake) 2/3 of total precipitation fall on farm/lake and watershed Healt h Protective Most of total precipitation occurs when the farm/lake are downwind of the source.

Wind direction Farm/lake are downwind 40% of the time Health Protective Farm/lake located in the predominantly downwind direction

. Temporal dominance of wind d irection based on data from Yakima, W ashington , where wind is predominantly from the west. Wind speed 2.8 m/sec Health Protective Low wind speed (5 th percentile of long

- term averages for contiguous United States ); increases net deposition to lake/watershe d. Air temperature 298 K Neutral Typical for summer temperatures in central and southern United States

. Mixing height 710 m Health Protective Relatively low long

- term average mixing height (5 th percentile of long

- term averages for contiguous United Stat es ); increases estimated air concentration. **Watershed and Water Body Characteristics** Evaporation of lake surface water 700 mm/yr Neutral Based on sensitivity analyses, value is not expected to under

- or overestimate concentration in surface water.
Sur face runoff into lake Equal to 40% of total precipitation Health Protective Based on typical water flow in wetter U.S

. locations; higher runoff results in greater transfer of chemical to lake.
Surface water turnover rate in lake About 12 turnovers per yea r Neutral Consistent with calculated water balance; reasonable in light of published values for small lakes . Might overestimate flush ing rate if water inputs are also overestimated

. Note that after evapotranspiration, remaining water volume added via pre cipitation is assumed to flow into or through lake.TRIM

- Based T iered Screening Me thodology for RTR

Tier 1 Methodology 41 October 2014 Characteristic Value Neutral or Health Protective? Comments on Assumptions

Soil erosion from surface soil into lake Varies by parcel; ranges from 0.002 to 0.01 kg/m 2

- day Neutral Erosion rates calculated using the universal soil loss equation (USLE); inputs to USLE were selected to be generally conservative with regard to concentration in the pond (i.e

., higher erosion rates were favored)

. Might underestimate erosion for locations susceptible to high erosion rates

. Note that higher erosion increases concentration in lake (and f ish) but decreases levels in surface soil (and farm products)

. Aquatic food web structure and components Multi level; includes large, upper trophic

- level fish Health Protective Inclusion of upper trophic

- level fish and absence of large

- bodied herbivore/d etritivore fish favor higher concentrations of bioaccumulative chemicals and result in higher concentrations in consumed fish

. Linear food

- chain maximizes concentration of bioaccumulative chemicals in higher trophic

- level fish. **Parameters for Estimating Concentrations in Farm Food Chain Media**  Fraction of plants and soil ingested by farm animals that is contaminated 1.0 (all food and soil from contaminated areas) Health Protective Assumes livestock feed sources (including grains and silage) are derived fr om most highly impacted locations.

Soil

- and air

- to

- plant transfer factors for produce and related parameters Typical (see Attachment B for details) Neutral Obtained from peer

- reviewed and standard EPA reference sources.

Biot ransfer factors for efficiency of uptake by animal of chemical in food/soil Typical (see Attachment B for details) Neutral Obtained from peer

- reviewed and standard EPA reference sources.

Bioavailability of chemicals in soil (f or soil ingested by animals) 1.0 (relative to bioavailability of chemical in plant matter) Health Protective Probably overestimates bioavailability in soil; many chemicals are less bioavailable in soil than in plants.

**Ingestion Exposure Assumptions** Inges tion rates for all farm produce/livestock types Person obtains all food sources from local farm; ingestion rate is 90 th percentile of rates for home

produced food items Health Protective All food derived from impacted farm; total food ingestion rate would exceed expected body weight

- normalized ingestion rates (prevents underestimating any individual food type).

Fish ingestion rate Adult: 373 g/day

Child age groups: 1 to 2: 108 g/day 3 to 5: 1 59 g/day 6 to 11: 2 68 g/day 12 to 19: 3 31 g/day Health Protect ive The adult rate is the 99 th percentile value for adult females from Burger (2002) and is considered representative of subsistence fisher s

. Rates for children are based on the 99 th percentile , consumer

- only fish ingestion rates from EPA 2002

. R ates wer e adjustedTRIM

- Based T iered Screening Me thodology for RTR

Tier 1 Methodology 42 October 2014 Characteristic Value Neutral or Health Protective? Comments on Assumptions

to be represent ative of the age groups used in the screening scenario. See Attachment B for a detailed discussion. Exposure frequency Consumption of contaminated food items occurs 365 days/yr Health Protective All m eals from local farm products.

Body weight Mean of national distribution Neutral Note that this does not affect the body

weight

- normalized rates for produce and animal products

. **Other Chemical**

**- Specific Characteristics** 
General chemical properties used in fate and transport modeling (Henry’s law, K ow , etc.) Varies Neutral Obtained from peer

- reviewed sources; intended to be representative of typical behavior and characteristics.

"General" physical properties (plant matter density, aquatic life biomass, alg al growth rate, etc.) Varies Neutral Obtained from peer

- reviewed sources; intended to be representative of typical behavior and characteristics.

Dose

- response values Varies Neutral to Health Protective Values used are those determined to be appropriate for risk assessment by OAQPS; values are developed to be health protective.



### 2.5 Evaluation of Screening Scenario

2.5.1 Introduction

The screening scenario developed for assessing multipathway human health risk for EPA’s Risk and Technology Review has been subject ed to a series of evaluations. As described previously t he major PB

- HAP categories of concern for this assessment are cadmium compounds (Section 2.5.2 ), mercury compounds (Section 2.5.3 ), dioxins ( Section 2.5.4 ), and POM (Section 2.5.5 ). The scenario evaluations were focused primarily on assessing the behavior of these HAP categories in the environment, the accumulation of these chemicals in ingestible food products, and the predominant pathways of human exposure.

2.5.2 Cadmium Compounds

S ome of the largest anthropogenic sources of cadmium to air are facilities that process, mine, or smelt cadmium

- zinc ores or cadmium

- zinc

- lead ores

, coal

- and oil

- fired boilers

, other urban and industrial facilities

, phosphate fertilizer manufacturing facilities

, road dust

, and municipal sewage sludge incinerators (ATSDR 2008).

2.5.2.1 Behavior in the Environment

Once emitted into the environment, airborne cadmium particl es can be transported over long distances before being d eposited

. Cadmium has been observed to partition primarily to soil when released to the environment (ATSDR 2008). The mobility of cadmium in soil dependsTRIM

- Based T iered Screening Me thodology for RTR

Tier 1 Methodology 43 October 2014 strongly on soil pH, clay content, and avail ability of organic matter

— factors that determine whether the cadmium is dissolved or sorbed in surface soil

. In general, c admium adsorbs to soil particles in the surface layers of the soil profile, but to a lesser degree than many other heavy metals (HSDB 2005). C admium also bi nds strongly to organic matter, rendering the metal relatively immobile in some soils. Nonetheless, some plants still can take up cadmium efficiently , thus providing an entry point for cadmium into the food chain (ATSDR 2008).

Ca dmium also enters surface waters , which can occur via atmospheric deposition, runoff and erosion, or wastewater streams. M ost cadmium compounds entering the water column are quickly removed through adsorption to organic matter in sediment or to other susp ended compounds. Cadmium that remains in the water column is expected to exist primarily in the dissolved state where it is available for uptake by aquatic organisms.

Freshwater fish accumulate cadmium primarily through direct uptake of the dissolved form through the gills and secondarily through the diet, which plays a variable role in total cadmium uptake (Reinfelder et al. 1998; Chen et al. 200 0; Saiki et al. 1995). Although some biomagnification of cadmium has been reported for aquatic food chains in saltwater systems, biomagnification in freshwater systems appears to be present only at lower trophic levels (Chen et al. 2000) and in narrowly defined niches (e.g., plankton/macroinvertebrate food chains; Croteau et al. 2005). Biomagnification factors (B MFs) of less than 1 generally have been reported for fish at higher trophic levels, indicating that cadmium concentrations generally biodiminish from lower to higher trophic levels (Chen et al. 2000 ; Mason et al. 2000).

For the RTR screening scenario, th e partitioning behavior modeled in TRIM.FaTE was consistent with the behavior of cadmium expected in the natural environment.

2.5.2.2 Concentrations in Ingestible Products

Most non

- inhalation exposure to cadmium outside of occupational settings is through dietary intake

. Available data indicate that cadmium accumulates in plants, aquatic organisms, and terrestrial animals, offering multiple ingestion exposure pathways (ATSDR 2008) . A ctual cadmium levels in ingestible products , however, varies based on type of foo d, agricultural and cultivation practices, atmospheric deposition rates, characteristics of environmental media, and presence of other anthropogenic pollutants

. Meat and fish generally contain lower amounts of cadmium overall, but cadmium can be highly co ncentrated in certain organ meats, such as kidney and liver (ATSDR 2008) . In a study of cadmium concentrations in 14 food groups (including prepared foods), m eat, cheese, and fruits generally contain ed low levels of cadmium (ATSDR 2008).

For the RTR scr eening scenario, the cadmium concentrations output by MIRC were consistent with reported values in all ingestible media products . The products with higher reported cadmium levels in the literature, including soil, plants, and fish, also contained the high er modeled concentrations.

2.5.2.3 Average Daily Dose (ADD)

To determine the media most relevant to exposure and risk, the ingestion exposure factors must be considered in addition to the estimated media concentrations (i.e., a higher concentration for a particu lar medium does not necessarily mean higher risk). In Exhibit 19 , the contribution s of ingestion exposure pathways to the average daily dose ( ADD ) (and thus the HQ) for the different age categories are presented. As shown in the exhibit , fish ingestion is the dominant exposure pathway across all age categories, accounting for nearly 100 percent of the ADD for all groups. The combined contribution from all other exposure pathways accounts for less than 0 .7 percent of the total AD D for all age groups. Most of the additional exposure was fromTRIM

- Based T iered Screening Me thodology for RTR

Tier 1 Methodology 44 October 2014 ingestion of fruits and vegetables. The highest ADD corresponds to children aged 1

– 2 years ; thus, the exposure corresponding to this group was used to determine the emission threshold for cad mium. In other words, the threshold emissions rate for cadmium is set at the level where the HQ for this age category is equal to 1.0.

Exhibit 19

. Estimated Contribution s of Modeled Food Types to Cadmium Ingestion Exposures and Hazard Quotients

2.5.3 Mercury Compounds

S ome of the largest anthropogenic sources of mercury to air are facilities that process, mine, or smelt mercury ores; industrial/commercial boilers; fossil fuel combustion activities (primarily coal); cement productio n facilities ; other urban and industrial facilities; and medical and municipal waste incinerators ( ATSDR 1999) . These facilities can emit a mixture of elemental and divalent mercury , mostly in the gaseous phase, with some divalent forms in particle

- bound phases ( EPA 1997 )

.
2.5.3.1 Behavior in the Environment

Once emitted into the environment, mercury undergoes changes in form and species as it moves through environmental media. Elemental mercury is the most prevalent species of mercury in the atmosphere. Due t o the long residence time of elemental mercury in the atmosphere, this compound is relatively well distributed, even on a global scale.

Divalent mercury is removed from the atmosphere at a faster rate than elemental mercury, and it can be transferred to th e surface near the emission source via wet or dry deposition where it appears to adsorb tightly to soil particles ( EPA 1997) or dissolved organic carbon. Divalent mercury in soil also can be methylated by microbes or reduced to elemental mercury and revol atilized back into the atmosphere. Most divalent mercury from atmospheric deposition willTRIM

- Based T iered Screening Me thodology for RTR

Tier 1 Methodology 45 October 2014 remain in the soil profile, however, in the form of inorganic compounds bound to soil organic matter. Although this complexing behavior with organic matter signific antly limits mercury transport, the ability of mercury to form these complexes greatly depends on soil conditions such as pH, temperature, and soil humic content. For example, mercury strongly adsorbs to humic materials and sesquioxides in soil at pH > 4 and in soils with high iron and aluminum content (ATSDR 1999). Small amounts of mercury in soil can be transported to surface water via runoff or leaching.

Mercury could also enter the water column through atmospheric fallout

. Once in the water body, d ivalent mercury can be methylated through microbial activity . In addition, divalent and methyl mercury can be further reduced to elemental mercury, which can volatilize and reenter the atmosphere

. Solid forms of inorganic mercury compounds could adsorb t o particulates in the water column or partition to the sediment bed ( EPA 1997 )

. The solubility of mercury in water depends on the species and form of mercury present as well as properties of the water such as water pH and chloride ion concentration (ATSD R 1999). Low pH favors the methylation of mercury in the water column, typically performed by sulfur

- reducing bacteria in anaerobic conditions . Methyl mercury is typically of greatest concern because it readily bio accumulate s and efficiently biomagnifie s in aquatic organisms

. A considerable amount (2 5

– 60 percent) of both divalent mercury compounds and methyl mercury is strongly bound to particulates in the water column ( EPA 1997). The remaining mercury is dissolved

. Most of the e lemental mercury produc ed as a result of reduction of divalent mercury volatilizes back into the atmosphere.

For the screening scenario, the partitioning behavior modeled in TRIM.FaTE generally was consistent with trends noted in the literature. Divalent mercury was the most prevalent species in modeled surface soil, surface water, and sediment compartments, while methyl mercury was the dominant species in fish

.
2.5.3.2 Concentrations in Ingestible Products

Available data indicate that mercury bioaccumulates in plants, aquatic organ isms, and terrestrial animals, providing multiple ingestion exposure pathways ( EPA 1997; ATSDR 1999). Low levels of mercury are found in plants, with leafy vegetables containing higher concentrations than potatoes, grains, legumes, and other vegetables an d fruits (ATSDR 1999; EPA 1997). Cattle demethylate mercury in the rumen and, therefore, store very little of the mercury they ingest by foraging or consuming silage or grain. Thus, mercury content in meat and cow’s milk is low (ATSDR 1999). Concentrati ons of methyl mercury in fish are generally highest in larger, older specimens at the higher trophic levels ( EPA 1997).

Although data on mercury in foods other than fish are not abundant in the literature, total mercury concentrations output by MIRC were generally consistent with the reported values that were available. The exposure pathways that most influenced the mercury HQs in the model are presented in Exhibit 20 . As shown, the dominant exposure pathway for all age groups is ingestion of fish. R elative to divalent mercury

, methyl mercury concentrations in fish were very high (approximately 95 percent of total mercury).TRIM

- Based T iered Screening Me thodology for RTR

Tier 1 Methodology 46 October 2014 Exhibit 20

. Estimated Contribution s of Modeled Food Types to Methyl Mercury Ingestion Exposures

2.5.3.3 Average Daily Dose

In Exhibit 20 , the contribution s of ingestion exposure pathways to the ADD (and thus the HQ) for methyl mercury across the different age categories are presented. As shown , fish is the do minant exposure pathway across all age categories, accounting for nearly 100 percent of the ADD for each group

. The combined contribution of all other exposure pathways accounts for less than 1 percent of the total ADD for all age groups. The high degree of exposure to methyl mercury through fish ingestion is attributed to the ease with which this compound bioaccumulate s and biomagnifies in fish and to the health protective ingestion assumptions used in the screening scenario. The highest ADD corresponds to children aged 1

– 2 years ; thus, the exposure corresponding to this group was used to determin e the emission threshold for mercury.

2.5.4 Dioxins

I ncineration and combustion processes are believed to be the primary emission sources for chlorinated dioxins ( ATS DR 1998)

. The five stationary s ource categories that generate the vast majority of 2,3,7,8

- TCDD emissions in the United States are municipal waste incineration, medical waste incineration, hazardous waste kilns from Portland cement manufacturing, secondar y aluminum smelting, and biological incineration

.TRIM

- Based T iered Screening Me thodology for RTR

Tier 1 Methodology 47 October 2014 2.5.4.1 Behavior in the Environment

Dioxins emitted to the atmosphere can be transported long distances in vapor form or bound to particulates, depositing in soils and water bodies in otherwise pristine locations far from the source. Although airborne dioxins are susceptible to wet and dry deposition, most dioxins emitted to the atmosphere through incineration/combustion processes are not deposited close to the source (ATSDR 1998).

In soil, dioxins strongly adsor b to organic matter and show very little vertical movement, particularly in soils with a high organic carbon content (ATSDR 1998). Most dioxins deposited in soil are expected to remain buried in the soil profile, with erosion of contaminated soil particle s the only significant mechanism for transport to water bodies.

The dry deposition of dioxins from the atmosphere to water bodies is another important transport process. Because of the hydrophobic nature of dioxins, most dioxins entering the water column are expected to adsorb to suspended organic particles or partition to bed sediment, which appears to be the primary environmental sink for this chemical group ( EPA 2004c). Although dioxins bound to aquatic sediment primarily become buried in the sediment compartment, some resuspension and remobilization of congeners can occur if sediments are disturbed by benthic organisms (ATSDR 1998).

Bioaccumulation factors (BAFs) in fish are high as a result of the lipophilic nature of chlorinated dioxins. Alt hough the processes by which freshwater fish accumulate dioxins are not well understood, both fish and invertebrates bioaccumulate congeners that have partitioned to sediment or have become suspended in water ( EPA 2004c ). B ecause most dioxins in the aquatic env ironment are adsorbed to suspended particles, h owever, direct uptake from the water is unlikely to be the primary route of exposure for most aquatic organisms at higher trophic levels ( ATSDR 1998). At lower trophic levels, the primary route of exposure ap pears to be through uptake of water in contaminated sediment pores , and the primary route of exposure in the higher trophic levels appears to be through food chain transfer. Following ingestion, some fish can slowly metaboliz e certain congeners, such as 2 ,3,7,8

- TCDD, and releas e the polar metabolites in bile. This process ultimately might limit bioaccumulation at higher trophic levels ( ATSDR 1998).

For the RTR screening scenario, the partitioning behavior modeled in TRIM.FaTE was consistent with the beha vior of 2,3,7,8

- TCDD expected in the natural environment. Also of note is that dioxins readily partition into breast milk due to the lipophilic nature of these compounds.

2.5.4.2 Concentrations in Ingestible Products

The primary source of non

- inhalation exposure to dioxins outside of occupational settings is through dietary intake , which accounts for more than 90 percent of daily dioxin exposure (ATSDR 1998). Available data indicate that dioxins concentrate in plants, aquatic organisms, and animals, offering mult iple ingestion exposure pathways . A ctual congener levels in ingestible products , however, can vary based on type of food, agricultural and cultivation practices, atmospheric deposition rates, characteristics of environmental media, and presence of other a nthropogenic pollutants . Dioxins appear to enter the terrestrial food chain primarily through vapor

- phase deposition onto surfaces of plant s , which are then consumed by larger animals

. Another major source of exposure to dioxins is through ingestion of c ontaminated soil by animals

. Observed trends indicate that meat, dairy, and fish consumption are the dominant exposure pathways, comprising 90 percent of dioxin dietary intake (ATSDR 1998). Consistent with the literature, the modeled concentration of 2, 3,7,8

- TCDD in the fish compartment for the screeningTRIM

- Based T iered Screening Me thodology for RTR

Tier 1 Methodology 48 October 2014 scenario was at least one order of magnitude greater than concentrations in the other compartments. Among the compartments with the lowest concentrations were fruits and vegetables, which do not readily accumulate 2,3,7,8

- TCDD.

Ingestion of breast milk during infancy and fish ingestion contribute to over 97 percent of lifetime dioxin exposure for 2,3,7,8

- TCDD in the screening scenario. Daily intakes of 2,3,7,8

- TCDD from cow’s milk, produce, and fish ha ve been reported in the literature to comprise 27 percent, 11 percent, and 10 percent, respectively, of the total daily intake in the general population. Some studies note that specific subpopulations, such as subsistence farmers and fisher s, however, mig ht have very different exposure profiles in which fish, meat, and dairy drive congener exposure (ATSDR 1998). Given the subsistence diet modeled in the RTR screening scenario, the high exposure from consumption of fish is appropriate within the context of this assessment

.
2.5.4.3 Lifetime Average Daily Dose (LADD)

T he contribution s of ingestion exposure pathways to the lifetime average daily dose (L ADD ) ( and thus life time cancer risk) for the modeled dioxin congeners are presented i n Exhib it 21

. Based on the modeling methodology and assumptions used, exposures via the breast milk pathway consistently account for approximately 30 percent of the lifetime exposure for all congeners, while exposure via fish, soil , and the various farm food ch ain pathways is highly variable across congeners. This variability can be explained in part by differences in the physiochemical properties that drive the environmental transport processes of these congeners ( e.g., K ow , molecular weight). The differences are also likely attributed to differences in the congener

- specific half

- life in abiotic media and the degree to which the congener is metabolized in biotic media.

2.5.5 Polycyclic Aromatic Hydrocarbons

PAHs can enter the atmosphere as a result of a variety of combustion processes, both natural and anthropogenic. S tationary emission sourc es account for approximately 80 percent of total annual PAH emissions . Alt hough the primary source of stationary source PAH emissions is thought to be residential wood burnin g, other processes such as power generation; incineration; coal tar, coke, and asphalt production; and petroleum catalytic cracking are also major contributors ( ATSDR 1995)

.TRIM

- Based T iered Screening Me thodology for RTR

Tier 1 Methodology 49 October 2014 Exhibit 21

. Estimate d Contributions of Modeled Food T ypes to Dioxin Ingestion Exposures

2.5.5.1 Behavior in the Environment

PAHs emitted to the atmosphere can travel long distances in vapo r form or attached to particl es, or they can deposit relatively close to an emission source via wet or dry deposition onto wat er, soil, and vegetation

. In the atmosphere, PAHs are found primarily in the particle

- bound phase, and atmospheric residence time and transport distances are highly influenced by climatic conditions and the size of the particles to which they are bound (A TSDR 1995).

As a result of sustained input from anthropogenic sources, PAHs are ubiquitous in soil. High molecular weight PAHs, such as benzo( a ) pyrene , strongly adsorb to organic carb on in soil, indicating that adsorption to soil particles will limit the mobility of these compounds following deposition to soil (ATSDR 1995).

Most PAHs enter the water column directly through atmospheric deposition (ATSDR 1995). Following deposition onto surface waters, approximately two

- thirds of PAHs adsorb strongly to

s ediment and suspended particles, while only small amounts revolatilize back to the atmosphere (ATSDR 1995). Aquatic organisms can accumulate PAHs via uptake from water, sediment, or food. Although fish and other organisms readily take up PAHs from contam inated food (e.g., aquatic insects, other benthic invertegrates, smaller fish)

, biomagnification generally does not occur because many organisms can rapidly metabolize PAHs (ATSDR 1995). As a result, concentrations of PAHs have generally been observed to decrease with increasing trophic levels (ATSDR 1995). Due to this ability to metabolize PAHs, BAFs in fish are not expected to be especially high. Sediment

- dwelling organisms can experience increasedTRIM

- Based T iered Screening Me thodology for RTR

Tier 1 Methodology 50 October 2014 exposure to PAHs through association (e.g., direct upt ake, consumption) with contaminated sediment (ATSDR 1995).

For the screening scenario, the partitioning behavior of benzo(a)pyrene is generally consistent with trends reported in the literature.

2.5.5.2 Concentrations in Ingestible Products

The primary source of n on

- inhalation exposure to benzo(a) pyrene outside of occupational settings is through dietary intake. Exposure can depend on the origin of the food (higher values

are often recorded at contaminated sites) and the method of food preparation (higher valu es have been reported for food that is smoked or grilled). PAHs have been observed to bioaccumulate in aquatic organisms and terrestrial animals through uptake of contaminated water, soil, and food. T hese compounds are readily metabolized by higher troph ic level organisms, including humans, however, so biomagnification is not considered to be significant ( ATSDR 1995). Plants accumulate PAHs primarily through atmospheric deposition, but chemical concentrations tend to be below detection levels. PAHs in m eat have been observed at concentrations below detection levels up to higher concentrations when the meat is smoked. Similar concentrations have been reported for fish, with smoked fish concentrations sometimes quadruple those found in terrestrial animals . Because PAH concentrations are highest in products that are smoked or grilled, most of the available data for benzo ( a ) pyrene in food is for products that have been prepared using these processes. As a result, reported values might be significantly highe r than those output by MIRC.

For the RTR screening scenario, concentrations output by MIRC were generally lower tha n the reported ranges for benzo(a) pyrene in ingestible products. This trend is likely the result of background exposure in reported measure ments and available data that are skewed toward concentrations in highly contaminated products. Considering these mitigating factors, the RTR screening scenario output concentrations are within the range of plausible values for PAHs in ingestible products

.
2.5.5.3 Lifetime Average Daily Dose

T he contribution s of ingestion exposure pathways to the LADD (and thus life time cancer risk) for various PAH s are presented i n Exhibit 22

. As shown

, the variability in the driving exposure pathways a cross PAHs is significant , with fish, beef, dairy, fruits, and vegetables comprising between 90 and 99 percent of exposure for different PAHs.

This variability can be accounted for in part by differences in the physiochemical properties that drive the envi ronmental fate and transport processes of these PAH s ( e.g., K ow , molecular

we ight, chemical structure), differences in the PAH

- specific half

- life in abiotic media

, and the degree to which the PAHs are metabolized in biotic media. The variability in exposu re pathways is consistent with information provided in the literature.

2.5.6 Summary

This assessment provide s a summary of the fate and transport processes and the major routes of exposure for the PB

- HAP categories of interest to EPA’s RTR Program

, as modeled in TRIM.FaTE. In general, the modeled behavior of the compounds is consistent with data found in the literature.

This assessment reveal s that fish ingestion is a major route of exposure for cadmium, mercury, dioxins, and PAHs . For organics (i.e., dioxins and PAHs), the farm

- food

- chain also is a major route of exposure, with beef and dairy contributing significantly to the L ADD.TRIM

- Based T iered Screening Me thodology for RTR

Tier 1 Methodology 51 October 2014 Exhibit 22

. Estimated Contributions of Modeled Food Types to PAH Ingestion Exposure s

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# J So i l

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• F ru i ts &

V egetables

• Poultry & Eggs

• Total Da iry

BeefTRIM

- Based Tiered Screening Methodolo gy for RTR

Tier 2 Methodology 52 October 2014 

## 3. Tier 2 Met hodology

3.1 Overview of Approach

The Tier 1 screening scenario is, by design, generic and health

- protective. It was constructed to allow for quick application to a large number of facilities in a source category with a minimal chance of returning false negat ives for risk. Based on screening assessments conducted for RTR to date, many facilities might not screen out of the Tier 1 assessment for some source categories . Because the Tier 1 screen uses numerous health

- protective assumptions, the assessment must b e refined to determine whether the facility is actually expected to pose a risk above levels of concern

. One way to refine the risk estimates is to conduct a site

- specific assessment where the Tier 1 model values and layout are replaced with site

- specific values and layouts wherever possible. However, this approach would not be feasible given the resource intensive nature of a site

specific assessment and the number of facilities that might not screen out of the Tier 1 assessment in some source categories.

As an intermediate approach, we instead refine our Tier 1 screening estimates to Tier 2 screening estimates. This is done by replacing some of the more health

- protective assumptions in a Tier 1 screen with more site

- appropriate values. Specifically, for Tier 2, the following parameter values are varied from their Tier 1 values:

• Meteorological characteristics, including the fraction of time the wind blows in the direction of each farm and lake ( using wind direction), the wind speed, the precipitation rate , and the mixing height; and

• Location s of fishable lake(s) relative to the facility (including the absence of a fishable lake)

. In addition, a refined fisher scenario is used to model risks associated with nearby lakes. The refined fisher scenario is bas ed on the idea that an adult fisher might fish from multiple lakes if the first (i.e., highest

- concentration) lake is unable to provide him an adequate catch to satisfy the assumed ingestion rate (i.e., 373 g/d for adults). This assessment uses the assumpt ion that the biological productivity limitation of each lake is 1 gram of fish per acre of water: meaning that in order to fulfill the adult ingestion rate, the fisher will need to fish from 373 total acres of lakes.

In selecting the fate and transport var iables listed above to include in Tier 2 adjustments , a balance was struck between: 1) the degree of impact on the potential risk estimate; 2) the ease of implementation in TRIM.FaTE; and 3) the ease of obtaining site

- specific values on a facility

by

- facil ity basis. Because of the expected variability in exposure parameters amongst any population living around a given facility, the exposure parameters pertaining to farm ingestion pathways remain fixed at their health

- protective Tier 1 levels. However, the f ish ingestion rates might vary on a site

- specific level using the refined fisher scenario based on the availability of lakes to fish

. Tier 2 screening assessments are performed for those facilities that do not screen out during the Tier 1 assessment. The overall implementation of Tier 2 is shown in Exhibit 23

. The starting point (shown in green) is the ratio of the facility emission rate for the PB

- HAP of concern to the Tier 1 threshold for that PB

- HAP . Next, t he facility

- specific estimates of the Tier 2 meteorological and lake

- location parameters listed above must be gathered for each facility (shown in red). Then, the associated TRIM.FaTE and MIRC risk estimates must be calculated (shown in orange). Because of the volume of faci lities that need to be evaluated in Tier 2, the implementation focuses on estimating refined risk using pre

- calculated databases (discussedTRIM

- Based Tiered Screening Methodolo gy for RTR

Tier 2 Methodology 53 October 2014 below) rather than gathering the input data and performing TRIM.FaTE and MIRC modeling speartely for each facility.

Exhibit 23

. Basic Process for Implementing Tier 2

First, databases of the relevant U.S. meteorological and lake data were created that can be accessed during a Tier 2 evaluation (shown in red in the figure). These meteorologic al and lake data are discussed in more detail in Section 3.5 . The meteorological database includes annual

average summary statistics on wind direction, wind speed, and precipitation for more than 800 surface stations paired wit h their closest upper

- air stations located throughout the United States. These surface data cover year 2011 and are the sameAERMOD

- ready data used by EPA OAQPS for RTR inhalation modeling

. As discussed below , in most cases the 2011 precipitation data were not used, and instead

, the 30

- year average annual precipitation data for each station

were used. The database of lakes, available from ESRI ® and based on U.S. Geological Survey data, includes information on the location, size, use or type designation, and name (if available) of all lakes in the United States. To focus on lakes that can support angling of upper trophic level fish, lakes used for disposal, evaporation, or treatment were excluded, and only lakes greater than 25 acres in area are included. Lak es larger than 100,000 acres in area are not included because the sizes of their watersheds and the complexity of their lake dynamics are not feasible to model with the TRIM.FaTE modeling system.

In parallel to the meterological and lake data collection, a series of TRIM.FaTE simulations was performed that systematically varied the values for four of the five selected fate and transport variables (shown in orange in the figure, consisting of lake location, wind speed, precipitation rate, and mixing height). Wind direction affects only whether the chemical mass advects toward the farm and lake, so the effect of site

- specific wind directions can be evaluated outside TRIM.FaTE simulations. These simulations do not simulate specific facilities; instead, four o r five alternative values for each of the four variables were selected using statistics on U.S. meteorological data or professional judgment to capture the expected range in the facility data. TRIM.FaTE simulations were performed for every possible combin ation of these variable values to enable the estimation of appropriate site

- specific threshold adjustment factors for scenarios with the corresponding characteristics. Based on the TRIM.FaTE results of these simulations 

### Identify Meteorology Statistics and Lake Distance Values (Reflecting the expected range of meteorology conditions and lake locations near most U.S. facilities)



### Run TRIM.FaTE/MIRC Test Cases to Create Matrix of Tier 2 Adjustment Factors (Reflecting all combinations of meteorology statistics and lake distance values) 

### Facility’s Tier 1 Screening Results

Identify Facility’s Closest

Meteorology Station and All Qualifying Lakes 

### One Time For Each Facility
Assessment

Identify and Apply Tier 2 Adjustment FactorsTRIM

- Based Tiered Screening Methodolo gy for RTR

Tier 2 Methodology 54 October 2014 (and the subsequent exposure and ri sk characterization, conducted using MIRC), a matrix of Tier 2 threshold adjustment factors was calculated, with each element of the matrix corresponding to a unique combination of values from each of the selected variables. The contribution of each exposu re media toward each scenario’s Tier 2 risk metric (i.e., the individual contributions of fish ingestion, soil ingestion, beef ingestion, etc. toward the total Tier 2 risk metric) was included in the matrix because the Tier 2 assessment separates chemical exposure from fish ingestion from exposure from farm food chain ingestion.

These TRIM.FaTE and MIRC simulations are used to estimate part of the Tier 2 threshold adjustment factors. Wind direction is the other part of the Tier 2 threshold adjustment factor s. The wind direction adjustment for a given facility is the ratio of the frequency that winds blow toward the Tier 1 farm and lake (43 percent of the time) and the frequency that winds blow toward the facility

- specific farm and lake in Tier 2. These Tier 2 threshold adjustment factors are multiplied by each other and represent the ratio between the risk metric (i.e., cancer risk or HQ) obtained using the Tier 1 screening scenario and the risk metric obtained from the Tier 2 TRIM.FaTE runs. For a given fac ility, an adjusted Tier 2 ratio (emissions compared to the emission threshold) can be estimated by dividing the Tier 1 emission ratio (the output of the Tier 1 screen) by the adjustment factor that best corresponds to the meteorological conditions present at the site and the presence and location of lakes at the site:

Tier 2 Ratio = Tier 1 Ratio ÷ Tier 2 Adjustment Factor

Matrices of threshold adjustment factors from the TRIM.FaTE and MIRC simulations were separately developed for the four PB

- HAPs that curr ently have screening emission thresholds in the Tier 1 process: benzo(a)pyrene (BaP, representative of PAHs), cadmium, divalent mercury and 2,3,7,8

- Tetrachlorodibenzo

- p

- Dioxin (TCDD, representative of dioxins). The wind direction threshold adjustment facto rs were irrespective of chemical (specific only to the facility). In implementing the Tier 2 process, a risk equivalency approach was used to convert speciated emissions of PAHs and dioxins into BaP and TCDD equivalents, respectively, similar to the Tier 1 screening approach.

Finally, to implement the Tier 2 screen, a Microsoft® Access TM tool was created that includes the database of meteorological data and lake data as well as the Tier 2 adjustment factors for the combinations of variables simulated in the Tier 2 TRIM.FaTE simulations. In the tool, each facility is matched with the same surface meteorological station used in the RTR inhalation risk assessment , and the values for the four relevant parameters at that station are recorded (wind speed, wind dir ection, precipitation rate, and mixing height). The tool also facilitates the identification of all qualifying lakes in the area surrounding the facility and their distances and directions relative to the facility . These five values become the set of faci lity

- specific parameters. The threshold adjustment factor corresponding to this set of site

- specific data is then estimated using the matrix of adjustment factors and the wind direction adjustment

. Wind direction values are used as

- is with no rounding or binning. If one of the four facility matrix variables (wind speed, precipitation rate, mixing height, or lake location) is between two of the computed levels available for that variable in the simulation matrix, the more health

- protective of the two levels is selected (i.e., the level that resulted in the smaller adjustment to the Tier 1 exposure). The Tier 1 screening emission threshold for a PB

- HAP is then divided by the appropriate adjustment factors to obtain an updated Tier 2 emission threshold for th at PB

- HAP at that facility. A facility then screens out in Tier 2 if the emission s are below the Tier 2 threshold for the fisher scenario and for the farmer scenario

.TRIM

- Based Tiered Screening Methodolo gy for RTR

Tier 2 Methodology 55 October 2014 

### 3.2 Selection of Site

- Specific Characteristics for the Tier 2 Assessment

The screening scen ario used to derive Tier 1 thresholds incorporates assumptions regarding meteorological conditions, the spatial configuration of the hypothetical exposure setting, physical parameters of the environment, and chemical

- specific parameters that result in gene rally health

- protective results. In Tier 2, selected assumptions used in the fate and transport modeling conducted using TRIM.FaTE are modified to reflect more site

- specific information for the facilities being evaluated. 19 To determine which scenario cha racteristics should be incorporated into the Tier 2 assessment , the following issues were considered for TRIM.FaTE model parameters:

• How sensitive are the modeled risks to a specific user

- input model parameter (e.g., wind direction, wind speed) ?

• Do the pla usible values for a given parameter span a large range when comparing different RTR facilty locations ?

• Which site

- specific characteristics can be found easily and reliably for facilities with emissions exceeding Tier 1 thresholds?

• In general, is the unce rtainty associated with the parameter high or low ?

• How complicated or time

- consuming is the incorporation of a given parameter (e.g., wind speed) into the screening scenario set

- up ?

Attachment D provides an exhibit showing all the TRIM.FaTE variables considered for the Tier 2 assessment . These variables were evaluated qualitatively using the criteria above to determine whether the variable was of high, medium, or low priority. The following five “high priority” variables were selected for implementation in the current Tier 2 assessment :

• Wind direction (the percent of time the wind blows toward the lake and farm),

• Wind speed,

• Precipitation,

• Mixing height, and

• Location of fishable lake s relative to the facility

. These parameters were judged to represent a balance between range of potential variability, ease of implementation within the modeling scheme used for RTR, and ease of obtaining site

specific values with a relatively high level of confidence.



### 3.3 Estimation of Adjustment Facto rs for Selected Site

- Specific Parameters

The purpose of including site

- specific detail for a facility evaluated in Tier 2 is to develop a more realistic estimate of risk associated with facility emissions. This purpose is achieved within the assessment by generating revised emission thresholds of potential concern specific to a given PB

- HAP on a facility

- by

- facility basis. However, instead of performing full

- fledged, site

- specific model runs for each facility that does not “screen out” in Tier 1, a set of generally applicable threshold adjustment factors for each PB

- HAP was developed based on a set of model runs.

19 Only TRIM.FaTE parameters were considered for inclusion in Tier 2 adjustments because of the difficulty in identifying substantial **location**

**- related**  differences in values for exposure factors (and other inputs to MIRC). The exposure characteristics used in MIRC are considered to be generally consistent across different locations and facilities.TRIM

- Based Tiered Screening Methodolo gy for RTR

Tier 2 Methodology 56 October 2014 For each PB

- HAP, these runs corresponded to unique combinations of values for wind speed, precipitation rate, mixing height, and lake location ( wind direction is assessed separately). These adjustment factors were based on a set of runs in which the values for these parameters were varied systematically. The wind direction adjustment factor is the ratio of the frequency that winds blow toward th e lake and farm in the Tier 1 screening scenario (43 percent) and the frequency that winds blow toward the facility’s farm and lake in Tier 2 , with a separate wind direction adjustment factor for each directional octant (i.e., separate factors for the farm and lakes in the northern octant, the farm and lakes in the northeastern octant, etc.) . The appropriate adjustment factors are then applied to the Tier 1 threshold for that PB

- HAP when evaluating a facility in Tier 2.

The analyses conducted to select th e parameters to derive the threshold adjustment factors are described in this section. Section 3.3.2 also describes the development of appropriate “bins” for the selected parameters. These bins correspond to the subset of par ameter values for which adjustment factors were calculated, based on the anticipated range of plausible values for facilities evaluated in RTR.

3.3.1 Selection Values for Variables of Interest

For each site

- specific parameter that is assessed in the Tier 2 asse ssment

, other than wind direction, adjustment factors were estimated that correspond to a set of four to six particular values for the parameter. The adjustment factor for wind direction directly relates Tier 1 and site

- specific Tier 2 wind direction freq uencies, with respect to the directions of the lake and farm. These individual adjustment factors can the be multiplied for a particular variable combination to get an overall adjustment factor. To facilitate this, bins were created for each parameter of i nterest and a relevant range of values, with the exception of wind direction (as described below, representative bins were not necessary for this parameter). The rationale for selecting the range for each bin for each parameter of interest is described be low.

Wind Direction : Within the context of the hypothetical exposure scenario used in Tier 1 (and under actual conditions), when t he frequency with which the wind blow s towards the modeled domain (i.e., where the hypothetical farm and lake are located) increases

, greater pollutant deposition will occur over and around the farm and lake

. T he percentage of time the wind blows toward the farm and lake is therefore positively correlated with ingestion exposure and risk

. In the screening scenario used to es timate Tier 1 thresholds , the wind is assumed to blow toward the modeled domain 3 days a week, or 43 percent of the time. This assumption is intended to approximate an unusually consistent long

- term wind pattern and is representative of wind direction pat terns in Yakima, Washington ( where the wind blows eastward approximately 40 percent of the time

, based on a review of wind direction data compiled by the Nati onal Weather Service)

.
I n the model runs to develop the Tier 2 bins , this factor was changed to 1, 2

, and 4 days per week, corresponding to 14 percent , 29 percent , and 57 percent of the time. This range of values was chosen to obtain a good understanding of the impact of wind direction on risk for the range of wind direction patterns likely to be pr esent at real facilities. Review of these results indicated that, within this modeling scenario, estimated ingestion exposure varies directly with percentage of time the wind blows toward the farm and lake. Given the exactly linear nature of the relation ship observed in model results obtained from these runs, the adjustment of the threshold for wind direction in Tier 2 is a direct, linear adjustment using the actual site value rather than an incremental, bin

- based approach. In other words, the Tier 1 thr eshold is adjusted for wind direction in direct proportion to the difference between conditions for the actual facility location and the wind direction parameters included in the Tier 1 screening scenario (i.e., blowing toward the lake/farm 43 percent of t he time on average).TRIM

- Based Tiered Screening Methodolo gy for RTR

Tier 2 Methodology 57 October 2014 Wind Speed : Although the impact of wind speed on non

- inhalation risks also is likely to depend on configurational parameters such as the location of farms and lakes, in general it is reasonable to assume that higher wind speeds lead t o more rapid chemical transfer out of the modeled domain

, allowing less time for chemical deposition and

, therefore

, less total near

- field deposition and a lower exposure and risk

. The Tier 1 screening assessment assumed a wind speed of 2.8 m/s, correspon ding to the 5 th percentile (i.e., slower) of annual average speed for the contiguous United States (distribution was based on data from a climate publication from the National Oceanic and Atmospheric Administration [NOAA], which used data from over 200 sta tions nationwide). 20 This value is similar to the annual average wind speeds of the U.S. Deep S outh. 21 In the model runs to develop the Tier 2 threshold adjustment factors

, we calculated the change to exposure resulting from increasing this value to 3.5 m/s , 4 m/s, and 5 m/s ( values also shown in Exhibit 24 ; 5 m/s is the 88 th percentile in the NOAA data). Based on these values, the bins used to classify wind speed are: 2.8

– 3.5 m/s, 3.5

– 4 m/s, 4

– 5 m/s and above 5 m/s. In all modele d cases, increasing the wind speed while maintaining constant lake location, wind direction, mixing height, and precipitation led to Tier 2 estimations of high

- end risk or hazard that were smaller than that of Tier 1. To ensure that the approach is health

- protective, a facility was assigned the lower end value of the bin into which it was placed. Facilities with wind speeds less than 2.8 m/s were assumed to have a wind speed of 2.8 m/s.

Precipitation : Higher levels of precipitation over the modeled domain are expected to increase

non

- inhalation risks by increasing particulate and gaseous wet deposition near

- field to the

source. The screening scenario used in Tier 1 assessment assumed an annual precipitation rate of 1,500 mm/year. This level of annual prec ipitation was estimated to represent rainy conditions in the U.S., such as parts of the U.S. Deep South and parts of the U.S. Northwest Coast 22 . Though the rate was an estimate, it does correspond to approximately the 95 th percentile (i.e., higher rate) pre cipitation in t he N ational Climatic Data Center’s (NCDC) 30

- year ( 1981

– 2010 ) data from U

. S

. stations

. 23 To estimate adjustment factors i n the Tier 2 assessment

, model simulations were conducted with the parameter value set to three lower values (i.e., 1,18 7 mm/year, 924 mm/year and 512 mm/year; values also shown in Exhibit 24 ), corresponding to the 75 th , 50 th and 25 th percentiles, respectively, of the NCDC data

. Locations with lower precipitation levels were assumed to have a mini mum precipitation of 512 mm/year. Based on these levels, the bins used to classify precipitation were: 0

- 512 mm/year, 512

- 924 mm/year, 924

- 1,187 mm/year, 1,187

- 1,500 mm/year and above 1,500 mm/year. In nearly all modeled cases, decreasing the precipitatio n rate while maintaining constant lake location, wind speed, wind direction, and mixing height led to Tier 2 estimations of high

- end risk or hazard that were smaller than that of Tier 1. To be health

- protective, a facility was assigned the upper end value of the bin in which it was placed. Facilities with precipitation levels above 1,500 mm/year were assumed to experience precipitation of 1,500 mm/year.

20 http://ols.nndc.noaa.gov/plolstore/plsql/olstore.prodspecific?prodnum=C00095

- PUB

- A0001#TABLES

– this website is updated yearly, so its current data may not match the data used to develop wind speed s for screening analyses.

21 National Climatic Data Center CliMaps (NCDC

- CliMaps) (2007).

22 National Climatic Data Center Historical Climate Series (NCDC

- HCS) (2007).

23 http://www.ncdc .noaa.gov/oa/climate/normals/usnormals.htmlTRIM

- Based Tiered Screening Methodolo gy for RTR

Tier 2 Methodology 58 October 2014

Exhibit 24

. Lake Distance and Meteorological Parameter Values for which Adjus tment Factors were Developed in Tier 2 a ,b

Parameter Value

Wind Speed (m/s) 2.8
3.5

4
5

Precipitation (mm/yr) 512
924 1,187 1,500

Mixing Height (m) 710
865 1,097 1,537

Lake Distance (km) No lake
2
5
10
20
40

a Bold indicates the value is equal to the value used in Tier 1.

b Wind direction is not shown here because its effect on modeled exposure and risk in TRIM.FaTE is linear.

Mixing Height: Greater mixing heights can dilute the concentration of pollutants in air, resulting in lower deposition and other transfer rates from air to surfaces and consequently also lower ingestion exposures. The Tier 1 screening assessment assumed a mixing height of 710 meters. This v alue is the 5 th percentile (i.e., lower) of annual average mixi ng heights for 463 U.S. locations, based on data obtained from EPA’s SCRAM Web site. 24 To estimate adjustment factors in the Tier 2 assessment , mdoel simulations were conducted with the parameter value set to three larger values (i.e., 865 m, 1,079 m, and 1,537 m; values also shown in Exhibit 24 ). These values correspond to North Little Rock, Arkansas ; Boise, Idaho ; and Tucson, Arizona ; and they are intended to encompass the range of annual average mixing heights experienced in di fferent parts of the United States. Based on these levels, the following bins were selected for categorization of mixing height: 710

– 865 m, 865

– 1,079 m, 1,079

– 1,537 m, and above 1,537. In all modeled cases, increasing the mixing heights while maintainin g constant lake location, wind direction, wind speed, and precipitation rate led to Tier 2 estimations of high

- end risk or hazard that were smaller than that of Tier 1. To be health

- protective, a facility was assigned the lower end value of the bin into wh ich it was placed. Facilities with mixing heights above 1,537 m and those below 710 m were assumed to have mixing heights of 1,537 m and 710 m, respectively.

Lake Location : Moving the lake included in the hypothetical Tier 1 scenario to a location farth er from the actual source in the modeled domain will reduce modeled (TRIM.FaTE) deposition to the lake and its watershed and consequently reduce exposures associated with

24 Support Center for Regulatory Atmospheric Modeling; http://www.epa.gov/scram001/tt24.htmTRIM

- Based Tiered Screening Methodolo gy for RTR

Tier 2 Methodology 59 October 2014 the fish consumption pathway, which is an important pathway of exposure for several c hemicals (for example, in the case of methyl mercury, it is by far the predominant exposure pathway). For the scenario modeled in Tier 1, the center of the lake was situated approximately 2 km from the source. To estimate lake location adjustment factors for use in Tier 2, we completed a series of model runs in which the lake was located 5 km, 10 km, 20 km, and 40 km from the source, as well as runs with no lake (values also shown in Exhibit 24 ). Accordingly, the bins used to cl assify lake location relative to the facility are: no lake, 2

– 5 km, 5

– 10 km, 10

– 20 km, 20

– 40 km, and 40

– 50 km (lakes farther than 50 km are not considered). To be health

protective, a facility was assigned the lower end value of the bin into which it was placed. A facility with a lake closer than 2 km was assumed to have a lake at 2 km.

In resituating the lake at these alternative locations, we maintained ratios consistent with those included in the Tier 1 screening scenario for (1) lake area to total la nd area in the modeled domain, (2) runoff watershed area to lake area, and (3) erosion watershed area to lake area. Exhibit 25 provides a diagram of the TRIM.FaTE layout in each of the alternate lake location simulations except no

- lake. We used a “thin” lake shape (i.e., downwind width much smaller than the cross

- wind length) that minimized the potential effect of declining deposition with distance from stack that might affect lakes that are long in the downwind direction. By

con trolling for these potentially confounding effects, we could isolate the effect of lake location on risk appropriately . Moving the lakes to increasing distances from the stack required expansion of the modeled domain. Maintaining the same overall ratio o f land area to lake area in each domain resulted in scenarios with increasingly large lake s, with surface area increasing with distance from the source. This approach also was taken for reasons of modeling convenience (i.e., taking into account resource r equirements associated with reconfiguring the TRIM.FaTE spatial layout). The changes in lake size between these four runs are not expected to have a substantial independent effect on exposure and risk because the effect of increased lake size is offset by greater total deposition and runoff . Furthermore, the lake depth was not changed, which might be as important a parameter as lake surface area in determining the chemical concentrations in the water column and sediment

. As noted above

, we set up the

con figurations to ensure that the lakes in the different scenarios received runoff and erosion from equivalent watersheds on a per surface area basis.

3.3.2 Estimation of Adjustment Factors

Adjustment factors were estimated for each variable described above and a pplied as divisors to the Tier 1 emissions thresholds. The resulting Tier 2 emissions thresholds are used to assess whether facilities with corresponding configurations carry some potential for significant multipathway health risks by comparing their actu al emissions to the Tier 2 thresholds

.
3.3.2.1 General Approach

The core principle in the estimation of adjustment factors is the assumption of direct proportionality of risk and emissions in the modeling approach used for RTR involving TRIM.FaTE and MIRC. Alth ough not strictly present across all variations due to feedback mechanisms and other processes encompassed by the TRIM.FaTE model, a generally linear relationship between risk and emissions has been observed across model simulations conducted for RTR. Thi s suggests that the ratio of total estimated exposures (and consequently risks) obtained for the screening scenario and any alternative configuration could be used as an adjustment factor to scale emissions for that specific alternative configuration. The risk in the alternative configuration following such a scaling of emissions would be equal to the risk in the screening scenario (which in Tier 1 was set a t a risk level of 1E−6 incremental lifetime cancer risk or an incremental HQ of 1, depending on the toxic effe ct of the chemical in question)

.TRIM

- Based Tiered Screening Methodolo gy for RTR

Tier 2 Methodology 60 October 2014 Exhibit 25 . Layouts for Tier 2 TRIM.FaTE Simulations Using Alternate Distances Betwe en the Facility and the Fishable Lake a

a The no

- lake scenario is not shown.TRIM

- Based Tiered Screening Methodolo gy fo r RTR

Tier 2 Methodology 61 October 2014 To account for potential interactions between the chosen Tier 2 variables, matrix adjustment factors were estimated by performing TRIM.FaTE runs for each unique combinati on of the specified variable values (that is, all permutations of the selected values for the wind speed, precipitation, mixing height, and lake location). Adjustment factors for each configuration were then estimated as the ratio of risks in the Tier 1 a ssessment to the estimated risk for the particular TRIM.FaTE configuration. This approach results in a large matrix of adjustment factors and has the advantage of accounting for all possible interaction effects between the variables. The contribution of e ach exposure media toward each scenario’s Tier 2 risk metric (i.e., the individual contributions of fish ingestion, soil ingestion, beef ingestion, etc. toward the total Tier 2 risk metric) was included in the matrix because the Tier 2 assessment separates chemical exposure from fish ingestion from exposure from farm food chain ingestion.

The adjustment factor for wind direction is handled separately. Based on TRIM.FaTE test runs, the fraction of the time the wind blows in the direction of the farm and l ake was observed to have a direct, linear effect on the resulting risk estimates. For this reason, it was not necessary to include this variable in the TRIM runs conducted to create the matrix of adjustment factors, as described above. Instead, the adjus tment factor is calculated with a linear factor that divides the Tier 1 value (0.43, or 43% of the time in the direction of the farm and lake) by the site

- specific facility value, as follows:

Adjustment Factor Wind Direction = 0.43 / (fraction of time wind blows towards domain)

The adjustment factor for wind direction is then multiplied by the matrix adjustment factor discussed above to obtain a consolidated threshold adjustment factor that accounts for all the five variables considered in Tier 2.

3.3.2.2 Incorpor ation of the Risk Equivalency Approach

The adoption of a risk

- equivalency approach to convert speciated emissions of PAHs and dioxins to BaP and TCDD equivalents, respectively, in Tier 1 required the development of risk equivalency factors (REFs) for each reported species in these groups. The REFs for PAHs and dioxins represent the ratio of the risk posed by a particular species to the risk posed by BaP and TCDD, respectively, at equivalent emissions rates in a given scenario.

The REFs can be represented a s the product of exposure equivalency factors (EEFs) and toxicity equivalency factors (TEFs). For the PAHs, this can be expressed as:

REF PAH = EEF PAH × TEF PAH
The EEFs for PAHs represent the ratio of the exposure to a particular species to the ex posure to BaP (and similarly for dioxins and TCDD) at equivalent emission rates. These ratios are thus specific to the TRIM.FaTE layout and input assumptions. The TEFs for PAHs and dioxins represent the ratio of the oral cancer slope factor (CSF) for a par ticular species to the CSF for BaP and TCDD, respectively, and are the same in Tier 1 and in Tier 2.

The EEFs depend on the TRIM.FaTE configuration, including layout and meteorlogical input values. For example, the exposure profile (i.e., how the different ingestion pathways contribute to total exposure and risk) is different for each PAH. For a PAH where fish is a dominant driver of risk, moving the lake will have a large effect on the overall risk; however, if produce is the dominant driver of risk, movin g the lake will have a much smaller effect on overall risk. Thus, for Tier 2, EEFs were recomputed for each of the representative scenarios modeled in Tier 2 separately for the PAH species currently evaluated (some based on direct TRIM.FaTE modeling, other s 15 based on K ow - based regression estimates) and the dioxin species currentlyTRIM

- Based Tiered Screening Methodolo gy fo r RTR

Tier 2 Methodology 62 October 2014 evaluated (most based on direct TRIM.FaTE modeling, and a small number assumed to behave like TCDD).

The following mathematical formulas demonstrate how the Tier 2 adjustment f actors are estimated for the PAH and dioxin species. The formulas presented below are for a representative PAH species, but they are also applied to dioxin species.

For a given PAH congener emitted at a rate E PAH at a facility , the risk

- equivalent BaP emi ssion level can be expressed as:

E

BAP EQIV = E PAH SPECIES × EEF PAH SPECIES × TEF PAH SPECIES
Then, the Tier 1 screening result is estimated by comparing the risk

- equivalent BaP emissions to the BaP emission threshold:

Ratio TIER 1_PAH SPECIES = E TIER 1_BAP EQUIV Threshold TIER 1_BAP
If the ratio is less than 1, the facility “screens out” of the Tier 1 assessment . Similarly, for Tier 2, the ratio of risk

- equivalent BaP emissions to the Tier 2 BaP threshold may be expressed as:

Ratio TIE R 2_PAH SPECIES = E TIER 2_BAP EQUIV Threshold TIER 2_BAP
Using the definition of the risk

- equivalent BaP emissions, this can be re

- expressed for a given PAH species as:

Ratio TIER 2_PAH SPECIES = E PAH SPECIES × EEF TIER 2_PAH SPECIES × TEF PAH SPECIES Threshold TIER 2_BAP
This expression may be further reconfigured, after some algebraic rearrangement, in terms of the Tier 1 ratio as:

Ratio TIER 2_PAH SPECIES = Ratio TIER 1_PAH SPECIES × Threshold TIER 1_BAP Threshold TIER 2_BAP × EEF TIER 2_PAH SPECIES EEF TIER 1_PAH SPECIES

Tier 2 Adjustment Factor PAH SPECIES = Threshold TIER 1_BAP Threshold TIER 2_BAP × EEF TIER 2_PAH SPECIES EEF TIER 1_PAH SPECIES
These equations simply state that to adjust the Tier 1 threshold to a Tier 2 thresho ld for a particular PAH species, the adjustment factor must include the ratio of the BaP Tier 1 and 2 thresholds (as is true for cadmium, mercury, and TCDD, as discussed above) and the ratio of the EEFs for the particular PAH species in Tier 2 and Tier 1. This additional EEF factor is needed to account for the fact that the EEFs are different for each Tier 2 TRIM.FaTE configuration.TRIM

- Based Tiered Screening Methodolo gy fo r RTR

Tier 2 Methodology 63 October 2014 Finally, the ratio of total BaP equivalents contributed by all PAH species at a given facility to the BaP Tier 2 threshold ma y be expressed, by summing the above expression, as:

Ratio TIER 2_ALL PAHs = � Ratio TIER 1_PAH SPECIES × Threshold TIER 1_BAP Threshold TIER 2_BAP × EEF TIER 2_PAH SPECIES EEF TIER 1_PAH SPECIES ALL PAHs

If the ratio of total BaP equival ents contributed by all PAH species to the BaP Tier 2 threshold is greater than 1, the facility would not have screened out of the Tier 2 assessment for the PAH group.

3.3.2.3 Refined Fisher Scenario

The refined fisher scenario is based on the idea that an adult f isher might fish from multiple lakes if the first (i.e., highest

- concentration) lake is unable to provide an adequate catch to satisfy the assumed ingestion rate (i.e., 373 g/d for adults). This assessment uses the assumption that the biological productivi ty limitation of each lake is 1 gram of fish per acre of water: meaning that in order to fulfill the adult ingestion rate, the fisher will need to fish from 373 total acres of lakes (see Attachment E for further discussion on l ake productivity)

.
Under the refined fisher scenario, a fisher fishes from each surrounding lake in order of highest chemical concentration in fish to lowest concentration and catches fish up to the biological productivity limit. A maximum travel radius i s used to maintain a realistic scenario. The total screening result to the fisher can be expressed as the sum of all screening results for each lake (which is based on the ingestion rate and fish concentration for each respective lake)

.
A 50 km radius of lakes is used to maintain a realistic scenario. Depending on the model configuration, very large modeling configurations put a strain on computing resources, and TRIM.FaTE can crash if the domain is very large or if the domain is divided into too many comp artments. In most scenarios, the vast majority of chemical deposition occurs across distances smaller than 50 km, so the added computing resources needed to model a domain larger than 50 km will usually not be offset by additional, substantial chemical dep osition and exposure. Indeed, wind speeds must exceed 13 m/s (approximately 29 mph) sustained across an hour in order for the chemical plume to travel farther than 50 km; those wind speeds are unlikely to occur across the many days or weeks needed to subst antially affect chronic exposure. In addition , a 50

- km limit also puts a reasonable constraint on the domain of lakes for the fisher scenario

. In order for the Tier 2 screening to remain health protective, it is assumed that the fisher visits lakes in ord er of high est to lowest with respect to risk . If the first lake is 373 acres or greater, the fisher will only travel to that lake. If the acreag e is smaller than 373 acres, the n the fishe r will travel to the next highest risk lake, and continues to travel to subsequent lakes until a total acreage of 373 acres is achieved, or until all qualifying lakes have been visited.

The associated risk for each lake is then scaled by the total adult ingestion rate (equations 1 and 2).

( 1 ) Scaled T 2 Fis h risk = T 2 Fis h risk × � Lake SA ( acs )
373 acs �

Equation 1: Scaled fish risk for each lake 1 to n

- 1TRIM

- Based Tiered Screening Methodolo gy fo r RTR

Tier 2 Methodology 64 October 2014 ( 2 ) Scaled T 2 risk = T 2 Fis h risk × � 373 acs

− ∑ ( SAs of Previously Vis ited Lakes ( acs ) )
373 acs �

Equation 2: Scaled fish risk for nth lake

The risk associated with each lake visited is then summed together for a final risk (equation 3).

( 3 ) Total Scaled T 2 Fis h risk = ( Eq

. 2 ) + � ( Eq

. 1 )
Equat ion 3: Total fish risk for scenario

If the total acreage of fishable lakes surrounding the facility fails to exceed 373 acres, then the risk for each lake is found using equation 1, and the total risk is the sum of all scaled risks (equation 4)

.
( 4 ) To t al Scaled T 2 Fis h risk = � ( Eq

. 1 )
Equation 4: Total fish risk if total acreage of lakes does not reach 373 acres



### 3.4 Preparing National Databases of Lake and Meteorological Data

To facilitate the effective application of the Tier 2 screening procedures, databases were prepared containing national

- scale information about lakes (locations and sizes) and meteorological data at available surface stations (including wind direction, wind speed, precipitation, and estimated mixing height). The deve lopment and content of these two databases are discussed in the following sections.

3.4.1 Processing Lake Data for Tier 2 Assessment

The lake database was built using a geospatial file (U.S. Water Bodies) provided by ESRI ® for their ArcGIS TM products. 25 Because t his geospatial file excluded water bodies in Alaska, Puerto Rico, and the U.S. Virgin Islands, it was augmented with water body information (directly from the USGS National Hydrography Dataset) for those other locations. The data generally have an estimate d horizontal accuracy of 50 m. For the Tier 2 assessment , we focused on the hundreds of thousands of water bodies classified as “Lake/Pond” or “Reservoir” but not designated for disposal, evaporation, or treatment. We refer to these water bodies simply a s “lakes” in the remainder of this document. The approximately 100,000 other water bodies (classified as canal/ditch, ice mass, inundation area, playa, stream/river, swamp marsh, or unclassified) were not included. In a more subjective step during the pro cessing of the lake database for a specific source category (when Tier 2 is run “operationally”), the lake names (when provided) are scrutinized manually, and lakes are removed from the assessment when their names suggest industrial or treatment use (e.g., wastewater treatment ponds, sludge ponds, fly ash ponds, paper mill ponds, sewage pools, etc.). In this respect, the lake dataset is never truly final; lake names are scrutinized each time a new set of facilities is assessed using the Tier 2 methods and t ools , which can lead to the permanent removal of some lakes from the dataset.

Early in the process of compiling this database, we encountered the question: “What size water body qualifies as a ‘lake’ for the purposes of this assessment?” The Tier 2 asse ssment must focus on lakes large enough to support relatively intensive angling pressure to be compatible

25 Specifically , the geospatial file in the ESRI ® Data & Maps 2009 Da ta Update for ArcGIS TM version 9.3.1. It was derived by the United States Geological Survey (USGS), EPA, and ESRI ® from the USGS National Hydrography Dataset (USGS 2012).TRIM

- Based Tiered Screening Methodolo gy fo r RTR

Tier 2 Methodology 65 October 2014 with the assumed exposure scenario. To estimate the relationship between high fish consumption rates, harvest rates

, and lake size, the following fi ve key assumptions were made. Information and citations to peer

- reviewed literature that support these assumptions are provided in Attachment E . Note that in the TRIM.FaTE model screening scenario, water

- column carnivores (WC Cs) are modeled as trophic level 4 (TL4) fish (e.g., pickerel, largemouth bass), with all of their diet consisting of smaller “prey” or “pan” fish in the water column that are simulated as trophic level 3 (TL3). The benthic carnivores (BCs) in TRIM.FaTE ar e modeled to represent an intermediate trophic level between 3 and 4, i.e., TL3.5. The BCs (e.g., catfish) obtain half of their diet from TL2 (benthic invertebrates that feed on detritus at the sediment surface) and half from TL3 fish in the benthic enviro nment. For the screening scenario, we assume that fisher s consume fish biomass in a ratio of 50:50 from the BC and WCC compartments, respectively. Together, these two fish compartments are referred to as piscivorous fish.

1. Piscivorous fish , when present, c omprise approximately 21 percent of the standing biomass of fish. BC fish represent 17 .5 percent of the standing fish biomass in natural lakes ; WCC fish account for 3 .5 percent of this total fish biomass. Thus, WCC (or TL4) fish represent the limiting co mpartment for fish harvesting and consumption.

2. Humans can harvest 10 percent of any single fish compartment without threatening the population due to overharvesting.

3. The minimum viable effective population size for a single fish species is at least 50 adul t fish for a local population to survive over the short term ( e.g., more than a decade)

. 4. Only 33 percent of the fish caught for consumption is edible fillet muscle

. A 0.33 edible fraction is used to estimate total fish biomass associated with human consum ption.

5. A total fish standing biomass of 40 g wet weight/m 2 represents an approximate upper bound for natural ponds and lakes in the United States.

Using the above assumptions and a series of equations (see Attachment E ), the ma ximum fish ingestion rates as a function of standing biomass and lake size were estimated. Exhibit 26 presents these findings, where the grey shading indicat ing when WCC fish would probably not be present, the white (unshaded) cel ls represent combinations of lake size and productivity that could sustain the listed fish ingestion rates over some time, and the yellow cells represent likely long

- term sustainability associated with more than 500 adult WCC fish in the lake (see Exhibit notes).

The Tier 2 assessment is intended to retain most of the health

- protective attributes of the screening scenario used in Tier 1 so that no facilities of potential concern erroneously “pass” the screen. For a given facility, the smaller the lake s ize threshold, the greater the number of lakes and the greater the probability that a lake is closer to the facility. Lakes closer to a facility will result in higher chemical concentrations in fish compared with lakes farther from the facility. Thus, Exhibit 26 was used to determine the smallest lake size that would support a TL4 population. At the assumed upper

- limit standing fish biomass of 40 g ww/m 2 , this corresponds to 25 acres (the first “white” box when moving from small er to larger lakes)

.
The fish ingestion rate supported by a 25

- acre lake is not as high as the adult ingestion rate used in the Tier 1 and Tier 2 assessments (i.e., 373 g ww fillet per day ). Exhibit 26 indicates that at a total fish biomass productivity of 40 g ww/m 2 , the maximum sustainable fish harvest from the WCC compartment would correspond to an ingestion rate of only 103 g/day (with half that from the WCC and half from BC). However, to be health protective and t o ensure t hat small lakes that might be more highly contaminated than estimated by the TRIM.FaTE screens were not eliminated, we selected 25 acres as the “cutoff” for the minimum size for an actual lake nearTRIM

- Based Tiered Screening Methodolo gy fo r RTR

Tier 2 Methodology 66 October 2014 a facility to be included in the Tier 2 assessment

. In add ition, l arger lakes (larger than 100,000 acres) were not considered since they cannot be readily modeled in TRIM.FaTE. For the purposes of proximity matching lakes to emitting facilities, the location of each lake is identified as the geographic centroid i nside the lake.

It should be noted that an individual l ake as small as 25 acres could be stocked annually at a rate adequate to support the assumed fish ingestion rate. For stocked fish, however, we would have to assume that when introduced to the lake, th e fish were uncontaminated by the chemicals of interest. Moreover, the period over which accumulation of chemical from the lake could occur would be roughly three to six months (fishing season) for the majority of the fish stocked as adults (i.e., at appro ximately 2 kg), instead of several years for fish hatched in the lake.

To place an upper bound on the radial distance from the source up to which lake

- derived risks need to be assessed, we took into account the limitations of TRIM.FaTE. We limited t he lake analysis to an outward radial bound of 50 km from the stack. For facilities with no lakes within 50 km, lake

- derived risk is assumed 0

.TRIM

- Based Tiered Screening Methodolo gy for RTR

Tier 2 Methodology 67 October 2014 Exhibit 26

. Maximum Fish I ngestion R ate (g/day) A ssociated with S ustainable F ishin g a

Standing Biomass (g ww/m 2 ) b Size of Pond or L ake (acres )

1 2 3 4 5 7.5 10 15 25 35 50 75 100 150 200 400
2 0 0 0 0 0 0 1 1 1 2 3 4 5 8 10 20
3 0 0 0 0 0 1 1 1 2 3 4 6 8 12 15 31
4 0 0 0 0 1 1 1 2 3 4 5 8 10 15 20 41
5.7 0 0 0 1 1 1 1 2 4 5 7 11 15 22 29 58
10 0 1 1 1 1 2 3 4 6 9 13 19 26 38 51 102
15 0 1 1 2 2 3 4 6 10 13 19 29 38 58 77 154
20 1 1 2 2 3 4 5 8 13 18 26 38 51 77 102 205
30 1 2 2 3 4 6 8 12 19 27 38 58 77 115 154 307
35 1 2 3 4 4 7 9 13 22 31 45 67 90 134 179 359
40 1 2 3 4 5 8 10 15 26 36 51 77 102 154 205 410
50 1 3 4 5 6 10 13 19 32 45 64 96 128 192 256 512
60 2 3 5 6 8 12 15 23 38 54 77 115 154 231 307 615
70 2 4 5 7 9 13 18 27 45 63 90 134 179 269 359 717
80 2 4 6 8 10 15 20 31 51 72 102 154 205 307 410 820
90 2 5 7 9 12 17 23 35 58 81 115 173 231 346 461 922
100 3 5 8 10 13 19 26 38 64 90 128 192 256 384 512 1025
110 3 6 8 11 14 21 28 42 70 99 141 211 282 423 563 1127
120 3 6 9 12 15 23 31 46 77 108 154 231 307 461 615 1229
130 3 7 10 13 17 25 33 50 83 117 166 25 0 333 499 666 1332

a Dark g ray shading indicates insufficient population size for T L 4 (WCC) fish (<50 adults) to be sustainable for more than a decade ; y ellow

- shaded cells indicate the likel ihood to provide long

- term sustainable fish population s with at le ast 500 TL4 adult fish present; w hite area indicates medium

- term sustainability

. b Represents the standing biomass of TL4 fish. At the upper

- limit standing biomass of 40 g ww/ m 2 estimated for natural lakes, 25 acres could support a water

- column TL4 fish population, but would provide for no more than 26 grams of fillet per day for a single fisher over a full year.TRIM

- Based Tiered Screening Methodolo gy for RTR

Tier 2 Methodology 68 October 2014 3.4.2 Processing Meteorological Data for Tier 2 Assessment

In addition to the lake database, a database of U.S. surface stations with complete dat a was created, so that each source category facility can be paired with the closest meteorological station data. This process of pairing dozens or hundreds of facilities with meteorological data is not unprecedented

. In their report to the Science Adviso ry Board (SAB) on the 1996 NATA, EPA described pairing each facility with the closest meteorological station in an inventory of over 350 meteorological stations nationwide, creating an average facility

- to

- station distance of less than 50 km in the 1996 NAT A (EPA 2001b) . In a separate 2009 report to the SAB on the RTR program, EPA described using 158 meteorological stations to choose from nationwide, with a standard practice of selecting the station nearest to each facility unless the facility provides onsi te meteorological data (EPA 2009) . Using 156 petroleum refineries as a sample data set, the average facility

- to

- station distance was 72 km. In both instances, t he SAB accepted this matching as standard practice when modeling large numbers of sources, al th ough they recommended providing high

- level siting maps (e.g., meteorological stations overlaid with terrain gradients or regional climate regimes) to qualify some of the uncertainties related to meteorological data in air dispersion modeling (EPA 2001a; EP A 2010 b )

. The current effort builds on this practice but increases the number of available meteorological stations as described below.

3.4.2.1 Sources of Meteorological Data

To construct a database of meteorological statistics for all available U.S. surface stati ons for use in multipathway screening assessments, EPA started with the same U.S. meteorology dataset used in RTR inhalation assessments. RTR inhalation assessments use data from 824 ASOS (Automated Surface Observing System) stations that record hourly and sub

- hourly measurements. These data represent the year 2011, and the surface stations are paired with their closest, regularly

- reporting upper

- air stations. This number of stations is far greater than the 350 and 158 stations

, respectively

, used in the 19 96 NATA report (EPA 2001b) and the 2009 RTR report (EPA 2009)

. The 2011 precipitation measurements reflect 2011 weather conditions, and, like any other year, some areas of the country experienced rainfall that was significantly less than normal, and some

a reas received much more rainfall than normal. To reduce this bias in precipitation data

, we used average annual precipitation data from the 1981

− 2010 National Climatic Data Center 30

- year normal dataset wherever possible. If 30

- year normal precipitation d ata were not available for a station, as was the case for a few stations in the dataset, we used the ASOS precipitation data as

- is

.
3.4.2.2 Coverage of Meteorological Stations Compared with Facility Locations

Exhibit 27 shows the proximit y of the evaluated meteorology sites to the locations of U.S. point source facilities from the 2005 NATA. Generally, the spatial density of the surface meteorological stations in this dataset was similar to the spatial densit y of the 2005 NATA facilities. That is, the density tends to be greatest in the Great Lakes region, along the East and West Coasts, and in the Southern Plains, and tends to be lowest in the Rockies (except Colorado) and Northern Plains

.TRIM

- Based Tiered Screening Methodolo gy for RTR

Tier 2 Methodology 69 October 2014 3.4.2.3 Data Processing

To facilitate application of the meteorological data to the Tier 2 assessment , EPA gathered wind information in directional octants that could be linked to the direction of the relevant lake s (see Introduction and Section 3.5 ). EPA divided the periphery aroun d a meteorological station into eight octants representing the direction toward which the wind was blowing:

N: >337.5 to 22.5 degrees NE: >22.5 to 67.5 degrees E: >67.5 to 112.5 degrees SE: >112.5 to 157.5 degrees S: >157.5 to 202.5 degr ees SW: >202.5 to 247.5 degrees W: >247.5 to 292.5 degrees NW: >292.5 to 337.5 degreesTRIM

- Based Tiered Screening Methodolo gy for RTR

Tier 2 Methodology 70 October 201 4 Exhibit 27

. The Locations of Meteorological Stations and Point Source Facilities a

a The 2005 NATA was the most recent, comprehensive, f inalized dataset of nationwide point source emitters of hazardous air pollutants, and it is used here only for illustrative purposes. The 2005 NATA used a meteorological dataset different from the one used in this report.TRIM

- Based Tiered Screening Methodology for RTR

Tier 2 Methodology 71 October 2014 A software program was develop ed to calculate the following statistics for each of these stations:

• Number of hourly observations,

• Number of hours with calm winds or missing winds,

• Percentage of time the wind blows into each octant (after excluding missing wind hours),

• Median wind speed blowing into each octant

,

• Median mixing h eight used (if heat flux > 0, convective mixing height was used, otherwise mechanical mixing height was used) , and

• Average annual precipitation (irrespective of wind octant and preferring 30

- year normal data if ava ilable).

Total annual precipitation data in the 2011 meteorological data included anomalies (relative to normal conditions) in areas that experienced extreme drought conditions or large rainfall surplusses. To address this, the 30

- year (1981

- 2010) average annual precipitation was used instead. 26

The choice of using median values for wind speed and mixing height was based on a comparison of median and mean values. For nearly all stations, the median value was smaller than the mean value; because smaller va lues are more health protective, we selected the median statistic for all stations.



### 3.5 Implementation of Tier 2 Assessment

The Tier 2 screening assessment is conducted using a Microsoft® AccessTM tool that was developed specifically for this purpose. Similar t o the Tier 1 screening assessment tool, the Tier 2 tool was created so that facilities that do not screen out during the Tier 1 assessment can be screened concurrently, if desired.

The Tier 2 tool requires three inputs to run: the results of the Tier 1 sc reening assessment, a list of facilities to model, associated meteorology stations that can be matched to using a proximity analysis, and the matrix of adjustments factors that are used to adjust the Tier 1 screening thresholds to the Tier 2 screening leve ls. The matrix is used to standardize the scenario based on the chemical and on the lake distance and meteorology characteristics of the assessed facility ( refer to Section 3.3 for a discussion of these adjustment factors ).

The tool contains procedures that match each facility to all lakes within a user

- defined radius (currently 50 km) that are within user

- defined size limitations (currently 25 acres to 100,000 acres), and based on water body type (e.g., they are not waste dispo sal or treatment facilities, retention ponds, etc). In addition, each facility is matched to the same meteorology station used in RTR inhalation assessments, which is usually the nearest meteorology station ; when the meteorology station match is not known from the RTR inhalation assessment, the tool conducts a proximity analysis. The tool allows the user to review matching lakes and exclude ones that are not considered suitable for modeling (e.g., based on names indicating industrial, waste, or treatment pu rposes, as discussed in Section 3.4.1 .) Any excluded lakes are recorded by the tool so that they are omitted from subsequent screening assessments.

26 30

- year average annual precipitation was obtained from the National Climatic Data Center (NCDC). http://www.ncdc.noaa.gov/oa/climate/normals/usnormals.html

.TRIM

- Based Tiered Screening Methodology for RTR

Tier 2 Methodology 72 October 2014 Per facility and PB

- HAP, the tool evaluates the screening result at each lake a nd hypothetical farm. For each lake, the initial calculations produce screening results consistent with the full screening scenario ingestion rate (i.e., 373 g/day for adults, regardless of assumed lake fish productivity), and then additional calculations adjust the screening results for the lakes based on the initial screening results and areas of the lakes (see Section 3.3.2.3 ). Each modeling scenario consists of a farm and a lake (or, in some cases, no lake )

. Alt hough the dis tance from the facility to the modeled farm remains fixed for each octant, the location of the lake affects chemical concentrations in some farm food chain media due to modeled runoff and erosion patterns that are directed mostly toward the lake. Therefore , there can be multiple different farm screening results in the same octant for the same facility, each corresponding to a different lake

distance scenario. The tool identifies the largest farm screening result from all scenarios evaluated for a facility. T he tool presents the final Tier 2 screening results for the fisher and farm er separately, including results specific to individual farm food chain media, for each chemical, and then summed together for each PB

- HAP group.

