Residual Risk Assessment for the Phosphate Fertilizer and Phosphoric Acid Source 

Categories in Support of the July 2015 Risk and Technology Review Final Rule

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Residual Risk Assessment for the

PHOSPHATE FERTILIZER PRODUCTION 

and 

PHOSPHORIC ACID MANUFACTURING SOURCE 

CATEGORIES 

in Support of the July 2015 Risk and Technology Review 

Final Rule

by

EPA’s Office of Air Quality Planning and Standards

Office of Air and Radiation

July 2015
Residual Risk Assessment for the Phosphate Fertilizer and Phosphoric Acid Source 

Categories in Support of the July 2015 Risk and Technology Review Final Rule

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Table of Contents

1. Introduction............................................................................................................................. 5

2. Methods................................................................................................................................... 6

2.1. Emissions and source data............................................................................................6

2.2. Dispersion modeling for inhalation exposure assessment............................................6

2.3. Estimating chronic human inhalation exposure ...........................................................8

2.4. Screening for potential acute impacts of concern ........................................................9

2.5. Multipathway human health and environmental risk screening.................................12

Multipathway Human Health Screening...........................................................................12

2.6. Dose-response assessment..........................................................................................16

2.6.1. Sources of chronic dose-response information...................................................16

2.6.2. Sources of acute dose-response information.......................................................24

2.7. Risk characterization..................................................................................................28

2.7.1. General................................................................................................................28

2.7.2. Mixtures..............................................................................................................29

2.7.3. Facility-wide risks...............................................................................................30

2.7.4. MACT-allowable risks........................................................................................30

3. Risk Results for Phosphate Fertilizer Production ................................................................. 31

3.1 Source category description and results.....................................................................31

3.2 Baseline risk characterization.....................................................................................33

4. Risk Results for the Phosphoric Acid Manufacturing Source Category............................... 38

4.1 Source category description and results.....................................................................38

4.2 Risk characterization..................................................................................................42

5. General Discussion of Uncertainties and How They Have Been Addressed ....................... 48

5.1 Exposure modeling uncertainties..................................................................................48

5.2 Uncertainties in the dose-response relationships...........................................................49

6 References................................................................................................................................. 58
Residual Risk Assessment for the Phosphate Fertilizer and Phosphoric Acid Source 

Categories in Support of the July 2015 Risk and Technology Review Final Rule

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Index of Tables

Table 2.2-1 AERMOD version 11103 model options for RTR modeling.................................7

Table 2.6-1 (a) Dose-Response Values for Chronic Inhalation Exposure to Carcinogens.....21

Table 2.6-1(b) Dose-Response Values for Chronic Oral Exposure to Carcinogens................22

Table 2.6-2 (a) Dose-Response Values for Chronic Inhalation Exposure to Noncarcinogens22

Table 2.6-2 (b) Dose-Response Values for Chronic Oral Exposure to Noncarcinogens.........24

Table 2.6-3 Dose-Response Values for Acute Exposure..........................................................27

Table 3.1-1 Summary of Emissions from the Phosphate Fertilizer Production Source 

Category and Availability of Dose-Response Values...............................................................32

Table 3.2-1 Summary of Source Category Level Inhalation Risks for Phosphate Fertilizer 

Production.................................................................................................................................33

Table 3.2-2 Summary of the “Tier 1 and 2” Multipathway Screening Analysis for the 

Phosphate Fertilizer Production Source Category ....................................................................36

Table 3.2-3 Summary of the “Tier 2 and 3” Multipathway Screening Analysis for Phosphate 

Fertilizer Production for Mississippi Phosphates (NEIMS059305) .........................................36

Table 3.2-4 Summary of Environmental Risk Screening Results for the Phosphate Fertilizer 

Production Source Category .....................................................................................................38

Table 4.1-1 Summary of Emissions from the Phosphoric Acid Manufacturing Source 

Category and Availability of Dose-Response Values...............................................................40

Table 4.2-1 Summary of Source Category Level Inhalation Risks for Phosphoric Acid 

Manufacturing...........................................................................................................................44

Table 4.2-2 Summary of Acute Refined Results for Phosphoric Acid Manufacturing............45

Table 4.2-3 Summary of the “Tier 1 and 2” Multipathway Screening Analysis for the 

Phosphoric Acid Manufacturing Source Category ...................................................................47

Table 4.2-4 Summary of Environmental Risk Screening Results for the Phosphoric Acid 

Manufacturing Source Category...............................................................................................48

Appendices

Appendix 1 Technical Support Document for HEM3 Modeling

Appendix 2 Meteorological Data for HEM3 Modeling

Appendix 3 Analysis of data on short-term emission rates relative to long-term emission 

rates

Appendix 4 Draft Detailed Risk Modeling Results

Appendix 5 Technical Support Document for TRIM-Based Multipathway Tiered Screening 

Scenario for RTR: Summary of Approach and Evaluation

Appendix 6 Dispersion Model Receptor Revisions and Additions

Appendix 7 Environmental Risk Screening

Appendix 8 Acute Noncancer Risk Analysis for Phosphate Fertilizer and Phosphoric Acid 

 Manufacturing

Appendix 9 Technical Support Document: Human Health Multipathway Residual Risk 

 Screening Assessment for the Phosphate Fertilizer Production and Phosphoric 

 Acid Manufacturing Source Categories
Residual Risk Assessment for the Phosphate Fertilizer and Phosphoric Acid Source 

Categories in Support of the July 2015 Risk and Technology Review Final Rule

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Index of Acronyms

AERMOD American Meteorological Society/EPA Regulatory Model

AEGL Acute exposure guideline level

ASTDR US Agency for Toxic Substances and Disease Registry

CalEPA California Environmental Agency

ERPG Emergency Response Planning Guideline

HAP Hazardous Air Pollutant

HEM Human Exposure Model

HI Hazard index

HQ Hazard quotient

IRIS Integrated Risk Information System

LOAEL Lowest Observed Adverse Effect Level

MACT Maximum Achievable Control Technology

MIR Maximum Individual Risk

MOA Mode of action

NAC National Advisory Committee

NAAQS National Ambient Air Quality Standard

NATA National Air Toxics Assessment

NEI National Emissions Inventory

NOAEL No Observed Adverse Effect Level

NPRM Notice of Proposed Rulemaking

PB-HAP Persistent and Bioaccumulative - HAP

POM Polycyclic organic matter

REL Reference exposure level

RfC Reference concentration

RfD Reference dose

RTR Risk and Technology Review

TOSHI Target-organ-specific hazard index

URE Unit risk estimate
Residual Risk Assessment for the Phosphate Fertilizer and Phosphoric Acid Source 

Categories in Support of the July 2015 Risk and Technology Review Final Rule

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1. Introduction

Section 112 of the Clean Air Act (CAA) establishes a two-stage regulatory process for 

addressing emissions of hazardous air pollutants (HAPs) from stationary sources. In the first 

stage, section 112(d) requires the Environmental Protection Agency (EPA, or the Agency) to 

develop technology-based standards for categories of sources (e.g., petroleum refineries, pulp 

and paper mills, etc.) [1]. EPA has largely completed the initial Maximum Achievable 

Control Technology (MACT) standards as required under this provision. Under section 

112(d)(6), EPA must review each of these technology-based standards at least every eight 

years and revise a standard, as necessary, “taking into account developments in practices, 

processes and control technologies.” In the second stage, EPA is required under section 

112(f)(2) to assess the health and environmental risks that remain after implementation of the 

MACT standards. If additional risk reductions are necessary to protect public health with an 

ample margin of safety or to prevent an adverse environmental effect, EPA must develop 

standards to address these remaining risks. This second stage of the regulatory process is 

known as the residual risk stage. For each source category for which EPA issued MACT 

standards, the residual risk stage must be completed within eight years of promulgation of the 

initial technology-based standard.

In December of 2006 we consulted with a panel from the EPA's Science Advisory Board 

(SAB) on the “Risk and Technology Review (RTR) Assessment Plan” and in June of 2007, 

we received a letter with the results of that consultation. Subsequent to the consultation, in 

July of 2009 a meeting was held with an SAB panel for a formal peer review of the “Risk and 

Technology Review (RTR) Assessment Methodologies” [2]. We received the final SAB 

report on this review in May of 2010 [3]. Where appropriate, we have responded to the key 

messages from this review in developing our current risk assessments and we will be 

continuing our efforts to improve our assessments by incorporating updates based on the SAB 

recommendations as they are developed and become available. Our responses to the key 

recommendations of the SAB are outlined in the memo entitled, “EPA’s Actions in Response 

to Key Recommendations from the SAB Review of RTR Risk Assessment Methodologies” 

[4]. 

This document contains the methods and the results of baseline risk assessments (i.e., after the 

implementation of the MACT standard) performed for the phosphate fertilizer production and 

phosphoric acid manufacturing source categories. The methods discussion includes 

descriptions of the methods used to develop refined estimates of chronic inhalation exposures 

and human health risks for cancer and noncancer endpoints, as well as descriptions of the 

methods used to screen for acute health risks, chronic non-inhalation health risks, and adverse 

environmental effects.
Residual Risk Assessment for the Phosphate Fertilizer and Phosphoric Acid Source 

Categories in Support of the July 2015 Risk and Technology Review Final Rule

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2. Methods

2.1. Emissions and source data

Data from a CAA section 114 information collection request (ICR) were used for this 

assessment. In April 2010, we issued the ICRs to all U.S. phosphate fertilizer and phosphoric 

acid manufacturers to gather information needed to conduct the technology review and 

residual risk requirements of the CAA. The ICRs requested available information regarding 

process equipment, control devices, phosphate and acid production, inventory data for all 

point and fugitive emissions, practices used to control fugitive emissions, and other aspects of 

facility operations, including stack parameters and locations. Additional survey information 

from this source category was collected in January 2014 and used in the assessment, namely 

new mercury and hydrofluoric acid (HF) emissions collected from plants that utilize calciners. 

Next, EPA engineers who have extensive knowledge of the characteristics of this industry 

performed an engineering and quality assurance/quality control review of the data to identify 

limitations and issues. Finally, EPA engineers contacted facility and industry representatives 

to clarify details and resolve issues with their data submissions. 

The 2005 National-Scale Air Toxics Assessment (NATA) National Emissions Inventory 

(NEI) was updated for these source categories with data obtained from industry through the 

ICR process. The emissions data and corrections are described in more detail in the memo

entitled “Emissions Data Used in Residual Risk Modeling: Phosphoric Acid and Phosphate 

Fertilizer Production Source Categories” available in the docket for this action. 

2.2. Dispersion modeling for inhalation exposure assessment

Both long- and short-term inhalation exposure concentrations and associated health risk from 

each facility in the source category of interest were estimated using the Human Exposure 

Model in combination with the American Meteorological Society/EPA Regulatory Model 

(AERMOD) dispersion modeling system (HEM-3). The approach used in applying this 

modeling system is outlined below, and further details are provided in Appendix 1. The 

HEM-3 performs three main operations: atmospheric dispersion modeling, estimation of 

individual human exposures and health risks, and estimation of population risks. This section 

focuses on the dispersion modeling component. The exposure and risk characterization 

components are discussed in other subsections of Sections 2 and 3.

The dispersion model in the HEM-3 system, utilizing EPA’s AERMOD version 11103 The 

version available at the time of the analysis), Gaussian plume dispersion model for modeling 

point, area, and volume sources of continuous air emissions from facility applications [5]. 

Further details on AERMOD can be found in the AERMOD Users Guide [6]. The model is 

used to develop annual average ambient concentrations through the simulation of hour-byhour dispersion from the emission sources into the surrounding atmosphere. Hourly emission 

rates used for this simulation are generated by evenly dividing the total annual emission rate 

from the inventory into the 8,760 hours of the year.
Residual Risk Assessment for the Phosphate Fertilizer and Phosphoric Acid Source 

Categories in Support of the July 2015 Risk and Technology Review Final Rule

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The first step in the application of the HEM3 modeling system is to estimate ambient 

concentrations at locations of interest. The AERMOD model options employed are 

summarized in Table 2.2-1 and are discussed further below.

Table 2.2-1 AERMOD version 11103 model options for RTR modeling

Modeling Option Selected Parameter for chronic exposure

Type of calculations Hourly Ambient Concentration

Source type Point, area represented as pseudo point source

Receptor orientation Polar (13 rings and 16 radials)

Discrete (census block centroids) and user-supplied receptors

Terrain characterization Actual from USGS 1-degree DEM data

Building downwash Not Included

Plume deposition/depletion Not Included

Urban source option No

Meteorology 1 year representative NWS from nearest site (over 200 

stations)

In HEM-3, meteorological data are ordinarily selected from a list of over 200 National 

Weather Service (NWS) surface observation stations across the continental United States, 

Alaska, Hawaii, and Puerto Rico (the number of stations available at the time of the analysis). 

In most cases the nearest station is selected as representative of the conditions at the subject 

facility. Ideally, when considering off-site meteorological data, most site-specific dispersion 

modeling efforts will employ up to five years of data to capture variability in weather patterns 

from year to year. However, because we had an insufficient number of appropriately 

formatted model input files derived from available meteorological data, we modeled only a 

single year, typically 1991. While the selection of a single year may result in underestimation of long-term ambient levels at some locations, likewise it may result in overestimation at others. For each facility identified by its characteristic latitude and longitude 

coordinates, the closest meteorological station was used in the dispersion modeling. 

Appendix 2 (Meteorological Data Processing Using AERMET for HEM-3) provides a 

complete listing of stations and assumptions along with further details used in processing the 

data through AERMET. The sensitivity of model results to the selection of the nearest 

weather station and the use of one year of meteorological data is discussed in “Risk and 

Technology Review (RTR) Risk Assessment Methodologies” [2].

The HEM-3 system estimates ambient concentrations at the geographic centroids of census 

blocks (using the 2010 Census), and at other receptor locations that can be specified by the 

user. See Appendix 6 to this document (Dispersion Model Receptor Revisions and Additions) 

for a discussion of user receptors and centroid location changes specific to these source 

categories. The model accounts for the effects of multiple facilities when estimating 

concentration impacts at each block centroid. Typically we combined only the impacts of 
Residual Risk Assessment for the Phosphate Fertilizer and Phosphoric Acid Source 

Categories in Support of the July 2015 Risk and Technology Review Final Rule

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facilities within the same source category, and assessed chronic exposure and risk only for 

census blocks with at least one resident (i.e., locations where people may reasonably be 

assumed to reside rather than receptor points at the fenceline of a facility). Chronic ambient 

concentrations were calculated as the annual average of all estimated short-term (one-hour) 

concentrations at each block centroid. Possible future residential use of currently uninhabited 

areas was not considered. Census blocks, the finest resolution available in the census data, 

are typically comprised of approximately 40 people or about ten households. 

In contrast to the development of ambient concentrations for evaluating long-term exposures, 

which was performed only for occupied census blocks, worst-case short-term (one-hour) 

concentrations were estimated both at the census block centroids and at points nearer the 

facility that represent locations where people may be present for short periods, but generally 

no nearer than 100 meters from the center of the facility (note that for large facilities, this 

100-meter ring could still contain locations inside the facility property). Since short-term 

emission rates were needed to screen for the potential for hazard via acute exposures, and 

since the ICR contains only annual emission totals, we generally apply the assumption to all 

source categories that the maximum one-hour emission rate from any source is ten times the 

average annual hourly emission rate for that source.1

 More description of our short-term 

assessment can be found in section 2.4 of this document, Acute Risk Screening and Refined 

Assessment. 

Census block elevations for HEM-3 modeling were determined nationally from the US 

Geological Service 1/3 Arc Second National Elevation Dataset, which have a spatial 

resolution of about 10 meters. Elevations of polar grid points used in estimating short- and 

long-term ambient concentrations were assumed to be equal to the highest elevation of any 

census block falling within the polar grid sector corresponding to the grid point. If a sector 

does not contain any blocks, the model defaults the elevation to that of the nearest block. If 

an elevation is not provided for the emission source, the model uses the average elevation of 

all sectors within the innermost model ring. AERMOD adjusts the plume’s flow if nearby 

elevated hills are expected to influence the wind patterns. For details on how hill heights 

were estimated and used in the AERMOD modeling see Appendix 2 of this document, 

Technical Support Document for HEM3 Modeling. 

2.3. Estimating chronic human inhalation exposure

We used the estimated annual average ambient air concentration of each HAP at each census 

block centroid as a surrogate for the lifetime inhalation exposure concentration of all the 

people who reside in the census block. That is, the risk analysis did not consider either the

short-term or long-term behavior (mobility) of the exposed populations and its potential 

influence on their exposure.

 

We did not address short-term human activity for two reasons. First, our experience with 

NATA assessments (which modeled daily activity using EPA’s HAPEM model) suggests 

that, given our current understanding of microenvironment concentrations and daily activities, 

modeling short-term activity would, on average, reduce risk estimates about 25 percent for 

 1

 In situations where we have information on hourly emissions, we will adjust the multiplier when evaluating 

acute risks.
Residual Risk Assessment for the Phosphate Fertilizer and Phosphoric Acid Source 

Categories in Support of the July 2015 Risk and Technology Review Final Rule

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particulate HAPs; it would also reduce risk estimates for gaseous HAPs, but typically by 

much less. Second, basing exposure estimates on average ambient concentrations at census 

block centroids may underestimate or overestimate actual exposure concentrations at some 

residences. Further reducing exposure estimates for the most highly exposed residents by 

modeling their short-term behavior could add a systematic low bias to these results.

We did not address long-term migration nor population growth or decrease over 70 years, 

instead basing the assessment on the assumption that each person’s predicted exposure is 

constant over the course of their lifetime which is assumed to be 70 years. In assessing cancer 

risk, we generally estimated three metrics: the maximum individual risk (MIR), which is 

defined as the risk associated with a lifetime of exposure at the highest concentration (note 

that the MIR can be estimated for each source as well as for the entire source category); the 

population risk distribution; and the cancer incidence. The assumption of not considering 

short or long-term population mobility does not bias the estimate of the MIR nor does it affect 

the estimate of cancer incidence since the total population number remains the same. It does, 

however, affect the shape of the distribution of individual risks across the affected population, 

shifting it toward higher estimated individual risks at the upper end and reducing the number 

of people estimated to be at lower risks, thereby increasing the estimated number of people at 

specific risk levels.

2.4. Screening for potential acute impacts of concern

In establishing a scientifically defensible approach for the assessment of potential health risks 

due to acute exposures to HAP, we followed the same general approach that has been used for 

developing chronic health risk assessments under the residual risk program. That is, we 

developed a tiered, iterative approach. This approach to risk assessment was endorsed by the 

National Academy of Sciences in its 1993 publication “Science and Judgment in Risk 

Assessment” and subsequently was adopted in the EPA’s “Residual Risk Report to Congress” 

in 1999. 

The assessment methodology is designed to eliminate from further consideration those 

facilities for which we have confidence that no acute adverse health effects of concern will 

occur. To do so, we use what is called a tiered, iterative approach to the assessment. This 

means that we begin with a screening assessment, which relies on readily available data and 

uses conservative assumptions that in combination approximate a potential worst-case 

exposure. The result of this screening process is that either the facility being assessed poses 

no potential acute health risks (i.e., it “screens out”), or that it requires further, more refined

assessment. A refined assessment could use industry- or site-specific data on the temporal 

pattern of emissions, the layout of emission points at the facility, the boundaries of the 

facility, and/or the local meteorology. In some cases, all of these site-specific data would be 

needed to refine the assessment; in others, lesser amounts of site-specific data can be used to 

determine that acute exposures are not a concern, and significant additional data collection is

not necessary. 

Acute health risk screening was performed for each facility as the first step. When we 

identify acute impacts which exceed their relevant benchmarks, we pursue refining our acute 

screening estimates to the extent possible using available data. In some cases, this may 
Residual Risk Assessment for the Phosphate Fertilizer and Phosphoric Acid Source 

Categories in Support of the July 2015 Risk and Technology Review Final Rule

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include the use of a facility-specific emissions multiplier to estimate the peak hourly emission 

rates from the average rates (rather than the default factor of 10). In other cases, this may 

entail determining the actual physical layout and boundaries of a facility and surrounding 

communities to more accurately gauge where people might reasonably be exposed for an 

hour.

When screening for potentially significant acute exposures, we used an estimate of the highest 

hourly ambient concentration at any off-site location as the surrogate for the maximum 

potential acute exposure concentration for any individual. These hourly concentrations are 

based on hourly emissions, as described above. Since information is not usually available on 

short-term emission rates, we generally apply the assumption that the maximum one-hour 

emission rate from any source is ten times the average annual hourly emission rate for that 

source. (The average hourly emissions rate is defined as the total emissions for a year divided 

by the total number of operating hours in the year.) This choice of a factor of ten for acute 

screening was originally based on engineering judgment. 

To develop a more robust peak-to-mean emissions factor, and in response to one of the key 

messages from the SAB consultation on our RTR Assessment Plan, we performed an analysis 

using a short-term emissions dataset from a number of sources located in Texas (originally 

reported on by Allen et al. 2004) [7]. In that report, the Texas Environmental Research 

Consortium Project compared hourly and annual emissions data for volatile organic 

compounds for all facilities in a heavily-industrialized 4-county area (Harris, Galveston, 

Chambers, and Brazoria Counties) over an eleven-month time period in 2001. We obtained 

the dataset and performed our own analysis, focusing on sources which reported emitting high 

quantities of HAP over short periods of time (see Appendix 3 of this document, Analysis of 

data on short-term emission rates relative to long-term emission rates). Most peak emission 

events were less than twice the annual average, and the highest was a factor of 74 times the 

annual average, and the 99th percentile ratio of peak hourly emission rate to the annual hourly 

emission rate was 9. Based on these results, we generally chose the factor of ten for all initial 

screening. This factor is intended to cover all possible hourly peaks associated with routinelyvariable emissions. While there have been some documented emission excursions above this 

level, our analysis of the data from the Texas Environmental Research Consortium suggests 

that this factor should cover more than 99 percent of the short-term peak gaseous or volatile 

HAP emissions from typical industrial sources. We have no data relating specifically to peak 

short-term emissions of particulate HAP. In the absence of source category-specific data, we 

use this same default approach for particulate emissions as well. 

For phosphate fertilizer production facilities an acute multiplier of 10 (x) the average 1-hr 

emission rates was applied to all sources. 

For phosphoric acid manufacturing facilities a multiplier of 10 (x) was applied for point 

sources based on the ICR data and engineering knowledge of the source category processes 

and for emissions from fugitive sources (gypsum ponds and cooling ponds) an acute 

multiplier of 1 was applied. 
Residual Risk Assessment for the Phosphate Fertilizer and Phosphoric Acid Source 

Categories in Support of the July 2015 Risk and Technology Review Final Rule

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As mentioned above, when we identify acute impacts which exceed their relevant 

benchmarks, we pursue refining our acute screening estimates to the extent possible. For the 

phosphate fertilizer production source category, all facilities screened out for acute risks from 

the initial HEM run, refer to Appendix 8 to this document (Acute Noncancer Risk Analysis 

for Phosphate Fertilizer and Phosphoric Acid Manufacturing), Table 1 to view initial acute 

screen risk values.

The hydrogen fluoride emissions from the phosphoric acid manufacturing gypsum ponds 

drove the acute risks. This emission source was considered stable and homogeneous and not 

subject to major fluctuations in hourly emissions over the year and therefore an acute 

multiplier of 1 was applied. This was applied to all area sources in the entire source category 

except for one facility that had site-specific data for their gypsum ponds, an acute multiplier 

of 2 was applied for this facility. This data was considered unique to the facility and not 

applicable for any other site. For further information on the acute multipliers applied for 

these source categories refer to the memo entitled “Emissions Data Used in Residual Risk 

Modeling: Phosphoric Acid and Phosphate Fertilizer Production Source Categories” 

available in the docket for this action.

In summary, we used conservative assumptions for emission rates, meteorology, and exposure 

location, and refined data where available. We used the following assumptions in our acute 

assessment approach:

• Peak 1-hour emissions were based upon specific emission process groups varying 

from 1 to 10 times the average 1-hour emission rates except for area source emissions 

from phosphoric acid manufacturing facilities, an acute hourly multiplier of 1 was 

used except for 1 site we had site-specific data to calculate an hourly multiplier.

• For facilities with multiple emission points, peak 1-hour emissions were assumed to 

occur at all emission points at the same time.

• For facilities with multiple emission points, 1-hour concentrations at each receptor 

were assumed to be the sum of the maximum concentrations due to each emission 

point, regardless of whether those maximum concentrations occurred during the same 

hour. 

• Worst-case meteorology (from one year of local meteorology) was assumed to occur 

at the same time the peak emission rates occur. The recommended EPA local-scale 

dispersion model, AERMOD, is used for simulating atmospheric dispersion.

• A person was assumed to be located downwind at the point of maximum impact 

during this same 1-hour period, but no nearer to the source than 100 meters.

• The maximum impact was compared to multiple short-term health benchmarks for the 

chemical being assessed to determine if a possible acute health risk might exist. These 

benchmarks are described in section 2.6 of this report.
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2.5. Multipathway human health and environmental risk screening

Multipathway Human Health Screening

The potential for significant human health risks due to exposures via routes other than 

inhalation (i.e., multipathway exposures) was screened by first determining whether any 

sources emitted any hazardous air pollutants known to be persistent and bioaccumulative in 

the environment (PB-HAP)2. The PB-HAP compounds or compound classes are identified 

for the screening from the EPA’s Air Toxics Risk Assessment Library [8]. Examples of 

PB-HAP are cadmium compounds, chlordane, chlorinated dibenzodioxins and furans, DDE, 

heptachlor, hexachlorobenzene, hexachlorocyclohexane, lead compounds, mercury 

compounds, methoxychlor, polychlorinated biphenyls (PCB), polycyclic organic matter

(POM), toxaphene, and trifluralin. There were PB-HAP emissions identified for both of the 

source categories addressed in this risk document. For the phosphorate fertilizer production 

source category, we identified emissions of cadmium compounds, lead compounds, and 

mercury compounds. For the phosphoric acid manufacturing source category, we identified 

emissions of cadmium compounds, lead compounds, mercury compounds, POM, and dioxin. 

With respect to PB-HAP emissions other than lead, emissions were evaluated for potential 

non-inhalation risks using a tiered screening approach which was developed for use with the 

EPA’s peer-reviewed Total Risk Integrated Methodology – Fate, Transport, and Ecological 

Exposure (TRIM.FaTE3) model. With this approach, we first determine whether the facilityspecific emission rates of each of the emitted PB-HAP are large enough to create the potential 

for significant non-inhalation human health risks under reasonable worst-case conditions. To 

facilitate this step, we developed emission rate levels for each PB-HAP using a hypothetical 

upper-end screening exposure scenario developed for use in conjunction with TRIM.FaTE. 

The exposure scenario includes a generic farming/fishing exposure scenario that simulates a 

subsistence environment. We conducted a sensitivity analysis on the screening scenario to 

ensure that its key design parameters would represent the upper end of the range of possible 

values, such that it would represent a conservative but not impossible scenario. The PB-HAP 

emissions from each facility in the source category were compared to the emission rate levels 

for each of the PB-HAP identified to assess the potential for significant human health risks via 

non-inhalation pathways. For the purpose of developing emission rates for the multipathway 

screen, we derived emission levels for each PB-HAP (other than lead) at which the maximum 

human health risk would be 1 in a million for lifetime cancer risk or a hazard quotient of 1.0 

for noncancer impacts. We call this application of the TRIM.FaTE model the Tier 1 Screen. 

See Appendix 5 of this document, (Technical Support Document for TRIM-Based 

Multipathway Screening Scenario for RTR: Summary of Approach and Evaluation) for a 

complete discussion of the development and testing of the screening scenario, as well as for 

the values of facility-level emission rates developed for screening potentially significant 

 2 Although the two-letter chemical symbol for lead is Pb, in this assessment PB-HAP refers to the many air 

pollutants known to be persistent and bioaccumulative in the environment. In instances where the report is 

specifically referring to lead, it is spelled out (i.e., the two-letter chemical symbol for lead is not used in this 

document). 

3 EPA’s Total Risk Integrated Methodology (General Information) http://www2.epa.gov/fera/total-riskintegrated-methodology-trim-general
Residual Risk Assessment for the Phosphate Fertilizer and Phosphoric Acid Source 

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multipathway impacts. 

If the PB-HAP emissions for a facility exceed the Tier screening emission rate, we conduct a 

Tier 2 multipathway screen. In the Tier 2 screen, the location of each facility that exceeded 

the Tier 1 emission level is used to refine some of the assumptions associated with the 

environmental scenario while maintaining the exposure scenario assumptions. We then adjust 

the risk-based Tier 1 screening level for each PB-HAP for each facility based on an 

understanding of how exposure concentrations estimated for the screening scenario change 

with meteorology and environmental assumptions. The screening scenarios developed 

include 2 separate receptors; 1) subsistence fisher and 2) subsistence farmer. This step creates 

a facility-specific emission level for each PB-HAP (i.e., the level for cadmium could be 

different for every facility), unlike the Tier 1 emission level which was constant for the same 

PB-HAP at different facilities (i.e., the level for cadmium was the same for every facility). 

The subsistence fisher scenario assumes a high-end fish consumption rate of 373 grams/day.

4

Also, the assessment uses the assumption that the biological productivity limitation of each 

lake is 1 gram of fish per acre of water Thus, in order to fulfill the adult ingestion rate, we 

may need to model more than one lake, refer to Appendix 5 of this document (Technical 

Support Document for TRIM-Based Multipathway Screening Scenario for RTR: Summary of 

Approach and Evaluation) for a complete discussion of the bioassay studies used to support 

this assumption. 

If we need to include more than one lake to achieve the 373 g/day ingestion rate, we begin 

with the lake with highest chemical concentration of a given PB-HAP group, and “fish” up to 

the lake’s biological productivity. We then systematically proceed to other lakes based on 

concentration, until the 373 g/day limit is met. A maximum travel radius of 50 km relative to 

the facility is used to maintain a realistic scenario. The final Tier 2 screening result for the 

fisher can be expressed as the sum of the risk from each lake that is fished (which is based on 

the amount of fish ingested for each lake multiplied by the fish concentration). If the highestconcentration 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 model 

includes 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 acres of lake, the risk reflects a reduced ingestion 

rate based on the lake acreage. The farmer scenario (same approach for all Tiers) involves an 

individual living for a 70-year lifetime on a farm homestead in the vicinity of the source and 

consuming produce grown on, and meat and animal products raised on, the farm. The 

ingestion rate used for these food groups are set at the 90th percentile of EPA’s “Exposure 

Factors Handbook: 2011 Edition”, and is considered an upper-bound level. [9 ]

Facility emissions of PB-HAP that do not exceed these new Tier 2 screening levels are 

considered to pose no significant risks. When facilities exceed the Tier 2 screening levels, it 

does not mean that multipathway impacts are significant, only that we cannot rule out that 

possibility based on the results of the screen. See Appendix 5 of this document (Technical 

 4 Burger, J. 2002. Daily consumption of wild fish and game: Exposures of high end 

recreationalists. Environmental Health Research. 12(4):343–354
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Support Document for TRIM-Based Multipathway Screening Scenario for RTR: Summary of 

Approach and Evaluation) for a complete discussion of the Tier 2 screen. 

If the PB-HAP emissions for a facility exceed the Tier 2 screening emissions rate, and data 

are available, we may decide to conduct a more refined multipathway assessment. This next 

iteration of assessment could be an extensive refined assessment that replaces as many of the 

assumptions made in the Tier 2 screen with site-specific data as possible or it could be 

focused on a few parameters such as adequacy of the lake, and/or the effective plume height

and the use of time-series meteorology in conjunction with hourly effective release heights. 

We refer to the latter as the Tier 3 screen. In the Tier 3 screen, the lakes included in the 

screen are scrutinized for accessibility (i.e., private or public) and appropriateness (i.e., 

fishable lake v. industrial waste pond). In addition, a Tier 3 screen may include an 

examination of the site-specific meteorological mixing height relative to the effective plume 

rise. As with the Tier 2 screen, the Tier 3 step creates a facility-specific emission rate level 

for each PB-HAP (i.e., the level for cadmium could be different for every facility). Also as 

with the Tier 2 screen, if facilities exceed the Tier 3 screening levels, it does not mean that 

multipathway impacts are significant, only that we cannot rule out that possibility based on 

the results of the screen. See Appendix 5 of this document (Technical Support Document for 

TRIM-Based Multipathway Screening Scenario for RTR: Summary of Approach and 

Evaluation) for a complete discussion of the Tier 3 screen. For the phosphate fertilizer 

production source category, we did conduct a Tier 3 screening assessment for Mississippi 

Phosphates Corporation in Pascagoula, Mississippi. In evaluating the potential multipathway 

risks from emissions of lead compounds, rather than developing a screening emission rate for 

them, we compared maximum estimated chronic atmospheric concentrations with the current 

National Ambient Air Quality Standard (NAAQS) for lead. Values below the NAAQS were 

considered to have a low potential for multipathway risks.

The NAAQS value, a public health policy judgment, incorporated the Agency’s most recent 

health evaluation of air effects of lead exposure for the purposes of setting a national ambient 

air quality standard. In setting this value, the Administrator promulgated a standard that was 

requisite to protect public health with an adequate margin of safety. We consider values 

below the level of the primary NAAQS to protect against multipathway risks because as 

mentioned above, the primary NAAQS is set as to protect public health with an adequate 

margin of safety. However, ambient air lead concentrations above the NAAQS are 

considered to pose the potential for increased risk to public health. We consider this NAAQS 

assessment to be a refined analysis given: 1) the numerous health studies, detailed risk and 

exposure analyses, and level of external peer and public review that went into the 

development of the primary NAAQS for lead, combined with: 2) the site-specific dispersion 

modeling used in this assessment to estimated ambient lead concentrations due to primary 

aluminum emissions. It should be noted, however, that this comparison does not account for 

possible population exposures to lead from sources other than the one being modeled; for 

example, via consumption of water from untreated local sources or ingestion of locally grown 

food. Nevertheless, the Administrator judged that such a standard would protect, with an 

adequate margin of safety, the health of children and other at-risk populations against an array 

of adverse health effects, most notably including neurological effects, particularly 

neurobehavioral and neurocognitive effects, in children (73 FR 67007). The Administrator, in 
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setting the standard, also recognized that no evidence-or risk based bright line indicated a 

single appropriate level. Instead a collection of scientific evidence and other information was 

used to select the standard from a range of reasonable values (73 FR 67006).

We further note that comparing ambient lead concentrations to the NAAQS for lead, 

considering the level, averaging time, form and indicator, also informs whether there is the 

potential for adverse environmental effects. This is because the primary lead NAAQS, set to 

protect against adverse welfare effects (including adverse environmental effects), has the 

same averaging time, form, and level as the primary standard. Thus, ambient lead 

concentrations above the NAAQS for lead also indicate the potential for adverse 

environmental effects.

Environmental Risk Screening

The EPA has developed a screening approach to examine the potential for adverse 

environmental effects as required under section 112(f)(2)(A) of the CAA. The environmental 

screen focuses on the following seven environmental HAP:

• Five persistent bioaccumulative HAP (PB-HAP) – cadmium, dioxins/furans, POM, 

mercury (both inorganic mercury and methyl mercury), and lead,

• Two acid gases – hydrogen chloride (HCl) and hydrogen fluoride (HF).

HAP that persist and bioaccumulate are of particular environmental concern because they 

accumulate in the soil, sediment, and water. The acid gases – HCl and HF – were included 

due to their well-documented potential to cause direct damage to terrestrial plants.

For the environmental risk screening analysis, EPA first determined whether any facilities in 

the source category emitted any of the seven environmental HAP. If one or more of the seven 

environmental HAP evaluated are emitted by at least one facility in the source categories we 

proceed to the second step of the evaluation.

For cadmium, mercury, POM, and dioxins/furans, the environmental screening analysis 

consists of two tiers. In the first tier, TRIM.FaTE modeling was conducted under worst-case 

environmental conditions to determine whether the facility-specific off-site emission rates of 

each of the emitted environmental HAP exceeded ecological benchmarks represented as 

emissions levels. If off-site emissions from a facility do not exceed the Tier 1 levels the 

facility “passes” the screen, and therefore, is not evaluated further under the screening 

approach. If off-site emissions from a facility exceed the Tier 1 levels, we evaluate the 

facility further in Tier 2.

In Tier 2 of the environmental screening analysis, the screening emission levels are adjusted 

to account for local meteorology and the actual location of lakes in the vicinity of facilities 

that did not pass the Tier 1 screen. If off-site emissions from a facility do not exceed the Tier 

2 levels, the facility passes the screen, and is typically not evaluated further. If off-site 

emissions from a facility exceed the Tier 2 levels, the facility does not pass the screen, and, 

therefore, may have the potential to cause adverse environmental effects. Such facilities are 
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evaluated further to investigate factors such as the magnitude and characteristics of the area of 

exceedance.

For acid gases, the environmental screening analysis evaluates the potential phytotoxicity and 

reduced productivity of plants due to chronic exposure to acid gases. The environmental risk 

screening methodology for acid gases is a single-tier screen that compares the average off-site 

ambient air concentration over the modeling domain to ecological benchmarks for each of the

acid gases. For purposes of ecological risk screening, EPA identifies a potential for adverse 

environmental effects to plant communities from exposure to acid gases when the average 

concentration of the HAP around a facility exceeds the LOAEL ecological benchmark. In 

such cases, we further investigate factors such as the magnitude and characteristics of the area 

of exceedance (e.g., land use of exceedance area, size of exceedance area) to determine if 

there is an adverse environmental effect.

For lead compounds, we currently do not have the ability to calculate media concentrations 

using the TRIM.FaTE model. However, air concentrations of lead are already calculated as 

part of the human health exposure and risk analysis using the HEM3-AERMOD air dispersion 

model. To evaluate the potential for adverse environmental effects from lead, we compare the 

average modeled air emission concentrations of lead from each facility in the source category 

emissions to the level of the secondary National Ambient Air Quality Standard (NAAQS) for 

lead. The secondary lead NAAQS is a reasonable means of evaluating environmental risk 

because it is set to provide substantial protection against adverse welfare effects which can 

include “effects on soils, water, crops, vegetation, man-made materials, animals, wildlife, 

weather, visibility and climate, damage to and deterioration of property, and hazards to 

transportation, as well as effects on economic values and on personal comfort and wellbeing.” 5

 

2.6. Dose-response assessment

2.6.1. Sources of chronic dose-response information 

Dose-response assessment (carcinogenic and non-carcinogenic) for chronic exposure (either 

by inhalation or ingestion) for the HAP reported in the emissions inventories for the source

categories in this document were based on the EPA Office of Air Quality Planning and 

Standards’ existing recommendations for HAPs [10], which were also used for NATA [11]. 

This information has been obtained from various sources and prioritized according to (1) 

conceptual consistency with EPA risk assessment guidelines and (2) level of peer review 

received. The prioritization process was aimed at incorporating into our assessments the best 

 5 A secondary standard, as defined in Section 109(b)(2), must “specify a level of air quality the 

attainment and maintenance of which, in the judgment of the Administrator, based on criteria, is 

requisite to protect the public welfare from any known or anticipated adverse effects associated with 

the presence of [the] pollutant in the ambient air.” Welfare effects as defined in section 302(h) (42 

U.S.C. 7602(h)) include, but are not limited to, “effects on soils, water, crops, vegetation, man-made 

materials, animals, wildlife, weather, visibility and climate, damage to and deterioration of property, 

and hazards to transportation, as well as effects on economic values and on personal comfort and 

well-being.”
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available science with respect to dose-response information. The recommendations are based 

on the following sources, in order of priority: 

1) EPA has developed dose-response assessments for chronic exposure for many of the 

pollutants in this study. These assessments typically provide a qualitative statement 

regarding the strength of scientific data and specify a reference concentration (RfC, for 

inhalation) or reference dose (RfD, for ingestion) to protect against effects other than 

cancer and/or a unit risk estimate (URE, for inhalation) or slope factor (SF, for ingestion) 

to estimate the probability of developing cancer. The RfC is defined as an “estimate (with 

uncertainty spanning perhaps an order of magnitude) of a continuous inhalation exposure 

to the human population (including sensitive subgroups) that is likely to be without an 

appreciable risk of deleterious effects during a lifetime.” Similarly, the RfD is “an 

estimate (with uncertainty spanning perhaps an order of magnitude) of a daily oral 

exposure to the human population (including sensitive subgroups) that is likely to be 

without an appreciable risk of deleterious effects during a lifetime.” The URE is defined 

as “the upper-bound excess cancer risk estimated to result from continuous lifetime 

exposure to an agent at a concentration of 1 μg/m3 in air.” The SF is “an upper bound, 

approximating a 95 percent confidence limit, on the increased cancer risk from a lifetime 

exposure to an agent. This estimate, [is] usually expressed in units of proportion (of a 

population) affected per mg/kg-day...” EPA disseminates dose-response assessment 

information in several forms, based on the level of review. The Integrated Risk 

Information System (IRIS) [12] is an EPA database that contains scientific health 

assessment information, including dose-response information. All IRIS assessments since 

1996 have also undergone independent external peer review. The current IRIS process 

includes review by EPA scientists, interagency reviewers from other federal agencies, and 

the public, and peer review by independent scientists external to EPA. New IRIS values 

are developed and old IRIS values are updated as new health effects data become 

available. Refer to the “IRIS Track” website for detailed information on status and 

scheduling of current individual IRIS assessments and updates 

(http://cfpub.epa.gov/ncea/iristrac/index.cfm). EPA’s science policy approach, under the 

current carcinogen guidelines, is to use linear low-dose extrapolation as a default option 

for carcinogens for which the mode of action (MOA) has not been identified. We expect 

future EPA dose-response assessments to identify nonlinear MOAs where appropriate, 

and we will use those analyses (once they are peer reviewed) in our risk assessments. At 

this time, however, there are no available carcinogen dose-response assessments for 

inhalation exposure that are based on a nonlinear MOA.

2) US Agency for Toxic Substances and Disease Registry (ATSDR). ATSDR, which is part 

of the US Department of Health and Human Services, develops and publishes Minimum 

Risk Levels (MRLs) [13] for inhalation and oral exposure to many toxic substances. As 

stated on the ATSDR web site: “Following discussions with scientists within the 

Department of Health and Human Services (HHS) and the EPA, ATSDR chose to adopt a 

practice similar to that of the EPA's Reference Dose (RfD) and Reference Concentration 

(RfC) for deriving substance specific health guidance levels for non neoplastic endpoints.” 

The MRL is defined as “an estimate of daily human exposure to a substance that is likely 

to be without an appreciable risk of adverse effects (other than cancer) over a specified 
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duration of exposure.” ATSDR describes MRLs as substance-specific estimates to be 

used by health assessors to select environmental contaminants for further evaluation. 

Exposures above an MRL do not necessarily represent a threat, and MRLs are therefore 

not intended for use as predictors of adverse health effects or for setting cleanup levels.

3) California Environmental Protection Agency (CalEPA). The CalEPA Office of 

Environmental Health Hazard Assessment has developed dose-response assessments for 

many substances, based both on carcinogenicity and health effects other than cancer. The 

process for developing these assessments is similar to that used by EPA to develop IRIS 

values and incorporates significant external scientific peer review. As cited in the 

CalEPA Technical Support Document for developing their chronic assessments6: “The 

guidelines for developing chronic inhalation exposure levels incorporate many 

recommendations of the U.S. EPA [14] and NAS [15].” The non-cancer information 

includes available inhalation health risk guidance values expressed as chronic inhalation 

reference exposure levels (RELs) [16]. CalEPA defines the REL as “the concentration 

level at or below which no health effects are anticipated in the general human population.” 

CalEPA's quantitative dose-response information on carcinogenicity by inhalation 

exposure is expressed in terms of the URE [17], defined similarly to EPA's URE. 

 

As mentioned above, in order to characterize non-cancer chronic effects, and in response to key 

recommendations from the SAB, the EPA selects dose-response values that reflect the best 

available science for all HAP included in RTR risk assessments7

. More specifically, for a given 

HAP, EPA examines the availability of inhalation reference values from the sources included in 

our tiered approach (e.g., IRIS first, ATSDR second, Cal EPA third) and determines which 

inhalation reference value represents the best available science. Thus, as new inhalation reference 

values become available, EPA will typically evaluate them and determine whether they should be 

given preference over those currently being used in RTR risk assessments. In developing 

chronic risk estimates, we adjusted dose-response values for some HAPs based on 

professional judgment, as follows: 

1) There is an existing IRIS RfC for manganese (Mn) published in 19938

. This value was in the 

RTR risk assessment supporting the 2011 Ferroalloys Notice of Proposed Rulemaking. 

However, since the 2011 proposal ATSDR has published an assessment of Mn toxicity (2012) 

which includes a chronic inhalation reference value (i.e., an ATSDR Minimal Risk Level or 

MRL)9

. Both the 1993 IRIS RfC and the 2012 ATSDR MRL were based on the same study 

(Roels et al., 1993). In developing their assessment, ATSDR used updated dose-response 

modeling methodology (benchmark dose approach) and considered recent pharmacokinetic 

 6 Air Toxics Hot Spots Program, Risk Assessment Guidelines, Part III - Technical Support Document

For the Determination of Non-cancer Chronic Reference Exposure Levels. Air Toxicology and Epidemiology 

Section, Office of Environmental Health Hazard Assessment, California Environmental Protection Agency. 

February 2000 (http://www.oehha.ca.gov/air/chronic_rels/pdf/relsP32k.pdf) 7 The SAB peer review of RTR Risk Assessment Methodologies is available at: 

http://yosemite.epa.gov/sab/sabproduct.nsf/4AB3966E263D943A8525771F00668381/$File/EPA-SAB-10-007-

unsigned.pdf. 8 USEPA Integrated Risk Information System Review of Manganese (1993) available at 

http://www.epa.gov/iris/subst/0373.htm. 9 Agency for Toxic Substances & Disease Registry Toxicological Profile for Manganese (2012) available at 

http://www.atsdr.cdc.gov/toxprofiles/tp.asp?id=102&tid=23.
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findings to support their MRL derivation. In the interest of using the best available science, 

the EPA has chosen to rely on the ATSDR MRL for Mn in the current secondary aluminum 

proposal. 

2) In the case of nickel compounds, to provide a health-protective estimate of potential 

cancer risks, we used the IRIS URE value for nickel subsulfide in this risk assessment. 

Based on past scientific and technical considerations, the determination of the percent of 

nickel subsulfide was considered a major factor for estimating the extent and magnitude of 

the risks of cancer due to nickel-containing emissions. Nickel speciation information for 

some of the largest nickel-emitting sources (including oil combustion, coal combustion, 

and others) suggested that at least 35 percent of total nickel emissions may be soluble 

compounds and that the URE for the mixture of inhaled nickel compounds (based on 

nickel subsulfide, and representative of pure insoluble crystalline nickel) could be derived 

to reflect the assumption that 65 percent of the total mass of nickel may be carcinogenic. 

Based on consistent views of major scientific bodies (i.e., National Toxicology Program 

(NTP) in their 12th Report of the Carcinogens (ROC)10, International Agency for Research 

on Cancer (IARC)11, and other international agencies)12 that consider all nickel 

compounds to be carcinogenic, we currently consider all nickel compounds to have the 

potential of being carcinogenic to humans. The 12th Report of the Carcinogens states that 

the “combined results of epidemiological studies, mechanistic studies, and carcinogenic 

studies in rodents support the concept that nickel compounds generate nickel ions in target 

cells at sites critical for carcinogenesis, thus allowing consideration and evaluation of 

these compounds as a single group.” Although the precise nickel compound (or 

compounds) responsible for the carcinogenic effects in humans is not always clear, studies 

indicate that nickel sulfate and the combinations of nickel sulfides and oxides encountered 

in the nickel refining industries cause cancer in humans (these studies are summarized in a 

review by Grimsrud et al., 201013). The major scientific bodies mentioned above have 

also recognized that there are differences in toxicity and/or carcinogenic potential across 

the different nickel compounds. 

Therefore, in the inhalation risk assessment for this supplemental proposal, to take a 

conservative approach, we considered all nickel compounds to be as carcinogenic as 

nickel subsulfide and applied the IRIS URE for nickel subsulfide without a factor to 

reflect the assumption that 100 percent of the total mass of nickel may be as carcinogenic 

as pure nickel subsulfide. However, given that there are two additional URE values14

 10 National Toxicology Program (NTP), 2011. Report on carcinogens. 12th ed. Research Triangle Park, NC: 

US Department of Health and Human Services (DHHS), Public Health Service. Available online at 

http://ntp.niehs.nih.gov/ntp/roc/twelfth/roc12.pdf

11 International Agency for Research on Cancer (IARC), 1990. IARC monographs on the evaluation of 

carcinogenic risks to humans. Chromium, nickel, and welding. Vol. 49. Lyons, France: International Agency 

for Research on Cancer, World Health Organization Vol. 49:256.

12 World Health Organization (WHO, 1991) and the European Union’s Scientific Committee on Health and 

Environmental Risks (SCHER, 2006). 13 Grimsrud TK and Andersen A. Evidence of carcinogenicity in humans of water-soluble nickel salts. J Occup 

Med Toxicol 2010, 5:1-7. Available online at http://www.occup-med.com/content/5/1/7. 14 Two UREs (other than the current IRIS values) have been derived for nickel compounds as a group: one 

developed by the California Department of Health Services 
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derived for exposure to mixtures of nickel compounds, as a group, that are 2-3 fold lower

than the IRIS URE for nickel subsulfide, the EPA also considers it reasonable to use a 

value that is 50 percent of the IRIS URE for nickel subsulfide for providing an estimate of 

the lower end of the plausible range of cancer potency values for different mixtures of 

nickel compounds. 

3) Where possible for emissions of unspecified mixtures of HAP categories such as metal 

compounds and POM, we apply category-specific chemical speciation profiles appropriate 

to the source category to develop a composite dose-response value for the category. 

4) Where POM emissions were not speciated into individual compounds, we applied the 

same simplifying assumptions to assessments that are used in NATA [18]. The NATA 

approach partitions POM into eight different non-overlapping “groups” that are modeled 

as separate pollutants. Each POM group comprises POM species of similar carcinogenic 

potency, for which we can apply the same URE.