The tool also can incorporate the results of Tier 3 assessments. Since the tool allows the user to manually exclude lakes, if any lakes are removed from the assessment in Tier 3, those lakes can be removed in the tool and the Tier 2 screening can be repeated with the revised lake database. If Tier 3 plume

- rise or time

- series assessments are conducted, the resulting Tier 3 adjustment factors can be entered into the tool and it will update the Tier 2 screening results to produce Tier 3 screening results.

The tool outputs several results tabl es, including an intermediate table that provides information on each lake and farm, including the fish ingestion and screening

- level risk associated with each lake . The final screening results for each facility and PB

- HAP group are also output. A summary table identifies the facilities exceeding the Tier 2 emission screening threshold of each PB

- HAP group (separately for the fisher and farmer) and the largest screening results. All intermediate and final results tables present the Tier 1 and Tier 2 screeni ng results side

- by

- side for comparison. Exhibit 28 through Exhibit 30 provide screen shots of tool output tables.

Exhibit 28

. Example of the Summary Output Table from Tier 2 T ool a

a The screen shot examples shown in this section are for actual facilities in actual source categories ; however their source categories, NEI IDs, and coordinates have been altered or masked so the data are not linked to specific facilities. “SV” mea ns “screening value”, which is the ratio of emissions to emission screening threshold.TRIM

- Based Tiered Screening Methodology for RTR

Tier 2 Methodology 73 October 2014 Exhibit 29

. Example of the Facility Output Table from Tier 2 Tool a, b

a The screen shot examples shown in this section are for actual faci lities in actual source categories ; however their source categories, NEI IDs, and coordinates have been altered or masked so the data are not linked to specific facilities. “SV” means “screening value”, which is the ratio of emissions to emission screening threshold.

b Only a portion of the table is shown

. Individual farm ingestion media are in the table but only “soil” is displayed for space reasons

.

Exhibit 30

. Example of the Lake Assessment Output Table from Tier 2 Tool a ,b

a The screen shot examples shown in this section are for actual facilities ; however their source categories, NEI IDs, and coordinates have been altered or masked so the data are not linked to specific facilities. Only a portion of the table might be show n. The headers “Stationary Fisher” and “Traveling Fisher” are shorthand references to the initial fisher scenario (i.e., catches and consume s 373 g/d per lake, regardless of lake area) and the refined fisher scenario (i.e., assumes a 1 g/acre/d lake produc tivity), respectively. “SV” means “screening value”, which is the ratio of emissions to emission screening threshold.

b The table displayed reflect s a Tier 3 Screening Assesment having been conducted . In a Tier 2 Screening Assesssment, the Add’l Lake Asse ssment, Plume Rise Adj Factors, and Time Series Adj Factors would not be shown

.TRIM

- Based Tiered Screening Methodology for RTR

Tier 3 Methodology 74 October 2014 

## 4. Tier 3 Methodology

4.1 Overview of Approach

The Tier 3 screening assessment is an additional assessment that can be conducted , at the discretion of the risk assessor, on facilities that do not screen out with the Tier 2 assessment. The Tier 3 screening approach consists of three individual assessments that further refine the screening scenario (beyond the refinements in Tier 2) based on additional site

- specific data and evaluations. The assessments introduce additional site

- specificity to the screening scenario, requiring a potentially higher level of effort than the Tier 2 assessment, but still a much lower level of effort than the full site

- specific assessment. One of the Tier 3 as sessments (i.e., the lake assessment) results in the rescreening of the facility’s emissions using the Tier 2 methods and using a revised lake database. The other two assessments (i.e., the plume

- rise and time

- series assessments) each result in an adjustme nt factor to be applied to the screening result reflecting the Tier 3 lake assessment. The adjustment factors of the plume

- rise and time

- series assessments cannot be compounded (i.e., the time

- series assessment, if conducted, supplants the plume

- rise asses sment already conducted).

The remainder of this section discusses each of these assessments.



### 4.2 Lake Assessment

Other than verifying a facility’s emission rates, one of the simplest assessments that can be conduct ed (beyond the Tier 2 methods) is to assess t he existence, the potential purpose, the accessibility and fishability, and the suitability of lakes for the models and methods used in Tier 2. The full USGS dataset of lakes and reservoirs nationwide that is used in Tier 2 does not contain information on lake accessibility or fishability. In addition, it occasionally identifies a lake that no longer exists (e.g., has evaporated or been drained) or it uses a classification that might not accurately reflect the lake’s purpose or type. A erial and street

- view imagery and internet searches can be used to quickly ascertain if an assessed lake actually exists, if it is likely not fished (e.g., appears swampy or covered in algae), if it is likely used for industrial or waste disposal/treatment purposes, and/or if i t is adjacent to or connected to a river or saltwater body (estuaries, rivers, and saltwater areas are not ideal for the assessment models and methods).

For example, the blue outline in Exhibit 31 identifies a lake from the USGS d ataset that originally qualified for Tier 2 based on the information provided in that dataset, but aerial imagery shows it is likely evaporated or drained. Similarly, the outline in Exhibit 32 identifies a lake that originally qua lified, but aerial imagery shows that it is directly adjacent to an industrial facility and likely used only for on

- site industrial purposes.

For facilities undergoing Tier 3 screening, all lakes are assessed from which the fish er catches and consumes fis h according to the refined Tier 2 fisher methods discussed in Section 3

. A ll lakes meeting the criteria discussed in the previous paragraphs are permantly removed from the screening assessment so that they are no longer used in any screening assessment. If a lake is removed from the facility’s assessment, it might be necessary for the hypothetical fisher to catch and consume fish from additional lake(s) in order to fish a total of 373 acres. In this case, additional lakes that w ere not fished previously are evaluated , using the methods discussed in Section 3 , in descending order of initial Tier 2 fisher screening result and lake area.

If any lakes are removed from the screening, the screening assessme nt is conducted again (using the Tier 2 methods) with the revised lake dataset. Although this additional lake assessment is conducted based on the Tier 2 screening results of a particular set of facilities, lakes removed during that assessment could affect the screening results of other facilities in theTRIM

- Based Tiered Screening Methodology for RTR

Tier 3 Methodology 75 October 2014 source category beyond that original set. This can happen if an assessed facility is within 50 km of another assessed facility, whereby removing a lake could affect the screening results of both facilities. It should be noted, however, that s creening results across different facilities are not summed

.
Exhibit 31 . Example of Lake Removed from Screening

— Likely Evaporated or Drained

Note: Aerial imagery from ESRI World Imagery (2014 ).

Exhibit 32 . Example of Lake Removed from Screening

— Likely an Industrial Lake

Note: Aerial imagery from ESRI World Imagery (2014). This map was created during the assessment of a specific source category, thus the reference to “the assessment in this report” in the text box.TRIM

- Based Tiered Screening Methodology for RTR

Tier 3 Methodology 76 October 2014 

### 4.3 Plume

- rise Assessment

In Tiers 1 and 2, all chemicals are emitted inside the mixing layer and are available for ground

level exposure. In reality, the physical height of an emission source, in combinati on with the temperature and velocity of the chemical plume as it leaves the source, can cause some of the chemical plume to reach areas above the mixing layer. In TRIM.FaTE modeling, the chemical mass deposited above the mixing layer (i.e., the model’s upp er

- air layer) is unavailable for ground

- level exposure (i.e., the upper

- air layer functions as a chemical sink). Many emission sources in RTR are physically taller than the mixing height during some hours, and hot exit gas temperatures (i.e., buoyancy) and /or high exit gas velocities (i.e., momentum) can further elevate the chemical plume.

The Tier 3 methodology utilizes methods summarized by Seinfeld and Pandis (1998) to estimate how often a facility’s emissions reach the upper

- air sink, which decreases av ailability for ground

- level exposure. The methods to estimate the amount of chemical lost to the upper

- air sink require use of the facility’s corresponding meteorology data (e.g., air temperature and vertical temperature gradient, wind speed, and atmospher ic stability), the mass of the PB

- HAP emitted from each source, the physical characteristics of the sources (i.e., release height, inside diameter at the release point, and exit gas temperature and velocity), and an estimate of the size of the facility.

F or each relevant emission source, estimates of the hourly effective release height (i.e., sum of actual release height and plume rise) are compared to the hourly mixing height to determine the mass of chemical remaining in the mixing layer. T he mass of che mical remaining in the mixing layer, summed across the sources, is compared to the total emitted mass of the chemical. This results in an adjustment factor expressing the fraction of emissions remaining in the mixing layer after accounting for release heig ht and plume rise. T his final plume

- rise adjustment factor is then applied to the Tier 2 screening results.



### 4.4 Assessment of Time

- series Meteorology and Effective Release Heights

As discussed in Section 3 , the Tier 2 screening re sults are based on the average meteorological conditions prevailing at the facility being assessed. The use of time

- series meteorology data, which captures hour

- by

- hour changes in each of the assessed meteorological parameters instead of using constant ave rage values, increases the accuracy of the estimates of potential risk by accounting for potential statistical interactions between the meteorology parameters and by improving the method of accounting for mass advected into the modeling domain.

Tier 2 scr eening result s also are based on a constant emission release height that is always within the mixing layer. Including hourly effective release heights along with hourly meteorology data further increases the accuracy of the screening results

.
For a facilit y undergoing a time

- series assessment, a time series of hourly effective release heights is developed and the Tier 2 spatial scenario that best matches each lake from which the hypothetical fisher fishes is identified . Using these data along with the facil ity’s matching hourly meteorology data, the facility’s emission rates, and the fish ingestion rates, chemical fate and transport is modeled in TRIM.FaTE and corresponding screening

- level risks are calculated using MIRC. This results in a screening

- level ri sk value for each lake. If the farmer scenario is specifically being assessed, the modeling will also result in a screening

- level risk value for each farm. T he screening

- level risk associated with each lake is multiplied by the percent of daily ingested fi sh caught from the lake; summing those products across all modeled lakes results in the screening

- level fisher risk (i.e., fisher screening result ) associated with the evaluated PB

-TRIM

- Based Tiered Screening Methodology for RTR

Tier 3 Methodology 77 October 2014 HAP emitted by the facility. The time

- series

- adjusted Tier 3 screening resu lt s are compared to the Tier 2 screening result s calculated previously.TRIM

- Based Tiered Screening Methodology for RTR

Tier 3 Methodology 78 October 2014 

## 5. References

ATSDR (Agency for Toxic Substances and Disease Registry). 1995. Toxicological profile for polycyclic aromatic hydrocarbons. Atlanta, GA: U.S. Department of Health and Hum an Services, Public Health Service.

ATSDR. 1998. Toxicological profile for chlorinated dibenzo

- p

- dioxins. Atlanta, GA: U.S. Department of Health and Human Services, Public Health Service.

ATSDR. 1999. Toxicological profile for Mercury. Atlanta, GA: U.S. De partment of Health and Human Services, Public Health Service.

ATSDR. 2008. Toxicological profile for Cadmium. Atlanta, GA: U.S. Department of Health and Human Services, Public Health Service.

Burger, J. 2002. Daily consumption of wild fish and game: Exposu res of high end recreationists. International Journal of Environmental Health Research 12:343

- 354.
Cal/EPA (California Environmental Protection Agency) Office of Environmental Health Hazard Assessment (OEHHA). 2000. Air Toxics Hot Spots Program Risk Assess ment Guidelines; Part IV, Exposure Assessment and Stochastic Analysis Technical Support Document. Section 6, Dermal Exposure Assessment. September. Available at: http://www.oehha.ca.gov/air /hot_spots/pdf/chap6.pdf

. Chen, C.Y., R.S. Stemberger, B. Klaue, J.D. Blum, P.C. Pickhardt, and C.L. Folt. 2000. Accumulation of heavy metals in food web components across a gradient of lakes. Limnology and Oceanography 45(7): 1525

- 1536.

Croteau, M., S.N. Luoma, and A.R. Stewart. 2005. Trophic transfer of metals along freshwater food webs: Evidence of cadmium biomagnification in nature. Limnology and Oceanography 50(5):1511

- 1519.
EPA 1997. Mercury Study Report to Congress. Volume III: Fate and Transport of Mercury in the Environment. EPA

- 452/R

- 97

- 005. Office of Air Quality Planning and Standards and Office of Research and Development. December.

EPA 1998. Methodology for Assessing Health Risks Associated with Multiple Pathways of Exposure to Combustor Emiss ions. National Center for Environmental Assessment. Cincinnati, OH. EPA

- 600/6

- 90/003. December.

EPA 1999. Short Sheet: IEUBK Model Soil/Dust Ingestion Rates. EPA

- 540

- F

- 00

- 007; OSWER

9285.7

- 33. Washington, D.C.: Office of Solid Waste and Emergency Response ; December. Available at: http://www.epa.gov/superfund/lead/products/ssircolo.pdf

.
EPA 2001a. NATA

– Evaluating the National

- scale Air Toxics Assessment, 1996 DATA

– An SAB Advisory. U .S EPA Science Advisory Board. EPA

- SAB

- EC

- ADV

- 02

- 001. 12/2001.

EPA 2001b. National

- scale Air Toxics Assessment for 1996, Draft for EPA Science Advisory Board Review. EPA Office of Air Quality Planning and Standards. EPA

- 453/R

- 01

- 003. 01/18/2001.

EPA 2002a. Total Risk Integrated Methodology: TRIM.FaTE Technical Support Document. Volume II: Description of Chemical Transport and Transformation Algorithms. EPA

- 453/R

-TRIM

- Based Tiered Screening Methodology for RTR

Tier 3 Methodology 79 October 2014 02

- 011b. Office of Air Quality Planning and Standards: Research Triangle Park, NC. September.

EP A 2002b. Evaluation of TRIM.FaTE, Volume I: Approach and Initial Findings. EPA

- 453/R

- 02

- 0012. Office of Air Quality and Planning Standards: Research Triangle Park, NC. September.

EPA 2003. Multimedia, Multipathway, and Multireceptor Risk Assessment (3MRA) Modeling System, Volume II: Site

- based, Regional, and National Data. SAB Review Draft. EP

- 530/D

03

- 001b. Office of Research and Development, Athens, GA, and Research Triangle Park, NC, and Office of Solid Waste, Washington, DC. July. Available at: http://www.epa.gov/epaoswer/hazwaste/id/hwirwste/risk03.htm

. EPA 2004a. Air Toxics Risk Assessment Reference Library; Volume 1

– Technical Resource Document, Part III, Human Health Risk As sessment: Multipathway Chapter 14, Overview and Getting Started: Planning and Scoping the Multipathway Risk Assessment. Office of Air Quality Planning and Standards, Research Triangle Park, NC. April. Available at: http://www.epa.gov/ttn/fera/data/risk/vol_1/chapter_14.pdf

. EPA 2004b. Evaluation of TRIM.FaTE Volume III: Model Comparison Focusing on Dioxin Test Case. EPA

- 453/R

- 04

- 002. Available at: http://www.epa.gov/ttn/fera/data/tr im/eval_rept_vol3_2005.pdf

EPA 2004c. Exposure and Human Health Reassessment of 2,3,7,8

- Tetrachlorodibenzo

- p

Dioxin (TCDD) and Related Compounds. Volume 2: Properties, Environmental Levels, and Background Exposures. Dioxin Reassessment, NAS Review Draft. U.S. Environmental Protection Agency, Washington, D.C., EPA/600/P

- 00/001Cb. Available at: http://www.epa.gov/ncea/pdfs/dioxin/nas

- review/

EPA 2005a. Human Health Risk Assessment Protocol for H azardous Waste Combustion Facilities (including the Hazardous Waste Companion Database of chemical

- specific parameter values). U.S. Environmental Protection Agency, Office of Solid Waste and Emergency Response, Washington, DC. EPA

- 530

- R

- 05

- 006. September.

EPA 2005b. Evaluation of TRIM.FaTE, Volume II: Model Comparison Focusing on Mercury Test Case. EPA

- 453/R

- 05

- 002. Office of Air Quality and Planning Standards: Research Triangle Park, NC. July.

EPA 2005c. Supplemental Guidance for Assessing Susceptibility from Early

- Life Exposure to Carcinogens. EPA

- 630/R

- 03

- 003F. Risk Assessment Forum: Washington, DC. March.

EPA 2006. Risk and Technology Review (RTR) Assessment Plan. Office of Air and Radiation. November 20, 2006. Available at: http://www.epa.gov/sab/panels/consul_risk_and_tech_assessment_plan.htm

.
EPA 2007. Prioritized Chronic Dose

- Response Values for Screening Risk Assessments (Table 1). Office of Air Quality Planning and Standards; June 12, 2007. Available at: http://www.epa.gov/ttn/atw/toxsource/summary.html

. EPA 2009. Risk and Technology Review (RTR) Risk Assessment Methodologies: For Review by the EPA ’s Science Advisory Board with Case Studies

– MACT I Petroleum Refining Sources and Portland Cement Manufacturing. EPA Office of Air Quality Planning and Standards. EPA

- 452/R

- 09

- 006. 06/2009.TRIM

- Based Tiered Screening Methodology for RTR

Tier 3 Methodology 80 October 2014 EPA 2010a Development of a Relative Potency Factor (RPF) Approac h for Polycyclic Aromatic Hydrocarbon (PAH) Mixtures (External Review Draft). Washington, DC, EPA/635/R

- 08/012A February. Available at: http://cfpub.epa.gov/ncea/iris_drafts /recordisplay.cfm?deid=194584

EPA 2010 Review of EPA’s draft entitled, “Risk and Technology Review (RTR) Risk Assessment Methodologies: For Review by the EPA’s Science Advisory Board with Case Studies

– MACT I Petroleum Refining Sources and Portland Ceme nt Manufacturing.” EPA Science Advisory Board. EPA

- SAB

- 10

- 007. 05/07/2010.

EPA 2011a Exposure Factors Handbook: 2011 edition. National Center for Environmental Assessment, Washington, DC; EPA/600/R

- 09/052F. Available from the National Technical Information Service, Springfield, VA, and online at http://www.epa.gov/ncea/efh.

EPA 2011b Human Health Multipathway Residual Risk Assessment for the Secondary Lead Smelting Source Category. Draft Report. Prepared by ICF International for EPA Risk and Exposure Assess ment Group. 02/15/2011.

EPA 2011c Technical Support Document: Case Study Analyses of Potential Local

- scale Human Health Risks Associated with Mercury Emissions from Electric Utility Steam

- generating Units. Draft. Prepared by ICF International for EPA Offic e of Air Quality Planning and Standards. 02/17/2011.

EPA 2014a Technical Support Document: Human Health Multipathway Residual Risk Assessment for the Ferroalloys Production Source Category. Draft. Prepared by ICF International for EPA Office of Air Quality Planning and Standards. 02/21/2014.

EPA 2014b Technical Support Document: Human Health Multipathway Residual Risk Assessment for the Petroleum Refineries Sector. Draft. Prepared by ICF International for EPA Office of Air Quality Planning and Standards. 01 /31/2014.

ESRI (Environmental Systems Research Institute). 2014. World Imagery. Accessed June 03, 2014. Available at: http://www.arcgis.com/home/item.html?id=10df2279f9684e4a9f6a7f08febac2a9.

Hazardous Substances Data Bank (HSDB). 2005. Bethesda, MD: Natio nal Library of Medicine, U.S. [Last Revision Date 06/23/2005]. Cadmium Compounds; Hazardous Substances Databank Number: 6922. Available at: http://toxnet.nlm.nih.gov/cgi

- bin/sis/htmlgen?HSDB

Holzworth, G.C. 1972. Mixing Heights, Wind Speeds, and Potential for Urban Air Pollution Throughout the Contiguous United States,” AP

- 101, January 1972, U.S. Environmental Protection Agency, Office of Air Programs, Research Triangle Park, North Carolina.

ICF (ICF International). 2005. Memorandum: TRIM.FaTE Screening Sce nario: Aquatic Food Web Analysis; submitted to Deirdre Murphy and Terri Hollingsworth, U.S. EPA, from Margaret McVey and Rebecca Kauffman, ICF Consulting. October 18.

Mason R.P., J. Laporte, and S. Andres. 2000. Factors controlling the bioaccumulation of

m ercury, methylmercury, arsenic, selenium, and cadmium by freshwater invertebrates and fish. Archives for Environmental Contamination and Toxicology 38(3):283

- 97.

McKone, T.E., A. Bodnar, and E. Hertwich. 2001. Development and evaluation of state

- specific l andscape data sets for multimedia source

- to

- dose models. University of California at Berkeley. Supported by U.S. Environmental Protection Agency (Sustainable TechnologyTRIM

- Based Tiered Screening Methodology for RTR

Tier 3 Methodology 81 October 2014 Division, National Risk Management Research Laboratory) and Environmental Defense Fund. July. LBNL

- 43722.

Morton, F.I. 1986. Practical estimates of lake evaporation. Journal of Climate and Applied Meteorology 25:371

- 387.

NCDC (National Climatic Data Center). 1995. Hourly United States Weather Observations (HUSWO) 1990

- 1995.

National Climatic Data Center. (2007). Cimate Maps of the United States (CLIMAPS). Available online at http://cdo.ncdc.noaa.gov/cgi

- bin/climaps/climaps.pl

National Climatic Data Center (NCDC). (2012) Quali ty Controlled Local Climatological Data. Available online at http://cdo.ncdc.noaa.gov/qclcd/QCLCD?prior=N

Reinfelder, J.R., N.S. Fisher, S.N. Luoma, J.W. Nichols, and W.

- X. Wang. 1998. Trace elem ent trophic transfer in aquatic organisms: A critique of the kinetic model approach. The Science of the Total Environment 219: 117

- 135.

Saiki, M.K., D.T. Castleberry, T.W. May, B.A. Martin, and F.N. Bullard. 1995. Copper, cadmium, and zinc concentrations i n aquatic food chains from the upper Sacramento River (California) and selected tributaries. Arch. Environ. Contam. Toxicol. 29:484

- 491.

Seinfeld, J.H., and S.N. Pandis. 1998. Atmospheric Chemistry and Physics: From Air Pollution to Climate Change, Wiley

- I nterscience, New York, pp. 931

– 933.
Stull, R.B. 1988. An Introduction to Boundary Layer Meteorology. Kluwer Academic Publishers, The Netherlands. 452 pp.

Turner, D.B. 1970. Workbook of Atmospheric Dispersion Estimates. PHS Publication No. 999

- AP

- 26. U.S. D epartment of Health, Education, and Welfare, National Air Pollution Control Administration, Cincinnati, Ohio.

USGS (U.S. Geological Survey). (2012) National Hydrography Dataset. Available online at http://nhd.usgs.gov/

. USGS (U.S. Geological Survey). 1987. National Water Summary 1987

– Hydrologic Events and Water Supply and Use. USGS Water

- Supply Paper 2350. J.E. Carr, E.B. Chase, R.W. Paulson, and D.W. Moody, Compilers.

Van den Berg, M., L.S. Birnbaum, M. Denison, M. DeVito, W. Farlans, M. Feeley, H. Fiedler, H. Hakansson, A. Hanberg, L.. Haws, M. Rose, S. Safe, D. Schrenk, C. Tohyama, A. Tritscher, J. Tuomisto, M. Tysklind, N. Walker, and R.E. Peterson. 2006. The 2005 World Health Organization reevaluation of human and mammalian toxic equivalency factors for dioxins and dioxin

- like compounds. Toxicol Sci. 93(2): 223

- 41.
Wischmeier, W.H., and D. Smith. 1978. Predicting Rainfall Erosion Losses: A Guide to Conservation Planning. USDA

- ARS Agriculture Handbook No. 537, Wa shington, D.C. 58 pp.TRIM

- Based Tiered Screening Methodology for RTR

[This page intentionally left blank.]TRIM

- Based Tiered Screening Methodology for RTR

Attachment A i October 2014 TRIM.FaTE Inputs 

## TRIM .FaTE Inputs Attachment A.[This page intentionally left blank.]TRIM

- Based Tiered Screening Methodology for RTR

Attachment A iii October 2014 TRIM.FaTE Inputs Exhibits, Attachment A

Exhibit A

- 1. TRIM.FaTE Simulation Parameters fo r the TRIM.FaTE Screening Scenario

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

- 1

Exhibit A

- 2. Meteorological Inputs for the TRIM.FaTE Screening Scenario

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

- 2

Exhibit A

- 3. Air Parameters for the TRIM.FaTE Screening Scenario

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

- 3

Exhibit A

- 4. Soil and Groundwater Parameters for the TRIM.FaTE Screening Scenario

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

- 4

Exhibit A

- 5. Runoff Assumptions for the TRIM.FaTE Screening Scenario

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

- 6

Exhibit A

- 6. USLE Erosion Parameters for the TRIM.FaTE Screening Scenario

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

- 7

Exhibit A

- 7. Terrestrial Plant Placement for the TRIM.FaTE Screening Scenario

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

- 8

Exhibit A

- 8. Terrestrial Plant Parameters for the TRIM.FaTE Screening Scenario

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

- 9

Exhibit A

- 9. Surface Water Parameters for the TRIM.FaTE Screening Scenario

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

- 11

Exhibit A

- 10. Sediment Parameters for the TRIM.FaTE Scree ning Scenario

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

- 12

Exhibit A

- 11. Aquatic Animals Food Chain, Density, and Mass for the TRIM.FaTE Screening Scenario

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

- 13

Exhibit A

- 12 . Cadmium Chemical

- Specific Parameters for the TRIM.FaTE Screening Scenario

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

- 14

Exhibit A

- 13. Mercury Chemical

- Specific Parameters for the TRIM.FaTE Screening Scenario

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

- 15

Exhibit A

- 14. PAH Chemical

- Specific Parameters for the TRIM.FaTE Screening Scenario

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

- 16

Exhibit A

- 15. Dioxin Chemical

- Specific Parameters for the TRIM .FaTE Screening Scenario

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

- 18

Exhibit A

-- 16. Cadmium Chemical

- Specific Parameters for Abiotic Compartments in the TRIM.FaTE Screening Scenario

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

- 20

Exhibit A

- 17. Mercury Chemical

- Specific Parameters for Abiotic Compartments in the TRIM.FaTE Screening Scenario

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

- 21

Exhibit A

- 18. PAH Chemical

- Specific Parameters for Abiotic C ompartments in the TRIM.FaTE Screening Scenario

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

- 25

Exhibit A

- 19. Dioxin Chemical

- Specific Parameters for Abiotic Compartments in the TRIM.FaTE Screening Scenario

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

- 28

Exhibit A

- 20. Cadmium Chemical

- Specific Parameters for Plant Compartments in the TRIM.FaTE Screening Scenario

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

- 32

Exhibit A

- 21. Mercury Chemical

- Specific Pa rameters for Plant Compartments in the TRIM.FaTE Screening Scenario

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

- 33

Exhibit A

- 22. PAH Chemical

- Specific Parameters for Plant Compartments in the TRIM.FaTE Screening Scenario

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

- 34

Exhibit A

- 23. PAH Chemical

- Specific Parameters for Plant Compartments in the TRIM.FaTE Screening Scenario

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

- 35TRIM

- Based Tiered Screening Methodology for RTR

Attachment A iv October 2014 TRIM.FaTE Inputs Exhibit A

- 24. Cadmium Chemical

- Specific Parameters for Aquatic Species in the TRIM.FaTE Screening Scenario

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

- 37

Exhibit A

- 25. Mercury Chemical

- Specific Parameters for Aquatic Species in the TRIM.FaTE Screening Scenario

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

- 39

Exhibit A

- 26. PAH Chemical

- Specific Parameters for Aquatic Species in the TRIM.FaTE Screening Scenario

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

- 40

Exhibit A

- 27. Dioxin Chemi cal

- Specific Parameters for Aquatic Species in the TRIM.FaTE Screening Scenario

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

- 43TRIM

- Based Tiered Screening Methodology for RTR

Attachment A A

- 1 October 2014 TRIM.FaTE Inputs This attachment provides tables of the modeling inputs for the TRIM.FaTE screening scenario. Exhibit A

- 1 present s runtime settings for TRIM.FaTE. Exhibit A

- 2 and Exhibit A

- 3 present meteorological and air parameters , respectively, entered into the model. Exhibit A

- 4

. , Exhibit A

- 5 , and Exhibit A

- 6 present the parameters for soil and groundwater, runoff assumptions, and the USLE (universal soil loss equation) erosion parameters, respectively, for the screening scenario. Exhibit A

- 7 and Exhibit A

- 8 present terrestrial parameters. Exhibit A

- 9 through Exhibit A

- 11 present lake parameters, and Exhibit A

- 12 through Exhibit A

- 2 7 present paramet ers specific to the chemical s modeled in the scenario.

Exhibit A

- 1 . TRIM.FaTE Simulation Parameters for the TRIM.FaTE Screening Scenario

Parameter Name Units Value Used Ref erence Start of simulation date/time 1/1/1990, midnight Consistent with met data.
End of simulation date/time 1/1/2040, midnight Consistent with met data set; selected to provide a 50

- year modeling period. Simulation time step hr 1 Selected value. Outp ut time step a hr 4 Selected value.

a Output time step is set in TRIM.FaTE using the scenario properties "simulationStepsPerOutputStep" and "simulationTimeStep."

.TRIM

- Based Tiered Screening Methodology for RTR

Attachment A A

- 2 October 2014 TRIM.FaTE Inputs Exhibit A

- 2

. Meteorological Inputs for the TRIM.FaTE Screening Scenario

Parameter Name Units Value Used Reference **Meteorological Inputs** Air temperature degrees K 298 USEPA 2005. Horizontal wind speed m/sec 2.8 5th percentile annual average value for contiguous U nited S tates , calculated from 30 yrs of annual normal temperature values. Vertical wind speed m/sec 0.0 Assumption; vertical wind speed not used by any of the algorithms in the version of the TRIM.FaTE library used for screening.
Wind direction degrees clockwise from N (blowing from) 3

- days

- on 4

- days

- off On is defined as time during which wind is blowing into the model domain. A conservative estimate of time during which wind should blow into the modeling domain was determined by evaluating HUSWO; it w as concluded that a conservative estimate would be approximately 42% of the time.
Rainfall r ate m 3 [rain]/m 2 [surface area]

- day varies daily 1.5 m/yr is the maximum statewide 30

- year (1971

- 2000) average for the contiguous United States, excluding Rhode Isl and because of extreme weather conditions on Mt. Washington. Data obtained from the National Climatic Data Center at http://www.ncdc.noaa.gov/oa/climate/online/ccd/nrmpcp.txt. The precipitation frequency was 3

- days

- on:4

- days

- off based on data from Holzwo rth, 1972. Mixing height (used to set air VE property named “top”) m 710 5th percentile annual average mixing heights (calculated from daily morning and afternoon values), for all stations on SCRM (40 state, 70 stations). isDay_SteadyState_forAir unitles s

-- Value not used in current dynamic runs (would need to be reevaluated if steady

state runs are needed). isDay_SteadyState_forOther unitless

--TRIM

- Based Tiered Screening Methodology for RTR

Attachment A A

- 3 October 2014 TRIM.FaTE Inputs Exhibit A

- 3

. Air Para meters for the TRIM.FaTE Screening Scenario

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

- 08 Bidleman 1988 Density of air g/cm 3 0.0012 US EPA 1997 b Dust density kg[dust]/m 3 [dust] 1,400 Bidleman 1988 Fraction orga nic matter on particulates unitless 0.2 Harner and Bidleman 1998TRIM

- Based Tiered Screening Methodology for RTR

Attachment A A

- 4 October 2014 TRIM.FaTE Inputs Exhibit A

- 4

. Soil and Groundwater Parameters for the TRIM.FaTE Screening Scenario

Parameter Name Units V alue Used Reference **Surface Soil Compar t**  **ment Type**
Air content volume[air]/volume[compartment] 0.28 McKone et al. 2001. Average vertical velocity of water (percolation) m/day 8.22E

- 04 Assumed to be 0.2 times average precipitation for site. Boundary laye r thickness above surface soil m 0.005 Thibodeaux 1996; McKone et al. 2001 (Table 3). Density of soil solids (dry weight) kg[soil]/m 3 [soil] 2600 Default in McKone et al. 2001 (Table 3)

. Thickness

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

- tilled a m 0.20 USEPA 2005.
Erosion fraction unitless varies b See E xhibit 5

. Fraction of area available for erosion m 2 [area available]/m 2 [total] 1 Assumption ; area assumed rural.

Fraction of area available for runoff m 2 [area available]/m 2 [total] 1 Ass umption ; area assumed rural.
Fraction of area available for vertical diffusion m 2 [area available]/m 2 [total] 1 Assumption ; area assumed rural.

Fraction s and unitless 0.25 Assumption

. Organic carbon fraction unitless 0.008 U.S. average in McKone et al. 2001 (Table 16 and A

- 3). pH unitless 6.8 Assumption

. Runoff fraction unitless varies b See Exhibit A

- 5

. Total erosion rate kg [soil]/m 2 /day varies b See Exhibit A

- 6

. Total runoff rate m 3 [water]/m 2 /day 1.64E

- 03 Calculated using scenario

- specific precipitation rate and assumptions associated with water balance.
Water content volume[water]/volume[compartment] 0.15 McKone et al. 2001 **Root Zone Soil Compartment Type**
Air content volume[air]/volume[compartm ent] 0.25 McKone et al 2001 (Table 16). Average vertical velocity of water (percolation) m/day 8.22E

- 04 Assumed as 0.2 times average precipitation for New England in McKone et al. 2001. Density of soil solids (dry weight) kg[soil]/m 3 [soil] 2,600 McKone e t al. 2001 (Table 3).
Fraction s and unitless 0.25 Assumption

.TRIM

- Based Tiered Screening Methodology for RTR

Attachment A A

- 5 October 2014 TRIM.FaTE Inputs Exhibit A

- 4

. Soil and Groundwater Parameters for the TRIM.FaTE Screening Scenario

Parameter Name Units Value Used Reference **Root Zone Soil Comp artment Type**  **, continued**
Thickness

- untilled a m 0.79 McKone et al. 2001 (Table 16

- U.S. a verage).
Thickness

- tilled a m 0.6 Adjusted from McKone et al. 2001 (Table 16). Organic carbon fraction unitless 0.008 McKone et al. 2001 (Table s 16 and A

- 3, U.S.

a verage). pH unitless 6.8 Assumption

. Water content volume[water]/volume[compartment] 0.15 McKone et al. 2001 **Vadose Zone Soil Compartment Type**
Air content volume[air]/volume[compartment] 0.22 McKone et al. 2001 (Table 17). Average vertical velocity o f water (percolation) m/day 8.22E

- 04 Assumed as 0.2 times average precipitation for New England in McKone et al. 2001. Density of soil solids (dry weight) kg[soil]/m 3 [soil] 2,600 Default in McKone et al. 2001 (Table 3).
Fraction s and unitless 0.35 Assump tion

. Thickness a m 1.4 McKone et al. 2001 (Table 17). Organic carbon fraction unitless 0.003 McKone et al. 2001 (Table s 16 and A

- 3, U.S.

a verage). pH unitless 6.8 Assumption

. Water content volume[water]/volume[compartment] 0.21 McKone et al. 2001 (Tabl e 17

- national average).
**Ground w**  **ater Compartment Type**
Thickness a m 3 McKone et al. 2001 (Table 3).
Fraction s and unitless 0.4 Assumption

. Organic carbon fraction unitless 0.004 Assumption

. pH unitless 6.8 Assumption

. Porosity volume[total pore space ]/volume[compartment] 0.2 Default in McKone et al. 2001 (Table 3).

Density of s olid material in aquifer kg[soil]/m 3 [soil] 2,600 Default in McKone et al. 2001 (Table 3).

a Set using the volume element properties file

. b See separate tables ( Exhibit A

- 5 and Exhibit A

- 6 ) for erosion/runoff fractions and total erosion rates.TRIM

- Based Tiered Screening Methodology for RTR

Attachment A A

- 6 October 2014 TRIM.FaTE Inputs Exhibit A

- 5

. Runoff Assumptions for the TRIM.FaTE S creening Scenario
Originating Compartment Destination Compartment Runoff/Erosion F raction SurfSoil_Source SurfSoil_N1 0.0 SurfSoil_S1 0.0 sink 1.0 SurfSoil_N1 SW_Pond 1.0 SurfSoil_Source 0.0

SurfSoil_N6 0.0 SurfSoil_S1 0.0 sink 0.0 SurfSoil_ S1 SW_Pond 1.0 SurfSoil_Source 0.0

SurfSoil_N1 0.0 sink 0.0 SurfSoil_N6 SW_Pond 1.0 SurfSoil_N1 0.0 SurfSoil_N7 0.0 sink 0.0 SurfSoil_N7 SW_Pond 1.0 SurfSoil_N6 0.0 SurfSoil_N3 0.0 sink 0.0 SurfSoil_N3 SW_Pond 1.0 SurfSoil_N7 0.0 S urfSoil_N4 0.0 sink 0.0 SurfSoil_N4 SW_Pond 1.0 SurfSoil_N3 0.0 SurfSoil_N5 0.0 SurfSoil_S4 0.0 sink 0.0 SurfSoil_S4 SW_Pond 1.0 SurfSoil_N4 0.0 SurfSoil_S5 0.0 sink 0.0 SurfSoil_N5 a SW_Pond 0.0 SurfSoil_N4 0.5 SurfSoil_S5 0.5 sink 0.0 SurfSoil_S5 a SW_Pond 0.0 SurfSoil_N5 0.0 SurfSoil_S4 1.0 sink 0.0

a Assumes that N5 is higher ground tha n S5, and half of the runoff flows into N4, and the other half in to S5. Assumes all runoff from S5 flows into S4.TRIM

- Based Tiered Screening Methodology for RTR

Attachment A A

- 7 October 2014 TRIM.FaTE In puts Exhibit A

- 6

. USLE Erosion Parameters for the TRIM.FaTE Screening Scenario

Soil Parcel Area Rainfall/Erosivity Index Soil Erodibility Index Length

Slope Factor Land Use Cover Mgmt Factor Supportin g Practices

Factor Unit Soil Loss Sediment Delivery

Ratio a Calculated (Adjusted)

Erosion

Rate

Code m 2 R (100 ft

- ton/ac) K (ton/ac/(100 ft

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

N1 5.8E+04 300 0.39 1.5 grass 0.1 1 17.5 5 0.010779 0.533 0.005740
N6 4.1E+04 300 0.39 1.5 crops 0.2 1 35.1 0.021557 0.557 0.012014
N7 7.3E+04 300 0.39 1.5 grass 0.1 1 17.55 0.010779 0.518 0.005580
N3 3.5E+05 300 0.39 1.5 grass 0.1 1 17.55 0.010779 0.385 0.004151
N4 2.0E+06 300 0.39 1.5 fores t 0.1 1 17.55 0.010779 0.309 0.003331
N5 6.7E+06 300 0.39 1.5 forest 0.1 1 17.55 0.010779 0.196 0.002116
S1 5.8E+04 300 0.39 1.5 grass 0.1 1 17.55 0.010779 0.533 0.005740
S4 2.0E+06 300 0.39 1.5 forest 0.1 1 17.55 0.010779 0.309 0.003331
S5 6.7E+06 300 0.39 1.5 forest 0.1 1 17.55 0.010779 0.196 0.002116

a Calculated using SD R = a * (AL) - b ; where a is the empirical intercept coefficient (based on the size of the watershed), AL is the total watershed area receiving deposit ion (m 2 ), and b is the empirical slope coefficient (always 0.125).TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment A A

- 8 October 2014 TRIM.FaTE Inputs Exhibit A

- 7

. Terrestrial Plant Placement for the TRIM.FaTE Screening Scenario

Surface Soil Volume Element Surface Soil Depth (m) Coniferous Forest Grasses/ Herbs None

Source 0.01 x

N1 0.01 x N6 0.20 (tilled) x N7 0.01 x N3 0.01 x N4 0.01 x N5 0.01 x S1 0.01 x S4 0.01 x S5 0.01 xTRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment A A

- 9 October 2014 TRIM.FaTE Inputs Exhibit A

- 8

. Terrestrial Plant Parameters for the TRIM.FaTE Screening Scenario

Parameter Name Units Coniferous a Grass/Herb a
Value Used Reference Value Used Reference
**Leaf Compartment Type** Allow exchange 1=yes, 0=no 1

- S easonal b

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

m 2 [area] 5.0 Harvard Forest, dom. red oak and red maple, CDIAC website 5.0 Mid

- range of 4

- 6 for old fields, R.J. Luxmoore, ORNL. Calculate wet deposition interception fraction ( Boolean ) 1=yes, 0=no 0 Assumption

. 0 Assumption

.

Corre ction exponent, octan o l to lipid unitless 0.76 From roots, Trapp 1995. 0.76 From roots, Trapp 1995.

Degree stomatal opening unitless 1 Assumed value of 1 for daytime (stomatal diffusion is turned off at night using a different property, IsDay). 1 Assumed value of 1 for daytime (stomatal diffusion is turned off at night using a different property, IsDay).

Density of wet leaf kg/m 3 820 Paterson et al. 1991. 820 Paterson et al. 1991. Leaf wetting factor m 3.00E

- 04 1E

- 04 to 6E

- 04 for different crops and ele ments, Muller and Prohl 1993. 3.00E

- 04 1E

- 04 to 6E

- 04 for different crops and elements, Muller and Prohl 1993.

Length of leaf m 0.01 Assumption

. 0.05 Assumption

. Lipid content kg/kg wet weight 0.00224 European beech, Riederer 1995. 0.00224 European beech , Riederer 1995. Litter fall rate 1/day 0.0021 value assumes 1st

- order relationship and that 99% of leaves fall over 6 years S easonal c

-

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

.
Vegetation attenuation factor m 2 /kg 2.9 Grass/hay, Baes et al. 1984. 2.9 Grass/hay, Baes et al. 1984. Water content unitless 0.8 Paterson et al. 1991. 0.8 Paterson et al. 1991. Wet deposition interception fraction unitless 0.2 Calculated based on 5 ye ars of local met data, 1987

- 1991. 0.2 Calculated based on 5 years of local met data, 1987

- 1991. Wet mass of leaf per soil area kg[fresh leaf]/m 2 [area] 2.0 Calculated from leaf area index, leaf thickness (Simonich and Hites, 1994), density of wet foliage. 0.6 Calculated from leaf area index and Leith 1975.

**Particle on Leaf Compartment Type** Allow exchange 1=yes, 0=no 1

- S easonal b

-TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment A A

- 10 October 2014 TRIM.FaTE Inputs Exhibit A

- 8

. Terrestrial Plant Parameters for the TRIM.FaTE Screening Scenar io

Parameter Name Units Coniferous a Grass/Herb a
Value Used Reference Value Used Reference
**Particle on Leaf Compartment Type , continued**  Volume particle per area leaf m 3 [leaf particles]/m 2 [leaf] 1.00E

- 09 Based on particle density and size distribution for atmospheric particles measured on an adhesive surface, Coe and Lindberg 1987. 1.00E

- 09 Based on particle density and size distribution for atmospheric particles measured on an adhesive surface, Coe and Lindberg 1987. **Root Compartment Type**

**– Nonwoody O**  **nly** Allow exchange 1=yes, 0=no S easonal b

- Correction exponent, octanol to lipid unitless 0.76 Trapp 1995.

Lipid content of root kg/kg wet weight 0.011 Calculated. Water content of root kg/kg wet weight 0.8 Assumption

. Wet density of r oot kg/m 3 820 Soybean, Paterson et al. 1991. Wet mass per soil area kg/m 2 1.4 Temperate grassland, Jackson et al. 1996. **Stem Compartment Type**

**– Nonwoody Only**  Allow exchange 1=yes, 0=no S easonal b

- Correction exponent, octanol to lipid unitl ess 0.76 Trapp 1995

.
Density of phloem fluid kg/m 3 1,000 Assumption

. Density of xylem fluid kg/cm 3 900 Assumption

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

Fraction of transpiration flow

r ate that is phloem rate unitless 0.05 Paterson et al. 1991.

Lipid content of stem kg/kg wet weight 0.00224 Leaves of European beech, Riederer 1995. Water content of stem unitless 0.8 Paterson et al. 1991 Wet density of stem kg/m 3 830 Ass umption

. Wet mass per soil area kg/m 2 0.24 Calculated from leaf and root biomass density.

a See Exhibit A

- 7 for assignment of plant types to surface soil compartments.

b Begins March 9 (set to 1), ends November 7 (set to 0). Nationwide 80th percentile.

c Begins November 7, ends December 6; rate = 0.15/day during this time (value assumes 99 percent of leaves fall in 30 days).TRIM

- Base d Tiered Screening Methodolo gy for RTR

Attachment A A

- 11 October 2014 TRIM.FaTE Inputs Exhibit A

- 9

. Surfa ce Water Parameters for the TRIM.FaTE Screening Scenario

Parameter Name Units Value Used Reference Algae carbon content (fraction) unitless 0.465 APHA 1995.

Algae density in water column g[algae]/L[water] 0.0025 Millard et al. 1996 as cited in ICF 2005. Algae growth rate 1/day 0.7 Hudson et al. 1994 as cited in Mason et al. 1995b

. Algae radius um 2.5 Mason et al. 1995b. Algae water content (fraction) unitless 0.9 APHA 1995. Average algae cell density (per vol ume cell, not water) g[algae]/m 3 [algae] 1,0 00,000 Mason et al. 1995b, Mason et al. 1996. Boundary layer thickness above sediment m 0.02 Cal EPA 1993.

Chloride concentration mg/L 8.0 Kaushal et al. 2005. Chlorophyll concentration mg/L 0.0029 ICF 2005. Depth a m 3.18 WI DNR 200 7

- calculation base d on relationship between drainage basin and lake area size.
Dimensionless viscous sublayer thickness unitless 4 Ambrose et al. 1995.

Drag coefficient for water body unitless 0.0011 Ambrose et al. 1995.

Flush rate 1/year 12.17 Calculated based on pond d imensions and flow calculations.
Fraction Sand unitless 0.25 Assumption

. Organic carbon fraction in suspended sediments unitless 0.02 Assumption

.
pH unitless 7.3 Assumption

. Suspended sediment deposition velocity m/day 2 USEPA 1997 b

.
Total suspended sediment concentration kg[sediment]/m 3 [water column] 0.05 USEPA 2005.

Water temperature degrees K 298 USEPA 2005.

a Set using the volume element properties named "top" and "bottom."TRIM

- Base d Tiered Screening Methodolo gy for RTR

Attachment A A

- 12 October 2014 TRIM.FaTE Inputs Exhibit A

- 10

. Sediment Parameters for the TRIM.FaTE Screening Scenario

Parameter Name Units Value Used Reference Depth a m 0.05 McKone et al. 2001 (Table 3).
Fraction s and unitless 0.25 Assumption

. Organic carbon fraction unitless 0.02 McKo ne et al. 2001 (Table 3). Porosity of the sediment

zone volume[total pore space]/volume[sediment compartment] 0.6 USEPA 2005

.

Solid material density in sediment kg[sediment]/m 3 [sediment] 2,600 McKone et al. 2001 (Table 3). pH unitless 7.3 Assumption

. S ediment resuspension velocity m/day 6.69E

- 05 Calculated from water balance model.

a Set using the volume element properties named "top" and "bottom."TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment A A

- 13 October 2014 TRIM.FaTE Inputs Exhibit A

- 11

. Aquati c Animals Food Chain, Density, and Mass for the TRIM.FaTE Screening Scenario

Aquatic Biota (Consuming

Organism) Fraction Diet

Biomass (kg/m 2 ) Body Weight

(kg) ReferenceAlgaeZooplanktonBenthicInvertebrateWaterColumnHerbivoreBenthicOmnivoreWaterColumnOmnivoreBenthicCarnivoreWaterColumnCarnivore

Benthic Invertebrate 0% 0% 0% 0% 0% 0% 0% 0% 0.020 2.55E

- 04 Assumption

.

Water Column Herbivore 0% 100% 0% 0% 0% 0% 0% 0% 0.002 0.025 Assumption

.

Benthic Omnivore 0% 0% 100% 0% 0% 0% 0% 0% 0.002 2.50E

- 01 Assumption

. Water Column Omnivore 0% 0% 0% 100% 0% 0% 0% 0% 0.001 0.25 Assumption

.

Benthic Carnivore 0% 0% 50% 0% 50% 0% 0% 0% 0.001 2.0 Assumption

. Water Column Carnivore 0% 0% 0% 0% 0% 100% 0% 0% 0.0002 2.0 Assumption

.
Zooplankton 100% 0% 0 % 0% 0% 0% 0% 0% 0.0064 5.70E

- 08 Assumption

.TRIM

- Based Tiered Scree ning Methodolo gy for RTR

Attachment A A

- 14 October 2014 TRIM.FaTE Inputs Exhibit A

- 12

. Cadmium Chemical

- Specific Parameters for the TRIM.FaTE Screening Scenario

Parameter Name a Units Value Reference

CAS number b unitless 7440

- 43

- 9

- Diffusion coefficient in pure air m 2 [air]/day 0.71 USEPA 1999 (Table A

- 2

- 35). Diffusion coefficient in pure water m 2 [water]/day 8.16E

- 05 USEPA 1999 (Table A

- 2

- 35). Henry's Law c onstant Pa

- m 3 /mol 1.00E

- 37 USEPA 199 9 (Table A

- 2

- 35; assumed to be zero). Melting point degrees K 594 ATSDR 1999. Molecular weight g/mol 112.41 ATSDR 1999.
Octanol

- air partition coefficient (K oa ) m 3 [air]/m 3 [octanol]

- -

Octanol

- carbon partition coefficient (K oc ) - -

Octanol

- water part ition coefficient (K ow ) L[water]/kg[octanol]

- -

a All parameters in this table are TRIM.FaTE chemical properties.

b Th is CAS numbers appl ies to elemental Cd; however, the cations of cadmium are being modeled.TRIM

- Based Tiered Scree ning Methodolo gy for RTR

Attachment A A

- 15 October 2014 TRIM.FaTE Inputs Exhibit A

- 13

. Mercury Chemical

- Specific Parameters for the TRIM.FaTE Screening Scenario

Parameter Name Units Value Reference Hg(0) b Hg(2) b MHg b

CAS number unitless 7439

- 97

- 6 14302

- 87

- 5 22967

- 92

- 6

- Diffusion coe fficient in pure air m 2 [air]/day 0.478 0.478 0.456 USEPA 1997 b

.

Diffusion coefficient in pure water m 2 [water]/day 5.54E

- 05 5.54E

- 05 5.28E

- 05 USEPA 1997 b

.
Henry's Law constant Pa

- m 3 /mol 719 7.19E

- 05 0.0477 USEPA 1997 b

.
Melting p oint degrees K 234 5.50E+0 2 443 CARB 1994. Molecular weight g/mol 201 201 216 USEPA 1997 b

. Octanol

- water partition coefficient (K ow ) L[water]/kg[octanol] 4.15 3.33 1.7 Mason et al. 1996.

Vapor washout ratio m 3 [air]/m 3 [rain] 1,200 1.6E+06 0 USEPA 1997 b

, based on Petersen et al. 1 995.

a All parameters in this table are TRIM.FaTE chemical properties.

b On this and all following tables, Hg(0) = elemental mercury, Hg(2) = divalent mercury, and MHg = methyl mercury.TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment A A

- 16 October 2014 TRIM.FaTE Inputs Exhibit A

- 14

. PAH Chemical

- Specific Parameters for the TRIM.FaTE Screening Scenario

Parameter Name Units Value 2Methyl 712DMB Acenaphthene Acenaphthylene BaA BaP BbF BghiP
CAS number unitless 91

- 57

- 6 57

- 97

- 6 83

- 32

- 9 208

- 96

- 8 56

- 55

- 3 50

- 32

- 8 205

- 99

- 2 191

- 24

- 2
Diffusion coefficient in pure air m 2 /day 0.451 0.691 0.009 0.388 0.441 0.372 0.009 0.190

Diffusion coefficient in pure water m 2 /day 6.70E

- 05 6.91E

- 05 8.64E

- 05 6.03E

- 05 7.78E

- 05 7.78E

- 05 8.64E

- 05 4.54E

- 05

Henry's Law constant Pa

- m 3 /mol 50.56 0.20 18.50 12.70 1.22 0.07 0.05 0.03

Melting point degrees K 307.75 396.65 366.15 365.65 433 452 441 550.15 Molecular weight g/mol 142.20 256.35 154.21 152.20 228.29 252.32 252.32 276.34
Octanol

- water partition coefficient (K ow ) L[water]/L[oc tanol] 7.24E+03 6.31E+05 8.32E+03 1.00E+04 6.17E+05 9.33E+05 6.03E+05 4.27E+06

Parameter Name Units Value
BkF Chr DahA Fluoranthene Fluorene IcdP
CAS number unitless 207

- 08

- 9 218

- 01

- 9 53

- 70

- 3 206

- 44

- 0 86

- 73

- 7 193

- 39

- 5
Diffusion coefficient in p ure air m 2 /day 0.009 0.009 0.009 0.009 0.009 0.009

Diffusion coefficient in pure water m 2 /day 8.64E

- 05 8.64E

- 05 8.64E

- 05 8.64E

- 05 8.64E

- 05 8.64E

- 05

Henry's Law constant Pa

- m 3 /mol 0.04 0.53 0.0 076 1.96 9.81 0.03

Melting point degrees K 490 531 539 383.15 383.15 437 Molecular weight g/mol 252.32 228.29 278.33 202.26 166.20 276.34
Octanol

- water partition coefficient (K ow ) L[water]/L[octanol] 8.71E+05 5.37E+05 3.16E+06 1.45E+05 1.51E+04 5.25E+0 6TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment A A

- 17 October 2014 TRIM.FaTE Inputs Exhibit A

- 1 4. PAH Chemical

- Specific Parameter s for the TRIM.FaTE Screening Scenario

Parameter Name Units Reference

CAS number unitless

- Diffusion coefficient in pure air m 2 /day USEPA 2005. Exceptions include USEPA 1997 a (7,12

- Dimethylbenz(a)anthracene), and USEPA 2007 (2

- Methylnaphthalene, Ace naphthylene, and Benzo(g,h,i)perylene) Diffusion coefficient in pure water m 2 /day USEPA 2005. Exceptions include USEPA 1997 a (7,12

- Dimethylbenz(a)anthracene), and USEPA 2007 (2

- Methylnaphthalene, Acenaphthylene, and Benzo(g,h,i)perylene) Henry's Law cons tant Pa

- m 3 /mol USEPA 2005. Exceptions include USEPA 2003 (2

- Methylnaphthalene) HSDB 2001a (7,12

- Dimethylbenz(a)anthracene), HSDB 2001b (Acenaphthylene), and HSDB 2001c (Benzo(g,h,i)perylene) Melting point degrees K Budavari 1996. Exceptions include USEPA 2003 (2

- Methylnaphthalene), HSDB 2001a (7,12

- Dimethylbenz(a)anthracene), HSDB 2001b (Acenaphthylene), HSDB 2001c (Benzo(g,h,i)perylene), and USEPA 2005 (Acenaphthene, Fluoranthene, and Fluorene) Molecular weight g/mol Budavari 1996. Exceptions include USE PA 2003 (2

- Methylnaphthalene), HSDB 2001a (7,12

- Dimethylbenz(a)anthracene), HSDB 2001b (Acenaphthylene), HSDB 2001c (Benzo(g,h,i)perylene), and USEPA 2005 (Acenaphthene, Fluoranthene, and Fluorene)
Octanol

- water partition coefficient (K ow ) L[water]/L[octa nol] Hansch et al. 1995. Exceptions include Passivirta et al. 1999 (Acenaphthylene, Benzo(k)fluoranthene, and Indeno(1,2,3

- cd)pyrene), and Sangster 1993 (Benzo(b)fluoranthene)TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment A A

- 18 October 2014 TRIM.FaTE Inputs Exhibit A

- 15

. Dioxin Chemical

- Specific Parameters for the TRIM.FaTE Screening Scenario

Parameter Name Units Value1,2,3,4,6,7,8,9-OCDD1,2,3,4,6,7,8,9-OCDF1,2,3,4,6,7,8-HpCDD1,2,3,4,6,7,8-HpCDF1,2,3,4,7,8,9-HpCDF1,2,3,4,7,8-HxCDD1,2,3,4,7,8-HxCDF

CAS number unitless 3268

- 87

- 9 39001

- 02

- 0 35822

- 46

- 9 67562

- 39

- 4 55673

- 89

- 7 39227

- 28

- 6 70648

- 26

- 9
Diffusion coefficient in pure air m 2 /day 0.751 0.168 0.782 0.176 0.176 0.816 0.183

Diffusion coefficient in pure water m 2 /day 6.91E

- 05 6.91E

- 05 6. 91E

- 05 6.91E

- 05 6.91E

- 05 6.91E

- 05 6.91E

- 05

Henry's Law constant Pa

- m 3 /mol 0.684 0.19 1.22 1.43 1.42 1.08 1.45 Melting p oint degrees K 603.0 259.0 538.0 236.5 222.0 546.0 499.0 Molecular weight g/mol 460 443.76 425.2 409.31 409.31 391 374.87
Octanol

- wat er partition coefficient (K ow ) L[water]/L[octanol] 1.58E+08 1.00E+08 1.00E+08 2.51E+07 7.94E+06 6.31E+07 1.00E+07

Parameter Name Units Value1,2,3,6,7,8-HxCDD1,2,3,6,7,8-HxCDF1,2,3,7,8,9-HxCDD1,2,3,7,8,9-HxCDF1,2,3,7,8-PeCDD1,2,3,7,8-PeCDF2,3,4,6,7,8-HxCDF

CAS number unitless 57653

- 85

- 7 57117

- 44

- 9 19408

- 74

- 3 72918

- 21

- 9 40321

- 76

- 4 57117

- 41

- 6 60851

- 34

- 5
Diffusion coefficient in pure air m 2 /day 0.816 0.183 0.816 0.183 0.854 0.192 0.183

Diffusion coefficient in pure water m 2 /day 6.91E

- 05 6.9 1E

- 05 6.91E

- 05 6.91E

- 05 6.91E

- 05 6.91E

- 05 6.91E

- 05

Henry's Law constant Pa

- m 3 /mol 1.11 0.741 1.11 1.11 0.263 0.507 1.11 Melting point degrees K 558.0 506.0 517.0 509.0 513.0 499.0 512.5 Molecular weight g/mol 390.84 374.87 390.84 374.87 356.4 340.42 374 .87
Octanol

- water partition coefficient (K ow ) L[water]/L[octanol] 1.62E+08 8.24E+07 1.62E+08 3.80E+07 1.86E+07 6.17E+06 8.31E+07TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment A A

- 19 October 2014 TRIM.FaTE Inputs Exhibit A

- 1 5. Dioxin Chemical

- Specific Parameters for the TRIM.FaTE Screening Scenario

Parameter Name Units Value

Reference2,3,4,7,8-PeCDF2,3,7,8-TCDD2,3,7,8-TCDF

CAS number unitless 57117

- 31

- 4 1746

- 01

- 6 51207

- 31

- 9

- Diffusion coefficient in pure air m 2 /day 0.192 0.899 0.203 US EPA 2005

Diffusion coefficient in pure water m 2 /day 6.91E

- 05 4.84E

- 05 5.19E

- 05 US EPA 2 005

Henry's Law constant Pa

- m 3 /mol 0.505 3.33 1.46 US EPA 2005 Melting point degrees K 469.3 578.0 500.0 Mackay et al. 2000. Exceptions include USEPA 2000a (1,2,3,6,7,8

- HxCDD, 1,2,3,7,8,9

- HxCDF, and 1,2,3,7,8

- PeCDD), ATSDR 1998 (1,2,3,6,7,8

- HxCDF, 1,2,3, 7,8

- PeCDF, and 2,3,4,6,7,8

- HxCDF), and NLM 2002 (1,2,3,7,8,9

- HxCDD) Molecular weight g/mol 340.42 322 306 Mackay et al. 2000. Exceptions include ATSDR 1998 (1,2,3,6,7,8

- HxCDF, 1,2,3,7,8,9

- HxCDF, 1,2,3,7,8

- PeCDD, 1,2,3,7,8

- PeCDF, and 2,3,4,6,7,8

- HxCDF) and NLM 2002 (1,2,3,6,7,8

- HxCDD and 1,2,3,7,8,9

- HxCDD)
Octanol

- water partition coefficient (K ow ) L[water]/L[octanol] 3.16E+06 6.31E+06 1.26E+06 Mackay et al. 1992 as cited in USEPA 2000b. Exceptions include Mackay et al. 2000 (1,2,3,4,7,8,9

- HpCDF), USEPA 200 0a (1,2,3,6,7,8

- HxCDD, 1,2,3,6,7,8

- HxCDF, 1,2,3,7,8,9

- HxCDD, 1,2,3,7,8,9

- HxCDF, and 2,3,4,6,7,8

- HxCDF), and Sijm et al. 1989 as cited in USEPA 2000b (1,2,3,7,8

- PeCDD)TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment A A

- 20 October 2014 TRIM.FaTE Inputs Exhibit A

- 16

. Cadmium Chemical

- Specific Parameters for Abiotic Compartments in the TRIM.FaTE Screening Scenario

Parameter Name Units Value Reference

**Air Compartment Type** Particle dry deposition velocity m/day 260 Calculated from Mu h lbaier and Tis s ue 1980.
Washout r atio m 3 [air]/m 3 [rain] 200,000 MacKay et al. 1986. **Surface Soil Compartment Type** Use input characteristic depth ( Boolean ) 0 = no, Else = yes 0 Assumption

.
**Root Zone Soil Compartment Type** Use input characteristic depth ( Boolean ) 0 = no, E lse = yes 0 Assumption

.
**Vadose Zone Soil Compartment Type** Use input characteristic depth ( Boolean ) 0 = no, Else = yes 0 Assumption

.
**Surface Water Compartment Type**
Ratio of concentration in water to concentration in algae to concentration dissolved in w ater L[water]/g[algae wet wt] 1.87 McGeer et al. 2003.TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment A A

- 21 October 2014 TRIM.FaTE Inputs Exhibit A

- 17

. Mercury Chemical

- Specific Parameters for Abiotic Compartments in the TRIM.FaTE Screening Scenario

Par ameter Name Units Value Reference Hg(0) Hg(2) MHg **Air Compartment Type** Particle dry deposition velocity m/day 500 500 500 CalTOX value cited in McKone et al. 2001. Demethylation rate 1/day N/A N/A 0 Assumption

. Methylation rate 1/day 0 0 0 Assumpti on

. Oxidation r ate 1/day 0.00385 0 0 Low end of half

- life range (6 months to 2 years) in USEPA 1997 b

. Reduction rate 1/day 0 0 0 Assumption

. Washout r atio m 3 [air]/m 3 [rain] 200,000 200,000 200,000 Assumption

. **Surface Soil Compartment Type** Use input cha racteristic depth ( Boolean ) 0 = no, Else = yes 0 0 0 Assumption

.

Soil

- water partition coefficient L[water]/kg[soil wet wt] 1,000 58,000 7,000 USEPA 1997 b

.
Vapor dry deposition velocity m/day 50 2500 0 Hg(0)

- from Lindberg et al. 1992 ; Hg(2)

- estimate by USEPA using the Industrial Source Complex (ISC) Model

- [See Vol. III, App. A of the Mercury Study Report (USEPA 1997 b )]. Demethylation rate 1/day N/A N/A 0.06 Range reported in Porvari and Verta 1995 is 3E

- 2 to 6E

- 2 /day; value is average maximum pote ntial demethylation rate constant under anaerobic conditions. Methylation rate 1/day 0 0.001 0 Range reported in Porvari and Verta 1995 is 2E

- 4 to 1E

- 3 /day; value is average maximum potential methylation rate constant under anaerobic conditions.
Oxidati on rate 1/day 0 0 0 Value assumed in USEPA 1997 b

. Reduction rate 1/day 0 1.25E

- 05 0 Value used for untilled surface soil (2cm), 10% moisture content, in USEPA 1997 b ; general range is (0.0013/day)*moisture content to (0.0001/day)*moisture content for fores ted region (Lindberg 1996; Carpi and Lindberg 1997).TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment A A

- 22 October 2014 TRIM.FaTE Inputs Exhibit A

- 17 . Mercury Chemical

- Specific Parameters for Abiotic Compartments in the TRIM.FaTE Screening Scenario

Parameter Name Units Value Reference Hg(0) Hg(2) MHg Root Zone Soil Compartment Ty pe Use input characteristic depth ( Boolean ) 0 = no, Else = yes 0 0 0 Assumption

. Soil

- water partition coefficient L[water]/kg[soil wet wt] 1,000 58,000 7,000 USEPA 1997 b.

Demethylation rate 1/day N/A N/A 0.06 Range reported in Porvari and Verta 1995 is 3E

- 2 to 6E

- 2 /day; value is average maximum potential demethylation rate constant under anaerobic conditions. Methylation rate 1/day 0 0.001 0 Range reported in Porvari and Verta 1995 is 2E

- 4 to 1E

- 3 /day; value is average maximum potential methylation r ate constant under anaerobic conditions.
Oxidation rate 1/day 0 0 0 Value assumed in USEPA 1997 b

. Reduction rate 1/day 0 3.25E

- 06 0 Value used for tilled surface soil (20cm), 10% moisture content, in USEPA 1997 b (Lindberg 1996; Carpi and Lindberg 1997). **Vadose Zone Soil Compartment Type** Use input characteristic depth ( Boolean ) 0 = no, Else = yes 0 0 0 Assumption

. Soil

- water partition coefficient L[water]/kg[soil wet wt] 1,000 58,000 7,000 USEPA 1997 b

.
Demethylation rate 1/day N/A N/A 0.06 Range repor ted in Porvari and Verta 1995 is 3E

- 2 to 6E

- 2 /day; value is average maximum potential demethylation rate constant under anaerobic conditions. Methylation rate 1/day 0 0.001 0 Range reported in Porvari and Verta 1995 is 2E

- 4 to 1E

- 3 /day; value is average maximum potential methylation rate constant under anaerobic conditions.
Oxidation rate 1/day 0 0 0 Value assumed in USEPA 1997 b

. Reduction rate 1/day 0 3.25E

- 06 0 Value used for tilled surface soil (20cm), 10% moisture content, in USEPA 1997 b (Lindberg 1996; Carpi and Lindberg 1997).TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment A A

- 23 October 2014 TRIM.FaTE Inputs Exhibit A

- 17 . Mercury Chemical

- Specific Parameters for Abiotic Compartments in the TRIM.FaTE Screening Scenario

Parameter Name Units Value Reference Hg(0) Hg(2) MHg **Groundwater Compartment Type**
Soil

- water partition coefficient L[water]/kg[soil wet wt] 1,000 58,000 7,000 USEPA 1997 b

.
Demethylation rate 1/day N/A N/A 0.06 Range reported in Porvari and Verta 1995 is 3E

- 2 to 6E

- 2 /day; value is average maximum potential demethylation rate constant under anaerobic condi tions. Methylation rate 1/day 0 0.001 0 Range reported in Porvari and Verta 1995 is 2E

- 4 to 1E

- 3 /day; value is average maximum potential methylation rate constant under anaerobic conditions.
Oxidation rate 1/day 1.00E

- 08 0 0 Small default nonzero value (0 assumed in USEPA 1997 b ).
Reduction rate 1/day 0 3.25E

- 06 0 Value used for tilled surface soil (20cm), 10% moisture content, in USEPA 1997 b (Lindberg 1996; Carpi and Lindberg 1997). **Surface Water Compartment Type** Algal surface area

- specific uptake rat e constant nmol/[μm2

- day

- nmol] 0 2.04E

- 10 3.60E

- 10 Assumes radius = 2.5mm, Mason et al. 1995b, Mason et al. 1996; Hg(0) assumed same as Hg(2).
D ow ("overall K ow ") L[water]/kg[octanol] 0

- a

- b Mason et al. 1996.
Solids

- water partition coefficient L[water] /kg[solids wet wt] 1,000 100,000 100,000 USEPA 1997 b

.
Vapor dry deposition velocity m/day N/A 2500 USEPA 1997 b (Vol. III, App. A). Demethylation rate 1/day N/A N/A 0.013 Average range of 1E

- 3 to 2.5E

- 2/day from Gilmour and Henry 1991. Methylation rat e 1/day 0 0.001 0 Value used in EPA 1997; range is 1E

- 4 to 3E

4/day (Gilmour and Henry 1991).
Oxidation rate 1/day 0 0 0 Assumption

. Reduction rate 1/day 0 0.0075 0 Value used in USEPA 1997 b ; reported values range from less than 5E

- 3/day for depths great er than 17m, up to 3.5/day (Xiao et al. 1995; Vandal et al. 1995; Mason et al. 1995a; Amyot et al. 1997).TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment A A

- 24 October 2014 TRIM.FaTE Inputs Exhibit A

- 17 . Mercury Chemical

- Specific Parameters for Abiotic Compartments in the TRIM.FaTE Screening Scenario

Parameter Name Units Value Referenc e Hg(0) Hg(2) MHg **Sediment Compartment Type**
Solids

- water partition coefficient L[water]/kg[solids wet wt] 3,000 50,000 3,000 USEPA 1997 b

.
Demethylation rate 1/day N/A N/A 0.0501 Average range of 2E

- 4 to 1E

- 1/day from Gilmour and Henry 1991. Methyl ation rate 1/day 0 1.00E

- 04 0 Value used in EPA 1997 b ; range is 1E

- 5 to 1E

- 3/day, Gilmour and Henry 1991.
Oxidation rate 1/day 0 0 0 Assumption

. Reduction rate 1/day 0 1.00E

- 06 0 Inferred value based on presence of Hg(0) in sediment porewater (USEPA 1997 b ; Vandal et al. 1995).

a TRIM.FaTE Formula Property, which varies from 0.025 to 1.625 depending on pH and chloride concentration.

b TRIM.FaTE Formula Property, which varies from 0.075 to 1.7 depending on pH and chloride concentration.TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment A A

- 25 October 2014 TRIM.FaTE Inputs Exhibit A

- 18

. PAH Chemical

- Specific Parameters for Abiotic Compartments in the TRIM.FaTE Screening Scenari o

Parameter Name Units Value

2Methyl 712DMB Acenaph

thene Acenaph

thylene BaA BaP BbF BghiP BkF
**Air Compartment Type** Particle dry deposition velocity m/day 500 500 500 500 500 500 500 500 500
Half

- life day 0.154 0.092 0.3 0.208 0.125 0.046 0.596 0.215 0.458
Washout r atio 200000 200000 200000 200000 200000 200000 200000 200000 200 000 **Surface Soil Compartment Type** User input characteristic depth ( Boolean ) 0 = No, Else = Yes 0 0 0 0 0 0 0 0 0

Half

- life day 18 24 56 66.5 680 530 610 415 2140 **Root Zone Soil Compartment Type** User input characteristic depth ( Boolean ) 0 = No, Else = Yes 0 0 0 0 0 0 0 0 0

Half

- life day 18 24 56 66.5 680 530 610 415 2140 **Vadose Zone Soil Compartment Type** User input characteristic depth ( Boolean ) 0 = No, Else = Yes 0 0 0 0 0 0 0 0 0

Half

- life day 36 48 112 133 1360 1060 1220 830 4280
**Groundwater Com partment Type** 
Half

- life day 36 48 112 133 1360 1060 1220 830 4280 **Surface Water Compartment Type**
Ratio Of Conc In Algae To Conc Dissolved In Water (g[chem]/kg[algae]) / (g[chem]/L[water]) 2.6 333.4 3 3.7 325 510 317 1539 473

Half

- life day 78 216 25 184 0.375 0.138 90 1670 62.4 **Sediment Compartment Type**
Half

- life day 2290 2290 2290 2290 2290 2290 2290 2290 2290TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment A A

- 26 October 2014 TRIM.FaTE Inputs Exhibit A

- 1 8. PAH Chemical

- Specific Parameters for Abiotic Compartments in the TRIM.FaTE Screening Scenario

Parameter Name Units Value Refer ence Chr DahA Fluoran

thene Fluor

ene IcdP
**Air Compartment Type** Particle dry deposition velocity m/day 500 500 500 500 500 McKone et al. 2001

. Half

- life day 0.334 0.178 0.46 0.46 0.262 Howard et al. 1991 / upper bound measured or estimated value. Ex ceptions include ATSDR 2005 (2

- Methylnaphthalene), USEPA 1998 (7,12

- Dimethylbenz(a)anthracene, Benzo(g,h,i)perylene, and Fluoranthene) / average of range, HSDB 2001d (Acenaphthene), HSDB 2001b (Acenaphthylene), and Spero et al. 2000 (Fluorene)

. Washout r a tio 200000 200000 200000 200000 200000 Mackay et al. 1986

. **Surface Soil Compartment Type** User input characteristic depth ( Boolean ) 0 = No, Else = Yes 0 0 0 0 0 Assumption.

Half

- life day 1000 940 275 33 730 MacKay et al. 2000 / average of range. Exce ptions include ATSDR 2005 (2

- Methylnaphthalene), USEPA 1998 (7,12

- Dimethylbenz(a)anthracene, Benzo(g,h,i)perylene, and Fluoranthene) / average of range, HSDB 2001d (Acenaphthene), HSDB 2001b (Acenaphthylene), and HSDB 2001e (Fluorene)

. **Root Zone Soil Comp artment Type**  User input characteristic depth ( Boolean ) 0 = No, Else = Yes 0 0 0 0 0 Assumption.

Half

- life day 1000 940 275 33 730 Howard et al. 1991 / upper bound measured or estimated value. Exceptions include ATSDR 2005 (2

- Methylnaphthalene), USEPA 19 98 (7,12

- Dimethylbenz(a)anthracene, Benzo(g,h,i)perylene, and Fluoranthene) / average of range, HSDB 2001d (Acenaphthene), HSDB 2001b (Acenaphthylene), and HSDB 2001e (Fluorene)

. **Vadose Zone Soil Compartment Type** User input characteristic depth ( Boolean ) 0 = No, Else = Yes 0 0 0 0 0 Assumption.TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment A A

- 27 October 2014 TRIM.FaTE Inputs Exhibit A

- 1 8. PAH Chemical

- Specific Parameters for Abiotic Compartments in the TRIM.FaTE Screening Scenario

Parameter Name Units Value Reference Chr DahA Fluora

n - thene Fluor

ene IcdP
**Vadose Zone Soil Compartm ent Type**  **, continued**
Half

- life day 2000 1880 550 66 1460 Howard et al. 1991 / upper bound measured or estimated value. Exceptions include ATSDR 2005 (2

- Methylnaphthalene), USEPA 1998 (7,12

- Dimethylbenz(a)anthracene, Benzo(g,h,i)perylene, and Fluoranthene) / twice average of range, HSDB 2001d (Acenaphthene) / multiplied by 2, HSDB 2001b (Acenaphthylene) / multiplied by 2, and HSDB 2001e (Fluorene) / multiplied by 2

. **Groundwater Compartment Type**
Half

- life day 2000 1880 550 66 1460 Howard et al. 1991 / uppe r bound measured or estimated value. Exceptions include ATSDR 2005 (2

- Methylnaphthalene), USEPA 1998 (7,12

- Dimethylbenz(a)anthracene, Benzo(g,h,i)perylene, and Fluoranthene) / twice average of range, HSDB 2001d (Acenaphthene) / multiplied by 2, HSDB 2001b (Acenaphthylene) / multiplied by 2, and HSDB 2001e (Fluorene) / multiplied by 2

. **Surface Water Compartment Type** RatioOfConcInAlgaeToConcD issolvedInWater (g[chem]/kg[algae])/ (g[chem]/L[water]) 280 1388 67.4 5.8 1653 K ow from Del Vento and Dachs 2002

.