The emissions inventory for the phosphate fertilizer production and phosphoric acid 

manufacturing source categories includes emissions of HAP with available chronic 

quantitative inhalation dose-response values. Of these, 22 are classified as known, probable, 

or possible carcinogens, with quantitative cancer dose-response values available. These 22

HAPs, their quantitative inhalation chronic cancer dose-response values, and the source of 

each value are listed in Table 2.6-1(a). The POM compounds and dioxin with chronic oral 

cancer dose-response values available (for which multipathway screening assessments were 

performed) are listed in Table 2.6-1(b). Fifty HAP have quantitative inhalation chronic 

noncancer threshold values available, four of these HAP (mercury, lead, dioxin, and 

cadmium), for which a multipathway assessment was performed, also have quantitative oral 

chronic noncancer threshold values available. These 50 HAP, their threshold values, and the 

source of the value are listed in Table 2.6-2(a) and Table 2.6-2(b).

 (http://www.arb.ca.gov/toxics/id/summary/nickel_tech_b.pdf) and the other by the Texas Commission on 

Environmental Quality (http://www.epa.gov/ttn/atw/nata1999/99pdfs/healtheffectsinfo.pdf).
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Table 2.6-1 (a) Dose-Response Values for Chronic Inhalation Exposure to 

Carcinogens

URE (unit risk estimate for cancer)15 = cancer risk per μg/m3 of average lifetime exposure. 

Sources: IRIS = EPA Integrated Risk Information System, EPA OAQPS = interim value 

recommended by the EPA Office of Air Quality Planning & Standards, CAL = California 

EPA Office of Environmental Health Hazard Assessment, Conv. Oral = Oral unit risk 

converted to inhalation.

Pollutant CAS 

Number16

URE7

(1/μg/m3)

Source

1,2,3,6,7,8-hexachlorodibenzo-p-dioxin 57653857 3.3 EPA-ORD

2,4-dinitrotoluene 121142 0.000089 CAL

Acetaldehyde 75070 0.0000022 IRIS

Arsenic compounds 7440382 0.0043 IRIS

Benzene17 71432 0.0000078 IRIS

Benzyl chloride 100447 0.000049 CAL

Beryllium compounds 7440417 0.0024 IRIS

Bromoform 75252 0.0000011 IRIS

Cadmium compounds 7440439 0.0018 IRIS

Chromium (VI) compounds 18540299 0.012 IRIS

Ethyl benzene 100414 0.0000025 CAL

Ethylene dibromide 106934 0.0006 IRIS

Ethylene dichloride 107062 0.000026 IRIS

Ethylene oxide 75218 0.000088 CAL

Formaldehyde18 50000 0.000013 IRIS

Methyl tert-butyl ether 1634044 0.00000026 CAL

Methylene chloride 75092 0.000000016 IRIS

Naphthalene 91203 0.000034 CAL

Nickel compounds 7440020 0.00048 EPA OAQPS

Polycyclic Organic Matter19 (POM)

 Benzo[a]pyrene 50328 0.00176 CAL

Tetrachloroethene 127184 0.00000026 IRIS

 15 The URE is the upper-bound excess cancer risk estimated to result from continuous lifetime exposure to an 

agent at a concentration of 1 μg/m3 in air. URE’s are considered upper bound estimates meaning they represent 

a plausible upper limit to the true value. 

16 Chemical Abstract Services identification number. For groups of compounds that lack a CAS number we 

have used a surrogate 3-digit identifier corresponding to the group’s position on the CAA list of HAPs. 17 The EPA IRIS assessment for benzene provides a range of plausible UREs. This assessment used the highest 

value in that range, 7.8E-06 per ug/m3

. The low end of the range is 2.2E-06 per ug/m3. 18 The EPA has used the CIIT URE value, 5.5X10-9 per mg/m3

, to characterize formaldehyde cancer risk in some 

instances.

19 Assigned the URE associated with a mixture of POM compounds having a similar potency. Details of this 

method, also used in the National Air Toxics Assessments, are available at 

http://www.epa.gov/nata2002/methods.html#pom 
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Table 2.6-1(b) Dose-Response Values for Chronic Oral Exposure to Carcinogens

SF (oral slope factor for cancer) = cancer risk per mg/kg/d of average lifetime exposure. Sources: IRIS 

= EPA Integrated Risk Information System, CAL = California EPA Office of Environmental Health 

Hazard Assessment, EPA/OAQPS = interim value recommended by the EPA Office of Air Quality 

Planning and Standards, EPA ORD = EPA Office of Research and Development, HEAST = EPA Health 

Effects Assessment Tables

Pollutant

CAS 

Number

SF

(1/mg/kg/d) Source

Dioxin/Furans

 Hexachlorodibenzo-p-dioxin 19408743 6200 IRIS

 2,3,7,8-Tetrachlorodibenzo-p-dioxin 1746016 150000 EPA ORD

Methylene chloride 75092 0.002 IRIS

Polycyclic organic matter (POM)

 Benzo(a)pyrene 50328 7.3 IRIS

Table 2.6-2 (a) Dose-Response Values for Chronic Inhalation Exposure to 

Noncarcinogens

RfC (reference inhalation concentration) = an estimate (with uncertainty spanning perhaps an 

order of magnitude) of a continuous inhalation exposure to the human population (including 

sensitive subgroups) that is likely to be without an appreciable risk of deleterious effects 

during a lifetime. Sources: IRIS = EPA Integrated Risk Information System, CAL = 

California EPA Office of Environmental Human Health Assessment, ATSDR = US Agency 

for Toxic Substances Disease Registry, HEAST = EPA Health Effects Assessment Tables, 

EPA OAQPS = EPA Office of Air Quality Planning & Standards

Pollutant CAS 

Number8

RfC 

(mg/m3)

Source20

2,4-dinitrotoluene 121142 0.007 CAL

2-chloroacetophenone 532274 0.00003 IRIS – L/M

Acetaldehyde 75070 0.009 IRIS - L

Acrolein 107028 0.00002 IRIS - M

Antimony compounds 7440360 0.0002 IRIS - L

Arsenic compounds 7440382 0.000015 CAL

Benzene 71432 0.03 IRIS - M

Beryllium compounds 7440417 0.00002 IRIS - M

Cadmium compounds 7440439 0.00001 ATSDR

Carbon disulfide 75150 0.7 IRIS - M

Chlorobenzene 108907 1 CAL

Chloroform 67663 0.098 ATSDR

Chromium (VI) compounds 18540299 0.0001 IRIS - M

Cobalt compounds 7440484 0.0001 ATSDR

Cumene 98828 0.4 IRIS - M

Cyanide & Cyanide cpds

 Cyanide compounds21 57125 0.0008 IRIS – L/M

 20 The descriptors L (low), M (medium), and H (high) have been added for IRIS RfC values to indicate the 

overall level of confidence in the RfC value, as reported in IRIS.

21 The value for hydrogen cyanide was used as a surrogate for all cyanide compounds without an RfC.
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Diethanolamine 111422 0.003 CAL

Ethyl benzene 100414 1 IRIS - L

Ethyl chloride 75003 10 IRIS - M

Ethylene dibromide 106934 0.009 IRIS - M

Ethylene dichloride 107062 2.4 ATSDR

Formaldehyde 50000 0.0098 ATSDR

Hydrochloric acid 7647010 0.02 IRIS - L

Hydrofluoric acid 7664393 0.014 CAL

Isophorone 78591 2 CAL

Lead compounds 7439921 0.00015 EPA OAQPS

Manganese compounds 7439965 0.0003 ATSDR

Mercury compounds

 Elemental gaseous mercury 7439976 0.0003 IRIS - M

 Gaseous divalent mercury 201 0.0003 IRIS - M

 Particulate divalent mercury 202 0.0003 IRIS - M

Methyl bromide 74839 0.005 IRIS - H

Methyl chloride 74873 0.09 IRIS - M

Methyl isobutyl ketone 108101 3 IRIS - L/M

Methyl methacrylate 80626 0.7 IRIS - M/H

Methyl tert-butyl ether 1634044 3 IRIS - M

Methylene chloride 75092 0.6 IRIS – M/H

Naphthalene 91203 0.003 IRIS - M

n-hexane 110543 0.7 IRIS - M

Nickel compounds 7440020 0.00009 ATSDR

Phenol 108952 0.2 CAL

Propionaldehyde 123386 0.008 IRIS – L/M

Selenium compounds 7782492 0.02 CAL

Styrene 100425 1 IRIS - M

Tetrachloroethene 127184 0.04 IRIS -M

Toluene 108883 5 IRIS - H

Vinyl acetate 108054 0.2 IRIS - H

Xylenes (mixed) 1330207 0.1 IRIS - M

 m-Xylene22 108383 0.1 IRIS - M

 o-Xylene22 95476 0.1 IRIS - M

 p-Xylene22 106423 0.1 IRIS - M

 22 The RfC for mixed xylene was used as a surrogate.
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Table 2.6-2 (b) Dose-Response Values for Chronic Oral Exposure to Noncarcinogens

RfD (reference dose) = an estimate (with uncertainty spanning perhaps an order of magnitude) of a 

continuous oral exposure to the human population (including sensitive subgroups) that is likely to be 

without an appreciable risk of deleterious effects during a lifetime. 

Sources: IRIS = EPA Integrated Risk Information System, ATSDR = US Agency for Toxic Substances and 

Disease Registry, HEAST = EPA Health Effects Assessment Tables

Pollutant CAS Number

RfD

(mg/kg/d) Source 

Cadmium compounds 7440439 0.0005 IRIS - H

Mercury and Mercury Compounds23

 Mercuric chloride 7439976 0.0003 IRIS - H

 Methyl mercury 22967926 0.0001 IRIS - H

 Phenylmercuric acetate 62384 0.00008 IRIS - L

2,3,7,8-Tetrachlorodibenzo-p-dioxin 1746016 0.0000000007 IRIS - H

Tetrachloroethene 127184 0.006 IRIS - M

Tetraethyl lead 78002 0.0000001 IRIS - M

2.6.2. Sources of acute dose-response information 

Hazard identification and dose-response assessment information for preliminary acute 

inhalation exposure assessments is based on the existing recommendations of OAQPS for 

HAPs [19]. Depending on availability, the results from screening acute assessments are 

compared to both “no effects” reference levels for the general public, such as the California 

Reference Exposure Levels (RELs), as well as emergency response levels, such as Acute 

Exposure Guideline Levels (AEGLs) and Emergency Response Planning Guidelines 

(ERPGs), with the recognition that the ultimate interpretation of any potential risks associated 

with an estimated exceedance of a particular reference level depends on the definition of that 

level and any limitations expressed therein. Comparisons among different available 

inhalation health effect reference values (both acute and chronic) for selected HAPs can be 

found in an EPA document of graphical arrays.24

California Acute Reference Exposure Levels (RELs). The California Environmental 

Protection Agency (CalEPA) has developed acute dose-response reference values for many 

substances, expressing the results as acute inhalation Reference Exposure Levels (RELs). 

The acute REL (http://www.oehha.ca.gov/air/pdf/acuterel.pdf) is defined by CalEPA 

as “the concentration level at or below which no adverse health effects are anticipated 

for a specified exposure duration. [20]. RELs are based on the most sensitive, 

relevant, adverse health effect reported in the medical and toxicological literature. 

RELs are designed to protect the most sensitive individuals in the population by the 

inclusion of margins of safety. Since margins of safety are incorporated to address 

 23 The multipathway exposure assessment for mercury included fate and transport analysis, that included 

separate oral exposure estimates for divalent mercury and methylmercury.

24 See: U.S. EPA. Graphical Arrays of Chemical-Specific Health Effect Reference Values for Inhalation 

Exposures [Final Report]. (U.S. Environmental Protection Agency, Washington, D.C., EPA/600/R-09/061, 

2009. http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=211003.)
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data gaps and uncertainties, exceeding the REL does not automatically indicate an 

adverse health impact.” Acute RELs are developed for 1-hour (and 8-hour) exposures. 

The values incorporate uncertainty factors similar to those used in deriving EPA’s 

Inhalation Reference Concentrations (RfCs) for chronic exposures (and, in fact, 

California also has developed chronic RELs).

Acute Exposure Guideline Levels (AEGLs). AEGLs are developed by the National 

Advisory Committee (NAC) on Acute Exposure Guideline Levels (NAC/AEGL) for 

Hazardous Substances, and then reviewed and published by the National Research 

Council. As described in the Committee’s “Standing Operating Procedures (SOP)”

(http://www.epa.gov/opptintr/aegl/pubs/sop.pdf), AEGLs “represent threshold exposure 

limits for the general public and are applicable to emergency exposures ranging from 10 

min to 8 h.” Their intended application is “for conducting risk assessments to aid in the 

development of emergency preparedness and prevention plans, as well as real time 

emergency response actions, for accidental chemical releases at fixed facilities and from 

transport carriers.” The document states: “the primary purpose of the AEGL program and 

the NAC/AEGL Committee is to develop guideline levels for once-in-a-lifetime, shortterm exposures to airborne concentrations of acutely toxic, high-priority chemicals.” In 

detailing the intended application of AEGL values, the document states that, “It is 

anticipated that the AEGL values will be used for regulatory and nonregulatory purposes 

by U.S. Federal and State agencies, and possibly the international community in 

conjunction with chemical emergency response, planning, and prevention programs. 

More specifically, the AEGL values will be used for conducting various risk assessments 

to aid in the development of emergency preparedness and prevention plans, as well as 

real-time emergency response actions, for accidental chemical releases at fixed facilities 

and from transport carriers.” The NAC/AEGL defines AEGL-1 and AEGL-2 as:

“AEGL-1 is the airborne concentration (expressed as ppm or mg/m3

) of a substance above 

which it is predicted that the general population, including susceptible individuals, could 

experience notable discomfort, irritation, or certain asymptomatic nonsensory effects. 

However, the effects are not disabling and are transient and reversible upon cessation of 

exposure.”

“AEGL-2 is the airborne concentration (expressed as ppm or mg/m3) of a substance above 

which it is predicted that the general population, including susceptible individuals, could 

experience irreversible or other serious, long-lasting adverse health effects or an impaired 

ability to escape.”

“Airborne concentrations below AEGL-1 represent exposure levels that can produce mild 

and progressively increasing but transient and nondisabling odor, taste, and sensory 

irritation or certain asymptomatic, nonsensory effects. With increasing airborne 

concentrations above each AEGL, there is a progressive increase in the likelihood of 

occurrence and the severity of effects described for each corresponding AEGL. Although 

the AEGL values represent threshold levels for the general public, including susceptible 

subpopulations, such as infants, children, the elderly, persons with asthma, and those with 

other illnesses, it is recognized that individuals, subject to unique or idiosyncratic 
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responses, could experience the effects described at concentrations below the 

corresponding AEGL.”

Emergency Response Planning Guidelines (ERPGs). The American Industrial Hygiene 

Association (AIHA) has developed Emergency Response Planning Guidelines (ERPGs) [21] 

for acute exposures at three different levels of severity. These guidelines represent 

concentrations for exposure of the general population (but not particularly sensitive persons) 

for up to 1 hour associated with effects expected to be mild or transient (ERPG-1), 

irreversible or serious (ERPG-2), and potentially life-threatening (ERPG-3). 

ERPG values https://www.aiha.org/getinvolved/AIHAGuidelineFoundation/EmergencyResponsePlanningGuidelines/Pages/default.a

spx) are described in their supporting documentation as follows: “Emergency Response 

Planning Guidelines (ERPGs) were developed for emergency planning and are intended as 

health based guideline concentrations for single exposures to chemicals. These guidelines 

(i.e., the ERPG Documents and ERPG values) are intended for use as planning tools for 

assessing the adequacy of accident prevention and emergency response plans, including 

transportation emergency planning and for developing community emergency response plans. 

The emphasis is on ERPGs as planning values: When an actual chemical emergency occurs 

there is seldom time to measure airborne concentrations and then to take action.” ERPG-1

and ERPG-2 values are defined by AIHA as follows:

“ERPG-1 is the maximum airborne concentration below which it is believed that nearly all 

individuals could be exposed for up to 1 hour without experiencing other than mild 

transient adverse health effects or without perceiving a clearly defined, objectionable 

odor.”

“ERPG-2 is the maximum airborne concentration below which it is believed that nearly all 

individuals could be exposed for up to 1 hour without experiencing or developing 

irreversible or other serious health effects or symptoms which could impair an individual's 

ability to take protective action.”

In assessing the potential risks associated with acute exposures to HAP, we do not follow a 

prioritization scheme and therefore we consider available dose-response values from multiple 

authoritative sources. 

The emissions inventory for the phosphate fertilizer production and phosphoric acid 

manufacturing source categories includes emissions of 35 HAP with relevant and available 

quantitative acute dose-response threshold values. These HAPs, their acute threshold values, 

and the source of the value are listed below in Table 2.6-3.
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Table 2.6-3 Dose-Response Values for Acute Exposure

Pollutant

CAS 

Number

AEGL-1

(1-hr)

(mg/m3)

AEGL-2

(1-hr)

(mg/m3)

ERPG1

(mg/m3)

ERPG-2

(mg/m3) REL 

Acetaldehyde 75070 81 490 18 360 0.47

Acrolein 107028 0.069 0.23 0.11 0.34 0.0025

Arsenic and Arsenic Compounds

 Arsenic compounds 7440382 0.0002

Benzene 71432 170 2600 160 480 1.3

Benzyl chloride 100447 5.2 52 0.24

Beryllium compounds 7440417 0.025

Carbon disulfide 75150 40 500 3.1 160 6.2

Chlorobenzene 108907 46 690

Chloroform 67663 310 240 0.15

Cumene 98828 250 1500

Dimethyl sulfate 77781 0.12 0.62

Ethyl benzene 100414 140 4800

Ethylene dibromide 106934 130 180

Ethylene dichloride 107062 200 810

Formaldehyde 50000 1.1 17 1.2 12 0.055

n-Hexane 110543 12000

Hydrochloric acid 7647010 2.7 33 4.5 30 2.1

Hydrofluoric acid 7664393 0.82 20 1.6 16 0.24

Mercury (elemental) 7439976 1.7 2 0.0006

Methyl bromide 74839 820 190 3.9

Methyl chloride 74873 1900 830

Methyl hydrazine 60344 3.2

Methyl methacrylate 80626 70 490

Methyl tert-butyl ether 1634044 180 2100

Methylene chloride 75092 690 1900 1000 2600 14

Phenol 108952 58 89 38 190 5.8

Propionaldehyde 123386 110 620

Styrene 100425 85 550 210 1100 21

Tetrachloroethene 127184 240 1600 680 1400 20

Toluene 108883 750 4500 190 1100 37

Vinyl acetate 108054 24 630 18 260

Xylenes (mixed) 1330207 560 4000 22

 m-Xylene 22 108383 22

 o-Xylene22 95476 22

 p-Xylene22 106423 22
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2.7. Risk characterization

2.7.1. General

The final product of the risk assessment is the risk characterization, in which the information 

from the previous steps is integrated and an overall conclusion about risk is synthesized that is 

complete, informative, and useful for decision makers. In general, the nature of this risk 

characterization depends on the information available, the application of the risk information 

and the resources available. In all cases, major issues associated with determining the nature 

and extent of the risk are identified and discussed. Further, the EPA Administrator’s March 

1995 Policy for Risk Characterization [22] specifies that a risk characterization “be prepared 

in a manner that is clear, transparent, reasonable, and consistent with other risk 

characterizations of similar scope prepared across programs in the Agency.” These principles 

of transparency and consistency have been reinforced by the Agency’s Risk Characterization 

Handbook [23], in 2002 by the Agency’s information quality guidelines [24], and in the 

OMB/OSTP September 2007 Memorandum on Updated Principles for Risk Analysis25, and 

are incorporated in these assessments.

Estimates of health risk are presented in the context of uncertainties and limitations in the data 

and methodology. Through our tiered, iterative analytical approach, we have attempted to 

reduce both uncertainty and bias to the greatest degree possible in these assessments, within 

the limitations of available time and resources. We provide summaries of risk metrics 

(including maximum individual cancer risks and noncancer hazards, as well as cancer 

incidence estimates) along with a discussion of the major uncertainties associated with their 

derivation to provide decision makers with the fullest picture of the assessment and its 

limitations.

For each carcinogenic HAP included in an assessment that has a potency estimate available, 

individual and population cancer risks were calculated by multiplying the corresponding 

lifetime average exposure estimate by the appropriate URE. This calculated cancer risk is 

defined as the upper-bound probability of developing cancer over a 70-yr period (i.e., the 

assumed human lifespan) at that exposure. Because UREs for most HAPs are upper-bound 

estimates, actual risks at a given exposure level may be lower than estimated, and could be 

zero.

For EPA’s list of carcinogenic HAPs that act by a mutagenic mode-of-action [25], we applied 

EPA’s Supplemental Guidance for Assessing Susceptibility from Early-Life Exposure to 

Carcinogens [26]. This guidance has the effect of adjusting the URE by factors of 10 (for 

children aged 0-1), 3 (for children aged 2-15), or 1.6 (for 70 years of exposure beginning at 

birth), as needed in risk assessments. In this case, this has the effect of increasing the 

estimated life time risks for these pollutants by a factor of 1.6. In addition, although only a 

small fraction of the total POM emissions may be reported as individual compounds, EPA 

 25Memorandum for the Heads of Executive Departments and Agencies - Updated Principles for Risk Analysis

(September 19, 2007), From Susan E. Dudley, Administrator, Office of Information and Regulatory Affairs, 

Office of Management and Budget; and Sharon L. Hays, Associate Director and Deputy Director for Science, 

Office of Science and Technology Policy 

(http://www.whitehouse.gov/sites/default/files/omb/memoranda/fy2007/m07-24.pdf )
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expresses carcinogenic potency for compounds in this group in terms of benzo[a]pyrene 

equivalence, based on evidence that carcinogenic POM have the same mutagenic mechanism 

of action as does benzo[a]pyrene. For this reason, EPA implementation policy [27] 

recommends applying the Supplemental Guidance to all carcinogenic PAHs for which risk 

estimates are based on relative potency. Accordingly, we applied the Supplemental Guidance 

to all unspeciated POM mixtures.

Increased cancer incidence for the entire receptor population within the area of analysis was 

estimated by multiplying the estimated lifetime cancer risk for each census block by the 

number of people residing in that block, then summing the results for the entire modeled 

domain. This lifetime population incidence estimate was divided by 70 years to obtain an 

estimate of the number of cancer cases per year.

Unlike linear dose-response assessments for cancer, noncancer health hazards generally are 

not expressed as a probability of an adverse occurrence. Instead, “risk” for noncancer effects 

is expressed by comparing an exposure to a reference level as a ratio. The “hazard quotient” 

(HQ) is the estimated exposure divided by a reference level (e.g., the RfC). For a given HAP, 

exposures at or below the reference level (HQ ≤ 1) are not likely to cause adverse health 

effects. As exposures increase above the reference level (HQs increasingly greater than 1), 

the potential for adverse effects increases. For exposures estimated to be above the RfC, the 

risk characterization includes the degree of confidence ascribed to the RfC values for the 

compound(s) of concern (i.e., high, medium, or low confidence) and discusses the impact of 

this on possible health interpretations. 

The risk characterization for chronic effects other than cancer is expressed in terms of the HQ 

for inhalation, calculated for each HAP at each census block centroid. As discussed above, 

RfCs incorporate generally conservative uncertainty factors in the face of uncertain 

extrapolations, such that an HQ greater than one does not necessarily suggest the onset of 

adverse effects. The HQ cannot be translated to a probability that adverse effects will occur, 

and is unlikely to be proportional to adverse health effect outcomes in a population.

Screening for potentially significant acute inhalation exposures also followed the HQ 

approach. We divided the maximum estimated acute exposure by each available short-term 

threshold value to develop an array of HQ values relative to the various acute endpoints and 

thresholds. In general, when none of these HQ values are greater than one, there is no 

potential for acute risk. In those cases where HQ values above one are seen, additional 

information is used to determine if there is a potential for significant acute risks.

2.7.2. Mixtures

Since most or all receptors in these assessments receive exposures to multiple pollutants 

rather than a single pollutant, we estimated the aggregate health risks associated with all the 

exposures from a particular source category combined.

To combine risks across multiple carcinogens, our assessments use the mixtures guidelines’ 

[28,29] default assumption of additivity of effects, and combine risks by summing them using 

the independence formula in the mixtures guidelines.
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In assessing noncancer hazard from chronic exposures, in cases where different pollutants 

cause adverse health effects via completely different modes of action, it may be inappropriate 

to aggregate HQs. In consideration of these mode-of-action differences, the mixtures 

guidelines support aggregating effects of different substances in specific and limited ways. 

To conform to these guidelines, we aggregated non-cancer HQs of HAP that act by similar 

toxic modes of action, or (where this information is absent) that affect the same target organ. 