Ha lf

- life day 1.626 97.8 160 8.5 750 Howard et al. 1991 / upper bound measured or estimated value. Exceptions include HSDB 2005 (2

- Methylnaphthalene), HSDB 2001a (7

- 12 Dimethylbenz(a)anthracene), HSDB 2001d (Acenaphthene), HSDB 2001b (Acenaphthylene), and H SDB 2001c (Benzo(g,h,i)perylene), Montgomery 2000 (Fluoranthene), and Boyle 1985 (Fluorene)

. **Sediment Compartment Type**
Half

- life day 2290 2290 2290 2290 2290 Mackay et al. 1992 / PAH values are the mean half

- life of the log class that Mackay et al. assig ned for sediment, except for BbF and IcdP, which were not i n Table 2.3 of Mackay et. al.TRIM

- Based Tiered Screening Methodolo gy for RTR

At tachment A A

- 28 October 2014 TRIM.FaTE Inputs Exhibit A

- 19

. Dioxin Chemical

- Specific Parameters for Abiotic Compartments in the TRIM.FaTE Screening Scenario

Parameter Name Units Value1,2,3,4,6,7,8,9-OCDD1,2,3,4,6,7,8,9-OCDF1,2,3,4,6,7,8-HpCDD1,2,3,4,6,7,8-HpCDF1,2,3,4,7,8,9-HpCDF1,2,3,4,7,8-HxCDD

**Air Compartment Type** Deposition Velocity m/day 500 500 500 500 500 500

H alf

- life day 162 321 64 137 122 42
Washout Ratio m 3 [air]/m 3 [rain] 91000 22000 64000 32000 32000 9000 **Surface Soil Compartment Type** Input characteristic depth m 0.08 0.08 0.08 0.08 0.08 0.08 Use input characteristic depth ( Boolean ) 0 = No, Else = Yes 0 0 0 0 0 0

Halflife day 3650 3650 3650 3650 3650 3650 **Root Zone Soil Compartment Type** Input characteristic depth m 0.08 0.08 0.08 0.08 0.08 0.08 Use input characteristic depth 0 = No, Else = Yes 0 0 0 0 0 0

Half

- life day 3650 3650 3650 3650 3650 3650 **Vadose Zone Soil Compartment Type** Input characteristic depth m 0.08 0.08 0.08 0.08 0.08 0.08 Use input characteristic depth ( Boolean ) 0 = No, Else = Yes 0 0 0 0 0 0

Half

- life day 1008 1008 1008 1008 1008 1008 **Groundwater Compartment Type**
Half

- life da y 1008 1008 1008 1008 1008 1008 **Surface Water Compartment Type** Ratio Of Conc In Algae To Conc Dissolved In Water (g[chem]/g[algae])/ (g[chem]/L[water]) 5.31 4.54 4.54 2.83 1.9 3.88

Half

- life day 0.67 0.58 47 0.58 0.58 6.3 **Sediment Compartment Type**
Hal f

- life day 1095 1095 1095 1095 1095 1095TRIM

- Based Tiered Screening Methodolo gy for RTR

At tachment A A

- 29 October 2014 TRIM.FaTE Inputs Exhibit A

- 1 9. Dioxin Chemical

- Specific Parameters for Abiotic Compartments in the TRIM.FaTE Screening Scenario

Parameter Name Units Value1,2,3,4,7,8-HxCDF1,2,3,6,7,8-HxCDD1,2,3,6,7,8-HxCDF1,2,3,7,8,9-HxCDD1,2,3,7,8,9-HxCDF1,2,3,7,8-PeCDD

**Air Compartment Type** Deposition v elocity m/day 500 500 500 500 500 500
Half

- life day 78 28 55 28 51 18
Washout r atio m 3 [air]/m 3 [rain] 10000 9000 10000 9000 10000 18000 **Surface Soil Compartment Type** Input characteri stic depth m 0.08 0.08 0.08 0.08 0.08 0.08 Use input characteristic depth ( Boolean ) 0 = No, Else = Yes 0 0 0 0 0 0

Half

- life day 3650 3650 3650 3650 3650 3650 **Root Zone Soil Compartment Type** Input characteristic depth m 0.08 0.08 0.08 0.08 0.08 0.08
U se input characteristic depth 0 = No, Else = Yes 0 0 0 0 0 0

Half

- life day 3650 3650 3650 3650 3650 3650 **Vadose Zone Soil Compartment Type** Input characteristic depth m 0.08 0.08 0.08 0.08 0.08 0.08 Use input characteristic depth ( Boolean ) 0 = No, Else = Yes 0 0 0 0 0 0

Half

- life day 1008 1008 1008 1008 1008 1008 **Groundwater Compartment Type**
Half

- life day 1008 1008 1008 1008 1008 1008 **Surface Water Compartment Type** Ratio Of Conc In Algae To Conc Dissolved In Water (g[chem]/g[algae])/ (g[chem]/L[wate r]) 2.06 5.36 4.25 5.36 3.26 1.55

Half

- life day 0.58 6.3 0.58 6.3 0.58 2.7 **Sediment Compartment Type**
Half

- life day 1095 1095 1095 1095 1095 1095TRIM

- Based Tiered Screening Methodolo gy for RTR

At tachment A A

- 30 October 2014 TRIM.FaTE Inputs Exhibit A

- 1 9. Dioxin Chemical

- Specific Parameters for Abiotic Compartments in the TRIM.FaTE Screening Sc enario

Parameter Name Units Value1,2,3,7,8-PeCDF2,3,4,6,7,8-HxCDF2,3,4,7,8-PeCDF2,3,7,8-TCDD2,3,7,8-TCDF

**Air Compartment Type** Deposition v elocity m/day 500 500 500 500 500
Half

- life day 31 59 33 12 19
Washout r atio m 3 [air]/m 3 [rain] 13000 10000 14000 18000 19000 **Surface Soil Compartment Type** Input characteristic depth m 0.08 0.08 0.08 0.08 0.08 Use input characteristic depth ( Boolean ) 0 = No, Else = Yes 0 0 0 0 0

Half

- life day 3650 3650 3650 3650 3650 **Root Zone Soil Compartment Type** Input c haracteristic depth m 0.08 0.08 0.08 0.08 0.08 Use input characteristic depth 0 = No, Else = Yes 0 0 0 0 0
Half

- life day 3650 3650 3650 3650 3650 **Vadose Zone Soil Compartment Type** Input characteristic depth m 0.08 0.08 0.08 0.08 0.08 Use input charact eristic depth ( Boolean ) 0 = No, Else = Yes 0 0 0 0 0

Half

- life day 1008 1008 1008 1008 1008 **Groundwater Compartment Type**
Half

- life day 1008 1008 1008 1008 1008 **Surface Water Compartment Type** Ratio Of Conc In Algae To Conc Dissolved In Water (g[chem]/g [algae])/ (g[chem]/L[water]) 1.75 4.26 1.39 1.76 0.71

Half

- life day 0.19 0.58 0.19 2.7 0.18 **Sediment Compartment Type**
Half

- life day 1095 1095 1095 1095 1095TRIM

- Based Tiered Screening Methodolo gy for RTR

At tachment A A

- 31 October 2014 TRIM.FaTE Inputs Exhibit A

- 1 9. Dioxin Chemical

- Specific Parameters for Abiotic Compartments in the TRIM.FaTE Screening Scenario

Parameter Name Reference

**Air Compartment Type** Deposition v elocity McKone et al. 2001

. Half

- life Atkinson 1996 as cited in USEPA 2000 b ; vapor phase reaction with hydroxyl radical

. Washout r atio Vulykh et al. 2001

. **Surface Soil Com partment Type**  Input characteristic depth Not used (model set to calculate value)

. Use input characteristic depth ( Boolean ) Assumption.
Half

- life Mackay et al. 2000; the degradation rate was cited by multiple authors, value is for 2,3,7,8

- TCDD

. **Root Zon e Soil Compartment Type**  Input characteristic depth N ot used (model set to calculate value)

. Use input characteristic depth Assumption.
Half

- life Mackay et al. 2000; the degradation rate was cited by multiple authors, value is for 2,3,7,8

- TCDD

. **Vadose Z one Soil Compartment Type**  Input characteristic depth Not used (model set to calculate value). Use input characteristic depth ( Boolean ) Assumption

. Half

- life Average value of the range presented in Mackay et al. 2000; based on estimated unacclimated aero bic biodegradation half

- life, value is for 2,3,7,8

- TCDD. **Groundwater Compartment Type**
Half

- life Average value of the range presented in Mackay et al. 2000; based on estimated unacclimated aerobic biodegradation half

- life, value is for 2,3,7,8

- TCDD.
**Surf ace Water Compartment Type**  Ratio Of Conc In Algae To Conc Dissolved In Water Estimated from K ow value using model from DelVento and Dachs 2002
Half

- life Kim and O’Keefe 1998, as cited in USEPA 2000 b

. **Sediment Compartment Type**
Half

- life Estimation bas ed on Adriaens and Grbic

- Galic 1992,1993 and Adriaens et al. 1995 as cited in USEPA 2000 b

.TRIM

- Based Tiered Screening Methodolo gy for RTR

At tachment A A

- 32 October 2014 TRIM.FaTE Inputs Exhibit A

- 20

. Cadmium C hemical

- Specific Parameters for Plant Compartments in t he TRIM.FaTE Screening Scenario

Parameter Name Units Value Reference

**Leaf Compartment Type** Transfer factor to leaf particle 1/day 0.002 Assumption

. **Particle on Leaf Compartment Type**
Transfer factor to leaf 1/day 0.200 Assumption

. **Root Compartment Type** 

**- Grasses and Herbs**  **a**
Root to Root Soil Partition

Alpha of Steady State unitless 0.95 Henning et al. 2001.

Root to Root Soil Partition

Partitioning Coefficient m 3 [bulk root soil]/m 3 [root] 0.23 Nriagu 1980; based on average value calculated from va rious agricultural plant species.
Root to Root Soil Partition

Time to Reach Alpha day 28 Henning et al. 2001.

**Stem Compartment Type**

**- Grasses and Herbs**  **a** Transpiration stream concentration factor (TSCF) m 3 [soil pore water]/m 3 [xylem fluid] 0.45 Tsiros et al. 1999.

a Roots and stems are not modeled for deciduous or coniferous forest in the current version of TRIM.FaTE.TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment A A

- 33 October 2014 TRIM.FaTE Inputs Exhibit A

- 21

. Mercury Chemical

- Specific Parameters f or Plant Compartments in the TRIM.FaTE Screening Scenario

Parameter Name Units Value Reference Hg(0) Hg(2) MHg **Leaf Compartment Type** Transfer factor to leaf particle 1/day 0.002 0.002 0.002 A ssumed based on 1% of transfer factor from leaf particle to leaf. Demethylation rate 1/day N/A N/A 0.03 Calculated from Bache et al. 1973. Methylation rate 1/day 0 0 0 Assumed from Gay 1975, Bache et al. 1973. Oxidation rate 1/day 1.0E+06 0 0 A ssumed to be nearly instantaneous Reduction rate 1/day 0 0 0 Assump tion

. **Particle on Leaf Compartment Type** Transfer factor to leaf 1/day 0.2 0.2 0.2 Assumption

. Demethylation rate 1/day N/A N/A 0 Assumption

. Methylation rate 1/day 0 0 0 Assumption

. Oxidation rate 1/day 0 0 0 Assumption

. Reduction rate 1/day 0 0 0 As sumption

. **Root Compartment Type**

**- Grasses and Herbs**  **a** Alpha for root

- root zone bulk soil unitless 0.95 0.95 0.95 Selected value. Root/root

- zone

- soil

- water partition coefficient m 3 [bulk root soil]/ m 3 [root] 0 0.18 1.2 Hg 2

- geometric mean Leonard et al. 19 98, John 1972, Hogg et al. 1978; MHg

assumed, based on Hogg et al. 1978.
t

- alpha for root

- root zone bulk soil day 21 21 21 Assumption

. Demethylation rate 1/day N/A N/A 0 Assumption

. Methylation rate 1/day 0 0 0 Assumption

. Oxidation rate 1/day 0 0 0 A ssumption

. Reduction rate 1/day 0 0 0 Assumption

. **Stem Compartment Type**

**- Grasses and Herbs**  **a** Transpiration stream concentration factor (TSCF) m 3 [soil pore water]/m 3 [xylem fluid] 0 0.5 0.2 Calculation from Norway spruce, Scots pine, Bishop et al. 1998. Demethylation rate 1/day N/A N/A 0.03 Calculated from Bache et al. 1973. Methylation rate 1/day 0 0 0 Assumption

. Oxidation rate 1/day 0 0 0 Assumption

. Reduction rate 1/day 0 0 0 Assumption

.

a Roots and stems are not modeled for deciduous or coniferous forest in the current version of TRIM.FaTE.TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment A A

- 34 October 2014 TRIM.FaTE Inputs Exhibit A

- 22

. PAH Chemical

- Specific Parameters for Plant Compartments in the TRIM.FaTE Screening Scenario

Parameter Name Units Value

2Methyl 712DMB Acenaph

thene Acenaph

thylene BaA BaP BbF BghiP BkF
**Leaf Compartment Type**
Transfer factor to leaf particle 1/day 1.00E

- 04 1.00E

- 04 1.00E

- 04 1.00E

- 04 1.00E

- 04 1.00E

- 04 1.00E

- 04 1.00E

- 04 1.00E

- 04

Half

- life day 3.50 3.50 3.50 3.50 3.50 3.50 3.50 3.50 3.50 **Particle on Leaf Compartment Type**
Transfer factor to leaf 1/day 1.00E

- 04 1.00E

- 04 1.00E

- 04 1.00E

- 04 1.00E

- 04 1.00E

- 04 1.00E

- 04 1.00E

- 04 1.00E

- 04

Half

- life day 2.31 2.31 2.31 2.31 1.84 2.31 3.56 2.31 17.80 **Root Compartment Type**

**- Grasses and Herbs**  **a**

Half

- life day 34.60 34.60 34.60 34.60 34.60 34.60 34.60 34.60 34.60
Root soil

- water interaction

- alpha unitless 0.95 0.95 0.95 0.95 0.95 0.95 0.95 0.95 0.95

**Stem Compartment Type**

**- Grasses and Herbs**  **a**
Half

- life day 3.50 3.50 3.50 3 .50 3.50 3.50 3.50 3.50 3.50

a Roots and stems are not modeled for deciduous or coniferous forest in the current version of TRIM.FaTE.TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment A A

- 35 October 2014 TRIM.FaTE Inputs Exhibit A

- 23

. PAH Chemical

- Specifi c Parameters for Plant Compartments in the TRIM.FaTE Screening Scenario

Parameter Name Units Value
Reference Chr DahA Fluoran

thene Fluorene IcdP

**Leaf Compartment Type**
Transfer factor to leaf particle 1/day 1.00E

- 04 1.00E

- 04 1.00E

- 04 1.00E

- 04 1.00E

- 04 Assumption

.

Half

- life day 3.50 3.50 3.50 3.50 3.50 Edwards 1988 ( calculated from metabolic rate constant )

. **Particle on Leaf Compartment Type**
Transfer factor to leaf 1/day 1.00E

- 04 1.00E

- 04 1.00E

- 04 1.00E

- 04 1.00E

- 04 Assumption

.

Half

- life day 4.12 17 .80 2.31 2.31 17.80 Calculated as 2 times the measured photolysis half

- life from Mackay et al. 1992. Exceptions include values that have been set equal to Benzo(a)pyrene (2

- Methylnaphthalene; 7,12

- Dimethylbenz(a)anthracene; Acenaphthene; Acenaphthylene; B enzo(ghi)perylene; Fluoranthene; and Fluorene)

. **Root Compartment Type**

**- Grasses and Herbs**  **a**

Half

- life day 34.60 34.60 34.60 34.60 34.60 Edwards 1988 ( calculated from metabolic rate constant )

. Root soil water interaction

- alpha unitless 0.95 0.95 0.95 0. 95 0.95 Assumption

.
**Stem Compartment Type**

**- Grasses and Herbs**  **a**
Half

- life day 3.50 3.50 3.50 3.50 3.50 Edwards 1988 ( calculated from metabolic rate constant )

.

a Roots and stems are not modeled for deciduous or coniferous forest in the current version of T RIM.FaTE.TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment A A

- 36 October 2014 TRIM.FaTE Inputs Exhibit A

- 2 3. Dioxin Chemical

- Specific Parameters for Plant Compartments in the TRIM.FaTE Screening Scenario

Parameter Name Units Value Reference All Dioxins **Leaf Compartment Type** Transfer factor to leaf particle 1/day 0.003 Calculated a s 1 percent of transfer factor to leaf; highly uncertain.
Half

- life day 70 Arjmand and Sandermann 1985, as cited in Komoba et al. 1995; soybean root cell culture metabolism test data for DDE. **Particle on Leaf Compartment Type**
Transfer factor to leaf 1/d ay 0.3 Assumption b ased on USEPA 2000 b (an estimate for mercury) and Trapp 1995; highly uncertain.
Half

- life day 4.4 McCrady and Maggard 1993; photodegradation sorbed to grass foliage in sunlight; assumed 10 % sunlight per day. **Root Compartment Type**

**- Gra**  **sses and Herbs a** 
Half

- life day 70 Arjmand and Sandermann 1985, as cited in Komoba et al. 1995; soybean root cell culture metabolism test data for DDE.
Root s oil

- w ater i nteraction

- a lpha unitless 0.95 Assumption

. **Stem Compartment Type**

**- Grasses and Herbs**  **a**
Half

- life day 70 Arjmand and Sandermann 1985, as cited in Komoba et al. 1995; soybean root cell culture metabolism test data for DDE.

a Roots and stems are not modeled for deciduous or coniferous forest in the current version of TRIM.FaTE.TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment A A

- 37 October 2014 TRIM.FaTE Inputs Exhibit A

- 24

. Cadmium Chemical

- Specific Parameters for Aquatic Species in the TRIM.FaTE Screening Scenario

Parameter Name Units Value Reference

**Zooplankton Compartment Type**
Ab sorption rate constant L[water]/kg[fish wet wt]

- day 1500 Goulet 2007

. Assimilation efficiency from algae unitless 0.5 Goulet 2007

. Elimination rate constant 1/day 0.03 Goulet 2007

. **Benthic Invertebrate Compartment Type**
Sediment p artitioning

- a lpha of

e quilibrium unitless 0.95 Assumption.

Sediment partitioning

- p artition

c oefficient kg[bulk sed/kg[invertebrate wet wt] 0.27 Assumption.

Sediment p artitioning

- t ime to r each

a lpha of e quilibrium day 21 Hare et al. 2001

.
**Benthic Omnivore Compartment Typ e**  Assimilation efficiency from food unitless 0.1 Assumption b ased on Yan and Wang 2002. Absorption r ate c onstant unitless 1.23 Calculated based on body weight from regression in Hendriks and Heikens 200 1.
Elimination r ate c onstant unitless 1.73E

- 02 Assu mption. **Benthic Carnivore Compartment Type** Assimilation efficiency from food unitless 0.1 Assumption b ased on Yan and Wang 2002. Absorption r ate c onstant unitless 0.66 Calculated based on body weight from regression in Hendriks and Heikens 200 1.
Elimin ation r ate c onstant unitless 1.68E

- 03 Assumption.
**Water**

**- column Herbivore Compartment Type**  Assimilation efficiency from food unitless 0.1 Assumed value based on Yan and Wang 2002. Assimilation efficiency from plants unitless 0.1 Assumed value based on Ya n and Wang 2002. Absorption rate constant unitless 2.46 C alculated based on body weight from regression in Hendriks and Heikens 200 1.
Elimination rate constant unitless 1.73E

- 02 Assumption.TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment A A

- 38 October 2014 TRIM.FaTE Inputs Exhibit A

- 24 . Cadmium Chemical

- Specific Parameters for Aquat ic Species in the TRIM.FaTE Screening Scenario

Parameter Name Units Value Reference
**Water**

**- column Omnivore Compartment Type**  Assimilation efficiency from food unitless 0.1 Assumption b ased on Yan and Wang 2002. Assimilation efficiency from plants unitless 0.1 Assumption based on Yan and Wang 2002. Absorption rate constant unitless 1.23 Calculated based on body weight from regression in Hendriks and Heikens 200 1.
Elimination rate constant unitless 1.73E

- 02 Assumption.
**Water**

**- column Carnivore Compartment T**  **ype** Assimilation efficiency from food unitless 0.1 Assumption based on Yan and Wang 2002. Absorption rate constant unitless 0.66 Calculated based on body weight from regression in Hendriks and Heikens 200 1.
Elimination rate constant unitless 1.73E

- 02 As sumptionTRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment A A

- 39 October 2014 TRIM.FaTE Inputs Exhibit A

- 25

. Mercury Chemical

- Specific Parameters for Aquatic Species in the TRIM.FaTE Screening Scenario

Parameter Name Units Value Reference Hg(0) Hg(2) M Hg **Zooplankton Compartment Type**
Assimilation Efficiency From Algae unitless 0.2 0.015 0.5 Environment Canada 2002.
Half

- life day 1.0E+09 1.0E+09 1.0E+09 Assumption

. How Much Faster Hg Elimination Is Than For MHg unitless 3 3 1 Assumption

.
Methylation rate 1/day 0 0 0 Assumption

. Oxidation rate 1/day 0 1.0E+06 0 Assumption

. Reduction rate 1/day 0 0 0 Assumption

. **Benthic Invertebrate Compartment Type** Alpha of equilibrium for sediment partitioning unitless 0.95 0.95 0.95 Selected valu e (i.e., proport ion of equilibrium achieved by time “t”).
Benthic invertebrate

- bulk sediment partition coefficient kg[bulk sediment]/kg[invertebrate wet wt] 0.0824 0.0824 5.04 Hg(0)

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

- Saouter et al. 1991.

t

- alpha for equilib rium for sediment partitioning day 14 14 14 Experiment duration from Saouter et al. 1991. **All Fish Compartments Types a**  Elimination adjustment factor unitless 3 3 1 Trudel and Rasmussen 1997. Assimilation efficiency from food unitless 0.06 0.06 0.5 Willi ams et al. 2010. Demethylation rate 1/day N/A N/A 0 Assumption

. Methylation rate 1/day 0 0 0 Assumption

. Oxidation rate 1/day 1.0E+06 0 0 Assumption

. Reduction rate 1/day 0 0 0 Assumption

. **Water**

**- column Herbivore Compartment Type** 
Assimilation efficien cy from plankton unitless 0.06 0.06 0.5 Williams et al. 2010.

a Screening scenario includes: Benthic Omnivore, Benthic Carnivore, Water

- column Herbivore, Water

- column Omnivore, and Water

- column Carnivore.TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment A A

- 40 October 2014 TRIM.FaTE Inputs Exhibit A

- 26

. PAH Chemical

- Specific Parameters for Aquatic Species in the TRIM.FaTE Screening Scenario

Parameter Name Units Value

2Methyl 712DMB Acenaph

thene Acenaph

thylene BaA BaP BbF BghiP BkF

**Zooplankton Compa rtment Type**  Absorption rate constant L[water]/kg[fish wet wt]

- day 790 42650.94 42231 42302.18 42650.81 42652.78 42650.68 42655.77 42652.5

Assimilation efficiency from algae unitless 0.5 0.25 0.5 0.5 0.46 0.25 0.25 0.25 0.25

Elimination rate constant 1/d ay 169.68 2.03 148.07 123.44 2.073 1.3864 2.12 0.33 1.48

Half

- life day 0.007788 17 0.00239 0.00239 1.284 16.5 17 17 17 **Benthic Invertebrate Compartment Type**
Clearance constant unitless 100.6 100.6 100.6 100.6 100.6 100.6 100.6 100.6 100.6

V d (ratio of concentration in benthic invertebrates to concentration in water) ml/g 7235 7235 7235 7235 7235 7235 7235 7235 7235

Half

- life day 0.722 17 0.722 0.722 1.284 16.5 17 17 17 **All Fish Compartment Types a** 
Gamma fish unitless 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0. 2
Assimilation efficiency from food unitless 0.5 0.15 0.5 0.32 0.15 0.15 0.15 0.15 0.15

Half

- life day 0.2 2 0.2 0.2 0.408 1.925 2 2 2

a Screening scenario includes: Benthic Omnivore, Benthic Carnivore, Water

- column Herbivore, Water

- column Omnivore, and W ater

- column Carnivore.TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment A A

- 41 October 2014 TRIM.FaTE Inputs Exhibit A

- 2 6. PAH Chemical

- Specific Parameters for Aquatic Species in the TRIM.FaTE Screening Scenario

Parameter Name Units Value
Reference Chr DahA Fluoran

thene Fluorene IcdP

**Zooplankton Compartment Type** Absorption rate

c onstant L[water]/kg[fish wet wt]

- day 42649.95 42655.48 142000 15000 42655.93 K ow from Arnot et al. 2004. Exception is Berrojalbiz et al. 2009 (2

- Methylnaphthalene, Fluoranthene, and Fluorene)

. Assimilation efficiency from algae unitless 0.46 0.25 0.49 0.5 0.25 K ow from Arnot et al. 2004. Exception is maximum value from Wang and Wang 2006 (7,12

- Dimethylbenz(a)anthracene, Benzo(a)pyrene, Benzo(b)fluoranthene, Benzo(g,h,i)perylene, Benzo(k)fluoranthene, Dibenz(a,h)anthracene, and Indeno(1,2,3

- cd)pyrene)

. Eli mination rate constant 1/day 2.3746 0.4331 8.678 81.87 0.269 K ow from Arnot et al. 2004

.

Half

- life day 0.495 17 0.00239 0.0002476 17 McElroy 1990. Exceptions include Berrojalbiz et al. 2009 (2

- Methylnaphthalene, Fluoranthene, and Fluorene) and Moermond et al. 2007 (Benz(a)anthracene and Benzo(a)pyrene)

. **Benthic Invertebrate Compartment Type**
Clearance constant unitless 100.6 100.6 100.6 100.6 100.6 Stehly et al. 1990

. V d (ratio of concentration in benthic invertebrates to concentration in water) ml/g 723 5 7235 7235 7235 7235 Stehly et al. 1990

.

Half

- life day 0.495 17 0.722 0.722 17 Moermond et al. 2007

.TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment A A

- 42 October 2014 TRIM.FaTE Inputs Exhibit A

- 26 . PAH Chemical

- Specific Parameters for Aquatic Species in the TRIM.FaTE Screening Scenario

Parameter Name Units Value Reference Chr DahA Fluoran

thene Fluorene IcdP
**All Fish Compartment Types a** 
Gamma fish unitless 0.2 0.2 0.2 0.2 0.2 Thomann 1989

. Assimilation efficiency from food unitless 0.15 0.15 0.14 0.14 0.15 Lemair et al. 1992. Exceptions include Barber 2008 (2

- Methylnaphthalene and Acenaphthene) and Niimi and Palazzo 1986 (Acenaphthylene, Fluoranthene, and Fluorene)

. Half

- life day 0.533 2 0.165 0.2 2 Moermond et al. 2007

.

a Screening scenario includes: Benthic Omnivore, Benthic Carnivore, Water

- column Herbivore, Water

- column Omn ivore, and Water

- column Carnivore.TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment A A

- 43 October 2014 TRIM.FaTE Inputs Exhibit A

- 27 . Dioxin Chemical

- Specific Parameters for Aquatic Species in the TRIM.FaTE Screening Scena rio

Parameter Name Units Value1,2,3,4,6,7,8,9-OCDD1,2,3,4,6,7,8,9-OCDF1,2,3,4,6,7,8-HpCDD1,2,3,4,6,7,8-HpCDF1,2,3,4,7,8,9-HpCDF1,2,3,4,7,8-HxCDD1,2,3,4,7,8-HxCDF1,2,3,6,7,8-HxCDD1,2,3,6,7,8-HxCDF

**Zooplankton Compartment** Absorption r ate c onstant L[water]/kg[fish wet wt]

- day 86 40 8640 8640 8640 8640 8640 8640 8640 8640

Assimilation e fficiency from a lgae unitless 0.08 0.05 0.21 0.09 0.2 0.31 0.31 0.31 0.31
Elimination r ate c onstant 1/day 0.0102 0.016 0.016 0.0616 0.1829 0.0252 0.1474 0.0099 0.0194
Half

- life day 7E+06 7E+06 7E+ 06 7E+06 7E+06 7E+06 7E+06 7E+06 7E+06 **Benthic Invertebrate Compartment**
Clearance c onstant unitless 0 0 0 0 0 0 0 0 0
Sediment p artitioning p artition

c oefficient kg/kg 0.0013 0.0017 0.0055 0.0012 0.042 0.033 0.0081 0.013 0.02

Sediment p artitioning a lph a of

e quilibrium unitless 0.95 0.95 0.95 0.95 0.95 0.95 0.95 0.95 0.95

Sediment p artitioning t ime to

r each a lpha of e quilibrium days 120 42 120 42 42 120 42 120 42

V d (ratio of concentration in benthic invertebrates to concentration in water) ml/g 0 0 0 0 0 0 0 0 0

Half

- life day 5776.2 5776.2 5776.2 5776.2 5776.2 5776.2 5776.2 5776.2 5776.2 **All Fish Compartments a** 

Assimilation e fficiency from f ood unitless 0.08 0.05 0.21 b 0.09 0.2 0.3 1 c 0.31 0.31 0.31
Chemical u ptake r ate v ia g ill L[water]/kg[fish wet wt]

- day 11 6 56 25 50 102 200 300 200

Half

- life day 70 70 70 70 70 70 70 70 70TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment A A

- 44 October 2014 TRIM.FaTE Inputs Exhibit A

- 2 7. Dioxin Chemical

- Specific Parameters for Aquatic Species in the TRIM.FaTE Screening Scenario

Parameter Name Units Value1,2,3,7,8,9-HxCDD1,2,3,7,8,9-HxCDF1,2,3,7,8-PeCDD1,2,3,7,8-PeCDF2,3,4,6,7,8-HxCDF2,3,4,7,8-PeCDF2,3,7,8-TCDD2,3,7,8-TCDF

**Zooplankton Compartment** Absorption Rate Constant L[water]/kg[fish wet wt]

- day 8640 8640 8640 8640 8640 8640 8640 8640

Assimilation Efficiency from Algae unitless 0 .31 0.31 0.42 0.42 0.31 0.42 0.41 0.51
Elimination Rate Constant 1/day 0.0099 0.0413 0.0819 0.2316 0.0192 0.4331 0.2268 1.0375
Half

- life day 7E+06 7E+06 7E+06 7E+06 7E+08 7E+08 7E+06 7E+08 **Benthic Invertebrate Compartment**
Clearance Constant unitless 0 0 0 0 0 0 0 0 Sediment Partitioning Partition Coefficient kg/kg 0.015 0.067 0.098 0.024 0.072 0.17 0.205 0.056

Sediment Partitioning Alpha of Equilibrium unitless 0.95 0.95 0.95 0.95 0.95 0.95 0.95 0.95

Sediment Partitioning Time to Reach Alpha of Equil ibrium days 120 42 120 42 42 42 120 42

V d (ratio of concentration in benthic invertebrates to concentration in water) ml/g 0 0 0 0 0 0 0 0

Half

- life day 5776.2 5776.2 5776.2 5776.2 5776.2 5776.2 5776.2 5776.2 **All Fish Compartments a** 

Assimilation e fficie ncy from f ood unitless 0.31 0.31 0.42 0.42 0.31 0.42 0.41 0.51
Chemical u ptake r ate v ia g ill L[water]/kg[fish wet wt]

- day 300 200 700 300 200 400 600 400

Half

- life day 70 70 70 70 70 70 70 70TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment A A

- 45 October 2014 TRIM.FaTE Inputs Exhibit A

- 2 7. Dioxin Chemical

- Specific Parameters for Aquati c Species in the TRIM.FaTE Screening Scenario

Parameter Name Units Reference

**Zooplankton Compartment** Absorption r ate c onstant L[water]/kg[fish wet wt]

- day Zhang et al. 2011; used copepod k u value

.

Assimilation e fficiency from a lgae unitless Morrison et al. 1999. Exceptions include Niimi and Oliver 19 8 6 (1,2,3,4,6,7,8,9

- OCDD, 1,2,3,4,6,7,8,9

- OCDF), Berntssen et al. 2007 (1,2,3,4,6,7,8

- HpCDD, 1,2,3,4,6,7,8

- HpCDF), and 1,2,3,4,7,8,9

- HpCDF set conservatively as approximate linear interpolation between va lues for 1,2,3,4,7,8

- HxCDD and 1,2,3,4,6,7,8

- HpCDD / 1,2,3,4,6,7,8

- HpCDF (i.e., 0.3 to 0.1

– 0.2).
Elimination r ate c onstant 1/day Arnot and Gobas 2004; used K ow value

. Half

- life day Morrison et al. 1999; used metabolic rates for invertebrates

. **Benthic I nvertebrate Compartment** 
Clearance c onstant unitless Assumption.
Sediment p artitioning p artition c oefficient kg/kg Rubinstein et al. 1990; used TCDD data for sandworm

. Sediment p artitioning a lpha of e quilibrium unitless Rubinstein et al. 1990

. Sediment Partitioning Time to Reach Alpha of Equilibrium days Rubinstein et al. 1990

.

V d (ratio of concentration in benthic invertebrates to concentration in water) ml/g Assumption.

Half

- life day Rubinstein et al. 1990; used TCDD data for sandworm

. **All Fish Comp artmentsa**  Assimilation Efficiency from Food unitless Morrison et al. 1999. Exceptions include Niimi and Oliver 1996 (1,2,3,4,6,7,8,9

- OCDD, 1,2,3,4,6,7,8,9

- OCDF), Van den Berg et al. 1994 (1,2,3,4,6,7,8

- HpCDD), Berntssen et al. 2007 ( 1,2,3,4,6,7,8

- HpCDF), and 1,2,3,4,7,8,9

- HpCDF set conservatively as approximate linear interpolation between values for 1,2,3,4,7,8

- HxCDD and 1,2,3,4,6,7,8

- HpCDD / 1,2,3,4,6,7,8

- HpCDF (i.e., 0.3 to 0.1

– 0.2). Chemical Uptake Rate Via Gill L[water]/kg[fish wet wt]

- day Muir et al. 1985. Exception is Opperhuizen et al. 1986 (1,2,3,7,8,9

- HxCDF, 1,2,3,7,8

- PeCDD, 1,2,3,7,8

- PeCDF, 2,3,4,7,8

- PeCDF, 2,3,7,8

- TCDD, 2,3,7,8

- TCDF)

. Half

- life day Berntssen et al. 2007

.

a Screening scenario includes: Benthic Omnivore, Benthic Carnivore, Wat er

- column Herbivore, Water

- column Omnivore, and Water

- column Carnivore.

b 0.21 used for benthic omnivore, water column omnivore, and water column herbivore; 0.13 used for benthic carnivore and water column carnivore.

c 0.31 used for benthic omnivore, water c olumn omnivore, benthic carnivore, and water column carnivore; 0.37 used for water column herbivore.TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment A A

- 46 October 2014 TRIM.FaTE Inputs 

## References

Ad ria e n s, P

. , Q. Fu

, a n d D

. Gr b i c

- Galic. 1 9 9 5 . B i o a v ail a b ility a n d tra n s f o r m at i o n o f h i gh ly c h l o ri n a t e d d i b e nz o

- p

- dioxins a n d d i b e nz o f u ra n s i n a n a e r o b ic s o i l s a n d s e d i me n ts. E n v i r o nm e n tal S ci e n c e a n d Technology 2 9 ( 9 ): 2 25 2

- 22 60

.
Ad ria e n s, P

. , a n d D

. G r b ic

- Galic. 1 9 9 3 . R e du c ti v e d e c h l o ri n ati o n o f P C D D / F b y a n a e r o b ic c u lt u r e s a n d

s e d i me n ts. Or g a n o h al o g e n C o m p o und s 1 2 : 10 7

- 1 10

.
Ad ria e n s, P

. , a n d D

. G r b ic

- Galic. 1 9 9 2 . Eff e ct o f c o c o n ta m i n a n ts a n d c o n c e n tr a ti o n o n t h e a n a e r o b ic b i o tra n s f o r m a ti o n o f P C D D / F in m et h a n o g e n ic r i v er s e d i me n ts. Or g a n o h a l o g e n C om p o und s 8 : 2 0 9

- 2 12.

ATSDR (Ag e n cy f o r T o x i c Sub sta n c e s a n d D i s ease Re g ist r y). 2 0 05

. T o xi c o l o g ical p r o file f o r
n a ph t h al e n e , 1

- m e t h y l n a ph t h al e n e , a n d 2

- m e t h y l n a ph t h al e n e

. A v a i la b le at: h tt p : / / w ww

. a ts d r

. c d c .go v / T o x P r o file s / T P

. as p ? i d = 2 40 & ti d = 4 3

.
A T S D R. 1 9 99

. T o xi c o l o g ical p r o fil e f o r c a d m i u m

. A v aila b le at h tt p:/ / w ww

. a t s d r

. c d c .g o v / t o x p r o fil e s / t p

. as p ? i d = 4 8 & ti d = 1 5

.
A T S D R. 1 9 98

. T o xi c o l o g ical p r o file f o r c h l o r o d i b e nzo

- p

- d i o xi n s ( C DD s ). A v aila b l e at: h tt p: / / w w w

. ats d r

. c d c

. g o v / t o x p r o fil e s / t p. as p ? i d =9 3 8 & ti d = 1 9 4

.
A m b r o s e , R. A

., J r., T. A

. W o o l, a n d J

. L

. M ar t i n

. 1 9 95

. T h e wa t e r qu ali t y a n al y s i s s i m u lat i o n p r o g r a m , W AS P 5

, P art A : Mo d e l d o c u me n tat i o n

. A t h e n s, G A : U .S . E P A N at i o n al Ex p o s u re R e s e a r ch L a b o ra t o r y , Ec o s y s t e m s D i v is i o n

.
A P H A ( American Public Health Association ). 19 95

. S t a nd ard m et h o d s f o r t h e e x a m i n at i o n o f wa t e r a n d waste w a t e r. Was h i ng t o n , D C.

A m y o t, M

. , D

. Le a n , a n d G. M i erl e

. 1 9 9 7

. P h o t o c h e m i c al f o r m at i o n o f v o lati l e me r c u ry in h i g h arct i c lak e s. E n v ir

o n me n t al T o

xi

c o l o g y a n d C h e m ist r y 1 6 ( 10 ) : 2 0 5 4

- 2 0 6 3

.
A rj m a nd

, M

. , a n d H

. S a nd e r m a nn

. 1 9 8 5

. Me ta b o li s m o f DD T a n d r e l a t e d c o m pound s in c e ll s u s p e n s i o n
c u lt u r e s o f s oy b e an ( G l y

ci

n e m ax L

. ) a n d w h e at ( Trit u c u m a e s ti v u m L. ) Pe st i ci

d

e B i o c h e m ist r y a n d P

h

y

s i

o l o g y 2 3 : 3 89

.
A r n o t, J ., a n d F.A

. G o b as. 2 0 04 . A f o o d w e b b i o a cc u m u l ati o n m o d e l f o r o r g a n ic c h e m ica l s in a qu atic

ec o s y s te m s. E n v i r o n m e n tal T o

xic o l o g y a n d C h e m ist r y 23 ( 1 0 ) : 2 3 43

- 2 3 55.

A tki n s o n , R. 1 9 9 6

. A t m o s p h e ric c h e m ist r y o f P CBs, P C DD s a n d P C D F s. Iss u e s in E n v ir

o n me n tal S cie n ce a n d T e c hn o l o g y 6 : 53

- 72.

Bac h e , C .A.

, W .J . G u t e n m a nn , L

. E. S t. J o hn

, J r., R. D

. S w e e t, H

. H

. H atfi e l d

, a n d D

. J

. L isk. 1 9 73

. Me r c u ry a n d me t h y l me rc u r y c o n t e n t o f a g ric u lt u ral c r o p s g r o w n o n s o ils t r e a t e d with v ar i o u s
me rc u r y c o m p o und s. J o u r n al o f Ag r i c u lt u ral a n d Fo o d Ch e m ist r y 2 1 :6 07

- 6 1 3.

Ba e s, C. F

., III, R. D

. Sh ar p , A

. L . Sj o r ee n , a n d R

. W. Sh o r. 1 9 84 . A r e v iew a n d a n al y s i s o f p ar a m e t e rs f o r
ass e ssi n g tra n s p o rt o f e n v i r o nm e n tally r e le a s e d ra d i o nu cli d e s t h r o ug h a g ric u l t u r e . OR N L

- 5 7 8 6

. O ak Ri dg e N ati o n al L a b o ra t o r y , Oak Ri dg e , T N

.
Bar b e r, M .C. 20 08

. D ie t ary up take m o d els u s ed f o r m o d e li n g t h e b i o a c c u m u lat i o n o f o r g a n ic

c o n t a m i n a n ts in f i s h

. E n v i r o nm e n t al T o

x

i c o l o g y a n d C h em ist r y 2 7 ( 4 ) : 7 55

- 7 7 7.TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment A A

- 47 October 2014 TRIM.FaTE Inputs B e r n tsse n , M

. H .G

. , T .A . G i ske g jer de

, G. R o s e n l und

, B

. E. T o r ste n s en, a n d A

. K

. L u nd e b ye

. 20 07

. P r e d icti n g w o rld h e alth o r g a n i z ati o n t o xic e qu i v a l e n cy fac t o r d i o xin a n d d i o xi n

- li k e p o l y c h l o ri n a t e d b i ph e n y l le v e ls in fa r m e d A tl a n tic sa l m o n ( S a l m o sala r ) b as e d o n k n o wn l e v e ls in f ee d

. E

n v i r o n me n tal T o

xi

c o l o g y a n d Ch e m istr y 26 ( 1 ) : 1 3

- 2 3

. d o i : 1 0

. 1 8 97 / 06

- 1 2 2 r

. 1

.
B e rr o jal b i z , N.

, S

. L a c o r t e , A . Cal b e t, E. S ai z , C. Bar a ta, a n d J

. D ac h s. 2 0 09

. A cc u mu lati o n a n d c y cli n g
o f p o l y c y clic a r o m atic h y d r o c ar b o n s in zo o p la n k t o n

. E n v ir

o nm e n tal S c i e n ce a n d T e c hn o l o g y 4 3 ( 7 ) : 2 29 5

- 2 3 01

. d o i: 1 0

. 1 0 2 1 / e s 8 0 1 8 2 2 6

. Bidleman, T.F. 1 9 8 8. A t mo s pheric pro c ess e s. Environmental Science and Technology 2 2 : 3 6 1

- 3 67.

Bishop, K

. H

. ; L ee, Y

. H

. ; Munthe, J

. ; Dambrine, E. 1 9 9 8. X yl e m sap as a pathway for t otal mercury and methylmercury transport from s oils t o tree canopy in t he bor e al forest. Biogeochemistry 4 0: 1 01

- 1 13.

B oy l e, T. 1 9 8 5

. V ali d at i o n a n d p r e d icta b ility o f la b o ra t o ry m e t h o d s f o r ass e ssi n g t h e fate a n d e ff e cts o f

c o n t a m i n a n ts in a qu a tic ec o s y s t e ms . Ba l ti m o r e , M D : AS TM I n t e r n ati o n al.

B ud a v ari, S. [ e d

. ]

. 1 9 96

. T h e M e rck I nd e x

- A n e n c y cl op e d ia o f c h e m icals, d r ug s, a n d b i o l o g icals. W h ite h o u s e S t a ti o n , N J : M e rck a n d C o

. , I n c

. , p

. 1 78

.
C A RB ( California Air Resources Board ). 1 9 94

. D e ve l o p me n t o f i n t e r me d ia tra n sf e r fac t o rs f o r p o ll u ta n ts, V o l um e II: M e tals a n d n o n

- vo l a tile o r g a n ic c o m p o un d s. P B 95

- 2 6 0 6 91

. Calif o r n i a : A ir R e s o u rc e s B o ar d

. M arc h

.
Cal E P A ( California Environmental Protection Agency ). 1 9 93

. CalT O X

, A m u lt i m e d i a t o tal

- e x p o s u re
m o d e l f o r h a z ar d o u s

- was t e si t e s, P art I I : T h e d y n a m ic m u lt i me d ia tra n s p o r t a n d tra n s f o r m ati o n

. M o d e l P r e p ar e d f o r : T h e Offi c e o f S cie n tific A ffairs. De p a r t m e n t o f T o xic Sub sta n c e s C o n tr o l. S acr a m e n t o , Calif o r n ia. D e c e m b e r. D ra f t F i n al.

Car p i, A ., a n d S. E. L i ndb e r g

. 1 9 97

. Sun li gh t

- m e d iat e d e m iss i o n o f e l e m e n tal me r c u ry fr o m s o il

a m e nd e d with m un ici p al s e wa g e sl u dg e

. E n v i r o nm e n tal S ci e n c e a n d T e c hn o l o g y 3 1 ( 7 ) : 2 0 85

- 2 0 91.

C oe , J. M

. , a n d S. E. L i ndb e r g

. 1 9 87

. T h e m o r ph o l o g y a n d si z e d istri bu ti o n o f a t m o s ph e ric p a r ticl e s d e p o si t e d o n f o lia g e a n d i n e rt s u rfa c e s. J o u r n al o f t h e A ir P o ll u ti o n C o n t r o l A s s o c i ati o n 3 7 : 2 37

- 2 4 3.

Cra n k, J.

, N. R. M c F arla n e , J

. C. N e w b y , G

. D

. P a t e r s o n , a n d J. B. Pe d l e y

. 1 9 8 1

. D iff u s i o n p r o c e s s e s in

e n v ir o n me n tal s y st e m s. I n : P a t e r s o n e t al. 1 9 9 1

. Lo n d o n : M a c m illan P r e s s , L t d

.
D el V e n t o , S.

, a n d J

. D ac h s. 2 0 02

. P r e d i c ti o n o f up t a k e d y n a m ics o f p e rsis t e n t o r g a n ic p o ll u ta n ts b y b act e ria a n d phy t o p la n k t on

. E n v ir

o n me n tal T o

xi

c o l o g y a n d C h e m ist r y 2 1 ( 1 0 ) : 2 0 99

- 2 1 07.

E d war d s, N. T. 1 9 8 8

. A ss i m ilati o n a n d m e ta b o li s m o f p o l y c y clic ar o m at i c h y d r o ca r b o n s b y ve g e tat i o n

– an a pp r o ach t o t h is c o n t r o ve rsial i ss u e a n d s ugg e s ti on s f o r f u t u r e r e s e arc h

. I n : ( M

. C o o ke a n d A.J. De nn is, e d s

. ) P oly nuc le a r A r om a tic H y

d

r o ca r bon s: A D e cad e of P r og r e s s

. Ba t t e lle P r e s s, C o l u m bu s, O H ; pp

. 211

- 2 2 9.

Environment Canada . 2002

. Ecosystem Health Science

- Based Solutions: Canadian Tissue Residue Guidelines for the protection of Wildlife Consumers of Aquatic Biota: Methylmercury. Report No. 1

- 4.

Ga y , D

. D

. 1 9 75

. Bi o t ra n sf o r m at i o n a n d c h e m ical f o r m o f me rc u r y in p la n ts. I n t e r n ati o n al C o n f e r e n ce o n H ea v y M etals in t h e E n v i r on m e n t, pp

. 87

- 95

. V o l. II, P art 1

. Oc t o b e r.TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment A A

- 48 October 2014 TRIM.FaTE Inputs Gil m o u r, C

. C., a n d E .A

. H e n r y

. 1 9 9 1

. Me rc u r y me t h y lat i o n in a qu atic s y s t e m s af f e c t e d b y a cid d e p o sit i o n

. E n v ir

o n me n t al P o ll u t i o n 7 1 : 1 3 1

- 1 69.
Goulet, R.R., S. Krack, P.J. Doyle, L. Hare, B. Vigneault, and J.C. McGeer. 2007. Dynamic multipathway modeling of Cd bioaccumulation in Daphnia magna using waterbourne and dietborne exposures. Aquatic Toxicology

. 81: 1 17

- 125.

H a n sc h

, C

. , A

. L e o , a n d D

. H oe k m a n

. 1 9 95

. Ex p l o ri n g Q SA R

- H y d r o p h o b ic, e lec t r on ic, a n d ster i c
c o n sta n t s. Was h i ng t o n , D C : Am e rican C h em i cal So cie t y 15 4

.
H ar e , L ; T e ssie r , A ; and Warr e n , L

. 2 0 01

. Ca d m i u m a cc umu lati o n b y i n ve r t e b ra t e s l i v i n g at t h e
s e d i me n t

– wat e r i n t e rfa c e

. Environmental Toxicology and Chemistry 2 0 : 8 8 0

- 8 89.

H ar n e r, T., a n d Bi d l e m a n, T.F

. 1 9 98 . Octa n o l

- air p ar t it i o n c oe f f icie n t f o r d e s cri b i n g p ar t ic l e/ g as p a r titi o n i n g o f ar o m atic c o m p o und s in u r b an air. E n v i r o nm e n tal S ci e n ce a n d T e c h n o l o g y 3 2 : 14 94

- 1 5 02.

H S D B ( Hazardous Substances Data Bank ). 2 0 0 5

. B et h es d a, M D : N at i o n al L i b ra r y o f Me d ici n e , U. S

. [ L a s t R ev is i o n D a t e 1 0 / 2 7/ 2 0 05 ]

. 2

- M e t h y l n a ph t h a l e n e ; H a z ar d o u s Sub sta n c e s D ata b a n k Nu m b e r : 5 2 7 4

. A v aila b le a t : h t t p: // t o x n e t

. n l m .n i h.g o v / c g i

b i n / si s / s e a rc h / a ? db s+ h s d b : @ t e r m + @ D OC N O +5 2 7 4

.
H S D B. 2 0 0 1 a. Bet h es d a, M D : N at i o n al L i b ra r y o f Me d ici n e , U. S

. [ L a s t R ev is i o n D ate 0 8 / 0 9/ 2 0 01 ]

. 7

, 1 2

- D i m e t h y l b e nz ( a )a n t h rac e n e ; H a z ar d o u s Sub sta n c e s D ata b a n k N u m b e r : 2 9 38

. A v aila b le a t: h tt p: / / t o x n e t .n l m .n i h.g o v / c g i

- b i n/ sis / s e a rc h / a ? db s+ h s db : @ t e r m + @ D O C N O + 2 9 3 8

.
H S D B. 2 0 0 1 b

. B e t h e s d a, M D : N ati o n al L i b ra r y o f M e d ici n e , U. S

. [ L ast R ev is i o n D ate 0 8 / 0 9/ 2 0 01 ]

. A c e n a ph t h y l e n e ; H a z ar d ou s Sub sta n c e s D ata b a n k Nu m b e r: 2 6 6 1

. Av aila b le at: h t t p : / / t o x n e t .n l m .n i h.g o v / c g i

- b i n / si s / s e arc h / a ? db s+ h s db : @ t e r m + @ D O C N O + 2 6 6 1

.
H S D B. 2 0 0 1 c. Bet h es d a, M D : N at i o n al L i b ra r y o f Me d ici n e , U. S

. [ L a s t R ev is i o n D ate 0 8 / 0 9/ 2 0 01 ]

. B e nz o ( gh i) p e r y l e n e ; H a z a r d o u s Sub sta n c e s D a ta b a n k Nu m b e r: 6 1 7 7

. A v aila b le a t : h t t p: // t o x n e t

. n l m .n i h.g o v / c g i

- b i n / si s / s e a rc h / a ? db s+ h s d b : @ t e r m + @ D OC N O +6 1 7 7

. HSDB. 2001e. Bethesda, MD: National Library of Medicine, U.S. [Last Revision Date 08/09/2001]. Fluorene; Hazardous Substances Databank Number: 2165. Available at: http://toxnet.nlm.nih.gov/cgi

- bin/sis/search/a?dbs+hsdb:@term+@DOCNO+2165.

H S D B. 2 0 0 1 d

. B e t h e s d a, M D : N ati o n al L i b ra r y o f M e d ici n e , U. S

. [ L ast R ev is i o n D a te 0 8 / 0 9/ 2 0 01 ]

. A c e n a ph t h e n e ; H a z ar d o u s Sub sta n c e s D ata b a n k Numb e r: 2 6 59

. A v aila b le at: h t t p : / / t o x n e t .n l m .n i h.g o v / c g i

- b i n / si s / s e arc h / a ? db s+ h s db : @ t e r m + @ D O C N O + 2 6 59

.
H e nd riks, AJ ; and H eike n s, A

. 2 0 0 1 . T h e po wer o f si z e. 2

. Rate c o n s t a n ts a n d e qu ili b r i u m ra t i o s f o r
acc u m u lat i o n o f i no r g a n ic s ub sta n c e s r e lat e d to s p e c i e s w e i gh t. Environmental Toxicology and Chemistry 2 0 : 1 4 2 1

- 1 4 3 7

.
H e nn i ng , B. J

., H .G. Sn ym an , a n d T. A

. S

. A ve li ng

. 2 0 01

. P la n t

- s o il i n t e ract i o n s o f sl u dg e

- b o r n e h e a v y
m etals a n d t h e e f f e ct o n m ai z e (Z e a m a y s L

. ) s e e d li n g g r o wt h

. Wate r S A 27 ( 1 ) : 7 1

- 7 8

.
H o gg , T .J.

, J. R. B e tta n y , a n d J. W

. B. S te w art. 1 9 78

. T h e up take o f 2 0 3 H g

- la b e l e d m e rc u ry c o m p o und s b y b r o m e g rass f r o m irri g a t e d und ist u r b e d s o il c o l u m n s. J o u r n al o f E n v i r o n me n tal Q u alit y 7 : 4 4 5

- 4 50.

H o l z w o r t h

, G

. C. 1 9 72

. M ix i n g h ei gh ts, wi n d s p e e d s, a n d p o t e n tial f o r u r b an air p o ll u ti o n t h r o ugh o u t t h e
c o n ti gu o u s U n it e d S tat e s. P r e p ar e d f o r E P A Offi c e o f A ir P r o g ra m s. R e s e arch Tr i a ng le P ar k

, N C.TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment A A

- 49 October 2014 TRIM.FaTE Inputs H o war d

, P .H.

, R .S

. B o e t h li ng , W .F

. J ar v is, W

. M

. M ey l a n , a n d E

. M

. M ichal e n k o

. 1 9 91

. H a ndbo o k o f

e n v ir o n me n tal d e g ra d a ti o n rat e s. C h e ls e a, M i c h i g a n : L e wis P ub lis h e rs

Hud s o n , R

. , S.A . G h e ri n i, C .J . Watras, a n d D

. P o rc e lla. 1 9 94

. M o d e li n g t h e b i o g e o c h em i c al c y cle o f

me rc u r y in l a k e s : T h e M e r c u ry C y cli n g M o d e l ( M C M ) a n d its a pp licat i o n t o t h e M TL S t ud y L a k e s. I n : C .J. Watras a n d J. W. Hu cka b e e , e d s. M e rc u ry p o ll u t i o n i n t e g rati o n a n d s y n t h e sis. Le wis P ub lis h e r s. pp

. 4 7 3

- 5 2 3

.
IC F(ICF International ). 2 00 5

. M e m o ra nd u m : TRI M .F a TE sc r ee n i n g s c e n ari o : Aq u atic f oo d w e b a n a l y sis;

s ub m it t e d t o D e ir d re M u r p h y a n d T e rri H o lli ng s w o r t h

, U. S . E P A

, fr o m M ar g ar e t M c V e y a n d R e b e cca K a u ff m a n , ICF C o n s u lti ng

. Oct o b e r 18

.
J acks o n , R

. B

. , J . Ca n a d e ll, J. R. E h leri ng e r, H.A

. M o o n e y , O

. E. S ala, a n d E

. D

. S c hu l z e

. 1 9 96 . A g l o b al

a n al y sis o f r o o t d istri bu t i o n s f o r t e r r e strial b i o me s. O e c o l o g ia 1 0 8 : 3 89

- 4 11.

J o hn

, M

. K

. 1 9 7 2

. M e rc u ry up ta k e f r o m s o il b y v ar i o u s p la n t s p e c i e s. B u lletin o f E n v ir

o n me n tal C o n t a m i n at i o n a n d T o

xi

c o l o g y 8 : 77

- 80.

K a u s h al, S

. S

., P

. M . G r o f f m a n , G.E. L i k e n s, K .T. B e l t, W

. P

. S ta c k

, V.R. Ke ll y , L .E. Ba n d

, a n d G.T. F is h e r, 2 0 05 . I n c r e as e d S ali n i z at i o n o f Fr e sh Wa t er in t h e No r t h eastern U n ited S tates. P r o c e e d i ng s
o f t h e N ati o n al A ca d e m y o f S cie n c e s 1 0 2 : 1 3 5 1 7

- 1 3 5 20.

K i m , M

. , a n d P

. O ’ K e e f e

. 1 9 98

. T h e r o le o f n a t u ral o r g a n ic c o m p o und s in p h o t o s e n siti z e d d e g ra d at i o n
o f p o l y c h l o ri n at e d d i b e n z o

- p

- d i o xi n s a n d d i b e nz o f u ra n s. Or g a n o h al o g e n C o m p o und s 3 6 : 3 7 7

- 38 0

.
K o mo b a , D

. , C. L a n g e b ar te l s, a n d H

. S a nd e r m a nn

. 1 99 5

. M e ta b o lic p r o c e s s e s f o r o r g a n ic c h e m ica l s in p la n ts. I n : P la n t c o n t a m i n a ti o n mo d e li n g a n d si m u l a t i o n o f o r g a n ic c h e m ical p r o c e ss e s. Tra pp , S.

, a n d M c F arla n e , J. C

. , e d s

. , CRC P r e ss, B o ca R a t o n , F L

. P a g e s 6 9

- 1 0 3

.
Le it h

, H

. 1 9 75

. P r i m a r y p r odu cti v i t y in t h e b i o s ph e r e

. I n : H

. Le i th a n d R

. W. W h it a k e r. E c o l o g ical S t ud i e s, V o l um e 1 4

. Sp ri ng e r

- V e rl a g

.
L e m air, P

. , A

. M at h ie u , S

. C arri e r e , J.F

. N ar b o nn e , M

. L afa u ri e , a n d J . Gi ud ic e lli. 1 9 92

. H e p a tic b i o tra n s f o r m a ti o n e nzy m e s in a qu ac u lt u re E u r o p e an s e a b ass ( D i c e n trarc hu s la b rax ): ki n e tic p ara m e t e rs a n d i ndu cti o n with b e nz o ( a ) p y r e n e

. C o m p ara t i v

e Bi o c h em ist r y a n d P h

y

s i

o l

o g y 1 03 ( B ): 8 4 7

- 85 3

.
L e o n ar d

, T. L

., G.E. T a y l o r, J r

. , M .S . G u sti n , a n d G

. C .J

. F e r n a nd e z

. 1 9 98

. M e rc u ry a n d p la n ts in

c o n t a m i n ated s o il s : 1 . U p t a ke, p art i ti o n i ng , a n d e m i s s i o n t o t h e a t m o s ph ere. E n v i r o nm e n tal T

o

x

i c o l o g y a n d Ch em i s tr y 1 7 : 2 0 63

- 2 0 71.

L i ndb e r g , S .E

. , T. P

. M e ye r s, G.E. Ta y l o r, R.R. T u r n e r, a n d W. H

. S c h r oe d e r 1 9 92

. A t m o s ph e r e

- Su rface Exc h a ng e o f Me rc u r y t o a F o r e s t: R e s u lt s o f M o d e lli n g a n d Gra d ie n t App r o a c h e s. Journal of Geo ph ysical Research 97 ( D 2 ) : 2 5 19

- 2 5 28.

L i ndb e r g , S. E. 1 9 9 6

. F o r e s t s a n d t h e g l o b al b i o g e o c h e m ical c y c l e o f me rc u r y : T h e i m po r t ance o f und e rsta nd i n g air / v e g e t a t i o n e x c h a ng e p r o c e s s e s. I n : W. Ba e y a n s e t al

. , e d s. G l ob al a n d r e g i o n al

me rc u r y c y cl e s: S o u rc e s , f l u x e s, a n d m ass b ala n c e s. p p

. 3 5 9

- 3 80.

M ack a y , D

. , W

. Y. Sh i u , a n d K

. C. M a. 2 0 00

. P h y sica l

- c h e m ical p r o p e rt i e s a n d e n v i r o nm e n tal f a te h a ndb ook

. B o ca Ra t o n , F L : CRC P r e ss LL C.TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment A A

- 50 October 2014 TRIM.FaTE Inputs M ac k a y , D

., W.Y. Sh i u , a n d K .C

. M a. 1 9 92

. Ill u stra t e d h a ndb oo k o f p h y sical

- c h e m i cal p r o p e r ti e s a n d

e n v ir o n me n tal fa t e f o r o r g a n ic c h e m icals: p o l y nu clear ar o m atic h y d r o car b o n s , p o l y c h l o ri n at e d d i o xi n s, a n d d i b e nz o f u ra n s. C h e ls e a, M I: L e wis P ub lis h e rs.

M ack a y , D

. , S

. P at t e r s o n , a n d W. H

. Sc h r oe d e r. 1 9 8 6

. Mo d e l d e scri b i n g t h e ra t e s o f tra n s f e r p r o c e s s e s
o f o r g a n ic c h e m ica l s b e t w e e n at m o s ph e r e a n d w at e r. E n v ir

o n me n tal S ci e n c e a n d T e c hn o l o g y 2 0 ( 8 ) : 8 1 0

- 8 1 6.

M a s o n , R

. P

. , J. R. R e i n f e l d e r , a n d F. M

. M

. M o r e l. 1 9 96

. U p tak e , t o xici t y , a n d t r o ph i c tr a n sf e r o f m e rc u ry in a c o a stal d ia t o m

. E n v ir

on me n tal S cie n ce a n d T e c hn o l o g y 30 ( 6 ) : 1 8 3 5

- 1 8 4 5

.
M a s o n , R

. P

. , F. M

. M

. M o r e l, a n d H.F

. H e m o nd

. 1 9 9 5 a. T h e r o le o f m ic r oo r g a n i s m s in e l e m e n tal m erc u r y f o r m ati o n in n at u ral wat e r s. Wat e r, A ir, a n d S o il Po ll u ti o n 8 0 : 7 75

- 7 8 7.

M a s o n , R

. P

. , J. R. R e i n f e l d e r , a n d F. M

. M

. M o r e l. 1 9 95 b

. Bi o a c c u m u lat i o n o f me rc u ry a n d

me t h y l m e rc u r y

. Wat e r A ir a n d S o il P o ll u ti o n 8 0 ( 1

- 4 ) : 9 1 5

- 9 21.

M cCra dy

, J. K

. , a n d S

. P

. M a gg ar d

. 19 9 3 . U pd a t e a n d p h o t o d e g ra d ati o n o f 2

, 3

, 7

, 8

- t e trac h l o r o

- p

- d i o xin

s o r b e d t o g r a ss f o lia g e

. E n v ir

o nm e n tal S c i e n ce a n d T e c hn o l o g y 2 7 : 3 43

- 3 5 0

.
M cEl r oy , A . E. 1 9 90

. P o l y c y clic ar o m a tic h y d r o car b o n me t a b o li s m in t h e p o l y c h a e te N e r

e is v ir

e n s

. Aqu atic T o

xi

c o l o g y 18 ( 1 ) : 3 5

- 5 0

.
M cG e e r, J. C

. , K .V . Brix, J

. M

. S k e aff, D

. K

. D e F o re s t, S

. I. Bri gh a m , W .J

. Ad a m s, a n d A. Gree n

. 2 0 03

. I n v er s e r e lati o n s h ip b e t w ee n b i o c on c e n tra t i o n fac t o r a n d e x p o s u re c o n c e n tra t i o n f o r m e tals: i m p licat i o n s f o r h a z ard ass e s s me n t o f m e t a ls in t h e a qu at i c e n v ir o n m e n t. E n v i r o nm e n tal T o

x

i c o l og y
a n d C h e m ist r y ( 22 ) 5 : 1 0 1 7

– 1 0 37.

M c K o n e , T

. E

. , A

. B o dn ar, a n d E. H e rtw i c h

. 2 0 0 1

. D e v e l o pm e n t a n d e v al u at i o n o f stat e

- s p e cific la nd sca p e d ata s e t s f o r m u lti m e d ia s ou r ce

- t o

- d o se m o d e ls. U n i v e rsi t y o f Calif o r n ia at B e r k e le y

. Supp o r t e d b y t h e U. S . E n v ir o n me n t al P r o t e c t i o n Ag e n cy ( Su stai n a b le T e c hn o l o g y D i v is i o n , N at i o n al Risk M a n a g e me n t R e s e a r ch L a b o ra t o r y ) a n d E n v ir o n me n tal D e f e n se Fund

. Ju l y

. L B N L

- 4 3 7 2 2.

M illard, E.S., D

. D

. My l e s, O.E. J o hannss o n, and K

. M

. R a lph. 1 9 96

. P h y t o plank t o n ph o t o s y nth e s i s at t w o ind e x stat i o ns in L a k e Ont a rio 19 87

- 1 9 9 2 : as s e s s me nt o f the l o ng

- t e rm r e s p o nse to p h o sph o rus c o nt r o l. Canadian Journal of Fisheries and Aquatic Sciences 5 3 : 1 0 92

- 1 11 1

.
M o e r m o nd

, C. T. A

., T

. P . T r aas, I. R oe ssi n k

, K. V el t m a m , A.J

. H e nd riks, a n d A

. A

. K oe l m a n s. 2 0 07

. Mo d e li n g d ecrea s ed f oo d c h ain acc u m u lat i o n o f P AH s du e to st r o n g s o r p ti o n to c ar b o n ac e o u s
m a t e rials a n d me t a b o lic t ra n s f o r m a t i o n

. E n v i r o n me n tal S ci e n ce a n d T e c hn o l o g y 4 1 ( 1 7 ) : 61 85

- 6 1 91

. d o i: 10

. 10 2 1 /e s 0 7 0 2 3 6 4

.
Mo n t g o m e r y , J

. 20 00 . G r o u nd wat e r c h em i cals d e s k r e f ere n c e

. B o c a R a t o n , F L : CR C P r e ss L L C, p

. 1 7 0 1

. Mo rri son , H.A

., D.M

. W h itt le

, C .D

. Me tcalf e , a n d A

. J

. N ii m i. 1999

. App licat io n o f a f oo d w e b b i o acc umu lati o n mode l fo r t h e p r ed i c ti o n o f po l y c h l or i n at e d b i pheny l, dio xi n , a n d f u ran c ongene r
c on c ent rat ion s in La ke O nt ario a qu atic b i o ta. Canadian Journal of Fisheries and Aquatic Sciences 56(8 ) :1389

- 1400

. Morrison, H., Whittle, D., et al. 1999. Application of a food web bioaccumulation model for the prediction of polychlorinated biphenyl, dioxin, and furan congener concentrati ons in Lake Ontario aquatic biota. Can. J. Fish. Aquat. Sci. 56: 1389

– 1400 (1999).TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment A A

- 51 October 2014 TRIM.FaTE Inputs Muh l b aier, J ., a n d G

. T. Tiss ue

. 1980 . Ca dm i u m io n t h e sou t he rn b asin o f L a k e Mi c h i g a n

. Water, Air and Soil Pollution (15):45

- 49

.
M u ir, D

. C

. , W

. K

. M ars h all, a n d G

. R. We b s ter. 1 9 85 . B i o c o n c e n tr a t i on o f P C DD s b y fis h : E ffe c ts o f

mo l e c u lar s tr u ct u re a n d w at e r c h e m i s tr y

. Ch e m o s p h ere 14 (6 /7 ) : 8 29

- 8 3 3

.
M u ller, H. and Pr o hl, G. 1993. ECOSYS

- 87: A dynamic model for assessing radiological consequences of nuclear accidents. Health Physic s 64(3):232

- 252.

NLM ( National Library of Medicine ). 2002

. H a z ar dou s Sub sta n ce Da ta Ba n k ( HSD B). Av aila b le at: h tt p://to x ne t .n l m.n i h.gov/ c g i

- b i n/ sis /htm l gen?HSD B.

N ii m i, A ., a n d B. Oli v e r. 1 9 8 6 . Bi o l o g ical h alf

- l i ve s o f c h l o ri n at e d d i b e n z o

- p

- d i o xi n s a n d d i b e nz o f u ra n s in rai nb o w t r o u t ( S a l mo ga i r d

e n e ri ). E n v i r o nm e n tal T o

xi

c o l o g y 5 : 49

- 53.

N ii m i, A

. J, . a n d V. P a l a zzo

. 1 9 86 . B i o l o g ical h alf

- li v e s o f e i gh t p o l y c y clic ar o m a tic hyd r o car b o n s ( P AH s ) in rai nb o w t r o u t ( S al m o g air d

n e ri ). Wa t e r R e s e a r c h 2 0 (4 ) : 5 03

- 5 0 7.

N ria gu , J. O. 1 9 80

. C a d m i u m in t h e e n v ir o n me n t. P a r t I: E c o l o g ical c y cli ng

. N e w Y o rk: J o h n Wil e y & S o n s. C h a p t e r 1 5 : U p ta k e a n d e f f e cts o f c a d m i u m in h i gh e r p la n ts. pp

. 6 0 8

- 6 09.

O pp e r hu i z e n , A.

, W .J . Wa g e n aar, F .W. M

. v an d er W i e l en, M

. v an d e n B e r g , K . Ol i e , a n d F

. A

. P .C. G o b as. 1 9 86 . U p take a n d e l i m i n at i o n o f P C D D / P C D F c o ng e n e rs b y fish a f t e r a qu eo u s e x p o s u re t o a fl y

- ash e xtra c t f r o m a mu n ici p al i n ci ne r a t o r. C h e m o s ph e r e 1 5 ( 9

– 1 2 ) : 2 04 9

- 2 0 5 3

. d o i : 10

. 10 16 / 0 0 4 5

- 6 5 35 ( 8 6 ) 9 0 5 11

- 4

. Passivirta, J., Sinkkonen, S., Mikkelson, P., Rantio, T., Wania, F. (1999) Estimation of vapor pressures, solubilities and Henry’s law constants of selected persistent organic pollutants as functions of temperature. Chemo sphere 39, 811

– 832.

P at e r s o n , S.

, D

. M a c ka y , a n d A . Gla d m a n

. 1 9 91 . A f ug acity m o d e l o f c h em i cal up t ake b y p la n ts f r o m
s o il a n d air. Ch e m o s ph e r e 2 3: 5 3 9

- 5 65.

Pe t e rs e n , G

. , A

. I ve rf e l d t

, a n d J

. M un t h e

. 1 9 9 5

. A t m o s ph e ric m e rc u ry s p e c i e s o v e r C e n tral a n d N o rt h e rn E u r o p e

. Mo d e l calc u l a ti o n s a n d c o m p ari s o n w i th o b se r v at i o n s f r o m t h e N o r d ic A i r a n d P r e ci p itat i o n N e t w o rk f o r 1 9 8 7 a n d 1 9 8 8

. A t mo s p h e ric E n v ir

o n m e n t 2 9 : 47

- 68.

Po r v ari, P ., a n d M . Ve r ta. 1 9 9 5

. M et h y l m e rc u ry p r o d u cti o n in f l o o d e d s o ils: A la b o ra t o ry st ud y

. Wa t e r, A ir, a n d S o il Po ll u t i o n 8 0 : 7 65

- 7 7 3.

Rie d e r e r, M

. 1 99 5

. P a r tit i on i n g a n d tra n s p o rt o f o r g a n ic c h e m icals b e t w ee n t h e at m o s ph e ric

e n v ir o n me n t a n d l e a ve s. I n : Tra pp , S . a n d J . C. M c F arl a n e , e d s. P la n t c o n t a m i n at i on : M o d e li n g a n d si m u lat i o n o f o r g a n ic c h e m ical p r o c e ss e s. B o ca R a t o n , F L : Le w i s P ub lis h e r s. pp

. 15 3

- 1 90.

R ub i n st e i n , N. I

. , R .J

. P r u e ll, B

. K . Ta p li n , J.A

. L i V o lsi, a n d C

. B. N o r w o o d

. 1 9 9 0 . B i o a v aila b ility o f 2

, 3

, 7

, 8

- TC D D

, 2

, 3

, 7

, 8

- T C D F a n d P C Bs t o m ari n e b e n t h o s f r o m P assaic ri v e r s e d i me n t s. Ch e m o s ph e r e 2 0 : 1 09 7

- 1 1 02

. Sangster, J. (1993) LOGKOW, A Databank of Evaluated Octanol

- Water Partition Coefficients. 1st Edition, Montreal, Quebec, Canada.

S a o u t e r, E

. , F . Ri b e y r e , A

. B o ud o u , a n d R. M a u r y b rac h e t. 1 9 9 1

. H e xa g e n ia ri g i

d

a (E ph e m e r o p t e ra) as
a b i o l o g ical m o d e l in a qu a tic e c o t o xic o l o g y

- Ex p e ri m e n t al st ud ies o n me rc u r y tra n s f e rs f r o m
s e d i me n t. E n v ir

o n me n t al P o ll u t i o n 6 9 : 51

- 67.TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment A A

- 52 October 2014 TRIM.FaTE Inputs S ij m , D .T. H

. M

., H

. W e ve r, P

. J

. d e Vri e s, a n d A

. O pp e r h u i z e n

. 1 9 89

. Octan

- 1

- o l / w a t e r p ar t i ti o n
c oe ff i cie n ts o f p o l y c h l o ri n a t e d d i b e nz o

- p

- d i o xi n s a n d d i b e nz o f u ra n s: e x p e r i me n t al v al u e s d e t e r m i n e d with a s tirri n g m e t h o d

. Ch e m o s ph e r e 1 9 ( 1

- 6 ) : 2 6 3

- 26 6

.
S i mo n ic h

, S. L ., a n d R .A

. H i te s. 1 9 94

. I m p o r t a n c e o f v e g e tat i o n in r e m ov i n g p o l y c y clic ar o m a tic h y d r o car b o n s f r o m t h e a t m o s ph e r e

. N at

u r

e 3 7 0 : 49

- 51.

Sp e r o , J.

, B. De V i t o , a n d L

. T h e o d o r e

. [ e d s. ]

. 2 00 0 . R e gu lat o ry c h e m icals h a ndbo o k. N e w Y o r k

, N Y: CRC P r e ss, p

. 1 0 7 2

.
S t e h l y , G

. R

. , P .F

. L a nd r u m , M

. G. H e n r y , a n d C. K l e mm

. 1 9 90

. T o xi c o kin e t i c s o f P A H s in H e xa g e n ia E

n v ir

o n me n t al T o

xi

c o l o g y a n d Ch em i s tr y 9 : 1 67

- 1 7 4

.
T h i b o d e a u x, L .J

. 1 9 9 6 . E n v i r o nm e n tal c h e m o d y n a m ic s : M o v e me n t o f c h e m icals i n air, wa t e r, a n d s o il. N e w Y o r k

, N Y: J o h n Wi l e y a n d S o n s, I n c.

T ho m a nn , R .V

. 19 89 . B i o a c c u m u lat i o n mo d e l o f o r g a n i c

- c h em i cal d istri bu t i o n in a qu atic f o o d

- c h a i n s. E

n v ir

o n me n t al S cie n c e a n d T e c h n o l o g y 2 3 ( 6 ) : 6 9 9

- 707.

Tra pp , S

. 1 9 9 5

. M o d e l f o r up take o f x e n o b i o tics i n to p l a n ts. I n : Tra pp , S . a n d J . C. M c F arla n e , e d s. P la n t c o n t a m i n at i o n : M o d e li n g a n d si m u lat i o n o f o r g a n ic c h em ical p r o c e s s e s. B o ca R a t o n , F L : L e wis P ub lis h e rs. pp

. 1 07

- 1 5 1.

Tr ud e l, M

., a n d J. B. Ra s m u s s e n

. 1 9 9 7

. M o d e li n g t h e e l i m i n at i o n o f m e rc u ry b y fi s h

. E n v ir

o n m e n tal S ci e n ce a n d T e c h n o l o gy 3 1 : 1 7 16

- 1 7 22.

Tsir o s, I

. X

. ; R.B. A m b r o s e , a n d A . C h r o n o p o u l o u

- S e r eli. 1 9 99

. A ir

- v e g etat i o n p ar t i t i o n i n g o f t o xi c
c h em i c als in e n v ir o n me n t a l si m u lat i o n mo d e li ng . Gl o b al N E S T: T h e I n t ernational J o u r n al 1 ( 3 ) : 1 77

- 1 8 4.

US E P A ( U.S. Environmental Protection Agency ). 2 0 07

. D r aft risk asses s m e n t f o r t h e S ie m e n s Wa t er T e c hn o l o g i e s C o r p

. car b o n r e act i v ati o n facili t y P ark e r , A ri z o n a. App e nd ix F . C h e m i cal

- ph y sical p ara me t e rs f o r c o m p o und s n o t in U S E P A' s HH R A P

. A v aila b le at: h tt p : / / w ww

. e p a .g o v / re g i on 9/ w ast e / si e m e n s / pd f / Ri s k A s ses s m e n t / s i e m e n s

- ris k assess App x F.pd f.

U S E P A

. 2 0 05

. H u m an H e a l th Risk A ss e s s m e n t P r o t o c o l f o r H a z ar d o u s Wa s te C o m bu s t i o n F aciliti e s. C om p a n i o n D ata b as e

. A v aila b le a t: h tt p : / / w ww

. e p a .g o v / r e g i on 6 / 6 pd / rcr a _c / p r o t o c o l / p r o t o c o l .h t m.
USEPA 2003. Toxicological Review of 2

- Methylnaphthalene. Available online at: http://www.epa.gov/iris/toxreviews/1006tr.pdf

.
USEPA

. 2 0 00 a. E st i m a ti o n P r o g ram I n t e rfa c e ( E P I) s u i t e . Offi c e o f Po ll u t i o n P r e ve n t i o n a n d T o xics ( O P P T). A v aila b le a t: h tt p : / / w ww

. e p a .g o v/ o pp t / ex p o s u r e / d o c s / e p is u ite d l

. h t m

.
USEPA

. 2 0 00 b

. D raft e x p o s u r e a n d hu m an h e alth r e a s ses s m e n t o f 2

, 3

, 7

, 8

- t e t r ac h l o r o d i b e nzo

- p

- d i o xin T C DD ) a n d r e la te d c o m p o und s, V o l u m e 3 : P r o p e rt i e s, e n v ir o n me n tal l e ve ls, a n d b ack g r o un d

e x p o s u r e s, C h a p t e r 2 : P h y s i cal a n d c h e m i cal p r o p e r ties a n d fate a n d App e nd ix A . E P A / 6 0 0 / P

0 0 / 0 0 1 B c. A v aila b le at: h tt p : / / w ww

. e p a .g o v / n c e a /pd fs / d i o xi n / p art 1 / vo l u m e3 / c h ap

- 2 .pd f.

USEPA

. 1 9 99

. S c r ee n i n g le v el e c o l o g ical r i sk asses s me n t p r o t o c o l f o r h a z ar d o u s wa s te c o m b u sti o n f acil i ties. P e e r R e v i e w D ra f t, N o v em b e r. A v ail a b le at: h tt p : / / w ww

. e p a .g o v /e p a o sw e r / h a z was t e / c o m bu s t / e c o risk .h t mTRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment A A

- 53 October 2014 TRIM.FaTE Inputs USEPA

. 1 9 98

. C h em ical fate h alf

- li v es f o r T o xics Re l ease I n v e n t o ry (T RI) c h em i cals. Ju l y

. S RC T R 9 8

- 0 08

.
USEPA

. 1 9 97 a. Supp l eme n tal b a ck g r o un d d o c um e n t; N o ng r o und w a t e r p at h way risk as s es s m e n t ; P et r o le u m pr o c e ss w aste listing d e t e r m i n at i o n

. App e nd ix B. C o m p o un d s p e cif i c i npu t v al u e s. A v aila b le a t : h tt p : / / w ww

. e p a .g o v/ o s w /h a z ar d / was t e t y p e s / was t e i d / p e t r o r e f / a pp e ndb.pd f.

USEPA

. 1 9 97 b

. U .S . E n v ir o n m e n tal P r o t ect i o n Ag e n c y

. M erc u r y st ud y r e p o rt to c o ng r e ss. V o l um e III: F a te a n d tra n s po rt o f m e r c u ry in t h e e n v i r on me n t. Office o f A ir Q u ality P la nn i n g a n d S ta nd ar d s a n d Office o f Re s earch a n d D e v e l o pm e n t.

V an d e n B e r g , M

., J

. D e J o n gh

, H

. Po i g e r, a n d J. R. Ol s on

. 1 9 94

. T h e t o x i c o ki n e tics a n d me t a b o li s m o f p o l y c h l o ri n at e d d i b e n z o

- p

- d i o xi n s ( P C DD s) a n d d i b e nz o f u ra n s ( P C D F s ) a n d t h e ir r e le v a n c e f o r t o x i cit y

. CRC Critical R e v i e w s in T o

x

i c o l o g y 2 4 ( 1 ) :1

- 74.

V a nd al, G

. M

. , W .F

. F it zg e r a l d

, K

. R. R o lf hu s, a n d C .H. L a m b o r g

. 1 9 95

. M o d e li n g t h e e l e me n tal m e rc u ry
c y cle in P al l e t t e L ak e , Wis c o n si n , U SA

. W at

e r, A ir, a n d S o il Po ll u t i o n 8 0 : 7 89

- 7 9 8.

V u l yk h

, N ., a n d V. Sh ata l ov

. 2 0 01 . I n ve sti g a t i o n o f D i o x i n / Fu ran C o m po siti o n in E m issi o n s a n d in E n v ir o n me n t al M e d ia: S e l e cti o n o f C o ng e n e rs f o r M od e li ng . T e c hn ical N o te 6 / 20 01

. M e t e o r o l o g ical S y n t h e si z i n g C e n t e r

- E a st.

Wa ng , X

. , a n d W. X

. Wa ng

. 2 0 06 . B i o a c c u m u lat i o n a n d tra n sf e r o f b e nz o ( a ) p y r e n e in a s i m p lified

m ari n e f oo d c h ai n

. M ari n e E c o l o gy P r o g r ess S erie s 3 1 2 : 1 01

- 1 11.
Williams, J.J., J. Dutton, C.Y. Chen, and N.S. Fisher. 2010. Metal (As, Cd, Hg, and CH 3 Hg) bioaccumula tion from water and food by the benthic amphipod Leptocherius plumulosus

. Environmental Toxicology and Chemistry 29(8):1755

- 1761.

Wil m e r, C., a n d M

. F ric k e r. 1 9 96

. S t o m ata. S e c o n d e d

. N e w Y o r k

, N Y: C h a p m an a n d H all. p

. 1 2 1

.
WI D N R (Wi s c o n sin De p ar t me n t o f N at u ral R e s o u r c e s) 2 0 07

. A G u i d e to U nd e rs t a nd i n g t h e H y d r o l o g ic C o nd iti o n o f Wis c o n si n ’s L a ke Sup e r i o r W a t e rs h e d s. Wisc o n sin De p a r t me n t o f N at u ral R e s o u rc e s ( D N R).

X ia o , Z .F.

, D

. S t r o m b e r g , a n d O. L i ndq v ist. 1 9 9 5 . I n fl u e n ce o f hu m ic s ub sta n c e s o n ph o t o l y s is o f d i v al e n t me rc u r y in a qu e o u s s o l u t i on

. Wat e r, A ir, a n d S o il P o ll u ti o n 8 0 : 7 89

- 7 9 8.

Ya n , X ; and Wa ng , W X

. 2 0 0 2

. Ex p o s u re a n d po t e n tial f oo d c h ain tra n sf e r f ac t o r o f C d

, S e a n d Zn in

m ari n e fish L u tja nu s ar g e n ti m a c u l a t u s

. Marine Ecology Progress Series 2 3 8 : 1 7 3

- 1 86

.
Z h a ng , Q

. , L

. Ya ng , a n d W

. X . Wa ng

. 2 0 1 1 . B i o acc u m u l a ti o n a n d t r o ph ic t ra n sf e r o f d i o xi n s in m ari n e
c o p e p o d s a n d fis h

. E n v ir

o n me n tal P o ll u t i o n 1 5 9 ( 1 2 ) : 3 3 9 0

- 3 3 9 7

.[ This page intentionally left blank. ]TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B i Oct ober 2014 Description of MIRC 

## Description of Multimedia Ingestion Risk Calculator Attachment B. (MIRC) Used for RTR Exposure and Risk Estimates[ This page intentionally left blank. ]TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B iii October 2014 Description of MIRC 

## CONTENTS, ATTACHMENT B

1. Introduction

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

- 1

1.1. Purpose and Overview

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

- 1

1.2. Scope of MIRC

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

- 1

1.3. Use in EPA’s Air Toxics Program

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

- 2

1.4. MIRC Highlights

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

- 2

1.5. Organization of This Attachment

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

- 3

2. MIRC Overview

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

- 3

2.1. Software

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

- 4

2.2. Exposure Pathways

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

- 6

2.3. Receptor Groups

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

- 7

3. Exposure Algorithms

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

- 9

3.1. Farm Food Chain Algorithms

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

- 9 3.1.1. Estimating Chemical Concentrations in Produce

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

- 10 3.1.2. Estimating Chemical Concentrations in Animal Products

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

- 16

3.2. Chemical Intake Calculations for Adults and Non

- Infant Children

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

- 19 3.2.1. Chemical Intake from Soil Ingestion

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

- 21 3.2.2. Chemical Intake from Fish Ingestion

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

- 21 3.2.3. Chemical Intake from Fruit Ingestion

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

- 23 3.2.4. Chemical Intake from Vegetab le Ingestion

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

- 23 3.2.5. Chemical Intake from Animal Product Ingestion

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

- 25 3.2.6. Chemical Intake from Drinking Water Ingestion

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

- 27

3.3. Total Chemical Intake

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

- 27

3.4. Chemical Intake Calculations for Nursing Infants

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

- 28 3.4.1. Infant Average Daily Absorbed Dose

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

- 29 3.4.2. Chemical Concentration in Breast Milk Fat

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

- 30 3.4.3. Chemical Concentration in Aqueous Phase of Breast Milk

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

- 33 3.4.4. Alternative Model for Infant Intake of Methyl Mercury

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

- 35

4. Dose

- Response Values Used for Assessment

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

- 36

4.1. Cadmium

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

- 39

4.2. Dioxins (2,3,7,8

- TCDD)

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

- 39

4.3. Mercury

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

- 40

4.4. Polycyclic Organic Matter

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

- 40

5. Risk Estimation

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

- 42

5.1. Cancer Risks

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

- 42

5.2. Non

- cancer Hazard Quotients

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

- 44 5.2.1. Hazard Quotients for Chemicals with a Chronic RfD

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

- 45TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B iv October 2014 Description of MIRC 5.2.2. Hazard Quotients for Chemicals with RfD Based on Developmental Effects

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

- 45 5.2.3. Hazard Index for Chemicals with RfDs

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

- 45

6. Model Input Options

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

- 46

6.1. Environmental Concentrations

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

- 47

6.2

. Farm

- Food

- Chain Parameter Values

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

- 47 6.2.1. List of Farm

- Food

- Chain (FFC) Parameters

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

- 47 6.2.2. Produce Parameter Values

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

- 49 6.2.3. Animal Product Parameter Values

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

- 62

6.3. Adult and Non

- Infant Exposure Parameter Values

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

- 64 6.3.1. Body Weights

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

- 65 6.3.2. Water Ingestion Rates

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

- 66 6.3.3. Local Food Ingestion Rates

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

- 66 6.3.4. Local Fish Ingestion Rates

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

- 70 6.3.5. Soil Ingestion Rates

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

- 77 6.3.6. Total Food Ingestion Rates

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

- 77

6.4. Other Exposure Factor Values

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

- 78 6.4.1. Exposure Frequency

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

- 78 6.4.2. Fraction Contaminated

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

- 79 6.4.3. Preparation and Cooking Losses

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

- 79 6.4.4. Food Preparation/Cooking Ad justment Factor (FPCAF) for Fish B

- 81

6.5. Breast

- Milk Infant Exposure Pathway Parameter Values

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

- 82 6.5.1. Receptor

- specific Paramete rs

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

- 82 6.5.2. Chemical

- Specific Parameter Values

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

- 86

7. Summary of MIRC Default Exposure Parameter Settings

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

- 89

7.1. Default Ingestion Rates

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

- 89 7.1.1. Fish Ingestion Rates

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

- 90 7.1.2. Farm Food Chain In gestion

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

- 90

7.2. Default Screening

- Level Population

- Specific Parameter Values

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

- 92

7.3. Default Chemical

- Specific Parameter Values fo r Screening Analysis

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

- 93

7.4. Screening

- Level Parameter Values for Nursing Infant Exposure

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

- 94 7.4.1. Dioxins B

- 94 7.4.2. Methyl Mercury

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

- 95

8. References

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

- 96TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B v October 2014 Description of MIRC Exhibits, Attachment B

Exhibit B

- 1. Overview of MIRC Software Application for Performing Farm

- Food

Chain Ingestion Exposure and Risk Calculations

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

- 5

Exhibit B

- 2. Transfer Pathways for Modeled Farm Food Chain (FFC) Media

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

- 7

Exhibit B

- 3. Chemical Transfer Pathways for Produce

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

- 10

Exhibit B

- 4. Estimating Chemical Concentration in Abov eground Produce

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

- 10

Exhibit B

- 5. Chemical Transfer Pathways for Animal Products

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

- 16

Exhibit B

- 6. Oral Dose

- response Values Used to C alculate RTR Screening Threshold Emission Rates for PB

- HAP Chemicals a

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

- 37

Exhibit B

- 7. WHO 2005 Toxic Equivalency Factors (TEFs) for Dioxin Congeners

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

- 40

Exhibit B

- 8. Oral Dose

- response Values for Polycyclic Organic Matter (POM) Groups a

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

- 41

Exhibit B

- 9. MIRC Parameters Used to Estimate Chemical Concentrations in Farm Foo ds

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

- 48

Exhibit B

- 10. Chemical

- Specific Inputs for Produce Parameters for Chemicals Included in MIRC

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

- 49

Exhibit B

- 11. Chemical

- Spe cific Inputs by Plant Type for Chemicals in MIRC

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

- 51

Exhibit B

- 12. Non

- Chemical

- Specific Produce Inputs

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

- 61

Exhibit B

- 13. Animal P roduct Chemical

- specific Inputs for Chemicals Included in MIRC

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

- 62

Exhibit B

- 14. Soil and Plant Ingestion Rates for Animals

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

- 64

Ex hibit B

- 15. Mean and Percentile Body Weight Estimates for Adults and Children

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

- 65

Exhibit B

- 16. Estimated Daily Per Capita Mean and Percentile Water Ingestion Rates for Children and Adults a

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

- 66

Exhibit B

- 17. Summary of Age

- Group Specific Food Ingestion Rates for Farm Food Items

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

- 67

Exhibit B

- 18. Fish Ingestion Rates Used in Screening Assessment

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

- 72

Exhibit B

- 19. Daily Mean and Percentile Consumer

- Only Fish Ingestion Rates for Children and Adults ( IR CO,y ) a

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

- 74

Exhibit B

- 20. Fraction of Population Consuming Freshwater/Estuarine Fish on a Single Day ( F PC,y )

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

- 75

Exhibit B

- 21. Calculated Long

- term Mean and Percentile per capita Fish Ingestion Rates for Children and Adults ( IR PC,y )

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

- 75

Exhibit B

- 22. Calculated Mean and 90 th Percentile Per capita Fish Ingestion Rates for Populations of Recreational Fishers (IR PC,y )

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

- 76

Exhibit B

- 23. Daily Mean and Percentile Soil Ingestion Rates for Children and Adults

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

- 77

Exhibit B

- 24. Daily Mean and Percentile Per Capita To tal Food Intake for Children and Adults

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

- 78TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B vi October 2014 Description of MIRC Exhibit B

- 25. Fraction Weight Losses from Preparation of Various Foods

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

- 80

Exhibit B

- 2 6. Scenario

- and Receptor

- Specific Input Parameter Values Used to Estimate Infant Exposures via Breast Milk

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

- 83

Exhibit B

- 27. Average Body Weight for Infants

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

- 83

Exhibit B

- 28. Time

- weighted Average Body Weight for Mothers

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

- 84

Exhibit B

- 29. Infant Breast Milk Intake Rates

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

- 86

Exhibit B

- 30. Chemical

- specific Input Parameter Values for Breast Milk Exposure Pathway

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

- 87

Exhibit B

- 31. Farm Food Category Ingestion Rates for Health Protective Screening Sc enario for Farming Households

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

- 91

Exhibit B

- 32. Mean Body Weight Estimates for Adults and Children a

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

- 92

Exhibit B

- 33. Chemical

- Spe cific Parameter Values for Input to MIRC a

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

- 93

Exhibit B

- 34. Chemical and Animal

- Type Specific Biotransfer Factor (Ba) Values for Input to MIRC

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

- 94TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B vii October 2014 Description of MIRC Equations, Att achment B

Equation B

- 1. Chemical Concentration in Aboveground Produce

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

- 11 Equation B

- 2. Chemical Concentration in Aboveground Produce D ue to Root Uptake

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

- 11 Equation B

- 3. Chemical Concentration in Aboveground Produce Due to Deposition of Particle

- phase Chemical

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

- 12

E quation B

- 4. Chemical Concentration in Aboveground Produce Due to Air

- to

Plant Transfer of Vapor

- phase Chemical

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

- 12 Equation B

- 5. Conversion of Aboveground Produce Chemical Concentration from D ry

- to Wet

- Weight Basis

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

- 13 Equation B

- 6. Chemical Concentration in Belowground Produce: Nonionic Organic Chemicals

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

- 14 Equation B

- 7. Chemical Concentration in Belowground Produce: Inorganic Chemicals

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

- 15 Equation B

- 8. Conversion of Belowground Produce Chemical Concentration from Dry

- to Wet

- Weight Basis

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

- 15 Equation B

- 9. Chemical Concentration in Beef, Pork, or Total Dairy

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

- 16 Equation B

- 10. Chemical Concentration in Poultry or Eggs

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

- 17 Equation B

- 11. Incidental Ingestion of Chemical in Soil by Livestock

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

- 17 Equation B

- 12. Ingestion of Chemical in Feed by Livestock

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

- 18 Equation B

- 13. Chemical Concentration in Livestock Feed (All Aboveground)

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

- 18 Equation B

- 14. Chemical Concentration in Livestock Feed Due to Root Uptake

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

- 19 Equation B

- 15. Average Daily Dose for Specified Age Group and Food Type

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

- 19 Equation B

- 16. Chemical Intake from So il Ingestion

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

- 21 Equation B

- 17. Chemical Intake from Fish Ingestion

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

- 22 Equation B

- 18. Consumption

- weighted Chemical Concentration in Fish

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

- 22 Equation B

- 19. Chemical Intake from Consumption of Exposed Fruits

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

- 23 Equation B

- 20. Chemical Intake from Consumption of Protected Fruits

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

- 23 Equation B

- 21. Chemical Intake from Exposed Vegetables

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

- 24 Equation B

- 22. Chemical Intake from Protected Vegetables

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

- 24 Equation B

- 23. Chemical Intake from Root Vegetables

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

- 24 Equation B

- 24. Chemical Intake from Ingestion of Beef

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

- 25 Equation B

- 25. Chemical Intake from Dairy Ingestion

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

- 25 Equation B

- 26. Chemical Intake from Pork Ingestion

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

- 25 Equation B

- 27. Chemical Intake from Poultry Ingestion

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

- 26 Equation B

- 28. Chemical Intake from Egg Ingestion

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

- 26 Equation B

- 29. Chemical Intake from Drinking Water Ingestion

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

- 27TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B viii October 2014 Description of MIRC Equation B

- 30.

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

- 27 Equation B

- 31.

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

- 27 Equation B

- 32.

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

- 27 Equation B

- 33.

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

- 28 Equation B

- 34.

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

- 28 Equation B

- 35.

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

- 28 Equation B

- 36. Lifetime Average Daily Dose (LADD)

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

- 28 Equation B

- 37 . Average Daily Dose of Chemical to the Nursing Infant

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

- 29 Equation B

- 38. Chemical Concentration in Breast Milk Fat

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

- 30 Equation B

- 39. Daily Maternal Absorbed Intake

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

- 31 Equation B

- 40. Biological Elimination Rate Constant for Chemicals for Non

lactating Women

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

- 32 Equation B

- 41. Biological Elimination Constant for Lipophilic Chemicals for Lactating Women

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

- 32 Equation B

- 42. Chemical Concentration in Aqueous Phase of Breast Milk

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

- 33 Equation B

- 43. Fraction of Total Chemical in Body in the Blood Plasma Compartment

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

- 34 Equation B

- 44. Biological Elimination Rate Constant for Hydr ophilic Chemicals

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

- 35 Equation B

- 45. Calculation of Infant Average Daily Absorbed Dose of Methyl Mercury

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

- 36 Equation B

- 46. Calcul ation of Excess Lifetime Cancer Risk

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

- 43 Equation B

- 47.

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

- 44 Equation B

- 48.

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

- 44 Equation B

- 49.

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

- 44 Equation B

- 50.

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

- 44 Equation B

- 51.

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

- 44 Equa tion B

- 52.

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

- 44 Equation B

- 53.

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

- 44 Equation B

- 54. Hazard Quotient for Chemicals with a Chronic RfD

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

- 45 Equation B

- 55. Hazard Index Calculation

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

- 46 Equation B

- 56. Calculation of Age

- Group

- Specific and Food

- Specific Ingestion Rates

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

- 70 Equation B

- 57. Calculation of Alternative Age

- Group

- Specific Fish Ingestion Rates

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

- 73TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 1 October 2014 Description of MIRC 

## 1. Introduction

1.1. Purpose and Overview
This document provides a detailed description of the Multimedia Inges tion Risk Calculator (MIRC), a modeling tool and database designed to assist in estimating risks via multiple ingestion pathways, particularly for food products grown or raised at home or on a farm

. 27 MIRC was designed to estimate risks to humans from inge stion of produce or animal products, fish, and water in the vicinity of a source of chemical emissions to air. The user can evaluate either generalized (e.g., health protective default) or more site

- specific scenarios using the same tool. MIRC includes a database of exposure parameter values, offering the user the option of selecting mean, median, and upper percentile values for many parameters, data permitting. Generally health protective default values were assigned to each parameter in the tool and th e default configuration is used for initial risk screening efforts by EPA’s Office of Air Quality Planning and Standards’ (OAQPS) for Risk and Technology Review (RTR) multimedia risk assessments. MIRC also allows the user to define the farm food chain (FFC ) parameter values and receptor characteristics to better represent a site

- specific scenario.

With user

- input concentrations for one or more chemicals in air and soil and air

- to

- surface deposition rates, MIRC calculates the chemical’s concentrations in hom e

- or farm

- grown produce and animal food products using FFC algorithms adapted from EPA’s Human Health Risk Assessment Protocol for Hazardous Waste Combustion Facilities (hereafter referred to as HHRAP; EPA 2005a). MIRC uses these calculated concentration s, along with user

- input chemical concentrations for fish and drinking water, to estimate chemical intake rates, as average daily doses (ADDs), for adults, children, and nursing infants. Users can obtain chemical input concentrations and deposition rates from measurements at an actual site or from a transport and fate model, such as TRIM.FaTE, as is done for RTR risk assessment.

For a specified set of chemical concentrations and MIRC parameter options, MIRC calculates ADDs separately for adults, four age groups of children, and infants to reflect differences in food ingestion rates and diet at different lifestages. MIRC estimates age

- specific hazard quotients (HQs) as the ratio of age

- specific ADDs to the reference dose (RfD) for a chemical. The most ap propriate HQ for a chemical depends on its toxic mode of action and the duration of exposure required to produce an effect. MIRC also estimates average lifetime ADDs and compares those to cancer slope factors (CSFs) to estimate cancer risks. A breast mil k ingestion pathway is available to estimate exposure and risks to nursing infants.

MIRC was developed to be a flexible, transparent application. The tool includes chemical transfer and ingestion exposure algorithms and a database of parameter values, man y with several options, used by these equations

. The MIRC database includes values for the relevant physiochemical properties and toxicity reference values for more than 500 chemicals, including approximately 60 inorganics taken primarily from a database developed for HHRAP (EPA 2005a). Although designed for OAQPS ’ RTR assessments for sources of hazardous air pollutants (HAPs), the tool is flexible in its design and can be used to assess risks in many other contexts where soil and air concentrations are p redicted or measured.



### 1.2. Scope of MIRC For persistent and bioaccumulative (PB) chemicals, risks from direct inhalation of the chemical can be much less than risks from ingestion of the chemical in water, fish, and food products

27 Fully functional versions of MIRC have been developed in both Access TM

- based and ExcelTM

- based formats; how ever, MIRC currently is not publicly available.TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 2 October 2014 Description of MIRC grown in an area of chemical deposition. Vegetables and fruits in such areas can become contaminated directly by deposition of the airborne chemical to foliage, fruits, and vegetables or indirectly by root uptake of the chemical deposited to soils. Livestock can be exposed to the PB chemicals via ingestion of contaminated forage and incidental ingestion of contaminated soils.

For PB chemicals, evaluation of the inhalation pathway for air pollutants may reveal only a portion of the risk to individuals in such populations. Household s that consume high quantities of self

- caught fish or locally grown produce and animal products may be particularly susceptible to ingestion of chemicals transferred from air in the vicinity of an air emissions source. For PB chemicals in particular, ther efore, EPA developed methods of estimating risk from indirect exposure pathways associated with the deposition of airborne chemicals to gardens and farms, as described in HHRAP (EPA 2005 a).



### 1.3. Use in EPA’s Air Toxics Program

For PB

- HAPs, indirect exposure pa thways, such as ingestion, might contribute more to total risk than the inhalation pathway. EPA therefore developed several computer software tools to assist in evaluating exposure and risk from non

- inhalation pathways. EPA developed the Total Risk Integ rated Methodology (TRIM) Environmental Fate, Transport, and Ecological Exposure (TRIM.FaTE) computer program to simulate the release, transport, and fate of HAPs from a specific source throughout the area in which local (non

- source) chemical deposition is likely to be a concern. TRIM.FaTE models the transport of individual chemicals from the source through air by advection (wind) of particle

- and vapor

- phase chemical and deposition of the chemical from air to terrestrial and aquatic ecosystems by wet and d ry deposition. Movement of the chemical through a watershed via erosion and runoff, uptake by plants, and other abiotic and biotic transfer processes also are simulated. For the chemical that reaches surface waters, TRIM.FaTE models uptake and bioaccumul ation to trophic level (TL) 3 and 4 fish (i.e., pan fish and game fish, respectively).

MIRC was developed to process TRIM.FaTE results, in particular, air deposition rates and the concentrations of a chemical, after a specified duration of emissions, in several spatially explicit environmental compartments, including air, surface and root

- zone soils, surface and ground waters, and fish. MIRC uses those results to calculate exposure to the chemical through ingestion of locally grown foods, including vario us types of fruits and vegetables, poultry, swine, and dairy (and beef) cattle. MIRC also calculates the associated risks for individuals who consume those foods. MIRC was designed to use specific TRIM.FaTE results to estimate FFC concentrations, ingesti on exposures, and human health risks for OAQPS’ RTR assessments. It uses the same approach that OAQPS intends to implement directly in its TRIM system via three modules beyond TRIM.FaTE: TRIM Farm Food Chain, TRIM.Expo Ingestion , and TRIM.Risk

. 28



### 1.4. MIRC Highl ights Although designed to assist EPA OAQPS in its RTR assessments, MIRC is a stand

- alone software application that can be used in other contexts. A user can supply either measured or estimated chemical concentrations for soil, air, water, and fish, and a lso can provide air deposition rates likely for the location(s) of interest based on local meteorology. The user can accept the default values for many exposure parameters and screen for small possibilities of risk, or the user can select other options or overwrite parameter values to tailor the estimates to a specific scenario or location.

28 General information about the TRIM system is available at http://www.epa.gov/ttn/fera/trim_gen.html

.TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 3 October 2014 Description of MIRC MIRC complies with EPA’s latest guidelines for exposure and risk assessment, including HHRAP ; the Agency’s 2005 Guidelines for Carcinogen Risk Assessment (Cancer Guid elines)

, Supplemental Guidance for Assessing Susceptibility from Early

- Life Exposure to Carcinogens (Supplemental Guidance), and Guidance on Selecting Age Groups for Monitoring and Assessing Childhood Exposures to Environmental Contaminants ( EPA 2005b, c,d) ; and its 2008 Child

- Specific Exposure Factors Handbook ( EPA 2008a)

. In particular, MIRC provides several important capabilities:

• When provided air and soil concentrations, the MIRC software package allows rapid calculation of screening

- level exposures an d risks associated with household consumption of locally grown/raised foods.

• MIRC can calculate exposures and risks associated with incidental ingestion of surface soils, fish consumption, and drinking water.

• The tool calculates ADDs (i.e., chemical intake rates) for six “built

- in” age groups to allow use of age

- group

- specific body weights, ingestion rates, food preferences, and susceptibility to toxic effects.

• Its database of chemical information covers plant

- and animal

- specific transfer factors and oth er inputs that determine concentrations in farm food stuffs.

• Value options for receptor characteristics in the database include the mean and 50 th

, 90 th , 95 th , and 99 th percentile values where data permit.

• For carcinogens with a mutagenic mode of action, MI RC estimates a lifetime ADD using the three lifestages and potency adjustment factors recommended in EPA’s 2005 Cancer Guidelines and Supplemental Guidance.

• The data for exposure parameters in the tool have been updated to include the latest recommended va lues for children issued September 30, 2008, in the Agency’s Child

Specific Exposure Factors Handbook (CSEFH) (EPA 2008a).



### 1.5. O rganization of This Attachment Sections 2 through 5 of this attachment describe the exposure and risk models implemented in MIRC. S ection 2 provides an overview of the FFC exposure scenario and indicates options available to a user to tailor the scenario to specific applications. Section 3 describes the exposure algorithms use d in MIRC, including how ADDs are calculated. Section 4 presents the toxicity reference values included in MIRC to calculate risks. Section 5 describes the risk characterization algorithms in MIRC . Section 6 of this document describes data input options for the model. Section 7 describes the default parameterization of MIRC for application to health protective risk screening assessments, a nd Section 8 provides the references.

Note that the default parameterization described in Section 7 was used to estimate Tier 1 screening threshold emission rates of PB

- HAPs from RTR facilities. T hese emissions levels are assumed to pose negligible risk to subsistence communities in the vicinity of a facility emitting the PB

- HAPs to air. Users of MIRC can modify the default values for many of the parameters to better represent a specific exposure scenario.



## 2. MIRC Overview

The Multimedia Ingestion Risk Calculator (MIRC) software package is designed to allow rapid calculation of screening

- level exposures and risks associated with subsistence and recreational farmer/fisher population s in the vicinity of a source of chemical emissions to air

. The tool allows a user to assess human exposures via ingestion pathways, including drinking waterTRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 4 October 2014 Description of MIRC consumption, incidental soil ingestion, fish ingestion, and ingestion of ten types of farm food chain (FFC) product s : exposed fruits, protected fruits, exposed vegetables, protected vegetables, root vegetables, beef, total dairy, pork, poultry, and eggs. The tool also includes a breast milk ingestion and risk module for nursing infants

. For fruits and vegetables, the terms “exposed” and “protected” refer to whether the edible portion of the plant is exposed to the atmosphere

. The remainder of this overview consists of three sections. The first (Section 2.1 of this attachment ) provides a n overview of the MIRC software package. The second and third sections summarize the ingestion exposure pathways included in the tool and the “built

- in” receptor age categories, respectively ( Section s 2.2 and 2.3 of this attachment ).



### 2.1. Software

The MIRC application includes the following components:

• A graphical user interface through which the user locates and accesses various input and output tables.

• Input tables in which the user can enter env ironmental concentrations of a chemical estimated for air, soil, drinking water, and fish tissue.

• Internal chemical transfer and exposure algorithms and database of options for FFC algorithm parameter value, chemical

- specific inputs, and exposure factors.

• Tabulated outputs of calculated chemical concentrations in the various farm food products (e.g., fruits, vegetables, beef, eggs) and ADDs for those foods and for water and fish ingestion for each receptor category.

• Output tables with estimated cancer ris ks and non

- cancer hazard estimates associated with total ingestion exposure to each chemical for each receptor category.

Exhibit B

- 1 provides a flowchart displaying the types of required and optional inputs and the general flow of calculations carried out by the tool.TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 5 October 2014 Description of MIRC Exhibit B

- 1

. Overview of MIRC Software Application for Performing Farm

- Food

- Chain Ingestion Exposure and Risk Calculations

User Selects Receptor Characteristics
From Options or Over

- write Body Weight User Specifies Environmental Concentrations for Chemical of Conc ern

Air Root

- zone and Surface Soils Fish Drinking Water

User Option to Add Breast Milk Pathway yes

no User Selects BMP Parameter Values Uptake by foliage / Uptake by roots: Vegetables, Fruits Grains Hay, Grass Animal Products

Home Grown Food Product Ingestion Rates Fish and Water Ingestion Rates

Duration Breast Feeding; maternal and infant characteristics Average Daily Dose (ADD) for Age Group y; y =1 to 5

Chemical Intake with Food/ Medium Type i; i = 1 to 10

Lifetime Average Daily Dose

Risk Characterization Module Farm Food Chain Biotransfer Calculations

Breast Milk Exposure Module

ADD Maternal
[C] in milk

Infant Dose Adult ADD x absorption efficiency

Exposure Module

Chemical Toxicity Reference

Values:

SF and RfDs Age

- specific & Lifetime Exposure Doses

Lifetime Cancer Risks
Age

- Specific Hazard Quotients User Selects Chemical(s ) of Concern: For Each, MIRC Calculates RisksTRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 6 October 2014 Description of MIRC A f orm within the graphical user interface enable s the user to construct specific scenarios by choosing ingestion sources, receptor ages, and other input choices (e.g., diet composition, body weight p ercentiles). This feature facilitate s the assessment of va rious exposure scenarios. To begin an assessment , the user must supply values for the following chemical

- specific pa rameters for the scenario being evaluated:

• Air concentration of total chemical,

• Fraction chemical in air in vapor

- phase,

• Wet and dry deposi tion rates for particle

- phase chemical,

• Drinking water concentration,

• Chemical concentration in surface soils (two locations; can be tilled and untilled),

• Chemical concentration in root

- zone soils (two locations; can be tilled and untilled), and

• Chemical c oncentrations in upper trophic

- level fish.

Users can input measured values or values estimated by TRIM.FaTE or other models for these parameters.
The MIRC application uses the input data and a variety of empirical transfer factor values (included in its d atabase) to estimate chemical concentrations in nine categories of FFC food types (see Section 2.2 of this attachment ). The FFC algorithms and transfer factor values included in MIRC are based on those presented in Chapter 5 o f EPA’s HHRAP (EPA 2005 a )

. For outputs, MIRC is designed to calculate individual cancer risk and non

- cancer hazard quotients for one chemical at a time

. It is up to the risk assessor to determine if cancer risks or hazard quotients may be additive acros s two or more chemicals (i.e., if they cause toxic effects in the same target organ by the same mode of action, such as multiple polycyclic aromatic hydrocarbons ( PAHs ) that are carcinogenic by a mutagenic mode of action).

The tool assumes that an individu al is exposed via all of the pathways specified (e.g, fruits and vegetables, animal products, soil, etc.). The tool therefore is useful in estimating risk to the maximally exposed individuals in a risk assessment. To evaluate other receptor populations, the user must specify the each exposure scenario separately.



### 2.2. Exposure Pathways

MIRC estimates t he concentrations of chemicals i n FFC food categories grown in an area of airborne chemical deposition using algorithms and parameter values provided in HHRAP (E PA 2005a). FFC foods are evaluated in ten categories: exposed fruit, protected fruit, exposed vegetables, protected vegetables, root vegetables, beef, total dairy, pork, poultry, and eggs

. Exhibit B

- 2 summarize s the pathways by which chemicals are transferred to these food media

. Note that for a general Tier 1 screening

- level assessment, all of the pathways can be modeled,

as is the case for EPA’s Risk and Technology Review (RTR) calculation of screening threshold emission rates for persistent and bioaccumulative hazardous air pollutants (PB

- HAPs) ( EPA 2008b).TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 7 October 2014 Description of MIRC Exhibit B

- 2

. Transfer Pathways for Modeled Farm Food Chain (FFC) Media

Farm Food Media Chemical Transfer Pathways Exposed fruit and vegetables • Direct deposition from air of particle

- bound chemical

• Air

- to

- plant transfer of vapor phase chemical

• Root uptake from soil Protected fruit and vegetables (including root vegetables) • Root uptake from soil

Beef and total dairy (including milk) • Ingesti on of forage, silage, and grain a

• Soil ingestion
Pork • Ingestion of silage and grain a

• Soil ingestion Poultry and eggs • Ingestion of grain a

• Soil ingestion

a Chemical concentrations in forage, silage, and g rain are estimated via intermediate calculations analogous to those used for aboveground produce.

Produce types included in the FFC can accumulate a chemical directly from air and/or soil. For exposed produce, chemical mass is assumed to be transferred to plants from the air in two ways. First, particle

- bound chemical can deposit directly on the plant surface. Second, the uptake of vapor

- phase chemicals by plants through their foliage can occur. For both exposed and protected produce, the concentratio n in the plant derived from exposure to the chemical in soil is estimated using an empirical bioconcentration factor (BCF) that relates the concentration in the plant to the concentration present in the soil. For belowground root vegetables, a root

concen tration factor is applied. The algorithms used to estimate produce concentrations are presented in Section 3.1.1 of this attachment

. Chemical concentrations in animal products are estimated based on the amount of chemical consumed through the diet, including incidental ingestion of soil while grazing. The diet options for farm animals in MIRC include forage (plants grown on

- site for animal grazing, such as grass), silage (wet forage grasses, fresh

- cut hay, or o ther fresh plant material that has been stored and fermented), and feed grain products grown on the farm (e.g., corn, soybeans). All three animal feed products are assumed to accumulate chemical via root uptake from the soil. Forage and silage also can a ccumulate chemical via direct deposition of particle

- bound chemical and vapor transfer.

T he algorithms in MIRC are based on the assumptions that beef and dairy cattle consume all three feed products, while pigs consume only silage and grain and chickens consume only grain. The incidental ingestion of the chemical in soils during grazing or consumption of foods placed on the ground is estimated using empirical soil ingestion v alues . For secondary animal products (dairy products and eggs), chemical concen trations are estimated by applying a biotransfer factor to the estimated concentration in the “source” animal (cows and chickens, respectively). The algorithms used to estimate animal product concentrations are described in Section 3.1.2 of this attachment

.


### 2.3. Receptor Groups As noted in EPA risk assessment guidelines ( EPA 2005b,c,d, 2008 a ), exposures of children are expected to differ from exposures of adults due to differences in body weights, ingestion rates, dietary preference s, and other factors

. It is important, therefore, to evaluate the contribution of exposures during childhood to total lifetime risk using appropriate exposure factor values

.TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 8 October 2014 Description of MIRC EPA’s HHRAP ( Chapter 4, EPA 2005a) recommends assessing exposures for children and adults separately, but considers all non

- infant children in one category. Specifically, HHRAP recommends eight categories of receptor: farmer, child farmer, resident, child resident, fisher, child fisher, acute receptor, and nursing infant . Over time , different EPA programs have used different child age groupings to evaluate body weights, ingestion rates, and other parameter values needed to estimate chemical exposures and risks to children.

To improve the match between age groups used to estimate v alues across exposure parameters, i n 2005, EPA recommend ed a standard set of child age categories for exposure and risk assessments ( EPA 2005b)

. EPA recommended four age groups for infants: birth to < 1 month; 1 to < 3 months; 3 to < 6 months; and 6 to < 12 months

. For young children, EPA recommended an additional four age groups: 1 to < 2 years; 2 to < 3 years; 3 to < 6 years; and 6 to < 11 years

. Two age groupings were recommended for teenagers and young adults: 11 to < 16 years; and 16 to < 21 years

. These age groupings correspond to different developmental stages and reflect different food ingestion rates per unit body weight, with the highest ingestion rates occurring for the youngest, most rapidly growing, age groups.

For purposes of RTR assessment using MIRC, the selection of age categories is limited by the categories for which most of the FFC food ingestion rates have bee n calculated. In Chapter 13 of both its Exposure Factors Handbook (EFH; EPA 2011a) and its Child

- Specific Exposure Factors Han dbook ( CSEFH ; EPA 2008a) , EPA summarized home

- grown /raised food ingestion rates for four children’s age groups: 1 to < 3 years; 3 to < 6 years; 6 to < 12 years; and 12 to < 20 years

. Intake rates were not calculated for children younger than 1 year becau se infants are unlikely to consume those foods . They are more likely to be nursing or to be fed formula and other commercial baby

- food products.

Although the age groupings used to estimate FFC ingestion rates do not match precisely the groupings that EP A recommended in 2005 for Agency exposure assessments ( EPA 2005b), they are the only age

- groupings for which such data are available. The U.S

. Department of Agriculture’s (USDA’s) 1987

- 1988 Nationwide Food Consumption Survey (USDA 1992, 1993, 1994a) rema ins the most recent survey of ingestion rates for home

- grown foods , and EPA’s analysis of those data, published in its 2011 EFH, remains the most recent ly published major analysis of those data . Because ingestion of home

- grown produce and animal products are the primary exposure pathway s for which MIRC was developed, those are the age groupings used for all child parameter values used to estimate exposure and risk in MIRC.

Thus, i n MIRC, values for each exposure parameter were estimated for adults (20 to 70 years) and five children’s age groups:

• infants under 1 year (i.e., 0 to < 1 year);

• children ages 1 through 2 years (i.e., 1 to < 3 years);

• children ages 3 through 5 years (i.e., 3 to < 6 years);

• children ages 6 through 11 years (i.e., 6 to < 12 ye ars) ; and

• children ages 12 through 19 years (i.e., 12 to < 20 years).

See Sections 5.1 and 5.2 for descriptions of the risk characterization algorithms used to calculate cancer and non

- cancer eff ects, respectively, for the above age groupings. Exposure and risks to i nfants under 1 year of age are estimated only for the breast

- milk

- ingestion pathway

.TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 9 October 2014 Description of MIRC For assessment of cancer risks from early

- life exposure, EPA recognizes that infants and children may be more sensitive to a carcinogenic chemical than adults, with cancers appearing earlier in life or with lower doses experienced during childhood ( EPA 2005c, d)

. Thus, the “potency” of a carcinogen might be higher for infants and children than for ad ults

. To date, however, data by which to evaluate the relative sensitivity of children and adults to the same daily dose of a carcinogen remain limited

. Based on analyses of radioactive and other carcinogenic chemicals, EPA recommends evaluating two life stages for children separately from adults for chemicals that cause cancer by a mutagenic mode of action (MOA) : from birth to < 2 years and from 2 to < 16 years ( EPA 2005c,d)

. EPA also suggests that, as data become available regarding carcinogens with a m utagenic MOA , further refinements of these age groupings may be considered

. For assessing risks from exposures to carcinogenic chemicals that act via a mutagenic MOA

, the two early lifestages recommended by EPA ( EPA 2005c,d) also are included in MIRC:

• ch ildren under the age of 2 years (i.e., 0 to < 2 years); and

• children from 2 through 15 years (i.e., 2 to < 16 years).

Different age groupings are needed for the assessment of risks from carcinogenic chemicals with a mutagenic MOA and other carcinogens wi th other or unknown MOAs. Currently in MIRC, t he only PB

- HAPs with a mutagenic mode of carcinogenesis are the carcinogenic PAHs . See Section 5.1 for a description of the age

- dependent adjustment factors (ADAFs) that are used to calculate cancer risks for chemicals with a mutagenic MOA

. 

## 3. Exposure Algorithms
The exposure algorithms in MIRC are described below in four sections. Section 3.1 of this attachment presents the algorithms used to estimate ch emical concentration s in FFC foods from chemical concentrations in soil and air

. Pathway

- specific algorithms used to estimate chemical intakes by adults and non

- infant children are described in Section 3.2 , and total chemical intake calculations are described in Section 3.3

. Finally, the sets of algorithms used to estimate chemical intake via consumption of breast milk by nursing infants are described in Section 3.4

. A s noted previously, the exposure algorithms used in MIRC are based on those presented in HHRAP (EPA 2005b). Any differences between MIRC and HHRAP are explained in this section.



### 3.1. Farm Food Chain Algorithms

T he algorithms and parameters used to estimate che mical concentrations in produce and animal products are described in S ections 3.1.1 and 3.1.2 of this attachment , respectively. D iscussion s of the parameter value options and the va lues selected as default s in MIRC for RTR risk assessment are provided in Section 6.2 . The use of TRIM.FaTE to model chemical fate and transport in the environment prior to FFC calculations drives the most significant differen ce between the FFC algorithms included in HHRAP and the equations used for RTR. The approach in HHRAP uses estimated ambient air concentrations and deposition rates from dispersion model simulations that use unit emission rates. Chemical

- specific emissio n rates (adjusted for vapor and particle

- bound fractions) are then incorporated into some of the HHRAP FFC algorithms to calculate concentrations in FFC media. Soil concentrations are calculated using a similar approach in HHRAP. For assessment of multip athway exposures for RTR, TRIM.FaTE is used to estimate air concentrations, air

- to

- surface deposition rates, and soil concentrations, and these outputs are used in the FFC algorithms.TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 10 October 2014 Description of MIRC 

### 3.1.1. Estimating Chemical Concentrations in Produce Produce (vegetables and fr uits) can become contaminat ed directly by deposition of airborne chemical s to foliage and fruits or indirectly by uptake of chemical s deposited to the soil . Given these two contamination processes, produce is divided into two main groups: abov eground and belowground produce. Aboveground produce is divided into fruits and vegetables. These groups are further subdivided into “ exposed ” and “ protected ” depending on whether the edible portion of the plant is exposed to the atmosphere or is protected by a husk , hull, or other outer covering

.
Exhibit B

- 3 lists the pathways by which ch emicals are transferred to the FFC produce categories

. Note that for a general screening

- level assessment, all of the pathways can be modeled, as was done for EPA’s calculation of Tier 1 screening threshold emission rates for PB

HAPs in its RTR assessments (EPA 2008b) , and as described in the Technical Support Document . Sections 3.1.1.1 and 3.1.1.2 below describe the transfer pathways and algorithms for aboveground and belowground produce, respectively.

Exhibit B

- 3

. Chemical Transfer Pathways for Produce

Farm Food Med ia Chemical Transfer Pathways Aboveground Produce Exposed fruits and vegetables Direct deposition from air of particle

- bound chemical Air

- to

- plant transfer of vapor phase chemical Root uptake from soil
Protected fruits and vegetables Root uptake from s oil

Belowground Produce Root vegetables Root uptake from soil



### 3.1.1.1. Aboveground Produce For abovegrou nd exposed produce, chemical mass is assumed to be transferred to plants from the air in three ways, as illustrated in Exhibit B

- 4

. First, particle

- bound chemical can deposit directly on the plant surface via deposition ( Pd )

. Th e amount of chemical accumulated is estimated based on the areal fraction of chemical deposition intercepted by the plant surface, minus a loss factor that is intended to account for removal of deposited chemical by wind and rain and changes in concentrati on due to growth dilution

. Second, for chemical present in air in the vapor phase, the concentration of chemical accumulated by the plant’s foliage is estimated using an empirical air

- to

- plant biotransfer factor ( Pv )

. Third, the chemical concentration in the plant due to root uptake from the soil ( Pr AG

- produce ) is estimated using an empirical bioconcentration factor ( Br AG

- produce ) that relates the chemical Exhibit B

- 4

. Estimating Chemical Concentration in Aboveground Produce

Deposition of Particles ( Pd ) Root Uptake from Soil ( Pr AG

- produce ) Vapor Transfer

( Pv )

Chemical Concentration in

Aboveground ProduceTRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 11 October 2014 Description of MIRC concentration in the plant to the average chemical concentration in the soil at the root

- zone depth in the produce

- growing area ( Cs root

- zone_produce )

. The edible portions of aboveground protected produce are not subject to contamination via particle deposition ( Pd ) or vapor transfer ( Pv ). Therefore, root uptake of chemicals is the primary mechanism t hrough which aboveground protected produce becomes contaminated . As shown below, t he chemical concentration in the aboveground plant due to root uptake from soil ( Pr AG

- produce

- DW ) is estimated using an empirical bioconcentration factor ( Br AG

- produce

- DW ) that relates the chemical concentration in the plant to the average chemical concentration in the soil at the root

- zone depth in the produce

- growing area ( Cs root

- zone_produce ) . These equations all assume measurements on a dry

- weight (DW) basis.

Equation B

- 1

. Chemical Concentration in Aboveground Produce

where :

C

AG

- produce

- DW(i) = Concentration of chemical in edible portion of aboveground produce type i, exposed or protected, o n a dry

- weight (DW) basis (mg/kg produce DW)

Pd (i) = Chemical concentration in edible portion of aboveground produce type i due to deposition of particles (mg/kg produce DW); for protected aboveground produce, Pd equals zero

Pr

AG

- produce

- DW(i) = Chemical concentration in edible portion of aboveground produce type i, exposed or protected, due to root uptake from soil at the root

- zone depth of the produce growing area (mg/kg produce DW)

Pv (i) = Chemical concentration in edible portion of aboveground produc e type i due to air

- to

- plant transfer (μg/g [or mg/kg] produce DW); for protected aboveground produce, Pv equals zero

Equation B

- 2

. Chemical Concentration in Aboveground Produce Due to Root Uptake

where:

Pr

AG

- produce

- DW(i) = Concentration of chemical in edible portion of aboveground produce type i

, exposed or protected , due to root uptake from soil at root

- zone depth in the produce

- growing area, on a dry

- weight (DW) basis (mg /kg produce DW)

Cs root

- zone_produce = Average chemical concentration in soil at root

- zone depth in produce

- growing area (mg/kg soil DW)

Br

AG

- produce

- DW(i) = Chemical

- specific plant/soil chemical bioconcentration factor for edible portion of aboveground p roduce type i

, exposed or protected (g soil DW / g produce DW) ) ( ) ( ) ( ) ( P i i i DW produce AG i DW produce AG Pv Pd r C + + =

− − − −

) ( ce zone_produ

- root ) ( P i DW produce AG i DW produce AG Br Cs r

− − − − × =TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 12 October 2014 Description of MIRC Equation B

- 3

. Chemical Concentration in Aboveground Produce Due to Deposition of Particle

- phase Chemical

where:

Pd (i) = Chemical concentration in aboveground produce type i on a dry

- weight (DW) basis due to particle deposition (mg/kg produce DW); set equal to zero for protected aboveground produce Drdp = Average annual dry deposition of particle

- phase chemical (g/m 2

- yr)

Fw = Fraction of wet deposition that adheres to plant surfaces; 0.2 for anions, 0.6 for cations and most organics (unitless)
Drwp = Average annual wet deposition of particle

- phase chemical (g/m 2

- yr)

Rp (i) = Interception fraction of the edible port ion of plant type i (unitless)
kp (i) = Plant surface loss coefficient for plant type i (yr - 1 )

Tp (i) = Length of exposure to deposition in the field per harvest of the edible portion of plant type i (yr)

Yp (i) = Yield or standing crop biomass of the ed ible portion of plant type i (kg produce DW/m 2 )

Note that Equation B

- 3 differs from Equation 5

- 14 in HHRAP, from which it is derived

. In HHRAP, Equation 5

- 14 includes the term Q x ( 1

– Fv ) to indicate the emissions rate, in g/se c, of chemical from the source and the proportion of the chemical that remains in, or partitions to, the particle

- phase in the air

. Also in HHRAP, the dry and wet particle phase deposition rates, Dydp and Dywp, respectively, are normalized to the emission rate and are expressed in units of sec/m 2

- yr

.
With MIRC, the user inputs both the dry and wet particle

- phase deposition rates, Drdp and Drwp , respectively, in units of g/m 2

- yr for a specific location relative to an emissions source

. Those deposition ra tes might be values measured near that location or estimated using a fate and transport model, such as TRIM.FaTE, in conjunction with local meteorological information and emissions rate data

. The chemical emissions term used in HHRAP

, Q , therefore, is not used in MIRC ’s Equation B

- 3

. In addition, in MIRC, Drdp and Drwp , the average annual dry

and wet

- particle

- phase deposition rates , respectively, are in units of g/m 2

- yr . Users of TRIM.FaTE should note that the dry

- and wet

- part icle

- deposition rates output from TRIM.FaTE are in units of g/m 2

- d ay; therefore, users must adjust the TRIM.FaTE output values to units of g/m 2

- yr (i.e., multiply by 365 days/yr) before inputting values for Drdp and Drwp into MIRC.

Equation B

- 4

. Chemical Concentration in Aboveground Produce Due to Air

- to

- Plant Transfer of Vapor

- phase Chemical ) ( ) ( )) ( * ) ( ( ) ( ) ( 

### ) 1 ( )) ( ( 000

, 1

i i i Tp i kp i i 

### kp Yp e Rp Drwp Fw Drdp Pd

× − × × × + ×

= −

a i AG i AG i 

### VG Bv Fv Ca Pv ρ ) ( ) ( ) ( 

### × × ×

=TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 13 October 2014 Description of MIRC w here:

Pv (i) = Concentration of chemical in edible portion of aboveground produce ty pe i from air

- to

- plant transfer of vapor

- phase chemical on a dry

- weight (DW) basis (μg/g produce DW); set equal to zero for protected aboveground produce

Ca = Average annual total chemical concentration in air (g/m 3 )

Fv = Fraction of airborne chemical in va por phase (unitless)

Bv AG(i) = Air

- to

- plant biotransfer factor for aboveground produce type i for vapor

- phase chemical in air ([mg/g produce DW] / [mg/g air], i.e., g air/ g produce DW)

VG AG(i) = Empirical correction factor for aboveground exposed produc e type i to address possible overestimate of the diffusive transfer of chemical from the outside to the inside of bulky produce, such as fruit (unitless)

ρ a = Density of air (g/m 3 )

Note that Equation B

- 4 differs from Equation 5

- 18 in HHRAP, from which it is derived

. In HHRAP, Equation 5

- 18 includes the term Q x Fv to indicate the emissions rate, in g/sec, of chemical from the sou rce and the fraction of the chemical in vapor phase in the air

. HHRAP also includes the parameter Cyv , or the unitized yearly average air concentration of vapor

phase chemical in units of μg

- sec/g

- m 3

. For MIRC, the user inputs the average annual total ai r concentration of the chemical, Ca , for a specific location relative to the source in units of g /m 3 ; MIRC includes a chemical

- specific default value for Fv for chemicals included in its database. The air concentration might be a value measured near that location or a value estimated by a fate and transport model such as TRIM.FaTE . Users of TRIM.FaTE should n ote that the average annual concentration of the total chemical in air (i.e., total of both vapor and particulate phases)

, Ca , output from TRIM.FaTE is in units μ g/m 3 ; therefore, the user must adjust the value to units of g/m 3 (i.e., divide by 1,000 μg/g) b efore entering it in MIRC.

The calculations of chemical concentration in aboveground produce, ( C AG

- produce

- DW ), shown above, are on a dry

- weight (D W) basis. The family FFC food ingestion rates, on the other hand, are on a fresh

- or wet

- weight (WW) basis. MIRC therefore calculates the concentration in aboveground produce on a wet

- weight basis, C AG

- produce

- WW , using Equation B

- 5 and the moisture content ( MAF ) of the FFC food category.

Equation B

- 5

. Conversion of Aboveground Produce Chemical Concentration from D ry - to W et

- W eight Basis

where:

C

AG

- produce

- WW(i) = Chemical concentration in edible portion of aboveground produce type i on a wet

weight (WW) basis (mg/kg produce WW)

C

AG

- produce

- DW(i) = Chemical concentration in edible portion of aboveground produce type i on a dry

weight ( DW) basis (mg/kg produce DW)

MAF (i) = Moisture adjustment factor for aboveground produce type i to convert the chemical concentration estimated for dry

- weight produce to the corresponding chemical concentration for full

- weight fresh produce (percent water ) 

###        

− × =

− − − − 

### 100 ) 100 ( ) ( ) ( ) ( i i DW produce AG i WW produce AG 

### MAF C CTRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 14 October 2014 Description of MIRC 

### 3.1.1.2. Belowground Produce The equations by which chemical concentrations are estimated in belowground produce are different for nonionic organic chemicals than for inorganic chemicals and ionic organic chemicals.



### 3.1.1.2.1. Nonionic Organic Chemicals
For belowground pro duce , the nonionic organic chemical concentration in the tuber or root vegetable is derived from exposure to the chemical in soil and is estimated using an empirical root concentration factor ( RCF ) and the average chemical concentration in the soil at the root

zone depth in the produce

- growing area ( Cs root

- zone_produce ), as shown in Equation B

- 6

. The RCF relates the chemical concentration in the plant on a wet

- weight basis to the average chemical concentration in the root

- zone soi l ( Cs root

- zone_produce ) on a dry

- weight basis

. Belowground produce (i.e., tubers or root vegetables) are protected from the deposition and va por transfer by being covered by soil

. Therefore, root uptake of chemicals is the primary mechanism through which belowground produce becomes contaminated

. Equation B

- 6

. Chemical Concentration in Belowground Produce : Nonionic Organic Chemicals

where:

C

BG

- produce

- WW = Concentration of c hemical in belowground (BG) produce (i.e., tuber or root vegetable) on a wet

- weight (WW) basis (mg chemical/kg produce WW)*

Cs root

- zone_produce = Average chemical concentration in soil at root

- zone depth in produce

- growing area, on a dry

- weight (DW) basis (mg chemical/kg soil DW)

RCF = Chemical

- specific root concentration factor for tubers and root produce (L soil pore water/kg root WW) *

VG rootveg = Empirical correction factor for belowground produce (i.e., tuber or root vegetable) to account for possib le overestimate of the diffusive transfer of chemicals from the outside to the inside of bulky tubers or roots (based on carrots and potatoes) (unitless) *

Kds = Chemical

- specific soil/water partition coefficient (L soil pore water/kg soil DW)

UCF = Unit s conversion factor of 1 kg/L

* Note that there is only one type of BG produce; hence there are no plant

- type

- specific subscripts

.
The RCF, as developed by Briggs et al . (1982), is the ratio of the chemical concentration in the edible root on a wet

- weigh t basis to its concentration in the soil pore water. RCFs are based on experiments with growth solutions ( hydroponic) instead of soils; therefore, it is necessary to divide the soil concentration by the chemical

- specific soil/water partition coefficient ( Kds )

. There is no conversion of chemical concentratio ns in belowground produce from DW to WW because the values are already on a WW basis

. For nonionic organic chemicals, it is possible to predict RCF values and Kds values ( for a specified soil organic carbon content) from a n estimate of the chemical’s K ow from empirically UCF Kds VG RCF Cs rootveg produce zone root

× × ×

= −

_ WW

- produce

- BG CTRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 15 October 2014 Description of MIRC derived regression models. Those models are shown in HHRAP Appendix A

- 2, Equations A

- 2

- 14 and A

- 2

- 15 ( RCF ) and in Equations A

- 29 and A

- 2

- 10 ( Kds ). The RCF and Kds values so calculate d for many of the chemicals in HHRAP are included in the MIRC databas e (including the values for PAHs and dioxins)

.


### 3.1.1.2.2. Inorganic and Ionic Organic Chemicals
For inorganic chemicals and ionized organic chemicals, it is not possible to predict RCF or Kds value s from K ow . For inorganic chemicals, chemical specific empirical values for the root/soil bioconcentration factor must be used. The root/soil bioconcentration factor, now specified as Br BG

- produce

- DW , must be obtained from the literature for each inorga nic chemical on a DW basis. For inorganic chemicals, therefore, Equation B

- 7 is used instead of Equation B

- 6

.

Equation B

- 7

. C hemical Concentration in Belowground Produce: Inorganic Chemicals

w here :

C BG

- produce

- DW = Concentration of chemical in edible portion of aboveground produce, due to root uptake from soil at root

- zone depth in the produce

- growing area, on a dry

weight (D W) basis (mg/kg produce DW)

Cs root

- zone_produce = Average chemical concentration in soil at root

- zone depth in produce

- growing area (mg/kg soil DW)

Br

BG

- produce

- DW = Chemical

- specific root/soil chemical bioconcentration factor for edible portion of below ground produce (g soil DW / g produce DW)

VG rootveg = Empirical correction factor for belowground produce (as in Equation B

- 6 ) ( unitless )

As for the aboveground produce , the DW estimate of concentration of chemical in the root

v egetables must be transformed to a WW estimate, as shown in Equation B

- 8

. Equation B

- 8 . Conversion of Belowground Produce Chemical Con centration from Dry

- to Wet

- Weight Basis

where:

C BG

- produce

- WW = Chemical concentration in edible portion of belowground produce on a weight

weight (WW) basis (mg/kg produce WW)

C

BG

- produce

- DW = Concentration of chemical in edible portion of abovegroun d produce, due to root uptake from soil at root

- zone depth in the produce

- growing area, on a dry

weight (DW) basis (mg/kg produce DW)

MAF (BG) = Moisture adjustment factor (as in Equation B

- 5 , but single value for below ground produce) (percent water) 1 _ rootveg DW produce BG produce zone root -DW BG-produce VG Br Cs C × ×

= − − −



###      

− × =

− − − − 

### 100 ) 100 ( BG DW produce BG WW produce BG 

### MAF C CTRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 16 October 2014 Description of MIRC 

### 3.1.2. Estimating Chemical Concentrations in Animal Products Chemical concentrations in animal products are estimated based on the amount of chemical consumed by each animal group m through each plant feed type i ( Plant Ch

- In take ( i,m

) ) and incidental ingestion of soil for ground

- foraging animals ( Soil Ch

- Intake (m) )

. Exhibit B

- 5 summarizes the pathways by which chemicals are transferred to these home

- or farm

- raised animal food products

. Note that for a general screening

- level assessment, all of the pathways can be modeled, as is done for EPA’s RTR calculation of screening threshold emission rates for PB

HAPs (EPA 2008b).

The feed options for farm animals in MIRC include forage (plants grown on

- site for animal grazing, such as grass), silage (wet forage grasses, fresh

- cut hay, or other fresh plant material that has been stored and fermented), and grain products grown on the farm

. As seen in Exhibit B

- 5

, the algorithms in MIR C for chemical intake with plant feed s ( Plant Ch

- Intake (i,m) ) are based on the assumptions that beef and dairy cattle consume all three plant feed products, while pigs consume only silage and grain

, and chickens consume only grain

.

Exhibit B

- 5

. Chemical Transfer Pathways for Animal Products

Farm Food Media Chemical Transfer Pathways
Animal Products Beef and total dairy (including milk) • Ingesti on of forage, silage, and grai n a

• Incidental soil ingestion

Pork • Ingestion of silage and grain a

• Incidental soil ingestion Poultry and eggs • Ingestion of grain a

• Incidental soil ingestion

a Chemical concentrations in plant feed (i.e., forage, silage, and grain) are estimated via interm ediate calculations (see Equation B

- 13

, Equation B

- 14

, Equation B

- 3 , and Equation B

- 4 ).

Forage and silage are exposed to the air and can accumulate chemical s via direct deposition of particle

- bound chemical and tra nsfer of vapor

- phase chemical, while a ll animal feed grains are assumed to be protected from the air by a husk or pod (e.g., corn, soybeans)

. All three animal feed products are assumed to a ccumulate chemical via root uptake

. Chemical concentrations are estimated for animal feeds using algorithms analogous to those for aboveground farm produce described above . MIRC uses Equation B

- 9 to calculat e the concentration of chemical in beef, por k, or total dairy and Equation B

- 10 to calculat e the concentration of chemical in poultry or eggs

. The chemical concentration in mammalian farm animals (i.e., beef and pigs) is adjusted using a metabolism factor ( MF ) that accounts for endogenous degradation o f the chemical (see Equation B

- 9 )

. MF is set to 1.0 for chemicals that are not metabolized and for chemicals for which the metabolic degradation rate is unknown

. Although oth er vertebrates, including birds, are likely to have similar metabolic pathways for most chemicals, the health protective assumption is that birds do not metabolize any chemicals; therefore, the MF is omitted from Equation B

- 10 for poultry and eggs.

Equation B

- 9

. Chemical Concentration in Beef, Pork, or Total Dairy



###        

+ × × = 

# ∑

= − − n

i m i Intake Ch m Intake Ch m m mammal 

### Plant Soil MF Ba C

1 )

, ( ) ( ) ( ) (TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 17 October 2014 Description of MIRC where:

C mammal(m) = Concentration of chemical in mammalian animal product m , where m = be ef, pork, or total dairy (mg chemical/kg animal product WW)

Ba (m) = Chemical

- specific biotransfer factor for chemical in diet to chemical in animal food product m , where m = beef, pork, or total dairy ([mg chemical/kg animal product WW] / [mg chemical i ntake/day] or day/kg WW)

MF = Chemical

- specific mammalian metabolism factor that accounts for endogenous degradation of the chemical (unitless)

Soil Ch

- Intake(m) = Incidental ingestion of chemical in surface soils by livestock type m during grazing or con sumption of foods placed on the ground (mg/day); see Equation B

- 11 below

Plant Ch

- Intake(i,m) = For livestock (animal product) type m , ingestion of chemical from plant feed type i (mg chemical/kg livestock WW); see Equation B

- 12 below (If m = beef or total dairy, then n = 3 and i = forage, silage, and grain; m = pork, then n = 2 and i = silage and grain; m = poultry, then n = 1 and I = grain.)

Equation B

- 10

. Chemical Concentration in Poultry or Eggs

where:

C poultry(m) = Concentration of chemical in food product m , where m = poultry or eggs (mg chemical/kg animal product WW)

Ba (m) = Chemical

- specific biotransfer factor for fo od product m , where m = poultry or eggs (day/kg animal product WW)

Soil Ch

- Intake(m) = Incidental ingestion of chemical in surface soils by consumption of food on the ground (mg chemical/day) where m = poultry; see Equation B

- 11

Plant Ch

- Intake(i,m) = For poultry (and eggs), animal m , ingestion of the chemical in plant feed type i (mg chemical/day), which for poultry is limited to grain; see Equation B

- 12

In MIRC, t he incidental ingestion of the chemica l in soils by livestock during grazing or consumption of feed placed on the ground ( Soil Ch

- Intake (m) ) is estimated using empirical soil ingestion rate s ( Qs ) and a soil bioavailability factor for livestock ( Bs ), as shown in Equation B

- 11 . At this time, the default value for Bs in MIRC for all chemicals is 1.0 (i.e., t he chemical in soil is assumed to be 100 percent bioavailable to the animal ). This assumption may be reasonably accurate for the soil surface to which airborne chemic al is deposited. MIRC allows the user to enter a surface soil concentration for areas where livestock forage, Cs S

- livestock , that is distinct from the surface soil concentration input for areas where produce may be grown and where humans might incidentall y ingest soils (see Section 6.1 of this attachment )

. Equation B

- 11

. Incidental Ingestion of Chemical in Soil by Livestock 

# ( ) )

, ( ) ( ) ( ) ( m i Intake Ch m Intake Ch m m poultry Plant Soil Ba C

− − + × =

Bs Cs Qs Soil livestock s m Intake Ch × × =

− − ) (TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 18 October 2014 Description of MIRC where:

Soil Ch

- Intake(m) = Incidental ingestion of the chemical in surface soils by livestock type m during grazing or consumption of foods placed on the ground (mg chemical/day)
Qs (m) = Quantity of soil eaten by animal type m each day (kg soil DW/day)

Cs s

- livestock = Chemical concentration in surface soil in contaminated area where livestock feed (mg chemical/kg soil DW)

Bs = Soil bioavailability factor for livestock (unitless) (assumed to be the same for birds and mammals)

Animal ingestion of the chemical in feed is calcu lated for each type of livestock based on their assumed diets. For m = beef and dairy cattle, chemical intake is estimated for all three feed types: i = forage, silage, and grain

. For pork, chemical intake is estimated only for silage and grain

. The che mical intake for poultry is based on grain consumption only

. The intake of chemical with each f eed type, i

, Plant Ch

- Intake( i,m

) , is calculated separately according to Equation B

- 12 . Note that the animal feed ingestion rates are on a dry

- weight (DW) basis; hence, no DW to wet weight (WW) conversion is needed.

Equation B

- 12

. Ingestion of Chemical in Feed by Livestock

where:

Plant Ch

- Intake(i,m) = Ingest ion of chemical in plant feed type i (mg chemical/day), where i = forage, silage, or grain, for livestock type m

F

(i,m) = Fraction of plant feed type i obtained from contaminated area used to grow animal feed, where I = forage, silage, or grain (unitless) for livestock type m

Qp (i,m) = Quantity of plant feed type i consumed per animal per day (kg plant feed DW/day), where i = forage, silage, or grain, for livestock type m

C

feed(i) = Concentration of chemical in ingested plant feed type i (mg chemical/kg plant feed DW), where i = forage, silage, or grain

The concentrations of chemical in the three different types of plant feeds for livestock are calculated according to Equation B

- 13

. The equation is the same as that for abovegro und produ ce in Equation B

- 1 , w ith the exception that the concentrations are for plants used as animal feeds (not produce consumed by humans) and all types of plant feed (i.e., forage, silage, and grain) are aboveground

. Equation B

- 13

. Chemical Concentration in Livestock Feed (All Aboveground)

where:

C feed(i) = Concentration of chemical in plant feed type i on a dry

- weight (DW) basis (mg chemical/kg p lant feed DW), where i = forage, silage, or grain ) ( )

, ( )

, ( )

, ( i feed m i m i m i Intake Ch C Qp F Plant × × =

−

(i) (i) feed(i) feed(i) 

### Pv Pd Pr C + + =TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 19 October 2014 Description of MIRC Pr

feed(i) = Concentration of chemical in plant feed type i due to root uptake from soil (mg/kg DW), where i = forage, silage, or grain; see Equation B

- 14 below

Pd (i) = Concentrat ion of chemical in plant feed type i due to wet and dry deposition of particle

- phase chemical (mg/kg DW), where i = forage, silage, or grain; when i = grain, the Pd term equals zero

Pv (i) = Concentration of chemical in plant feed type i due to air

- to

- pla nt transfer of vapor

- phase chemical (μg/g [or mg/kg] DW) where i = forage, silage, or grain; when i = grain, the Pd term equals zero

MIRC calculates the chemical concentration in animal feed due to root uptake from the soil using Equation B

- 14

. The equation is the same as Equation B

- 2 , except that a Br value appropriate to grasses is used and MIRC allows for different soil concentrations in the area used to grow animal feed than in the a rea used to grow produce for human consumption (see Section 6.1 of this attachment , user inputs) . Note that for feed type i = grains , the Pd and Pv terms do not apply (are set to zero), because the feed products (i.e., corn ke rnels, soy beans) are protected from the air (i.e., by husks, pods).

Equation B

- 14 . Chem ical Concentration in Livestock Feed Due to Root Uptake

where:

Pr feed(i) = Concentratio n of chemical in plant feed type i due to root uptake from soil on a dry

- weight (DW) basis (mg chemical/kg plant feed DW), where i = forage, silage, or grain

Cs root

- zone_feed(i) = Average chemical concentration in soil at root

- zone depth in area used to grow plant feed type i (mg chemical/kg soil DW), where i = forage, silage, or grain

Br

feed(i) = Chemical

- specific plant

- soil bioconcentration factor for plant feed type i (kg soil DW/kg plant feed DW), where i = forage, silage, or grain

The algorithms us ed to calculate Pd (i) and Pv (i) when plant feed type i = forage and silage are identical to those used to calculate Pd (i) and Pv (i) for aboveground exposed produce (i.e., Equation B

- 3 and Equation B

- 4 , re spectively ).

There are no conversion s of DW feed to WW feed , because all feed ingestion rates for liv es tock are based on DW feed.



### 3.2. Chemical Intake Calculations for Adults and Non

- Infant Children MIRC calculates human chemical intake rates from the ingesti on of home

- grown foods as average daily doses (ADDs) normalized to body weight for each age grou p, chemical, and food type separately

. ADDs, calculated using Equation B

- 15 , are expressed in milligrams of chemical per kilogram of receptor body weight per day (mg/kg

- day).

Equation B

- 15

. Average Daily Dose for Specified Age Group and Food Type ) ( ) (

_ ) ( P i feed i feed zone root i feed Br Cs r × =

−

               

× × × ×

= days 365 (y)

(y) (y) (y) (i) (y,i) (i) (y,i) EF
AT BW ED FC IR C ADDTRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 20 October 2014 Description of MIRC w here :

ADD (y,i) = Average daily dose for age group y from food type or ingestion medium i (mg chemical/kg body weight

- day)

C (i) = Concentration of chemical in food type i harvested from the contaminated area (mg chemical/kg food or mg food/L water)

IR (y,i) = Ingestion rate for age group y of food type i (kg/day or L/day)

FC (i) = Fraction of food type i that was harvested from contaminated area (unitless)

ED (y) = Exposure duration for age group y (years)

BW (y) = Body weight for age group y (kg)

AT (y) = Averaging time for calculation of daily dose (years) for age group y , set equal to ED in MIRC

EF (y) = Annual exposure frequency for age group y (days)

Equation B

- 15 takes into account the chemical concentration in each food type i (or in water), the quantity of food brought into the h ome for consumption , the loss of some of the mass of the foods due to preparation and cooking, how much of the food is consumed per year, the amount of the food obtained from contaminated areas, and the consumer’s body weight (EPA 2011a, 2003a). In MIRC, ADDs are calculated separately for each chemical, home

- grown food type, and consumer age group.

ADD values, expressed as intakes, not absorbed doses, are appropriate for comparison with RfDs and for use with cancer slope factors ( C SFs) to estimate risk, as discussed in Section 5 of this attachment

. An exception is for the breast

- milk exposure pathway, where calculating the dose available to and absorbed by the nursing infant is rel ated to the dose absorbed by the mother as di scussed in Section 3.4 of this attachment

. MIRC evaluates only one exposure scenario at a time. For screening

- level assessments, all components of this equation are assumed to remain constant for consumers in a given age gro up over time (e.g., seasonal and annual variations in diet are not explicitly taken into account)

. To calculate an ADD (y ,i ) from the contaminated area for food group i over an entire lifetime of exposure, age

- group

- specific ingestion rates and body weight s are used for the age groups described in Section 2.3 of this attachment . In MIRC, the averaging time used to calculate the daily dose for an age group ( AT y ) is equal to the exposure duration for that group ( ED y ); therefore t hese variables drop out of Equation B

- 15

. For each chemical included in a screening scenario, total average daily exposure for age group y ( ADD (y) ) is estimated as the sum of chemical intake from all ingestion pathways combined: Note that the last exposure pathway is limited to infants

.

• Incidental soil ingestion;

• Ingestion of fish;

• Ingestion of homegrown fruits (exposed and protected);

• Ingestion of homegrown vegetables (exposed, protected, and root);

• Ingestion of animal products from home

- raised animals:

– Milk and other dairy products from cows,TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 21 October 2014 Description of MIRC – Beef products,

– Pork products, and

– Poultry and eggs;

• Ingestion of drinking water from specified source; and

• Ingestion of breast milk by infants.

The algorithms for the f irst six exposure pat hways listed above are described in Sections 3.2.1 through 3.2.6 of this attachment

. The algorithms for the breast

- milk ingestion pathway are described in Section 3.4

.


### 3.2.1. Chemical Intake from Soil Ingestion
Equation B

- 16 shows the equation used to estimate chemical intake through incidental ingestion of soil

. Equation B

- 16

. Chemical Intake from Soil Ingestion

where:

ADD Soil(y) = Average daily chemical intake from incidental ingestion of soil or ingestion by child in age group y (mg chemical/kg body weight

- day)

C

Soil = Concentration of chemical in soil from contamina ted area on a dry

- weight (DW) basis (μg/g soil DW)

IR Soil(y) = Soil ingestion rate for age group y (g DW/day)

FC Soil = Fraction of soil ingested that is from contaminated area (unitless)

BW (y) = Body weight for age group y (kg)

EF = Exposure frequency; number of days per year of exposure for family(ies) as specified for scenario ( ≤ 365 days)

Note: MIRC saves soil ingestion rates in units of mg/day (not g/day); therefore, there is an additional 0.001 g/mg conversion unit in the actual MIRC algorithm tha n shown here

.


### 3.2.2. Chemical Intake from Fish Ingestion Ingestion of locally caught fish is included as a possible exposure pathway in MIRC ( Equation B

- 17 ). Two types of fish are included in the exposure algorithm: tr ophic level 3.5 (abbreviated as TL3) fish, equivalent to benthic carnivores such as catfish and trophic level 4 (TL4) fish in the water column, equivalent to game fish such as lake trout and walleye. The chemical concentration in fish in Equation B

- 17 is estimated as the consumption

- weighted chemical concentration using Equation B

- 18

.        
   

 

   

 

× × ×

= days 365 EF
BW μg mg 0.001 FC IR C
ADD
(y) Soil Soil(y) Soil

(y) SoilTRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 22 October 2014 Description of MIRC Equation B

- 17

. Chemical Intake from Fish Ingestion

Equation B

- 18

. Consumption

- weighted Chemical Concentration in Fish



### C Fish 

### = ( C FishTL3 

### × F TL3 

### ) + ( C FishTL4 

### × F TL4 

### )

w here:

ADD Fish(y) = Average daily chemical intake from ingestion of local fish for age group y (mg/kg

day)

L1 Fish * = Weight of fish brought into home that is discarded during preparation (e.g., head, bones, liver, other viscera, belly fat, skin with fat) (unitless)

L2 Fish * = Loss of weight during cooking, such as evaporation and loss of fluids into pan (unitless)

C

Fish TL3 = Chemical concentration in whole fish for trophic level 3.5 (TL3) fish on a wet

weight (WW) basis (mg/kg WW)

C

Fish TL4 = Chemical concentration in whole fish for trophic level 4 ( TL4 ) fish on a wet

- weight (WW) basis (mg/kg WW)

F

TL3 = Fraction of fish intake that is from TL3 (unitless)

F

TL4 = Fraction of fish intake that is from TL4 (unitless)

C

Fish = Consumption

- weighted mean chemical concentr ation in total fish (i.e., as specified by Equation B

- 18 ) (mg/kg WW)

FC Fish = Fraction of local fish consumed derived from contaminated area (unitless)

BW (y) = Body weight for age y (kg)

IR Fish(y) * = Local fish ingestion rate f or age y (g WW/day)

EF = Exposure frequency; number of days per year of exposure for family(ies) as specified for scenario ( ≤ 365 days)

* Parameter values must be internally consistent. In contrast to the tables included in MIRC for ingestion rate opti ons for homegrown food products, which are based on the products as brought into the home from the field (see Section 6.3.3 of this attachment ), the tables of fish ingestion rate options included in MIRC are on an “as consumed” basis (i.e., after preparation and cooking losses), and L1 and L2 therefore are set equal to zero. If the user wishes to enter local fish ingestion rates on an “as harvested” basis, the user also should enter L1 and L2 values as specified in Section 6.4.3 of this attachment

. When whole fish are prepared for cooking, it is usual for the viscera, head, and fins to be removed, particularly for larger fish

. Many persons also remove (or do not eat) the skin, bones, and belly fat

. EPA has, therefore, estimated the proportion of the weight of whole fish that tends to be lost during preparation and cooking across a variety of fish species ( EFH; EPA 2011a ) and included those losses in its HHRAP algorithms for chemical intake from fi sh (L1 Fish and L2 Fish in Equation B

- 17 )

. 

## ( ) ( ) days 365 g kg 001 0
2 1 1 1        

×        

× × ×

×

− ×

− = EF
BW FC

. IR C
L L ADD
(y) Fish Fish(y) Fish

Fish Fish Fish(y)TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 23 October 2014 Description of MIRC 

### 3.2.3. Chemical Intake from Fruit Ingestion Average daily doses of a chemical from homegrown exposed fruits are calculated separately for exposed and protected fruits ( E quation B

- 19 and Equation B

- 20 , respectively).