This process creates, for each target organ, a target-organ-specific hazard index (TOSHI), 

defined as the sum of hazard quotients for individual HAP that affect the same organ or organ 

system. All TOSHI calculations presented here were based exclusively on effects occurring 

at the “critical dose” (i.e., the lowest dose that produces adverse health effects). Although 

HQs associated with some pollutants have been aggregated into more than one TOSHI, this 

has been done only in cases where the critical dose affects more than one target organ. 

Because impacts on organs or systems that occur above the critical dose have not been 

included in the TOSHI calculations, some TOSHIs may have been underestimated. As with 

the HQ, the TOSHI should not be interpreted as a probability of adverse effects, or as strict 

delineation of “safe” and “unsafe” levels. Rather, the TOSHI is another measure of the 

potential for adverse health outcomes associated with pollutant exposure, and needs to be 

interpreted carefully by health scientists and risk managers.

Because of the conservative nature of the acute inhalation screening and the variable nature of 

emissions and potential exposures, acute impacts were screened on an individual pollutant 

basis, not using the TOSHI approach.

2.7.3. Facility-wide risks

To help place the source category risks in context, we examined “facility-wide” risks using 

2005 NEI data and modeling as described in Section 2.2. For the facilities in these source 

categories, we estimated the maximum inhalation cancer and chronic noncancer risks 

associated with all HAP emissions sources at the facility, including emissions sources that are 

not part of the source categories but are located within a contiguous area and are under 

common control. We analyzed risks due to the inhalation of HAP for the populations residing 

within 50 kilometers of each facility. The results of these facility-wide assessments are 

summarized below in the Risk Characterization sections of this document. The complete 

results of the facility-wide assessments are provided in Appendix 4. 

2.7.4. MACT-allowable risks

The emissions data in the data sets for these seven source categories are estimates of actual 

emissions on an annual basis. The risk results presented in the following sections are based 

on these actual emissions. For these two source categories, the MACT-allowable emissions 

are estimated to be equivalent to the actual emissions except for certain emission sources. See

the memorandum titled “Emissions Data Used in Residual Risk Modeling: Phosphoric Acid 

and Phosphate Fertilizer Production Source Categories” available in the docket for this action. 

Therefore, except for certain sources, the risks based on MACT-allowable emissions would 

be equivalent to the actual risks presented below. See the source category risk 

characterization sections for details on the MACT-allowable risk. 
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3. Risk Results for Phosphate Fertilizer Production

3.1 Source category description and results

Phosphate fertilizer production facilities use beneficiated phosphate rock with acids and/or 

nitrates to produce different types and grades of fertilizers. Phosphate fertilizers are used in 

agricultural applications as a plant nutrient. Phosphoric Acid Manufacturing (40 CFR part 63 

subpart AA) and Phosphate Fertilizers Production (40 CFR part 63 subpart BB) were 

established as separate source categories by EPA. However, these operations are generally 

collocated, as phosphoric acid manufacturing facilities provide feedstock for phosphate 

fertilizer production facilities and much of the phosphoric acid produced in the U.S. is 

consumed in the manufacture of fertilizers. 

Phosphate fertilizers are classified according to the concentration of phosphorus pentoxide in 

the final product. Normal superphosphate is made by reacting phosphate rock with sulfuric 

acid. Granular triple superphosphate (GTSP) is made by reacting phosphoric acid (produced 

at a phosphoric acid facility) with beneficiated phosphate rock. Ammonium phosphates, of 

which diammonium phosphate (DAP) is the most common, are made by reacting phosphoric 

acid, sulfuric acid, and anhydrous ammonia. Emission points along the phosphate fertilizer 

production process include reactors, granulators, dryers, screens, and grinding and drying 

processes. 

Emissions data, collected via ICRs sent to all U.S. phosphate fertilizer, were used to conduct 

this assessment. The data include information regarding process equipment, control devices, 

phosphate and acid production, inventory data for all point and fugitive emissions, practices 

used to control fugitive emissions, and other aspects of facility operations, including stack 

parameters and locations.

We currently estimate that there are eleven phosphate fertilizer production facilities operating 

in the United States. Ten of these eleven facilities are co-located with phosphoric acid 

manufacturing facilities. The 2005 National-Scale Air Toxics Assessment (NATA) National 

Emissions Inventory (NEI) was updated for this category with data from industry and 

enhanced quality assurance of facility and emission locations. All eleven of these facilities 

are identified as major sources in the NEI. 

The emissions for the phosphate fertilizer production source category ICR data set (eleven

facilities) are summarized in Table 3.1-1. The total HAP emissions for the source category are 

approximately 105 tons per year. Based on these data, the HAP emitted in the largest 

quantities across these eleven facilities is hydrofluoric acid. This chemical accounts for 96

percent of the total emissions by mass. Persistent and bioaccumulative HAP (PB HAP)26

reported as emissions from these facilities include mercury, lead, and cadmium compounds. 

The following environmental HAP are emitted from the phosphate fertilizer production source 

 26 Persistent and bioaccumulative HAP are defined in the EPA’s Air Toxics Risk Assessment Library, Volume 1, 

EPA-453K-04-001A, and provided on the EPA’s Technology Transfer Network website for Fate, Exposure, and 

Risk Assessment at http://www2.epa.gov/fera/risk-assessment-and-modeling-air-toxics-risk-assessmentreference-library
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category and are included in the environmental risk screen: mercury compounds, cadmium, 

lead, and hydrofluoric acid.

Table 3.1-1 Summary of Emissions from the Phosphate Fertilizer Production Source Category and 

Availability of Dose-Response Values

HAPa Emissions 

(tpy)

Number of 

Facilities 

Reporting HAP 

(11 facilities in 

ICR data set)

Prioritized Inhalation Dose-Response Value 

Identified by OAQPSb

PBHAP? Unit Risk 

Estimate for 

Cancer?

Reference 

Concentration 

for Chronic 

Noncancer?

Health 

Benchmark 

Values for 

Acute 

Noncancer?

Hydrofluoric Acid 101 11  

Manganese Compounds 0.5 11  

Nickel Compounds 0.3 11   

Mercury Compounds 

 Elemental Gaseous Mercury 0.01 11    

 Gaseous Divalent Mercury 0.004 11   

 Particulate Divalent Mercury 0.004 11   

Lead Compounds 0.10 11 c  

Chromium Compounds 

 Chromium (III) Compounds 0.09 11

 Chromium (VI) Compounds 0.04 11  

Cadmium Compounds 0.03 11   

Arsenic Compounds 0.03 11    

Selenium Compounds 0.02 11  

Cobalt Compounds 0.02 11  

Antimony Compounds 0.01 11  

Beryllium Compounds 0.005 11    a Notes for how HAP were speciated for risk assessment:

• For most metals, emissions reported as the elemental metal are combined with metal compound emissions (e.g., 

“cadmium” emissions modeled as “cadmium & compounds”). In the absence of speciation information, we assume 

the reported mass is 100 percent metal.

• For emissions reported generically as “chromium” or “chromium & compound,” emissions are speciated 75% 

“chromium (III) compounds” and 25% “chromium (VI) compounds” for this source category. Chromium 

speciation profiles can be found on the EPA’s Technology Transfer Network website for emissions inventories at 

http://www.epa.gov/ttn/chief/net/2005inventory.html. 

• For emissions reported generically as “mercury” or “mercury & compounds,” emissions are speciated for this 

category as 80% elemental gaseous mercury, 10% gaseous divalent mercury, and 10% particulate divalent 

mercury. Mercury speciation profiles can be found on the EPA’s Technology Transfer Network website for 

emissions inventories at http://www.epa.gov/ttn/chief/net/2005inventory.html. 

b Specific dose-response values for each chemical are identified on EPA’s Technology Transfer Network website for air 

toxics at http://www2.epa.gov/fera/dose-response-assessment-assessing-health-risks-associated-exposure-hazardous-airpollutants

c There is no reference concentration for lead. In considering noncancer hazards for lead in this assessment, we compared 

maximum annual ambient exposure estimates to the National Ambient Air Quality Standards (NAAQS) for lead (0.15 

μg/m3). These NAAQS for lead were reviewed with revisions adopted in October 2008 

(http://www.epa.gov/air/lead/actions.html). The primary (health-based) standard is a maximum or not-to-be-exceeded, rolling 

three-month average, measured as total suspended particles (TSP). The secondary (welfare-based) standard is identical to the 

primary standard.
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3.2 Baseline risk characterization

This section presents the results of the risk assessment for the phosphate fertilizer production

source category. The basic chronic inhalation risk estimates presented here are the maximum 

individual lifetime cancer risk, the maximum chronic hazard index, and the cancer incidence. 

We also present results from our acute inhalation impact screening in the form of maximum 

hazard quotients, as well as the results of our preliminary screen for potential non-inhalation 

risks from PB HAP. Also presented are the HAP “drivers,” which are the HAP that 

collectively contribute 90 percent of the maximum cancer risk or maximum hazard index at 

the highest exposure location. A detailed summary of the facility-specific risk assessment 

results is available in Appendix 4 of this document, Draft Detailed Risk Modeling Results.

Baseline Inhalation Risk Assessment Results Based on Actual Emissions

Table 3.2-1 summarizes the preliminary inhalation risk results for this source category. The 

results indicate that maximum lifetime individual cancer risks are less than 1 in a million. 

The total estimated cancer incidence from this source category is 0.001 excess cancer cases 

per year. The maximum chronic noncancer target organ specific hazard index (TOSHI) value 

for the source category could be up to 0.003 associated with emissions of hydrogen fluoride, 

indicating no significant potential for chronic noncancer impacts. 

Worst-case acute hazard quotients (HQs) were calculated for every HAP shown in Table 2.6-

3 that has an acute benchmark. Based on maximum hourly emission estimates an emissions 

multiplier of 10 was used to estimate the peak hourly emission rates for this source category. 

See the memorandum entitled, “Emissions Data Used in Residual Risk Modeling: Phosphoric 

Acid and Phosphate Fertilizer Production Source Categories” for detailed description of how 

the maximum hourly emissions were developed for this source category (found in the docket 

for this rule making). No HAP in this source category had an acute HQ of greater than 1 for 

any benchmark. 

Table 3.2-1 Summary of Source Category Level Inhalation Risks for Phosphate 

Fertilizer Production

Result HAP “Drivers”

Facilities in Source Category

Number of Facilities Estimated to be in Source 

Category 11 n/a

Number of Facilities Modeled in Risk 

Assessment 11 n/a

Cancer Risks

Maximum Individual Lifetime Cancer Risk 

(in 1 million)

0.5 chromium (VI) compounds, nickel 

compounds
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Table 3.2-1 Summary of Source Category Level Inhalation Risks for Phosphate 

Fertilizer Production

Result HAP “Drivers”

Number of Facilities with Maximum Individual Lifetime Cancer Risk:

Greater than or equal to 100 in 1 

million 0 n/a

Greater than or equal to 10 in 1 million 0

Greater than or equal to 1 in 1 million 0

Chronic Noncancer Risks

Maximum Skeletal Hazard Index 0.003 Hydrogen fluoride

Number of Facilities with Maximum Skeletall Hazard Index:

Greater than 1 0 n/a

Acute Noncancer Screening Results

Maximum Acute Hazard Quotient

0.4

0.09

0.04

0.003

0.004

elemental gaseous mercury (REL)

hydrofluoric acid (AEGL-1)

hydrofluoric acid (ERPG-1)

hydrofluoric acid (AEGL-2)

hydrofluoric acid (ERPG-2)

Number of Facilities With Potential for Acute 

Effects 0 n/a

Population Exposure

Number of People Living Within 50 

Kilometers of Facilities Modeled 5,000,000 n/a

Number of People Exposed to Cancer Risk:

Greater than or equal to 100 in 1 

million 0 n/a

Greater than or equal to 10 in 1 million 0 n/a

Greater than or equal to 1 in 1 million 0 n/a

Number of People Exposed to Noncancer Skeletal Hazard Index:

Greater than 1 0 n/a

Estimated Cancer Incidence (excess cancer 

cases per year) 0.001 n/a

Contribution of HAP to Cancer Incidence

chromium (VI) compounds 65% n/a

nickel compounds 19% n/a

arsenic compounds 11% n/a

Baseline Inhalation Assessment Results Based on Allowable Emissions

Analysis of potential differences between actual emissions levels and the maximum emissions 

allowable under the MACT standards were also calculated for this source category. 

Based upon the above analysis we multiplied the modeled actual risks for the MIR facility 

with process site-specific multipliers to estimate allowable risks under the MACT. This 

approach was deemed sufficient due to the low actual modeled risks for the source category. 
Residual Risk Assessment for the Phosphate Fertilizer and Phosphoric Acid Source 

Categories in Support of the July 2015 Risk and Technology Review Final Rule

35

The maximum lifetime individual cancer risks based upon allowable are still less than 1 in a 

million. The maximum chronic noncancer TOSHI value for allowable emissions was 

estimated with an estimated HI = 0.003, the same as the actuals.

Baseline Multipathway Assessment Results Based on Actual Emissions

To identify potential multipathway health risks from PB-HAP other than lead, we first 

performed a screening analysis (Tier 1) that compared emissions of PB-HAP emitted from the 

phosphate fertilizer production source category (based on actual emissions) to screening

emission rates (see section 2.5) see Appendix 5 for a more detailed discussion of the 

screening emission rates. The PB-HAP emitted by facilities in this category include mercury 

compounds (11 facilities), lead compounds (11 facilities), and cadmium compounds (11 

facilities. We compared reported emissions of PB-HAP to the Tier 1 screening emission 

thresholds established by the EPA for the purposes of the RTR risk assessments. One facility 

(NEIMS059305) emitted divalent mercury (Hg2+) above the Tier 1 screening threshold level, 

exceeding the screening threshold by a factor of 20, and we found it necessary to conduct a 

Tier 2 screening assessment.

We refined our Tier 2 screen with some additional site-specific information (See Appendix 5 

of this document, Technical Support Document for TRIM-Based Multipathway Screening 

Scenario for RTR: Summary of Approach and Evaluation, for more information about the 

Tier 2 screen.). The additional site-specific information included the location of fishable 

lakes and local wind direction and speed. The result of this analysis was the development of 

site-specific emission screening levels for each of the four PB-HAP for the farmer and fisher 

scenarios. 

Similar to the results from the Tier 1 screen, an exceedance of a screening level in Tier 2 

cannot be equated with a risk value or a hazard quotient (or hazard index). Rather, it 

represents a high-end estimate of what the risk or hazard may be. For example, an 

exceedance of 2 for a non-carcinogen can be interpreted to mean that we have high 

confidence that the hazard would be lower than 2. Similarly, an exceedance of 30 for a 

carcinogen means that we have high confidence that the risk is lower than 30 in a million. 

Our confidence comes from the conservative, or health-protective, assumptions that are in the 

screens: we choose inputs from the upper end of the range of possible values for the 

influential parameters used in the screens; and we assume that the exposed individual exhibits 

ingestion behavior that would lead to a high total exposure. 

Based on this Tier 2 screening analysis, mercury emissions exceeded the non-cancer 

screening thresholds by a factor of 3 for the subsistence fisher. A review of the 3 lakes the 

fisher needed to fish to support his fish ingestion intake of 373 grams/day indicated the lakes 

were fishable. Due to the theoretical construct of the screening model, these factors are not 

directly translatable into estimates of risk or hazard quotients for these facilities. Table 3.2-2 

summarizes the results of the Tier 1 and Tier 2 screens. We further note that these results are 

biased high for purposes of screening and are therefore subject to significant uncertainties.
Residual Risk Assessment for the Phosphate Fertilizer and Phosphoric Acid Source 

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Table 3.2-2 Summary of the “Tier 1 and 2” Multipathway Screening Analysis for the 

Phosphate Fertilizer Production Source Category

To refine our mercury Tier 2 screen for Mississippi Phosphates, we proceeded to a Tier 3 

screen; a) we examined the set of lakes from which the fisher ingested fish. Any lakes that 

appeared to not be fishable or not publicly accessible were removed from the assessment, and 

the screening assessment was repeated. After we made the determination the 3 critical lakes 

were fishable, we analyzed b) plume rise data, a review of the hourly data indicated about a 

33 percent decrease in the Tier 2 screening value resulting in a value of 2; we than proceeded 

to step c) assessment of time-series meteorology and effective stack release heights. Using 

TRIM-FaTE for the Tier 2 screening scenario for Mississippi Phosphates we used time-series 

meteorology to calculate the cumulative loss of chemical being released above the mixing 

height as well as fate and transport effects for each lake body. This refined screen reduced the 

media concentration and exposures for each of the lakes and decreased the non-cancer

screening value to 1, refer to Table 3.2-3. 

For this source category our analysis indicates the multipathway risks are at levels that are 

acceptable and below a level of concern. For further discussion on the methods and results 

for each of the three Tier screens, see Appendix 9 of this document, Technical Support 

Document: Human Health Multipathway Residual Risk Screening Assessment for the 

Phosphate Fertilizer Production and Phosphoric Acid Manufacturing Source Categories.

Table 3.2-3 Summary of the “Tier 2 and 3” Multipathway Screening Analysis for 

Phosphate Fertilizer Production for Mississippi Phosphates (NEIMS059305)
Residual Risk Assessment for the Phosphate Fertilizer and Phosphoric Acid Source 

Categories in Support of the July 2015 Risk and Technology Review Final Rule

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The multipathway screens includes some hypothetical elements, namely the hypothetical 

farmer and fisher scenarios. We also included children in different age ranges and adults. It 

is important to note that even though the multipathway assessment has been conducted, no 

data exist to verify the existence of either the farmer or fisher scenario outlined above. The 

farmer scenario involves an individual living for a 70-year lifetime on a farm homestead in 

the vicinity of the source and consuming produce grown on, and meat and animal products 

raised on, the farm. The individual also incidentally ingests surface soil at the location of the 

farm homestead. The fisher scenario involves an individual who regularly consumes fish 

caught in freshwater lakes in the vicinity of the source of interest over the course of a 70-year 

lifetime. In addition, exposure estimates and risks for infants consuming contaminated breast 

milk were evaluated in the case of dioxins, with the assumption that the nursing mother was 

exposed to chemicals via one of the two scenarios described above. 

In evaluating the potential for multipathway effects from emissions of lead, modeled 

maximum annual lead concentrations were compared to the secondary NAAQS for lead

(0.15 μg/m3

). Results of this analysis estimate that the NAAQS for lead would not be 

exceeded at any off-site locations. 

Baseline Environmental Risk Screening Based on Actual Emissions

We conducted a screening-level evaluation of the potential for adverse environmental risks 

associated with emissions of the following environmental HAP from the phosphate fertilizer 

production source category: mercury, cadmium, lead compounds, and hydrofluoric acid. The 

results of the environmental screening analysis are summarized in Table 3.2-4. In our Tier 1

analysis, emissions of mercury and cadmium did not exceed the threshold emission rates for

any of the ecological benchmarks (either the LOAEL or NOAEL) for any facility in the 

source category. Therefore, we did not conduct a Tier 2 environmental screening analysis. 

For lead, estimates of air concentrations did not exceed the secondary lead NAAQS for any 

individually modeled point for any facility. For hydrofluoric acid, the average modeled 

concentration around each facility (i.e., the average concentration of all offsite data points in 

the modeling domain) did not exceed any ecological benchmark (either the LOAEL or 

NOAEL). In addition, each individual modeled concentration of hydrofluoric acid (i.e., each 

offsite data point in the modeling domain) was below the ecological benchmarks for all 
Residual Risk Assessment for the Phosphate Fertilizer and Phosphoric Acid Source 

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38

facilities. 

Table 3.2-4 Summary of Environmental Risk Screening Results for the Phosphate 

Fertilizer Production Source Category 

Environmental 

HAP

Number of Facilities In Category 

Exceeding

Percent of Modeled 

Area in Category 

Exceeding2

Tier 1 Screen Tier 2 Screen1

NOAEL LOAEL NOAEL LOAEL NOAEL LOAEL

PBHAP

Hg None None - - 0% 0% 

Cd None None - - 0% 0% 

Pb None None - - 0% 0%

Acid 

Gases3 HF NA None - - NA 0%

NA – Not applicable.

1- Tier 2 screen is only performed for PB-HAP (other than lead) when there are exceedances of the Tier 1

screen. The acid gas and lead screens are a single-tier.

2 - A value of 0% indicates that none of the modeled data points exceeded the benchmark. For PB-HAP the 

percent area is based on the Tier 2 results, if a Tier 2 analysis is performed. Otherwise, the percent area is based 

on the Tier 1 results. 

3 – For HF, we evaluated two benchmarks, one from Canada and the other from the State of Washington. 

Although, they are both considered to be LOELs - the level between a NOAEL and a LOAEL, we have listed the 

results under the LOAEL column. 

Facility-wide Inhalation Assessment Results

The facility-wide MIR and TOSHI, available in Appendix 4, are based on actual emissions, as 

collected in the 114 request, from all emissions sources at the identified facilities. The results 

indicate that 11 facilities with phosphate fertilizer production processes have facility-wide 

cancer MIR less than or equal to 1 in a million. The maximum facility-wide TOSHI is less 

than 1. 

4. Risk Results for the Phosphoric Acid Manufacturing Source 

Category

4.1 Source category description and results

Phosphoric acid manufacturing facilities produce phosphoric acid from beneficiated 

phosphate rock. Phosphoric acid is used as a food additive, chemical feedstock, and as a raw 

material for certain types of fertilizers and animal feed supplements. Phosphoric Acid 

Manufacturing (40 CFR part 63 subpart AA) and Phosphate Fertilizers Production (40 CFR 

part 63 subpart BB) were established as separate source categories by EPA. However, these 

operations are generally collocated, as phosphoric acid manufacturing facilities provide 

feedstock for phosphate fertilizer production facilities and much of the phosphoric acid 

produced in the U.S. is consumed in the manufacture of fertilizers. 
Residual Risk Assessment for the Phosphate Fertilizer and Phosphoric Acid Source 

Categories in Support of the July 2015 Risk and Technology Review Final Rule

39

Phosphate rock is the starting material for the production of all phosphate products. All 

phosphate rock deposits contain mercury, fluorides, carbonates, and radionuclides along with 

the phosphate nodules of the ore. Once the ore reaches the phosphoric acid production 

facility, phosphoric acid is produced using the wet method, in which beneficiated ground 

phosphate rock is reacted with sulfuric acid and weak phosphoric acid to produce phosphoric 

acid and phosphogypsum, a waste product. The phosphogypsum is disposed of on-site in 

waste piles known as “gypsum stacks.” Emission points along the phosphoric acid 

manufacturing process include wet process phosphoric acid process lines, superphosphoric 

acid process lines (including vacuum evaporation processes and submerged combustion 

processes), phosphate rock dryers, phosphate rock calciners, dryers, cooling ponds, and 

gypsum stacks.

Emissions data, collected via ICRs sent to all U.S. phosphoric acid manufacturers, were used 

to conduct this assessment. The data include information regarding process equipment, 

control devices, phosphate and acid production, inventory data for all point and fugitive 

emissions, practices used to control fugitive emissions, and other aspects of facility 

operations, including stack parameters and locations.

We currently estimate that there are twelve phosphoric acid manufacturing facilities operating 

in the United States. Ten of these twelve facilities are co-located with phosphate fertilizer 

production facilities. The 2005 National-Scale Air Toxics Assessment (NATA) National 

Emissions Inventory (NEI) was updated for this category with data from industry and 

enhanced quality assurance of facility and emission locations. Eleven of these twelve facilities 

are identified as major sources in the NEI, and one facility is identified as an area source. 

The emissions for the phosphoric acid manufacturing source category ICR data set (of twelve

facilities) are summarized in Table 4.1-1. The total HAP emissions for the source category are 

approximately 2,200 tons per year. Based on these data, the HAP emitted in the largest 

quantities across these twelve facilities are hydrofluoric acid and hydrochloric acid. These two

chemicals account for 99 percent of the total emissions by mass. Persistent and 

bioaccumulative HAP (PB HAP)27 reported as emissions from these facilities include 

cadmium compounds, chlorinated dibenzodioxins and furans (analyzed as 2,3,7,8-

tetrachlorodibenzo-p-dioxin), lead, mercury, and polycyclic organic matter (analyzed as 

benzo(a)pyrene). The following environmental HAP are emitted from the phosphoric acid 

manufacturing source category and are included in the environmental risk screen: mercury 

compounds, cadmium, POM, dioxins/furans, lead, hydrochloric acid and hydrofluoric acid.

 27 Persistent and bioaccumulative HAP are defined in the EPA’s Air Toxics Risk Assessment Library, Volume 1, 

EPA-453K-04-001A, and provided on the EPA’s Technology Transfer Network website for Fate, Exposure, and 

Risk Assessment at http://www2.epa.gov/fera/risk-assessment-and-modeling-air-toxics-risk-assessmentreference-library
Residual Risk Assessment for the Phosphate Fertilizer and Phosphoric Acid Source 

Categories in Support of the July 2015 Risk and Technology Review Final Rule

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Table 4.1-1 Summary of Emissions from the Phosphoric Acid Manufacturing Source Category and 

Availability of Dose-Response Values

HAPa Emissions (tpy)

Number of 

Facilities 

Reporting HAP 

(12 facilities in 

ICR data set)

Prioritized Inhalation Dose-Response Value 

Identified by OAQPSb

PBHAP?

Unit Risk 

Estimate 

for 

Cancer?

Reference 

Concentration 

for Chronic 

Noncancer?

Health 

Benchmark 

Values for 

Acute 

Noncancer?

Hydrofluoric Acid 2194 12   

Hydrochloric Acid 27 1   

Methyl Isobutyl Ketone 9 1  

Mercury Compounds

 Elemental Gaseous Mercury 0.1 12    

 Gaseous Divalent Mercury 0.001 12   

 Particulate Divalent Mercury 0.001 12   

Nickel Compounds 0.2 12   

Manganese Compounds 0.08 12  

Chromium Compounds 

 Chromium (III) Compounds 0.06 11

 Chromium (VI) Compounds 0.01 12  

Cyanide Compounds 0.06 1  

Selenium Compounds 0.05 12  

Formaldehyde 0.04 1   

Benzene 0.03 1    

Cadmium Compounds 0.02 12   

Lead Compounds 0.02 12 c  

Benzyl Chloride 0.02 1   

Isophorone 0.01 1   

Acetaldehyde 0.01 1   

Methyl Chloride 0.01 1   

Toluene 0.01 1   

Arsenic Compounds 0.009 12    

Propionaldehyde 0.008 1  

Acrolein 0.006 1   

Methylene Chloride 0.006 1    

Cobalt Compounds 0.005 12  

Methyl Hydrazine 0.004 1 

Methyl Bromide 0.004 1   

Carbon Disulfide 0.003 1   

Antimony Compounds 0.003 12  

Ethyl Benzene 0.002 1   

Polycyclic Organic Matter 

 Polycyclic Organic Matter 0.002 1   

 Benzo[a]pyrene 0.0000009 1   

N-hexane 0.001 1   
Residual Risk Assessment for the Phosphate Fertilizer and Phosphoric Acid Source 

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Table 4.1-1 Summary of Emissions from the Phosphoric Acid Manufacturing Source Category and 

Availability of Dose-Response Values

HAPa Emissions (tpy)

Number of 

Facilities 

Reporting HAP 

(12 facilities in 

ICR data set)

Prioritized Inhalation Dose-Response Value 

Identified by OAQPSb

PBHAP?

Unit Risk 

Estimate 

for 

Cancer?

Reference 

Concentration 

for Chronic 

Noncancer?

Health 

Benchmark 

Values for 

Acute 

Noncancer?

Naphthalene 0.001 1   

Chloroform 0.001 1  

Dimethyl Sulfate 0.001 1  

Ethyl Chloride 0.0009 1  

Ethylene Dichloride 0.0009 1   

Bromoform 0.0009 1  

Xylenes (mixed) 0.0008 1  

Methyl Tert-butyl Ether 0.0008 1    

Beryllium Compounds 0.0008 12    

Tetrachloroethene 0.0007 1    

Styrene 0.0006 1   

Chlorobenzene 0.0005 1   

Methyl Methacrylate 0.0004 1   

Phenol 0.0004 1   

Acetophenone 0.0003 1 

Vinyl Acetate 0.0002 1  

1,1,2,2-tetrachloroethane 0.0002 1 

2,4-dinitrotoluene 0.0002 1  

Cumene 0.0001 1  

O-xylene 0.0001 1   

Ethylene Dibromide 0.00003 1    

2-chloroacetophenone 0.000006 1  

Dioxins and Furans 

 1,2,3,6,7,8-

hexachlorodibenzo-p-dioxin 0.000000006 1    

a Notes for how HAP were speciated for risk assessment:

• For most metals, emissions reported as the elemental metal are combined with metal compound emissions (e.g., 

“cadmium” emissions modeled as “cadmium & compounds”). In the absence of speciation information, we assume 

the reported mass is 100 percent metal.

• For emissions reported generically as “chromium” or “chromium & compound,” emissions are speciated 75% 

“chromium (III) compounds” and 25% “chromium (VI) compounds” for this source category. Chromium 

speciation profiles can be found on the EPA’s Technology Transfer Network website for emissions inventories at 

http://www.epa.gov/ttn/chief/net/2005inventory.html. 

• For emissions reported generically as “mercury” or “mercury & compounds,” emissions are speciated for this 

category as 80% elemental gaseous mercury, 10% gaseous divalent mercury, and 10% particulate divalent 

mercury. Mercury speciation profiles can be found on the EPA’s Technology Transfer Network website for 

emissions inventories at http://www.epa.gov/ttn/chief/net/2005inventory.html. 

b Specific dose-response values for each chemical are identified on EPA’s Technology Transfer Network website for air 

toxics at http://www2.epa.gov/fera/dose-response-assessment-assessing-health-risks-associated-exposure-hazardous-airpollutants
Residual Risk Assessment for the Phosphate Fertilizer and Phosphoric Acid Source 

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42

c There is no reference concentration for lead. In considering noncancer hazards for lead in this assessment, we compared 

maximum annual ambient exposure estimates to the National Ambient Air Quality Standards (NAAQS) for lead (0.15 

μg/m3). These NAAQS for lead were reviewed with revisions adopted in October 2008 

(http://www.epa.gov/air/lead/actions.html). The primary (health-based) standard is a maximum or not-to-be-exceeded, rolling 

three-month average, measured as total suspended particles (TSP). The secondary (welfare-based) standard is identical to the 

primary standard.

4.2 Risk characterization

This section presents the results of the risk assessment for the phosphoric acid manufacturing

source category. The basic chronic inhalation risk estimates presented here are the maximum 

individual lifetime cancer risk, the maximum chronic hazard index, and the cancer incidence. 

We also present results from our acute inhalation impact screening in the form of maximum 

hazard quotients, as well as the results of our preliminary screen for potential non-inhalation 

risks from PB HAP. Also presented are the HAP “drivers,” which are the HAP that 

collectively contribute 90 percent of the maximum cancer risk or maximum hazard index at 

the highest exposure location. A detailed summary of the facility-specific risk assessment 

results is available in Appendix 4 of this document, Draft Detailed Risk Modeling Results.

Baseline Inhalation Risk Assessment Results Based on Actual Emissions

Table 4.2-1 summarizes the preliminary inhalation risk results for this source category. The 

results indicate that maximum lifetime individual cancer risks are less than 1 in a million. 

The total estimated cancer incidence from this source category is 0.0002 excess cancer cases 

per year. The maximum chronic noncancer target organ specific hazard index (TOSHI) value 

for the source category could be up to 0.2 associated with emissions of hydrofluoric acid, 

indicating no significant potential for chronic noncancer impacts. 

Worst-case acute hazard quotients (HQs) were calculated for every HAP that has an acute 

benchmark, two facilities were identified with HQ values > 1. For cases where the screening 

acute HQ was greater than 1, we further refined the estimates by determining the highest HQ 

value that is outside facility boundaries. The highest refined worst-case acute HQ value is 2

(based on the acute REL for hydrofluoric acid) as shown in Table 4.2-1. The HQ values of 2 

represent an upper-bound risk estimate for both facilities; the off-site locations for these sites 

were either located in a rural location in which public access is limited or in an off-site area 

that maybe owned by the facility. 

For phosphoric acid manufacturing facilities a multiplier of 10 (x) was developed for point 

sources based upon the ICR data and engineering knowledge of the source category processes 

and for emissions from area sources (gypsum ponds and cooling ponds) an acute multiplier of 

1 was used. The hydrogen fluoride emissions from the phosphoric acid manufacturing

gypsum ponds drove the acute risks. This emission source was considered stable and 

homogeneous and not subject to major fluctuations in hourly emissions over the year and 

therefore an acute refined multiplier of 1 was applied. This was applied to all area sources in 

the entire source category except for one facility that had site-specific data for their gypsum 

ponds, an acute multiplier of 2 was applied for this facility. This data was considered unique 

to the facility and not applicable for any other site. For further information on the acute 

multipliers applied for this source category refer to the memo entitled “Emissions Data Used 

in Residual Risk Modeling: Phosphoric Acid and Phosphate Fertilizer Production Source 
Residual Risk Assessment for the Phosphate Fertilizer and Phosphoric Acid Source 

Categories in Support of the July 2015 Risk and Technology Review Final Rule

43

Categories” available in the docket for this action. Table 4.2-2 provides more information on 

the refined acute risk estimates for HAP that had an acute HQ greater than 1 for any 

benchmark, refer to Appendix 8 to this document (Acute Noncancer Risk Analysis for 

Phosphate Fertilizer and Phosphoric Acid Manufacturing) Table 2 for further information. 

For acute risks, 4 facilities exceeded the HEM-3 screen for hydrofluoric acid exceeding the 

REL by a factor of 2. A refined analysis of these sites identified 2 facilities with off-site 

concentrations exceeding the REL. The worst-case maximum estimated 1-hour exposure to 

hydrofluoric acid outside the facility fence line for the phosphoric acid manufacturing source 

category is 530 ug/m3. This estimated worst-case exposure exceeds the 1-hour REL by a 

factor of 2 (HQREL = 2) and is below the 1-hour AEGL-1 (HQAEGL-1 = 0.6). 

Worst-case acute hazard quotients (HQs) were calculated for every HAP shown in Table 4-1

that has an acute benchmark, and the highest acute HQ (and associated HAP) is shown in 

Table 4.2-1. 
Residual Risk Assessment for the Phosphate Fertilizer and Phosphoric Acid Source 

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Table 4.2-1 Summary of Source Category Level Inhalation Risks for Phosphoric Acid 

Manufacturing

Result HAP “Drivers”

Facilities in Source Category

Number of Facilities Estimated to be in Source 

Category 12 n/a

Number of Facilities Modeled in Risk 

Assessment 12 n/a

Cancer Risks

Maximum Individual Lifetime Cancer Risk 

(in 1 million)

0.09 chromium (VI) compounds, arsenic 

compounds, nickel compounds

Number of Facilities with Maximum Individual Lifetime Cancer Risk:

Greater than or equal to 100 in 1 million 0 n/a

Greater than or equal to 10 in 1 million 0

Greater than or equal to 1 in 1 million 0

Chronic Noncancer Risks

Maximum Skeletal Hazard Index 0.2 hydrofluoric acid

Maximum Neurological Hazard Index 0.002 manganese compounds

Number of Facilities with Maximum Skeletal or Neurological Hazard Index:

Greater than 1 0 n/a

Acute Noncancer Refined Results

Maximum Acute Hazard Quotient

2

0.6

0.3

0.03

0.03

hydrofluoric acid (REL)

hydrofluoric acid (AEGL-1)

hydrofluoric acid (ERPG-1)

hydrofluoric acid (AEGL-2)

hydrofluoric acid (ERPG-2)

Number of Facilities With Potential for Acute 

Effects 2 hydrofluoric acid

Population Exposure

Number of People Living Within 50 Kilometers 

of Facilities Modeled 5,200,000 n/a

Number of People Exposed to Cancer Risk:

Greater than or equal to 100 in 1 million 0 n/a

Greater than or equal to 10 in 1 million 0 n/a

Greater than or equal to 1 in 1 million 0 n/a

Number of People Exposed to Noncancer Skeletal or Neurological Hazard Index:

Greater than 1 0 n/a

Estimated Cancer Incidence 

(excess cancer cases per year) 0.0002 n/a

Contribution of HAP to Cancer Incidence

chromium (VI) compounds 56% n/a

arsenic compounds 23% n/a

nickel compounds 12% n/a
Residual Risk Assessment for the Phosphate Fertilizer and Phosphoric Acid Source Categories in Support of the July 2015 

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Table 4.2-2 Summary of Acute Refined Results for Phosphoric Acid Manufacturing

MAXIMUM ACUTE HAZARD 

QUOTIENTS ACUTE DOSE-RESPONSE VALUES

Screening Results

Based on 

REL

Based on 

AEGL-1/

ERPG-1

Based on 

AEGL-2/

ERPG-2

REL

(mg/m3

)

AEGL-1

(1-hr) 

(mg/m3

)

ERPG-1 

(mg/m3

)

AEGL-2

(1-hr)

(mg/m3

)

ERPG-2 

(mg/m3

)

HAP

Max. 1-

hr. Air 

Conc. 

(mg/m3

)

Hydrofluoric acid 0.5 2 0.6 / 0.3 0.03 / 0.03 0.24 0.82 1.6 20 16

Notes on Screening Process:

1) The screening was performed for all emitted HAP with available acute dose-response values. Only those pollutants whose screening HQs were greater than 1 for at least 

one acute threshold value are shown in the table.

2) HAP with available acute dose-response values which are not in the table do not carry any potential for posing acute health risks, based on an analysis of currently 

available emissions data.

Notes on Acute Dose-Response Values:

REL – California EPA reference exposure level for no adverse effects. Most, but not all, RELs are for 1-hour exposures.

AEGL – Acute exposure guideline levels represent emergency exposure (1-hour) limits for the general public.

AEGL-1 is the exposure level above which it is predicted that the general population, including susceptible individuals, could experience effects that are 

 notable discomfort, but which are transient and reversible upon cessation of exposure.

AEGL-2 is the exposure level above which it is predicted that the general population, including susceptible individuals, could experience irreversible or other serious, 

 long-lasting adverse health effects or an impaired ability to escape.

ERPG – Emergency Removal Program guidelines represent emergency exposure (1-hour) limits for the general public.

ERPG-1 is the maximum level below which it is believed that nearly all individuals could be exposed for up to 1 hour without experiencing other than mild, transient 

 adverse health effects.

ERPG-2 is the maximum exposure below which it is believed that nearly all individuals could be exposed for up to 1 hour without experiencing or developing 

 irreversible or other serious health effects or symptoms which could impair an individual’s ability to take protective action.
Residual Risk Assessment for the Phosphate Fertilizer and Phosphoric Acid Source 

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Baseline Inhalation Assessment Results Based on Allowable Emissions

Analysis of potential differences between actual emissions levels and the maximum emissions 

allowable under the MACT standards were also calculated for this source category. Based 

upon the above analysis we multiplied the modeled actual risks for the MIR facility with 

process site-specific multipliers to estimate allowable risks under the MACT. This approach 

was deemed sufficient due to the low actual modeled risks for the source category. The 

maximum lifetime individual cancer risks based upon allowable are still less than 1 in a 

million. The maximum chronic noncancer TOSHI value for allowable emissions was 

estimated at an HI = 0.3.

Baseline Multipathway Assessment Results Based on Actual Emissions

To identify potential multipathway health risks from PB-HAP other than lead, we first 

performed a screening analysis (Tier 1) that compared emissions of PB-HAP emitted from the 

phosphoric acid manufacturing source category (based on actual emissions) to screening

emission rates (see section 2.5) see Appendix 5 for a more detailed discussion of the 

screening emission rates. The PB-HAP emitted by facilities in this category include mercury 

compounds (12 facilities), lead compounds (12 facilities), dioxin/furan compounds 

(1 facility), POM compounds (1 facility) and cadmium compounds (12 facilities). We 

compared reported emissions of PB-HAP to the Tier 1 screening emission thresholds 

established by the EPA for the purposes of the RTR risk assessments. One facility, PCS in 

Aurora, NC (NEI45571) emitted divalent mercury (Hg2+) and cadmium compounds above the 

Tier 1 screening threshold level, exceeding the mercury screening threshold by a factor of 7 

and cadmium by a factor of 2, we than proceeded to conduct a Tier 2 screening assessment for 

divalent mercury and cadmium.

For these PB-HAP’s and facility that did not screen out during our initial Tier 1 multipathway 

screening analysis, we improved our analysis with some additional site-specific information 

to develop a Tier 2 screen. (See Appendix 5 of this document, Technical Support Document 

for TRIM-Based Multipathway Screening Scenario for RTR: Summary of Approach and 

Evaluation, for more information about the Tier 2 screen.) The additional site-specific 

information included the location of fishable lakes and local wind direction and speed. The 

result of this analysis was the development of site-specific emission screening levels for each 

of the four PB-HAP for the farmer and fisher scenarios. 

Based on this Tier 2 screening analysis, the cadmium is below the non-cancer screening 

threshold with a value of 0.3 and at a value of 1 for mercury for the subsistence fisher

scenario. Both screening values for cadmium and mercury are below a level of concern. Due 

to the theoretical construct of the screening model, these factors are not directly translatable 

into estimates of risk or hazard quotients for these facilities. Table 4.2-3 summarizes the 

results of the Tier 1 and Tier 2 screens. We further note that these results are biased high for

purposes of screening and are therefore subject to significant uncertainties.
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Table 4.2-3 Summary of the “Tier 1 and 2” Multipathway Screening Analysis for the 

Phosphoric Acid Manufacturing Source Category

The Tier 2 Screen includes some hypothetical elements, namely the hypothetical farmer and 

fisher scenarios. It is important to note that even though the multipathway assessment has 

been conducted, no data exist to verify the existence of either the farmer or fisher scenario 

outlined above. The farmer scenario involves an individual living for a 70-year lifetime on a 

farm homestead in the vicinity of the source and consuming produce grown on, and meat and 

animal products raised on, the farm. The individual also incidentally ingests surface soil at 

the location of the farm homestead. The fisher scenario involves an individual who regularly 

consumes fish caught in freshwater lakes within a radius of 50 km of the emission source. In 

addition, exposure estimates and risks for infants consuming contaminated breast milk were 

evaluated in the case of dioxins, with the assumption that the nursing mother was exposed to 

chemicals via one of the two scenarios described above. 

Baseline Environmental Risk Screening Based on Actual Emissions

We conducted a screening-level evaluation of the potential for adverse environmental risks 

associated with emissions of the following environmental HAP from the phosphoric acid 

manufacturing source category: mercury compounds, cadmium, POM, dioxins, lead, 

hydrochloric acid and hydrofluoric acid. The results of the environmental screening analysis 

are summarized in Table 4.2-4. In our Tier 1 analysis, emissions of mercury, cadmium, POM, 

and dioxins did not exceed the threshold emission rates for any of the ecological benchmarks 

(either the LOAEL or NOAEL) for any facility in the source category. Therefore, we did not 

conduct a Tier 2 environmental screening analysis. For lead, there were no exceedances of 

the secondary lead NAAQS for any individual data point for any facility. 

For hydrochloric acid and hydrofluoric acid, the average modeled concentration around each 

facility (i.e., the average concentration of all offsite data points in the modeling domain) did 

not exceed any ecological benchmark (either the LOAEL or NOAEL). In addition, each 

individual modeled concentration of hydrochloric acid (i.e., each offsite data point in the 

modeling domain) was below the ecological benchmarks for all facilities. The results of the 
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environmental screen for hydrofluoric acid are summarized for each facility in Table 3 of 

Appendix 7. For hydrofluoric acid, less than 1 percent of the offsite modeling domain for the 

entire source category was above the Canadian LOEL ecological benchmark. The largest 

facility exceedance area based on the Canadian LOEL ecological benchmark represented 6

percent of that facility’s total offsite modeled area.

Table 4.2-4 Summary of Environmental Risk Screening Results for the Phosphoric Acid 

Manufacturing Source Category 

Environmental 

HAP

Number of Facilities In Category 

Exceeding

Percent of Modeled 

Area in Category 

Exceeding

Tier 1 Screen Tier 2 Screen1

NOAEL LOAEL NOAEL LOAEL NOAEL LOAEL

PBHAP

Hg None None - - 0% 0% 

Cd None None - - 0% 0% 

POM None None - - 0% 0% 

Dioxins None None - - 0% 0% 

Pb None None - - 0% 0%

Acid 

Gases3

HCl NA None - - 0% 0%

HF NA None - - NA 1%

NA – Not applicable.

1- Tier 2 screen is only performed for PB-HAP when there are exceedances of the Tier 1 screen. The acid gas 

screen is a one tier screen.

2 - A value of 0% indicates that none of the modeled data points exceeded the benchmark. For PB-HAP the 

percent area is based on the Tier 2 results, if a Tier 2 analysis is performed. Otherwise, the percent area is based 

on the Tier 2 results. 

3 – For HF, we evaluated two benchmarks, one from Canada and the other from the State of Washington. 

Although, they are both considered to be LOELs - the level between a NOAEL and a LOAEL, we have listed the 

results under the LOAEL column. The results presented in the table are the same for both the LOEL from 

Canada and the LOEL from the State of Washington (i.e., no facility showing exceedance of averaged values; 

1% of area in category showing and exceedance). For HCl, we evaluated one benchmark at the LOAEL level.

Facility-wide Inhalation Assessment Results

The facility-wide MIR and TOSHI, available in Appendix 4, are based on emissions as 

identified in the NEI, from all emissions sources at the identified facilities. The results of the 

facility-wide indicate that eleven facilities with phosphoric acid manufacturing processes have 

a facility-wide cancer MIR less than or equal to 1 in a million. The maximum facility-wide 

TOSHI for the source category is 0.2.

5. General Discussion of Uncertainties and How They Have Been 

Addressed

5.1 Exposure modeling uncertainties

Although every effort has been made to identify all the relevant facilities and emission points, 

as well as to develop accurate estimates of the annual emission rates for all relevant HAP, the 
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uncertainties in our emission inventory likely dominate the uncertainties in our exposure 

modeling estimates. The chronic exposure modeling uncertainties are considered relatively 

small in comparison, since we are using EPA’s refined local dispersion model with sitespecific parameters and reasonably representative meteorology. If anything, the population 

exposure estimates are biased high by not accounting for short- or long-term population 

mobility and by neglecting processes like deposition, plume depletion, and atmospheric 

degradation. Additionally, estimates of the maximum individual risk (MIR) contain 

uncertainty, because they are derived at census block centroid locations rather than actual 

residences. This uncertainty is known to create potential underestimates and overestimates of 

the actual MIR values for individual facilities, but, overall, it is not thought to have a 

significant impact on the estimated MIR for a source category. Finally, we did not factor in 

the possibility of a source closure occurring during the 70-year chronic exposure period, 

leading to a potential upward bias in both the MIR and population risk estimates; nor did we 

factor in the possibility of population growth during the 70-year chronic exposure period, 

leading to a potential downward bias in both the MIR and population risk estimates.

A sensitivity analysis performed for the 1999 NATA found that the selection of the 

meteorology dataset location could result in a range of chronic ambient concentrations which 

varied from as much as 17 percent below the estimated value to as much as 84 percent higher 

than the estimated value. This variability translates directly to the estimated exposures and 

risks in our assessment, indicating that the actual risks could vary from 17 percent lower to 84

percent higher than the estimated values.

We have purposely biased the acute screening results high, considering that they depend upon 

the joint occurrence of independent factors, such as hourly emissions rates, meteorology and 

human activity patterns. In cases where multiple acute threshold values are considered 

scientifically acceptable we have chosen the most conservative of these threshold values, 

erring on the side of overestimating potential health risks from acute exposures. In the cases 

where these results indicated the potential for exceeding short-term health thresholds postcontrols, we refined our assessment by developing a better understanding of the geography of 

the facility relative to potential exposure locations. 

5.2 Uncertainties in the dose-response relationships

In the sections that follow, separate discussions are provided on uncertainty associated with 

cancer potency factors and noncancer reference values. Cancer potency values are derived for 

chronic (lifetime) exposures. Noncancer reference values are generally derived for chronic 

exposures (up to a lifetime), but may also be derived for acute (<24 hours), short-term (>24 

hours up to 30 days), and subchronic (>30 days up to 10percent of lifetime) exposure 

durations, all of which are derived based on an assumption of continuous exposure throughout 

the duration specified. For the purposes of assessing all potential health risks associated with 

the emissions included in an assessment, we rely on both chronic (cancer and noncancer) and 

acute (noncancer) benchmarks, which are described in more detail below.

Although every effort is made to identify peer-reviewed dose-response values for all HAPs

emitted by the sources included in an assessment, some HAP have no peer-reviewed cancer 

potency values or reference values for chronic non-cancer or acute effects (inhalation or 
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ingestion). Since exposures to these pollutants cannot be included in a quantitative risk 

estimate, an understatement of risk for these pollutants at environmental exposure levels is 

possible.

Additionally, chronic dose-response values for certain compounds included in the assessment 

may be under EPA IRIS review, and revised assessments may determine that these pollutants 

are more or less potent than currently thought. We will re-evaluate risks if, as a result of these 

reviews, a dose-response metric changes enough to indicate that the risk assessment may 

significantly mischaracterize human health risk.

Cancer assessment

The discussion of dose-response uncertainties in the estimation of cancer risk below focuses 

on the uncertainties associated with the specific approach currently used by the EPA to 

develop cancer potency factors. In general, these same uncertainties attend the development 

of cancer potency factors by CalEPA, the source of peer-reviewed cancer potency factors 

used where EPA-developed values are not yet available. To place this discussion in context, 

we provide a quote from the EPA’s Guidelines for Carcinogen Risk Assessment [30] (herein 

referred to as Cancer Guidelines). “The primary goal of EPA actions is protection of human 

health; accordingly, as an Agency policy, risk assessment procedures, including default 

options that are used in the absence of scientific data to the contrary, should be health 

protective.” The approach adopted in this document is consistent with this approach as 

described in the Cancer Guidelines.

For cancer endpoints EPA usually derives an oral slope factor for ingestion and a unit risk 

value for inhalation exposures. These values allow estimation of a lifetime probability of 

developing cancer given long-term exposures to the pollutant. Depending on the pollutant 

being evaluated, EPA relies on both animal bioassay and epidemiological studies to 

characterize cancer risk. As a science policy approach, consistent with the Cancer 

Guidelines, EPA uses animal cancer bioassays as indicators of potential human health risk 

when other human cancer risk data are unavailable. 