Equation B

- 19 . Chemical Intake from Consumption of Exposed Fruits

Equation B

- 20 . Chemical Intake from Consumption of Protected Fruits

where:

ADD ExpFruit(y) ADD ProFruit(y) = Average daily chemical intake from ingestion of exposed fruit or protected fruit (depending on subscrip t) (mg chemical/kg body weight

- day)

L1 ExpFruit = Mean reduction in fruit weight resulting from removal of skin or peel, core or pit, stems or caps, seeds and defects, and from draining liquids from canned or frozen forms (unitless)

L1 ProFruit = Mean redu ction in fruit weight that results from paring or other preparation techniques for protected fruits (unitless)

L2 ExpFruit = Mean reduction in fruit weight that results from draining liquids from cooked forms of the fruit (unitless)

C

ExpFruit C

ProFruit = Chemical concentration in whole exposed fruits or whole protected fruits (depending on subscript) on a wet

- weight (WW) basis (mg chemical/kg exposed fruit WW)

EF = Exposure frequency; number of days per year of exposure for family(ies) as specified for sc enario ( ≤ 365 days)

FC ExpFruit FC ProFruit = Fraction of exposed fruits or protected fruits (depending on subscript) obtained from contaminated area (unitless)

IR ExpFruit(y) IR ProFruit(y) = Ingestion rate of home

- grown exposed fruits or protected fruits ( depending on subscript) for age y (g WW/kg body weight

- day)

Fruit ingestion rates in the survey were based on weights of unprepared fruits (e.g., one apple; one pear) or the weight of a can of fruit (e.g., 8 oz can)

. The weight of the fruit ingested is less than the initial weight owing to common preparation actions ( L1 ExpFruit and L1 ProFruit ; e.g., coring apples and pears; pe e ling apples; pitting cherries)

. Cooking of exposed fruit (e.g., berries, apples, peaches) often results in further weight loss t hat results from liquids lost during cooking and drained from the cooking vessel ( L2 ExpFruit ). EPA has assumed that cooking of protected fruit results in no loss of weight for the fruit.



### 3.2.4. Chemical Intake from Vegetable Ingestion MIRC includes three separat e algorithms for homegrown vegetables adapted from EPA’s HHRAP Modeling System (EPA 2005a ): one for exposed vegetables such as asparagus, broccoli, lettuce, and tomatoes ( although they are actually a fruit); one for protected vegetables 

# ( ) ( )        

×        

× × × ×

− ×

− = days 365 g kg 001 0 2 1 1 1 EF FC

. IR C L L ADD ExpFruit ) ExpFruit(y ExpFruit ExpFruit ExpFruit ) ExpFruit(y



## ( )        

×        

× × × ×

− = days 365 g kg 001 0 1 1 EF FC

. IR C L ADD ProFruit ) ProFruit(y ProFruit ProFruit ) ProFruit(yTRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 24 October 2014 Description of MIRC such as corn, cabba ge, soybeans, and peas; and one for root vegetables such as carrots, beets, and potatoes (see Equation B

- 21

, Equation B

- 22 , and Equation B

- 23 , respectively ).

Equation B

- 21 . Chemical Intake from Exposed Vegetables

Equation B

- 22 . Chemical Intake from Protected Vegetables

Equation B

- 23 . Chemical Intake from Root Vegetables

where:

ADD ExpVeg(y) ADD ProVeg(y) ADD RootVeg(y) = Average chemical intake from ingestion of exposed vegetables, protected

ve getables, or root vegetables (depending on subscript) for age group y (mg chemical/kg body weight

- day)

L1 ExpVeg = Mean net preparation and cooking weight loss for exposed vegetables (unitless); includes removing stalks, paring skins, discarding damaged le aves

L1 ProVeg = Mean net cooking weight loss for protected vegetables (unitless); includes removing husks, discarding pods of beans and peas, removal of outer leaves

L1 RootVeg = Mean net cooking weight loss for root vegetables (unitless); includes losses from removal of tops and paring skins

L2 RootVeg = Mean net post cooking weight loss for root vegetables from draining cooking liquids and removal of skin after cooking (unitless)

C

ExpVeg C

ProVeg C

RootVeg = Chemical concentration in exposed vegetables, p rotected vegetables, or root vegetables (depending on subscript) on a wet

- weight (WW) basis (mg chemical/kg vegetable WW)

EF = Exposure frequency; number of days per year of exposure for family(ies) as specified for scenario ( ≤ 365 days) FC ExpVeg FC ProVeg FC RootVeg = Fraction of exposed vegetables, protected vegetables, or root vegetables (depending on subscript) obtained from contaminated area (unitless)

IR ExpVeg(y) IR ProVeg(y) IR RootVeg(y) = Ingestion rate of exposed vegetables, protected vegetables, or root vegetables (depending on subscript) for age group y (g vegetable WW/kg body weight

- day) 

# ( )        

×        

× × × ×

− = days 365 g kg 001 0 1 1 EF FC

. IR C L ADD ExpVeg ExpVeg(y) ExpVeg ExpVeg ExpVeg(y)



# ( )        

×        

× × × ×

− = days 365 g kg 001 0 1 1 EF FC

. IR C L ADD ProVeg ProVeg(y) ProVeg ProVeg ProVeg(y)



# ( ) ( )        

×        

× × × ×

− ×

− = days 365 g kg 001 0 2 1 1 1 EF FC

. IR C L L ADD RootVeg RootVeg(y) RootVeg RootVeg RootVeg RootVeg(y)TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 25 October 2014 Description of MIRC 

### 3.2.5. Chemical Intake from Animal Product Ingestion Calculations of chemical intake from the consumption of farm animals and related food products are provided below i n Equation B

- 24 through Equation 2

- 28 for homegrown beef, dairy (milk), pork, poultry, and eggs, respectively.

Equation B

- 24

. Chemical Intake from Inge stion of Beef

where:

ADD Beef(y) = Average daily chemical intake from ingestion of beef for age group y (mg/kg

day)

L1 Beef = Mean net cooking loss for beef (unitless)

L2 Beef = Mean net post cooking loss for beef (unitless)

C

Beef = Concentration of cont aminant in beef (mg/kg WW))

EF = Exposure frequency; number of days per year of exposure for family(ies) as specified for scenario ( ≤ 365 days)

IR

Beef(y) = Ingestion rate of contaminated beef for age group y (g WW/kg

- day)

FCBeef = Fraction of beef cons umed raised on contaminated area or fed contaminated silage and grains (unitless)

Equation B

- 25

. Chemical Intake from Dairy Ingestion

where :

ADD Dairy(y) = Average daily chemi cal intake from ingestion of total dairy for age group y (mg/kg

- day)

C

Dairy = Average concentration of contaminant in total dairy (mg/kg WW)

EF = Exposure frequency; number of days per year of exposure for family(ies) as specified for scenario ( ≤ 365 day s)

IR Dairy(y) = Ingestion rate of contaminated total dairy for age group y (g WW/kg

- day)

FC Dairy = Fraction of total dairy products from contaminated area (unitless)

Equation B

- 26

. Chemical Intake from Pork Ingestion 

## ( ) ( )        

×        

× × × ×

− ×

− = days 365 g kg 001 0 2 1 1 1 EF FC

. IR C L L ADD Beef Beef(y) Beef Beef Beef Beef(y)

       

×        

× × × = days 365 g kg 001 0 EF FC

. IR C ADD Dairy Dairy(y) Dairy Dairy(y)



## ( ) ( )        

×        

× × × ×

− ×

− = days 365 g kg 001 0 2 1 1 1 EF FC

. IR C L L ADD Pork Pork(y) Pork Pork Pork Pork(y)TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 26 October 2014 Description of MIRC where:

ADD Pork(y) = Average daily chemical intake from ingestion of pork for age group y (mg/kg

day)

L1 Pork = Mean net cooking loss for pork (unitless); includes dripping and volatile losses during cooking; av eraged over various cuts and preparation methods

L2 Pork = Mean net post cooking loss for pork (unitless); includes losses from cutting, shrinkage, excess fat, bones, scraps, and juices; averaged over various cuts and preparation methods

C

Pork = Concentra tion of contaminant in pork (mg/kg WW)

EF = Exposure frequency; number of days per year of exposure for family(ies) as specified for scenario ( ≤ 365 days)

IR Pork(y) = Ingestion rate of contaminated pork for age y (g WW/kg

- day)

FC Pork = Fraction of pork o btained from contaminated area (unitless)

Equation B

- 27

. Chemical Intake from Poultry Ingestion

where:

ADDPoultry(y) = Average daily dose (chemical intake) from ingestion of poultry (mg/kg

- day) L1Poultry = Mean net cooking loss for poultry (unitless) L2Poultry = Mean net post cooking loss for poultry (unitless)

CPoultry = Concentration of chemical in poultry (mg/kg WW)

EF = Exposure frequency; number of days per year of e xposure for family(ies) as specified for scenario ( ≤ 365 days) IRPoultry(y) = Ingestion rate of poultry for age group y (g WW/kg

- day)

FC Poultry = Fraction of poultry from contaminated area or fed contaminated grains (unitless)

The reduction in the weigh t of beef, pork , and poultry during and after cooking may correlate with an increase or decrease in the concentration of the chemical in the food as consumed depending on the chemical and depending on the cooking method.

Equation B

- 28 . Chemical Intake from Egg Ingestion

where:

ADD Egg(y) = Average daily chemical intake from ingestion of eggs for age group y (mg/kg

day) 

# ( ) ( )        

×        

× × × ×

− ×

− = days 365 g kg 001 0 2 1 1 1 EF FC

. IR C L L ADD Poultry Poultry(y) Poultry Poultry Poultry Poultry(y)

       

×        

× × × = days 365 g kg 001 0 EF FC

. IR C ADD Egg Egg(y) Egg Egg(y)TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 27 October 2014 Description of MIRC C

Egg = Concentration of contaminant in eggs (mg/kg WW)

EF = Exposure frequency; number of days per year of exposure for family(ies) as specified for scenario ( ≤ 365 days)

IR Egg(y) = Ingestion rate of contaminated eggs for age group y (g WW/kg

- day)

FC Egg = Fraction of eggs obtained from contaminated area (unitle ss)



### 3.2.6. Chemical Intake from Drinking Water Ingestion If the user chooses to evaluate chemical ingestion via drinking water , the user specifies a chemical concentration in g/L (equivalent to mg/mL) based on their particular scenario. The chemical concentrati on could represent water from groundwater wells, community water, nearby surface waters, or other source

. For this exposure pathway, ingestion rates are in units of milliliters of water per day (mL/day ) (see Equation B

- 29 ).

Equ ation B

- 29 . Chemical Intake from Drinking Water Ingestion

where:

ADD DW(y) = Average daily chemical intake from ingestion of drinking water from local residential water source for age group y (mg/kg

- day)

C

DW = Concentration of contaminant in drinking water (g/L)

IR DW(y) = Drinking water ingestion rate for age group y (mL/day)

FC DW = Fraction of drinking water obtained from contaminated area (unitless)

BW (y) = Body weight of age group y (kg)

EF = Exposure frequency; number of days per year of exposure for family(ies) as specified for scenario ( ≤ 365 days)



### 3.3. Total Chemical Intake

To estimate the total ADD

, or intake of a chemical from all of the exposure media that a single ind ividual in each age group is expected to contact (e.g., soil, local fish, five types of home

grown produce, and five types of home

- raised animals or animal products), the media

- specific chemical intakes are summed for each age group

. Total average daily e xposure for a particular age group y ( ADD (y) ) is estimated as the sum of chemical intake from all ingestion pathways combined, as illustrated in Equation B

- 30 through Equation B

- 35 below.

Equations B

- 30 to B

- 35. Total Average Daily Dose of a Chemical for Different Age Groups Equation B

- 30

. Equation B

- 31

. Equation B

- 32

.        

×         × ×

= days 365 EF
BW FC IR C ADD
(y) DW DW(y) DW DW(y)

breastmilk ADD ADD = < )

1 ( 

# ∑

= −

− 

### = n

i i 

### ADD ADD 1 )

, 2 1 ( ) 2 1 ( 

# ∑

= −

− 

### = n

i i 

### ADD ADD 1 )

, 5 3 ( ) 5 3 (TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 28 October 2014 Description of MIRC Equation B

- 33

. Equation B

- 34

. Equation B

- 35

.

where i represents the i th food type or ingestion medium and n equals the total number of food types or ingestion media, and ADD parameters are defined below:

ADD (<1) = T otal average daily dose of chemical for infants less than one year from ingestion of breast milk (mg/kg

- day)

ADD (1

– 2) = Total average daily dose of chemical from all ingestion sources for children ages 1 through 2 years (mg/kg

- day)

ADD (3

– 5) = Total aver age daily dose for children ages 3 through 5 years (mg/kg

- day)

ADD (6

– 11) = Total average daily dose for children ages 6 through 11 years (mg/kg

- day)

ADD (12

– 19) = Total average daily dose for children ages 12 through 19 years (mg/kg

- day)

ADD (adult) = Tot al average daily dose for adult age 20 up to 70 years (mg/kg

- day)

The lifetime average daily dose ( LADD ) is calculated as the time

- weight average of the ADD values for each age group ( Equation B

- 36 ).

Equation B

- 36

. Lifetime Average Daily Dose (LADD)

The time

- weighting factors simply equal the duration of exposure for the specified age category in years divided by the total lifespan, as sumed to be 70 years

.


### 3.4. Chemical Intake Calculations for Nursing Infants The scientific literature indicates that infants can be exposed to some chemical s via their mothers’ breast milk . The magnitude of the exposure can be estimated from information on t he mother’s exposure, data on the partitioning of the chemical into various compartments of the mother’s body and into breast milk, and information on the infant ’ s consumption of milk and absorption of the chemica l

. To add this exposure pathway to the MIR C application

, we adapted exposure algorithms and default assumptions from EPA’s Methodology for Assessing Health Risks Associated with Multiple Pathways of Exposure to Combustor Emissions ( EPA 1998)

, hereafter referred to as MPE, as explained below

.
Not e that this pathway generally is of most concern for lipophilic bioaccumulative chemicals (e.g., dioxins) that can cause developmental effects

. The period of concern for the more hydrophilic chemicals that cause developmental effects generally is earlier, that is

, from conception to birth . Hydrophilic chemicals generally exchange well between the maternal and fetal blood supplies at the placenta. 

# ∑

= − − 

### = n

i i 

### ADD ADD 1 )

, 11 6 ( ) 11 6 ( 

# ∑

= −

− 

### = n

i i 

### ADD ADD 1 )

, 19 12 ( ) 19 12 ( 

# ∑

= 

### = n

i i adult adult 

### ADD ADD 1 )

, ( ) (



###      
+      
+      
+      
+      
+      

= < 

### 70 50
70 8
70 6
70 3
70 2
70

1 (adult) (12-19) (6-11) (3-5) (1-2) 1) ( 

### ADD ADD ADD ADD ADD ADD LADDTRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 29 October 2014 Description of MIRC 

### 3.4.1. Infant Average Daily Absorbed Dose The average daily dose of chemical absorbed by the infant ( DAI inf

) is estim ated in MIRC with Equation B

- 37 . This basic exposure equation relies on the concentration of the chemical in the breast milk, the infant’s breast

- milk ingestion rate ( IR milk ), the absorption efficiency of the chemical by the oral route of exposure ( AE inf

), the bodyweight of the infant ( BW inf

), and the duration of breast feeding ( ED ). Equation B

- 37 is EPA’s (EPA 1998) modification of an average daily dose for the infant model first published by Smith (198 7) and includes variables for both the concentration of the chemical in the breast milk fat ( C milkfat ) and the concentration of the chemical in the aqueous phase of breast milk ( C aqueous ). The remainder of the DAI inf

- associated equations assume that most chemicals of concern will partition either to the lipid phase or to the aqueous phase of breast milk, although some chemicals may partition significantly to both phases of milk. Thus, the remaining equations in MIRC assume that either C milkfat or C aqueous is equal to zero and hence drops out of the equation.

For the parameters in Equation B

- 37 (and the equations that follow) that are not calculated from another equation, an EPA default value and options for other values availabl e in MIRC for the infant breast

- milk

- exposure pathway are described in Section 6.4 of this attachment . The user also can overwrite those parameter values with a different value from the literature as appropriate.

Equation B

- 37

. Average Daily Dose of Chemical to the Nursing Infant

w here:

DAI inf = Average daily dose of chemical absorbed by infant (mg chemical/kg body weight

- day)

C

milkfat = Concentra tion of chemical in lipid phase of maternal milk (mg chemical/kg milk lipid; calculated using Equation B

- 38 )

f mbm = Fraction of fat in breast milk (unitless)

C

aqueous = Concentration of chemical in aqueous phase of maternal mil k (mg chemical/kg aqueous phase milk; calculated using Equation B

- 42 )

IR

milk = Infant milk ingestion rate over the duration of nursing (kg milk/day)

AE inf = Absorption efficiency of the chemical by the oral route of exposure (i .e., chemical

- specific fraction of ingested chemical that is absorbed by the infant) (unitless)

ED = Exposure duration, i.e., duration of breast feeding (days)

BW inf = Body weight of infant averaged over the duration of nursing (kg)

AT = Averaging tim e associated with exposure of interest; equal to ED (days)

As mentioned above, Equation B

- 37 includes terms for the chemical in both the lipid

- and non

lipid phases of milk. The remaining equations, however, assume that a chemi cal of concern will partition to the lipid or aqueous phase of breast milk, but not to both. Different models are used to estimate C milkfat (described in Section 3.4.2 of this attachment ) and C aqueous (described in Section 3.4.3 of this attachment ). 

## ( ) ( ) 

# ( ) 

# [ ]

AT BW ED AE IR f C f C DAI

inf inf milk mbm aqueous mbm milkfat inf 

### × × × ×

− × + ×

= 1TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 30 October 2014 Description of MIRC 

### 3.4.2. C hemical Concentration in Breast Milk Fat When developing the Methodology for Assessing Health Risks Associated with Multiple Pathways of Exposure to Combustor Emissions (MPE) (EPA 1998), EPA reviewed th ree first

order kinetics models for estimating chemical concentration in breast milk fat. The model selected for use in MIRC is the model selected in MPE. The other two models were not considered in MPE because one used a biotransfer factor (BTF) approac h considered more of a screening model than a predictive tool ( Travis et al. 1988 ) and the other assumed that the contaminant concentration in the maternal fat compartment is at steady state and that the concentration in breast milk fat is the same as in m aternal body fat ( Smith 1987 ). The model in MIRC is a changing

- concentration model that EPA adapted from a model by Sullivan et al

. (1991). The model, shown in Equation B

- 38 , estimates the average chemical concentration in the b reast milk over the entire period of breast feeding by reference to a maximum theoretical steady

- state concentration. Studies of lipophilic chemicals such as dioxins suggest that concentrations in the maternal milk are highest during the first few weeks o f breast feeding and then decrease over ti me (ATSDR 1998). Equation B

- 38 accounts for the changing concentration in breast milk fat, but estimates one average value to represent the concentration over the entire duration of breas t feeding. The model is dependent on the maternal body burden of the chemical and assumes that the chemical concentration in breast milk fat is the same as the concentration in general maternal body fat. According to reviewers of the model, this assumpti on warrants further investigation because milk fat appears to be synthesized in the mammary glands and may have lower chemical concentrations than general body fat stores (EPA 2001a).

Equation B

- 38

. C hem ical Concentration in Breast Milk Fat

w here:

C milkfat = Concentration of chemical in lipid phase of maternal milk (mg chemical/kg lipid)

DAI mat = Daily absorbed maternal chemical dose (mg chemical/kg maternal body weight

day; calculated using Equation B

- 39 )

f f = Fraction of total maternal body burden of chemical that is stored in maternal fat (mg chemical in body fat / mg total chemical in whole body; value from literature or EPA default

- see Section 6.5 of this attachment )

k

elim = Chemical

- specific total elimination rate constant for elimination of the chemical by non

- lactating women (per day; e.g., via urine, bile to feces, exhalation; value from literature or calculated using Equation B

- 40 )

f fm = Fraction of maternal body weight that is fat stores (unitless)

k

fat_elac = Chemical

- specific rate constant for total elimination of chemical in the lipid phase of milk during nursing (per day; value from literature or calculat ed using Equation B

- 41 )

t bf = Duration of breast feeding (days)

t pn = Duration of mother’s exposure prior to parturition and initiation of breast feeding (days) 

# ( )        

−        

− − × + × × ×

= − − bf elac fat pn elim t k

elac fat

elim t k

bf elac fat elac fat

elim

fm elim f

mat milkfat e k k e t k k k
f k f DAI C

_ 1 1 1

_ _ _TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 31 October 2014 Description of MIRC Equation B

- 38 relies on the daily mate rnal absorbed intake ( DAI mat ) to determine the concentration of the chemical in the breast milk fat. DAI mat is multiplied by the fraction of the chemical that is stored in maternal fat ( f f ) to determine the amount (i.e., mass) of chemical in the fat. Thi s product, divided by the chemical

- specific elimination rate constant ( k elim ) for non

lactating adult women and the fraction of the mother’s weight that is fat ( f fm ) , represents the maximum theoretical steady

- state concentration of the chemical in an adult woman. If used alone to estimate the chemical concentration in breast milk fat, the equation as explained thus far is likely to overestimate the chemical concentration in milk fat because it does not account for losses due to breast feeding. Alone, this term ( DAI mat f f / k elim f fm ) also assumes that the biological half

- life of the chemical in the mother’s breast milk fat is small relative to the duration of the mother’s exposure. However, for chemicals with half

- lives that are longer than the exposure d uration, which are the chemicals of concern in the applications of MIRC to date, an additional term is needed to determine the average concentration in the milk fat over the duration of her exposure.

To account for breast feeding losses and longer chemic al half

- lives in the mother than the exposure duration, an additional term is included in Equation B

- 38 . This term includes a fraction dependent on two rate constants, k elim and the elimination constant for a lipophilic chemical in lactating women via the lipid phase of breast milk ( k fat

_ elac ), the duration of the mother’s chemical exposure prior to nursing ( t pn ), and the duration of breast feeding ( t bf

). The whole body concentration ( DAI mat f f / k elim f fm ), the maximum theoretic al steady

- state concentration, is multiplied by the rate of elimination averaged over the duration of the mother’s exposure, including her exposure prior to and during lactation. To review the derivation of Equation B

- 38

, see App endix B of MPE ( EPA 1998).

To estimate an average daily dose absorbed by an infant’s mother, or DAI mat , the average daily dose ( ADD ) (in mg/kg

- day) for the chemical from all sources that MIRC calculates for adults ( ADD (adult) , described in Section 3.3 of this attachment

, Equation B

- 35 ), is multiplied by an absorption efficiency ( AE mat ) or fraction of the chemical absorbed by the oral route of exposure, as shown in Equation B

- 39 . The value for AE mat can be estimated from absorption efficiencies for adults in general. Available data for some chemicals, in particular some inorganic compounds, indicate AE values for ingestion exposures of substantially less than 100 percent

. For a few of these chemicals, data also indicate lower AEs for the chemical when ingested in food or in soil than when ingested in water (e.g., cadmium)

. For a screening level assessment, however, it is reasonable to either assume 100 percent for the AE mat or to use the higher AE mat of the food and water AE mat values if available; hence, a single AE mat parameter is included in Equation B

- 39

. Equation B

- 39

. Daily Maternal Absorbed Intake

w here:

DAI mat = Daily maternal dose of chemical absorbed from medium i (mg/kg

- day)

ADD (adult) = Average daily dose to the mother (mg/kg

- day) (calculated by MIRC

– see Section 3.3 of this attac hment

, Equation B

- 35 )

AE mat = Absorption efficiency of the chemical by the oral exposure route (i.e., chemical

specific fraction of ingested chemical that is absorbed) by the mother (unitless) (value from literature or EPA defaul t

– see Section 6.4 of this attachment ) mat (adult) mat AE ADD DAI × =TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 32 October 2014 Description of MIRC Equation B

- 35

, used to calculate ADD (adult), is based on many medium

- specific ingestion rates that are normalized to body weight . The adult body weights to wh ich the homegrown food ingestion rates are normalized are the body weights of the consumers in the original USDA survey (see Section 6.3.3 of this attachment ), which included both males and females

. An assumption in the breast

- milk exposure pathway is that those ingestion rates also are applicable to nursing mothers. The original data for ingestion rates for soil, drinking water, and fish are on a per person basis for males and females combined. MIRC divides those chemical in takes by an adult body weight for males and females combined as specified by the user (e.g., 71.4 kg mean value) to estimate the ADD normalized to body weight from those sources. If the user finds that those exposure media contribute the majority of the c hemical intake for the risk scenario under consideration, the user may use alternative ingestion rates for those media and alternative body weights for nursing women, as described in Section 6.5 of this attachment

.
Elimination rates for chemicals often are reported as the half

- life of the chemical in the body following a known dose of chemical. Many chemicals exhibit a two

- phase elimination process, the first being more rapid than the second. For screening risks for persistent and bioaccumulative chemicals, the half

- life of the slower phase of elimination, presumably from

non

- blood compartments of the body, is the more important of the two. Assuming first

- order kinetics, Equation B

- 40 is used to conve rt a measured half

- life for elimination of a chemical for adults or non

- lactating women to an elimination rate constant (EPA 1998). The equation can be used to estimate any kind of chemical loss rate constant from a measured chemical half

- life.

Equation B

- 40

. Biological Elimination Rate Constant for Chemicals for Non

- lactating Women

where:

k elim = Chemical

- specific elimination rate constant for elimination of the chemical fo r
non

- lactating women (per day; e.g., via urine, bile to feces, exhalation)

ln2 = Natural log of 2 (unitless constant)

h = Chemical

- specific biological half

- life of chemical for non

- lactating women (days)

For chemicals transferred from the body of lact ating women to breast milk, the rate of chemical elimination is augmented by the rate of chemical loss via the milk through breast feeding. The total elimination rate for lactating women sometimes is measured directly and reported in the literature. Wher e direct measurements are not available, and for chemicals that partition predominantly to the lipid

- phase of milk, EPA has used Equation B

- 41 to estimate the total chemical elimination rate for lactating women, k fat

_ elac (EPA 199 8 )

. Equation B

- 41

. Biological Elimination Constant for Lipophilic Chemicals for Lactating Women

where: 

### h k

elim 

### 2 ln

=

mat fm mbm f milk elim elac fat 

### BW f f f IR k k

× × × + =

_TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 33 October 2014 Description of MIRC k

fat_elac = Rate constant for total elimination of chemical during nu rsing (per day); accounts for both elimination by adults in general and the additional chemical elimination via the lipid phase of milk in nursing women

k

elim = Elimination rate constant for chemical from adults, including non

- lactating women (per day; e .g., via urine, bile to feces, exhalation; chemical

- specific; value from literatur e or calculated from half

- life using Equation B

- 40 )

IR

milk = Infant milk ingestion rate over the duration of nursing (kg/d)

f f = Fraction of total maternal body burden of chemical that is stored in maternal fat (mg chemical in body fat / mg chemical total in body; value from literature or EPA default)

f mbm = Fraction of fat in breast milk (unitless)

f fm = Fraction of maternal body weight that is fat stores (unitless)

BW mat = Maternal body weight over the entire duration of the mother’s exposure to the chemical including during pregnancy and lactation (kg)

Equation B

- 41 is based on a model from Smith (1987) and accounts for the additional elimination pathway for lipophilic chemicals via the breast milk fat. The term K fat

_ elac is estimated by adding an estimate of the first

- order elimination constant for breast feeding losses to k elim , which is the chemical

- specific tota l elimination rate constant for non

- lactating women. The breast feeding losses are estimated from the infant’s intake rate of breast milk ( IR milk ), the fraction of the total maternal body burden of the chemical that is stored in maternal body fat ( f f ), th e fraction of the mother’s breast milk that consists of fat (lipids) ( f mbm ), the mother’s body weight ( BW mat ), and the fraction of the mother’s weight that is body fat ( f fm ). In Equation B

- 41

, the value for the mother’s body weig ht should be specific to women of child

- bearing age, as opposed to a body weight value for both males and females that is used to estimate an adult average daily dose and the mother’s absorbed daily intake in Equation B

- 39 . Body weight values for the mother are described in Section 6.5 of this attachment . Smith’s (1987) model assumes that the chemical partitions to the lipid

- phase of breast milk to the same degree that it partitions into the mother’s body fat . For highly lipophilic compounds, losses from breast feeding can be larger than losses by all other pathways (EPA 1998).



### 3.4.3. Chemical Concentration in Aqueous Phase of Breast Milk When developing MPE (EPA 1998), EPA also considered models to estimate chemical concentrations in the aqueous phase of breast milk ( C aqueous ). EPA adapted Smith’s (1987) steady state concentration model for estimating C milkfat and developed the C aqueous model shown in Equation B

- 42 (EPA 1998). Che micals that would partition to the aqueous phase of human milk include water

- soluble chemicals, such as salts of metals, and other hydrophilic chemicals that may be in equilibrium with bound forms of the chemical in different tissues

. The C aqueous equatio n assumes that the chemical concentration in the aqueous phase of milk is directly proportional to the chemical concentration in the mother’s blood plasma

. The portion of chemical sequestered in red blood cells (e.g., bound to RBC proteins) is assumed to be unavailable for direct transfer to breast milk

. Equation B

- 42

. Chemical Concentration in Aqueous Phase of Breast Milk

pm elac aq bm pl mat aqueous 

### f k Pc f DAI C

× × ×

=

_TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 34 October 2014 Description of MIRC where:

C

aqueous = Concentration of chemical in aqueo us phase of maternal milk (mg/kg)

DAI mat = Daily absorbed maternal chemical dose (mg/kg

- day; calculated by Equation B

- 39 )

f pl = Fraction of chemical in the body (based on absorbed intake) that is in the blood pla sma compartment (unitless; value from literature or calculated by Equation B

- 43 )

Pc bm = Partition coefficient for chemical between the plasma and breast milk in the aqueous phase (unitless); assumed to equal 1.0

k

aq

_elac = Chem ical

- specific rate constant for total elimination of chemical in the aqueous phase of milk during nursing (per day; value from literature or calculated in Equation B

- 44 )

f pm = Fraction of maternal weight that is blood plasma (uni tless)

Equation B

- 42 is a steady

- state concentration model that, like the Equation B

- 38 for C milkfat , is dependent on the maternal absorbed daily intake ( DAI mat ). In Equation B

- 42

, DAI mat is multiplied by the fraction of the absorbed chemical that is circulating in the blood plasma compartment ( f pl

) and a partitioning coefficient for the chemical between plasma and the aqueous phase of breast milk ( Pc bm ). For highly water

- solub le chemicals that are not transported via special carrier molecules, the chemical is assumed to diffuse passively from the mother’s blood serum to the aqueous phase of her milk, in which case Pc bm would equal 1.0. The denominator includes the biological e limination constant for the chemical in the aqueous phase of breast milk in lactating women ( k aq_elac ) and the fraction of the mother’s weight that is plasma ( f pl

). Because the model assumes steady

- state, it does not account for chemical species with long half

- lives in the body or for body burden losses due to lactation

. These factors are important for highly lipophilic chemicals and for non

- lipophilic chemicals such as methyl mercury, lead, and cadmium that partition into body compartments such as red bl ood cells and bone

. While these latter chemicals or forms of these chemicals are water

- soluble when free, they have relatively long half

- lives because they are in equilibrium with the chemical bound to macromolecules in some tissue compartments

. Lead is of particular concern because it can be released from the bone into the blood during lactation, and thus into the breast milk (EPA 2001a). Due to this limitation, the model may over

- or underestimate exposure to the infant.

Because Equation B

- 42 is based on the relationship between the chemical concentrations in the aqueous phase of breast milk and the blood plasma, a value for the fraction of the chemical in the mother’s blood plasma ( f pl

) is required. Ideally, an empirical va lue for f pl should be used. If empirical values are not available, f pl can be estimated from Equation B

- 43 , provided that an empirical value can be found for the fraction of the chemical in the body that is in the mother’s whole blood compartment ( f bl ; EPA 1998).

Equation B

- 43

. Fraction of Total Chemical in Body in the Blood Plasma Compartment

w here:

f pl = Fraction of chemical in body (based on absorb ed intake) that is in the blood plasma compartment (unitless); chemical

- specific 

# ( ) bp RBC bp bp bl pl 

### f Pc f f f f

− + ×

= 1TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 35 October 2014 Description of MIRC f bl = Fraction of chemical in body (based on absorbed intake) in the whole blood compartment (unitless); chemical

- specific

f bp = Fraction of whole blood that is plasma (unit less)

PcRBC = Partition coefficient for chemical between red blood cells and plasma (unitless); chemical

- specific

If the fraction of the total chemical in the body that is in the whole blood compartment ( f bl

) is known for a given chemical, then the fract ion of that chemical that is in blood plasma depends only on the partition coefficient for the chemical between the red blood cells and the plasma ( Pc RBC ) and the fraction of whole blood that is plasma ( f bp ).

Another parameter for which a value is needed t o solve Equation B

- 42 is the total chemical elimination rate for lactating women for hydrophilic chemicals, k aq_elac . As for k fat_elac for lipophilic chemicals, k aq_elac for hydrophilic chemicals would be equal to k elim plus the loss rate for the chemical in the aqueous phase of breast

- milk during lactation. In the case of hydrophilic chemicals, EPA has yet to propose a term for the additional elimination of a chemical in the aqueous phase of milk from breast feeding. Given basi c physiological mechanisms, we assume that chemical loss rates via urine are likely to be significantly higher than loss rates from nursing, however. This is because the counter

- current anatomy of kidney tubules allows substantial concentration of chemica ls in the tubules for elimination in urine compared with the concentration in circulating blood and because of active secretion of some chemicals into urine. Therefore, the best estimation of elimination of hydrophilic chemicals by lactating women is simp ly k elim , the elimination of the chemical from a non

- lactating woman, as shown in Equation B

- 40 . The extent to which k elim is an underestimate of k aq_elac for a given chemical will determine the extent of health protective bias i n k aq_elac
. Equation B

- 44

. Biological Elimination Rate Constant for Hydrophilic Chemicals

w here :

k aq_elac = Chemical

- specific rate constant for total elimination of chemical by lactating women for hydrophilic chemicals (per day)

k

elim = Chemical

- specific rate constant for total elimination of chemical by non

- lactating women (per day; e.g., via urine, bile to feces, exhalation; value from literature or calculated from half

- li fe using Equation B

- 40 )



### 3.4.4. Alternative Model for Infant Intake of Methyl Mercury In this version of MIRC, we were unable to fully parameterize the aqueous model for mercury. In particular, no empirical value could be found for the s teady

- state fraction of total hydrophilic chemical body burden in the mother that is in the blood plasma (f pl , see Exhibit B

- 29 ). This parameter could be estimated using Equation B

- 43 if a suitable chemic al

- specific fraction of chemical in the body that is in the whole blood (f bl ) could be found. However, the value found for f bl is based on a single

- dose study and is not considered reliable for use in chronic exposure calculations.

We therefore conducted a literature search to identify existing physiologically based toxicokinetic (P BTK) models of lactational transfer of methylmercury (MeHg) in humans. Most PBTK models that we identified focused on gestational transfer of mercury between mother and elim elac aq k k =

_TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 36 October 2014 Description of MIRC fetus, including a PBTK dynamic compartmental model for gestational transfer of MeHg in humans developed by Gearhart et al . (1995, 1996), and reparameterized by Clewell et al

. (1999).

We did find, however, that Byczkowski and Lipscomb (2001) had added a lactat ional transfer module to the Clewell et al . (1999) model. Byczkowski and Lipscomb compared their model’s predictions to epidemiological data from mother

- nursing

- infant pairs obtained following an accidental high

- dose poisoning in Iraq (Amin

- Zaki et al . 19 76) and from 34 mother

- nursing

infant pairs examined in a low

- dose, chronic exposure environment (Fujita and Takabatake 1977). Using data from the Iraq incident, Byczkowski and Lipscomb (2001) found good agreement between their model’s predictions and the clinical data relating MeHg concentrations in breast milk to MeHg concentrations in infant’s blood with time following the poisoning. To compare their model’s predictions to data from chronic exposure to low doses of MeHg, Byczkowski and Lipscomb (2001) simulated MeHg intake for 500 days prior to conception, continued through gestation, and 6.5 months (200 days) of lactation. Their model’s predictions were consistent with Fujita and Takabatake’s (1977) study, although use of hair/blood partition coeffici ents based on the results of the 1977 study precluded use of this comparison as model validation. Both the model predictions and the mean values from the 1977 data indicated that the concentration of MeHg in the blood of nursing infants was close to the M eHg concentration in their mothers’ blood (approximately 0.025 to 0.027 mg/L, Figure 4 of report). At those blood concentrations, the PBTK model estimated the average mat ernal intake of MeHg to be 0.68 ± 0.33 (SD) μg/kg

- day and the average infant intake of MeHg to be 0.80 ± 0.38 μg/kg

- day. Therefore, for purposes of MIRC, the DAI inf of MeHg is estimated to be the same as the maternal intake per unit body weight ( Equation B

- 42 ).

Equation B

- 45

. Calculation of Infant Average Daily Absorbed Dose of Methyl Mercury

where:

DAI inf_MeHg = Average daily dose of MeHg absorbed by infant from breast milk (mg/kg

- day)

DAI mat_MeHg = Average daily dose of methyl mercury absorbed by the mother, predominantly from fish (mg/kg

- day)



## 4. Dose

- Response Values Used for Assessment

Chemical dose

- response values included in MIRC i nclude carcino genic potency slope factors f or ingestion and non

- cancer oral reference doses (RfDs) for chronic exposures . The dose

response values currently used in MIRC for RTR assessments are shown in Exhibit B

- 6 For some chemicals, OAQPS has identified dose

- response values for use in RTR (EPA 2007a), and these dose

- response values are used in MIRC for RTR assessments. In general, OAQPS chose these values based on the following hierarchy of sources: EPA’s Integrated Risk Information System (I RIS); the Centers for Disease Control’s Agency for Toxic Substances and Disease Registry (ATSDR); and the California Environmental Protection Agency’s (CalEPA’s) Office of Environmental Health Hazard Assessment ( OEHHA ) Toxicity Criteria Database . For PB

- HA P chemicals that are currently evaluated in MIRC but do not currently have dose

- response values identified by OAQPS for RTR, alternative methods for deriving values were used (see Sections 4.2 and 4. 4 of this attachment ). mat_MeHg inf_MeHg DAI DAI =TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 37 October 2014 Description of MIRC Exhibit B

- 6 . Oral Dose

- response Values Used to Calculate RTR Screening Threshold Emission Rates for PB

- HAP Chemicals a

Chemical CAS No. C ancer S lope F actor R e f erence D ose

Value (mg/kg

- day ) - 1 Source Value (mg/kg

day ) Source

**Inorganics**
Cadmium compounds in food b 7440439 not available 1.0E

- 03 IRIS Mercury (elemental) 7439976 not available not available
Mercuric chloride 7487947 not available 3.0E

- 0 4 IRIS Methyl mercury (MeHg) 22967926 not available 1.0E

- 04 IRIS
**Dioxins** 1,2,3,4,6,7,8

- Heptachlorodibenzo

- p

- dioxin 35822469 1.50E+03 Derived from WHO 2005 TEFs c not available

1,2,3,4,6,7,8

- Heptachlorodibenzofuran 67562394 1.50E+03 Derived from WHO 20 05 TEFs c not available

1,2,3,4,7,8,9

- Heptachlorodibenzofuran 55673897 1.50E+03 Derived from WHO 2005 TEFs c not available

1,2,3,4,7,8

- Hexachlorodibenzo

- p

- dioxin 39227286 1.50E+04 Derived from WHO 2005 TEFs c not available

1,2,3,4,7,8

- Hexachlorodibenzof uran 70648269 1.50E+04 Derived from WHO 2005 TEFs c not available

1,2,3,6,7,8

- Hexachlorodibenzo

- p

- dioxin 57653857 1.50E+04 Derived from WHO 2005 TEFs c not available

1,2,3,6,7,8

- Hexachlorodibenzofuran 57117449 1.50E+04 Derived from WHO 2005 TEFs c not ava ilable

1,2,3,7,8,9

- Hexachlorodibenzo

- p

- dioxin 19408743 6.20E+03 IRIS not available

1,2,3,7,8,9

- Hexachlorodibenzofuran 72918219 1.50E+04 Derived from WHO 2005 TEFs c not available

2,3,4,6,7,8

- Hexachlorodibenzofuran 60851345 1.50E+04 Derived from WHO 20 05 TEFs c not available

1,2,3,4,6,7,8,9

- Octachlorodibenzo

- p

- dioxin 3268879 4.50E+01 Derived from WHO 2005 TEFs c not available

1,2,3,4,6,7,8,9

- Octachlorodibenzofuran 39001020 4.50E+01 Derived from WHO 2005 TEFs c not available

1,2,3,7,8

- Pentachlorodiben zo

- p

- dioxin 40321764 1.50E+05 Derived from WHO 2005 TEFs c not available

1,2,3,7,8

- Pentachlorodibenzofuran 57117416 4.50E+03 Derived from WHO 2005 TEFs c not available

2,3,4,7,8

- Pentachlorodibenzofuran 57117314 4.50E+04 Derived from WHO 2005 TEFs c not av ailable

2,3,7,8

- Tetrachlorodibenzo

- p

- dioxin 1746016 1.50E+05 EPA ORD 7E

- 10 IRIS

2,3,7,8

- Tetrachlorodibenzofuran 51207319 1.50E+04 Derived from WHO 2005 TEFs c not availableTRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 38 October 2014 Description of MIRC Exhibit B

- 6 . Oral Dose

- response Values Used to Calculate RTR Screening Threshold Emission Rates for PB

- HAP Chemicals a

Chemical CAS No. C ancer S lope F actor R e f erence D ose

Value (mg/kg

- day ) - 1 Source Value (mg/kg

day ) Source

**Polycyclic Organic Matter**

1

- Methylnaphthalene 90120 5.0E

- 01 EPA 1999, POM Grou p 72002 d 7.0E

- 02 ATSDR

2

- Acetylaminofluorene 53963 1.0E+01 EPA 1999, POM Group 75002 d not available

2

- Chloronaphthalene 91587 5.0E

- 01 EPA 1999, POM Group 72002 d 8.0E

- 02 IRIS

2

- Methylnaphthalene 91576 5.0E

- 01 EPA 1999, POM Group 72002 d 5.0E

- 02 ATSDR

3

- M ethylcholanthrene 56495 2.2E+1 CalEPA not available 7,12

- Dimethylbenz(a)anthracene 57976 2.5 E+0 2 CalEPA not available

Acenaphthene 83329 5.0E

- 01 EPA 1999, POM Group 72002 d 6.0E

- 02 IRIS

Acenaphthylene 208968 5.0E

- 01 EPA 1999, POM Group 72002 d not availa ble

Anthracene 120127 0 e IRIS 3.0E

- 01 IRIS Benz(a)anthracene 56553 1.2E+00 CalEPA not available Benzo(a)pyrene 50328 7.3 E+0 0 IRIS not available Benzo(b)fluoranthene 205992 1.2E+00 CalEPA not available
Benzo(e)pyrene 192972 5.0E

- 01 EPA 1999, POM Group 72002 d not available

Benzo(g,h,i) perylene 191242 5.0E

- 01 EPA 1999, POM Group 72002 d not available
Benzo(k)fluoranthene 207089 1.2E+00 CalEPA not available
Carbazole 86748 2.0E

- 02 HEAST not available Chrysene 218019 1.2E

- 01 CalEPA not available
Diben zo(a,h)anthracene 53703 4.1E+00 CalEPA not available Dibenzo(a,i)pyrene 189559 1.2E+0 2 CalEPA not available Dibenzo(a,j)acridine 224420 1.2E+00 CalEPA not available

Fluoranthene 206440 5.0E

- 01 EPA 1999, POM Group 72002 d 4.0E

- 02 IRIS

Fluorene 86737 5.0 E

- 01 EPA 1999, POM Group 72002 d 4.0E

- 02 IRIS

Indeno(1,2,3

- c,d)pyrene 193395 1.2E+00 CalEPA not available

PAH, total 234 5.0E

- 01 EPA 1999, POM Group 71002 d not available

Perylene 198550 5.0E

- 01 EPA 1999, POM Group 72002 d not available

Phenanthrene 8501 8 0 e IRIS not available
Polycyclic Organic Matter 246 5.0E

- 01 EPA 1999, POM Group 71002 d not availableTRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 39 October 2014 Description of MIRC Exhibit B

- 6 . Oral Dose

- response Values Used to Calculate RTR Screening Threshold Emission Rates for PB

- HAP Chemicals a

Chemical CAS No. C ancer S lope F actor R e f erence D ose

Value (mg/kg

- day ) - 1 Source Value (mg/kg

day ) Source

Pyrene 129000 0 e IRIS 3.0E

- 02 IRIS

Retene 483658 5.0E

- 01 EPA 1999, POM Group 72002 d not available

ATSDR = Agency for Toxic Substances and Disease Reg istry, IRIS = Integrated Risk Information System

, EPA OAQPS = EPA’s Office of Air Quality Planning and Standards

, CalEPA = California EPA

, EPA ORD = EPA’s Office of Research and Development

, HEAST = EPA Health Effects Assessment Tables, TEF = toxic equival ency factor

a Values as of June 2012; these values may be updated as newer ones become available.

b There are RfDs for both water ingestion and food ingestion for cadmium

– the RfD for food is used.

c Dose

- response values for these dioxin congeners are not av ailable from EPA sources. CSFs for these congeners were derived as discussed in Section 4.2 of this attachment

. d The method to assign oral cancer slope factors to polycyclic organic matter (POM) without CSFs av ailable from other EPA sources is the same as that used in the 1999 National Air Toxics Assessment (EPA 1999). A complete description of the methodology is available at: http://w ww.epa.gov/ttn/atw/nata1999/99pdfs/pomapproachjan.pdf and is summarized in Section 4.4 of this attachment

. e W eight of evidence evaluation s indicated that the available data were adequate to determine that this chemical was not carcinogenic (EPA 2010 a )

,



### 4.1. Cadmium
EPA has developed two chronic RfDs for cadmium (Cd) , one for food and one for water, based on data in IRIS indicating a lower absorption efficiency of cadmium from food than from water

. The default RfD set in MIRC is the higher RfD for Cd compounds in food ( as described in Section A.2.3, the drinking water exposure pathway is not modeled in the screening scenario because the likelihood that humans would use a lake as a drinking water source is assumed to be low ). User s of MIRC who assess exposures via drinking water would need to use the RfD for Cd compounds in water (i.e., 5.0E

- 04 mg/kg

- day)

.


### 4.2. Dioxins (2,3,7,8

- TCDD)
For chemicals for which the critical health effect is developmental, either in utero and/or during the f irst months or years of life, the exposure duration and timing of exposure for comparison with the RfD (or comparable values) require special consideration. The most sensitive health endpoints for both mercury and 2,3,7,8

- TCDD are neurological effects dur ing development that have long

- lasting effects on learning and social behaviors. To ensure a protective risk characterization for these chemicals, it is important to use the shortest exposure duration appropriate, at the appropriate life stage, for compar ison with the toxicity reference values. This approach avoids “dilution” of an estimated average ADD that would result from averaging the lower daily chemical intake rates normalized to body weight for older children and adults with the potentially higher daily intake rates of infants over a longer exposure averaging period.

The convention for assessing risk from mixtures of dioxins is by application of toxic equivalency factors (TEFs) to dioxin concentrations, which are then expressed as toxic equivalents (TEQs). Of the dioxin congeners, 2,3,7,8

- TCDD is the most widely studied and considered to be one of the most toxic congeners. It is therefore assigned a TEF of 1, with the other dioxin congener TEQ concentrations scaled relative to 2,3,7,8

- TCDD concent rations on the basis of toxicity. For risk assessment of dioxins for RTR (with one exception), the World Health Organization (WHO) 2005 TEFs presented in Exhibit B

- 7 were used to derive the CSFs (shown in Exhibit B

- 6 ) for dioxin congeners without available EPA dose response values. The one exception is 1,2,3,7,8,9

- Hexachlorodibenzo

- p

- dioxin , whose TEF was based on data from IRIS.TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 40 October 2014 Description of MIRC Exhibit B

- 7 . WHO 2005 Toxic Equivalency Factors (TEFs) for Dioxin Congeners

Dioxin Congener CAS No. WHO 2005 Toxic Equivalency Factor a
1,2,3,4,6,7,8

- Heptachlorodibenzo

- p

- dioxin 35822469 0.01 1,2,3,4,6,7,8

- Heptachlorodibenzofuran 67562394 0.01
1 ,2,3,4,7,8,9

- Heptachlorodibenzofuran 55673897 0.01 1,2,3,4,7,8

- Hexachlorodibenzo

- p

- dioxin 39227286 0.1 1,2,3,4,7,8

- Hexachlorodibenzofuran 70648269 0.1 1,2,3,6,7,8

- Hexachlorodibenzo

- p

- dioxin 57653857 0.1 1,2,3,6,7,8

- Hexachlorodibenzofuran 57117449 0.1 1,2,3,7,8,9

- Hexachlorodibenzo

- p

- dioxin b 19408743 0.04 1,2,3,7,8,9

- Hexachlorodibenzofuran 72918219 0.1 2,3,4,6,7,8

- Hexachlorodibenzofuran 60851345 0.1 1,2,3,4,6,7,8,9

- Octachlorodibenzo

- p

- dioxin 3268879 3E

- 04 1,2,3,4,6,7,8,9

- Octachlorodibenzofuran 390010 20 3E

- 04 1,2,3,7,8

- Pentachlorodibenzo

- p

- dioxin 40321764 1 1,2,3,7,8

- Pentachlorodibenzofuran 57117416 0.03 2,3,4,7,8

- Pentachlorodibenzofuran 57117314 0.3 2,3,7,8

- Tetrachlorodibenzo

- p

- dioxin 1746016 1 2,3,7,8

- Tetrachlorodibenzofuran 51207319 0.1

a Sourc e: van den Berg et al. 2006 , with the one exception in the next footnote.

b For 1,2,3,7,8,9

- HexCDD, OAQPS identified an oral cancer slope factor from IRIS. For the purposes of these multipathway assessments , EPA uses the TEF derived from this IRIS oral CSF (6200 1/mg/kd/d, equaling a TEF of 0.041) rather than the WHO 2005 TEF of 0.1.



### 4.3. Mercury The RfD applies to the pregnant mother as well as young children. EPA has not specified the minimum exposure duration at the RfD level of exposure that is appropriate to use in characterizing risk ; we assume 10 years for women of childbearing age and 1 year for infants. We note that human exposures to MeHg are primarily through the consumption of fish and shellfish (EPA 2001b). EPA found that, on average, approximate ly 76 percent of the exposure to MeHg for women of childbearing age could be attributed to ingestion of mercury in freshwater and estuarine fish and shellfish, with the remaining 24 percent derived from marine fish and shellfish. Other sources accounted f or less than 0.06 percent of total exposures (EPA 2001b).



### 4.4. Polycyclic Organic Matter

Dose

- response values for some of the POM species that are included in the screening assessment were not identified by OAQPS; for these POM species, an alternative methodo logy for identifying CSFs was needed. Previously, for risk assessment of inhalation exposures to polycyclic organic matter (POM) for EPA’s National

- Scale Air Toxics Assessments (NATA) and for RTR, OAQPS developed an approach for characterizing risks associ ated with the individual POM species and POM groups reported in the National Emissions Inventory (NEI). I ndividual POM s were assigned to one of eight POM groups according to cancer potencies derived by EPA for IRIS and by CalEPA, and based on assumptions regarding relative carcinogenicity. OAQPS then estimated an inhalation CSF for each POM group. The same approach was used to derive oral CSFs for POMs without available CSFs. Each POM group (with all its memberTRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 41 October 2014 Description of MIRC POM species reported in NEI, not just the species currently evaluated in this assessment ) and the corresponding CSFs using this methodology are presented in Exhibit B

- 8

. These group CSFs are used only when OAQPS has not, for the purposes of RTR, identified a CSF specific to the individual chemical.

Exhibit B

- 8 . Oral Dose

- response Values for Polycyclic Organic Matter (POM) Groups a

Individual POM or POM Group CAS No. C ancer S lope F actor b (mg/kg

- day) - 1

**POM Group 71002** Benz(a)anthracene/chrysene (7

- PAH) 103

0.5 Total PAH 234 Polycyclic organic matter 246
16

- PAH 40
16

- PAH

– 7

- PAH 75040 **POM Group 72002**
Anthracene 120127

0.5 Pyrene 129000
Benzo (g,h,i) perylene 191242 Benzo(e)py rene 192972 Benzo(c)phenanthrene 195197 Perylene 198550 Benzo(g,h,i) f luoranthene 203123 Benzo(a)fluoranthene 203338
Fluoranthene 206440 Acenaphthylene 208968
1

- Methylpyrene 2381217
12

- Methylbenz(a)anthracene 2422794 Methylbenzopyrenes 247 Methylchrysene 248 Methylanthracene 26914181
Benzofluoranthenes 56832736
9

- Methylbenz(a)anthracene 779022
1

- Methylphenanthrene 832699 Acenaphthene 83329
Phenanthrene 85018
Fluorene 86737
2

- Methylnaphthalene 91576
2

- Chloronaphthalene 91 587 **POM Group 73002** 7,12

- Dimethylbenz(a)anthracene 57976 1000 **POM Group 74002** Dibenzo(a,i)pyrene 189559 100 Dibenzo(a,h)pyrene 189640TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 42 October 2014 Description of MIRC Exhibit B

- 8 . Oral Dose

- response Values for Polycyclic Organic Matter (POM) Groups a

Individual POM or POM Group CAS No. C ancer S lope F actor b (mg/kg

- day) - 1

**POM Group 75002**
3

- Methylcholanthrene 56495

10 Dibenzo(a,e)pyrene 192654
5

- Methylchrysene 3697243
Benzo ( a ) pyr ene 50328 Dibenzo(a,h)anthracene 53703 **POM Group 76002** Benzo(b+k)fluoranthene 102

1 Indeno(1,2,3

- c,d)pyrene 193395 Benzo(j)fluoranthene 205823 Benzo(b)fluoranthene 205992 Benzo(k)fluoranthene 207089 Dibenzo(a,j)acridine 224420 Benz(a)anthr acene 56553 **POM Group 77002** Chrysene 218019 0.1 **POM Group 78002** 

7

- PAH 75 0.5

a These group CSFs are used only when OAQPS has not, for the purposes of RTR, identified a CSF specific to the individual chemical.

b The method to assign oral cancer slope fa ctors to POM groups was the same as that used in the 1999 National Air Toxics Assessment (EPA 1999). A complete description of the methodology is available at: http://www.epa.gov /ttn/atw/nata1999/99pdfs/pomapproachjan.pdf

. 

## 5. Risk Estimation
For persistent and bioaccumulative hazardous air pollutants (PB

- HAPs), risks from inhalation of a chemical directly from air generally will be negligible compared with risks from ingestion of the chemical from foodstuffs grown in an area subject to air deposition of the chemical. Risk characterization for carcinogens with a linear mode of action at low doses is described in Section 5.1 of this attachment . Risk cha racterization for chemicals likely to exhibit a threshold for response (e.g., non

- cancer hazards) is described in Section 5.2

.


### 5.1. Cancer Risks

The estimated risk of developing cancer from exposure to a chemical from a specified source is characterized as the e xcess l ifetime c ancer r isk (ELCR)

. The ELCR represents the incremental probability of an individual developing cancer over a lifetime as a result of lifetime exposure to the chemical. For a known or suspected carcinogen wi th a low

- dose linear mode of action , the estimated ELCR is calculated as the product of the lifetime average daily dose (LADD) and the

cancer slope factor (CSF) :TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 43 October 2014 Description of MIRC Equation B

- 46

. Calculation of Excess Lifetime Cancer Risk

ELCR = LADD × C SF

w here :

ELCR = Estimated excess lifetime cancer risk from a chemical summed across all exposure pathways and media (unitless)

LADD = Lifetime average total daily dose from all exposure pathways and media (mg/kg

day)

CSF = Oral carcinogenic potency slope factor for chemical (per mg/kg

- day)

As described in Section 3.3 of this attachment , t he LADD (in mg/kg

- day) for a chemical is calculated to reflect age

- related diff erences in exposure rates that are experienced by a hypothetical individual throughout his or her lifetime of exposure

. Th e tot al chemical intake is normalized to a lifetime, which for the purposes of this assessment is assumed to be 70 years

.
EPA consi ders the possibility that children might be more sensitive than adults to toxic chemicals, including chemical carcinogens (EPA 2005b,c). Where data allow, EPA recommends development of lifestage

- specific cancer potency C SFs

. To date, EPA has developed a separate slope factor for early lifestage exposure for only one chemical ( i.e., 1,1,1

- trichloroethane ; EPA 2007b), and current data availability for most chemicals preclude this approach. EPA has, therefore, examined options for default adjustments of the CSF to protect children. To date, the only mode of action (MOA) for carcinogenesis for which EPA has adequate data to develop a reasonable quantitative default approach is mutagenesis ( EPA 2005 b, c) . For carcinogens with a mutagenic MOA for cancer, EPA c oncluded that the carcinogenic potency of a chemical may be approximately tenfold greater for the first 2 years of life (i.e., birth up to second birthday) and threefold greater for the next 14 years of life (i.e., ages 2 through 15) than for adults (EPA 2 005c). These conclusions are represented by age

dependent adjustment factors (ADAFs) of 10, 3, and 1 for the first two lifestages and for adults, respectively

. These three lifestages do not match the age categories for the home

- grown food ingestion rate s, the age categories in MIRC. As a consequence, ADAFs for the age groups in MIRC are adapted as time

- weighted average values as follows:

To estimate total lifetime risk from a lifetime of exposure to such a chemical, EPA recommends estimating the ca ncer risk for each of the three lifestages separately and then adding the risks for i = 1 to 6 age groups. 

## ( ) ( )

3 5 6 2 yr 1 3 yr 1 10 10

5 3 2 1

= = × + ×

= = <

)

- ( )

- ( 1) (

ADAF . ADAF ADAF 

## ( ) ( )

1 2 8 yrs 4 1 yrs 4 3 3

19 12 11 6

= = × + ×

= =

(adult) )

- ( )

- (

ADAF ADAF ADAFTRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 44 October 2014 Description of MIRC Equations B

- 47 to B

- 53 . Lifetime Cancer Ri sk: Chemicals with a Mutagenic MOA for Cancer Equation B

- 47

. Risk (<1) = ADD (0

– <1) × 10 × CSF × (1 yr/70 yr) Equation B

- 48

. Risk (1

–

2) = ADD (1

– 2) × 6.5 × CSF × (2 yr/70 yr) Equation B

- 49

. Risk (3

–

5) = ADD (3

– 5) × 3 × CSF × (3 yr/70 yr) Equation B

- 50

. Risk (6

– 11) = ADD (6

– 11) × 3 × CSF × (6 yr/70 yr) Equat ion B

- 51

. Risk (12

– 19) = ADD (12

– 19) × 2 × CSF × (8 yr/70 yr) Equation B

- 52

. Risk (adult) = ADD (adult) × 1 × CSF × (50 yr/70 yr)
Equation B

- 53

. **ELCR**

In other words, Equation B

- 53 indicates that the total excess lifetime cancer risk ( ELCR) equals the sum of the age

- group

- specific risks estimated by Equation B

- 47 through Equation B

- 52

, where:

Risk (<1) = R isk from chemical ingestion in first year of life

Risk (1

-

2) = Risk from chemical ingestion fro m first birthday through age 2 years

Risk (3

- 5) = Ri sk from chemical ingestion from age 3 through 5 years of age

Risk (6

- 11) = Risk from chemical ingestion from age 6 through 11 years of age

Risk (12

- 19) = Risk from chemical ingestion from age 12 through 19 years of age

Risk (adult) = Risk from chemical ingestion from age 20 to 70 years age

ADD (<1) = Average daily dose for infants under one year of age (mg/kg

- day)

ADD (1

- 2) = Average daily dose from first birthday through age 2 years of age (mg/kg

- day)

ADD (3

- 5) = Average daily dose from age 3 through 5 years of age (mg/kg

- day)

ADD (6

- 11) = Average daily dose from age 6 through 11 years of age (mg/kg

- day)

ADD (12

- 19) = Average daily dose from age 12 through 19 years of age (mg/kg

- day)

ADD (adult) = Averag e daily dose for adults age 20 to 70 years of age (mg/kg

- day)

CSF = Oral carcinogenic potency slope factor for chemical (per mg/kg

- day)

Risk (i) = R isk f rom chemical ingestion for the i th age group
ELCR = T otal extra lifetime cancer risk (incremental or extra risk)

n = Number of age groups (i.e., 6)



### 5.2. Non

- cancer Hazard Quotients

Non

- cancer risks are presented as hazard quotients (HQs), that is, the ratio of the estimated daily intake (i.e., ADD) to the reference dose (e.g., chronic RfD)

. If the HQ for a chemical is equal to or less than 1, EPA believes t hat there is no appreciable risk that non

- cancer health effects will occur . I f the HQ is greater than 1 then there is at least some possibility for an adverse health effect

. The larger the HQ value, the more likely it is that an adverse health effect may occur. 

# ∑

= 

### = n

i i 

### Risk 1 ) (TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 45 October 2014 Description of MIRC 

### 5.2.1. Hazard Quotients for Chemicals with a Chronic RfD For chemicals with a chronic RfD, MIRC calculates an HQ for each age group separately using Equation B

- 54 to indicate th e potential for adverse health effects associated with chronic exposure via ingestion pathways. T he HQ is the ratio of a long

- term, daily average exposure normalized to the receptor's body weight (i.e., ADD) to the RfD for that chemical . HQs are threshol d effects and are not additive across age groups.

Equation B

- 54

. Hazard Quotient for Chemicals with a Chronic RfD

where:

HQ = Hazard quotient for chemical (unitless)

ADD = Average daily ingested dose of chemical (mg/kg

- day) from all food types and ingested media for the age group

RfD = Chronic oral reference dose for chemical (mg/kg

- day)



### 5.2.2. Hazard Quotients for Chemicals with RfD Based on Developmental Effects

For chemicals for which the toxicity reference value is an RfD based on developmental effects

, a shorter exposure duration ( ED ) and averaging time ( AT ) may be required

. For this type of chemical (e.g., methylmercury, 2,3,7,8

- TCDD), the appropriate ED/AT and sensitive l ifestage for exposure may need to be estimated from the information provided in the critical developmental study(ies) from which the RfD was derived (e.g., in consultation with the RfD documentation in EPA’s IRIS or in a toxicological profile developed for the chemical)

. For screening

- level risk assessments, however, a health protective approach is to compare the highest ADD from among the child age categories provided in MIRC to the RfD , as is done for all PB

- HAPs. This approach ensures that the highest exposure from among the various age groups evaluate d is taken into consideration, regardless of which age group might be most relevant to the health effect of interest ( i.e., the age group on which the RfD is based).



### 5.2.3. Hazard Index for Chemicals with RfD s

Wh en conducting screening

- level assessments for multiple chemicals, it can be informative to calculate a hazard index (HI) for toxicologically similar chemicals (EPA 2000). The HI is the sum of HQs across chemicals (not age groups) as shown in Equation B

- 55

. As with the HQ, if the HI value is less than 1, adverse health effects are not expected for that suite of chemicals

. If the screening level HI exceeds 1, however, the risk assessor may in some instances, evaluate the assumptio ns of the screening

- level assessment to determine if more realistic local values are available for parameters that drive risk . In addition, the risk assessor may need to examine the mode of action (MOA) and target organ(s) for the chemicals with the highe st HQs to develop an appropriate approach to assessing their potential joint action. 

### RfD ADD HQ =TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 46 October 2014 Description of MIRC Equation B

- 55

. Hazard Index Calculation

HI = HQ 1 + HQ 2 ... HQ n

where:

HI = Hazard index (u nitless)
HQ 1 = Hazard Quotient for chemical 1 (unitless)
HQ 2 = Hazard Quotient for chemical 2 (unitless)

HQ n = Hazard Quotient for chemical n (unitless)

The HI approach can be appropriate for chemicals with the same MOA and same target organ ; however, MOA often is difficult to determine. An HI usually is “developed for each exposure route of interest, and for a single toxic effect or for toxicity to a single target organ” ( EPA 2000; p 79 ). If a receptor is exposed to multiple chemicals that affect dif ferent target organs or that operate by different MOAs, and if more than one HQ is close to 1, the risk assessor in some circumstances, may consider whether chemical interactions play a role in chemical toxicity ( EPA 2000)

. Exposures to more than one chem ical can result in a greater or lesser toxic response than might be predicted on the basis of one or the other chemical acting alone (toxicologically independent) or acting in concert (toxicologically similar chemicals)

. Users are referred to EPA’s Supple mentary Guidance for Conducting Health Risk Assessment of Chemical Mixtures for approaches to assessing the potential for adverse health effects from exposure to multiple chemicals ( EPA 2000)

. Note that users of MIRC are responsible for determining how t o interpret HQs for multiple chemicals

. 

## 6. Model Input Options
This section describes the input options currently included in MIRC . Required user inputs for environmental media concentrations and air deposition rates, such as those predicted by (output of) TRIM.FaTE, are described in Section 6.1 of this attachment . Values for farm

- food

chain (FFC) parameters for specific types of produce and animal products are discussed in Section 6.2 . Options for parameterizing receptor characteristics are described in Section 6.3

, including age

- group

- specific values for body weight, water ingestion, and food ingestion by food type. Options for other exposure parameter val ues in MIRC, such as exposure frequency and loss of chemical during food preparation and cooking, are provided in Section 6.4

.
Where values for chemical

- specific parameters are presented, values are presented only for PB

- HAP chemicals cu rrently evaluated using the TRIM

- based RTR screening scenario. The database included with MIRC contains chemical

- specific parameter values for a large number of chemicals (more than 500), because all of the chemical

- specific input data compiled by EPA for use in HHRAP were uploaded into MIRC. However, only chemicals that are PB

- HAPs evaluated for RTR are discussed in this document (see Sections 1.1.1 and 2.2.2 ), and the HHRAP inputs provided for oth er chemicals have not been reviewed or verified. The data presented in this chapter were reviewed and used to develop the set of modeling defaults used to calculate screening threshold emission rates for RTR. Note that the default values used to estimate RTR screening thresholds, and the justification for selecting a specific value from the data sets described in this chapter, are discussed in Chapter 7.TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 47 October 2014 Description of MIRC 

### 6.1. Environmental Concentrations

As noted in Section 2 of this attachment

, MIRC is intended to estimate exposures and risks to self

- sufficient farming and fishing families from ingestion of FFC media in an area of airborne chemical deposition

. The tool analyzes one exposure scenario at a time (e.g., adult farmer exposed to dioxi n from ingestion of beef) ; therefore, it is best used to evaluate a maximally exposed individual (MEI) or family when MIRC is used to screen for possible risks.

The following values specific to the air pollutant of concern are required inputs to MIRC:

• a si ngle air concentration (in g/m 3 );

• the fraction of chemical in the air that is in the vapor phase;

• air

- to

- surface deposition rates for both vapor

- and particle

- phase chemical in the air (in g/m 2

- yr);

• two fish tissue concentrations, one each for forage and g ame fish (i.e., fish in TL 3 and TL 4) (in mg/kg wet weight);

• concentrations in drinking water (in g/L); and

• four chemical concentrations in soil (in μg/g dry weight), one each for:

– surface soil in produce growing area,

– surface soil where livestock feed,

– root

- zone soil in produce growing area, and

– root

- zone soil in livestock feed growing area.

The MIRC software is configured to estimate ingestion exposures via drinking water for a specified chemical concentration in the drinking water source (e.g., ground water well)

. However, no exposure via drinking water is assumed to occur when calculating the Tier 1 screening thresholds . As discussed in Section 2.2.3 , t he drinking water exposure pathway is not modeled for the scenario dev eloped for the Tier 1 assessment because the likelihood that humans would use a lake as a drinking water source is assumed to be low.

The user must provide the inputs listed above; no default values are included for these parameters in MIRC

. Media concent rations output by TRIM.FaTE can be entered into the tool manually from model output files or can be imported directly from the TRIM.FaTE output files

. For RTR evaluations, a tool to facilitate this process was developed using a Microsoft ® Excel TM routine w ritten in Visual Basic

.


### 6.2. Farm

- Food

- Chain Parameter Values Using the chemical information specified in Section 6.1 above as inputs, MIRC calculate s chemical concentrations in foods that are commonly grown or raised on family fa rms: exposed and protected fruit s ; exposed and protected vegetables; root vegetables; beef; total dairy products; pork; and poultry and eggs

.


### 6.2.1. List of Farm

- Food

- Chain (FFC) Parameters

MIRC estimates chemical concentrations in the produce identified above using algorithms from HHRAP (EPA 2005a) as described in Section 3.2 of this attachment . P arameter values required for these HHRAP algorithms, including chemical

- specific med ia transfer factors (e.g.,TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 48 October 2014 Description of MIRC soil

- to

- plant transfer coe fficients) and plant

- and animal

- specific properties (e.g., plant interception fraction, quantity of forage consumed by cattle) , are included in tables in MIRC. As described in Section 7 of this attachment , the HHRAP

- recommend ed parameter values are the default values in MIRC; however, t hese and other inputs in MIRC can be revised as needed. describes the parameters that are included in the algorithms used to estimate chemical concentrations in the farm food categories. The parameter names and symbols are referenced in this section for plants/produce and animal products

.

Exhibit B

- 9

. MIRC Parameters Used to Estimate Chemical Concentrations in Farm Foods

Parameter Description Units
**Plants/Produce**
Br AG

- produce

DW(i) Chemical

- specific plant/soil chemical bioconcentration factor for edible portion of aboveground produce type i, exposed or protected Unitless ( g soil DW / g produce DW )

Bv AG(i) Chemical

- specific a ir

- to

- plant biotransfer factor for aboveground produce type i for vapor

- phase chemical in air U nitless ([mg chemical / g DW plant] / [mg chemical / g air])
Fw Fraction of wet deposition that adheres to plant surfaces; 0.2 for anions, 0 .6 for cations and most organics Unitless

Kd s Chemical

- specific s oil/water partition coefficient L soil pore water / kg soil DW kp (i) Plant

- specific surface loss coefficient for aboveground exposed produce and animal forage and silage yr - 1

MAF (i) Moistu re adjustment factor for aboveground produce type i to convert the chemical concentration estimated for dry

- weight produce to the corresponding chemical concentration for full

weight fresh produce Percent water

RCF Chemical

- specific r oot concentration fa ctor for tubers and root produce on a wet

- weight (WW) basis L soil pore water/ kg root WW Rp (i) Plant

- specific i nterception fraction for the edible portion of aboveground exposed produce or animal forage and silage Unitless

Tp (i) Length of plant exposur e to deposition per harvest of the edible portion of aboveground exposed produce or animal forage and silage Year

VG AG(i) Empirical correction factor for aboveground exposed produce type i to address possible overestimate of the diffusive transfer of chem ical from the outside to the inside of bulky produce, such as fruit Unitless

VG rootveg Empirical correction factor for belowground produce (i.e., tuber or root vegetable) to account for possible overestimate of the diffusive transfer of chemicals from th e outside to the inside of bulky tubers or roots (based on carrots and potatoes) Unitless

Yp (i) Plant

- specific y ield or standing crop biomass of the edible portion of produce or animal feed kg produce DW/m 2

**Animal Products**
Bs Soil bioavailability factor for livestock Unitless
MF Chemical

- specific mammalian metabolism factor that accounts for endogenous degradation of the chemical UnitlessTRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 49 October 2014 Description of MIRC Exhibit B

- 9

. MIRC Parameters Used to Estimate Chemical Concentrations in Farm Foods

Parameter Description Units
Ba (b eef ) Chemical

- specific b iotransfer factor for chemical in diet of cow to chemical in beef on a fresh

- wet (FW; e quivalent to WW) basis mg chemical/kg FW tissue/mg chemical/day or day/kg FW tissue
Ba (d airy ) Biotransfer factor in dairy day/kg FW tissue
Ba (p ork ) Biotransfer factor in pork day/kg FW tissue
Ba (p oultry ) Biotransfer factor in poultry day/kg FW tissue
B a (e ggs ) Biotransfer factor in eggs day/kg FW tissue Qs (m) Quantity of soil eaten by animal type m each day kg/day Qp (i,m) Quantity of plant feed type i consumed per animal type m each day kg/day

Source: EPA Source: EPA 2005 a DW = dry weight; FW = fre sh weight; WW = wet weight



### 6.2.2. Produce Parameter Values

Exhibit B

- 10 and Exhibit B

- 11 provide the chemical

- specific input values that are the current defaults for produce FFC food types in MIRC. Exhibit B

- 12 present s additional non

- chemical

specific input values for parameters used in the algorithms that calculate chemical concentrations in produce. Unless otherwise noted, the default parameter values were obtained from HHRAP

. O ptions for other parameter values are not included in MIRC at this time; however, the user can overwrite values if appropriate

. Refer to HHRAP ( EPA 2005 a , Chapter 5 and associated appendices) for detailed descriptions of these parameters and documentation of input values

.

Exhibit B

- 10

. Chemical

- Specific Inputs for Produce Parameters for Chemicals Included in MIRC

Chemical Fraction of Wet Deposition ( **Fw** ) (unitless) a Root Concentration Factor ( **RCF** ) (belowground) (L/kg) b Soil

- Water Partition Coefficient ( **Kds** ) (L/kg) c Chemical Air

- to

Plant Biotransfer Factor ( **Bv AG(i)**  ) (unitless) d **Inorganics** Cadmium compounds 0.6 NA 7.5E+01 NA e Mercury (elemental) 0.6 NA 1.0E+03 0 f

Merc uric chloride 0.6 NA 5.8E+04 1.8E+03 Methyl mercury 0.6 NA 7.0E+03 0 f

**PAHs**
2

- Methylnaphthalene 0.6 2.2E+02 5.0E+01 1.4E+00 7,12

- Dimethylbenz(a)anthrace

ne 0.6 6.8E+03 4.0E+03 4.2E+04

Acenaphthene 0.6 2.4E+02 3.9E+01 4.6E+00 Acenaphthylene 0.6 2.8E+02 6.8E+01 8.1E+00 Benz(a)anthracene 0.6 6.7E+03 2.9E+03 6.8E+03 Benzo(a)pyrene 0.6 9.2E+03 7.8E+03 1.7E+05TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 50 October 2014 Description of MIRC Exhibit B

- 10

. Chemical

- Specific Inputs for Produce Parameters for Chemicals Included in MIRC

Chemical Fraction of Wet Deposition ( **Fw** ) (unitless) a Root Concentration Factor ( **RCF** ) (belowground) (L/kg) b Soil

- Water Partition Coefficient ( **Kds** ) (L/kg) c Chemical Air

- to

Plant Biotransfer Factor ( **Bv AG(i)**  ) (unitless) d Benzo(b)fluoranthene 0.6 6.6E+03 3.8E+03 1.7E+05 Benzo(g,h,i)perylene 0.6 3.0E+04 2.6E+04 2.3E+06 Benzo(k)fluoranthene 0.6 8.7E+03 5.5E+03 2.8E+05 Chrysene 0.6 6.0E+03 3.4E+03 1.4E+04 Dibenz(a,h)anthracene 0.6 2.3E+04 1.4E+04 6.2E+06
Fluoranthene 0.6 2.2E+03 3.9E+02 9.0E+02
Fluorene 0.6 3.8E+02 6.2E+01 1.6E+01 Indeno(1,2,3

- cd)pyrene 0.6 3.5E+04 3.2E+04 2.8E+06
**Dioxins** OctaCDD, 1,2,3,4,6,7,8,9

- 0.6 4.8E+05 7.8E+05 2.4E+06

OctaCDF, 1,2,3,4,6,7,8,9

- 0.6 3.4E+05 4.9E+05 2.3E+06 HeptaCDD, 1,2,3,4,6,7,8

- 0.6 3.4E+05 4.9E+05 9.1E+05 HeptaCDF, 1,2,3,4,6,7,8

- 0.6 1.2E+05 1.2E+05 8.3E+05 HeptaCDF, 1,2,3,4,7,8,9

- 0.6 4.8E+04 3.9E+04 8.3E+05 HexaCDD, 1,2,3,4,7,8

- 0.6 2.4E+05 3.1E+05 5.2E+05 HexaCDF, 1,2,3,4,7,8

- 0.6 5.7E+04 4.9E+04 1.6E+05 HexaCDD, 1,2,3,6,7,8

- 0.6 4.9E+05 8.0E+05 5.2E+05 HexaCDF, 1,2,3,6,7,8

- 0.6 2.9E+05 4.1E+05 1.6E+05 HexaCDD, 1,2,3,7,8,9

- 0.6 4.9E+05 8.0E+05 5.2E+05 HexaCDF, 1,2,3,7,8,9

- 0.6 1.6E+05 1.9E+05 1.6E+05 HexaCDF, 2,3,4,6,7,8

- 0.6 2.9E+05 4.1E+05 1.6E+05 PentaCDD, 1,2,3,7,8

- 0.6 9.2E+04 9.2E+04 2.4E+05 PentaCDF, 1,2,3,7,8

- 0.6 3.9E+04 3.0E+04 9.8E+04 PentaCDF, 2,3,4,7,8

- 0.6 2.3E+04 1.6E+04 9.8E+04TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 51 October 2014 Description of MIRC Exhibit B

- 10

. Chemical

- Specific Inputs for Produce Parameters for Chemicals Included in MIRC

Chemical Fraction of Wet Deposition ( **Fw** ) (unitless) a Root Concentration Factor ( **RCF** ) (belowground) (L/kg) b Soil

- Water Partition Coefficient ( **Kds** ) (L/kg) c Chemical Air

- to

Plant Biotransfer Factor ( **Bv AG(i)**  ) (unitless) d
TetraCDD, 2, 3,7,8

- 0.6 4.0E+04 3.1E+04 6.6E+04 TetraCDF, 2,3,7,8

- 0.6 1.2E+04 6.2E+03 4.6E+04
Source: EPA 2005a. NA = not applicable.

a 6E

- 01 is the value for cations and most organic chemicals. As described in HHRAP (EPA 2005a), Appendix B (available at http:// www.epa.gov/osw/hazard/tsd/td/combust/finalmact/ssra/05hhrapapb.pdf), EPA estimated this value (EPA 1994a, 1995a) from a study by Hoffman et al. (1992) in which soluble gamma

- emitting radionuclides and insoluble particles tagged with gamma

emitting radionu clides were deposited onto pasture grass via simulated rain. Note that the values developed experimentally for pasture grass may not accurately represent all aboveground produce

- specific values. Also note that values based on the behavior of insoluble pa rticles tagged with radionuclides may not accurately represent the behavior of organic compounds under site

- specific conditions.

b For nonionic organic chemicals, as described in HHRAP (EPA 2005a), Appendix A (available at http://www.epa.gov/osw/hazard/ts d/td/combust/finalmact/ssra/05hhrapapa.pdf ), RCF is used to calculate the below

- ground transfer of contaminants from soil to a root vegetable on a wet

- weight basis as shown in Equation B

- 6 . EPA estimated chemical

specific values for RCF from empirical regression equations developed by Briggs et al. (1982) based on their experiments measuring uptake of compounds into barley roots from growth solution. Briggs’ regression equations allow calculation of RCF values from log Kow. For metals and mercuric compounds, empirical values for soil to root vegetable transfer on a dry

- weight basis are available in the literature, thus the RCF was not needed.

c As discussed in HHRAP (EPA 2005a), Appendix A, Kds describes the partitioning of a com pound between soil pore

- water and soil particles and strongly influences the release and movement of a compound into the subsurface soils and underlying aquife r. Kds values for mercuric compounds were obtained from EPA (1997b). Kds for cadmium compounds were obtained from EPA 1996. For all PAHs and dioxins, Kds was calculated by multiplying Koc times the screening scenario’s fraction organic carbon content (0.008). Empirical information for Koc was available for acenaphthene, benz(a)anthracene, benzo(a) pyrene, dibenz(a,h)anthracene, fluoranthene, and fluorene in USEAP 1996. For all other organic compounds, the Koc was calculated using the correlation equations presented in USEAP 2005a.

d As discussed in HHRAP (EPA 2005a), Appendix A, the value for mercur ic chloride was obtained from EPA 1997b. BvAG(i) values for PAHs were calculated using the correlation equation derived for azalea leaves as cited in Bacci et al. (1992), the n reducing this value by a factor of 100, as suggested by Lorber (1995), who conc luded that the Bacci factor reduced by a factor of 100 was similar to his own observations in various studies. The values for dioxins were obtained from Lorber and Pinsky (200 0).

e It is assumed that metals, with the exception of vapor

- phase elemental me rcury, do not transfer significantly from air into leaves. f Speciation and fate and transport of mercury from emissions suggest that BvAG(i) values for elemental and methyl mercury are likely to be zero (EPA 2005a).