Extrapolation of study data to estimate potential risks to human populations is based upon 

EPA’s assessment of the scientific database for a pollutant using EPA’s guidance documents 

and other peer-reviewed methodologies. The EPA Cancer Guidelines describes the Agency’s 

recommendations for methodologies for cancer risk assessment. EPA believes that cancer 

risk estimates developed following the procedures described in the Cancer Guidelines and 

outlined below generally provide an upper bound estimate of risk. That is, EPA’s upper 

bound estimates represent a “plausible upper limit to the true value of a quantity” (although 

this is usually not a true statistical confidence limit).28 In some circumstances, the true risk 

could be as low as zero; however, in other circumstances the risk could also be greater.29 

 28 IRIS glossary 

(http://ofmpub.epa.gov/sor_internet/registry/termreg/searchandretrieve/glossariesandkeywordlists/search.do?deta

ils=&glossaryName=IRIS%20Glossary ).

29 The exception to this is the URE for benzene, which is considered to cover a range of values, each end of 

which is considered to be equally plausible, and which is based on maximum likelihood estimates.
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When developing an upper bound estimate of risk and to provide risk values that do not 

underestimate risk, EPA generally relies on conservative default approaches.30 EPA also uses 

the upper bound (rather than lower bound or central) estimates in its assessments, although it 

is noted that this approach can have limitations for some uses (e.g. priority setting, expected 

benefits analysis).

Such health risk assessments have associated uncertainties, some which may be considered 

quantitatively, and others which generally are expressed qualitatively. Uncertainties may vary 

substantially among cancer risk assessments associated with exposures to different pollutants, 

since the assessments employ different databases with different strengths and limitations and 

the procedures employed may differ in how well they represent actual biological processes for 

the assessed substance. EPA’s Risk Characterization Handbook also recommends that risk 

characterizations present estimates demonstrating the impact on the assessment of alternative 

choices, data, models and assumptions [31]. Some of the major sources of uncertainty and 

variability in deriving cancer risk values are described more fully below. 

(1) The qualitative similarities or differences between tumor responses observed in 

experimental animal bioassays and those which would occur in humans are a source of 

uncertainty in cancer risk assessment. In general, EPA does not assume that tumor sites 

observed in an experimental animal bioassay are necessarily predictive of the sites at which 

tumors would occur in humans.31 However, unless scientific support is available to show 

otherwise, EPA assumes that tumors in animals are relevant in humans, regardless of target 

organ concordance. For a specific pollutant, qualitative differences in species responses can 

lead to either under-estimation or over-estimation of human cancer risks. 

(2) Uncertainties regarding the most appropriate dose metric for an assessment can also lead 

to differences in risk estimates. For example, the measure of dose is commonly expressed in 

units of mg/kg/d ingested or the inhaled concentration of the pollutant. However, data may 

support development of a pharmacokinetic model for the absorption, distribution, metabolism 

and excretion of an agent, which may result in improved dose metrics (e.g., average blood 

concentration of the pollutant or the quantity of agent metabolized in the body). Quantitative 

uncertainties result when the appropriate choice of a dose metric is uncertain or when dose 

metric estimates are themselves uncertain (e.g., as can occur when alternative 

 30 According to the NRC report Science and Judgment in Risk Assessment (NRC, 1994) “[Default] options are 

generic approaches, based on general scientific knowledge and policy judgment, that are applied to various 

elements of the risk-assessment process when the correct scientific model is unknown or uncertain.” The 1983 

NRC report Risk Assessment in the Federal Government: Managing the Process defined default option as “the 

option chosen on the basis of risk assessment policy that appears to be the best choice in the absence of data to 

the contrary” (NRC, 1983a, p. 63). Therefore, default options are not rules that bind the agency; rather, the 

agency may depart from them in evaluating the risks posed by a specific substance when it believes this to be 

appropriate. In keeping with EPA’s goal of protecting public health and the environment, default assumptions 

are used to ensure that risk to chemicals is not underestimated (although defaults are not intended to overtly 

overestimate risk). See EPA 2004 An Examination of EPA Risk Assessment Principles and Practices, 

EPA/100/B-04/001 available at: http://www.epa.gov/osa/pdfs/ratf-final.pdf 31 Per the EPA Cancer Guidelines: “The default option is that positive effects in animal cancer studies indicate 

that the agent under study can have carcinogenic potential in humans.” and “Target organ concordance is not a 

prerequisite for evaluating the implications of animal study results for humans.”
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pharmacokinetic models are available for a compound). Uncertainty in dose estimates may 

lead to either over or underestimation of risk.

(3) For the quantitative extrapolation of cancer risk estimates from experimental animals to 

humans, EPA uses scaling methodologies (relating expected response to differences in 

physical size of the species), which introduce another source of uncertainty. These 

methodologies are based on both biological data on differences in rates of process according 

to species size and empirical comparisons of toxicity between experimental animals and 

humans. For a particular pollutant, the quantitative difference in cancer potency between 

experimental animals and humans may be either greater than or less than that estimated by 

baseline scientific scaling predictions due to uncertainties associated with limitations in the 

test data and the correctness of scaled estimates. 

(4) EPA cancer risk estimates, whether based on epidemiological or experimental animal data, 

are generally developed using a benchmark dose (BMD) analysis to estimate a dose at which 

there is a specified excess risk of cancer, which is used as the point of departure (or POD) for 

the remainder of the calculation. Statistical uncertainty in developing a POD using a 

benchmark dose (BMD) approach is generally addressed though use of the 95 percent lower 

confidence limit on the dose at which the specified excess risk occurs (the BMDL), 

decreasing the likelihood of understating risk. EPA has generally utilized the multistage 

model for estimation of the BMDL using cancer bioassay data (see further discussion below).

(5) Extrapolation from high to low doses is an important, and potentially large, source of 

uncertainty in cancer risk assessment. EPA uses different approaches to low dose risk 

assessment (i.e., developing estimates of risk for exposures to environmental doses of an 

agent from observations in experimental or epidemiological studies at higher dose) depending 

on the available data and understanding of a pollutant’s mode of action (i.e., the manner in 

which a pollutant causes cancer). EPA’s Cancer Guidelines express a preference for the use 

of reliable, compound-specific, biologically-based risk models when feasible; however, such 

models are rarely available. The mode of action for a pollutant (i.e., the manner in which a 

pollutant causes cancer) is a key consideration in determining how risks should be estimated 

for low-dose exposure. A reference value is calculated when the available mode of action 

data show the response to be nonlinear (e.g., as in a threshold response). A linear low-dose 

(straight line from POD) approach is used when available mode of action data support a linear 

(e.g., nonthreshold response) or as the most common default approach when a compound’s 

mode of action is unknown. Linear extrapolation can be supported by both pollutant-specific 

data and broader scientific considerations. For example, EPA’s Cancer Guidelines generally 

consider a linear dose-response to be appropriate for pollutants that interact with DNA and 

induce mutations. Pollutants whose effects are additive to background biological processes in 

cancer development can also be predicted to have low-dose linear responses, although the 

slope of this relationship may not be the same as the slope estimated by the straight line 

approach. 

EPA most frequently utilizes a linear low-dose extrapolation approach as a baseline sciencepolicy choice (a “default”) when available data do not allow a compound-specific 

determination. This approach is designed to not underestimate risk in the face of uncertainty 
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and variability. EPA believes that linear dose-response models, when appropriately applied 

as part of EPA’s cancer risk assessment process, provide an upper bound estimate of risk and 

generally provide a health protective approach. Note that another source of uncertainty is the 

characterization of low-dose nonlinear, non-threshold relationships. The National Academy 

of Sciences has encouraged the exploration of sigmoidal type functions (e.g., log-probit 

models) in representing dose response relationships due to the variability in response within 

human populations. Another National Research Council (NRC) report [32] suggests that 

models based on distributions of individual thresholds are likely to lead to sigmoidal-shaped 

dose-response functions for a population. This report notes sources of variability in the 

human population: “One might expect these individual tolerances to vary extensively in 

humans depending on genetics, coincident exposures, nutritional status, and various other 

susceptibility factors...” Thus, if a distribution of thresholds approach is considered for a 

carcinogen risk assessment, application would depend on ability of modeling to reflect the 

degree of variability in response in human populations (as opposed to responses in bioassays 

with genetically more uniform rodents). Note also that low dose linearity in risk can arise for 

reasons separate from population variability: due to the nature of a mode of action and 

additivity of a chemical’s effect on top of background chemical exposures and biological 

processes.

As noted above, EPA’s current approach to cancer risk assessment typically utilizes a straight 

line approach from the BMDL. This is equivalent to using an upper confidence limit on the 

slope of the straight line extrapolation. The impact of the choice of the BMDL on bottom line 

risk estimates can be quantified by comparing risk estimates using the BMDL value to central 

estimate BMD values, although these differences are generally not a large contributor to 

uncertainty in risk assessment (Subramaniam et. al., 2006) [33]. It is important to note that 

earlier EPA assessments, including the majority of those for which risk values exist today, 

were generally developed using the multistage model to extrapolate down to environmental 

dose levels and did not involve the use of a POD. Subramaniam et. al. (2006) also provide

comparisons indicating that slopes based on straight line extrapolation from a POD do not 

show large differences from those based on the upper confidence limit of the multistage 

model.

(6) Cancer risk estimates do not generally make specific adjustments to reflect the variability 

in response within the human population — resulting in another source of uncertainty in 

assessments. In the diverse human population, some individuals are likely to be more 

sensitive to the action of a carcinogen than the typical individual, although compound-specific 

data to evaluate this variability are generally not available. There may also be important life 

stage differences in the quantitative potency of carcinogens and, with the exception of the 

recommendations in EPA’s Supplemental Cancer Guidance for carcinogens with a mutagenic 

mode of action, risk assessments do not generally quantitatively address life stage differences. 

However, one approach used commonly in EPA assessments that may help address variability 

in response is to extrapolate human response from results observed in the most sensitive 

species and sex tested, resulting typically in the highest URE which can be supported by 

reliable data, thus supporting estimates that are designed not to underestimate risk in the face 

of uncertainty and variability.
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Chronic noncancer assessment

Chronic noncancer reference values represent chronic exposure levels that are intended to be 

health-protective. That is, EPA and other organizations which develop noncancer reference

values (e.g., the Agency for Toxic Substances and Disease Registry – ATSDR) utilize an 

approach that is intended not to underestimate risk in the face of uncertainty and variability. 

When there are gaps in the available information, uncertainty factors (UFs) are applied to 

derive reference values that are intended to be protective against appreciable risk of 

deleterious effects. Uncertainty factors are commonly default values32 e.g., factors of 10 or 3, 

used in the absence of compound-specific data; where data are available, uncertainty factors 

may also be developed using compound-specific information. When data are limited, more 

assumptions are needed and more default factors are used. Thus there may be a greater 

tendency to overestimate risk—in the sense that further study might support development of 

reference values that are higher (i.e., less potent) because fewer default assumptions are 

needed. However, for some pollutants it is possible that risks may be underestimated.

For non-cancer endpoints related to chronic exposures, EPA derives a Reference Dose (RfD) 

for exposures via ingestion and a Reference Concentration (RfC) for inhalation exposures. 

These values provide an estimate (with uncertainty spanning perhaps an order of magnitude) 

of a daily oral exposure (RfD) or a continuous inhalation exposure (RfC) to the human 

population (including sensitive subgroups) that is likely to be without an appreciable risk of 

deleterious effects during a lifetime.33 To derive values that are intended to be “without 

appreciable risk,” EPA’s methodology relies upon an uncertainty factor (UF) approach

[34],[35] which includes consideration of both uncertainty and variability.

 

EPA begins by evaluating all of the available peer-reviewed literature to determine noncancer endpoints of concern, evaluating the quality, strengths and limitations of the available 

studies. EPA typically chooses the relevant endpoint that occurs at the lowest dose, often 

using statistical modeling of the available data, and then determines the appropriate point of 

departure (POD) for derivation of the reference value. A POD is determined by (in order of 

preference): (1) a statistical estimation using the benchmark dose (BMD) approach; (2) use of 

the dose or concentration at which the toxic response was not significantly elevated (no 

observed adverse effect level— NOAEL); or (3) use of the lowest observed adverse effect 

level (LOAEL).

 32 According to the NRC report Science and Judgment in Risk Assessment (NRC, 1994) “[Default] options are 

generic approaches, based on general scientific knowledge and policy judgment, that are applied to various 

elements of the risk-assessment process when the correct scientific model is unknown or uncertain.” The 1983 

NRC report Risk Assessment in the Federal Government: Managing the Process defined default option as “the 

option chosen on the basis of risk assessment policy that appears to be the best choice in the absence of data to 

the contrary” (NRC, 1983a, p. 63). Therefore, default options are not rules that bind the agency; rather, the 

agency may depart from them in evaluating the risks posed by a specific substance when it believes this to be 

appropriate. In keeping with EPA’s goal of protecting public health and the environment, default assumptions 

are used to ensure that risk to chemicals is not underestimated (although defaults are not intended to overtly 

overestimate risk). See EPA 2004 An examination of EPA Risk Assessment Principles and Practices,

EPA/100/B-04/001 available at: http://www.epa.gov/osa/pdfs/ratf-final.pdf 

33 See IRIS glossary
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A series of downward adjustments using default UFs is then applied to the POD to estimate 

the reference value [36]. While collectively termed “UFs”, these factors account for a 

number of different quantitative considerations when utilizing observed animal (usually 

rodent) or human toxicity data in a risk assessment. The UFs are intended to account for: (1) 

variation in susceptibility among the members of the human population (i.e., inter-individual 

variability); (2) uncertainty in extrapolating from experimental animal data to humans (i.e., 

interspecies differences); (3) uncertainty in extrapolating from data obtained in a study with 

less-than-lifetime exposure (i.e., extrapolating from subchronic to chronic exposure); (4) 

uncertainty in extrapolating from a LOAEL in the absence of a NOAEL; and (5) uncertainty 

when the database is incomplete or there are problems with applicability of available studies.

When scientifically sound, peer-reviewed assessment-specific data are not available, default 

adjustment values are selected for the individual UFs. For each type of uncertainty (when 

relevant to the assessment), EPA typically applies an UF value of 10 or 3 with the cumulative 

UF value leading to a downward adjustment of 10-3000 fold from the selected POD. An UF 

of 3 is used when the data do not support the use of a 10-fold factor. If an extrapolation step 

or adjustment is not relevant to an assessment (e.g., if applying human toxicity data and an 

interspecies extrapolation is not required) the associated UF is not used. The major 

adjustment steps are described more fully below.

1) Heterogeneity among humans is a key source of variability as well as uncertainty. 

Uncertainty related to human variation is considered in extrapolating doses from a subset or 

smaller-sized population, often of one sex or of a narrow range of life stages (typical of 

occupational epidemiologic studies), to a larger, more diverse population. In the absence of 

pollutant-specific data on human variation, a 10-fold UF is used to account for uncertainty 

associated with human variation. Human variation may be larger or smaller; however, data to 

examine the potential magnitude of human variability are often unavailable. In some 

situations, a smaller UF of 3 may be applied to reflect a known lack of significant variability 

among humans.

2) Extrapolation from results of studies in experimental animals to humans is a 

necessary step for the majority of chemical risk assessments. When interpreting animal data, 

the concentration at the POD (e.g. NOAEL, BMDL) in an animal model (e.g. rodents) is 

extrapolated to estimate the human response. While there is long-standing scientific support 

for the use of animal studies as indicators of potential toxicity to humans, there are 

uncertainties in such extrapolations. In the absence of data to the contrary, the typical 

approach is to use the most relevant endpoint from the most sensitive species and the most 

sensitive sex in assessing risks to the average human. Typically, compound specific data to 

evaluate relative sensitivity in humans versus rodents are lacking, thus leading to uncertainty 

in this extrapolation. Size-related differences (allometric relationships) indicate that typically 

humans are more sensitive than rodents when compared on a mg/kg/day basis. The default 

choice of 10 for the interspecies UF is consistent with these differences. For a specific 

chemical, differences in species responses may be greater or less than this value.

Pharmacokinetic models are useful to examine species differences in pharmacokinetic 

processing and associated uncertainties; however, such dosimetric adjustments are not always 
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possible. Information may not be available to quantitatively assess toxicokinetic or 

toxicodynamic differences between animals and humans, and in many cases a 10-fold UF 

(with separate factors of 3 for toxicokinetic and toxicodynamic components) is used to 

account for expected species differences and associated uncertainty in extrapolating from 

laboratory animals to humans in the derivation of a reference value. If information on one or 

the other of these components is available and accounted for in the cross-species 

extrapolation, a UF of 3 may be used for the remaining component.

3) In the case of reference values for chronic exposures where only data from shorter 

durations are available (e.g., 90-day subchronic studies in rodents) or when such data are 

judged more appropriate for development of an RfC, an additional UF of 3 or 10-fold is 

typically applied unless the available scientific information supports use of a different value.

4) Toxicity data are typically limited as to the dose or exposure levels that have been 

tested in individual studies; in an animal study, for example, treatment groups may differ in 

exposure by up to an order of magnitude. The preferred approach to arrive at a POD is to use 

BMD analysis; however, this approach requires adequate quantitative results for a meaningful 

analysis, which is not always possible. Use of a NOAEL is the next preferred approach after 

BMD analysis in determining a POD for deriving a health effect reference value. However, 

many studies lack a dose or exposure level at which an adverse effect is not observed (i.e., a 

NOAEL is not identified). When using data limited to a LOAEL, a UF of 10 or 3-fold is 

often applied. 

5) The database UF is intended to account for the potential for deriving an 

underprotective RfD/RfC due to a data gap preventing complete characterization of the 

chemical’s toxicity. In the absence of studies for a known or suspected endpoint of concern, a 

UF of 10 or 3-fold is typically applied.

Acute noncancer assessment

Many of the UFs used to account for variability and uncertainty in the development of acute 

reference values are quite similar to those developed for chronic durations, but more often 

using individual UF values that may be less than 10. UFs are applied based on chemicalspecific or health effect-specific information (e.g., simple irritation effects do not vary 

appreciably between human individuals, hence a value of 3 is typically used), or based on the 

purpose for the reference value (see the following paragraph). The UFs applied in acute 

reference value derivation include: 1) heterogeneity among humans; 2) uncertainty in 

extrapolating from animals to humans; 3) uncertainty in LOAEL to NOAEL adjustments; and 

4) uncertainty in accounting for an incomplete database on toxic effects of potential concern. 

Additional adjustments are often applied to account for uncertainty in extrapolation from 

observations at one exposure duration (e.g., 4 hours) to arrive at a POD for derivation of an 

acute reference value at another exposure duration (e.g., 1 hour). 

Not all acute reference values are developed for the same purpose and care must be taken 

when interpreting the results of an acute assessment of human health effects relative to the 

reference value or values being exceeded. Where relevant to the estimated exposures, the 
Residual Risk Assessment for the Phosphate Fertilizer and Phosphoric Acid Source 

Categories in Support of the July 2015 Risk and Technology Review Final Rule

57

lack of threshold values at different levels of severity should be factored into the risk 

characterization as potential uncertainties. 
Residual Risk Assessment for the Phosphate Fertilizer and Phosphoric Acid Source 

Categories in Support of the July 2015 Risk and Technology Review Final Rule

58

6 References

 

1. US EPA. National Emission Standards for Hazardous Air Pollutants. 

http://www.epa.gov/ttn/atw/mactfnlalph.html. 

2 US 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-452/R-09-006. 

http://www.epa.gov/ttn/atw/rrisk/rtrpg.html.

3. US EPA, 2010. SAB’s Response to EPA’s RTR Risk Assessment Methodologies. 

http://yosemite.epa.gov/sab/sabproduct.nsf/4AB3966E263D943A8525771F00668381/$File/EP

A-SAB-10-007-unsigned.pdf

4. US EPA, 2010. Memorandum from Dave Guinnup to RTR Wood Furniture Docket, entitled,

“EPA’s Actions in Response to Key Recommendations of the SAB Review of RTR Risk 

Assessment Methodologies.

5. US EPA, 2005. Revision to the Guideline on Air Quality Models: Adoption of a Preferred

General Purpose (Flat and Complex Terrain) Dispersion Model and Other Revisions; Final Rule. 

40 CFR Part 51. http://www.epa.gov/EPA-AIR/2005/November/Day-09/a21627.htm

6. US EPA, 2004. Users’ guide for the AMS/EPA regulatory model – AERMOD. EPA-454/B03-001. http://www.epa.gov/scram001/7thconf/aermod/aermodugb.pdf.

7. Allen, D., C. Murphy, Y. Kimura, W. Vizuete, T. Edgar, H. Jeffries, B.-U. Kim, M. Webster, 

and M. Symons, 2004. Variable industrial VOC emissions and their impact on ozone 

formation in the Houston Galveston Area. Final Report: Texas Environmental Research 

Consortium Project H-13. 

http://files.harc.edu/Projects/AirQuality/Projects/H013.2003/H13FinalReport.pdf

8. US EPA, 2004. Air Toxics Risk Assessment Reference Library, Volume 1. EPA-453-K-04- 

001A. http://www2.epa.gov/fera/dose-response-assessment-assessing-health-risks-associatedexposure-hazardous-air-pollutants

9. US EPA, September 2011. Exposure Factors Handbook: 2011 Edition; 

 http://www.epa.gov/ncea/efh/pdfs/efh-complete.pdf

10. US EPA, 2005. Table 1. Prioritized Chronic Dose-Response Values (2/28/05). Office of 

Air Quality Planning and Standards. http://www2.epa.gov/fera/dose-response-assessmentassessing-health-risks-associated-exposure-hazardous-air-pollutants

11. US EPA, 2005. 1999 National Air Toxics Risk Assessment. 

http://www.epa.gov/ttn/atw/nata1999

12. US EPA, 2006. Integrated Risk Information System. http://www.epa.gov/iris/index.html.
Residual Risk Assessment for the Phosphate Fertilizer and Phosphoric Acid Source 

Categories in Support of the July 2015 Risk and Technology Review Final Rule

59

 

13. US Agency for Toxic Substances and Disease Registry. 2006. Minimum Risk Levels (MRLs) 

for Hazardous Substances. http://www.atsdr.cdc.gov/mrls/index.html.

14. US EPA, 1994. US Environmental Protection Agency. Methods for Derivation of 

Inhalation Reference Concentrations and Application of Inhalation Dosimetry. 

EPA/600/8-90/066F. Office of Research and Development. Washington, DC: U.S.EPA.

15. NRC, 1994. National Research Council. Science and Judgment in Risk Assessment. 

Washington, DC: National Academy Press.

16. CA Office of Environmental Health Hazard Assessment, 2014. Chronic Reference 

Exposure Levels Adopted by OEHHA as of January 2014. 

http://www.oehha.ca.gov/air/allrels.html. 

17. CA Office of Environmental Health Hazard Assessment, 2005. Technical Support Document 

for Describing Available Cancer Potency Factors, May 2005. 

http://www.oehha.ca.gov/air/hot_spots/pdf/may2005Hotspots.pdf

18. US EPA, 2006. Approach for modeling POM. Technical support information for the 1999 

National Air Toxics Assessment. 

http://www.epa.gov/ttn/atw/nata1999/99pdfs/pomapproachjan.pdf.

19. US EPA, 2014. Table 2. Acute Dose-Response Values for Screening Risk Assessments 

(5/09/2014). Office of Air Quality Planning and Standards; http://www2.epa.gov/fera/doseresponse-assessment-assessing-health-risks-associated-exposure-hazardous-air-pollutants

20. CA Office of Environmental Health Hazard Assessment, 2014. All Acute Reference 

Exposure Levels developed by OEHHA as of January 2014. 

http://www.oehha.ca.gov/air/allrels.html

21. American Industrial Hygiene Association , 2013. Current AIHA ERPG Values. 

https://www.aiha.org/getinvolved/AIHAGuidelineFoundation/EmergencyResponsePlanningGuidelines/Pages/default.a

spx

22. US EPA, 1995. Guidance for Risk Characterization. Science Policy Council. 

http://www.epa.gov/OSA/spc/pdfs/rcguide.pdf.

23. US EPA, 2000. Risk Characterization Handbook. EPA 100-B-00-002.

24. US EPA, 2002. EPA’s Guidelines for Ensuring and Maximizing the Quality, Objectivity, 

Utility, and Integrity of Information Disseminated by the Environmental Protection Agency. 

EPA Office of Environmental Information. EPA/260R-02-008. 

http://www.epa.gov/quality/informationguidelines/documents/EPA_InfoQualityGuidelines.pdf

25. US EPA, 2006. Performing risk assessments that include carcinogens described in the 

Supplemental Guidance as having a mutagenic mode of action. Science Policy Council Cancer 

Guidelines Implementation Workgroup Communication II: Memo from W.H. Farland dated 

14 June 2006. http://www.epa.gov/osa/spc/pdfs/CGIWGCommunication_II.pdf
Residual Risk Assessment for the Phosphate Fertilizer and Phosphoric Acid Source 

Categories in Support of the July 2015 Risk and Technology Review Final Rule

60

 

26. US EPA, 2005. Supplemental guidance for assessing early-life exposure to carcinogens. 

EPA/630/R-03003F. http://www.epa.gov/ttn/atw/childrens_supplement_final.pdf.

27. US EPA, 2005. Science Policy Council Cancer Guidelines Implementation Workgroup 

Communication I: Memo from W.H. Farland dated 4 October 2005 to Science Policy 

Council. http://www.epa.gov/osa/spc/pdfs/canguid1.pdf

28. US EPA, 1986. Guidelines for the Health Risk Assessment of Chemical Mixtures. EPA-630-

R-98-002. http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=22567

29. US EPA, 2000. Supplementary Guidance for Conducting Health Risk Assessment of 

Chemical Mixtures. EPA-630/R-00-002. 

http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=20533

30. US EPA, 2005. Guidelines for Carcinogen Risk Assessment (2005). U.S. Environmental 

Protection Agency, Washington, DC, EPA/630/P-03/001F, 2005. 

http://www.epa.gov/cancerguidelines/

31. US EPA, 2000. Risk Characterization Handbook. EPA 100-B-00-002.

32. NRC (National Research Council) 2006. Assessing the Human Health Risks of 

Trichloroethylene. National Academies Press, Washington DC.

33. R.P. Subramaniam, P. White and V.J. Cogliano. 2006. Comparison of cancer slope factors 

using different statistical approaches, Risk Anal. Vol 26, p. 825–830.

34. US EPA. 1993. Reference Dose (RfD): Description and Use in Health Risk Assessments. 

http://www.epa.gov/iris/rfd.htm

35. US EPA. 1994. Methods for Derivation of Inhalation Reference Concentrations and 

Application of Inhalation Dosimetry. 

http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=71993

36. US EPA. 2002. A Review of the Reference Dose and Reference Concentration Processes. 

http://www.epa.gov/raf/publications/pdfs/rfd-final.pdf
Appendix 1 

Technical Support Document for HEM-3 Modeling 
Modeling for the Residual Risk and Technology Review 

Using the Human Exposure Model 3 – AERMOD Version

Updated 8/7/2008 

Technical Support Document 

Prepared for: 

U. S. Environmental Protection Agency 

Office of Air and Radiation 

Office of Air Quality Planning and Standards 

Emissions Standards Division, Risk and Exposure Group 

Research Triangle Park, NC 27711 

Prepared by: 

EC/R Incorporated 

501 Eastowne Drive, Suite 250 

Chapel Hill, North Carolina 27514 
ii 

Disclaimer

The research described in this document has been funded by the 

United States Environmental Protection Agency contracts 68-D01-076 and 68-D-06-119 to EC/R Incorporated. Although it has 

been subject to the Agency's review, it does not necessarily reflect 

the views of the Agency, and no official endorsement should be 

inferred. 
iii 

Contents

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

2. Overview of the HEM3– AERMOD System..............................................................................2

2.1 Preparation of Dispersion Modeling Inputs ........................................................................3

2.1.1 Compiling Emission Source Data ..............................................................................3

2.1.2 Defining the Modeling Domain and Receptors .........................................................4

Treatment of Nearby Census Blocks and Screening for Overlapping Blocks ...............4

Polar receptor network ...................................................................................................5

Elevations and hill heights for model receptors .............................................................5

2.1.3 Selection of Meteorological Data ..............................................................................6

2.2 Running of AERMOD ........................................................................................................6

2.2.1 AERMOD Dispersion Options Used by HEM3 ........................................................6

2.2.2 Use of Dilution Factors ..............................................................................................7

2.3 Postprocessing of AERMOD Results in HEM3 .................................................................7

2.3.1 Calculation of Impacts at Modeled Receptors ...........................................................8

2.3.2 Interpolation of Impacts at Outer Census Blocks ......................................................9

2.3.3 Calculation of Population Exposures and Incidence ...............................................10

2.3.4 Model Outputs .........................................................................................................10

2.4 Data Libraries Used in HEM3 ..........................................................................................11

2.4.1 Chemical Health Effects Information ......................................................................11

2.4.2 Census Block Locations and Elevation Data ...........................................................12

2.4.3 Meteorological Data .................................................................................................12

2.4.4 Gaseous Deposition Parameters ...............................................................................13

3. Modeling for the Residual Risk Technology Review ..............................................................14

3.1 Emission Source Inputs .....................................................................................................14

3.2 Pollutant Cross-Referencing .............................................................................................15

3.3 Meteorological Data..........................................................................................................15

3.4 Model Options Selected ....................................................................................................18

3.4.1 Urban or Rural Dispersion Characteristics ..............................................................19

3.4.2 Deposition and Plume Depletion .............................................................................19

3.4.3 Cutoff Distance for Modeling of Individual Blocks ................................................20

3.4.4 Plant Boundary Assumptions ...................................................................................20

3.5 Modeling of Multiple Facilities ........................................................................................20

4. Quality Assurance .....................................................................................................................21

4.1 Engineering Review ..........................................................................................................21

4.2 Geographic Checks ...........................................................................................................21

5. Uncertainties .............................................................................................................................24
iv 

6. References .................................................................................................................................31

 

Figures

Figure 3-1. Meteorological data available in the HEM3 library for AERMOD. ..........................18

Figure 4-1. Illustration of a quality assurance test for Census block coordinates located on plant 

property. .............................................................................................................................23

Tables

Table 2-1. Parameters Used to Delineate the Modeling Domain in HEM3 ...................................4

Table 3-1. Groupings Used for Polycyclic Organic Matter in the RTR Phase II Modeling Effort 

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

Table 3-2. Modeling Domain and Model Set-Up Options in the HEM-3 Model As Used in the 

Residual Risk and Technology Review Assessments ........................................................19

Table 5-1. Summary of General Uncertainties Associated with Risk and Technology Review 

Risk Assessment ................................................................................................................26
1 

1. Introduction

 This document describes the modeling approach used to estimate the risks to human 

populations in support of the Residual Risk and Technology Review currently being carried out 

by the U.S. Environmental Protection Agency (EPA). The model used in these risk assessments 

was the Human Exposure Model, Version 3 (HEM3). HEM3 incorporates the AERMOD, a state 

of the art air dispersion model developed under the direction of the American Meteorological 

Society / Environmental Protection Agency Regulatory Model Improvement Committee 

(AERMIC). 

 Section 2 of this report provides an overview of the HEM3 system; and Section 3 

describes inputs and choices made in implementing the model for the RTR program. Quality 

assurance efforts undertaken in the modeling effort are discussed in Section 4, and uncertainties 

associated with the modeling effort are discussed in Section 5. 
2 

2. Overview of the HEM3– 

AERMOD System

 HEM3 performs three main operations: dispersion modeling, estimation of population 

exposure, and estimation of human health risks. Two options are provided for dispersion 

modeling. The first is the American Meteorological Society (AMS) EPA Regulatory Model 

(AERMOD).1,2 The second option is the Industrial Source Complex - Short Term, Version 3 

(ISCST3) model. Both of these dispersion models handle a wide range of different source types 

which may be associated with an industrial source complex, including stack sources, area 

sources, and volume sources. AERMOD was used for the RTR modeling effort. 

 To prepare dispersion modeling inputs and carry out risk calculations, HEM3 draws on 

four data libraries, which are provided with the model. The first is a library of meteorological 

data for over 120 stations, which are used for dispersion calculations. A second library of 

Census block internal point locations and populations provides the basis of human exposure 

calculations (Census, 2000). This Census library also includes the elevation of each Census 

block, which can also be used in dispersion calculations, at the option of the model user. A third 

library of pollutant unit risk estimates and reference concentrations is used to calculate 

population risks. These unit risk estimates and reference concentrations are based on the latest 

values recommended by EPA for hazardous air pollutants (HAP) and other toxic air pollutants 

(EPA, 2007). The fourth data library is used only with AERMOD and only when the user opts to 

compute deposition or plume depletion. This database provides deposition parameters for 

gaseous pollutants. 

 HEM3 has been implemented in two versions: a single-plant version, and a community 

and sector version. The latter was used in this modeling effort. Both versions operate under the 

same general principles. In essence, the community and sector version provides a platform for 

running the single plant version multiple times. In both versions, source location and emissions 

data are input through a set of ExcelTM spreadsheets. The main difference is in the user interface 

for other model inputs. The single plant includes a graphical user interface (GUI) for the 

selection of various dispersion modeling options. In the community and sector version of 

HEM3, a control file replaces many of these GUI inputs. 

 The model estimates cancer risks and noncancer adverse health effects due to inhalation 

exposure at Census block internal point locations, and at other receptor locations that can be 

specified by the user. Cancer risks are computed using EPA’s recommended unit risk estimates 

for Hazardous Air Pollutants (HAP) and other toxic air pollutants. The resulting estimates 

reflect the excess cancer risk for an individual breathing the ambient air at a given receptor site 

24-hours per day over a 70-year lifetime. The model estimates the numbers of people exposed to 

various cancer risk levels. In addition, HEM3 estimates the total incremental cancer risks for 

people living within different distances of the modeled emission sources. 
3 

 Potential noncancer health effects due to chronic exposures are quantified using hazard 

quotients and hazard indices for various target organs. The “hazard quotient” for a given 

chemical and receptor site is the ratio of the ambient concentration of the chemical to the 

reference concentration. The “hazard index” for a given organ is the sum of hazard quotients for 

substances that affect that organ. HEM3 computes target-organ-specific hazard indices (TOSHI) 

for HAPs and other toxic air pollutants, and estimates the numbers of people exposed to different 

hazard index levels. In addition, maximum short term concentrations are computed for all 

pollutants, and concentrations are compared with threshold levels for acute health effects. 

 The following sections outline the methodologies used in the HEM3 system. Section 2.1 

describes the preparation of dispersion modeling inputs, Section 2.2 describes the running of 

AERMOD, Section 2.3 describes calculations performed by HEM3 to calculate risks and 

exposures, and Section 2.4 details the sources and methods used to produce HEM3’s data 

libraries. The HEM3 User’s Manual provides additional details on the input data and algorithms 

used in the model.3

 Specific model options used in the RTR are discussed in Chapter 3. 

2.1 Preparation of Dispersion Modeling Inputs

 HEM3 compiles data that will be needed for dispersion modeling, and prepares an input 

file suitable for running AERMOD. The dispersion modeling inputs can be divided into three 

main components: emission source data, information on the modeling domain and receptors for 

which impacts will be computed, and meteorological data. 

2.1.1 Compiling Emission Source Data

 A series of ExcelTM spreadsheet files are used to specify the emissions and configuration 

of the facility to be modeled. At a minimum, two files are needed: a pollutant emission file, and 

an emission location file. The emission file includes an emission source identification code for 

each emission source at the facility, the names of pollutants emitted by each source, and the 

emission rate for each pollutant. In addition, if the model run is to incorporate deposition or 

plume depletion, the emission file must also specify the percentage of each pollutant that is in the 

form of particulate matter. The balance is assumed to be in vapor form. 

 The emission location file includes the coordinates of each source, as well as information 

on the configuration and other characteristics of the source. HEM3 can analyze stack sources, 

area sources, or volume sources.a

 For stack sources, the location file must provide the stack 

height, stack diameter, emission velocity, and emission temperature. The file must also provide 

dimensions for each area or volume source, as well as the height of the source above the ground. 

For area sources, the angle of rotation from north can also be specified. The user can also 

 

I

For definitions of stack, area, and volume sources, please see the AERMOD documentation. AERMOD 

provides the capability to analyze some other emission source configurations, such as storage piles, however these 

configurations are not supported by HEM3.
4 

provide the terrain elevation at the base of each source, and the controlling hill height for each 

source. (The controlling hill height is used in AERMOD’s flow calculations. Calculation of the 

hill height is discussed in Section 2.4.2.) If these are not provided, HEM3 will calculate terrain 

elevations and controlling hill heights based on elevations and hill heights for the Census blocks 

nearest to the facility. 

 If particulate deposition or plume depletion are to be considered, then HEM-3 requires a 

third input file to specify the particle size distribution and scavenging parameters for various 

particle size ranges. Another optional file can be used to specify building dimensions if building 

wake effects are to be modeled. 

2.1.2 Defining the Modeling Domain and Receptors

 HEM3 defines a modeling domain for each facility that is analyzed based on parameters 

specified by the model user. These parameters are summarized in Table 2-1. The modeling 

domain is circular, and is centered on the facility, with a radius specified by the user. For the 

RTR analysis, the radius of the modeling domain is 50 kilometers (km). HEM3 identifies all of 

the Census block locations in the modeling domain from its Census database, and divides the 

blocks into two groups based on their distance from the facility. For the inner group of Census 

blocks (closest to the facility), each block location is modeled as a separate receptor in 

AERMOD. The cutoff distance for modeling individual Census blocks is determined by the 

model user. The model user can also provide an ExcelTM spreadsheet specifying additional 

locations to be included as model receptors in AERMOD. These additional discrete receptors 

may include plant boundary locations, monitoring sites, individual residences, schools, or other 

locations of interest. 

 For Census blocks in the outer group, 

beyond this modeling cutoff distance, 

emissions impacts are interpolated based on 

modeling results for a polar receptor network. 

The user also specifies an “overlap” distance, 

within which Census block coordinates will 

be considered to be on plant property. The 

following paragraphs provide more details on 

the treatment of blocks near the facility, on 

the polar receptor network, and on the 

determination of receptor elevations and 

controlling hill heights to be used in 

AERMOD. 

Treatment of Nearby Census Blocks and 

Screening for Overlapping Blocks

 Census block locations near the 

facility are modeled as separate receptors 

Table 2-1. Parameters Used to Delineate 

the Modeling Domain in HEM3

Parameter 

Typical 

value 

Modeling domain size – maximum 

distance to be modeled a 50 km 

Cutoff distance for modeling of 

individual blocks b

2,000 – 

3,000 m

Overlap distance – where receptors will 

be considered to be on plant property b 30 m 

Polar receptor network 

Distance to the innermost ring a 100 m 

Number of concentric rings 13 

Number of radial directions 16 

 aMeasured from the center of the facility. 

 b

Measured from each stack at the facility, and 

from the edges of each area or volume source. 
5 

within AERMOD. The cutoff distance for modeling of individual Census blocks is chosen by 

the user, and is tpically between 2000 and 3000 meters. This distance is not measured from the 

center of the facility, but is the minimum distance from any source at the facility. Therefore, any 

Census block location that is within the cutoff distance from any emission source is treated as a 

discrete AERMOD receptor. 

 HEM3 checks Census blocks that are very close to the facility in order to assess whether 

they overlap any area or volume emission sources. In addition, the user can specify an overlap 

distance, within which receptors will be considered to be on plant property. The default value 

for the overlap distance is 30 meters, or approximately equal to the width of a narrow buffer and 

a roadway. HEM3 tests each nearby receptor to determine whether it is within this distance from 

any stack or from the perimeter of any area or volume source. If a receptor falls within this 

distance, HEM-3 will not calculate risks based on the location of that receptor, but will instead 

assume that the risks associated with the receptor are the same as the highest predicted value for 

any receptor that is not overlapping. The location for calculating the default impact may be 

either another Census block, one of the polar grid receptors, or one of the additional discrete 

receptor locations provided by the model user. 

Polar receptor network

 The polar receptor network used in HEM3 serves three functions. First, it is used to 

estimate default impacts if one or more Census locations are inside the overlap cutoff distance is 

used to represent the plant boundary. Second, it is used to evaluate potential acute effects that 

may occur due to short-term exposures in unpopulated locations outside the plant boundary. 

Third, the polar receptor network is used to interpolate long-term and short-term impacts at 

Census block locations that are outside the cutoff distance for modeling of individual blocks. 

 The user defines the inner radius for the polar receptor network, the number of concentric 

rings to be analyzed, and the number of radial directions. The inner radius of the polar network 

should the minimum distance from the facility center that is generally outside of plant property. 

(For complex plant shapes, it is sometimes useful to specify an inner ring that encroaches on 

facility property in some directions.) The default, and also the minimum inner radius for the 

polar network is 100 meters (m). A typical run includes 13 concentric rings and 16 radial 

directions. HEM3 will distribute the radial directions evenly around the plant. For the 

concentric rings, the model will generate logarithmic progression of distances starting at the 

inner ring radium and ending at the outer radius of the modeling domain. In the single plant 

version of HEM3, the user can change these ring distances to meet the needs of a specific study. 

Elevations and hill heights for model receptors

 The user can specify whether HEM3 will include the effects of terrain when running 

AERMOD. If the terrain option is selected, HEM3 obtains elevations and controlling hill heights 

for Census block receptors from its internal Census location library. Section 2.4.2 describes the 

derivation of these elevations and hill heights. 
6 

 Elevations and controlling hill heights for the polar grid receptors are also estimated 

based on values from the Census library. HEM3 divides the modeling domain into sectors based 

on the polar receptor network, with each Census block assigned to the sector corresponding to 

the closest polar grid receptor. Each polar grid receptor is then assigned an elevation based on 

the highest elevation for any Census block in its sector. The controlling hill height is also set to 

the maximum hill height within the sector. If a sector does not contain any blocks, the model 

defaults to the elevation and controlling hill height of the nearest block outside the sector. 

2.1.3 Selection of Meteorological Data

 In addition to source and receptor information, AERMOD requires surface and upper air 

meteorological observations in a prescribed format. The model user can select a meteorological 

station from the HEM3 meteorological data library, or add new files to the library if site-specific 

data are available. If the user does not specify a meteorological station, HEM3 will select the 

closest station to the center of the modeling domain. 

2.2 Running of AERMOD

 Based on the user input data and other data described in the previous section, HEM3 

produces an input file suitable for AERMOD. HEM3 then runs AERMOD as a compiled 

executable program. No changes have been made from the version of AERMOD released to the 

public by EPA. The following sections give additional information on how AERMOD is used 

within HEM3. 

2.2.1 AERMOD Dispersion Options Used by HEM3

 AERMOD provides a wide array of options for controlling dispersion modeling 

calculations. In general, HEM3 uses the regulatory default options when running AERMOD.1 

These options include the following: 

! consideration of stack-tip downwash (except for Schulman-Scire downwash) 

! use of calm processing routines 

! use of missing-data processing routines 

! consideration of terrain effects 

However, the model user can opt to exclude the effects of terrain when running HEM3. The 

following additional options are available to the HEM3 user: 

! calculation of wet and dry deposition rates for vapor and particulate mater 

! consideration of plume depletion (due to deposition) when calculating air concentrations 

! consideration of building wake effects 

! calculation of short term impacts 

As noted in Section 2.1, the calculation of deposition or depletion and the consideration of 

building wake effects require additional user inputs. 
7 

 The user can opt to analyze short term impacts on a number of different time scales (e.g. 

1 hour, 2 hours, etc.), however only one short term time scale can be selected per run. If the user 

chooses to analyze short term impacts, a multiplier must be specified to reflect the ratio between 

the maximum short term emission rate and the long term average emission rate. The default 

multiplier for short term emissions is a factor of 10. This means that in the default case the 

maximum short term emission rate is assumed to be 10 times the long term average emission 

rate. The multiplier can be set to 1.0 if emissions from the facility are known to be constant. 

 HEM3 uses the AERMOD TOXICS run option, which optimizes the area source 

integration routine to reduce model runtimes. The TOXICS Sampled Chronological Input Model 

(SCIM) is not used, since the HEM3 AERMOD meteorological library only includes one year of 

observations for each station. (The SCIM option can be used to skip a portion of the hourly 

observations when meteorological data are available for multiple years.) 

2.2.2 Use of Dilution Factors

 To save computer run time when analyzing the impacts of multiple pollutants, HEM3 

does not model each pollutant separately. Instead, AERMOD is used to compute a series of 

dilution factors, specific to each emission source and receptor. The dilution factor for a 

particular emission source and receptor is defined as the predicted ambient impact from the given 

source and at the given receptor, divided by the emission rate from the given source. 

 If the user chooses not to analyze deposition or plume depletion, the dilution factor does 

not vary from pollutant to pollutant. If either deposition or depletion is chosen with the 

AERMOD dispersion model option of HEM3, separate dilution coefficients must be computed 

for each gaseous pollutant. In addition, separate dilution factors must be computed for different 

components of particulate matter if the components do not have the same particle size 

distribution. In the current version of HEM3, this can be done by creating a separate emission 

record for each pollutant emitted by from each source. (Common location data and source 

configurations can be used for different pollutant records representing the same emission 

source.) 

2.3 Postprocessing of AERMOD Results in HEM3

 HEM3 estimates total excess cancer risks and potential chronic noncancer health effects 

for all Census block locations in the modeling domain, all user-defined receptors, and all points 

in the polar receptor network. Potential chronic noncancer health effects are expressed in terms 

of TOSHI. Based on the results for Census blocks and other receptors, HEM-3 estimates the 

maximum individual risk and maximum TOSHI for populated receptors, and determines the 

locations of these maximum impacts. The model also determines the concentrations of different 

pollutants at the sites of the maximum risks, and the contributions of different emission sources 

to these maximum estimated risks. It should be noted that the locations may differ for the 

maximum individual cancer risk and for the hazard indices for different target organs. 
8 

 For acute impacts, HEM3 calculates the maximum short term concentrations for all 

pollutants emitted by the facility. These maximum short term concentrations are compared with 

various thresholds for acute health effects. 

 At the option of the model user, HEM3 will also compute the long term and short term 

predicted ambient concentrations of all pollutants emitted by the plant at all of the receptors in 

the modeling domain. In addition, pollutant contributions from each emission source at the 

facility are computed under this option. 

 Section 2.3.1. describes methods used to calculate cancer risks and hazard indices for 

receptors that are explicitly modeled using AERMOD. Section 2.3.2 describes the interpolation 

approach used to estimate cancer risks and hazard indices at Census blocks that are not explicitly 

modeled. 

2.3.1 Calculation of Impacts at Modeled Receptors

 As noted in Section 2.2.2, HEM3 does not model each pollutant separately unless 

deposition or depletion is being analyzed. Instead, AERMOD is used to compute a series of 

dilution factors, specific to each emission source and receptor. HEM3 also conserves computer 

memory by computing cancer risks and hazard indices directly, without recording the 

concentration of each pollutant at each receptor. The following algorithms are used to compute 

cancer risks and TOSHI for chronic noncancer health effects. 

For cancer risk: 

CRT = i, j CRi, j

CRi, j = DFi, j  CF  k [Ei, k  UREk]

For TOSHI: 

TOSHIT = i, j TOSHIi, j

TOSHIi, j = DFi, j  CF  k [Ei, k  RfCk ]

where: 

 CRT = total cancer risk at a given receptor (probability for one person) 

i, j = the sum over all sources i and pollutant types j (particulate or gas) 

 CRi, j = cancer risk at the given receptor for source i and pollutant type j 

 DFi, j = dilution factor [(g/m3

) / (g/sec)] at the given receptor for source i and pollutant 

type j 

 CF = conversion factor, 0.02877 [(g/sec) / (ton/year)] 

k = sum over all pollutants k within pollutant group j (particulate or gas) 

 Ei, k = emissions of pollutant k from source i and in pollutant type j 

 UREk = cancer unit risk estimate for pollutant k 
9 

 TOSHIT = total target-organ-specific hazard index at a given receptor 

 TOSHIi, j = target-organ-specific hazard index at the given receptor for source i and 

pollutant type j 

 RfCk = noncancer health effect reference concentration for pollutant k 

 If the cancer unit risk estimate is not available for a given chemical, then that chemical is 

not included in the calculation of cancer risk. Likewise, if the noncancer reference concentration 

is not available for a given chemical, that chemical is not included in the calculation of hazard 

indices. Note also that separate reference concentrations are used for acute and chronic hazard 

indices. 

 HEM3 computes short term concentrations and records the highest short term 

concentration for each pollutant. In addition, the user can opt to compute and record the short 

term and long concentrations at each receptor. Concentrations are computed as follows. 

Long term concentrations: 

ACT, k = i ACi, k

ACi, k = DFi, j  CF  Ei, k

Short term concentrations: 

ACT = i ACi, k

ACi, k = DFi, j  CF  Ei, k  M 

where: 

 ACT, k = total estimated ambient concentration for pollutant k at a given receptor 

i = the sum over all sources i (g/m3) 

 ACi, k = estimated ambient concentration of pollutant k at the given receptor as a 

result of emissions from source i (g/m3

) 

 M = ratio between the estimated maximum short term emission rate and the long term 

average emission rate (dimensionless) 

2.3.2 Interpolation of Impacts at Outer Census Blocks

 For Census blocks outside of the distance cutoff for individual block modeling, HEM-3 

estimates cancer risks and hazard indices by interpolation from the polar receptor network. 