Exhibit B

- 11 . Chemical

- Specific Inputs by Plant Type for Chemicals in MIRC

Compound Name Plant Part Plant

- Soil Bio

Concentration Factor ( **Br** AG

- produce

- DW(i) ) (unitless) a Empirical Correction Factor

- Belowgrou nd Produce ( **VG** rootveg ) (unitless) b Empirical Correction Factor

Aboveground Produce ( **VG AG(i)**  ) (unitless) c **Inorganics**

Cadmium compounds Exp. Fruit 1.3E

- 01

- 1.0E+00 Exp. Veg. 1.3E

- 01

- 1.0E+00 Forage 3.6E

- 01

- 1.0E+00
Grain 6.2E

- 02

- - Prot. Fru it 1.3E

- 01

- - Prot. Veg. 1.3E

- 01

- - Root 6.4E

- 02 1.0E+00

- Silage 3.6E

- 01

- 5.0E

- 01TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 52 October 2014 Description of MIRC Exhibit B

- 11 . Chemical

- Specific Inputs by Plant Type for Chemicals in MIRC

Compound Name Plant Part Plant

- Soil Bio

Concentration Factor ( **Br** AG

- produce

- DW(i) ) (unitless) a Empirical Correction Factor

- Belowgrou nd Produce ( **VG** rootveg ) (unitless) b Empirical Correction Factor

Aboveground Produce ( **VG AG(i)**  ) (unitless) c

Mercury (elemental) Exp. Fruit

- - 1.0E+00 Exp. Veg.

- - 1.0E+00 Forage

- - 1.0E+00
Grain

- - - Prot. Fruit

- - - Prot. Veg.

- - - Root

- 1.0E+00

- S ilage

- - 5.0E

- 01

Mercuric chloride Exp. Fruit 1.5E

- 02

- 1.0E+00 Exp. Veg. 1.5E

- 02

- 1.0E+00 Forage 0.0E+00

- 1.0E+00
Grain 9.3E

- 03

- - Prot. Fruit 1.5E

- 02

- - Prot. Veg. 1.5E

- 02

- - Root 3.6E

- 02 1.0E+00

- Silage 0.0E+00

- 5.0E

- 01

Methyl me rcury Exp. Fruit 2.9E

- 02

- 1.0E

- 02 Exp. Veg. 2.9E

- 02

- 1.0E

- 02 Forage 0.0E+00

- 1.0E+00
Grain 1.9E

- 02

- - Prot. Fruit 2.9E

- 02

- - Prot. Veg. 2.9E

- 02

- - Root 9.9E

- 02 1.0E

- 02

- Silage 0.0E+00

- 5.0E

- 01 **PAHs**

2

- Methylnaphthalene Exp. Fruit 2.3 E

- 01

- 1.0E+00 Exp. Veg. 2.3E

- 01

- 1.0E+00 Forage 2.3E

- 01

- 1.0E+00
Grain 2.3E

- 01

- - Prot. Fruit 2.3E

- 01

- - Prot. Veg. 2.3E

- 01

- - Root 4.4E+00 1.0E+00

- Silage 2.3E

- 01

- 5.0E

- 01

7,12

- Dimethylbenz(a)anthra

cene Exp. Fruit 1.7E

- 02

- 1.0E

- 02 Exp. Veg. 1.7E

- 02

- 1.0E

- 02 Forage 1.7E

- 02

- 1.0E+00
Grain 1.7E

- 02

- - Prot. Fruit 1.7E

- 02

- - Prot. Veg. 1.7E

- 02

- - Root 1.7E+00 1.0E

- 02

- Silage 1.7E

- 02

- 5.0E

- 01TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 53 October 2014 Description of MIRC Exhibit B

- 11 . Chemical

- Specific Inputs by Plant Type for Chemicals in MIRC

Compound Name Plant Part Plant

- Soil Bio

Concentration Factor ( **Br** AG

- produce

- DW(i) ) (unitless) a Empirical Correction Factor

- Belowgrou nd Produce ( **VG** rootveg ) (unitless) b Empirical Correction Factor

Aboveground Produce ( **VG AG(i)**  ) (unitless) c

Acenaphthene Exp. Fruit 2.1E

- 01

- 1.0E+00 Exp. Veg. 2.1E

- 01

- 1.0E+00
F orage 2.1E

- 01

- 1.0E+00
Grain 2.1E

- 01

- - Prot. Fruit 2.1E

- 01

- - Prot. Veg. 2.1E

- 01

- - Root 6.2E+00 1.0E+00

- Silage 2.1E

- 01

- 5.0E

- 01

Acenaphthylene Exp. Fruit 1.9E

- 01

- 1.0E

- 02 Exp. Veg. 1.9E

- 01

- 1.0E

- 02 Forage 1.9E

- 01

- 1.0E+00
Grain 1.9E

- 01

- - Prot. Fruit 1.9E

- 01

- - Prot. Veg. 1.9E

- 01

- - Root 4.1E+00 1.0E

- 02

- Silage 1.9E

- 01

- 5.0E

- 01

Benz(a)anthracene Exp. Fruit 1.7E

- 02

- 1.0E

- 02 Exp. Veg. 1.7E

- 02

- 1.0E

- 02 Forage 1.7E

- 02

- 1.0E+00
Grain 1.7E

- 02

- - Prot. Fruit 1 .7E

- 02

- - Prot. Veg. 1.7E

- 02

- - Root 2.3E+00 1.0E

- 02

- Silage 1.7E

- 02

- 5.0E

- 01

Benzo(a)pyrene Exp. Fruit 1.4E

- 02

- 1.0E

- 02 Exp. Veg. 1.4E

- 02

- 1.0E

- 02 Forage 1.4E

- 02

- 1.0E+00
Grain 1.4E

- 02

- - Prot. Fruit 1.4E

- 02

- - Prot. Veg. 1.4E

- 02

- - Root 1.2E+00 1.0E

- 02

- Silage 1.4E

- 02

- 5.0E

- 01TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 54 October 2014 Description of MIRC Exhibit B

- 11 . Chemical

- Specific Inputs by Plant Type for Chemicals in MIRC

Compound Name Plant Part Plant

- Soil Bio

Concentration Factor ( **Br** AG

- produce

- DW(i) ) (unitless) a Empirical Correction Factor

- Belowgrou nd Produce ( **VG** rootveg ) (unitless) b Empirical Correction Factor

Aboveground Produce ( **VG AG(i)**  ) (unitless) c

Benzo(b)fluoranthene Exp. Fruit 1.8E

- 02

- 1.0E

- 02 Exp. Veg. 1.8E

- 02

- 1.0E

- 02 Forage 1.8E

- 02

- 1.0E+00
Grain 1.8E

- 02

- - Prot. Fruit 1.8E

- 02

- - Prot. Veg. 1.8E

- 02

- - Root 1.7E+00 1.0E

- 02

- Silage 1.8E

- 02

- 5.0E

- 01

Benzo(g,h,i)perylene Exp. Fruit 5.7E

- 03

- 1.0E

- 02 Exp. Veg. 5.7E

- 03

- 1.0E

- 02 Forage 5.7E

- 03

- 1.0E+00
Grain 5.7E

- 03

- - Prot. Fruit 5.7E

- 03

- - Prot. Veg. 5.7E

- 03

- - Root 1.1E+00 1.0E

- 02

- Silage 5.7E

- 03

- 5 .0E

- 01

Benzo(k)fluoranthene Exp. Fruit 1.4E

- 02

- 1.0E

- 02 Exp. Veg. 1.4E

- 02

- 1.0E

- 02 Forage 1.4E

- 02

- 1.0E+00
Grain 1.4E

- 02

- - Prot. Fruit 1.4E

- 02

- - Prot. Veg. 1.4E

- 02

- - Root 1.6E+00 1.0E

- 02

- Silage 1.4E

- 02

- 5.0E

- 01

Chrysene Exp. Fru it 1.9E

- 02

- 1.0E

- 02 Exp. Veg. 1.9E

- 02

- 1.0E

- 02 Forage 1.9E

- 02

- 1.0E+00
Grain 1.9E

- 02

- - Prot. Fruit 1.9E

- 02

- - Prot. Veg. 1.9E

- 02

- - Root 1.7E+00 1.0E

- 02

- Silage 1.9E

- 02

- 5.0E

- 01TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 55 October 2014 Description of MIRC Exhibit B

- 11 . Chemical

- Specific Inputs by Plant Type for Chemicals in MIRC

Compound Name Plant Part Plant

- Soil Bio

Concentration Factor ( **Br** AG

- produce

- DW(i) ) (unitless) a Empirical Correction Factor

- Belowgrou nd Produce ( **VG** rootveg ) (unitless) b Empirical Correction Factor

Aboveground Produce ( **VG AG(i)**  ) (unitless) c

Dibenz(a,h)anthracene Exp. Fruit 6.8E

- 03

- 1.0E

- 02
E xp. Veg. 6.8E

- 03

- 1.0E

- 02 Forage 6.8E

- 03

- 1.0E+00
Grain 6.8E

- 03

- - Prot. Fruit 6.8E

- 03

- - Prot. Veg. 6.8E

- 03

- - Root 1.6E+00 1.0E

- 02

- Silage 6.8E

- 03

- 5.0E

- 01

Fluoranthene Exp. Fruit 4.0E

- 02

- 1.0E

- 02 Exp. Veg. 4.0E

- 02

- 1.0E

- 02
Fora ge 4.0E

- 02

- 1.0E+00
Grain 4.0E

- 02

- - Prot. Fruit 4.0E

- 02

- - Prot. Veg. 4.0E

- 02

- - Root 5.6E+00 1.0E

- 02

- Silage 4.0E

- 02

- 5.0E

- 01

Fluorene Exp. Fruit 1.5E

- 01

- 1.0E

- 02 Exp. Veg. 1.5E

- 01

- 1.0E

- 02 Forage 1.5E

- 01

- 1.0E+00
Grain 1.5E

- 01

- - Prot. Fruit 1.5E

- 01

- - Prot. Veg. 1.5E

- 01

- - Root 6.2E+00 1.0E

- 02

- Silage 1.5E

- 01

- 5.0E

- 01

Indeno(1,2,3

- cd)pyrene Exp. Fruit 5.1E

- 03

- 1.0E

- 02 Exp. Veg. 5.1E

- 03

- 1.0E

- 02 Forage 5.1E

- 03

- 1.0E+00
Grain 5.1E

- 03

- - Prot. Fruit 5.1E

- 03

- - Prot. Veg. 5.1E

- 03

- - Root 1.1E+00 1.0E

- 02

- Silage 5.1E

- 03

- 5.0E

- 01TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 56 October 2014 Description of MIRC Exhibit B

- 11 . Chemical

- Specific Inputs by Plant Type for Chemicals in MIRC

Compound Name Plant Part Plant

- Soil Bio

Concentration Factor ( **Br** AG

- produce

- DW(i) ) (unitless) a Empirical Correction Factor

- Belowgrou nd Produce ( **VG** rootveg ) (unitless) b Empirical Correction Factor

Aboveground Produce ( **VG AG(i)**  ) (unitless) c
**Dioxins**

OctaCDD, 1,2,3,4,6,7,8,9

- Exp. Fruit 7.1E

- 04

- 1.0E

- 02 Exp. Veg. 7.1E

- 04

- 1.0E

- 02 Forage 7.1E

- 04

- 1.0E+00
Grain 7.1E

- 04

- - Prot. Fruit 7.1E

- 04

- - Pr ot. Veg. 7.1E

- 04

- - Root 6.1E

- 01 1.0E

- 02

- Silage 7.1E

- 04

- 5.0E

- 01

OctaCDF, 1,2,3,4,6,7,8,9

- Exp. Fruit 9.2E

- 04

- 1.0E

- 02 Exp. Veg. 9.2E

- 04

- 1.0E

- 02 Forage 9.2E

- 04

- 1.0E+00
Grain 9.2E

- 04

- - Prot. Fruit 9.2E

- 04

- - Prot. Veg. 9.2E

- 04

- - Root 6.8E

- 01 1.0E

- 02

- Silage 9.2E

- 04

- 5.0E

- 01

HeptaCDD, 1,2,3,4,6,7,8

- Exp. Fruit 9.2E

- 04

- 1.0E

- 02 Exp. Veg. 9.2E

- 04

- 1.0E

- 02 Forage 9.2E

- 04

- 1.0E+00
Grain 9.2E

- 04

- - Prot. Fruit 9.2E

- 04

- - Prot. Veg. 9.2E

- 04

- - Root 6.8E

- 01 1.0E

- 02

- Silage 9.2E

- 04

- 5.0E

- 01

HeptaCDF, 1,2,3,4,6,7,8

- Exp. Fruit 2.0E

- 03

- 1.0E

- 02 Exp. Veg. 2.0E

- 03

- 1.0E

- 02 Forage 2.0E

- 03

- 1.0E+00
Grain 2.0E

- 03

- - Prot. Fruit 2.0E

- 03

- - Prot. Veg. 2.0E

- 03

- - Root 9.4E

- 01 1.0E

- 02

- Silage 2.0E

- 03

- 5.0E

- 01TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 57 October 2014 Description of MIRC Exhibit B

- 11 . Chemical

- Specific Inputs by Plant Type for Chemicals in MIRC

Compound Name Plant Part Plant

- Soil Bio

Concentration Factor ( **Br** AG

- produce

- DW(i) ) (unitless) a Empirical Correction Factor

- Belowgrou nd Produce ( **VG** rootveg ) (unitless) b Empirical Correction Factor

Aboveground Produce ( **VG AG(i)**  ) (unitless) c

HeptaCDF, 1,2,3,4,7,8,9

- Exp. Fruit 4.0E

- 03

- 1.0E

- 02 Exp. Veg. 4.0E

- 03

- 1.0E

- 02 Forage 4.0E

- 03

- 1.0E+00
Grain 4.0E

- 03

- - Prot. Fruit 4.0E

- 03

- - Prot. Veg. 4.0E

- 03

- - Root 1.2E+00 1.0E

- 02

- Silage 4.0E

- 03

- 5.0E

- 01

HexaCDD, 1,2,3,4,7,8

- Exp. Fruit 1.2E

- 03

- 1.0E

- 02 Exp. Veg. 1.2E

- 03

- 1.0E

- 02 Forage 1.2E

- 03

- 1.0E+00
Grain 1.2E

- 03

- - Prot. Fruit 1.2E

- 03

- - Prot. Veg. 1.2E

- 03

- - Root 7.6E

- 01 1.0E

- 02

- Silage 1.2E

- 03

- 5.0E

- 01

HexaCDF, 1,2,3,4,7,8

- Exp. Fruit 3.5E

- 03

- 1.0E

- 02 Exp. Veg. 3.5E

- 03

- 1.0E

- 02 Forage 3.5E

- 03

- 1.0E+00
Grain 3.5E

- 03

- - Prot. Fruit 3.5E

- 03

- - Prot. Veg. 3.5E

- 03

- - Root 1.2E+00 1.0E

- 02

- Silage 3.5E

- 03

- 5.0E

- 01

HexaCDD, 1,2,3,6,7,8

- Exp. Fruit 7.0E

- 04

- 1.0E

- 02 Exp. Veg. 7.0E

- 04

- 1.0E

- 02 Forage 7.0E

- 04

- 1.0E+00
Grain 7.0E

- 04

- - Prot. Fruit 7.0E

- 04

- - Prot. Veg. 7.0E

- 04

- - Root 6.1E

- 01 1.0E

- 02

- Silage 7.0E

- 04

- 5.0E

- 01TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 58 October 2014 Description of MIRC Exhibit B

- 11 . Chemical

- Specific Inputs by Plant Type for Chemicals in MIRC

Compound Name Plant Part Plant

- Soil Bio

Concentration Factor ( **Br** AG

- produce

- DW(i) ) (unitless) a Empirical Correction Factor

- Belowgrou nd Produce ( **VG** rootveg ) (unitless) b Empirical Correction Factor

Aboveground Produce ( **VG AG(i)**  ) (unitless) c

HexaCDF, 1,2,3,6,7,8

- Exp. Fruit 1.0E

- 03

- 1.0E

- 02 Exp. Veg. 1.0E

- 03

- 1.0E

- 02 Forage 1.0E

- 03

- 1.0E+00
Grain 1.0E

- 03

- - Prot. Fruit 1.0E

- 03

- - Prot. Veg. 1.0E

- 03

- - Root 7.1E

- 01 1.0E

- 02

- Silage 1.0E

- 03

- 5.0E

- 01

HexaCDD, 1,2,3,7,8,9

- Exp. Fruit 7.0E

- 04

- 1.0E

- 02 Exp. Veg. 7.0E

- 04

- 1.0E

- 02 Forage 7.0E

- 04

- 1.0E+ 00
Grain 7.0E

- 04

- - Prot. Fruit 7.0E

- 04

- - Prot. Veg. 7.0E

- 04

- - Root 6.1E

- 01 1.0E

- 02

- Silage 7.0E

- 04

- 5.0E

- 01

HexaCDF, 1,2,3,7,8,9

- Exp. Fruit 1.6E

- 03

- 1.0E

- 02 Exp. Veg. 1.6E

- 03

- 1.0E

- 02 Forage 1.6E

- 03

- 1.0E+00
Grain 1.6E

- 03

- - Prot. Fruit 1.6E

- 03

- - Prot. Veg. 1.6E

- 03

- - Root 8.5E

- 01 1.0E

- 02

- Silage 1.6E

- 03

- 5.0E

- 01

HexaCDF, 2,3,4,6,7,8

- Exp. Fruit 1.0E

- 03

- 1.0E

- 02 Exp. Veg. 1.0E

- 03

- 1.0E

- 02 Forage 1.0E

- 03

- 1.0E+00
Grain 1.0E

- 03

- - Prot. Fruit 1.0E

- 03

- - Prot. Veg. 1.0E

- 03

- - Root 7.1E

- 01 1.0E

- 02

- Silage 1.0E

- 03

- 5.0E

- 01TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 59 October 2014 Description of MIRC Exhibit B

- 11 . Chemical

- Specific Inputs by Plant Type for Chemicals in MIRC

Compound Name Plant Part Plant

- Soil Bio

Concentration Factor ( **Br** AG

- produce

- DW(i) ) (unitless) a Empirical Correction Factor

- Belowgrou nd Produce ( **VG** rootveg ) (unitless) b Empirical Correction Factor

Aboveground Produce ( **VG AG(i)**  ) (unitless) c

PentaCDD, 1,2,3,7,8

- Exp. Fruit 2.4E

- 03

- 1.0E

- 02 Exp. Veg. 2.4E

- 03

- 1.0E

- 02 Forage 2.4E

- 03

- 1.0E+00
Grain 2.4E

- 03

- - Prot. Fruit 2.4E

- 03

- - Prot. Veg. 2.4E

- 03

- - Root 1.0E+00 1.0E

- 02

- Silage 2.4E

- 03

- 5.0E

- 01

PentaCDF, 1,2,3,7,8

- Exp. Fruit 4.6E

- 03

- 1.0E

- 02 Exp. Veg. 4.6E

- 03

- 1.0E

- 02 Forage 4.6E

- 03

- 1.0E+00
Grain 4.6E

- 03

- - Prot. Fruit 4.6E

- 03

- - Prot. Veg. 4.6E

- 03

- - Root 1.3E+00 1.0E

- 02

- Silage 4.6E

- 03

- 5.0E

- 01

PentaCDF, 2,3,4,7,8

- Exp. Fruit 6.8E

- 03

- 1.0E

- 02 Exp. Veg. 6.8E

- 03

- 1.0E

- 02 Forage 6.8E

- 03

- 1.0E+00
Grain 6.8E

- 03

- - Prot. Fruit 6.8E

- 03

- - Prot. Veg. 6.8E

- 03

- - Root 1.5E+00 1.0E

- 02

- Silage 6.8E

- 03

- 5.0E

- 01

TetraCDD, 2,3,7,8

- Exp. Fruit 4.5E

- 03

- 1.0E

- 02 Exp. Veg. 4.5E

- 03

- 1.0E

- 02 Forage 4.5E

- 03

- 1.0E+00
Grain 4.5E

- 03

- - Prot. Fruit 4.5E

- 03

- - Prot. Veg. 4.5E

- 03

- - Root 1.3E+00 1.0E

- 02

- Silage 4.5E

- 03

- 5.0E

- 01TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 60 October 2014 Description of MIRC Exhibit B

- 11 . Chemical

- Specific Inputs by Plant Type for Chemicals in MIRC

Compound Name Plant Part Plant

- Soil Bio

Concentration Factor ( **Br** AG

- produce

- DW(i) ) (unitless) a Empirical Correction Factor

- Belowgrou nd Produce ( **VG** rootveg ) (unitless) b Empirical Correction Factor

Aboveground Produce ( **VG AG(i)**  ) (unitless) c

TetraCDF, 2,3,7,8

- Exp. Fruit 1.2E

- 02

- 1.0E

- 02 Exp. Veg. 1.2E

- 02

- 1.0E

- 02 Forage 1.2E

- 02

- 1.0E+00
Grain 1.2E

- 02

- - Prot. Fruit 1.2E

- 02

- - Prot. Veg. 1.2E

- 02

- - Root 1.9E+00 1.0E

- 02

- Silage 1.2E

- 02

- 5.0E

- 01

a As discussed in HHRAP ( EPA 2005a), the Br AG

-

produ ce

- DW(i) for aboveground produce and forage accounts for the uptake from soil and the subsequent transport of contaminants through the roots to the aboveground plant parts

. For organics, correlation equations to calculate values for Br on a dry weight bas is were obtained from Travis and Arms (1988)

. For cadmium, Br values were derived from uptake slope factors provided in EPA 1992

. Uptake slope is the ratio of contaminant concentration in dry weight plant tissue to the mass of contaminant applied per hec tare soil

. Br aboveground values for mercuric chloride and methyl mercury were calculated using methodology and data from Baes, et al. (1984)

. Br forage values for mercuric chloride and methyl mercury (on a dry weight basis) were obtained from EPA 1997b

. The HHRAP methodology assumes that elemental mercury doesn’t deposit onto soils

. Therefore, it’s assumed that there is no plant uptake through the soil.

b As discussed in HHRAP ( EPA 2005 a ), Appendix B, VG rootveg represents an empirical correction factor that reduces produce concentration

. Because of the protective outer skin, size, and shape of bulky produce, transfer of lipophilic chemicals ( i.e., log K ow greater than 4) to the center of the produce is not likely

. In addition, typical preparation techn iques, such as washing, peeling, and cooking, further reduce the concentration of the chemical in the vegetable as consumed by removing the high concentration of chemical on and in the outer skin, leaving the flesh with a lower concentration than would be the case if the entire vegetable were pureed without washing

. For belowground produce, HHRAP ( EPA 2005 a ) recommends using a VG r ootveg value of 0.01 for PB

- HAP with a log K ow greater than 4 and a value of 1.0 for PB

- HAP with a log K ow less than 4 based on information provided in EPA 1994b

. In developing these values, EPA (1994b) assumed that the density of the skin and the whole vegetable are equal (potentially overestimating the concentration of PB

- HAP in belowground produce due to root uptake).

c As discu ssed in HHRAP ( EPA 2005 a ), Appendix B, VG ag represents an empirical correction factor that reduces ab oveground produce concentration and was developed to estimate the transfer of PB

- HAP into leafy vegetation versus bulkier aboveground produce (e.g., apples )

. Because of the protective outer skin, size, and shape of bulky produce, transfer of lipophilic PB

- HAP (log K ow greater than 4) to the center of the produce is not likely

. In addition, typical preparation techniques, such as washing, peeling, and cooki ng, further reduces residues

. For aboveground produce, HHRAP ( EPA 2005 a ) recommends using a VG ag value of 0.01 for PB

- HAP with a log K ow greater than 4 and a value of 1.0 for PB

- HAP with a log K ow less than 4 based on information provided in EPA 1994 b. I n developing these values, EPA (1994 b ) assumed the following: (1) translocation of compounds deposited on the surface of aboveground vegetation to inner parts of aboveground produce would be insignificant (potentially underestimating t he concentration of P B

- HAP in aboveground produce due to air

- to

- plant transfer); (2) the density of the skin and the whole vegetable are equal (potentially overestimating the concentration of PB

- HAP in aboveground produce due to air

- to

- plant transfer); and (3) the thickness of vegetable skin and broadleaf tree skin are equal (effects on the concentration of PB

- HAP in aboveground produce due to air

- toplant transfer unknown)

. For forage, HHRAP recommends a VG ag value of 1.0, also based on information provided in EPA 1994 b. A VG ag value for silage is not provided in EPA 1994 b ; the VGag value for silage of 0.5 was obtained from NC DEHNR (1997); however, NC DEHNR does not present a specific rationale for this recommendation

. Depending on the composition of the site

specific sila ge, this value may under

- or overestimate the actual value

.TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 61 October 2014 Description of MIRC Exhibit B

- 12

. Non

- Chemical

- Specific Produce Inputs

Plant Part Interception Fraction

( **Rp (i)**  ) (unitless) a Pla nt Surface

Loss Coefficient ( **k**

**p (i)**  ) (1/year) b Length of Plant Exposure to Deposition ( **Tp (i)**  ) (year) c Yield or Standing

Crop Biomass

( **Yp (i)**  ) (kg/m 2 ) d Plant Tissue

Specific Moisture Adjustment Factor ( **MAF (i)** 

) (percent) e Exposed Vegetable 0.982 18 0.16 5.66 92
Protected Fruit NA NA NA NA 90
Protected Vegetable NA NA NA NA 80
Forage (animal feed) 0.5 18 0.12 0.24 92

Exposed Fruit 0.053 18 0.16 0.25 85 Root Vegetables NA NA NA NA 87 Silage (animal feed) 0.46 18 0.16 0.8 92 Grain (animal feed) NA NA NA NA 90
Source: EPA 2005a. NA = not applicable.

a Baes et al

. (1984) used an empirical relationship developed by Chamberlain (1970) to identify a correlation between initial Rp values and pasture grass productivity (standing crop biomass [ Yp ]) to calculate Rp values for exposed vegetables, exposed fruits, forage, and silage

. Two key uncertainties are associated with using these values for Rp : (1) Chamberlain’s (1970) empirical relationship developed for pasture grass may not accurately represent abovegroun d produce

. (2) The empirical constants developed by Baes et al

. (1984) for use in the empirical relationship developed by Chamberlain (1970) may not accurately represent the site

- specific mixes of aboveground produce consumed by humans or the site

- specifi c mixes of forage or silage consumed by livestock.

b The term kp is a measure of the amount of chemical that is lost to natural physical processes (e.g., wind, water) over time

. The HHRAP

- recommended value of 18 yr - 1 (also recommended by EPA 1994a and 1998 ) represents the midpoint of a range of values reported by Miller and Hoffman (1983)

. There are two key uncertainties associated with using these values for kp : (1) The recommended equation for calculating kp includes a health protective bias in that it d oes not consider chemical degradation processes

. ( 2) Given the reported range of kp values from 7.44 to 90.36 yr - 1 , plant concentrations could range from about 1.8 times higher to about 5 times lower than the plant concentrations estimated in FFC media us ing the midpoint kp value of 18.

c HHRAP ( EPA 2005a) recommends using a Tp value of 0.16 years for aboveground produce and cattle silage

. This is consistent with earlier reports by EPA (1994a, 1998) and NC DEHNR (1997), which recommended treating Tp as a c onstant based on the average period between successive hay harvests

. Belcher and Travis (1989) estimated this period at 60 days

. Tp is calculated as 60 days ÷ 365 days/year = 0.16 years

. For forage, the average of the average period between successive h ay harvests (60 days) and the average period between successive grazing (30 days) is used (that is, 45 days), and Tp is calculated as (60 days + 30 days)/ 2 ÷ 365 days/yr = 0.12 yr

. Two key uncertainties are associated with use of these values for Tp : (1) The average period between successive hay harvests (60 days) may not reflect the length of the growing season or the length between successive harvests for site

- specific aboveground produce crops

. The concentration of chemical in aboveground produce due to direct (wet and dry) deposition ( Pd ) will be underestimated if the site

- specific value of Tp is less than 60 days, or overestimated if the site

specific value of Tp is more than 60 days.

d Yp values for aboveground produce and forage were calculated usin g an equation presented in Baes et al

. (1984) and Shor et al

. (1982): Yp = Y hi / A hi , where Y hi = Harvest yield of i th crop (kg DW) and A hi = Area planted to i th crop (m2), and using values for Y h and A h from USDA (1994b and 1994c)

. A production

- weighted U.S

. average Yp of 0.8 kg DW/m2 for silage was obtained from Shor et al

. 1982.

e MAF represents the plant tissue

- specific moisture adjustment factor to convert dry

- weight concentrations into wet

- weight concentrations (which are lower owing to the dilution by water compared with dry

- weight concentrations)

. Values obtained from Chapter 10 of EPA's 2003 SAB Review materials for 3MRA Modeling System, Volume II, “Farm Food Chain and Terrestrial Food Web Data” ( EPA 2003a), which references EPA 1997c

. Note that the value for grain used as animal feed is based on corn and soybeans, not seed grains such as barley, oats, or wheat.TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 62 October 2014 Description of MIRC 

### 6.2.3. Animal Product Parameter Values MIRC also requires chemical

- specific inputs for many of the animal product algorithms. The relevant values are shown in Exhibit B

- 13 for the chemicals included in MIRC to date. The HHRAP algorithms require additional inputs for the animal products calculations that are not specific to PB

- HAPs , but are specific to the animal and animal product type. The soil and plant ingestion rates recommended in HHRAP for beef cattle, dairy cattle, swine, and chicken are provided in Exhibit B

- 14

.

Exhibit B

- 13

. Animal Product Chemical

- specific Inputs for Chemicals Included in MIRC

Compound Name Soil Bio

Availability Factor ( **Bs** ) (unitless) Biotransfer Factors (Ba m ) (day/kg FW tissue) a and Metabolism Factors (MF) ( unitless) b

Mamm al Non

- mammal
Beef ( **Ba beef**  ) Dairy ( **Ba dairy**  ) Pork ( **Ba pork**  ) **MF** Eggs ( **Ba eggs**  ) Poultry ( **Ba poultry**  ) **MF**
**Inorganics** Cadmium compounds 1 1.2E

- 04 6.5E

- 06 1.9E

- 04 1 2.5E

- 03 1.1E

- 01 NA Mercury (elemental) 1 0 0 0 1 0 0 NA
Mercuric chloride 1 1.1E

- 04 1.4E

- 06 3. 4E

- 05 1 2.4E

- 02 2.4E

- 02 NA Methyl mercury 1 1.2E

- 03 1.7E

- 05 5.1E

- 06 1 3.6E

- 03 3.6E

- 03 NA
**PAHs**
2

- Methylnaphthalene 1 2.4E

- 02 5.0E

- 03 2.9E

- 02 0.01 1.0E

- 02 1.7E

- 02 NA 7,12

- Dimethylbenz(a)anthra

cene 1 3.9E

- 02 8.3E

- 03 4.8E

- 02 0.01 1.7E

- 02 2.9E

- 02 NA

Acenap hthene 1 2.5E

- 02 5.2E

- 03 3.0E

- 02 0.01 1.0E

- 02 1.8E

- 02 NA Acenaphthylene 1 2.6E

- 02 5.5E

- 03 3.1E

- 02 0.01 1.1E

- 02 1.9E

- 02 NA Benz(a)anthracene 1 3.9E

- 02 8.3E

- 03 4.8E

- 02 0.01 1.7E

- 02 2.9E

- 02 NA Benzo(a)pyrene 1 3.8E

- 02 8.0E

- 03 4.6E

- 02 0.01 1.6E

- 02 2.8E

- 02 N A Benzo(b)fluoranthene 1 3.9E

- 02 8.3E

- 03 4.8E

- 02 0.01 1.7E

- 02 2.9E

- 02 NA Benzo(g,h,i)perylene 1 2.9E

- 02 6.1E

- 03 3.5E

- 02 0.01 1.2E

- 02 2.1E

- 02 NA Benzo(k)fluoranthene 1 3.8E

- 02 8.0E

- 03 4.6E

- 02 0.01 1.6E

- 02 2.8E

- 02 NA Chrysene 1 4.0E

- 02 8.4E

- 03 4.8E

- 02 0. 01 1.7E

- 02 2.9E

- 02 NA Dibenz(a,h)anthracen

e 1 3.1E

- 02 6.5E

- 03 3.8E

- 02 0.01 1.3E

- 02 2.3E

- 02 NA

Fluoranthene 1 4.0E

- 02 8.5E

- 03 4.9E

- 02 0.01 1.7E

- 02 3.0E

- 02 NA
Fluorene 1 2.9E

- 02 6.1E

- 03 3.5E

- 02 0.01 1.2E

- 02 2.1E

- 02 NA Indeno(1,2,3

- cd)pyrene 1 2.7E

- 02 5.8 E

- 03 3.3E

- 02 0.01 1.2E

- 02 2.0E

- 02 NA

**Dioxins** OctaCDD, 1,2,3,4,6,7,8,9

- 1 6.9E

- 03 1.4E

- 03 8.3E

- 03 1 2.9E

- 03 5.1E

- 03 NA

OctaCDF, 1,2,3,4,6,7,8,9

- 1 8.8E

- 03 1.8E

- 03 1.1E

- 02 1 3.7E

- 03 6.5E

- 03 NA
HeptaCDD, 1,2,3,4,6,7,8

- 1 8.8E

- 03 1.8E

- 03 1.1E

- 02 1 3.7E

- 03 6.5E

- 03 NATRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 63 October 2014 Description of MIRC Exhibit B

- 13

. Animal Product Chemical

- specific Inputs for Chemicals Included in MIRC

Compound Name Soil Bio

Availability Factor ( **Bs** ) (unitless) Biotransfer Factors (Ba m ) (day/kg FW tissue) a and Metabolism Factors (MF) ( unitless) b

Mamm al Non

- mammal
Beef ( **Ba beef**  ) Dairy ( **Ba dairy**  ) Pork ( **Ba pork**  ) **MF** Eggs ( **Ba eggs**  ) Poultry ( **Ba poultry**  ) **MF**
HeptaCDF, 1,2,3,4,6,7,8

- 1 1.6E

- 02 3.5E

- 03 2.0E

- 02 1 6.9E

- 03 1.2E

- 02 NA
HeptaCDF, 1,2,3,4,7,8,9

- 1 2.4E

- 02 5.1E

- 03 3.0E

- 02 1 1.0E

- 02 1.8E

- 02 NA

HexaCDD, 1,2,3,4,7,8

- 1 1.1E

- 02 2.3E

- 03 1.3E

- 02 1 4.6E

- 03 8.1E

- 03 NA HexaCDF, 1,2,3,4,7,8

- 1 2.3E

- 02 4.8E

- 03 2.8E

- 02 1 9.6E

- 03 1.7E

- 02 NA HexaCDD, 1,2,3,6,7,8

- 1 6.8E

- 03 1.4E

- 03 8.2E

- 03 1 2.9E

- 03 5.0E

- 03 NA HexaCDF, 1,2,3,6,7,8

- 1 9.7E

- 03 2.0E

- 03 1.2E

- 02 1 4.1E

- 03 7.1E

- 03 NA HexaCDD, 1,2,3,7,8,9

- 1 6.8E

- 03 1.4E

- 03 8.2E

- 03 1 2.9E

- 03 5.0E

- 03 NA

Hex aCDF, 1,2,3,7,8,9

- 1 1.4E

- 02 2.9E

- 03 1.7E

- 02 1 5.8E

- 03 1.0E

- 02 NA HexaCDF, 2,3,4,6,7,8

- 1 9.6E

- 03 2.0E

- 03 1.2E

- 02 1 4.1E

- 03 7.1E

- 03 NA PentaCDD, 1,2,3,7,8

- 1 1.8E

- 02 3.9E

- 03 2.2E

- 02 1 7.8E

- 03 1.4E

- 02 NA PentaCDF, 1,2,3,7,8

- 1 2.6E

- 02 5.5E

- 03 3.2E

- 02 1 1 .1E

- 02 1.9E

- 02 NA PentaCDF, 2,3,4,7,8

- 1 3.1E

- 02 6.5E

- 03 3.8E

- 02 1 1.3E

- 02 2.3E

- 02 NA TetraCDD, 2,3,7,8

- 1 2.6E

- 02 5.5E

- 03 3.2E

- 02 1 1.1E

- 02 1.9E

- 02 NA TetraCDF, 2,3,7,8

- 1 3.6E

- 02 7.7E

- 03 4.4E

- 02 1 1.5E

- 02 2.7E

- 02 NA
Source: EPA 2005a, unless otherwi se indicated. NA = not applicable.

a As discussed in HHRAP (EPA 2005a), Appendix A, biotransfer factors for mercury compounds were obtained from EPA 1997b. Consi dering speciation, fate, and transport of mercury from emission sources, elemental mercury is assumed to be vapor

- phase and hence is assumed not to deposit to soil or transfer into aboveground plant parts. As a consequence, there is no transfer of elemental mercury into a nimal tissues. Biotransfer factors for cadmium compounds were obtained from EPA 1995b. Biotransfer factors for dioxins and PAHs were calculated from chemical octanol

- water partitioning coefficients (K ow values) using the correlation equation from RTI (2005) and assuming the following fat contents: milk

- 4%; beef

- 19%; pork

- 23 %; poultry

- 14%; and eggs

- 8%.

b As discussed in HHRAP (EPA 2005a), EPA (1995c) recommends using a metabolism factor (MF) to account for metabolism of PAHs by mammals to offset the amount of bioaccumulation suggested by biotransfer factors. EPA has recomm ended an MF of 0.01 for bis(2

- ethylhexyl)phthalate (BEHP) and 1.0 for all other chemicals (EPA 1995d). For MIRC, an MF of 0.01 is also used to calculate concentrations of PAHs in food products from mammalian species based on the work of Hofelt et al. (200 1). This factor takes into account the P450

- mediated metabolism of PAHs in mammals; applying this factor in our approach reduced the concentrations of chemicals in beef, pork, and dairy by two orders of magnitude.TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 64 October 2014 Description of MIRC Exhibit B

- 14

. Soil and Plant Ingestion Rates for Animals

Animal Soil Ingestion Rate

– **Qs (m)**  (kg/day) a Plant Part Consumed by Animal Plant Ingestion Rate

– **Qp (I,m)**  (kg/day)

Beef cattle b 0.5 Silage 2.5
For age 8.8
Grain 0.47

Dairy cattle c 0.4 Silage 4.1 Forage 13.2
Grain 3.0

Swine d 0.37 Silage 1.4
Grain 3.3 Chicken (eggs) e 0.022 Grain 0.2

Source: EPA 2005 a HHRAP (Chapter 5).

a Beef cattle : NC DEHNR (1997) and EPA (1994 b ) recommended a soil in gestion rate for subsistence beef cattle of 0.5 kg/day based on Fries (1994) and NAS (1987)

. As discussed in HHRAP, Fries (1994) reported soil ingestion to be 4 percent of the total dry matter intake

. NAS (1987) cited an average beef cattle weight of 590 kg, and a daily dry matter intake rate (non

- lactating cows) of 2 percent of body weight

. This results in a daily dry matter intake rate of 11.8 kg DW/day and a daily soil ingestion rate of about 0.5 kg/day

. Dairy cattle : NC DEHNR (1997 ) and EPA (1994 b ) recommended a soil ingestion rate for dairy cattle of 0.4 kg/day based on Fries (1994) and NAS (1987)

. As discussed in HHRAP, Fries (1994) reported soil ingestion to be 2 percent of the total dry matter intake

. NAS (1987) cited an average beef cattle w eight of 630 kg and a daily dry matter intake rate (non

- lactating cows) of 3.2 percent of body weight

. This resulted in a daily dry matter intake rate of 20 kg/day DW, and a daily soil ingestion rate of approximately 0.4 kg/day

. Uncertainties associated with Qs include the lack of current empirical data to support soil ingestion rates for dair y cattle and the assumption of uniform contamination o f soil ingested by cattle

. Swine : NC DEHNR (1997) recommended a soil ingestion rate for swine of 0.37, estimat ed by assuming a soil intake that is 8% of the plant ingestion rate of 4.3 kg DW/day

. Uncertainties include the lack of current empirical data to support soil ingestion rates and the ass umption of uniform contamination of the soil ingested by swine. Chick en : HHRAP (EPA 2005a) assumes that chickens consume 10 percent of their total diet (which is approximately 0.2 kg/day grain) as soil, a percentage that is consistent with the study from Stephens et al

. (1995)

. Uncertainties include the lack of current

em pirical data to support soil ingestion rates for chicken and the assumption of uniform contamination of soil ingested by chic ken.

b The beef cattle ingestion rates of forage, silage, and grain are based on the total daily intake rate of about 12 kg DW/ day ( based on NAS [1987] reporting a daily dry matter intake that is 2 percent of an average beef cattle body weight of 590 kg) and are supported by NC DEHNR (1997), EPA (1994 b and 1990), and Boone et al

. (1981)

. The principal unc ertainty associated with these Qp values is the variability between forage, silage, and grain ingestion rates for cattle

. c The dairy cattle ingestion rates of forage, silage, and grain are based on the total daily intake rate of about 20 kg DW/day (NAS 1987; EPA 199 2 ) as recommended by NC DEHNR (1997)

. Uncertainties include the proportion of each food type in the diet, which varies from location to location

. Assuming uniform contamination of plant materials consumed by cattle also introduces uncertainty.

d Swine are not grazing anima ls and are assumed not to eat forage (EPA 1998). EPA (1994 b and 1998) and NC DEHNR (1997) recommended including only silage and grains in the diet of swine. EPA ( 1995c) recommended an ingestion rate of 4.7 kg DW/day for a swine, referencing N AS (1987)

. Assuming a diet of 70 percent grain and 30 percent silage ( EPA 1990), HHRAP estimated ingestion rates of 3.3 kg DW/day (grain) and 1.4 kg DW/day (silage). Uncertainties associated with Qp i nclude variability of the proportion of grain and silage in the di et, which varies from location to location

. e Chickens consume grain provided by the farmer. The daily quantity of grain feed consumed by chicken is assumed to be 0.2 kg/day (Ensminger ( 1980 ) , Fries ( 1982 ) , and NAS ( 1987)

. Uncertainties associated with this variable include the variability of actual grain ingestion rates from site to site

. In addition, assuming uniform contamination of plant materials consumed by chicken introduces some uncertainty.



### 6.3. Adult and Non

- Infant Exposure Parameter Values

The e xposure parameters included in MIRC and the ir default and other value options are summarized in the following subsections . The default values were selected to result in a highly health protective screening scenario . Parameter value options were primarily obtained or estimated from EPA’s Exposure Factors Handbook (EFH ; EPA 2011a ) and Child

- Specific Exposure Factors Handbook (CSEFH; EPA 2008a)

. Where values were reported for age groupings other than those used in MIRC (see Section 2.3 above for MIRC age groups), time

weighted average values were estimated for the MIRC age groups from the available data

.TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 65 October 2014 Description of MIRC In MIRC, ingestion rates for h ome

- p roduce d farm food items are included for exposed fruit, protected fruit, exposed vegetable s, protected vegetables, root vegetables, beef, total dairy, pork, poultry, and eggs . Those ingestion rates are already normalized to body weight (i.e., g wet weight /kg

- day) (EPA 2011a). The body weight parameter values presented in Exhibit B

- 15

, therefore, are not applied in the chemical intake (ADD) equations for these food types.

In MIRC, i ngestion rates also are included for drinking water (mL/day), soil (mg/day), and fish (g/day). These ingestion rates, however, are on a p er person basis (i.e., not normalized for body weight). The body weight parameter values presented in Exhibit B

- 15 , therefore, are applied in the chemical intake (ADD) equations for these media.



### 6.3.1. Body Weights Body weight ( BW ) opti ons included in MIRC include mean, 5 th , 10 th , 50 th , 90 th , and 95 th percentile value s for adults and the five children’s age groups : < 1 year; 1

– 2 years; 3

– 5 years; 6

– 11 years; and 12

– 19 years

. For its default screening assessment, EPA uses the mean BW for each age group. The BWs currently in the MIRC database are listed in Exhibit B

- 15

. In general, BW values for the five children’s age groups were calculated from the summary data provided in Table 8

- 3 of EPA’s 2008 CSEFH . For purposes of comparison, alternative BW values f or children ages 12 through 19 years also were estimated using data from Portier et al

. ( 2007 ). These values are listed in the last row of Exhibit B

- 15 , but are not included in MIRC. The means calculated using the two methods for children ages 12 through 19 years were essentially identical at 64 kg

. The other percentile values for this age group using the two methods differed by approximately 10 percent or less.

Exhibit B

- 15

. Mean and Percentile Body Weight Estimates for Adults and Children

Lifestage

(years) Duration (years) Body Weight (kg)
Mean 5 th 10 th 50 th 90 th 95 th

Adult a (20

- 70) 50 80.0 a 52.9 56 69.3 89.7 97.6
Child < 1 b 1 7.83 6.03 6.38 7.76 9.24 9.66
Child 1

- 2 c 2 12.6 9.9 10.4 12.5 14.9 15.6
Chi ld 3

- 5 d 3 18.6 13.5 14.4 17.8 23.6 26.2
Child 6

- 11 e 6 36.0 22.1 24.0 33.5 51.2 58.6
Child 12

- 19 f 8 64.2 39.5 45 64.2 83.5 89 [Child 12

- 19 g 8 64.3 41.1 44.6 60.9 88.5 98.4]

a BW represents the recommended body weight from EPAs 2011 EFH. Although the 18 to 74 year age category in EPA’s EFH does not match exactly the age 20 to 70 year categorization of adults in MIRC, the magnitude of error in the mean and percentile body weights is likely to be very small (i.e., less than 1%).

b Each BW represents a time

- weighted average of body weights for age groups birth to <1 month, 1 to <3 months, 3 to <6 months, and 6 to <12 months from Table 8

- 3 of the 2 008 CSEFH

. Original sample sizes for each of these age groups can also be found in Table 8

- 3.

c Each BW represents a time

- weighted average of body weights for age groups 1 to <2 years and 2 to <3 years from Table 8

- 3 of the 2008 CSEFH

. Original sample siz es for each of these age groups can also be found in Table 8

- 3.

d BWs obtained directly from Table 8

- 3 of the 2008 CSEFH (age group 3 to <6 years).

e Each BW represents a time

- weighted average of body weights for age groups 6 to <11 years and 11 to <16 years from Table 8

- 3 of the 2008 CSEFH

. Original sample sizes for each of these age groups can also be found in Table 8

- 3

. f Mean BW estimated using Table 8

- 22 of the 2008 CSEFH, which is based on NHANES IV data as presented in Portier et al. (2007) . This esti mate was c alculated as the average of the 8 single

- year age groups from 12 to 13 years through 19 to 20 years

. Values for the other percentiles were estimated using Portier et al., 2007

. g Each BW represents a time

- weighted average of body weights for ag e groups 11 to <16 years and 16 to <21 years from Table 8

- 3 of the 2008 CSEFH . E stimated values include 11

- year

- olds and individuals through age 20, which contributes to uncertainty in the estimates for 12 to 19 years . Those values are provided for compa rison purposes only and are not included in MIRC.TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 66 October 2014 Description of MIRC 

### 6.3.2. Water Ingestion Rates MIRC also includes the option of calculating chemical ingestion via drinking water obtained from surface

- water sources or from wells (i.e., from groundwater) in the contaminated area

. Users have the option in MIRC to set drinking water ingestion rates to zero or to revise the drinking water ingestion rates in MIRC to better reflect site

- specific water uses

. The 2008 CSEFH recommends values for drinking water ingestion rates for child ren based on a study reported by Kahn and Stralka (2008)

. Table 3

- 4 of the CSEFH provides per capita estimates of community water ingestion rates by age categories

. Community water ingestion includes both direct and indirect ingestion of water from the t ap. Direct ingestion is defined as direct consumption of water as a beverage, while indirect ingestion includes water added during food or beverage preparation

. The source of these data is the 1994

- 1996 and 1998 U.S. Department of Agriculture’s ( USDA ’s) Continuing Survey of Food Intakes by Individuals ( CSFII ) ( USDA 2000 )

. Exhibit B

- 16 includes the drinking water ingestion rates for children that are included in MIRC

. Mean and percentile adult drinking water ingestion rates were obtained from EPA (2004 b ), which presents estimated per capita water ingestion rates for various age categories based on data collected by the USDA’s 1994

– 1996 and 1998 CSFII (USDA 2000 )

. Adult ingestion rates, presented in Exhib it B

- 16 , represent community water ingestion, both direct and indirect as defined above, for males and females combined, ages 20 years and older

.

Exhibit B

- 16

. Estimated Daily Per Capita Mean and Percentile Water Ingestio n Rates for Children and Adults a

Lifestage (years) Ingestion Rates, Community Water (mL/day)
Mean 50 th 90 th 95 th 99 th

Child <1 b 504 482 969 1113 1440
Child 1

- 2 c 332 255 687 903 1318
Child 3

- 5 d 382 3 16 778 999 1592
Child 6

- 11 e 532 417 1149 1499 2274
Child 12

- 19 f 698 473 1641 2163 3467
Adult g 1219 981 2534 3087 4567
Sources: EPA 2004, 2008a *T he sample size does not meet minimum reporting requirements as described in EPA 2008 a. For some of these MIRC age groupings, the values are based on the time

- weighted average value for 2 or more age ranges from CSEFH Table 3

- 4 . O ne or more age ranges within the group may not meet the minimum reporting requirements , but not necessarily all of them fall within this category.

a Source is Kahn and Stralka 2008 , also presented in the CSEFH ( EPA 2008 a).

b Each IR represents a time

- weighted average of ingestion rates for age groups birth to <1 month, 1 to <3 months, 3 to <6 months, and 6 to <12 months from Table 3

- 4 of the 2008 CSEFH

. c Each IR represents a time

- weighted average of ingestion rates for age groups 1 to <2 years and 2 to <3 years from Table 3

- 4 of the 2008 CSEFH.

d Each IR represents the ingestion rate for age group 3 to <6 years from Table 3

- 4 of the 2 008 CSEFH.

e Each IR represents the ingestion rate for age group 6 to <11 years from Table 3

- 4 of the 2008 CSEFH . This value r epresents a health protective (i.e., slightly low) estimate of IR for ages 6 through 11 years since 11

- year olds are not included in this CSEFH age group. f Each IR represents a time

- weighted average of ingestion rates for age groups 11 to <16 years, 16 to <18, and 18 to <21 years from Table 3

- 4 of the 2008 CSEFH

. Note that estimated values include 11

- year

- olds and individuals throug h age 20, which contributes to uncertainty in the estimates for 12 to 19 years.

g Adult drinking water ingestion rates were obtained from EPA (2004 b ), Appendix E, Part I, Table A1 for community water, both sexes (ages 20+), direct plus indirect water ingest ion.



### 6.3.3. Local Food Ingestion Rates

MIRC includes m ean, median, 90 th , 95 th , and 99 th percentile food

- specific ingestion rates (IRs) for consumers

- only of farm food chain (FFC) media for adults and children

. The mean andTRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 67 October 2014 Description of MIRC percentile values are from EPA’s anal ysis of data from the USDA’s 1987 to 1988 Nationwide Food Consumption Survey (NFCS) (USDA 1993), as presented in Chapter 13 of the Agency’s Exposure Factors Handbook (i.e., Intake Rates for Various Home Produced Food Items) ( EPA 2011a). Consumers

- only mea ns that individuals who did not report eating a specified type of food during the three

- day period covered by the food ingestion part of the survey were not included in the analysis of ingestion rates for that food type. The questionnaire included the opt ions for a household to self

- identify in one or more of five categories: as a household that gardens, raises animals, hunts, fishes, or farms. As of September 2008, that survey was the most recent NFCS available ( EPA 2008a, CSEFH), and we are not aware of any that might be more recent. 29

For the adult age group in MIRC, data were compiled on food

- specific IRs separately for two types of households as indicated in the “Response to Questionnaire” ( EPA 2011a , Chapter 13): (1) households that farm (F) and (2) households that garden or raise animals (HG for homegrown)

. This division reflects EPA’s data analysis

. EPA tabulated IRs for fruits and vegetables separately for households that farm and households that garden . Similarly, EPA tabulated IRs for animals and animal products for households that farm and for households that raise animals

. Thus, the first type of household, F, represents farmers who may both grow crops and raise animals and who are likely to consume more homegrown/raised foods than the

seco nd type of household

. The second type of household, HG, represents the non

- farming households that may consume lower amounts of home

- grown or raised foods (i.e., HG encompasses both households that garden and households that raise animals)

.
Th e food

- spe cific ingestion rates are based on the amount of each food type that households that farm (F) or households that garden and raise animals (HG) produced and brought into their homes for consumption and the number of persons consuming the food. EPA averaged the actual consumption rate for home

- grown foods over the 1

- week survey period.

The default food

- specific ingestion rates in MIRC for adults are those for farming households (F) in Exhibit B

- 17 . The user can specify use of the non

- farming household (HG) ingestion rates if they are more appropriate for the user’s exposure scenario.

Exhibit B

- 17

. Summary of Age

- Group Specific Food Ingestion Rates for Farm Food Items

Product Child (age in yr) Adult (20

– 70

yrs) <1 1

– 2 3

– 5 6

– 11 12

– 19
**Mean ingestion rates (**  **g/kg**

**- day**  **)**
Beef a N/A 4.14 4.00 3.77 1.72 1.93 Dairy b N/A 91.6 50.9 27.4 13.6 2.96 Eggs a N/A 2.46 1.42 0.86 0.588 0.606 Exposed Fruit a N/A 6.14 2.60 2.52 1.33 1.19 Exposed Vegetable a N/A 3.48 1.74 1.39 1.07 1.38
Pork a N/A 2.23 2.15 1.50 1.28 1.10 Poultry a N/A 3.57 3.35 2.14 1.50 1.37

29 Note that EPA’s 2008 CSEFH does not distingui sh between exposed and protected fruits and vegetables when recommending food ingestion rates based on the same data set for the same age categories

. EPA’s 1997 analysis for its EFH therefore remains the most appropriate data source for use in MIRC.TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 68 October 2014 Description of MIRC Exhibit B

- 17

. Summary of Age

- Group Specific Food Ingestion Rates for Farm Food Items

Product Child (age in yr) Adult (20

– 70

yrs) <1 1

– 2 3

– 5 6

– 11 12

– 19

Protected Fruit a N/A 16.6 12.4 8.50 2.96 5.19 Protected Vegetable a N/A 2.46 1.30 1.10 0.78 0.862
Root Ve getable a N/A 2.52 1.28 1.32 0.94 1.03 Water (mL/day) c N/A 332 382 532 698 1218 **Median ingestion rates (g/kg** 

**- day)** 
Beef a N/A 2.51 2.49 2.11 1.51 1.55 Dairy b N/A 125 66.0 34.4 15.5 2.58 Eggs a N/A 1.51 0.83 0.561 0.435 0.474 Exposed Fruit a N/A 1.82 1.11 0.61 0.62 0.593 Exposed Vegetable a N/A 1.89 1.16 0.64 0.66 0.812
Pork a N/A 1.80 1.49 1.04 0.89 0.802 Poultry a N/A 3.01 2.90 1.48 1.30 0.922
Protected Fruit a N/A 7.59 5.94 3.63 1.23 2.08 Protected Vegetable a N/A 1.94 1.04 0.79 0.58 0.564 Root Vegetabl e a N/A 0.46 0.52 0.57 0.56 0.59 Water (mL/day) c N/A 255 316 417 473 981
**90 th**  **percentile ingestion rates (g/kg** 

**- day)**  **d**

Beef a N/A 9.49 8.83 11.4 3.53 4.41 Dairy b N/A 185 92.5 57.4 30.9 6.16 Eggs a N/A 4.90 3.06 1.90 1.30 1.31 Exposed Fruit a N/A 12.7 5.41 6.98 3.41 2.37 Exposed Vegetable a N/A 10.7 3.47 3.22 2.35 3.09
Pork a N/A 4.90 4.83 3.72 3.69 2.23 Poultry a N/A 7.17 6.52 4.51 3.13 2.69
Protected Fruit a N/A 44.8 32.0 23.3 7.44 15.1 Protected Vegetable a N/A 3.88 2.51 2.14 1.85 1.81 Root Vegetable a N/ A 7.25 4.26 3.83 2.26 2.49 Water (mL/day) c N/A 687 778 1149 1640 2534
**95 th**  **percentile ingestion rates (g/kg** 

**- day)** 
Beef a N/A 12.9 12.5 12.5 3.57 5.83 Dairy b N/A 167 89.9 56.0 32.3 7.80 Eggs a N/A 5.38 3.62 2.37 1.43 1.59 Exposed Fruit a N/A 14.6 6.07 11. 7 4.78 3.38 Exposed Vegetable a N/A 11.9 6.29 5.47 3.78 4.46
Pork a N/A 6.52 6.12 4.73 6.39 2.60 Poultry a N/A 8.10 7.06 5.07 3.51 3.93
Protected Fruit a N/A 48.3 35.1 26.9 11.4 19.2 Protected Vegetable a N/A 9.42 5.10 3.12 2.20 2.83 Root Vegetable a N/A 1 0.4 4.73 5.59 3.32 3.37TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 69 October 2014 Description of MIRC Exhibit B

- 17

. Summary of Age

- Group Specific Food Ingestion Rates for Farm Food Items

Product Child (age in yr) Adult (20

– 70

yrs) <1 1

– 2 3

– 5 6

– 11 12

– 19
Water (mL/day) c N/A 903 999 1499 2163 3087
**99 th**  **percentile ingestion rates (g/kg** 

**- day)** 
Beef a N/A 20.9 19.8 13.3 4.28 6.84 Dairy b N/A 180 87.2 54.8 34.7 9.20 Eggs a N/A 16.2 11.2 8.19 4.77 1.83 Exposed Fruit a N/A 25.2 32.5 15.7 5 .9 13.0 Exposed Vegetable a N/A 12.1 7.36 13.3 5.67 8.42
Pork a N/A 8.71 9.74 6.61 4.29 3.87 Poultry a N/A 9.63 10.24 6.12 4.60 4.93
Protected Fruit a N/A 109 71.2 58.2 19.1 34.4 Protected Vegetable a N/A 9.42 5.31 5.40 2.69 5.56 Root Vegetable a N/A 10.4 4.73 7.47 5.13 7.57 Water (mL/day) c N/A 1318 1592 2274 3467 4567

a Primary source for values was the 1987

– 1988 NFCS survey; compiled results are presented in Chapter 13 of 2011 Exposure Factors Handbook ( EPA, 2011 a ) . When data were unavailable for a particular age group, intake rate for all age groups was used multiplied by the age

- specific ratio of intake based on national population intake rates from CSFI I.

b Primary source for values was 1987

– 1988 NFCS survey, compiled results presented in Chapter 13 of 2011 Exposure Factors Handbook ( EPA, 2011 a ) . When data were unavailable for a particular age group, intake rate for all age groups was used multiplied by the age

- specific ratio of intake based on national population intake rates from an NHANES 2003

– 2006 analysis in Chapter 11 of the Exposure Factors Handbook.

c Primary source for children less than 3 years of age was a Ka h n and Stralka ( 2008 ) analysis of CSFII data, and from EPA’s analysis of NHANES 200 3

– 2006 data for children and adults greater than three. All data tables that were used and justifications for data sources are presented in Chapter 3 of the 2011 Exposure Factors’ Handbook.

d Default ingestion rate percentile used in MIRC for Tier 1 assess ments and chemical threshold calculations

.
For children, EPA estimated food

- specific IRs for four age categories ( EPA 2011a ): 1

– 2 years, 3

– 5 years, 6

– 11 years, and 12

– 19 years

. Sample sizes were insufficient to distinguish IRs for children in different types of households; hence, for children, a single IR value represents both F and HG households for a given food type and age category ( Exhibit B

- 17 )

. For some food types and age categories, there were insufficient data for EPA to provide consumer

- only intake rates (i.e., data set for the subpopulation consisted of fewer than 20 observations ). T he HHRAP methodology , Section 6.2.2.2, recommend s a method by which to calculate the “missing” age

specific consumer

- only ingestion rate s , as explained below . Fo od

- specific intake rates (IRs) for those child age groups and food items not include d in Chapter 13 of the 2011 EFH , that is IR age_ group _x , were derived using the following information:

• Mean or percentile

- specific consumer

- only intake of the farm food item, as brought into the home, for the total NFCS survey population (from EFH Chapter 13)

– IRCO_total;

• Mean or percentile

- specific per capita intake of the food type from all sources, as consumed, for the specific child age group, from Chapter 3 of the CSFII Analysis of Food Intake Distributions (EPA 2003c)

– IRPC, age_group_x; and

• Mean or percentile

- specific per capita intake of the farm food item for the total CSFII survey population (from Chapter 3 of EPA 2003c)

– IRPC_total.TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 70 October 2014 Description of MIRC T he ratio of IR PC, age_ group _x to IR PC_tot al from the CSFII data shows the consumption rate of a particular food type by a specific age group relative to the consumption rate for that food type for the population as a whole

. The ratio of IR CO, age

_ group

_ x to IR CO_total , that is the consumption rate of a particular food type by a specific age group (consumers only) relative to the consumption rate for that food type for the NFCS survey population as a whole (consumers only), should be approximately the sa me

. Given the assumption that the two ratios are equal, Equation B

- 56 was used to calculate the “missing” age

- specific consumer

- only IRs :
Equation B

- 56

. Calculation of Age

- Group

- Specific and Food

- Specific Ingestion Rates

where:

IR CO, age_group_x = Mean or percentile

- specific consumer

- only intake of the food type from all sources, as consumed, for the specific child age group X

IR CO_total = Mean or pe rcentile

- specific consumer

- only intake of the farm food item, as brought into the home, for the total NFCS survey population

IR PC, age_group_x = Mean or percentile

- specific per capita intake of the food type from all sources, as consumed, for the specifi c child age group X from the CSFII

IR PC_total = Mean or percentile

- specific per capita intake of the farm food item for the total CSFII survey p opulation

In this discussion, per capita (as opposed to consumer

- only ) indicates the intake rates are based o n the entire population rather than the subset of the population that ingests the particular food category (i.e., consumers). Here, the use of per capita ingestion rates are recommended by the HHRAP methodology because no consumer

- only percentile

- specific intakes are provided for the different age groups.

The above calculation implicitly assumes that the distribution of the consumption rate for a food type for a specific age group (consumers only) has the same shape as the distribution of the consumption r ate for a food type for a specific age group in the general population ( per capita ). Otherwise, the separate calculation of each percentile might yield intake estimates that decrease as the percentile increases. This calculation artifact could occur if t he shapes of the two distributions differ in the upper percentiles (or “tails”) of the distributions.

In the instances where the above calculations were used to fill data gaps in the above exhibit, only the dairy child

- specific age group intake estimates are not strictly increasing with increasing percentile. The distributions likely track better (and thus the above assumption of equal ratios is more reasonable) for lower percentiles, with deviations occurring due to outlier ingestion rates based on only a few respondents in the tails of the distributions. The MIRC defaults use the 90 th percentile ingestion estimates, which are likely more reliable than the 95 th or 99 th percentile estimates in this particular calculation.



### 6.3.4. Local Fish Ingestion Rates

6.3.4.1. Screen ing Scenario The USDA’s 1987 to 1988 Nationwide Food Consumption Survey (NFCS) (USDA 1993, 1994a), as presented in Chapter 13 of the Agency’s Exposure Factors Handbook (i.e., Intake Rates for 

### IR IR IR IR

PC_total oup_x PC, age_gr CO_total oup_x CO, age_gr 

### ×

=TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 71 October 2014 Description of MIRC Various Home Produced Food Items) (EPA 2011a)

, includes family

- c aught fish ingestion rates by age category. There are several disadvantages, however, to using that data source to estimate fish ingestion rates. First, due to inadequate sample sizes, EPA did not report fish ingestion rates for children less than 6 year s of age. Second, the NFCS data were collected more than two decades ago. Third, the reported fish ingestion rates are for ages 6 to 11 and 12 to 19 and are based on 29 and 21 individuals in each age category, respectively (EPA 2011a, Table 13

- 20). Fina lly, the ingestion rates from NFCS data are based on total weight of fish as brought into the home, and do not include losses from preparation of the fish (i.e., removal of inedible parts and, possibly, the skin). Estimates of preparation losses for fish intended to apply to the NFCS fish ingestion rate data are very uncertain and are based on a wide variety of freshwater, estuarine, and marine fish, and squid (EPA 2011a, Table 13

- 69). Additionally, when considering the multipathway screening methodology, it is important that potential health effects to those individuals who are most likely to have the greatest PB

- HAP exposure are not underestimated and, therefore, ingestion rates that are reflective of subsistence fisher ingestion rates are desired. There fore, a more recent survey was sought that included larger sample sizes, data for children younger than six years, ingestion rates for the parts of fish actually consumed, and ingestion rates reflective of subsistence fisher ingestion rates.

Taking all of these issues into consideration, the default adult fish ingestion rate selected for use in MIRC is 373 g/day, which is the the estimated 99 th percentile of fish ingestion rates for

woman fisher s as reported by Burger (2002). This rate is based upon ingesti on of “wild

- caught” fish, which includes freshwater, estuarine, and marine species, while our screening scenarios focus only on freshwater fish from lakes. This is notable because a number of studies indicate that fish ingestion rates are limited by speci es and habitat (i.e., lake, river, estuary, and ocean) and that the majority of the fish consumed in the United States are from river, marine and estuarine habitats versus lakes. Thus, although the fish ingestion rate for this group of subsistence fishers is not the highest fish ingestion rate available for use by EPA, we do believe it strikes the appropriate balance between being health protective and having screening scenarios so conservative that they are of limited use in the decision making process. This high

- end fish ingestion rate is appropriate in the context of the conservative screening scenario used in the RTR process. This methodology is particularly applicable for national rulemakings given that it is very likely that subsistence woman fisher s of child bearing age are located throughout the United States. Finally, we note that using a high

- end subsistence fish ingestion rate is consistent with section 112 of the CAA, which focuses on risks associated with maximally exposed individuals.

Becaus e Burger (2002) did not estimate fish ingestion rates for children, another data source was needed to develop ingestion rates for the child age categories that are used in MIRC. The child ingestion rates need to be consistent with the Burger adult ingesti on rate, reflective of subsistence fisher ingestion rates, and based on adequate sample sizes. To satisfy these requirements, data on child ingestion rates from EPA’s Estimated Per Capita Fish Consumption in the United States (EPA 2002) were selected for use. Specifically, the estimated 99 th percentile of as

- prepared

, consumer

- only ingestion rates for finfish and shellfish were selected (see Section 4.2.1.1 Table 5 of EPA 2002).The original data were collected as part of the 1994

– 96 and 1998 USDA Continuin g Survey of Intakes by Individuals (CSFII; USDA 2000). Values reflect “as prepared” ingestion rates for which cooking and preparation losses ( L1 and L2 ) did not need to be considered. “Total” fish as reported in this source represent s consumption of finf ish plus shellfish.

Because the child age categories used in MIRC differ from the CSFII age categories presented in EPA 2002, the CSFII data were adjusted for use in MIRC. The CSFII data did not provide ingestion rates for the 1

– 2 year age category. To e stimate ingestion rates for this age group,TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 72 October 2014 Description of MIRC EPA used the ingestion rate for the 3

– 5 year age category scaled downward by the ratio of the mean body weight of the 1

– 2 year age group to the mean body weight of the 3

– 5 year age group. Because MIRC uses a 3

– 5 year age category, no adjustment was needed for CSFII data from that age category. For the 6

– 11 and 12

– 19 age categories, time

- weighted average ingestion rates were calculated based on the CSFII ingestion rates. Exh ibit B

- 18 provides the fish ingestion rates used in the screening assessment

.

Exhibit B

- 18

. Fish I ngestion R ates Used in Screening Assessment

Ingestion Rates (g/day)
Infants

<1 yr Child 1

– 2 yrs Child 3

– 5 yrs Child 6

– 11 yrs Child 12

– 19 yrs Adult 20

– 70 yrs
NA 107.7 a 159.0 b 268.2 c 331.0 c 373 d

a A fish ingestion rate for ages 1

- 2 years was not available . The v alue represents the consumer

- only fish ingestion rate for ages 3 to 5 from EPA (2002) (Section 4 .2.1.1 Table 5 [freshwater/estuarine habitat]), scaled down by the ratio of the mean Child 1

- 2 body weight to the mean Child 3

- 5 body weight

. b This value represents the consumer

- only fish ingestion rate for ages 3 to 5 from EPA (2002), Section 4 .2.1.1 Table 5 (freshwater/estuarine habitat) rounded to the nearest full number

. c These values represent time

- weighted average consumer

- only fish ingestion rate s based on ingestion rates from EPA (2002), Section 4 .2.1.1 T able 5 (freshwater/estuarine habitat)

. d This value represents the 99 th percentile ingestion rate of wild caught fish for women as reported by Burger (2002)

.


### 6.3.4.2. Other Values Available in MIRC EPA’s (2002) analysis of freshwater and estuarine fish consumption from the USDA’s Continuing Survey of Food Intake by Individuals (CSFII) for 1994

- 96 and 1998 was chosen to provide fish ingestion rate options by age category in MIRC. Although the fish consumption rates reported in the CSFII include all sources, commerc ial and self

- caught, for purposes of screening level risk assessments, it was assumed that all freshwater and estuarine fish consumed are self

- caught. The inclusion of commercially obtained and estuarine fish will overestimate locally caught freshwater fi sh ingestion rates for most populations in the United States; however, it also might underestimate locally caught fish ingestion rates for some populations (e.g., Native Americans, Asian and Pacific Island communities, rural African American communities). Because consumption of locally caught fish varies substantially from region to region in the United States and from one population or ethnic group to the next, users of MIRC are encouraged to use more locally relevant data whenever available.

For childr en, MIRC also includes values for the mean and the 90th, 95th, and 99th percentile fish per

- capita ingestion rate s (freshwater and estuarine fish only) based on EPA’s analysis of 1994

- 96 and 1998 CSFII data ( EPA 2002, 2008a) . Those rates include individua ls who eat fish and those who do not eat fish. As shown in EPA’s 2008 CSEFH, Table 10

- 7, the 90 th percentile per capita ingestion rate s estimated from the two

- day CSFII recall period are zero for some child age groups

. Although not presented in CSEFH Tab le 10

- 7, median ingestion rate s for all child age groups would be zero (considering the “consumer only” sample sizes [CSEFH Table 10

- 9] relative to the “ per

- capita ” sample sizes in Table 10

- 7)

. The high percentile fish ingestion rate s that are zero resu lt from the short duration of the CSFII recall period (two days) compared with the averaging time of interest (a year) and the relatively infrequent consumption of fish (e.g., on the order of once a week to once a month or less) compared with the near dail y ingestion of other types of food products (e.g., dairy, produce, meat)

. Use of zero for fish ingestion rate s, however, is not useful in MIRC

. As a result, an alternative method was used to estimate fish ingestion rate s for children and adults that coul d provide reasonable, non

- zero values for all age groups and percentiles

.TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 73 October 2014 Description of MIRC The alternative, age

- group

- specific fish ingestion rate s were derived using values for each age group, y :

• Mean or other appropriate percentile consumer

- only fish ingestion rate s for age group y , IRCO, y , from EPA’s Estimated Per Capita Fish Consumption in the United States (EPA 2002), Section 5.2.1.1, Table 5, for freshwater/estuarine habitat. 30

• Fraction of the population consuming freshwater/estuarine fish, FPC, y , calculated as

co nsumer

- only sample size / U.S. population sample for age group y . The data to calculate th o se fractions are available in the 2008 CSEFH and EPA 2002.

Equation B

- 57 was used to calculate the alternative, per capita fish ingestion rate s by age group 

### ( IR PC,y 

### ):

Equation B

- 57

. Calculation of Alternative Age

- Group

- Specific Fish Ingestion Rates

where:

IR PC,y = Per capita fish ingestion rate for age group y (g/day)

IR CO,y = Consumer

- only fish ingestion rates for age group y (g/day) ( EPA 2002, Section 5.2.1.1, Table 5, for freshwater/estuarine habitat)

F

PC,y = Fraction of the population consuming freshwater/estuarine fish, calculated as

consumer

- only sample size / total U.S

. population sample size for age group y (unitless) (2008 CSEFH, EPA 2002)

In the above, per capita (as opposed to consumer

- only ) indicates the intake rates are based on the entire population rather than the subset of the population tha t ingests the particular food category. Here, per capita ingestions are recommended by the HHRAP methodology because

no consumer

- only percentile

- specific intakes are provided for the different age groups.

The mean and percentile consumer

- only fish ingesti on rates for children and adults and the fraction of the population consuming freshwater/estuarine fish used in calculating long

- term per capita fish ingestion rates by age group are presented in Exhibit B

- 19 and Exhibit B

- 20 . The mean and percentile per capita fish ingestion rates estimated using this methodology are summarized in Exhibit B

- 21 and are available in MIRC. The fish ingesti on rates provided in Exhibit B

- 21 and included in MIRC are intended to represent the harvest and consumption of fish in surface waters in a hypothetical depositional area

. For site

- specific application of this tool, users should consider using more localized survey dat a to estimate more appropriate fish ingestion rates

. The fishing season varies substantially across the United States by latitude, and fish consumption patterns also vary by type of water body (e.g., ponds, lakes, rivers, streams, estuaries, coastal marin e), cultural heritage, and general geographic area

. Therefore, use of more localized information is encouraged

. Note that as indicated in Exhibit 16 and Exhibit 18 , in developing the scr eening threshold emission rates, health protective fish ingestion rates for child and adult fish consumers that more closely represent exposures of a high

- end recreational fisher were used.

30 Most of these data also are provided in Table 10

- 9 of the CSEFH; the median values, however, are not presented in the CSEFH, and values for the mean and all other percentiles are slightly different due to rounding. y PC, y CO, y PC, F IR IR × =TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 74 October 2014 Description of MIRC As noted in Section 6.4.3 of this at tachment , if the user overwrites the fish IRs shown in Exhibit B

- 21 with fresh

- weight as caught values (e.g., values obtained from a local creel survey), the user is advised to set non

- zero values for the preparation and cooking l oss factors L1 and L2 in Equation B

- 15 . Suggested values are presented in Section 6.4.3

.

Exhibit B

- 19

. Daily Me an and Percentile **Consumer**

**- Only**  Fish Ingestion Rates for Children and Adults ( **IR CO,y**  ) a

Lifestage (years) Ingestion Rates, All Fish (g/day)
Mean 50th 90 th 95 th 99 th

Child <1 NA NA NA NA NA
Child 1

- 2 b 27.31 15.61 64.46 87.60 138.76 *
Child 3

- 5 c 40.31 23 .04 95.16 129.31 204.84 *
Child 6

- 11 d 61.49 28.46 156.86 * 247.69 * 385.64 *
Child 12

- 19 e 79.07 43.18 181.40 * 211.15 * 423.38 *
Adult f 81.08 47.39 199.62 * 278.91 505.65 *
Sources: EPA 2002, 2008a N A = not applicable; it is assumed that children < 1 year of ag e do not consume fish.

* Indicates that the sample size does not meet minimum reporting requirements as described in EPA 2002

. Owing to the small sample sizes, these upper percentiles values are highly uncertain.

a Per capita fish ingestion (FI) rates for c hildren by age group are available from Chapter 10 of the CSEFH ( EPA 2008a); however, all 50th and some 90th percentile ingestion rates are zero

. Per capita FI rates were therefore estimated as described in Equation B

- 57 to provi de reasonable, non

- zero values for all age groups and percentiles.

b A fish IR for ages 1

- 2 years was not available . The v alue represents the consumer

- only fish ingestion rate for ages 3 to 5 from EPA (2002) (Section 5.2.1.1 Table 5 [freshwater/estuarine h abitat]), scaled down by the ratio of the mean Child 1

- 2 body weight to the mean Child 3

- 5 body weight

. c These values represent the consumer

- only fish ingestion rate for ages 3 to 5 from EPA (2002), Section 5.2.1.1 Table 5 (freshwater/estuarine habitat)

. Sample size = 442.

d These values represent the consumer

- only fish ingestion rate for ages 6 to 10 from EPA (2002), Section 5.2.1.1 Table 5 (freshwater/estuarine habitat)

. Sample size = 147.

e These values represent the time

- weighted average per capita fi sh ingestion rate for ages 11 to 15 and 16 to 17 years from EPA (2002), Section 5.1.1.1 Table 5 (freshwater/estuarine habitat); the value may underestimate ingestion rate for ages 12 to 19 years

. Sample size = 135.

f These values r epresent the consumer

- onl y fish ingestion rate for individuals 18 years and older from EPA (2002), Section 5.2.1.1 Table 4 (freshwater/estuarine habitat)

. Sample size = 1,633.TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 75 October 2014 Description of MIRC

Exhibit B

- 20

. Fraction of Population Consuming Freshwater/Estuarine Fish on a Single Day ( **F PC,y**  )

Lifestage (years) Fraction Consuming Fish
Child 3

- 5 0.0503 a
Child 6

- 11 0.0440 b

Child 12

- 19 0.0493 c

Adult 0.08509 d

Sources: EPA 2002, 2008a

a This value was calculat ed using the ages 3 to 5 sample size for consumers only divided by the sample size for the U.S. population divided by 2 to represent the proportion consuming fish on a single day (the consumers

- only group includes individuals who consumed fish on at least one of two survey days) to match the one

- day ingestion rate.

b As in footnote a, the value was calculated using the ages 6 to 10 sample size for consumers only divided by the sample size f or U.S. population divided by 2.

c The value was calculated by summing the ages 11 to 15 and 16 to 17 sample sizes for consumers only and dividing by both by the sum of the sample sizes for U.S. population and by a factor of 2.

d The value was calculated using the ages 18 and older sample size for consumers only divided by th e sample size for U.S. population from Section 5.1.1.1 Table 4. The result was divided by 2 to represent a one

- day sampling period in order to match the

one

- day ingestion rate.

Exhibit B

- 21

. Calculated Long

- term Mean and Percentile **per capita**  Fish Ingestion Rate s for Children and Adults ( **IR PC,y**  )

Lifestage (years) Ingestion Rates, All Fish (g/day)
Mean 50th 90 th 95 th 99 th

Child <1 NA NA NA NA NA
Child 1

- 2 a 1.37 0.79 3.24 4.41 6.98
Child 3

- 5 b 2.03 1.16 4.79 6.51 10.3
Child 6

- 11 c 2.71 1.25 6.90 10.9 17.0
Child 12

- 19 d 3.90 2.13 8.95 10.4 20.9
Adult e 6.90 4.03 16.99 23.73 43.02

Sources: EPA 2002, 2008a N A = not applicable; it is assumed that children < 1 year of age do not consume fish.

a Values were c alculated as (consumer

- only IR for Child 1

- 2) x (fraction of population consuming fish for Child 3

- 5).

b Values were c alculated as (consumer

- only IR for Child 3

- 5) x (fraction of population consuming fish for Child 3

- 5 ).

c Values were c alculated as (consumer

- only IR for Child 6

- 11) x (fraction of population consuming fish for Child 6

- 11).

d Values were c alculated as (consumer

- only IR estimated for Child 12

- 19) x (fraction of population estimated to consume fish for Child 12

- 19).

e Values were c alculated as (consumer

- only IR for Adults) x (fraction of population consuming fish for Adults).TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 76 October 2014 Description of MIRC MIRC also includes values for the mean and the 90th percentile fish ingestion rates for recreational fisher s, black and female recreat ional fisher s, and fisher s of Hispanic, Laotian, and Vietnamese descent which are shown in Exhibit B

- 22 . These latter three populations are culturally or economically disposed to higher rates of fish ingestion tha n the general population. Recreational fisher values are from the EFH (EPA, 2011a). Black and female recreational fisher s ingestion rates are presented in Burger (2002). The fish ingestion rates for Hispanic, Laotian, and Vietnamese populations were deri ved from a study by Shilling et al . (2010) of contaminated fish consumption in California’s Central Valley Delta. Shilling et al . (2010) reported mean and 95 th percentile ingestion rates for each subpopulation. In part due to the low sample size in the S hilling study (n of 30 to 45), 95 th percentile values were believed to be unrealistically high. The 90 th percentile ingestion rate estimates presented in Exhibit B

- 22 for Hispanic, Laotian, and Vietnamese fishers w ere derived by EPA using information from Shilling et al . (2010; EPA, 2010).

Exhibit B

- 22 . Calculated Mean and 90 th Percentile Per capita Fish Ingestion Rates for Populatio ns of Recreational Fishers (IR PC,y )

Subpopulation

Percentile Units Recrea

tional Fisher a Female Recrea

tional Fisher b Black Recrea

tional Fisher b Hispanic Recrea

tional Fisher c Laotian Recrea

tional Fisher c Vietnamese Recrea

tional Fisher c

**Ingestion of Fish** 

Mean g/day 8 39.1 171 25.8 47.2 27.1
90th g/day 11 123 446 98 144.8 99.1

a 1997 Exposure Factors Handbook (USEPA, 1997 a )

b Burger (2002) weights are “as consumed” for locally caught fish.

c Shilling, F., A. White, L. Lippert, and M. Lubell. 2010. Co ntaminated fish consumption in California’s Central Valley Delta. Environmental Research 110:334

- 344.

Applications to date of MIRC have used whole fish concentrations estimated by TRIM.FaTE

. The proportion of lipid in TL3 and TL4 fish in TRIM.FaTE is as sumed to be 5.7 percent (by weight) for the whole fish, based on information provided by Thomann (1989)

. The lipid content of the part(s) of the fish normally consumed is likely to be less than 5.7 percent. For example, EPA estimated a consumption

- weight ed mean lipid value for fillets of fish from TL3 to be 2.6 percent and from TL4 to be 3.0 percent (Table 6

- 9 in EPA 2003b)

. If a user of MIRC wishes to account for reduced chemical concentration in fillet compared with whole fish for lipophilic chemicals, the user can specify a “preparation” loss of chemical (see Section 6.4 of this attachment ).

For lipophilic chemicals (e.g., log K ow greater than 4), which partition primarily into the fatty tissues of fish, much of the higher concentration tissues might be stripped from the fish during preparation (e.g., belly fat, viscera which includes fat in liver, etc

. , fat under skin)

. The degree to which the concentration of chemical in a fillet is less than the average total concentrati on in the whole fish is chemical specific

. Assuming the chemical concentration in the fillet is the same as in the whole fish may result in a health protective bias for highly lipophilic chemicals . For persons who prefer to consume fillets with the skin on and do not discard belly fat

, assuming the same concentration of chemical in the fish consumed as in the whole fish also is protective.TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 77 October 2014 Description of MIRC 

### 6.3.5. Soil Ingestion Rates Adult gardeners and farmers may incidentally ingest soils from gardening activities, and gardeni ng and farming families might ingest soil particles that adhere to exposed fruits and exposed and belowground vegetables. Soils that are re

- suspended in the air by wind can resettle on exposed fruits and vegetables. Children may incidentally ingest soils in those ways, but in addition, children playing outdoors may ingest soils directly or by hand

- to

- mouth activities during play. MIRC includes soil ingestion rate options by age group for these types of exposures

. MIRC does not include options fo r childr en who may exhibit pica, or the recurrent ingestion of unusually high amounts of soil (i.e., on the order of 1,000

- 5,000 mg/day or more) ( EPA 2008 a )

. Data on soil ingestion rates are sparse; the soil ingestion rates listed in E xhibit B

- 23 and included in MIRC are based on very limited data, as is evident from the values listed. The studies evaluated by EPA for children generally focused on children between the ages of 1 and 3 to 6 years and were not specific to families that g arden or farm. The default ingestion rates in MIRC are the 90 th percentile values, as for other ingestion rate parameters.

Exhibit B

- 23 . Daily Mean and Percentile Soil Ing estion Rates for Children and Adults

Age Group

(years) Soil Ingestion Rate (mg/day)
Mean a 50 th a 90 th 95 th 99 th

Child < 1 NA
Child 1

- 2 50 50 200 b 200 b 200 b

Child 3

- 5 50 50 200 b 200 b 200 b

Child 6

- 11 50 50 201 c 331 d 331 d

Child 12

- 19 50 50 201 c 331 d 331 d

Adult 20

- 70 20 20 201 c 331 d 331 d

Sources: EPA 2008 a , EPA 2011 a

a For mean and 50 th percentile soil ingestion rates for children, value represents a “central tendency” estimate from EPA’s 2008 CSEFH, Table 5

- 1. For adults, value is the recommended m ean value for adults from EPA’s 2011 EFH, Chapter 5, Table 5

- 1.

b V alues are the recommended “upper percentile” value for children from EPA’s 2011 EFH, Chapter 4, Table 4

- 23. The 2008 CSEFH and 2011 E F H included a high

- end value associated with pica only, but this value has not been used.

c V alues are 90 th percentile adult ingestion rates calculated in Stanek et al. 1997 ; used to represent older children and adults.

d V alues are 95 th percentile adult ingestion rates calculated in Stanek et al. 1997 ; used to r epresent older children and adults.



### 6.3.6. Total Food Ingestion Rates Although not included in MIRC for deterministic screening

- level exposure and risk assessments, total food ingestion rates w ould be included in any probabilistic module develop ed for MIRC

. Th e total food ingestion rates presented in Exhibit B

- 24 will be used to normalize or to truncate the sum of food

- specific ingestion rates to reasonable values

. This procedure is particularly important when chemical intake from mul tiple upper

- percentile food ingestion rates for different types of food are added together . Individuals representing the upper percentile ingestion rate for one food category might not be the same individuals who reported high percentile ingestion rates f or one or any of the other food categories.TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 78 October 2014 Description of MIRC Exhibit B

- 24 . Daily Mean and Percentile **Per Capita** Total Food Intake for Children and Adults

Lifestage (years) Percent of G roup Consuming Food Mean 50th 90 th 95 th 99 th

**Total Food Intake (g/day, as consumed)**
Child < 1 a 67.0%

- 99.7% h 322 270 599 779 1152
Child 1

- 2 b 100% 1,032 996 1537 1703 2143
Child 3

- 5 c 100% 1,066 1,020 1,548 1,746 2,168
Child 6

- 11 d 100% 1,118 1,052 1,6 42 1,825 2,218
Child 12

- 19 e 100% 1,197 1,093 1,872 2,231 2,975
Adult f 100% 1,100 1,034 1,738 2,002 2,736 **Total Food Intake (g/kg**

**- day, as consumed)** 
Child < 1 a 67.0%

- 99.7% h 39 34 72 95 147
Child 1

- 2 b 100% 82 79 125 144 177
Child 3

- 5 c 100% 61 57 91 1 02 132
Child 6

- 11 d 100% 40 38 61 70 88
Child 12

- 19 e 100% 21 19 34 40 51
Adult g 100% 14.8 13.9 23.7 27.6 35.5

Sources: EPA 2005e, 2008a

a These values represent a time

- weighted average for age groups birth to <1 month (N=88), 1 to <3 months (N=245), 3 t o <6 months (N=411), and 6 to <12 months (N=678) from Table 14

- 3 of the 2008 CSEFH

. These values represent a time

- weighted average for age groups 1 to <2 years (N=1,002) and 2 to <3 years (N=994) from Table 14

- 3 of the 2008 CSEFH

. c These values were o b tained from Table 14

- 3 of the 2008 CSEFH (age group 3 to <6 years, N=4,112).

d These values were o btained from Table 14

- 3 of the 2008 CSEFH (age group 6 to <11 years, N=1,553) . These values r epresents

a health protective (i.e., slightly low) estimate for a ges 6 through 11 years since 11

- year olds are not included in this CSEFH age group.

e These values represent a time

- weighted average for age groups 11 to <16 years (N=975) and 16 to <21 (N=743) years from Table 14

- 3 of the 2008 CSEFH

. Note that estimated v alues include 11

- year

- olds and individuals through age 20, which contributes to uncertainty in the estimates.

f These values represent a time

- weighted average for age groups 20 to 39 years (N=2,950) and 40 to 69 years (N=4,818) from Table 5B of the 2005 EPA analysis of CSFII

. g These values represent a time

- weighted average for age groups 20 to 39 years (N=2,950) and 40 to 69 years (N=4,818) from Table 5A of the 2005 EPA analysis of CSFII

. h Percents consuming foods from Table 14

- 3 of the 2008 CSEFH includ e: 67.0% (birth to <1 month); 74.7% (1 to <3 months); 93.7% (3 to <6 months); and 99.7% (6 to <12 months)

. Infants under the age of 1 that consume breast milk are classified as “non

consumers” of food.



### 6.4. Other Exposure Factor Values
The other exposure pa rameters included in the MIRC algorithms are exposure frequency (Section 6.4.1 ), fraction of the food type obtained from the contaminated area (Section 6.4.2 ), and reduction in the weight of the foo d types during preparation and cooking (Section 6.4.3 ). For the breast milk ingestion pathway, additional exposure parameters are included in the FFC algorithms (Section 6.5 ).



### 6.4.1. Exposure Frequency

The exposure frequency (EF) represents the number of d ays per year that an individual

consumes home

- produced food items that are contaminated with the chemical being evaluated. In MIRC, the default value for EF is 365 days/year for all exposure sources a nd all potential receptors. This assumption is consistent with the food ingestion rates used in MIRC (i.e., daily intake rates equivalent to annual totals divided by 365 days) and does not imply that residents necessarily consume home

- produced food produc ts every day of the year

.TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 79 October 2014 Description of MIRC If the user wishes to evaluate daily intake rates based on shorter averaging times, the user can overwrite both the food

- specific ingestion rates and the EF for each home

- grown food product. Users of MIRC might want to specify a lower EF values for various food types where residents obtain some of their diet from commercial sources and where consumption of home grown produce is seasonal.



### 6.4.2. Fraction Contaminated

The fraction contaminated (FC) represents the fraction of each food pr oduct consumed that is contaminated by the chemical at a level consistent with environmental concentrations in the area of concern (e.g., area with maximum deposition rates). Obviously, the most health protective assumption is that all food products co nsumed (i.e., 100 percent) are from the location represented by the chemical concentrations input into MIRC.

For non

- infant children and the adult age cohorts, MIRC includes the default FC of 1, assuming that 100 percent of the food product consumed is p roduced by households that farm, garden, or raise animals

. The user can vary this default FC value for individual food products to tailor the assessment to a particular exposure scenario

.


### 6.4.3. Preparation and Cooking Losses
Food preparation and c ooking loss es are included in the FFC exposure calculations to account for the amount of a food product as brought into the home that is not ingested due to loss durin g preparation, cooking, or post

- cooking . These losses need to be accounted for in the ADD equations because the food ingestion rates calculated from the USDA 1 987 to 1988 NFCS are based on the weight of home grown produce and animal products brought from the field into the house prior to any type of preparation. Not all of the produce or products were eventually ingested. In general, some parts of the produce and products are discarded during preparation while other parts might not be consumed even after cooking (e.g., bones). Thus, the actual food ingested is generally less than the amount brought in to the home.

Three distinct types of preparation and cooking losses are included in the ingestion exposure algorithms in MIRC: (1) loss of parts of the food type from paring (i.e., removing the skin from vegetables and fruits) or other types of preparation (e.g., removing pits, coring, deboning)

, (2) additional loss of weight for the food type during cooking (e.g., evaporation of water), and (3) post

- cooking loss es (e.g., non

- consumption of bones, draining cooking liquid [e.g., spinach]). MIRC includes mea n values for these three types of preparation and c ooking loss es for exposed fruit, protected fruit, exposed vegetables, protected vegetables, root vegetables, beef, pork, poultry, and fish . Different types of losses apply to different types of foods. Th erefore, the losses can be represented by only two parameters, L1 and L2 , the definitions of which vary according to the food type as explained in the endnotes in Exhibit B

- 25 . All preparation and cooking loss parameter values we re estimated as specified in the exhibit’s endnotes from data presented in Chapter 13 of the EPA’s 1997 and 2011 EFH (EPA 1997a and 2011a).TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 80 October 2014 Description of MIRC Exhibit B

- 25

. Fraction Weight Lo sses from Preparation of Various Foods

Product Mean Cooking, Paring, or Preparation Loss (Cooking Loss Type 1 [L1]) (unitless) a Mean Net Post Cooking (Cooking Loss Type 2 [L2]) (unitles s) b

Exposed Fruit c 0.244 0.305 Exposed Vegetable 0.162 d NA
Prote cted Fruit 0.29 e NA Protected Vegetable 0.088 f NA Root Vegetable g 0.075 0.22
Beef 0.27 0.24
Pork 0.28 0.36 Poultry 0.32 0.295 h

Fish i 0.0 0.0

Source: EPA 1997 a and 2011a NA = Not Available

a For fruits , includes losses from draining cooked forms

. Fo r vegetables , includes losses due to paring, trimming, flowering the stalk, thawing, draining, scraping, shelling, slicing, husking, chopping, and dicing and gains from the addition of water, fa t, or other ingredients

. For meats , includes dripping and vol atile losses during cooking.

b For fruits , includes losses from removal of skin or peel, core or pit, stems or caps, seeds and defects; may also include losses from removal of drained liquids from canned or frozen forms

. For vegetables , includes losses fro m draining or removal of skin

. For meats , includes losses from cutting, shrinkage, excess fat, bones, scraps, and juices.

c These values represent average s of means for all fruits with available data (except oranges) (Table 13

- 6).

d This value r epresents an average of means for all exposed vegetables with available data (Table 13

- 7)

. Exposed vegetables include asparagus, broccoli, cabbage, cucumber, lettuce, okra, peppers, snap beans, and tomatoes.

e This value was set equal to the v alue for oranges (Table 1 3

- 6).

f This value r epresents an average of means for all protected vegetables with available data (Table 13

- 7)

. Protected vegetables include pumpkin, corn, peas, and lima beans.

g These values r ep resent average s of means for all root vegetables with availa ble data (Table 13

- 7)

. Root vegetables include beets, carrots, onions, and potatoes.

h This value r epresents an average of means for chicken and turkey (Table 13

- 5).

i If the user changes fish ingestion rates to match a survey of the whole weight of fish br ought into the home from the field (divided by the consumers of the fish), an appropriate value for L1 would be 0.3 1 and an appropriate L2 would be 0.11 ( EPA 2011a).

There are substantial uncertainties associated with the L1 and L2 parameters, including the wide variation in values across produce types that were averaged together to recommend a central tendency value for each. For example, t he L2 factor does not distinguish between weight loss during cooking by water evaporation, which might leave the ch emical in the food (chemical not lost) , and pouring the cooking liquid down the drain (chemical lost) or using the liquid to create a sauce (chemical not lost) . In addition, the concentration of chemical might be highest in t he skin, which often is discar ded , and lowe r in the consumed portion of many bulky fruits and vegetable s. Finally, the data EPA used to evaluate L1 included negative losses (i.e., weight gains) due to hydration of dried vegetables (e.g., peas and lima beans), which increases the range of L1 values across different vegetables.

Note that the default L1 and L2 values for fish are set to zero

. That is because the data source for the fish ingestion rates is not the USDA’s 1987 to 1988 NFCS (USDA 1993, 1994 a ) as reported in EPA’s EFH , whi ch reported food as brought into the home, as is the case for the other food categories

. Instead, the fish IR data included in MIRC are based on parts actually consumed, and so no loss processes for preparation are needed

.
If the user manually changes f ish ingestion rates to match a local survey of the whole weight of fish brought into the home from the field (divided by number of persons consum ing the fish), the user should also set the L1 and L2 parameter to non

- zero values.TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 81 October 2014 Description of MIRC 

### 6.4.4. Food P reparation/ C ookin g A djustment F actor (FPCAF) for Fish In addition to estimating the weight of the food that is lost to preparation and cooking, there also can be changes in the chemical concentrations due to cooking. Because the fish consumption rates are “as consumed” and the fish concentration is based on uncooked fish, adjustments should be made to reflect the chemical concentrations in fish after cooking. In order to account for this phenomenon, an FPCAF can be applied to the uncooked fish concentration to estimate

a co ncentration in cooked fish. The following sections discuss FPCAFs for each of the 4 PB

HAPs.



### 6.4.4.1. Mercury In the U.S. EPA Revised Technical Support Document: National

- Scale Assessment of Mercury Risk to Populations with High Consumption of Self

- caught Freshwat er Fish (EPA 2011 b ), a food preparation/cooking adjustment factor (FPCAF) of 1.5 was used to adjust methyl

- mercury (MeHg) concentrations in consumed fish (i.e., a 50% increase in MeHg concentration due to cooking). C ooking fish typically increases MeHg lev els per unit fish (as consumed) because MeHg concentrates in the muscle

, while preparation involves removal primarily of non

- muscle elements of the fish. The value is based on a study by Morgan et al. (1997).



### 6.4.4.2. Cadmium Similar to mercury, cadmium will bind to muscle and will be retained during the cooking process. As such, the same FPCAF of 1.5 that is used for mercury is assumed for cadmium.



### 6.4.4.3. Dioxin
Dioxins are lipophilic and have been demonstrated to be lost during cooking. Based on a literature review, an FPCAF of 0.7 to is applied to account for these losses during the cooking process. A brief summary of supporting literature follows.

• Schecter et al. (1998) found that the mass of PCDD and PCDF in fresh catfish fillet (skin on) decreased by about 50 percent per serving portion during cooking. Given the simultaneous losses of moisture/fats during broiling of the catfish, the PCDDs and PCDFs concentrations decreased by 33 percent (i.e., multiply uncooked concentration in fresh fish by a factor of 0.66 = 0.70 t o one significant digit).

• Reinert et al. (1972) reported higher losses of another highly lipophilic chemical, DDT, from cooking fish fillets of bloaters, yellow perch, lake trout, and coho salmon. Concentrations of DDT in fish fillet portions for lake trou t and coho salmon, top predators, were reduced by 64 to 72 percent by frying or broiling, primarily through preferential loss of fat (and lipophilic DDT) during cooking. The investigators did not report skin on or off; however, they used steak cuts instead of flat fillets, which provide a smaller ratio of skin to muscle than is the case for fillets that constitute one side of the fish.

• Zabik and Zabik (1995) quantified the reduction in TCDD concentration of cooked, with the skin off, fillets compared with u ncooked fillet with skin for fish harvested from the Great Lakes. Concentrations in the cooked fish with the skin off were reduced relative to the raw fillet with the skin on by approximately 44 percent for walleye, 80 percent for white bass, and 61 percen t for lake trout. Comparing losses of TCDD for fillets cooked with the skin on versus fillets that were both skinned and cooked, Zabik and Zabik (1995) found reductions in TCDD concentrations of approximately 43 percent for Chinook Salmon cooked with the s kin on and 57 percent for c hinook s almon cooked withTRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 82 October 2014 Description of MIRC the skin off. They found a 37 percent reduction of TCDD concentration for carp fillets cooked with the skin on and 54 percent reduction if the skin was removed.

The three studies listed above indicate th at the 0.7 factor is not likely to overestimate loss of PCDD/PCDFs from fish during cooking (pan frying, broiling, grilling). Reductions in TCDD concentrations could be much higher with skin removal and trimming of fat.



### 6.4.4.4. PAHs

While it is reasonable to assum e that there might be losses of lipophilic PAHs during the cooking process, there is insufficient information to distinguish what the net loss (or gain) during cooking might be because cooking can create PAHs from proteins in the tissue. The literature ack nowledges these competing forces, but does not provide information to disentangle the gain and loss mechanisms. As such, a neutral approach was taken, which is to assume an adjustment factor of 1.0 (i.e., no adjustment) for PAHs.



### 6.5. Breast

- Milk Infant Exposu re Pathway Parameter Values Values used for parameters in the breast

- milk exposure pathway algorithms (see Section 3.4 of this attachment ) can be scenario

- specific, receptor

- specific, and/or chemical

- specific and might be empir ically derived or estimated by an appropriate model. For parameters that are scenario

specific or for which empirical values are required, the default values provided in MIRC are listed. For parameters for which MIRC calculates values, the appropriate eq uation is listed. Scenario

- and receptor

- specific parameters are discussed in Section 6.5.1 and chemical

specific parameters are discussed in Section 6.5.2

.


### 6.5.1. Receptor

- specific Parameters
Receptor

- specific values are needed for parameters that describe the characteristics or activities of the exposed individual. In this context, there are two relevant receptors: the mother and the infant. Exhibit B

- 26 lists the parameters and their default values. The text that follows describes the input value or value options for each exposure parameter required by MIRC to calculate the infant absorbed chemical intake rate, or DAI inf

. For parameter values that can be estimated when emp irical values are not available, see the equation description in Section 3.4 of this attachment

.TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 83 October 2014 Description of MIRC Exhibit B

- 26 . Scenario

- and Receptor

- Specific Input Parameter Values Used to Estimate Infant Exposures via Breast Mil k

Parameter Description Default Value

AT Averaging time for infant’s exposure via breast milk, i.e., duration of nursing (days) =ED

BW inf Body weight of infant (kg) averaged over du ration of nursing exposure 7.8

BW mat Body weight of mother (kg) averaged over duration of mother’s exposure 66

DAI mat Daily absorbed intake of chemical by mother (mg/kg

- day) Equation B

- 36
ED Exposure duration for infant, i.e., duration of breast feeding (days) =AT
AT/ED Averaging time divided by exposure duration 1.0
f bp Fraction of mother’s whole blood that is plasma (unitless) 0.65
f fm Fraction of mother’s body weight that is fat (unitless) 0.30
f mbm Fraction of fat in mo ther’s breast milk (unitless) 0.04
f pm Fraction of mother’s body weight that is plasma (unitless) 0.046
IR milk Mean infant milk ingestion rate over duration of nursing (kg/day) 0.709

t bf Duration of breast feeding (days) 365

t pn Duration of maternal ch emical exposure prior to nursing (days) 3285

Averaging time ( AT ) and exposure duration ( ED ). AT refers to the time over which the infant’s exposure to the chemical of concern is averaged. ED refers to the duration of the infant’s exposure. For the ex posure scenario considered for this age group, both AT and ED equal the duration of the nursing period, and they therefore cancel each other out in the infant average daily dose equation

. Infant body weight ( BW **inf**

)

. The user selects a value for BW inf , the time

- weighted average body weight of the infant over the entire duration of breast feeding, based on the age at which the infant stops breast feeding. For example, if the infant breast feeds for one year, the user should select the body weight for an infant th at is averaged from birth to the first birthday

. Similarly, if an infant breast feeds for 6 months, the user should select the body weight for an infant that is averaged from birth to six months . Because the default breast feeding duration ( t bf

) is one year (i.e., 365 days), the default infant body weight is 7.8 kg

, which is the time

- weighted average for the mean infant body weight between birth and the first birthday from EPA’s 2008 Child Specific Exposure Factors Handbook ( CSEFH; EPA 2008 a )

. Exhibit B

- 27 presents additional values for the infant body weight parameter that the user can select instead of the MIRC default.

Exhibit B

- 27

. Av erage Body Weight for Infants

Statistic 0 to < 6 months (kg) 0 to < 12 months (kg) 0 to < 18 months (kg) 0 to < 24 months (kg)
Mean 6.5 7.8 a 9.0 9.6
5 th percentile 5.0 6.0 7.0 7.5
10 th percentile 5.3 6.4 7.4 7.8
15 th percentile 5.5 6.7 7.7 8.2
25 th percentile 5.8 7.0 8.1 8.7
50 th percentile 6.4 7.8 8.9 9.5
75 th percentile 7.1 8.6 9.9 10.5
85 th percentile 7.4 9.0 10.3 11.0TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 84 October 2014 Description of MIRC Exhibit B

- 27

. Av erage Body Weight for Infants

Statistic 0 to < 6 months (kg) 0 to < 12 months (kg) 0 to < 18 months (kg) 0 to < 24 months (kg)
Mean 6.5 7.8 a 9.0 9.6
90 th percentile 7.7 9.2 10.6 11.3
95 th percentile 8.0 9.7 11.1 11.8

Source: EPA ( 2008a ) ; each value is the time

- weighted ave rage from the data summaries presented in the CSEFH, Table 8

- 3.

a MIRC default

Maternal body weight ( BW **mat** )

. This parameter represents the body weight of the mother averaged over the entire duration of the mother’s exposure to the chemical of concern. T he maternal body weight is needed to calculate the biological elimination constant for the lipophilic chemical in lactating women ( k fat_elac ). MIRC assumes that the mother will be pregnant for 9 months (i.e., 0.75 year) and will be lactating for 1 year. The MIRC default maternal body weight also assumes that the mother has been exposed for 10 years total. For 8.25 years, she is not pregnant or lactating, for 0.75 year she is pregnant, and for 1 year she is lactating. The MIRC default BW mat of 66 kg is b ased on CSFII data compiled by EPA for non

- lactating and non

pregnant women between the ages of 15 and 44 (i.e., women of child

- bearing age), lactating women, and pregnant women (EPA 2004). Exhibit B

- 28 presents additional values for the maternal body weight parameter which the user may choose to use instead of the MIRC default. The BW mat value is not the value that MIRC uses to estimate the mother’s absorbed daily intake ( DAI mat ). The daily ingestion rates for home

- grown/raised food products in MIRC are for men and women combined, with the rates normalized to body weight. The ingestion rates for soil, water, and fish included in MIRC are not normalized to body weight but are based on both men and women. For those ingestion rat es, MIRC uses an average body weight value for males and females to estimate the average daily dose (intake) of the chemical in mg/kg

- day. These values are subject to the assumption that the body

- weight normalized ingestion rates and resulting ADD values are applicable to nursing mothers.

Exhibit B

- 28

. Time

- weighted Average Body Weight for Mothers

Statistic Weight (kg)

Mean 66.0 a

5 th 47.1
10 th 50.2
25 th 54.3
50 th 62.0
75 th 72.0
90 th 85.7
95 th 97.0

Source: EPA 2004

a MIRC default value

Exposure duration ( ED )

. See discussion of AT and ED above.
Fraction of mother’s whole blood that is plasma ( f **bp** )

. Steinbeck (1954) reported that plasma volume accounts for approxima tely 60 percent of the total blood volume in non

- lactating human females (EPA 1998). Harrison (1967) and Ueland (1976) reported plasma volumes between 63 to 70 percent in postpartum women (EPA 1998). The default value in MIRC of 65 percent ( 0.65 )TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 85 October 2014 Description of MIRC is the value recommended by EPA in its Methodology for Assessing Health Risks Associated with Multiple Pathways of Exposure to Combustor Emissions (MPE, EPA 1998).

Fraction of mother’s body weight that is fat ( f f **m** )

. A limitation of using a steady

- state, instead of a dynamic partitioning, model for lactational transfer of chemicals is that several key parameters change over the course of exposure. For example, Equation B

- 38 , used to estimate the concentration of a lipophilic chemical in breast milk fat, assumes that the mother’s body fat will remain constant over the entire duration of breast feeding (t bf ), which is unlikely to be true (EPA 2001a). Another limitation of the single analytic model is that chemical transfer rates from blood to milk are unlikely to be the same as the rate of mobilization of the chemical from fat stores to the blood (EPA 2001a). Studies cited in ATSDR’s toxicological profile for chlorinated dibenzo

- p

dioxins show a correlation between percent body fat and the elimination rate of dioxins, with longer half

- lives for dioxins in individuals with a higher proportion of fat in their bodies (ATSDR 1998). In the context of a screening model, however, EPA recommends a default value for the fraction of a mother’s body comprised of fat of 0.3 based on data and discussions presented by Smith (1987) and Sullivan et al. (1991) (EPA 1998). A fraction of 0.3 indicates that 30 percent of the mother’s body weight is fat, which is a health protective value (EPA 2001a). To esta blish a health protective screening scenario, the MIRC default value for f fm is 0.30

. Fraction of fat in mother’s breast milk ( f **mbm** )

. The C milkfat model ( Equation B

- 38 ) assumes that a constant fraction of breast milk is fat, even though there is evidence that indicates variation in the fat content of breast milk throughout lactation (Sim and McNeil 1992). Different studies suggest a fat content of breast milk in humans of between 1 and 5 percent (Jensen 1987, Schecter et al

. 1994 , Hong et al

. 1994, McLachlan 1993, Bates et al

. 1994, NAS 1991, Butte et al

. 1984, Maxwell and Burmaster 1993, EPA 2011a, Smith 1987, Sullivan et al . 1991). The MIRC default value for f mbm of 0.04 (i.e., 4 percent) is the value EPA recommended for MPE (E PA 1998).

Fraction of maternal weight that is plasma ( f **pm** ) . Altmann and Dittmer (1964) estimated that plasma volume for adult women ranged from 37 to 60 mL/kg of body weight and averaged about 45 mL/kg. Ueland (1976) observed that the average plasma vo lume of women 6 weeks postpartum was 45 mL/kg of body weight. Using a value of 1.026 for the specific gravity of plasma from Conley (1974), EPA estimated a value of 0.046 for the fraction of maternal weight that is plasma (EPA 1998). The MIRC default for f pm therefore is 0.046

. Infant breast milk ingestion rate ( IR **milk** ) . Milk ingestion rates vary with several factors, including the age and size of the infant and use of other foods such as formula. Based on its review of a several studies, EPA recommende d time

- weighted average and upper percentile milk ingestion rates for infants that nurse for six and for twelve months (EPA 2011a, Table 15

- 3). To estimate an “average” value, EPA first estimated study

- sample

- size weighted average values for 1 through 12 months of age and then developed time

- weighted average milk ingestion rates from those (EPA 2011a). EPA estimated an upper percentile (upper bound) value as the mean plus two standard deviations. MIRC converts the ingestion rates measured volumetrically (mL/day) to mass

- based estimates (kg/day) assuming the density of human milk to be 1.03 g/mL (reported by NAS 1991 and recommended by EPA 2011a). The resulting values are shown in the first two rows of Exhibit B

- 29 . The MIRC scr eening

- level default of 980 mL/day is an upper

bound estimate based on a one

- year nursing period.

Exhibit B

- 29 also includes the recommended values for four non

- overlapping age categories from the CSEFH (EPA 2008a, Table 15

- 1). The values demonstrate that although infants grow substantially from birth to one year of age, the “upper bound” estimates of their milk ingestion rates are very close to 1 liter per day at all stages of development in the first year.TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 86 October 2014 Description of MIRC Exhibit B

- 29

. Infant Breast Milk Intake Rates

Age Category Average (mL/d) Average (kg/d) “Upper Bound” (mL/d) “Upper Bound” (kg/d) Reference

1 to 6 months 742 0.764 1,033 1.064 EPA 2011a †

0 to < 12 months 688 0.709 980 a 1.01 a EPA 2011a †

0 to < 1 month 510 0.525 950 0.979 EPA 2008 a ††

1 to < 3 months 690 0.711 980 1.01 EPA 2008 a †

3 to < 6 months 770 0.793 1,000 1.03 EPA 2008 a †

6 to < 12 months 620 0.639 1,000 1.03 EPA 2008 a †

a MIRC defau lt ; † Based on review of multiple studies ; †† Based on a single study

Duration of breast feeding ( t **bf**

)

. This parameter is equal to the infant’s exposure duration ( ED ) and the infant’s averaging time ( AT ). In its MPE Methodology, EPA asserts a health pr otective value for the duration of breast feeding of 1 year (i.e., 365 days) and a central tendency estimate of 6 months (180 days) (EPA 1998). Reviewers of MPE noted that 365 days may be overly health protective, given that only 20 percent of infants are breast fed for 6 months, at which point alternative foods are introduced, at least in addition to breast milk (EPA 2001a). Nonetheless, to establish a health protective screening scenario, the MIRC default for t bf is 365 days.

Duration of the mother’s ex posure to the chemical of concern prior to nursing ( t **pn** )

. The model shown as Equation B

- 38 includes this parameter to reduce the over

- estimate of chemical concentration in milk fat that occurs if the model is applied to a chemica l with a long biological half

- life (e.g., many years). The factor is needed for applications of the model to scenarios with a brief exposure duration (e.g., beginning a few months prior to the start of nursing ) relative to the chemical half

- life. As the duration of an exposure scenario increases to me et and exceed the chemical half

- life, however, the overestimate that occurs without this parameter is reduced. For example, assume a chemical biological half

- life of 8 years and a nursing period of 1 year. If exposure of the mother starts at the beginning of nursing, using Equation B

- 38 without the t pn term results in an over

- estimate of the concentration of the chemical in breast milk by a factor of 28.1 compared with the predictio n using Equation B

- 38 with the t pn term (EPA 1998, Table 9

- 6). However, at longer pre

- natal exposures of the mother, the magnitude of the over

- estimate is reduced: for a 10

- year exposure, the magnitude of the overestimate without the t pn term is 2.28, and for a 30

- year exposure, the overestimate is reduced to 1.39.

For purposes of the screening

- level assessment , we assume an exposure duration equal to the MIRC default half

- life for dioxins , or 10 years

. Only 3

, 285 d ays of that period are pre

- natal (i.e., 3650 minus 365 days, assuming 1 year lactation period). Although longer exposure periods are possible for the screening scenario, there is sufficient uncertainty in the model to merit accepting a health protective bias for this parameter value.



### 6.5.2. Chemical

- Specific Parameter Values

The chemical

- specific parameters in the breast

- milk pathway in MIRC are listed in Exhibit B

- 30

. Note that the parameters for which values are needed are different for the lip ophilic chemicals (i.e., dioxins), for which lactational transfer is assumed to occur via milk fat, and inorganic chemicals, for which the transfer is assumed to occur via the aqueous phase of breast milk (i.e., mercury). All dioxin congeners were assumed to manifest identical values as TCDD in regard to breast milk

- related parameters.TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 87 October 2014 Description of MIRC Exhibit B

- 30

. Chemical

- specific Input Parameter Values for Breast Milk Exposure Pathway

Parameter and Description 2,3,7,8

- TCDD MeHg

AE inf Infant absorption efficiency of the chemical by the oral route of exposure (i.e., fraction of ingested chemical that is absorbed by the infant; unitless) 1.0 (default) 1.0 (default)

AE mat Maternal absorp tion efficiency of the chemical by the oral route of exposure (i.e., fraction of ingested chemical that is absorbed by the mother; unitless) 1.0 (default) 1.0 (default)

f bl Fraction of steady

- state total body burden of hydrophilic chemical in mother that is in the mother’s whole blood compartment (unitless) NA 0.059 (Kershaw et al

. 1980) a

f f Fraction of steady

- state lipophilic chemical body burden in mother that is stored in body fat (unitless) ≥0.90 (ATSDR 1992) NA

f pl Fraction of steady

- state total hyd rophilic chemical body burden in mother that is in the blood plasma compartment (unitless) NA Not yet identified b

h Biological half

- life for chemical in non

- lactating women (days) 3650 (EPA 1994c) 50 (Sherlock et al

. 1984)
k aq_elac Rate constant for tota l elimination of hydrophilic chemicals by lactating women (per day) NA = k elim

k elim Rate constant for elimination of chemical for non

lactating women (per day; related to chemical half

life) 1.9E

- 04 b 1.4E

- 02 c

k fat_elac Rate constant for total eliminati on of lipophilic chemicals by lactating women (per day) Est. using Equation B

- 41 NA

Pc bm Partition coefficient for hydrophilic chemical between maternal blood plasma and aqueous phase of breast milk (g milk/g plasma; model assump tion) NA 1.0 (model assumption)

Pc RBC Partition coefficient for hydrophilic or protein

- bound chemical between red blood cells (RBC) and plasma in maternal blood (mL whole blood/mL RBC) NA 40 (Hollins et al

. 1975)

NA = not applicable . ND = not yet deter mined from literature.

a This value is based on a single

- dose study and may not be appropriate for a chronic exposure model.

b An empirical value for this variable is currently missing for application of model.

c This value was calculated from biological h alf

- life ( h ) using Equation B

- 40

. Absorption efficiency of the chemical by the oral route of exposure for the infant ( AE **inf**

)

. The models included in MIRC assume that the AE inf from the lipid phase of breast milk is equal to th e AE inf from the aqueous phase of the milk. Reviewers of the model stated that this assumption may not be valid and that ideally, the equation DAI inf would include variables for the AE inf from the breast milk fat and the AE inf from the aqueous phase of br east milk (EPA 2001a). However, since the MIRC assumption is that chemicals will partition to either the lipid or aqueous phase of milk, it is not necessary at this time to have multiple AE inf values for a given chemical. If data on the AE from the mothe r or an adult but not for the infant are available, data for the adult may be used for AE inf

. Reviewers also recommended that chemical

- specific values come from studies that account for absorption of the chemical from milk, because absorption from other m atrices (e.g., solid foods) may not be relevant (EPA 2001a). If chemical

- specific data are not available for adults or infants, a health protective default value for AE inf for a screening level assessment is 1 .0, which assumes 100 percent absorption (EPA 1998).TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 88 October 2014 Description of MIRC The default value for AE inf in MIRC for both MeHg and dioxin is 1.0

. For ingested lipophilic chemicals, it is reasonable to assume that absorption will be high ( EPA 2004c). ATSDR (1998) reported that dioxins are well absorbed by the oral route o f exposure, with one human experiment indicating more than 86 percent absorption. It is EPA policy to assume 100 percent absorption for chemicals with reported AEs of 50 percent or higher (EPA 2004c). MeHg also is well absorbed, with measured values as h igh as 95 percent, and so a value of 100 percent is used in MIRC (EPA 2001b).

Absorption efficiency of the chemical by the oral route of exposure for the mother ( AE **mat** )

. The default value for bo th dioxins and MeHg is 1.0, as described in the previous para graph.

Fraction of total maternal chemical body burden that is in the whole blood ( f **bl**

)

. The default value fo r MeHg in MIRC, 0.059, is fro m Kershaw et al

. (1980), which reported kinetics of blood deposition and clearance of MeHg in humans

. Individuals consumed one meal of fish that contained between 18 and 22 μg Hg/kg body weight

. The fraction of the dose deposited in the blood volume after mercury was fully distributed in tissues was 5.9 percent or 0.059. This study used a single

- dose and thus may not be appropriate for a chronic exposure analysis.

Fraction of total maternal chemical body burden that is in body fat ( f **f** )

. Based on ATSDR’s Toxicological Profile for Selected PCBs (ATSDR 1992) and Sullivan et al . (1991), EPA concluded that the “fraction o f ingested contaminant stored in fat may be >90%” for lipophilic chemicals such as PCBs and dioxins (EPA 1998). This statement was interpreted to mean that 90 percent of the maternal body burden of chemical at “steady state” is located in body fat for dio xins at steady state.

Fraction of total maternal chemical body burden that is in blood plasma ( f **pl**

)

. For hydrophilic chemicals, this parameter represents the steady

- state fraction of the total chemical in the body that is circulating in the blood plasma . Values for f pl may be available for some chemicals in the scientific literature. No value for this parameter for methyl mercury has been identified from the literature at this time. A value can be calculated using Equation B

- 43 . However , this equation requires a reliable value for f bl , and the value found for mercury may not be appropriate for a chronic exposure analysis (see above).

Chemical half

- life in non

- lactating women ( h ) . In general, highly lipophilic chemicals tend t o have relatively long biological half

- lives. EPA estimates that the half

- life for dioxins is between 7 and 10 years (EPA 1994a). ATSDR estimates that the half

- life for 2,3,7,8

- TCDD in particular may be as long as 12 years (ATSDR 1998)

. To establish a h ealth protective screening scenario, the MIRC default half

- life for dioxins is set to 10 years or 3650 days

.
The half

- life for methylmercury is on the order of weeks, not years

. Greenwood et al

. (1978) measured blood clearance rates for MeHg in lactatin g Iraqi women exposed accidentally to MeHg via bread prepared from wheat treated with a fungicide that contained MeHg

. The data indicated a mean half

- life for MeHg of approximately 42 days

. Sherlock et al . (1984) reported

an average measured half

- l ife fo r MeHg of 50 days with a range of 42

- 70 days . The MIRC default for MeHg is set to the longer average half

- life of 50 days.

Chemical elimination rate constant for lactating women

– aqueous ( k **aq_elac** )

. The parameter k

aq_elac is equal to k elim plus the loss rate for the chemical in the aqueous phase of breast

- milk during lactation. EPA has yet to propose a term for the additional elimination of a chemical in the aqueous phase of milk from breast feeding. In the absence of empirical values, a reasonable ass umption for water soluble chemicals is that k aq_elac is equal to k elim as discussed for Equation B

- 43 . The extent to which k elim is an underestimate of k aq_elac for a given chemical will determine the extent of health protective bias in k aq_elac
.TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 89 October 2014 Description of MIRC Chemical elimination rate constant for non

- lactating women ( k e **lim** ). Although values for this parameter often are reported directly in the literature, MIRC estimates k elim from chemical half

life assuming first

- order kinetics as shown i n Equation B

- 40 . For example, for a biological half

life of 3,650 days for dioxins, k elim is estimated to be 1.9E

- 04 per day. Assuming a biological half

- life of 50 days for MeHg, the value for k elim is estimated to be 0.014 per day

. Rate constant for total elimination of lipophilic chemicals by lactating women ( k **fat_elac** ). Although values for this parameter might be found in the scientific literature for some chemicals, in MIRC, k

fat_elac for dioxins is calculated from Equation B

- 41 . When the parameters in that equation are set to the default values in MIRC for dioxins, MIRC estimates a value of 0.0015 per day for k

fat_elac
. Partition coefficient for chemical between maternal blood plasma and aqueous ph ase of breast milk ( Pc **bm** ). The aqueous model, presented in Equation B

- 42 , assumes that the concentrations in the plasma and aqueous phase of breast milk are directly proportional (EPA 1998). Therefore, the default value for this parameter for MeHg in MIRC is 1.0.

Partition coefficient for chemical between red blood cells and plasma in maternal blood ( Pc **RBC** )

. Chemical

- specific values for this parameter should be located in the scientific literature. If chemical

- specific values are unavailable and it is assumed that there is equal distribution of the chemical in the plasma and red blood cells, EPA suggests a default value of 1.0 ( EPA 1998)

. F or MeHg, MIRC includes a va lue of 40 b ased on Hollins et al . (1975) study of cats expos ed to MeHg, which reported a ratio of radio

- labeled mercury in red blood cells to plasma of 97.7 to 2.3 (i.e., ratio of 42.5).



## 7. Summary of MIRC Default Exposure Parameter Settings

T he default settings included in MIRC are intended to be characteristic of a health protective (but plausible) exposure scenario that results in a negligible or extremely low chance of underestimating risk. These default parameter values were used to derive the screening threshold emission rates used for screening emissions of P B

- HAPs from sources included in RTR risk assessments. These values are the default, or initial setting, for parameter values in MIRC as described in Section 6 of this attachment . This section summarizes the default parameter values used to calculate screening thresholds.

This chapter is organized to present the chemical

- and scenario

- specific inputs to MIRC by data type. The screening

- level analysis uses 90 th percentile ingestion rates for soil, breast milk, and farm food ite ms and 99 th percentile ingestion rates for fish (presented in Section 7.1 ) and population

- specific characteristic assumptions (presented in Section 7.2 ), that are generally health protective in natu re. Screening thresholds were derived for five RTR chemical species: benzo(a)pyrene, cadmium, mercuric chloride, methyl mercury, and 2,3,7,8

- TCDD; Section 7.3 presents chemical

- specific parameter inputs for these five chemical s. Finally, Section 7.4 presents default parameter values for the nursing infant exposure scenario, which applied only to dioxin and methyl mercury as discussed in Section 3.4

.


### 7.1. Default Ingestion Ra tes The screening

- level (or default ) values for ingestion rates for soil, breast milk, and for each farm food item are equal to the 90 th percentile of the distribution of national data for that ingestion medium. In general, these values were obtained from the 2011 Exposure Factors Handbook or the 2008 Child

- Specific Exposure Factors Handbook (see Exhibit B

- 31 ). Fish ingestion rates are also available from these sources; however, as described in Section 6.3.4 , these sources were not used to obtain fish ingestion rates.TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 90 October 2014 Description of MIRC 

### 7.1.1. Fish Ingestion Rates

Th e adult fish ingestion rate was obtained from Burger (2002) , a study that examined daily consumption of wild

- caught fish for high

- end recreationalists (white, bla ck and female) in South Carolina. For female high

- end consumers of wild

- caught fish, Burger identified average and higher

- percentile consumption rates as follows: 39.1 g/day (mean), 123 g/day (90 th percentile), 172 g/day (95 th percentile), and 373 g/day ( 99 th percentile). As shown in Exhibit B

- 31 and discussed in Section 6.3.4 , f or adults, the rate of fish ingestion assumed in the screening scenario is 373 g/day , which corresponds to the 99 th percenti le value estimated by Burger for adult females

. T his value was selected to be representative of subsistence fishers

. For the child age groups, as discussed in Section 6.3.4 , the baseline fish ingestion rates for the screening scenario are based on “as prepared” total freshwater/estuarine fish ingestion rates at the 99 th percentile of the distribution for the consumer

- only population (i.e., inclusive only of people who consume fish, rather than per

- captita rates, which include b oth consumers and non

consumers), as estimated in EPA (2002)

, Section 4 .2.1.1

. Some adjustments were necessary because the age groups evaluated for RTR (which correspond to the age groups for which farm food ingestion rates are available) do not all direct ly correspond to the age groups in the EPA (2002) report. As described in Section 6.3.4 , these adjustments convert the available age

specific data on fish ingestion rates to the age

- specific values needed for MIRC.

For the sc reening

- level fish ingestion exposure scenario, the consumer evaluated is an individual who regularly consumes a large amount of fish that he or she has caught locally over the course of a 70

- year lifetime. Modeled exposures are intended to encompass thos e of a subsistance fisher whose diet comprises a substantial proportion of fish. The scenario is not, however, intended to represent the maximum possible exposure an individual subsistence fisher might experience.

Although the fish ingestion rates pres ented here are representative of the 99 th percentile of the evaluated data set, the use of these inputs (compared with 90 th percentile values used for other food types) is not considered to be inconsistent. This is due to the idiosyncrasies of the survey d ata on fish consumption, the fact that the data sets for homegrown foods and fish are not parallel, and the consideration of rates appropriate for subsistence fishers, as described above.

As discussed above, EPA believes that use of these fish ingestion r ates strike s the appropriate balance between being health protective and having screening scenarios so conservative that they are of limited use in the decision making process. This high

- end fish ingestion rate is appropriate in the context of the conserva t ive screening scenario used in the RTR process and is applicable for national rulemakings given that it is very likely that subsistence woman fisher s of child bearing age are located throughout the United States. Using a high

- end subsistence fish ingestio n rate also is consistent with section 112 of the CAA, which focuses on risks associated with maximally exposed individuals.



### 7.1.2. Farm Food Chain Ingestion

The default settings assume that a ll food types are obtained from the area of chemical deposition specifi ed by the user (i.e., fraction of food from contaminated area = 1.0).

For estimates of screening threshold emission rates for PB

- HAPS, environmental concentrations and air deposition rates were estimated using TRIM.FaTE for the area of maximal deposition in the vicinity of a hypothetical facility, and thus represent risks estimated for a maximally exposed individual/farm/family.TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 91 October 2014 Description of MIRC Exhibit B

- 31 also includes a sum of the 90th percentile ingestion rates for homegrow n food categories and 99th percentile fish ingestion to show the implied total food ingestion rate associated with setting multiple food

- type

- specific ingestion rates at upper percentiles. Because these upper percentile values for each farm food category are likely to reflect different individuals, it is likely that addition of multiple upper percentile intake values will exceed the total food ingestion rates expected for the general population. This sum is shown on the third row from the bottom (Total Fo od : Homegrown Only).

The second row from the bottom presents the 90 th percentile of the distribution of individual total food ingestion rates from the USDA’s 1994

- 96 and 1998 Continuing Survey of Food Intakes by Individuals (CSFII) (USDA 2000) data sets, as analyzed by EPA (EPA 2005e). The total ingestion rate for the farming households (third row from bottom) takes into account the cooking losses typical of each food category to provide a better comparison with the 90 th percentile individual total food i ngestion rates from CSFII (which are based on consumption of prepared foods). The final row of Exhibit B

- 31 shows the likely magnitude of the overestimates by age category by presenting the ratio of the two preceding rows. The v alues in this row demonstrate the potential for overestimating intake by using upper percentile values for all food groups. This bias may be considered when evaluating the results estimated by MIRC.

Exhibit B

- 31 . Farm Food Category Ingestion Rates for Health Protective Screening Scenario for Farming Households

Product Screening

- Level Consumer Ingestion Rate Units Infants

<1 yr Child 1

– 2 yrs Child 3

– 5 yrs Child 6

– 11 yr s Child 12

– 19 yrs Adult 20

– 70 yrs

**Farm Food Item**
Beef a NA 9.49 8.83 11.4 3.53 4.41 g/kg

- day Dairy b NA 185 92.5 57.4 30.9 6.16 g/kg

- day Eggs a NA 4.90 3.06 1.90 1.30 1.31 g/kg

- day Exposed Fruit a NA 12.7 5.41 6.98 3.41 2.37 g/kg

- day Exposed Vegetable a NA 10.7 3.47 3.22 2.35 3.09 g/kg

- day
Pork a NA 4.90 4.83 3.72 3.69 2.23 g/kg

- day Poultry a NA 7.17 6.52 4.51 3.13 2.69 g/kg

- day
Protected Fruit a NA 44.8 32.0 23.3 7.44 15.1 g/kg

- day Protected Vegetable a NA 3.88 2.51 2.14 1.85 1.81 g/kg

- day Root Vegeta ble a NA 7.25 4.26 3.83 2.26 2.49 g/kg

- day
**Other**

Breast milk c 1.01 NA NA NA NA NA kg/day
Soil (dry) NA 200 d 200 d 201 e 201 e 201 e mg/day Fish (per individual) f NA 107.7 g 159.0 g 268.2 h 331.0 h 373 g/dayTRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 92 October 2014 Description of MIRC Exhibit B

- 31 . Farm Food Category Ingestion Rates for Health Protective Screening Scenario for Farming Households

Product Screening

- Level Consumer Ingestion Rate Units Infants

<1 yr Child 1

– 2 yrs Child 3

– 5 yrs Child 6

– 11 yr s Child 12

– 19 yrs Adult 20

– 70 yrs **Total Food Ingestion Rates for Comparison Only (not i n MIRC; excludes soil and water)**  Total Food: Homegrown only i NA 259 142 99 51 35.5 g/kg

- day

Total Food: All Sources j NA 125 91 61 34 23.7 g/kg

- day Overestimate (ratio of Homegrown/Total) NA 2.1 1.6 1.6 1.5 1.3 (unitless)

Sources: EPA 2011 a , EPA 2008 a , unless otherwise noted. NA = not applicable

a Primary source for values was the 1987

– 1988 NFCS survey; compiled results are presented in Chapter 13 of the 2011 Exposure Factors Handbook ( EPA, 2011 a ) . When data were unavailable for a particular age group, the intake rate for all age groups was multiplied by the age

- specific ratio of intake based on national population intake rates from CSFII.

b Primary source for values was 1987

– 1988 NFCS survey, compiled results are presented in Chapter 13 of the 2011 Exposure Factors Handbook ( EPA, 2011 a ) . When data were unav ailable for a particular age group, the intake rate for all age groups was multiplied by the age

- specific ratio of intake based on national population intake rates from an NHANES 2003

– 2006 analysis in Chapter 11 of the Exposure Factors Handbook.

c Infants a re assumed to consume only breast milk for one year.

d These values are the recommended “upper percentile” value for children from EPA’s 2011 EFH, Chapter 4, Table 4

- 23. The 2008 CSEFH and 2011 EFH included a high

- end value associated with pica only, but this value has not been used.

e These values are 90th percentile adult ingestion rates calculated in Stanek et al. 1997, and they are used to represent older children and adults.

f The ingestion rate for adults was obtained from Burger (2002) and is the 99 t h percentile value for adult females considered high

- end recreationists ; this value is believed to be representative of subsistence fishers . The 99 th percentile values for children were d erived based on EPA’s Estimated Per Capita Fish Consumption in the U nited States (2002)

— Section 4. 2 .1.1 Table 5 (for child age categories) adjusted and scaled. Values reflect “as prepared” ingestion rates.

g The fish ingestion rate for children aged 3

– 5 years was obtained directly from Section 4.2.1.1, Table 5 in the EPA (2002) report (value presented is rounded) ; for these children, the RTR age

- group range matches the EPA (2002) age category. Fish ingestion rates for children less than 3 years old, however, were not provided. Therefore, f or children aged 1

– 2 years, the f ish ingestion rate was calculated using the ingestion rate for children aged 3

– 5 years scaled downward by the ratio of the mean body weight of children aged 1

– 2 years to the mean body weight of children aged 3

– 5

- years.
h Time

- weighted average ingestion r ates were calculated using t he EPA (2002) fish ingestion estimates in order to adjust for the differences between the age group ranges used for the RTR screening and those presented in the 2002 EPA report.

i Sum of post

- cooking food ingestion rates. This e stimate is calculated by multiplying the food ingestion rates on previous rows (excluding soil and water) by (1

- L 1 )x(1

- L 2 ), where L 1 and L 2 are the loss rates from Exhibit B

- 25

. The rows are then summed to get the total post

cook ing ingestion rate. j 90 th percentile total food intake rates from EPA 2008a and 2005e based on CSFII data 1994

- 96 and 1998; see Section 6.3.6 of this document.



### 7.2. Default Screening

- Level Population

- Specific Parameter Values

The screening

- level values for body weights (BWs) for the RTR screening threshold analysis, which serve as the default values in MIRC, are mean values and are presented in Exhibit B

- 32

. As stated in Section 6 of this attachment

, EPA recommends using the mean BW for each age group when using upper percentile values for medium ingestion rates. Use of the mean body weights introduces no bias toward over

- or underestimating risk.

Exhibit B

- 32

. Mean Body Weight Estimates for Adults and Childre n a

Lifestage (years) Duration (years) Mean Body Weight (kg)
Adult b (20

- 70) 50 80.0
Child < 1 c 1 7.83
Child 1

- 2 c 2 12.6
Child 3

- 5 d 3 18.6TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 93 October 2014 Description of MIRC Exhibit B

- 32

. Mean Body Weight Estimates for Adults and Childre n a

Lifestage (years) Duration (years) Mean Body Weight (kg)
Child 6

- 11 e 6 36.0
Child 12

- 19 f 8 64.2

a Sources: EPA 1997, 2008a

b EPA

- recommended value (EPA 2011a).

d These values were obtained directly f rom Table 8

- 3 of the 2008 CSEFH.

e Each BW represents a time

- weighted average of BWs for age groups 6 to <1 1 years and 11 to <16 years from Table 8

- 3 of the 2008 CSEFH

. Original sample sizes for each of these age groups can also be found in Table 8

- 3.

f These values were calculated as time

- weighted average BW for age groups 11 to <16 years and 16 to <21 years f rom Table 8

- 3 of the 2008 CSEFH . The direction of the possible bias is unknown. The values match the estimate based on Table 8

- 22 of the NHANES IV data as presented by Portier et al. (2007).



### 7.3. Default Chemical

- Specific Parameter Values for Screening Anal ysis

Exhibit B

- 33 presents chemical

- specific parameter values for input to MIRC for the screening

level analysis. Values for bioavailability when ingested in soil ( Bs ), mammalian metabolism factors ( MF ), correction factors for b elowground produce ( VG rootveg ), wet deposition fractions ( Fw ), air to plant transfer factors ( Bv AG ), root concentration factors ( RCF ), and soil

- water partition coefficient ( Kds ) are presented in Exhibit B

- 33

.

Exhibit B

- 33 . Chemical

- Specific Parameter Values for Input to MIRC a

Parameter Description Benzo(a)

- pyrene Cadmium Mercuric chloride Methyl mercury 2,3,7,8

- TCDD Units

Bs Soil bioavailability factor for livestock 1 1 1 1 1 unitless

MF Mammalian metabolism factor 0.01 1 1 1 1 unitless

VGrootveg Empirical correction factor for belowground produce, i.e., tuber or root vegetable, to account for possible overestimate of the transfer of chemicals f rom the outside to the inside of bulky tubers or roots (based on carrots and potatoes) 0.01 1 1 0.01 0.01 unitless

Fw Fraction of wet deposition that adheres to plant surfaces; 0.2 for anions, 0.6 for cations and most organics 0.6 0.6 0.6 0.6 0.6 unitlessTRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 94 October 2014 Description of MIRC Exhibit B

- 33 . Chemical

- Specific Parameter Values for Input to MIRC a

Parameter Description Benzo(a)

- pyrene Cadmium Mercuric chloride Methyl mercury 2,3,7,8

- TCDD Units

BvAG Air

- to

- plant biotransfer factor for aboveground produce for vapor

phase chemical in air 174,523 0 1,800 0 65,500 [mg/g produce DW]/[mg/g air]

RCF Chemical

- specific root concentration factor for tubers and root produce 9,180 0 0 0 40,002 L soil pore water/kg root WW

Kds Chemical

- specific soil/water partition coefficient 7,750 75 58,000 7,000 31,126 L soil pore water/kg soil DW

a Values presented in this exhibit are also presented in previous exhibits; however exact values used in the assessment are presented here, rather than values restricted by significant figures. In addition, only values for those chemicals that are specifically used in the screening assessment are provided here.

Only single estimates were developed for each of these parameter s for HHRAP (EPA 2005a), and the potential direction and magnitude of bias toward over

- or underestimating risks were not investigated in this assessment . The inputs that are both chemical

- specific and plant

- type

specific, as presented in Exhibit B

- 11 , are not repeated here. Again, only single estimates were developed for these parameters and the potential direction and magnitude of bias toward over

or underestimating risks were not investigated. Finally, Exhibit B

- 34 presents biotransfer factors for each of the chemicals and animal types for which screening threshold emissions were calculated.

Exhibit B

- 34 . Chemical and Animal

- Type Specific Biotransfer Factor (Ba) Values for Input to MIRC
([mg chemical/kg WW tissue or dairy] / [mg chemical intake/day] = day/kg WW tissue or dairy)
Chemical Beef Dairy Pork Eggs Poultry Benzo(a)pyrene 3.8E

- 02 8.0E

- 03 4.6E

- 02 1.6E

- 02 2.8E

- 02
Cadmium 1.2E

- 04 6.5E

- 06 1.9E

- 04 2.5E

- 03 1.1E

- 01
Mercuric c hloride 1.1E

- 04 1.4E

- 06 3.4E

- 05 2.4E

- 02 2.4E

- 02 Methyl m ercury 1.2E

- 03 1.7E

- 05 5.1E

- 06 3.6E

- 03 3.6E

- 03 2,3,7,8

- TCDD 3.6E

- 02 7.7E

- 03 4.4E

- 02 1.5E

- 02 2.7E

- 02



### 7.4. Screening

- Level Parameter Values for Nursing Infant Exposure MIRC is configured to evaluate risk to nursing infants exposed to dioxins and to methylmercury (MeHg) in their mother’s milk for a family farming and catching fish in the area of maximal air deposition of chemical. Input valu es were summarized in Section 6.5 of this attachment

.


### 7.4.1. Dioxins For dioxins, chemical intake via breast milk by nursing infants was estimated using the model presented in EPA’s Methodology for Assessing Health Risks Associated with Multiple Pathways of Exposure to Combustor Emissions (MPE, EPA 199 8). The assumption that lactational transfer of dioxins to the infant occurs via the lipid

- phase of milk appears reasonable. TheTRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 95 October 2014 Description of MIRC following screening

- level assumptions used in that mod el should bias the results toward health

protective estimates of risks.

• Duration of nursing is a full year and no other foods or liquids are consumed by the infant; a more “typical” value would be six months.

• Absorption efficiency of dioxin in food or milk by mother and infant are 100 percent; this assumption might overestimate absorption but probably by no more than 15 percent (see Section 6.5.2 ).

• The fat content of human milk is assumed to be 4 percent, a value toward the high end of the reported range of values (1 to 5 percent).

• The maternal chemical intake is estimated using upper percentile ingestion rates for the different homegrown foods (see discussion for Exhibit B

- 31 ); this assumption might

ove restimate total ingestion of homegrown foods by a factor of more than 2 (see Exhibit B

- 31 )

.

• If the fraction of the maternal body burden of dioxin that is in the body fat compartment is greater than 90 percent, as suggested by ATSD R (1998), then actual exposures of the infant may be less than estimated.

There also are parameter values and model assumptions for the lipid

- phase breast

- milk pathway for which possible bias is unknown.

• The accuracy of the model is unknown; it has not bee n verified or validated with empirical data.

• Using a half

- life of 10 years for dioxins may over

- or under

- estimate risks.

Finally, there is one assumption that might possibly introduce some bias toward underestimating risks. The model results are sensitiv e to the biological half

- life of the chemical in the mother relative to the length of her exposure prior to the lactation period. Using an exposure duration for the mother equal to the assumed half

- life for dioxins, 10 years, may underestimate the duratio n of exposure of the mother.



### 7.4.2. Methyl Mercury For MeHg, empirical data from a single human study (Fujita and Takabatake 1977) was used in conjunction with a physiologically based pharmacokinetic (PBPK) model of lactational transfer of MeHg developed and part ially validated by Byczkowski and Lipscomb (2001) to support a very simple predictive model

. Both the human data and the PBPK model indicated that for relatively low MeHg exposures, the concentration of MeHg in the nursing infant’s blood is similar to its concentration in the mother ’ s blood

. The PBPK model suggested in addition that the average daily dose of MeHg absorbed from milk by the nursing infant ( DAI inf

) is indistinguishable from the dose of MeHg absorbed by its mother from her food ( DAI mat )

. The data are limited, and the model includes various assumptions; however, there is no known directional bias in the estimates

.TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 96 October 2014 Description of MIRC 

## 8. References
Altman, P.L., and D.S. Dittmer, Eds. 1964. Biology Data Book. Volume 1. Bethesda, MD. 263

- 264 (As cited in EPA 1998).

Amin

- Zaki, L., S. Elhassani, M.A. Majeed, T.W. Clarkson, R.A. Doherty, M.R. Greenwood, and T. Giovanoli

- Jakubczak. 1976. Perinatal methylmercury poisoning in Iraq. Am. J. Diseases in Children 130: 1070

- 1076 (As cited in Byczkowski and Lipscomb 2001).

ATSDR (Agency for Toxic Substances and Disease Registry) . 1992. Toxicological profile for

s elected PCBs (Arochlor

- 1260, 1254, 124 8, 1242, 1232, 1221, and 1016). Atlanta, GA: U.S

. Department of Health and Human Services, Public Health Service (As cited in EPA 1 998 )

.
ATSDR

. 1998

. Toxicological profile for chlorinated dibenzo

- p

- dioxins

. Atlanta, GA: U.S

. Department of Health and Human Services, Public Health Service.

Bacci E., M. Cerejeira, C. Gaggi, G. Chemello, D. Calamari, and M. Vighi. 1992. Chlorinated dioxin s: Volatilization from soils and b i oconcentration in plant leaves. Bull. Environ. Contam. Toxicol. 48: 401

- 408.

Baes, C.F., R.D. Sharp, A.L. Sjoreen, and R.W. Shor. 1984. Review and analysis of parameters and assessing transport of environmentally released radionuclides through a griculture. ORNL

- 5786. Oak Ridge National Laboratory. Oak Ridge, Tennessee. September.

Bates, M.N., D.S. Hannah, S.J. Buckland, J.A. Taucher, and T. van Mannen. 1994. Chlorinated organic contaminants in breast milk of New Zealand wo men . Environmental Health Perspectives 102(Supplement 1): 211

- 217.

Belcher , G.D., and C.C. Travis. 1989. Modeling s upport for the RURA and municipal waste combustion projects: Final report on sensitivity and uncertainty analysis for the terrestrial food ch ain m odel

. Interagency Agreement No. 1824

- A020

- A1, Office of Risk Analysis, Health and Safety Research Division, Oak Ridge National Laboratory. Oak Ridge, Tennessee. October.

Boone, F.W., Y.C. Ng, and J.M. Palm. 1981. Te rrestrial p athway s of radionuclide p articulates. Health Physics 41:735

- 747.
Briggs, G.G., R.H. B romilow, and A.A. Evans. 1982. Relationships b etween lipophilicity and r oot

u ptake and translocation of non

- ionized chemicals by barley. Pesticide Science 13: 495

- 504 (As cited in EPA 2005a, Appen dix A

- 2).

Burger J. 2002 . Daily consumption of wild fish and game: Exposures of high end recreationalists. International Journal of Environmental Health Research 12 : 343

- 354

. Butte, N.F., C. Garza, E.O. Smith, and B.L. Nichols. 1984. Human milk intake and g rowth in exclusively breast

- fed infants. The Journal of Pediatrics 104(2):187

- 195.
Byczkowski, J.Z., and J.C. Lipscomb. 2001. Physiologically based pharmacokinetic modeling of the lactational transfer of methylmercury. Risk Analysis 21(5): 869

- 882.

Chamber lain, A.C. 1970. Interception and retention of r adio active aerosols by vegetation. Atmospheric Environment 4: 57

- 78.TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 97 October 2014 Description of MIRC Clewell, H.J, J.M. Gearhart, R.P. Gentry, T.R Covington, C.B. VanLandingham, C.B. Crump, and A.M. Shipp. 1999. Evaluation of the uncertaint y in an oral reference dose for methylmercury due to interindividual variability in pharmacokinetics. Risk Analysis 19: 547

- 558 (As cited in Byczkowski and Lipscomb 2001).

Conley, C.L. 1974. The blood. Medical Physiology 13 ( 2 ). V.B. Moutcasle, Ed. 1997. Th e C.V. Mosby Company, St. Louis, MO. 1027

- 1046 (As cited in EPA 1998).

Ensminger, M.E. 1980. Poultry Science

. Interstate Printers and Publishers, Inc. Danville, Illinois.

Fries, G. F. 1982. Potential polychlorinated biphenyl residues in animal products fr om application of contaminated sewage sludge to land. J. Environ. Qual. 11:14.

Fri es, G.F. 1994. Personal c ommuni cation b etween G.F. Fries, U.S. Department of Agriculture, and Glenn Rice and Jennifer Windholtz, U.S. Environmental Protection Agency, Offi ce of Research and Development. Agricultural Research Service. March.

Fujita, M., and E. Takabatake. 1977. Mercury levels in human maternal and neonatal blood, hair and milk. Bull. Environ. Contam. Toxicol. 18(2): 205

- 209.

Gearhart, J.M., H.J. Clewell III, K. S. Crump, A.M. Shipp, and A. Silvers. 1995. Pharmacokinetic dose estimates of mercury in children and dose

- response curves of performance tests in a large epidemiological study. Water, Air, Soil Pollut. 80: 49

- 58 (As cited in Byczkowski and Lipscomb 2001).

Gearhart, J., T. Covington, and H. Clewell III. 1996. Application of a physiologically based pharmacokinetic model for MeHg in a dose reconstruction of the Iraqi accidental exposures. Paper presented at the Fourth International Conference on Mercury as a Global Pollutant, Congress Cen tre, Hamburg, Germany; August (As cited in Byczkowski and Lipscomb 2001).

Greenwood, M.R., T.W. Clarkson, R.A. Doherty, A.H. Gates, L. Amin

- Zaki, S. Elhassani, and M.A. Majeed. 1978. Blood clearance half

- times in lactating and nonlactating members of a population exposed to mercury. Environ. Res. 16: 48

- 54.

Harrison, K.A. 1967. Blood volume changes in severe anemia in pregnancy. Lancet 1(7480):20

- 25.

Hofelt, C.S., M. Honeycutt, J.T. McCoy, and L.C. Haws. 2001. Development of a metabolism factor for polycyclic aromatic hydrocarbons for use in multipathway risk assessments of hazardous waste combustion facilities. Reg. Toxicol. Pharmacol . 33:60

- 65.

Hoffman, F.O., K.M. Thiessen, M.L. F rank, and B.G. Blaylock. 1992. Quantification o f the interception and initial retention of radioactive contaminants deposited on pasture grass by simulated rain. Atmospheric Environ. 26 a( 18 ) : 3313

- 3321

. Hollins, J.G., R.F. Willes, F.R. Bryce, S.M. Charbonneau, and I.C. Munro. 1975. The whole body reten tion and tissue distribution of Hg methylmercury in adult cats. Toxicol. App. Pharmacol. 33: 438

- 449.
Hong, C.S., J. Xiao, A.C. Casey, B. Bush, E.F. Fitzgerald, and S.A. Hwang. 1994. Mono

- ortho and non

- ortho

- substituted polychlorinated biphenyls in human milk from Mohawk and control women: Effects of maternal factors and previous lactation. Arch. Environ. Contam. Toxicol. 27(3): 431

- 437.TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 98 October 2014 Description of MIRC Jensen, A.A. 1987. Polychlorinated biphenyls (PCBs), polychlorodibenzo

- p

- dioxins (PCDDs) and polychlorodibenzofurans (PC DFs) in human milk, blood and adipose tissue. Sci. Total Environ. 64(3): 259

- 293.

Kahn, H.

, and K. Stralka. 2008. Estimated daily average per capita water ingestion by child and adult age categories based on USDA’s 1994

- 96 and 1998 continuing survey of foo d intakes individuals. J. Expo. Anal. Environ. Epidemiol. 1

- 9.

Kershaw

, T

. G

. , T

. W

. Clarkson

, and P

. H

. Dhahir

. 1980. The relationship between blood levels and dose of methylmercury in man. Arch. Environ. Health 35(1): 28

- 36.

Lorber, M. 1995 . Development of an air

- to

- plant vapor phase t ransfer for d ioxins and f urans. Presented at the 15th I nternational Symposium on Chlorinate d Dioxins and Related Compounds

. August 21

- 25, 1995 in Edmonton, Canada. Abstract in Organohalogen Compounds 24:179

- 186

. Lorber, M., and P. Pinsky. 2000. An evaluation of three empirical air

- to

- leaf models for polychlori nated dibenzo

- p

- dioxins and dibenzofurans. Chemosphere 41(6):931

- 41.

Maxwell, N.I., and D.E. Burmaster. 1993. A simulation model to estimate a distribution of lipid intake from breast milk during the first year of life. Journal of Exposure Analysis and Env ironmental Epidemiology 3(4): 383

- 406.
McLachlan, M.S. 1993. Digestive tract absorption of polychlorinated dibenzo

- p

- dioxins, dibenzofurans and biphenyls in a nursing infant. Toxicology and Applied Pharmacology 123(1): 68

- 72.

Miller, C.W., and F.O. Hoffma n. 1983. An examination of the environmental half

- time for radionuclides deposited on vegetation. Health Physics 45(3): 731

- 744.
Morgan, J.N., M.R. Berry, and R.L. Graves. 1997. Effects of Commonly Used Cooking Practices on Total Mercury Concentration in F ish and Their Impact on Exposure Assessments. Journal of Exposure Analysis and Environmental Epidemiology 7(1):119

- 133.

NAS (National Academy of Sciences)

. 1987. Predicting Feed intake of Food

- Producing A nimals

. National Research Council, Committee on Anim al Nutrition, Washington, D.C.

NAS. 1991. Nutrition During L actation . National Academies Press. Washington, DC.

NC DEHNR ( North Carolina Department of Health, Envi ronment, and Natural Resources)

. 1997. North Carolina Protocol for Performing Indirect Expos ure Risk Assessments for Hazardous Waste Combustion Units

. January

. Portier K., J. Tolson, and S. Roberts. 2007. Body weight distributions for risk assessment. Risk Anal. 27(1) : 11

- 26

. Reinert, RE; Stewart, D; Seagran, HL. 1972. Effects of dressing and coo king on DDT concentrations in certain fish from Lake Michigan. Journal of Fisheries Research Board of Canada. 29(5): 525

- 529.

RTI. 2005. Methodology for predicting cattle biotransfer f actors. Prepared for U.S. Environmental Protection Agency (EPA) Office o f Solid Waste. EPA Contract No. 68

- W

- 03

- 042. September. Available at: http://www.epa.gov/waste/hazard/tsd/td/combust/ finalmact/ssra/btfreportfull05.pdf

.TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 99 October 2014 Description of MIRC Schecter, A., P. Fürst, C. Fürst, O. Päpke, M. Ball, J. Ryan, H. Cau, L. Dai, H. Quynh, H.Q. Cuong, N. Phuong, P. Phiet, A. Beim, J. Constable, J. Startin, M. Samedy, and Y. Seng. 1994. Chlorinated dioxins and dibenzofurans in human tissue from general pop ulations: A selective review. Environmental Health Perspectives 102(Supplement 1): 159

- 171.
Sherlock, J., D. Hislop, G. Newton, G. Topping, and K. Whittle. 1984. Elevation of mercury in human blood from controlled ingestion of methylmercury in fish. Human Toxicology 3: 117

- 131 (As cited in USFDA 2009).

Shilling, F., White, A. Lippert, L, and M. Lubell. 2010. Contaminated fish consumption in California's Central Valley Delta. Environmental Research 110: 334

- 344.

Shor, R.W., C.F. Baes, and R.D. Sharp. 1982. Agricultural p roduction in the United States by

c ount y: A compilation of information from the 1974 census of agriculture for use in terrestrial food

- chain transport and assessment m odels

. Oak Ridge National Laboratory Publication. ORNL

- 5786

Sim, M.R. and J .J. McNeil. 1992. Monitoring chemical exposure using breast milk: A methodological review. American Journal of Epidemiology 136(1): 1

- 11.

Smith, A.H. 1987. Infant exposure assessment for breast milk dioxins and furans derived from waste incineration emissi ons. Risk Analysis 7:347

- 353.

Stanek, E.J., E.J. Calabrese, R. Barnes, P. Pekow. 1997. Soil ingestion in adults

– results of a second pilot study. Toxicol. Environ. Safety 36:249

- 257.

Steinbeck, A.W. 1954. Plasma and blood volumes of normal Australian fema les. Australian Journal of Experimental Biology and Medical Science 32(1): 95

- 9.

Stephens, R.D., M. Pe treas, and G.H. Hayward. 1995. Biotransfer and bioaccumulation of dioxins and furans from soil: Chickens as a model for foraging animals. Science Total En vironment 175: 253

- 273. July 20

. Sullivan, M.J., S.R. Custance, and C.F. Miller. 1991. Infant exposure to dioxin in mother’s milk resulting from maternal ingestion of contaminated fish. Chemosphere 23(8

- 10): 1387

- 1396

. Thomann, R.V. 1989. Bioaccumulation model of organic

- chemical distribution i n aquatic food

chains. Environ. Sci. Technol

. 23(6): 699

- 707.

Travis, C.C.

, and A.D. Arms. 1988. Bioconcentration of organics in beef, milk, and v egetation. Environ. Sci. Technol. 22:271

- 274.

Travis, C.C., H.A. Hatte mer

- Frey and A.A. Arms. 1988. Relationship between dietary intake of organic chemicals and their concentrations in human adipose tissue and breast milk. Arch. Environ. Contam. Toxicol. 17:473

- 478.

Ueland, K. 1976. Maternal cardiovascular dynamics. VII. Mat ernal cardiovascular dynamics. Intrapartum blood volume changes. Am. J. Obstetrics Gynecol

. 126(6): 671

- 677.
USDA (U.S. Department of Agriculture). 1992. Changes in Food Consumption and E xpe nditures in American H ouseholds during the 1980’s. USDA, Washingto n, D.C. Statistical Bulletin o. 849. (As cited in EPA 1997)TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 100 October 2014 Description of MIRC USDA. 1993. Food and Nutrient Intakes by I ndividuals in the United States, 1 Day, 1987

- 88. Nationwide Food Consumption Survey 1987

- 88, NFCS Report No. 87

- I

- 1. (As cited in EPA 1997)

USDA. 1994a. F ood Consumption and Dietary Levels of H ouseholds in the United States, 1987

- 88. Agricultural Research Service, Report No. 87

- H

- 1. (As cited in EPA 1997)

USDA . 1994b. Vegetables 1993 Summary . National Agricultural Statistics Service, Agricultural Statistics Board. Washington, D.C. Vg 1

- 2 (94). Jan.

USDA . 1994c. Noncitrus Fruits and Nuts 1993 Summary . National Agricultural Statistics Service, Agricultural Statistics Board, Washington, D.C. Fr Nt 1

- 3 (94).

USDA

. 2000. 1994

– 96, 1998 Continuing Survey of Food In takes by Individuals (CSFII) . CD

ROM. Agricultural Research Service, Beltsville Human Nutrition Research Center, Beltsville, MD. Available from the National Technical Information Service, Springfield, VA, Accession Number PB

- 2000500027. (As cited in EPA 20 08a, Chapter 14)

EPA (U S. E nvironmental Protection Agency)

. 1990. Interim Final Methodology for Assessing Health Risks Associated with Indirect Exposure to Combustor Emissions

. Environmental Criteria and Assessment Office. ORD.EPA

- 600

- 90

- 003. January.

EPA

. 1992. Technical Support Document for the Land Application of Sewage Sludge : Volumes I and II. EPA 822/R

- 93

- 001a. Office of Water. Washington, D.C.

EPA

. 1994a. Revised Draft Guidance for Performing Screening Level Risk Analysis at Combustion Facilities Bur ning Hazardous Wastes. Attachment C, Draft Exposure Assessment Guidance for RCRA Hazardous Waste Combustion Facilities. Office of Emergency and Remedial Response. Office of Solid Waste. December 14.

EPA

. 1994b. Estimating Ex posure to Dioxin

- Like Compounds. Volume II: Properties, Sources, Occurrence, and Background Exposures. External Review Draft. Office of Research and Development. Washington, DC. EPA/600/6

- 88/005Cc. June.

EPA

. 1994c. Estimating Exposure to Dioxin

- Like Compounds. External Review Draft. Off ice of Research and Development, Washington, D.C. EPA/600/6

- 88/005Cb. June. Available at: http://oaspub.epa.gov/eims/eimscomm.getfile?p_download_id=438673

.
EPA

. 1995a. Review Draft Development of Human Health

- Based and Ecologically

- Based Exit Criteria for the Hazardous Waste Identification Project

. Volumes I and II. Office of Solid Waste. March 3.

EPA

. 1995b. Memorandum Regarding Further Studies for Modeling the Indirect Expos ure Impacts from Combustor Emissions

. From Mathew Lorber, Exposure Assessment Group, and Glenn Rice, Indirect Exposure Team, Environmental Criteria and Assessment Office. Washington, D.C. January 20.

EPA

. 1995c. Further Issues for Modeling the Indirect Exp osure Impacts from Combustor Emissions

. O ffice of Research and Development

. Washington, D.C. January 20.

EPA

. 1995d. Waste Technologies Industries Screening Human Health Risk Assessment (SHHRA): Evaluation of Potential Risk from Exposure to Routine Operat ing Emissions

. Volume V. External Review Draft. U.S. EPA Region 5, Chicago, Illinois.TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 101 October 2014 Description of MIRC EPA. 1996. Soil Screening Guidance: User’s Guide. Office of Solid Waste and Emergency Response, Washington D.C. EPA/540/R

- 96/018, April 1996.

EPA

. 1997a. Exp osure Factor s Handbook. Volumes I, II, and III. Office of Research and Development, Washington, D.C. EPA

- 600

- P

- 95

- 002Fa,b,c. August. Available at: http://www.epa.gov/nceawww1/efh/

.
EPA

. 1997b. M ercury Study Report to Co ngress. Volume III: Fate and Transport of Mercury in the Environment. O ffice of Air Quality Planning and Standards and Office of Research and Development. E PA

- 452/R

- 97

- 005. December.

EPA

. 1997c. Parameter Guidance Document. National Center for Environment al Assessment, NCEA

- 0238

.
EPA

. 1998. Methodology for Assessing Health Risks Associated with Multiple Pathways of Exposure to Combustor Emissions. National Center for Environmental Assessment, Cincinnati, OH. EPA

- 600

- R

- 98

- 137. Available at: http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=55525

.
EPA. 1999. 1999 National

- Scale Air Toxics Assessment Results; Approach for Modeling POM

. Available at: http://www.epa.gov/ttn/atw/nata1999/nsata99.html

.
EPA

. 2001 a

. Peer Review of EPA’s Hazardous Waste Identification Rule Risk Assessment Model: Breast milk exposure model for the HWIR 3MRA Model . Prepared by Eastern Research Gro up for EPA Office of Solid Wastes. 68

- W5

- 0057.

EPA

. 2001b. Water Quality Criterion for the Protection of Human Health: Methylmercury. Office of Water, Office of Science and Technology. Washington D.C. EPA

- 823

- R

- 01

- 001. January. Available at: http://www.epa.gov/ncea/raf/pdfs/chem_mix/chem_mix_08_2001.pdf

.
EPA

. 2002. Estimated Per capita Fish Consumption in the United States. Office of Water, Of fice of Science and Technology, W ashi ngton, D

. C. EPA

- 821

- C

- 02

- 003. August. Available at: http://www.epa.gov/waterscience/fish/files/consumption_report.pdf

.
EPA

. 2003a. Chapter 10 In: Multimedia, Multipathway, and Multireceptor Risk Assessment (3MRA) Modeling System, Volume II: Site

- based, Regional, and National Data. SAB Review Draft. EP

- 530/D

- 03

- 001b. Office of Research and Development, Athens, GA, and Research Triangle Park, NC, and Office of Solid Waste, Wa shington, D.C. July. Available at: http://www.epa.gov/osw/hazard/wastetypes/wasteid/hwirwste/risk03.htm

.
EPA

. 2003b. Methodology for Deriving Ambient Water Quality Criteri a for the Protection of Human Health (20 00): Technical Support Document. Volume 2: Development of National Bioaccumulation Factors

. Office of Water, Office of Science and Technology, Washington, D.C. EPA

- 822

- R

- 03

- 030. December. Available at: http://www.epa.gov/waterscience/criteria/humanhealth/method/tsdvol2.pdf

.
EPA

. 2003c. CSFII Analysis of Food Intake Distributions. Office of Research and Development, National Center for Environmental Assessment, Washington, D.C. EPA

- 600

- R

- 03

- 29. Available at: http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=56610

.
EPA

. 2004. Estimated Per capita Water Ingesti on and Body Weight in the United States

– An Update. Office of Water, Office of Science and Technology, Washington, D.C. EPA

- 822

- R

-TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 102 October 2014 Description of MIRC 00

- 001. October. Available at: http://www .epa.gov/waterscience/criteria/drinking/percapita/2004.pdf

EPA

. 2004c. Risk Assessment Guidance for Superfund. Volume I: Human Health Evaluation Manual (Part E, Supplemental Guidance for Dermal Risk Assessment), Final . Office of Superfund Remediation and Technology Innovation, Washington, D.C. EPA/540/R/99/005; OSWER 9285.7

- 02EP; NTIS PB99

- 963312. July.

EPA

. 2005a. Human Health Risk Assessment Protocol for Hazardou s Waste Combustion Facilities. Office of Solid Waste and Emergency Response, Washington, DC. EPA

- 530

- R

05

- 006. September. Available at: http://www.epa.gov/osw/hazard/tsd/td/combust/risk.htm

.
EPA

. 2005b. Guidance on Selecting Age Groups for Monitoring and Assessing Childhood Expo sures to Environmental Contaminants. Risk Assessment Forum. Washington, DC. November. EPA/630/P

- 03/003F. Available at: http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=146583

.
EP A

. 2005c. Guidelines for Carcinogen Risk Assessment. Risk Assessment Forum, Washington, DC. EPA/630/P

- 03/001F. March. Available fr om: http://www.epa.gov/IRIS/cancer032505

- final.pdf

.
EPA

. 2005d. Supplemental Guidance for Assessing Susceptibility from Early

- Life Exposure to Carcinogens. Risk Assessment Forum: Washington, D.C. EPA

- 630/R

- 03

- 003F. March. Available at: http://www .epa.gov/ttn/atw/childrens_supplement_final.pdf

.
EPA

. 2005e. Analysis of Total Food Intake and Composition of Individual's Diet Based on the U.S. Department of Agriculture's 1994

- 96, 1998 Continuing Survey of Food Intakes By Individuals (CSFII) (Final). O ffice of Research and Development, National Center for Environmental Assessment, Washington, D.C. EPA/600/R

- 05/062F. Available at: http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid =132173

.
EPA

. 2007a. Prioritized Chronic Dose

- Response Values for Screening Risk Assessments (Table 1). Office of Air Quality Planning and Standards; June 12, 2007. Available at: http://www. epa.gov/ttn/atw/toxsource/summary.html

.
EPA

. 2007b. Toxicological Review of 1,1,1

- Trichloroethane (CAS No. 71

- 55

- 6) In Support of Summary Information on the Integrated Risk Information System (IRIS). Office of Research and Development, Washington, DC. EP A/635/R

- 03/006. August. Available at: http://www.epa.gov/iris

EPA

. 2008a. Child

- Specific Exposure Factors Handbook. Office of Research and Development, Washington, D

. C

. EPA/600/R

- 06/096F. September. Available at: http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=199243

.
EPA

. 2008b. Draft Report on EPA OAQPS Risk and Technology Review Methodologies: For Review by the EPA Science Advisory Boar d; Case Studies

– MACT I Petroleum Refining Sources, Portland Cement Manufacturing. Office of Air Quality Planning and Standards, Office of Air and Radiation, Research Triangle Park, NC. July 14, 2008.

EPA

. 2010

. U.S. EPA. Development of a Relative Potency Factor (RPF) Approach for Polycyclic Aromatic Hydrocarbon (PAH) Mixtures (External Review Draft). Washington, DC, EPA/635/R

- 08/012A February. Available at: http://cfpub.epa .gov/ncea/iris_drafts/recordisplay.cfm?deid=194584TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 103 October 2014 Description of MIRC EPA

. 2011a

. Exposure Factors Handbook: 2011 Edition

. Office of Research and Development, Washington, D.C. EPA/600/R

- 090/052F. September

. Available at: http://cfpub.epa.gov/ncea/risk/recordisplay.cfm?deid=236252

.
EPA

. 2011b

. Revised Technical Support Document: National

- Scale Assessment of Mercury Risk to Populations with High Consumption of Self

- caught Freshwater Fish; In Support o f the Appropriate and Necessary Finding for Coal

- and Oil

- Fired Electric Generating Units. Office of Air Quality Planning and Standards

, Research Triangle Park, NC

. EPA

- 452/R

- 11

- 009 . Dec ember

. Van den Berg, M., L.S. Birnbaum, M. Denison, M. De vito, W. Far lans, M. Feeley, H. Fiedler, H. Hakansson, A. Hanberg, L.. Haws, M. Rose, S. Safe, D. Schrenk, C. Tohyama, A. Tritscher, J. tuomisto, M. Tysklind, N. Walker, and R.E. Peterson. 2006. The 2005 World Health Organiza tion reevaluation of human and m ammalian to xic equivalency factors for dioxins and dioxin

- like compounds. Toxicol Sci. 93(2): 223

- 41.

Zabik, ME; Zabik, MJ. 1995. Tetra

- chlorodibenzo

- p

- dioxin residue reduction by cooking/processing of fish fillets harvested from the Great Lakes. Bulletin of Environ mental Contamination and Toxicology. 55:264

- 269.[This page intentionally left blank.]TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment C i October 2014 Dermal Risk Screening 

## Dermal Risk Screening Attachment C.[ This page intentionally left blank. ]TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment C iii October 2014 Dermal Risk Screening 

## CONTENTS , ATTACHMENT C

1. Hazard Identification and Dose Response Assessment

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

- 1

2. Dermal Exposure Estimation

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

- 2

2.1. Equations for Estimating Dermal Exposure

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

- 2

2.2. Exposure Factors and Assumptions

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

- 3

2.3. Receptor

- Specific Parameters

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

- 4

2.4. Scenario

- Specific Parameters

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

- 4

2.5. Chemical

- Specific Parameters

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

- 5

3. Screening

- Level Cancer Risks and Non

- Cancer Hazard Quotients

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

- 6

3.1. Dermal Cancer Risk

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

- 6

3.2. Dermal Hazard Quotient

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

- 7

4. Dermal Screening Results

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

- 7

5. References

................................ ................................ ................................ ....................... 10TRIM

- Based T iered Screening Methodology for RTR

Attachment C iv October 2014 Dermal Risk Screening Exhibi t s, Addendum 3

Exhibit C

- 1. Cancer Slope Factors and Reference Doses Based on Absorbed Dose

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

- 2
Exhibit C

- 2. Receptor

- Specific Body Surface Area Assumed to be Exposed to Chemi cals

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

- 4
Exhibit C

- 3. Scenario

- Specific Exposure Values for Water and Soil Contact

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

- 5
Exhibit C

- 4. Chemical

- Specific Dermal Exposur e Values for Water and Soil Contact

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

- 6
Exhibit C

- 5. Summary of Dermal Non

- Cancer Hazards

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

- 8
Exhibit C

- 6. Summary of Dermal Cancer Risks

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

- 9TRIM

- Based Tiered Screening Methodolo gy for R TR

Attachment C C

- 1 October 2014 Dermal Risk Screening Non

- inhalation exposure to PB

- HAPs can occur by way of the dermal pathway through contact with PB

- HAP

- contaminated soil and water. H owever, dermal absorption of chemicals that are originally airborne is generall y a relatively minor pathway of exposure compared to other exposure pathwa ys ( EPA 2006, Cal/EP A 2000) . This section demonstrates that for the conservative tiered screening scenario developed for RTR multipathway evaluation, the dermal exposure route is no t a significant risk pathway when compared to the ingestion pathway. In general, the RTR multipathway assessment followed the protocol for evaluating a reasonable maximum exposure as described in EPA’s Risk Assessment Guidance for Superfund (RAGS), Volume I: Human Health Evaluation Model, Part E, Supplemental Guidance for Dermal Risk Assessment ( EPA 2004)

. 

## 1. Hazard Identification and Dose Response Assessment
To assess the potential contribution of dermal exposure to non

- inhalation exposure, w e evaluated th e potential for cancer and chronic non

- cancer effects for the four PB

- HAPs currently assessed in the multipathway scre ening evaluation for RTR: cadmium, divalent mercury, 2,3,7,8

- TCDD, and benzo(a)pyrene

. EPA has not developed carcinogenic potency slope f actors ( C SFs) and non

- cancer reference doses (RfDs) specifically for evaluating potential human health concerns associated with dermal exposure to PB

- HAPs. Instead, dermal toxicity values can be derived from oral toxicity values via route

- to

- route extrapo lation by adjusting for gastrointestinal (GI) absorption

. EPA recommends making this adjustment only when GI absorption of the chemical is significantly less than 100% (i.e., less than 50 percent)

. Otherwise, a default value of complete (100 percent) ora l absorption is assumed, and no adjustment is made ( EPA 2004).

The absorbed cancer slope factor ( C SF ABS ) is based on the oral cancer slope factor ( C SF O ) and the fraction of the contaminant absorbed in the gastrointestinal track (ABS GI ), as follows:

where:

CSF ABS = Absorbed slope factor (mg/kg

- day) - 1

CSF o = Oral slope factor (mg/kg

- day) - 1

ABS GI = Fraction of chemical absorbed in gastrointestinal tract (unitless)

T he absorbed reference dose (RfD ABS ) is based on the oral reference dose (RFD O ) and th e fraction of the contaminant absorbed in the gastrointestinal tract (ABS GI ) , as shown below

.

where:

RfD ABS = Absorbed reference dose (mg/kg

- day)

RfD o = Oral reference dose (mg/kg

- day)

ABS GI = Fraction of chemical absorbed in gastrointestinal tract (un itless) GI o ABS 

### ABS CSF CSF =

GI o ABS 

### ABS RfD RfD × =TRIM

- Based Tiered Screening Methodolo gy for R TR

Attachment C C

- 2 October 2014 Dermal Risk Screening The GI absorptions for 2,3,7,8

- TCDD and all polycyclic aromatic hydrocarbons (PAHs) (which includes benzo[a]pyrene) were estimated to be greater than 50 percent. Therefore, as shown in Exhibit C

- 1 , no adjustments to the available oral CSFs were required. For cadmium and divalent mercury, adjustments were made based on absorption data provided in RAGS Part E, Exhibit 4

- 1. The RfDs for dermal exposure to cadmium and divalent mercury are also shown in Exhibit C

- 1

.
Exhibit C

- 1 . Cancer Slope Factors and Reference Doses Based on Absorbed Dose

PB

- HAP F raction of C ontaminant A bsorbed in GI T ract ( ABS GI ) (unitless) A bsor bed C ancer Slope F actor ( CSF ABS ) a (mg/kg

- day) - 1 Absorbed R eference D ose ( RfD ABS ) a

(mg/kg

- day)
Cadmium Compounds 0.05 NA 2.5E

- 05

Divalent Mercury 0.07 NA 2.1E

- 05

2,3,7,8

- TCDD No adjustment required b 1.5E+05 1.0E

- 09

Benzo[a]pyrene No adjustment requir ed b 1.0E+01 NA

NA = Not applicable

a Oral dose response values are presented in Appendix 2 . Only the resulting adjusted dose response values are presented in this table.

b According to RAGS Part E, Exhibit 4

- 1, GI absorption is expected to be greater than 50%.



## 2. Dermal Exposure Estimation

Dermal exposures and risk s resulting from absorption of the chemical through the skin from contact with contaminated water and soil w ere evaluated for the RTR screening scenario. Individuals were assumed to be exposed on a fraction of their bodies (i.e., their head, forearms, hands, lower legs, and feet) to contaminated soil from the TRIM.FaTE surface soil parcel with the highest concentration (N1) on a daily basis. For the water evaluation, individuals were assumed to b e exposed to contaminated surface water with the same PB

- HAP concentration as the TRIM.FaTE screening scenario lake over their entire bodies on a daily basis.



### 2.1. Equations for Estimating Dermal Exposure

The general equation used to estimate dermal absorbed d ose (DAD) for water or soil is shown below, and is expressed in milligrams of PB

- HAP per kilogram of receptor body weight per day (mg/kg

- day). DADs are calculated separately for the water and soil pathways and then added together for each age group

.

wh ere:

DA event = Absorbed dose per event; chemical

- specific; equation for DA event also differs depending on water or soil contact (mg/cm 2

- event)

EV = Event frequency (events/day)

ED = Exposure duration (years) 

### AT BW SA EF ED EV DA DAD event



### × × × × ×

=TRIM

- Based Tiered Screening Methodolo gy for R TR

Attachment C C

- 3 October 2014 Dermal Risk Screening EF = Exposure frequency (days/year)

SA = Ski n surface area available for contact (cm 2 )

BW = Body weight (kg)

AT = Averaging time; for non

- cancer effects, equals ED x 365 days/year; for cancer effects, equals 70 years x 365 days/year (days)

DA event is estimated to be the total dose absorbed throug h the skin at the end of exposure and the equation for calculation is different for organic and inorganic chemicals in water and for soil. The equations for calculating these chemical

- specific DA event values for water contact are provided in RAGS Part E, Chapter 3 (see Equations 3.2

– 3.4). For soil, the equation for calculating these chemical

- specific DA event values is provided in RAGS Part E, Chapter 3 (see Equation 3.12).

Water

– Organic Chemicals:

Water

– Inorganic Chemicals:

Soil

– All Chemical s:

where:

DA event = Absorbed dose per event (mg/cm 2

- event)
C w C

s = Chemical concentration in water (mg/cm 3 ) or soil (mg/kg)

K

p = Chemical

- specific dermal permeability coefficient of compound in water (cm/hr)

FA = Chemical

- specific fraction absorbed; accounts for loss due to the regular shedding of skin cells of some chemical originally dissolved into skin (unitless)



## τ event = Chemical

- specific lag time per event (hr/event)

t event = Receptor

- specific event duration (hr/event)

AF = Receptor

- and activity

- specific adherence factor of soil to skin (mg/cm 2

- event)

ABS = Chemical

- specific dermal absorption fraction (unitle ss)

CF = Conversion factor (10 - 6 kg/mg)



### 2.2. Exposure Factors and Assumptions The exposure parameters included in this assessment and their default and other value options are summarized in this subsection

. Default values were selected to result in a highly conservative estimate of exposure (i.e., exposures are likely overestimated). Parameter values were primarily obtained or estimated from RAGS Part E ( EPA 2004) and the Child

- Specific Exposure Factors Handbook ( CSEFH

, EPA 2008)

. Receptor

- and scenario

- spe cific exposure assumptions are discussed first, and a discussion of chemical

- specific parameters values follows . Estimated water and soil exposure concentrations are presented at the end of this subsection. 

### π τ event

event p w event 

### t 6 K FA 2 C DA × × × × × =

event p w event t K C DA × × =

CF ABS AF C DA s event × × × =TRIM

- Based Tiered Screening Methodolo gy for R TR

Attachment C C

- 4 October 2014 Dermal Risk Screening



### 2.3. Receptor

- Specific Parameters
Dermal exposures an d risks were estimated for the same age groups used in the ingestion exposure assessment: adults (ages 20 to 70 years) and five child age groups: <1 year; 1 to 2 years; 3 to 5 years; 6 to 11 years; and 12 to 19 years. The body weight values used in the ingestion exposure assessment were used in the dermal exposure assessment.

Body surface areas (SAs) for water and soil exposures for adults were calculated using Appendix C, Exhibit C

- 1

, of RAGS Part E. For children, SAs for water and soil exposures for the five children’s age groups were estimated using Table s 7

- 1 and 7

- 2 of the CSEFH, respectively. For SA (water), individuals were assumed to shower or bathe in the water with 100 percent of their body exposed. For SA (soil), it was assumed that indivi duals were exposed on a fraction of their total body, specifically their head, forearms, hands, lower legs, and feet. Based on information provided in RAGS Part E, the SA for forearms was calculated using the SA for arms and assuming a forearm

- to

- arm rati o of 0.45, and the SA for lower legs was estimated using the SA for legs and assuming a lower leg

- to

- leg ratio of 0.4.

Values for body SA by age group are summarized in Exhibit C

- 2

.
Exhibit C

- 2 . Receptor

- Specific Body Surface Area Assumed to be Exposed to Chemicals

Age Group a (years) Surface Area for Water Exposure (cm 2 ) Surface Area for Soil Exposure (cm 2 )
Adult 20

- 70 18,150 g 6,878 h

Child <1 b 3 ,992 1,772
Child 1

- 2 c 5,700 2,405
Child 3

- 5 d 7,600 3,354
Child 6

- 11 e 10,800 4,501
Child 12

- 19 f 17,150 6,906

a Sources for the child groups included Table 7

- 1 (total body surface area for SA

- Water), and Table 7

- 2 (fraction of total body surface area for SA

- Soil) of the 2008 CSEFH.

b Represents a time

- weighted average for age groups birth to <1 month, 1 to <3 months, 3 to <6 months, and 6 to <12 months.

c Represents a time

- weighted average for age groups 1 to <2 years and 2 to <3 years.

d Values for age group 3 to <6 years in the 2008 CSEFH.

e Values for age group 6 to <11 years in the 2008 CSEFH. Represents a conservative (i.e., slightly low) estimate for ages 6 through 11 years since 11

- year olds are not included in this CSEFH age group.

f Represents a t ime

- weighted average for age groups 11 to <16 years and 16 to <21 years. Note that estimated values include 11

- year

- olds and individuals through age 20, which contributes to uncertainty in the estimates for 12 to 19 years.

g Represents the average total su rface area of adults from Table C

- 1 of RAGS Part E.

h Represents the average surface area of adults for head, forearms, hands, lower legs, and feet from Table C

- 1 of RAGS Part E.



### 2.4. Scenario

- Specific Parameters

Exhibit C

- 3 summarize s the exposure values related to frequency and duration of contact. In general, these are the recommended defaults for calculating a reasonable maximum exposure (RME) for a residential scenario as proposed by EPA in RAGS Part E, Chapter 3.TRIM

- Based Tiered Screening Methodolo gy for R TR

Attachment C C

- 5 October 2014 Dermal Risk Screening Exhibit C

- 3 . Scenario

- Specific Exposure Values for Water and Soil Contact

Exposure Parameter Receptor Value Source
**Water Contact** Event Duration (t event ) (hr/event) Child 1 Reasonable maximum exposure scenario for showering/bathing from RAGS Part E, Exhibit 3

- 2 Adult 0.58

**Soil Contact** Soil Adherence Factor (AF) (mg/cm 2 ) Child 0.2 For children, value is geometric mean value for children playing (wet soil) and for adults, value is geometric mean value for an adult farmer from RAGS Part E, Exhibit 3

- 3 Adult 0.1

**Both Media** Event Frequency (EV) (events/day) All 1 Reasonable maximum exposure scenario from RAGS Part E, Exhibits 3

- 2 and 3

- 5. Exposure Frequency (EF) (days/year) All 3 50

Exposure Duration (ED) (years) Child <1 1 Represents the number of years included in the age group; also used in ingestion exposure calculations. Child 1

- 2 2
Child 3

- 5 3
Child 6

- 11 6
Child 12

- 19 8
Adult 20

- 70 50 Averaging Time (AT) (da ys) For cancer assessment, an AT equal to a lifetime (70 years) × 365 days/year is used. Same value used in ingestion exposure calculations. For non

- cancer assessment, an AT equal to the exposure duration (ED)

× 365 days/year is used, so AT will vary by r eceptor group. Same value used in ingestion exposure calculations.



### 2.5. Chemical

- Specific Parameters

The chemical

- specific parameters required to quantitatively evaluate dermal pathway exposures are listed in Exhibit C

- 4 . For the w ater concentration in the dermal analysis, t he modeled TRIM.FaTE chemical concentration in the screening scenario pond at the screening threshold emission rate was used. For the soil concentration, the modeled TRIM.FaTE chemical concentration in surface s oil in parcel N1 ( untilled soil, closest to facility ) of the screening scenario at threshold emission rate was used. This same soil concentration was also used in ingestion exposure calculations for soil ingestion.

Dermal absorption of chemicals in water is based on the use of a dermal permeability coefficient ( K p ), which measures the rate that a chemical penetrates the skin. Dermal absorption of soil

bound chemicals is based on the use of a dermal absorption fraction (ABS), which is a measure of how much of a chemical the skin absorbs through contact with soil.TRIM

- Based Tiered Screening Methodolo gy for R TR

Attachment C C

- 6 October 2014 Dermal Risk Screening Exhibit C

- 4 . Chemical

- Specific Dermal Exposure Values for Water and Soil Contact

PB

- HAP Cadmium Divalent Mercur y 2,3,7,8

- TCDD Benzo[a] pyrene Source

Chemical concentration in Water (Cw) (mg/cm 3 ) 2.4E

- 08 1.9E

- 09 2.6E

- 18 2.1E

- 13 TRIM.FaTE modeled concentration in screening scenario pond
Chemical concentration in Soil (Cs) (mg/kg) 6.9E

- 02 6.3E

- 02 2.2E

- 10 1.4E

- 04 TRI M.FaTE modeled concentration in surface soil in parcel N1 in screening scenario Permeability coefficient in water (Kp) (cm/hour) 0.001 0.001 0.81 0.7 Values from RAGS Part E, Exhibits B

- 3 (organics) and B

4 (inorganics)
Fraction absorbed water (FA) (unit less) NA NA 0.5 1.00 Values from RAGS Part E, Exhibits B

- 3; only used for organic chemicals Lag time per event ( event) (hr/event) NA NA 6.82 2.69 Values from RAGS Part E, Exhibits B

- 3; only used for organic chemicals Dermal absorption fraction (ABS) from soil (unitless) 0.001 0.045 a 0.03 0.13 Values from RAGS Part E, Exhibit 3

- 4, unless otherwise noted

a Value obtained from Bioavailability in Environmental Risk Assessment (Hrudey et al. 1996).



## 3. Screening

- Level Cancer Risks and Non

- Cancer Hazard Quotients
Toxicity values we re used in conjunction with exposure information to evaluate the potential for cancer risks and non

- ca ncer health hazards. Risk estimation methods are presented below.



### 3.1. Dermal Cancer Risk

Cancer risk for the dermal route was calculated as the product of the age

- specific DAD s and the absorbed CSF for each chemical, as follows:

Dermal Cancer Risk = DAD × C SF ABS

where:

DAD = Dermal Absorbed Dose (mg/kg

- day)

CSF ABS = Absorbed cancer slope factor (mg/kg

- day) - 1

Lifetime d ermal cancer r isks were calculated for 2,3,7, 8

- TCDD and benzo[a] pyrene

. The total risk accounts for dermal exposures that an individual m ight receive from these PB

- HAP s in water plus soil over his or her lifetime (7 0 years).TRIM

- Based Tiered Screening Methodolo gy for R TR

Attachment C C

- 7 October 2014 Dermal Risk Screening



### 3.2. Dermal Hazard Quotient
Dermal hazard quotient (HQ) was estimated as the ratio of age

- specific DAD s to the absorbed RfD for each chemical , as shown below:

Dermal HQ = DAD / RfD ABS

where:

DAD = Dermal Absorbed Dose (mg/kg

- day)

RfD ABS = Absorbed reference dose (mg/kg

- day)

The aggregate H Q accounts for exposures that a n individual in a receptor group may receive from the PB

- HAP in water and soil over the exposure duration. Non

- cancer hazard is not additive across the age groups evaluated here

. 

## 4. Dermal Screening Results

Exhibit C

- 5 presents a summary of estimated dermal non

- cancer hazards by age group. A summary of estimated lifetime dermal cancer risks is provided in Exhibit C

- 6 . The highest HQ value w as 0.006 (representing divalent mercury exposure f or children less than 1 year of age) or less. This is approximately 1 70 times less than the potential ingestion hazard quotients associated with the screening scenario (i.e. emissions of divalent mercury in the screening scenario resulted in an ingestion hazard quotient of 1) . The highest estimated individual lifetime cancer risk associated with potential dermal exposures was 4.1 E

- 0 9 for benzo[a]pyrene; this value is approximately 240 times smaller than the ingestion risk (i.e., 1E

- 06) estimated for the

s ame screening threshold emission rate.TRIM

- Based Tiered Screening Methodolo gy for R TR

Attachment C C

- 8 October 2014 Dermal Risk Screening Exhibit C

- 5 . Summary of Dermal Non

- Cancer Hazards

0 0.0000005 0.000001 0.0000015 0.000002 0.0000025 0.000003 0.0000035 0.000004 0.0000045

Child <1 Child 1

- 2 Child 3

- 5 Child 6

- 11 Child 12

- 19 Adult 20

- 70Hazard Quotient

Receptor 2,3,7,8

- TCDD
Water

Soil

Total

0 0.0001 0.0002 0.0003 0.0004 0.0005 0.0006 0.0007

Child <1 Child 1

- 2 Child 3

- 5 Child 6

- 11 Child 12

- 19 Adult 20

- 70Hazard Quotient

Receptor Cadmium
Water

Soil

Total

0 0.001 0.002 0.003 0.004 0.005 0.006 0.007

Child <1 Child 1

- 2 Child 3

- 5 Child 6

- 11 Child 12

- 19 Adult 20

- 70Hazard Quotient

Receptor Divalent Mercury
Water

Soil

TotalTRIM

- Based Tiered Screening Methodolo gy for R TR

Attachment C C

- 9 October 2014 Dermal Risk Screening Exhibit C

- 6 . Summary of Dermal Cancer Risks

PB

- HAP Dermal Lifetime Cancer Risk Magnitude of Difference a

**2,3,7,8**

**- TCDD** 
Water 2.64E

- 10 >3,700
Soil 1.49E

- 11 >67,300
Total 2.79E

- 10 >3,500 **Benzo[a]pyrene**
Water 1.50E

- 09 >600
Soil 2.63E

- 09 >3 00
Total 4.12E

- 09 >200

a Represents the magnitude of difference b etween the e stimated d ermal r isk and the p otential i ngestion r isk associated with the s creening s cenario

. Based on these results and taking into consideration the extremely conservative na ture of the dermal exposure calculations, it was assumed that it is not necessary to incorporate dermal exposures in calculating multipathway screening threshold levels. Specifically, the daily exposure durations of 0.58 hour for adults and 1 hour for chi ldren used to calculate dermal exposure from water are highly conservative and assume that the individual is bathing in surface water taken directly from a contaminated lake or is swimming in the lake for 350 days of the year. The exposure frequency of 35 0 days and corresponding skin surface area available for contact with contaminated soils (i.e., head, hands, arms, legs, and feet) likely also grossly overestimates dermal exposure to soil.TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment C 10 October 2014 Dermal Risk Screening 

## 5. References
Cal/EPA ( California Environmental Protection Agency ) O ffice of Environmental Health Hazard Assessment (OEHHA ). 2000. Air Toxics Hot Spots Program Risk Assessment Guidelines; Part IV, Exposure Assessment and Stochastic Analysis Technical Support Document. Section 6, Dermal Exposure Assessment. September. Avail able at: http://www.oehha.ca.gov/air/hot_spots/pdf/chap6.pdf

. Hrudey, S.E., W. Chen, and C.G. Roussex, 1996. Bioavailability in environmental risk assessment. CRC Press, Inc, Lewis publish ers.

EPA (Environmental Protection Agency). 2004. Risk Assessment Guidance for Superfund Volume 1: Human Health Evaluation Manual (Part E, Supplemental Guidance for Dermal Risk Assessment). EPA/540/R99/005. Available at: http://www.epa.gov/oswer/riskassessment/ragse/index.htm

EPA . 2006. Risk and Technology Review (RTR) Assessment Plan. Office of Air and Radiation. November 20. Available at: http://www.epa.gov/sab/panels/consul_risk_and_tech_assessment_plan.htm

.
EPA . 2008. Child

- Specific Exposure Factors Handbook. Office of Research and Development, Washington, D.C. EPA/600/R

- 06/096F. September. Availab le at: http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=199243

.TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment D i October 2014 RIM.FaTE Parameters 

## Summary of TRIM.FaTE Parameters Considered for Attachment D. Inclusion in Tier 2 Assessment[This page intentionally left b lank.]TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B , Addendum 1 iii October 2014 Tier 2 TRIM.FaTE P arameters Exhibits, Addendum 1

Exhibit Add B1

- 1. TRIM.FaTE Parameters Considered for Inclusion in Tier 2 Analysis

................................ ................................ ................................ ............ D

- 1[This page intentionally left blank.]TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment D D

- 1 October 2014 Tier 2 TRIM.FaTE Parameters E xhibit D

- 1 . TRIM.FaTE Parameters Considered for Inclusion in Tier 2 Assessment

Parameter Mechanism of Potential Influence in TRIM Assessment of Parameter Significance and Ease in Implementation Uncertainty in Site

- Specific Data for Facilities Priority for Inclusion

**Meteorological Parameters**
Wind direction (% of time wind blows toward the lake and farm) In previous runs, direct deposition accounted for the bulk of chemica l input onto farms and into lakes. Because wind direction is strongly correlated to direct deposition in a given location , media concentrations are potentially highly sensitive to this parameter. Also, because the percentage of time the prevailing wind blo ws in the direction of lakes and farms can vary considerably across locations, differences in this parameter might also result in significant changes in important environmental concentrations. Highly Significant : Previous sensitivity analyses have confirm ed this to be a very sensitive parameter in the Tier 1 Screening modeling set

- up. Changing the fraction of time the wind blows toward the lake and farm by a factor of two corresponds to a change in the risk by a factor of two.

Low Effort to Implement: Th is variable is relatively straightforward to vary in the Tier 2 screening scenario s

. Low to Moderate: The average fraction of time the wind blows in a given direction can be estimated for any surface meteorological station. Then, facilities can be linked t o the closest surface meteorological station. High

Wind speed Wind speed can affect the location of the “peak” concentration and deposition patterns in a given model configuration, as well as the risk

- distance profile. Highly Significant : Previous sensiti vity analyses have confirmed this to be a very sensitive parameter. However, wind speed does not vary widely across U.S. locations which could reduce its potential influence.

Low Effort to Implement: This variable is relatively straightforward to vary in the Tier 2 screening scenario s

. Low to Moderate: The annually

averaged wind speed can be estimated for any surface meteorological station. Then, facilities can be linked to the closest surface meteorological station. HighTRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment D D

- 2 October 2014 Tier 2 TRIM.FaTE Parameters E xhibit D

- 1 . TRIM.FaTE Parameters Considered for Inclusion in Tier 2 Assessment

Parameter Mechanism of Potential Influence in TRIM Assessment of Parameter Significance and Ease in Implementation Uncertainty in Site

- Specific Data for Facilities Priority for Inclusion

Precipitation Chemicals for which wet vapor or wet particle deposition processes are important are likely to be sensitive to the assumed level of precipitation

. Highly Significant : Previous sensitivity analyses have indicated a relatively high sensitivity of risk to precipitation for most PB

HAPs (PAHs, cadmium, and mercury)

.
Moderate Effort to Implement: In implementing changes in precipitation in TRIM, care must be taken to also preserve the overall water balance in the model. Low to Moderate: The annually

averaged precipitation rate can be estimated for the subset of surface meteorological stations that capture rainfall data. Then, facilities can be linked to the closest surface meteorological station with available data. HighTRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment D D

- 3 October 2014 Tier 2 TRIM.FaTE Parameters E xhibit D

- 1 . TRIM.FaTE Parameters Considered for Inclusion in Tier 2 Assessment

Parameter Mechanism of Potential Influence in TRIM Assessment of Parameter Significance and Ease in Implementation Uncertainty in Site

- Specific Data for Facilities Priority for Inclusion
Mixing height Greater mixing heights increase the dispers ion of pollutants in the atmosphere and consequently reduce deposition to the ground in the areas around the stack. This is likely to be a highly sensitive parameter if there is a sizeable variation in mixing heights between facilities. Highly Significant : Previous sensitivity analyses have shown risk to be very sensitive to mixing height.

Low Effort to Implement: This variable is relatively straightforward to vary in the Tier 2 screening scenario s

. Moderate to High: Mixing height estimates are available f or upper air meteorological stations, and this set of stations is more limited than the set of surface meteorological stations. Each surface station can be linked to the closest upper air station to estimate the average mixing height. Then, facilities can be linked to the closest surface meteorological station. The relative uncertainty in mixing height for a given facility is high, given diurnal variations in mixing height and the smaller number of upper air stations. HighTRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment D D

- 4 October 2014 Tier 2 TRIM.FaTE Parameters E xhibit D

- 1 . TRIM.FaTE Parameters Considered for Inclusion in Tier 2 Assessment

Parameter Mechanism of Potential Influence in TRIM Assessment of Parameter Significance and Ease in Implementation Uncertainty in Site

- Specific Data for Facilities Priority for Inclusion
**Configurational Parameters**
Dista nce of lake from stack Deposition is known to decrease with distance from stack, although this relationship also depends on meteorological parameters such as wind speed and wind direction. Significance Difficult to Determine: Limited results from previou s TRIM model runs show an inconclusive relationship between risk and distance from stack, possibly as a result of limited statistical power. Some studies in the literature show a definite decreasing risk gradient with distance but others report too many co nfounding factors to isolate the precise relationship.

Moderate Effort to Implement: This variable requires updates to the layout coordinates and requires more effort to vary in the Tier 2 screening scenario s than the meteorological parameters. Low: The lakes within a given radius of each facility can be found using ArcGIS TM (see section 4). High

Distance of farm from stack Deposition is known to decrease with distance from stack, although this relationship also depends on meteorological parameters such a s wind speed and wind direction. Significance Difficult to Determine: Limited results from previous TRIM model runs show an inconclusive relationship between risk and distance from stack, possibly as a result of limited statistical power. Some studies in the literature show a definite decreasing risk gradient with distance but others report too many confounding factors to isolate the precise relationship.

Moderate Effort to Implement: This variable requires updates to the layout coordinates and requires more effort to vary in the Tier 2 screening scenario s than the meteorological parameters. High: Although the distance to the farm will likely affect risk, it is difficult to determine the precise land parcels near each facility that are actually used for f arming now or in the future. MediumTRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment D D

- 5 October 2014 Tier 2 TRIM.FaTE Parameters E xhibit D

- 1 . TRIM.FaTE Parameters Considered for Inclusion in Tier 2 Assessment

Parameter Mechanism of Potential Influence in TRIM Assessment of Parameter Significance and Ease in Implementation Uncertainty in Site

- Specific Data for Facilities Priority for Inclusion

Watershed: lake area ratio A higher watershed:lake area ratio potentially increases the chemical input of water

- soluble or particle

attached chemicals into the lake. But the associated higher flush rate will likely

re duce this effect. Significance Difficult to Determine : Changes in the watershed to lake ratio affect risk, but the interaction depends on other variables involved in the water balance.

Moderate Effort to Implement: In implementing changes in the watershed :lake ratios in TRIM, care must be taken to also preserve the overall water balance in the model. High: The portion of land serving as a watershed to a particular lake is difficult to determine. Medium

Area and depth of lake A higher lake area would

ca pture more deposition but this effect might be counterbalanced by the ensuing larger volume of water, which reduces chemical concentration. Similarly, a deeper lake would also reduce concentrations, but this effect might be counterbalanced by the ensuing l ower flush rates at a constant level of precipitation/runoff. Significance Difficult to Determine : The impact of these parameters is inconclusive based on current studies using the TRIM model.

Moderate Effort to Implement: The lake area variable require s updates to the layout coordinates and requires more effort to vary in the Tier 2 screening scenario s than the meteorological parameters. In implementing changes in these variables in TRIM, care must be taken to also preserve the overall water balance in the model. High: While the area of lakes near a facility can be determined using GIS, the depth cannot. MediumTRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment D D

- 6 October 2014 Tier 2 TRIM.FaTE Parameters E xhibit D

- 1 . TRIM.FaTE Parameters Considered for Inclusion in Tier 2 Assessment

Parameter Mechanism of Potential Influence in TRIM Assessment of Parameter Significance and Ease in Implementation Uncertainty in Site

- Specific Data for Facilities Priority for Inclusion

**Physical Parameters**
Flush rate A higher flush rate out of the lake would result in a higher rate of chemical output from the lake, assuming c onstant inflow and volume. Significance Difficult to Determine : The impact of this parameter is inconclusive based on current studies using the TRIM model.

Moderate Effort to Implement: In implementing changes in the flush rate in TRIM, care must be taken to also preserve the overall water balance in the model. High: The flush rate of a lake cannot be determined easily for any lake found near a facility. In addition, erosion rates, watershed information, and lake depth needed to estimate the flushing rate are not readily available. Medium

Runoff rate and fraction A higher runoff rate (or fraction) would likely result in greater chemical input into the lake for some chemicals but also potentially a higher flush rate out of the lake. Significance Difficult to Determine : The impact of this parameter is inconclusive based on current studies using the TRIM model.

Moderate Effort to Implement: In implementing changes in the runoff rate and fraction in TRIM, care must be taken to also preserve the overall water balance in the model. High: As with the flush rate, the runoff rate and fraction for any lake near a facility cannot be readily determined. Medium

Erosion rate and fraction A higher erosion rate would likely result in greater chemical input into the lake for particle

- bound chemicals. It would also result in greater chemical transport onto farmlands, but this might be counterbalanced by equally greater erosion off farmland. Highly Significant : Previous analyses have shown risk to be sensitive to this param eter for some chemicals.

Moderate Effort to Implement: In implementing changes in the erosion rate and fraction in TRIM, care must be taken to also preserve the overall water balance in the model. High: As with the flush rate, the erosion rate and fracti on for any lake near a facility cannot be readily determined. MediumTRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment D D

- 7 October 2014 Tier 2 TRIM.FaTE Parameters E xhibit D

- 1 . TRIM.FaTE Parameters Considered for Inclusion in Tier 2 Assessment

Parameter Mechanism of Potential Influence in TRIM Assessment of Parameter Significance and Ease in Implementation Uncertainty in Site

- Specific Data for Facilities Priority for Inclusion

**Chemical Parameters** Methylation/ demethylation rates (Hg) For Hg, methylation and demethylation rates in lake sediment and surface water are potentially sensitive parameters affecting risk. A literature survey has indicated a relatively high range for rate constants describing these processes. Highly Significant : Previous analyses run in TRIM have confirmed the high sensitivity of these parameters for Hg.

Low Effort to Implement: This variable is relatively straightforward to vary in the Tier 2 screening scenario s. High: The specific methylation / deme t hylation rates for mercury in the vicinity of a specific facility cannot be readily determined. Low

Total phosphorus levels in the l ake The total phosphorus content of a lake is used as part of the TRIM.FaTE parameterization process to estimate the biomass content of different trophic levels. These biomass levels affect the biomagnification of chemicals up the food chain and potential ly risk to human consumers of fish. Not Significant : Previous analyses have shown limited sensitivity to total phosphorus levels. This is likely because the empirical equations predicting biomass in each trophic level depend in similar ways on the level of total phosphorus. So changes in total phosphorus do not significantly affect the ratio of biomass between the different trophic levels.

Low Effort to Implement: This variable is relatively straightforward to vary in the Tier 2 screening scenario s

. High: The total phosphorus levels in lakes near a specific facility cannot be readily determined. Low[This page intentionally left blank.]TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment E i October 2014 Lake Size Analysis 

## Analysis of Lake Size and Attachment E. Sustainable Fish Population[This page intentionally left blank.]TRIM

- Based Ti ered Screening Methodolo gy for RTR

Attachment E iii October 2014 Lake Size Analysis 

## CONTENTS , ADDENDUM 2

1. Introduction

................................ ................................ ................................ ..................... E

- 1

2. Angler Behavior

................................ ................................ ................................ .............. E

- 1

3. Fish Biology

................................ ................................ ................................ .................... E

- 2

3.1. Lake Productivity

................................ ................................ ................................ ... E

- 2

3.2. Proportion of Fish Biomass by Trophic Level

................................ ......................... E

- 4

3.3. Minimum Viable Population Size

................................ ................................ ............ E

- 5

4. Summary of Assumptions for the Lake Size Analysis

................................ ...................... E

- 6

5. Equations Used to Determine Lake Fish Populations

................................ ...................... E

- 6

6. References

................................ ................................ ................................ ..................... E

- 8TRIM

- Based Ti ered Screening Methodolo gy for RTR

Attachment E iv October 2014 Lake Size Analysis [This page intentionally left blank.]TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment E E

- 1 October 2014 Lake Size Analysis 

## 1. Introduction
Identifying the smal lest size of a lake that might maintain self

- sustaining populations of fish from trophic levels (TL) 3 and 4 and is sufficient to support at least one fisher at a specified fish ingestion rate requires consideration of many factors. Some factors depend on assumptions about the behavior of fisher s who consume fish from a lake ( see Section 2 below ). Other factors depend upon the general biology of fish populations in North American ecoregions (see Section 3 ). Based on evaluation of these factors, a set of assumptions was developed to support the estimation of minimum lake sizes that are needed to sustain a particular total human ingestion rate in grams/day (see Section 4 ) . Then, equations were developed (see Section 5 ) that were used to create Exhibit 26 (in Section 3.4 of the main body of the report) , which was used to determine the thres hold lake size of 25 acres.



## 2. Fisher Behavior
Several assumptions regarding fisher behavior are important for estimating a minimum lake size that is fishable. The first is a conservative assumption that fisher s (and their family members) consume about 50:50 top carnivore fish from both the benthos and from the water column. Benthic carnivores (BC), such as catfish and chub that consume benthic invertebrates and small benthic fish, can grow to large sizes (e.g., 2 kg). Assuming a diet of 50 percent benthic in vertebrates and 50 percent small benthic fish (e.g., young of the year that feed on algae or detritus), the BC fish category would represent trophic level (TL) 3.5. Pelagic piscivores

— water column carnivores (WCC)

— include species such as largemouth bass, lake trout, pickerel, and walleye. The WCC are modeled as TL 4, with 100 percent of their diet comprised of water column omnivores (WCO, e.g., pan fish such as bluegill and white perch), and the diet of the WCO is assumed to be 100 percent minnow

- sized fis h that feed on zooplankton and algae . The assumption of 50:50 WCC and BC consumption is conservative , because smaller, pan fish, are not included. Although fisher s might prefer to catch and consume the pelagic (TL4) game fish species, their generally low er abundance compared with fish that also consume benthic invertebrates dictates that fisher s will more often capture the benthic (TL3.5) carnivores. Some TL2 (herbivorous) fish such as carp attain “catchable” size, but they generally are not popular fish for consumption and are not considered here.

. A second assumption is that fisher s and their family members consume only the fillet portion of a fish. According to Ebert et al. (1993)

, the edible fraction of fish as a proportion of total fresh body we ight is 0.4 for salmon, 0.78 for smelt, and 0.3 for all other species. EPA recommends use of 0.30 for the consumable fraction of fish (USEPA, 1989). For this assessment , a 0.33 edible fraction for TL4 fish was assumed

. That factor is roughly one

- third

, which we considered preferable to 0.30 to account for some proportion of salmon likely in the diet. This factor is used in the lake size analysis to estimate total fish biomass required to support specified human fish consumption rates.

A third assumptio n relates to ingestion rates of the fisher or fisher family. Fish ingestion rates used for the purpose of the Tier 2 assessment are the same as those in Tier 1 and are consistent with subsistence fisher ingestion rates (see Exhibit 18

. Summary of RTR Tier 1 Screening Scenario Assumptions ).

The final assumption is that the lake must support a sustainable fishery despite fish harvesting by humans; in other words, the lake shouldn’t be “fished out” by the harvest r ate required to meet the subsistence fisher fish ingestion rate. The productivity of any particular fishery (local population of a species of fish) and the proportion of adult fish that can be harvested for human consumption are difficult values to estima te, and models to predict sustainable harvests of different fisheries are numerous and complex. Species

- specific parameters of key to suchTRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment E E

- 2 October 2014 Lake Size Analysis models include fecundity with age and size; survivorship of eggs, fry, and juveniles to sexual maturity (recruitment ); natural predation pressures; and temporal variation in food availability. For the purpose of this analysis, simplifying assumptions are required. In the analysis by Håkanson and Boulion (2004), which included a survey of 122 lakes, the authors noted t hat a typical loss from fishing by birds, mammals, and humans would be approximately 10 percent of the fish biomass in the prey fish compartment (T L 3) and 10 percent of the biomass in the predator fi sh compartment (TL4). The authors also found that as ove rall lake productivity increased, the biomass of prey (TL3) fish increased more rapidly than the biomass of predator (TL4) fish. For our lake size analysis, we assumed that fisher s can harvest 10 percent of the biomass of pelagic WCC fish without diminish ing the fish population size

. 

## 3. Fish Biology
Fish life histories also are key to estimating the minimum surface area of a pond or lake that could support a sustainable fish population of WCC

. The productivity and trophic structure of fish communities in p onds and lakes across the United States are varied. Thus, any set of assumptions is unlikely to all hold true at any given location. Nonetheless, three factors are important to any estimate of a minimum lake surface area for sustainable pelagic TL4 fishi ng: the general productivity of a lake (expressed as grams of fish wet weight per meter squared, g ww/m 2 ); the maximum likely proportion of the total fish biomass in a lake that is comprised of the top trophic level fish; and the minimum viable population (MVP) size required for the fish species to be self

- sustaining in the short term (for at least a few decades).



### 3.1. Lake Productivity The general productivity of a lake depends on many factors, including latitude, seasonal temperatures, and nutrients supporti ng the base of the food web. For lakes at approximately the same latitude in the same climate, nutrients play a key role in the total fish biomass that a lake might support. In a regression analysis of data on total phosphorus (TP) and fish biomass for 3 1 lakes across North America, Europe, and Russia, Nürnberg (1996) summarized the “limits” among three TP

- defined lake trophic status categories with respect to total fish wet weight biomass per unit area:

Oligo

- meso (TP = 10 μg/L) = 1.9 g ww/m 2

Meso

- eutro (TP = 30 μg/L) = 3.7 g ww/m 2

Eutro

- hypereutro (TP = 100 μg/L) = 8.5 g ww/m 2

Nürnberg (1996) also summarized total fish biomass limits from Bachmann et al. (1996) for the

same lake trophic status cat egories based on a sample of 60 lakes in Florida:

Oligo

- meso (TP = 10 μg/L) = 7.4 g ww/m 2

Meso

- eutro (TP = 30 μg/L) = 10.6 g ww/m 2

Eutro

- hypereutro (TP = 100 μg/L) = 15.6 g ww/m 2

As might be expected, for the same TP concentrations, standing fish bi omass per unit area in the Florida lakes is two to three times higher than standing fish biomass for more northerly lakes with shorter growing seasons.

Hanson and Legget (1982) estimated the relationship betwe en TP and standing stock of fish using a regres sion model based on samples from 21 lakes ranging in sur face area from 0.1

– 25,000 hectares (~0.25

– 62,000 acres) and located between 0° and 56° N latitude andTRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment E E

- 3 October 2014 Lake Size Analysis 121° E to 122° W longitude. Their linear regression relating TP to total fish standing biomass ( B) had a correlation coefficient ( r 2 ) of 0.84 :

B = 0.792 + 0 .07 2 (TP)

where:

B = total fish biomass (kg/h ectare )

TP = total phosphorous ( μg/L )

T he regression model of Hanson and Legget (1982) predict ed total fish biomass densities in lakes of 3.0

– 9.5 g w w/m 2 for TP concentrations ranging from 10

– 50 μg/L for oligo

- mesotrophic to mid

- range eutrophic lakes. Another regression model from Hoyer and Canfield (1991) predicted fish biomass densities in streams of 2.6

– 6.6 g ww/m 2 over the same range of TP concent rations.

In general, for very small lakes, relatively low fish productivity is likely. For example, Demers et al. (2001) found fish standing biomass values of 2.73 and 3.81 g ww/m 2 in two lakes of 27 and 22 acres (11 and 9 hectares), respectively, in sout h

- central Ontario. Brönmark and Weisner (1996) reported on aquatic communities from a sample of 44 small ponds in southern Sweden (most were less than 5 hectares ≈ 12 acres ). They found no fish in 5 of the smaller ponds (mean surface area of 0.20 ± 0.097 acres)

— which also exhibited lower TP concentrations than the larger ponds

— and no pisc ivorous fish in another 11 of the 44 ponds (mean surface area of 0.46 ± 0.27 acres). For the 28 ponds with piscivorous (TL4) fish present, the mean pond surface area was 1.4 (±1.3 SD) acres.

Scientists have also examined the relationship between TP and total fish biomass in reservoirs. Yurk and Ney (1989) examined the relationsh ip between TP and standing stock of fish in 22 reservoirs in southern Appalachia sampled in 1973. Their logarithmic regression relating TP to total fish standing biomass (B) used the following equation and had an r 2 of 0.75:

Log 10 (B) = 1.07 + 1.14 × Log 1 0 (TP)

Use of the equations from Hanson and Legget (1982) and Yurk and Ney (1989) yielded similar predications of total fish biomass at low to intermediate TP concentrations. At low TP (e.g., 10 μg/L), predictions of total fish biomass were 3.0 g ww/m 2 (Y urk and Ney, 1989) and 1.6 g ww/m 2 (Hanson and Legget, 1982); at high TP (e.g., 100 μg/L), fish biomass predicted by the two models were 15.5 and 22.4 g ww/m 2 , respectively.

Ideally, one would have data indicating TP levels in lakes in the vicinity of faci lities for a Tier 2 assessment . Such data, however, are rarely readily available. For purposes of the screening assessment, therefore, we assume that fish productivity per unit area is independent of lake size over a wide range of lake sizes and that TP levels are unknown.

Leidy and Jenkins (1977) reported analyses of several large data sets to support modeling of fish productivity and carrying capacity in reservoirs across the United States for the National Reservoir Research Program. The analyses incl uded studies of fish standing biomass by species in 61 reservoirs across the midwestern and eastern United States sampled at different times between 1952 and 1975. Only reservoirs of at least 500 acres (202 hectares) in size were included, with some excee ding 65,000 acres (in the Missouri drainage basin). Considering all 61 reservoirs, the mean biomass density of fish was 41.3 (± 30.4 SD) g ww/m 2 . The minimumTRIM

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Attachment E E

- 4 October 2014 Lake Size Analysis and maximum total fish biomass densities were 3.2 and 133.2 g ww/m 2 , respectively, and the media n value was 30.9 g ww/m 2 . Reservoirs typically have large drainage basins, which in some areas can receive excess TP from large expanses of agricultural areas.

In summary, the fish productivity in lakes and reservoirs can vary by more than three orders of magnitude. The reservoirs surveyed by Leidy and Jenkins (1977) in general were much larger (and were often more shallow and nutrient rich) than the natural lakes surveyed by others d iscussed above

. T he mean standing fish biomass of approximately 41.3 g ww /m2 from the reservoir survey is likely to be higher than a mean value for representative sample s of natural lakes in the United States

. For the purpose of estimating the minimum lake size that would sup port a sustainable WCC fishery, we rounded that va lue down to a single significant digit of 40 g ww/m 2 as the upper limit for total fish biomass in a lake. That standing biomass is higher than predicted by the regression models of Hanson and Legget (1982), Yurk and Ney (1989), and Nürnberg (1996) at a hig h total phosphorus of 100 μg/L (where phosphorous is the limiting nutrient) . Less productive lakes would support fewer fish per unit area, and, therefore, would have to be larger to support a specified fish ingestion rate.



### 3.2. Proportion of Fish Biomass by Tro phic Level

As indicated previously , for the Tier 2 assessment , the proportion of fish in a fisher ’s diet that consists of WCC (TL4) and BC (TL3.5) is assumed to be 50:50 by biomass (not numbers) for lakes that support the four trophic levels. In smaller l akes, TL4 fish are likely to be missing or rare, with TL3 fish in the water column (e.g., sunfish) being the highest trophic level supported by the primary productivity (algal/plant production) in some lakes. As a “rule of thumb” in ecology, 10 percent or less of the energy produced at one trophic level usually can be converted to biomass in the next trophic level (i.e., approximately 90 percent loss of energy) per trophic step. However, with different species having different energy assimilation efficien cies and with smaller species generally having higher turnover rates than larger species, the 10 percent energy rule does not necessarily translate into a standing biomass pyramid of similar proportions. In this section, the proportion of fish (based on b iomass) that might be expected in the WCC and the BC fish compartments relative to total standing fish biomass are examined assuming that the lake is large enough to support WCC (pelagic TL4 fish).

Examination of several studies of fish biomass by trophic level indicated that top trophic level fish might comprise up to 20 percent of the standing fish biomass in many locations. Ploskey and Jenkins (1982) estimated that piscivorous fish, both those that are generally free

- swimming or pelagic (e.g., pike, gar , walleye) and those that rest and forage primarily in the benthos (e.g., various species of catfish, suckers ) com prise 22 percent of the total fish biomass in DeGray Lake, Arkansas (averaged across several years). Leidy and Jenkins (1977) estimated that 18 percent of the fish biomass across the 61 reservoirs they examined was piscivorous (minimum of 14 percent and maximum 24 percent). Demers et al. (2001) categorized 2 percent and 15 percent of the total fish biomass in two small lakes of 27 and 22 acres in size, respectively, as piscivorous/benthivorous fish ( e.g., largemouth bass

, creek chub) ; primary benthivores (e.g., catfish, suckers) dominated at >70 percent in both lakes.

One of the more recent food web models for freshwater lakes is that of Håka nson and Boulion (2004). They designed their model to predict productivity and standing crop of prey and predatory fishes in lakes of northern Europe. The authors acknowledged that fish feeding patterns are complicated by the fact that fish change their feeding preferences as they age. Some fish species consume zooplankton or benthic invertebrates in their first year, and switch to small fish and then to larger fish as they mature and grow in size

.TRIM

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- 5 October 2014 Lake Size Analysis Håkanson and Boulion (2004) created a “distribution co efficient” to indicate what proportion of the total fish biomass in a lake is prey versus predatory fish. Based on data from 122 lakes in Europe and North America, they concluded that 27 percent by biomass is a “normal” portion of predatory fish in a bala nced system. They noted further, however, that for eutrophic lakes with TP levels >100 μg/L, the proportion of fish represented by piscivores declines to less than 20 percent

. The piscivores included both benthic and pelagic species. We note that most benthic piscivores also consume benthic macroinvertebrates.

Based on the information above , the top trophic level fish are assumed to comprise 21 percent of the total fish biomass. With the bulk of productivity in lakes originating from detritus in the benthos, the total biomass of strictly pelagic game fish is expected to be less than that of benthic fish. Therefore, for purpose of this lake size analysis, the piscivorous/benthivorous fish were separated into two compartments, with 17.5 percent of the total fish biomass in a benthic carnivore ( TL3 .5 or BC ) compartment and 3.5 percent of the t otal in a pelagic piscivore ( WCC ) compartment. Thus, the TL4 fish, when present, represent the limiting compartment for fish harvesting and consumption.



### 3.3. Minimum Viable Population Size The final step in estimating the minimum lake size that can support s ustainable fishing of its WCC fish species is to invoke the concept of minimum viable population (MVP) size. MVP is a concept used frequently in conservation biology for animals and is defined as the smallest population that will persist for a specified d uration (e.g., 100 years) with a given probability (e.g., 95 percent). MVP for any given species and location depends on many attributes of the species biology (e.g., body size, reproductive rate, home range size, corridors between populations, variabilit y in environmental characteristics that impact fecundity and survival). At lower numbers, the likelihood of population extinction increases due to environmental and demographic stochasticity (Menzie et al., 2008). As for fisheries biology, entire text bo oks have been dedicated to applied population ecology with population simulations incorporating demographic and life

- history characteristics, spatial separation of habitat patches and metapopulations, the probability of local catastrophes, genetic variatio n (e.g., drift), and other factors with predictions of time

- to

- extinction or probability of extinction within specified time periods (e.g., Soulé, 1987; Akçakaya et al., 1999). Consideration of such models in population

level ecological risk assessment ha s begun, but faces many challenges (Barnthouse et al., 2008). Moreover, that level of effort is beyond the resources available for screening

- level assessments

. Much of the initial work on MVP investigated the genetic minima required for short

- term survival

, continuing adaptation to environmental change, and ultimately evolution. Inbreeding has been considered the primary threat to short

- term survival and genetic drift the principal threat to losing the genetic variation required for adaptation (Shaffer, 19 87). S everal analyses (Senner

, 1980 ; Franklin

, 1980 ; Soulé

, 1980 ; Frankel and Soulé , 1981; Lande and Barrowclough

, 1987) have led to the conclusion that minimum effective population sizes on the order of 50 are required for short

- term survival (e.g., seve ral generations, decades), while effective population sizes on the order of 500 are necessary to provide adequate genetic variation for continuing adaptation over the long term (e.g., tens of generations, centuries for some animals) ( Shaffer, 1981; 1987 ). Effective population size, N e , is a measure of the rate of genetic drift (loss of genetic diversity or inbreeding), and its definition generally depends on the population in question (Rieman and Allendorft, 2001). N e can be estimated mathematically based on stochastic behavior of gene frequencies in a diploid population. Simple models assume a fixed population size, constant fecundity, specified sex ratio, and no overlap between generations (see studies cited in NRC, 1986). For animals with 50:50 sex ra tios, the effective population size is essentially the same as the actual breeding adult population size (Ewens et al., 1987). One of the most extensiveTRIM

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Attachment E E

- 6 October 2014 Lake Size Analysis population viability analyses in the United States has been conducted on the spotted owl (Boyce, 1993)

. Given the number and complexity of factors that influence MVP, however, including the definitions of time horizon (e.g., 100 years) and probability of survival ( e.g., 95 percent ), population biologists caution against using a “rule of thumb ” for MVP acr oss circumstances (Ewens et al.

, 1987).

Note that the MVP is appropriate for a single species of fish, not for generic categories of fish such as WCC or TL4. For this Tier 2 assessment

, the MVP of 50 associated with short

- term population survival was assu med for a TL4 fish species isolated in a lake. In reality, short

- term extirpations from a lake can be countered by purposeful introductions from other lakes or during flooding events. Thus, an MVP of 500 was not considered necessary for game fish in lake s.



## 4. Summary of Assumptions for the Lake Size Analysis
The following assumptions were used in processing lake d ata for the Tier 2 assessment and in estimat ing the relationship between fish ingestion and sustainable harvest rates and lake size (see Section 3.4.1 of Attachment B ).

1. Piscivorous fish ( WCC and BC ), when present, comprise approximately 21 percent of the standing biomass of fish (ignoring seasonal changes). The BC fish represent 17 .5 percent of the stan ding fish biomass; WCCs account for 3 .5 percent of the total fish biomass. Thus, WCC fish, when present, represent the limiting compartment for fish harvesting and consumption.

2. H umans can harvest 10 percent of the biomass of a fish compartment without thr eatening the population due to overharvesting.

3. The MVP size for a single WCC species is at least 50 adult fish for a local population to survive over the short term (more than a decade)

. 4. O nly 33 percent of the fish is edible fillet muscle

. 

## 5. Equations Used to Determine Lake Fish Populations

The standing biomass of WCC ( TL4 ) fish supported in Lake X can be calculated as the total standing biomass of fish (Total SB) multiplied by 0.035, based on the assumption that TL4 fish represent approximately 3.5 percent of the standing biomass in Lake X

.

WCC SB = Total SB x Fraction WCC ( Equation 1)

where:

WCC SB = Standing biomass of WCC fish ( g wet weight [ww]/m 2 ) in Lake X

Total SB = Total standing biomass of fish ( g ww/m 2 ) in Lake X

Fraction WCC = Fraction of WCC fish in La ke X ( i.e., 0.035)

Using WCC SB and the size of Lake X (Lake Size) , the total number of WCC fish supported in Lake X can be calculated using Equation 2 below

.

No

. WCC = Lake Size x WCC SB x CF

B W a ( Equation 2 )

where:TRIM

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Attachment E E

- 7 October 2014 Lake Size Analysis No. WCC = Total number of WCC fish in Lake X

Lake Size = Size of Lake X ( acres )

WCC SB = Standing biomass of WCC fish ( g ww/m 2 ; from Equation 1 )

CF = 4047 (unit conversion factor m 2 / acre )

BW a = Body weight of adult TL4 fis h ( 2000 g; assumed )

The likely a nnual p roductivity of WCC fish (kg/year) in Lake X can be estimated using Equation 3

.

Productivity WCC = Lake Size x WCC SB x CF 1

CF 2 (Equation 3)

where:

Productivity WCC = Likely annual productivity of WCC fish in Lake X (kg/year)

Lake Size = Size of the Lake X ( acres )

WCC SB = Standing biomass of WCC f ish ( g ww/m 2 ; from Equation 1 )

CF1 = 4047 (unit conversion factor 1, m 2 / acre )

CF2 = 1000 (unit conversion factor 2, g/kg )

The maximum fish ingestion rate (g/day) for WCCs plus BCs associated with sustainable fishin g can be predicted using Equation 4 . It assumes that the fisher s consume 50 percent WCC and 50 percent BC, represented by the factor of 2 in Equation 4

.

Max Sustain IR ( BC + WCC ) = 2 x Productivity WCC x FF x HF x CF 1

CF 2 ( Equation 4 )

where:

Max Sustain IR ( BC + WCC ) = Predicted maximum sustainable ingestion rate for BC and WCC fish (g/day) Productivity WCC = Likel y annual productivity of WCC fish in Lake X (kg /year; from Equation 3 )

FF = Fillet fraction; represents the assumed edible portion of fish ( 0.33; unitless )

HF = Annual harvest fraction (0.10; unitless )

CF1 = 1000 ( unit conversion factor 1, g/kg )

CF2 = 365 (unit conversion factor 2, days/year )TRIM

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- 8 October 2014 Lake Size Analysis 

## 6. References
Akçakaya, H.R., Burgman, M.A., and Ginzburg, L.R. 1999. Applied Population Ecology: Principles and Computer Exercises using RAMAS EcoLab. Sunderland, MA: Sinauer Associates, Inc. Publishers.

Bachmann, RW; Jones, BL; Fox, DD; Hoyer, M; Bull, LA; Canfield, DE. 1996. Relations between trophic state indicators and fish in Florida (USA) lakes. Canadian Journal of Fisheries and Aquatic Sciences. 53:842

- 855.

Barnthouse, L .W., Munns, W.R. Jr., and Sorensen, M.T. 2008. Population

- Level Ecological Risk Assessment. Boca Raton, FL: CRC Taylor & Francis Group; Pnesacola, FL: Society of Environmental Toxicology and Chemistry (SETAC).

Boyce, M.S. 1993. Population viability analysi s: adaptive management for threatened and endangered species. In: Wildlife Management Institute, Transactions of the 58th North American Wildlife and Natural Resources Conferences. Proceedings WWRC

- 93

- 19, pp. 520

- 527.

Brönmark, C. and S.E.B. Weisner. 1996. Decoupling of cascading trophic interactions in a freshwater benthic food chain. Oecologia 108: 534

- 541.

Demers, E., McQueen, D.J., Ramcharan, C.W., and A. Perez

- Fuentetaja. 2001. Did piscivores regulate changes in fish community structure? Advanc. Limnol . 56: 49

- 80.

Ebert, E., N. Harrington, K. Boyle, J. Knight, and R. Keenan. 1993. Estimating consumption of freshwater fish among Maine anglers. N. Am. J. Fisheries Manage. 13: 737

- 745.

Ewens, W.J., Brockwell, P.J., Gani, J.M., and Resnick, S.I. 1987. Minim um viable population sizes in the presence of catastrophes. In: M.E. Soulé (ed.) Viable Populations for Conservation. United Kingdom, Cambridge: Cambridge University Press, pp. 59

- 68.

Frankel, O.H., and Soulé, M.E. 1981. Conservation and evolution. United Kingdom, Cambridge: Cambridge University Press.

Franklin, I.R. 1980. Evolutionary change in small populations. In: M.E. Soulé and B.A. Wilcox (eds.) Conservation Biology: An Evolutionary

- Ecological Perspective. Sunderland, MA: Sinauer Associates; pp. 135

- 150. As cited by Shaffer, 1987.
Håkanson, L. and V.V. Boulion. 2004. Modeling production and biomasses of prey and predatory fish in lakes. Hydrobiologia 511: 125

– 150.
Hanson, J.M., and W.C. Leggett. 1982. Empirical prediction of fish biomass and yield. Ca n. J. Fish. Aquat. Sci. 39: 257

- 263.

Hoyer, M.V., and D.E. Canfield, Jr. 1991. A phosphorus

- fish standing crop relationship for streams? Lake Reserv. Manage. 7: 25

- 32.
Lande, R., and Barrowclough, G.F. 1987. Effective population size, genetic variation, a nd their use in population management. In: M.E. Soulé (ed.) Viable Populations for Conservation. United Kingdom, Cambridge: Cambridge University Press, pp. 87

- 123.

Leidy, G.R., and Jenkins, R.M. 1977. The development of fishery compartments and population rate coefficients for use in reservoir ecosystem modeling. Contract Report Y

- 77

- 1 preparedTRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment E E

- 9 October 2014 Lake Size Analysis by the National Reservoir Research Program, U.S. Fish and Wildlife Service, for the U.S. Army Corps of Engineers, Waterways Experiment Station, Vicksburg, Mississipp i, USA.

Menzie, C., Bettinger, N., Fritz, A., Kapustka, L., Regan, H., Moller, V., and Noel, H. 2008. Population protection goals. In: Barnthouse, L.W., Munns, W.R. Jr., and Sorensen, M.T. 2008. Population

- Level Ecological Risk Assessment. Boca Raton, FL: CRC Taylor & Francis Group; Pnesacola, FL: Society of Environmental Toxicology and Chemistry (SETAC), pp. 41

- 68.
NRC (National Research Council). 1986. Ecological Knowledge and Environmental Problem Solving. Committee on the Applications of Ecological Theo ry to Environmental Problems. Washington, DC: National Academies of Science Press.

Nürnberg, G.K. 1996. Trophic state of clear and colored, soft

- and hardwater lakes with special consideration of nutrients, anoxia, phytoplankton and fish. Journal of Lake a nd Reservoir Management 12(4): 432

- 447.

Ploskey, G.R., and R.M. Jenkins. 1982. Biomass model of reservoir fish and fish

- food interactions, with implications for management. North American Journal of Fisheries Management 2(2): 105

- 121.

Rieman, B.E., and All endorf, F.W. 2001. Effective population size and genetic conservation criteria for bull trout. N. A. J. Fisheries Manage. 21:756

- 764.
Senner, J.W. 1980. Inbreeding depression and the survival of zoo populations. In: M.E. Soulé and B.A. Wilcox (eds.) Conser vation Biology: An Evolutionary

- Ecological Perspective. Sunderland, MA: Sinauer Associates; pp. 209

- 244. As cited by Shaffer, 1987.

Scaffer, M.L. 1981. Minimum population sizes for species conservation. Bioscience 31: 131

- 134.

Shaffer, M. 1987. Minimum via ble populations: coping with uncertainty. In: M.E. Soulé (ed.) Viable Populations for Conservation. United Kingdom, Cambridge: Cambridge University Press, pp. 69

- 86.
Soulé, M.E. 1980. Thresholds for survival: maintaining fitness and evolutionary potential. In: M.E. Soulé and B.A. Wilcox (eds.) Conservation Biology: An Evolutionary

- Ecological Perspective. Sunderland, MA: Sinauer Associates; pp. 151

- 170. As cited by Shaffer, 1987.

Soulé, M.E. (ed.) 1987. Viable Populations for Conservation. United Kingdom, Ca mbridge: Cambridge University Press.

USEPA (US Environmental Protection Agency). 1989. Assessing human health risks from chemically contaminated fish and shellfish: a guidance manual. EPA

- 503/8

- 89

- 002. Washington, DC.

Yurk, J.J., and J.J. Ney. 1989. Phosp horus

- fish community biomass relationships in southern Appalachian reservoirs: can lakes be too clean for fish? Lake and Reservoir Management 5(2): 83

- 90.[This page intentionally left blank.]