Impacts at the polar grid receptors are estimated using AERMOD modeling results and the 

algorithms described in Section 2.3.1. The impacts at each outer Census block are interpolated 

from the four nearest polar grid receptors. The interpolation is linear in the angular direction, 

and logarithmic in the radial direction, as summarized in the following equations: 

Ia, r = I1, r + (I2, r – I1, r)  (a – A1)  (A2 – A1) 
10 

IA1, r = exp{ln(IA1,R1) + [ln(IA1,R2) – ln(IA1,R1)]  [ln r) – ln(R1)]  [ln(R2) – ln(R1)]} 

IA2, r = exp{ln(IA2,R1) + [ln(IA2,R2) – ln(IA2,R1)]  [ln r) – ln(R1)]  [ln(R2) – ln(R1)]} 

where: 

 Ia, r = the impact (cancer risk, hazard index, or concentration) at an angle, a, from north, 

and radius, r, from the center of the modeling domain 

 a = the angle of the target receptor, from north 

 r = the radius of the target receptor, from the center of the modeling domain 

 A1 = the angle of the polar network receptors immediately counterclockwise from the 

target receptors 

 A2 = the angle of the polar network receptors immediately clockwise from the target 

receptor 

 R1 = the radius of the polar network receptors immediately inside the target receptor 

 R2 = the radius of the polar network receptors immediately outside the target receptor 

2.3.3 Calculation of Population Exposures and Incidence

 Using the predicted impacts for Census blocks, HEM3 estimates the numbers of people 

exposed to various cancer risk levels and TOSHI levels. This is done by adding up the 

populations for receptors that have predicted cancer risks or TOSHI above the given threshold. 

 The model also estimates the total annual excess cancer risk (incidence) for the entire 

modeling region. The following equation is used: 

TCR = m [CRm  Pm ]  LT 

where: 

 TCR = the estimated total annual cancer risk, or incidence, (cancers/year) to the 

population living within the modeling domain 

m = the sum over all Census blocks m within distance the modeling domain 

 CRm = the total lifetime cancer risk (from all modeled pollutants and emission sources) at 

Census block m 

 Pm = the population at Census block m 

 LT = the average lifetime used to develop the cancer unit risk factor, 70 years 

2.3.4 Model Outputs

 The following is a summary of the outputs produced by HEM3. These are written to a 

collection of files in ExcelTM and dBaseTM format. 

! Maximum long term impacts at populated locations 

" maximum lifetime individual cancer risk 

" maximum TOSHI for the following health effects 

- respiratory system effects 

- liver effects 
11 

- neurological system effects 

- developmental effects 

- reproductive system effects 

- kidney effects 

- ocular system effects 

- endocrine system effects 

- hematological system effects 

- immunological system effects 

- skeletal system effects 

- spleen effects 

- thyroid effects 

" locations of the maximum cancer risk and TOSHI 

" Census block identification codes for the maximum cancer risk and TOSHI, and 

number of people in the Census block 

" contributions of different chemicals and emission sources to the maximum risk 

and TOSHI 

! Maximum acute impacts 

" maximum short term ambient concentration for each chemical 

" threshold levels for acute health effects of each chemical (compared with the 

maximum short term concentrations) 

" locations of the maximum impacts for different chemicals 

" Census block identification codes at the locations of maximum concentration, and 

number of people in the block 

" contribution of each emission source at the facility to the maximum short term 

concentration of each chemical 

! Outputs for all receptors 

" maximum individual cancer risk and TOSHI (all target organs) for each Census 

block and each user-specified discrete receptor (monitoring sites, etc.) 

" maximum individual cancer risk and TOSHI (all target organs) for each polar grid 

receptor 

" estimated deposition (optional) 

" predicted ambient concentration resulting from each emission source at each 

Census block (optional) 

! Population exposures and total cancer risk, or incidence 

" estimated numbers of people exposed to different levels of lifetime individual 

cancer risk (1 in a million, 1 in 100,000, etc.) 

" estimated numbers of people exposed to different levels of TOSHI (1, 2, 10, etc.) 

" total cancer risk, or incidence, in estimated cancer deaths per year, over the entire 

modeling domain 

2.4 Data Libraries Used in HEM3

2.4.1 Chemical Health Effects Information
12 

 HEM3 includes a library of available health effects data for HAPs. For each pollutant, 

the library includes the following parameters, where available: 

! unit risk estimate for cancer 

! reference concentration for chronic noncancer health effects 

! reference concentrations for acute health effects 

! target organs affected by the chemical for chronic health effects 

Unit risk estimates and reference concentrations included in the HEM3 chemical library have 

been taken from EPA’s database of recommended dose-response factors for HAPs.4

2.4.2 Census Block Locations and Elevation Data

 The HEM3 Census library includes Census block identification codes, locations, 

populations, elevations, and controlling hill heights for all of the over 5 million Census blocks 

identified in the 2000 Census. The location coordinates reflect the internal point of the block, 

which is a point selected by the Census to be roughly in the center of the block. For complex 

shapes, the internal point may not be in the geographic center of the block. Locations and 

population data for Census blocks in the 50 states and Puerto Rico were extracted from the 

LandView® database.5

 Locations and populations for blocks in the Virgin Islands were obtained 

from the Census web site. 

 The elevation of each Census block in the continental U.S. and Hawaii was estimated by 

interpolating the elevation at the block’s internal point from the U.S. Geological Service’s 

1:250,000 Digital Elevation Model (DEM) data.6 These data have a resolution of 3 arc seconds, 

or about 90 meters. The estimation of the elevation of each Census block in Alaska and the U.S. 

Virgin Islands was performed with analysis tools in the ArcGis 9.1 software application. The 

point locations of the Census block centroids in Alaska and the Virgin Islands were overlaid with 

a raster layer of North American Digital Elevation Model elevations (in meters). Each Census 

block point was assigned an elevation value based on the closest point in the ArcGis elevation 

raster file. 

 Hill heights were determined with base on the algorithm used in AERMAP, the 

AERMOD terrain processor.7

 In order to save run time and resources, the HEM3 Census block 

elevation database was substituted for the DEM data generally used in AERMAP. As noted 

above, the Census block elevations were originally derived from the DEM database. To 

determine the controlling hill height for each Census block, a cone was projected away from the 

block location, representing a 10% elevation grade. The controlling hill height was selected 

based on the highest elevation above that 10% grade (in accordance with the AERMAP 

methodology). The distance cutoff for this calculation was 100 km. (This corresponds to an 

elevation difference, at a 10% grade, of 10,000 m, which considerably exceeds the maximum 

elevation difference in North America.) 

2.4.3 Meteorological Data
13 

 HEM3 includes an extensive library of meteorological data to support both the 

AERMOD and ISCST3 dispersion models. 150 meteorological stations have been preprocessed 

for AERMOD as part of the RTR effort. In addition, 87 stations have previously been processed 

for ISCST3. Section 3.3 discusses the preparation of meteorological data for the RTR. 

2.4.4 Gaseous Deposition Parameters

 HEM3 provides options to compute the deposition of air pollutants, and to take into 

account the impacts of plume depletion due to deposition of gaseous and particulate pollutants. 

If either of these options is selected by the model user for gaseous pollutants, a number of 

pollutant properties are required by AERMOD. These include the diffusivity of the pollutant in 

air, the diffusivity of the pollutant in water, the Henry’s Law constant, and a parameter reflecting 

the cuticular resistance to uptake of the pollutant by leaves rCL).8

 HEM3 includes a library of 

these parameters for most gaseous HAPs. This library is based on an compendium of gaseous 

deposition parameters developed by Argonne National Laboratories.9

 It should be noted, 

however, that the deposition and depletion options of HEM3 and AERMOD were not used in the 

this component of the RTR analysis. 
14 

3. Modeling for the Residual Risk 

Technology Review 

 This section discusses the general approach used to implement the AERMOD version of 

HEM3 for the RTR modeling analysis. Separate reports have been prepared for all of the emission 

source categories included in the RTR analysis. These reports provide information on the 

emissions inputs and results for specific emission categories. 

3.1 Emission Source Inputs

 HEM3 and AERMOD require detailed data on emissions from each emission source 

included in the modeling analysis. These data include: 

! pollutants emitted 

! emission rate for each pollutant 

! emission source coordinates 

! stack height (or emission height for fugitive sources) 

! stack diameter (or configuration of fugitive sources) 

! emission velocity 

! emission temperature 

 Emissions data for the modeling effort were taken from the 2002 National Emissions 

Inventory (NEI) Final Version 1.10 These data include HAP emission rates, emission source 

coordinates, stack heights, stack diameters, flow rates, and exit temperatures. EPA performed an 

engineering review of the NEI data. In cases where new or better data were known to exist for a 

particular source category, that information was integrated into the data used in modeling for that 

category. For each source category, the emissions are summarized in the source category specific 

report. Detailed computer files containing all emission and release characteristics are available on 

the RTR website. 

 As noted in the previous section, industrial emission sources can be characterized in 

AERMOD and other dispersion models as point, area, or volume sources. The NEI includes a 

framework for characterizing area and volume sources; however, these fields are not filled in the 

2002 Final Version 1 of the NEI. Therefore, fugitive emissions are generally characterized as low 

point sources with minimal exit velocities. For some categories, additional information was 

available on the configuration of fugitive emission sources. This information was incorporated 

into the emissions database as part of the engineering review. Thus, fugitive emission sources 

were characterized as area or volume sources when sufficient configuration information was 

available. 
15 

3.2 Pollutant Cross-Referencing

 Because the NEI is developed from a number of different data sources, a single chemical 

may be listed in the inventory under different names (i.e. a “common name” and one or more 

structure-based names). In addition, pollutant groupings such as polycyclic organic matter 

(POM), can be listed in the NEI under the names of individual member compounds, and under 

different synonyms (e.g. polynuclear aromatic hydrocarbons). HEM3 requires an exact match in 

the chemical name in order to link emissions to the appropriate dose-response factors. The model 

will not process any pollutant that is not specifically listed in the chemical library (see Table 2-1). 

Therefore, all of the HAP names used in the NEI were linked to the appropriate chemical names in 

the HEM3 reference file. 

 The individual POM chemicals listed in the NEI were grouped into seven subclasses. 

Table 3-1 shows these groupings, and gives the cancer unit risk estimate used for each subclass. 

Emissions of metal compounds were also adjusted using algorithms developed for the Emissions 

Modeling System for Hazardous Air Pollutants (EMS-HAP) under the National-scale Air Toxics 

Assessment (NATA). A mass adjustment factor was applied to the emissions of metal compounds 

to account for a particular portion (e.g., the lead portion of lead sulfate) or to partition them among 

multiple pollutant categories (e.g. chromium arsenate into chromium VI compounds and arsenic 

compounds). In addition, where no specific compound information was available, metals were 

speciated into appropriate oxidation states (e.g. chromium compounds into chrome VI and chrome 

III) based on factors that have been developed for specific source categories and average factors 

that have been developed for the inventory as a whole. The adjustment factors and speciation 

factors were taken from the HAP Table module of EMS-HAP.11 ,12

3.3 Meteorological Data

 AERMOD meteorological data for the RTR analysis were derived from three sources: (1) 

AERMET processing of 122 National Weather Service (NWS) surface observation stations, (2) 

State agencies, and (3) industry (on-site). The 122 NWS sites were selected to provide thorough 

coverage of the U.S. For the RTR analysis, the average distance between a modeled facility and 

the applicable meteorological station was 72 km. One year of hourly data for these sites were 

processed through the AERMET program, and this is documented in “Meteorological Data 

Processing Using AERMET.”13 Four states provided AERMOD-ready meteorology files. These 

states were: Alabama (5 stations), Florida (4 stations), North Carolina (6 stations), and Wisconsin 

(22 stations). Eleven of the state supplied meteorology sites were co-located with sites in the list 

of 122. The data at the state supplied sites were more recent, so the state data for the 11 co-located 

sites were used. In addition, for the RTR petroleum refinery modeling, two refineries (Fairbanks, 

Alaska and the Virgin Islands) provided on-site AERMOD-ready meteorology files. These on-site 

meteorological data files were used with their respective refinery facilities for the HEM3 refinery 

modeling. Figure 3-1 shows the location of the 150 surface meteorological stations used in the 

RTR analysis. 

 As noted above, 122 NWS stations were processed through the AERMET program. To 

estimate the parameters needed by AERMOD, AERMET requires hourly surface weather 
16 

observations and the full twice-daily upper air soundings (i.e., meteorological variables reported at 

all levels). The surface and upper air stations are paired to produce the required input data for 

AERMOD. To simplify processing and minimize the amount of quality assurance needed, the 

analysis was restricted to meteorological data collected prior to the installation of the Automated 

Surface Observation System (ASOS). The ASOS has previously been found to omit the ceiling 

height for a large percentage of the observations at a number of meteorological stations. 

Installation and operation of ASOS equipment began in 1992; therefore, data for 1991 were 

processed with AERMET (note; the most recent version of AERMET has corrected this issue and 

is now compatible with ASOS data, processing of meteorological data in the future will utilize this 

revised version of AERMET and more recent years of meteorology). Data were retrieved from 

products available from the National 

Table 3-1. Groupings Used for Polycyclic Organic Matter in the RTR Phase II 

Modeling Effort

POM subgroup Names used in the NEI 

Cancer URE 

used for the 

subgroup 

(m3/g) 

POM 71002 16-PAH 0.000055 

 PAH, total 0.000055 

Polycyclic Organic Matter 0.000055 

POM72002 2-Chloronaphthalene 0.000055 

 2-Methylnaphthalene 0.000055 

 Acenaphthene 0.000055 

 Acenaphthylene 0.000055 

 Anthracene 0.000055 

 Benzo(c)phenanthrene 0.000055 

 Benzo[e]Pyrene 0.000055 

 Benzo[g,h,i,]Perylene 0.000055 

 Fluoranthene 0.000055 

 Fluorene 0.000055 

 Perylene 0.000055 

 Phenanthrene 0.000055 

 Pyrene 0.000055 

POM 73002 7,12-Dimethylbenz[a]Anthracene 0.1 

POM 74002 3-Methylcholanthrene 0.01 

POM 75002 5-Methylchrysene 0.001 

 Benzo[a]Pyrene 0.001 

 Dibenzo[a,h]Anthracene 0.001 

POM 76002 B[j]Fluoranthen 0.0001 

 Benz[a]Anthracene 0.0001 

 Benzo[b]Fluoranthene 0.0001 

 Benzo[k]Fluoranthene 0.0001 

 Indeno[1,2,3-c,d]Pyrene 0.0001 
17 

POM 77002 Chrysene 0.00001 

Climatic Data Center (NCDC). The surface data for 1991 were retrieved from the Hourly United 

States Weather Observation (HUSWO) CD. Upper air soundings were obtained from the 

Radiosonde Data of North America CDs produced by NCDC and the Forecast Systems Laboratory 

(FSL). 

 The input to and output from AERMET were examined for indications of missing input 

data. The 1200 Greenwich Mean Time (GMT) sounding is required for AERMET to calculate the 

convective boundary layer height and several associated parameters. There were many isolated 

days in which the 1200 GMT sounding was missing for many of the stations. However there also 

were several stations for which there were two or more consecutive days of missing 1200 GMT 

soundings. To minimize the impact on the output from AERMET, upper air data from a 

representative upper air station were substituted for those periods for which the 1200 GMT 

sounding was missing for two or more consecutive days. 

 One of the important requirements of applying the AERMOD model is the specification of 

surface characteristics for use in processing the meteorological data using AERMET. Application 

of AERMET requires specification of the surface roughness length, the Bowen ratio (an indicator 

of surface moisture), and the albedo (an indicator of surface reflectivity). These surface 

characteristics are used by AERMET to calculate the level of shear-induced mechanical turbulence 

generated by flow over the surface and for the energy balance calculations used in the 

determination of the Monin-Obukhov stability parameter and the convective velocity scale. For 

this application, the following surface characteristics were used: 

! Surface roughness length = 0.25 m. At the airport meteorological site, the surface 

roughness Includes runways, terminal buildings and other airport structures. In addition, 

off-airport structures often are within 3 kilometers of the measurement site. his 

combination of land covers suggests a value of 0.2 – 0.3 meters is appropriate. 

! Bowen ratio = 1.0. Representing an equal partition of the heat fluxes 

! Albedo = 0.15. Representing conditions for all seasons, including winter without 

continuous snow cover. 

The file STNS.TXT located on the HUSWO CD was used for the anemometer heights required by 

AERMET. These heights are to the nearest meter and were deemed appropriate for use in this 

application. 

 Two problems in the AERMET computer program were fixed before completing this 

application. The first pertained to hourly surface observations. Incorrect missing indicators in the 

code for wind speed and direction were output when the data were extracted from the raw input 

file in Stage 1, which caused the program to stop running in Stage 3. The AERMET program was 

changed to use correct missing indicators. The second problem was associated with the upper air 

soundings. AERMET would not process an entire station’s data if the surface pressure for the first 

sounding was less than 850 millibars (mb). Surface pressures less than 850 mb are not uncommon 

for stations at high elevations. The AERMET program was modified to expand the allowable 

surface pressures. 
18 

Figure 3.1. Locations of RTR AERMOD Meteorological Stations 

 In early 2007, AERMOD was modified to version 07026 which checks the header record 

of the surface meteorological input file to verify that its version number is 06341. As a result of 

this modification, the 122 NWS stations were re-processed through a newer version of AERMET 

(version 06431) to create meteorological data files compatible with AERMOD version 07026. 

The meteorology data received from the States were already at version 06341. 

3.4 Model Options Selected

 HEM3 presents a number of options for characterizing the modeling domain and data 

sources. As many sources were modeled in this assessment, established defaults and common 

practices were relied on to make these choices. The choices available to a HEM3 user and the 

selections that were made in this assessment are presented in Table 3-2. Some of the key 

selections are discussed in more detail in the paragraphs below. 

 Although routine emissions are not expected to vary significantly with time, nonroutine 

(upset) emissions can be significant relative to routine emissions. Upset emissions occur during 

periods of startup, shutdown, and malfunction. Upset emissions are not likely for equipment or 

storage tanks, but do result from malfunctioning control devices and leaks in cooling tower heat 

exchangers. There is some limited data on upset emissions available,14 but no facility-specific 
19 

analyses of these data were performed to characterize short-term emissions from these emission 

sources, and upset emissions were not modeled for the RTR risk assessments. 

Table 3-2. Modeling Domain and Model Set-Up Options in the HEM-3 Model As Used in 

the Residual Risk and Technology Review Assessments

Option Selection

Dispersion model: AERMOD or ISCST3 AERMOD 

Type of analysis: chronic, acute, or both Both 

Averaging time for short term impacts 1-hour 

Multiplier for short term emissions 10 a

Urban or rural dispersion characteristics Rural 

Include terrain impacts Yes 

Include building wake effects No 

Calculate deposition (wet, dry, or both) No 

Include impacts of plume depletion due to deposition No 

User-specified receptor locations (for plant boundary sites or other sites of interest) No 

Modeling domain size – maximum distance to be modeled 50 km 

Cutoff distance for modeling of individual blocks 3 km b

Overlap distance where receptors are considered to be on plant property – measured 

from each source 

30 m c

Polar receptor network specifications 

Distance from the plant center to the innermost ring 100 m c

Number of rings 13 

Number of directions 16 

Meteorology data Closest site 

 a For the secondary aluminum production category, the ratio of maximum short-term emissions to 

long-term average emissions was set to 100, based on the engineering review. 

 b The individual block modeling cutoff was increased for categories and for some facilities to ensure 

that the maximum individual risk values were not interpolated. 

 c

 The overlap distance and inner ring distance were adjusted for some facilities to avoid modeling 

locations that are on plant property (see section 4.2).

3.4.1 Urban or Rural Dispersion Characteristics

 Since 51 source categories were considered in these screening-level RTR assessments, the 

rural option was chosen to be most conservative (i.e., more likely to overestimate risk results). 

3.4.2 Deposition and Plume Depletion

 This modeling analysis did not take into account the depletion of pollutant concentrations 

in the plume due to wet or dry deposition. In addition, reactivity and decay were not considered. 
20 

It is possible that this approach, may overestimate air concentrations and therefore risk. However, 

one of the main metrics used by EPA in the residual risk program is the risk to the individual most 

exposed (maximum risk). Because the maximum risk usually occurs at a receptor very close to 

the emission source, it is unlikely to be influenced by altered plume dispersion characteristics of 

this type. For more refined, multipathway assessments, EPA will consider deposition and 

depletion. 

3.4.3 Cutoff Distance for Modeling of Individual Blocks

 The cutoff distance for modeling individual Census blocks was initially set to 3 km. This 

distance generally ensures that the maximum individual cancer risk and the maximum TOSHI are 

modeled explicitly and not interpolated. Following the modeling run, the results for each facility 

were checked to determine whether the maximum impacts were located inside the modeling cutoff 

distance. If the maximum impacts were outside the cutoff distance any of the impacts were 

significant, then HEM3 was rerun for the facility with an expanded modeling distance cutoff. In 

general, this was done if the cancer risk exceeded 1 in 10 million or any TOSHI exceeded 0.1. 

However, the risks for these facilities were generally very low, since the maximum impacts were 

generally only interpolated when the nearest Census block was more than 3 km from the facility. 

3.4.4 Plant Boundary Assumptions

 The main input mechanisms for incorporating plant boundary information in HEM3 are the 

overlap distance, the distance to the innermost polar receptor ring, and user-specified receptor 

locations. The NEI does not provide information on plant boundaries. Therefore, the userspecified receptor file was not used in this analysis. Conservative default assumptions were used 

for the overlap distance and the innermost polar receptor ring. However, these were adjusted for 

some categories where plant sites are known to be large. In addition, satellite imagery was used to 

check the plant boundary assumptions for facilities with large projected impacts. These checks are 

discussed further in the section on Quality Assurance (Section 4). 

3.5 Modeling of Multiple Facilities

 HEM3 models one facility at a time. However, clusters of nearby facilities may impact the 

same people, resulting in higher risk to those people. To account for this situation, risks were 

summed at each Census block for all facilities affecting the Census block. 

 As described earlier (Section 2.3.4), HEM3 produces detailed output tables containing the 

risk and population for every Census block in the modeling domain. These detailed tables were 

combined for all facilities in a source category and the risk for each Census block was summed. 

Thus, the effect of multiple facilities in the same source category on the same receptor could be 

estimated. The resulting “combined facility” or “cluster-effect” Census block risks were used to 

calculate population exposure to different cancer risk levels and source category incidence. 
21 

4. Quality Assurance

 The National Emissions Inventory is subject to an extensive program of quality assurance 

(QA) and quality control (QC). The QA/QC program for the point source component of the NEI 

is documented in a separate report, available from the NEI website.15 This section describes QA 

activities carried out under the RTR modeling analysis. 

4.1 Engineering Review

 In addition to the standardized QA steps taken for the entire NEI, EPA performed an 

engineering review of NEI data for the emission source categories included in the RTR analysis. 

This engineering review included two main components. The first component addressed the list of 

facilities included in each source category. EPA engineers reviewed independent sources of 

information to identify all sources in the category that are included in the NEI. In addition, EPA 

reviewed the list of sources represented as part of each category in the NEI to make sure that the 

facilities actually manufacture products characteristic of the source category. 

 The second component of the engineering review focused on the appropriateness of facility 

emissions. EPA reviewed the list of HAPs emitted by each facility to make sure that the 

pollutants were appropriate to the source category. In addition, EPA engineers review the 

magnitude of those HAP emissions. In cases where new or better data were known to exist for a 

particular source category, that information was integrated into the data used in modeling for that 

category. In these cases, the source category specific documents provide additional details on the 

emissions inputs used. 

4.2 Geographic Checks

 The NEI QA process includes some basic checks on location data for point sources. The 

coordinates for each source are checked to ensure that they are in the county that has been 

specified for the source. If this is not the case, or if no geographic coordinates are available for the 

emission source, then the coordinates are set to a default location based on the nature of the 

emission source category.15 In addition, coordinates for all emission sources at a given facility are 

checked to ensure that they are withing 3 km of one another. 

 As part of the RTR modeling analysis, additional geographical QA checks were made for 

some facilities. Facilities subjected to these additional checks include: 

! cases where the initial estimates of maximum risks were particularly high 

" maximum individual cancer risk of over 1 in 10,000 

" any maximum TOSHI above 10 

! cases where no Census blocks were identified within 3 km of the facility 

 The emission source coordinates were reviewed for each of these facilities, and compared 

with the address reported for the facility. Both the coordinates and the facility address were 
22 

entered into Internet mapping sites which provide the option of viewing aerial or satellite 

photography. These sites include Google Earth® (earth.google.com/), Google Maps® 

(local.google.com/), MapQuest® (www.mapquest.com) and Microsoft Terraserver® 

(www.terraserver.com/). If the address and the coordinates represented the same location, then the 

coordinates were taken to be correct. If the locations were different, then the aerial photographs 

for the address and the coordinate location were reviewed to determine whether either photograph 

included an industrial facility. Generally, where the two locations were different, the facility 

address was found to be correct. In some cases, this comparison could not be made because the 

reported address was a Post Office box or a headquarters address. Where this occurred, the aerial 

photograph for the coordinate location was reviewed to determine whether an industrial facility 

was located at or near the location. If emission source coordinates were found to be incorrect, 

HEM3 was rerun using corrected coordinates. These changes are described in the source category 

documents. 

 For the high-risk facilities, the coordinates used to represent the most impacted Census 

blocks were also reviewed. This review drew on detailed Census block boundary maps and aerial 

photographs. Large industrial facilities will frequently occupy one or more entire Census blocks. 

However, these blocks may also include one or more residences on the periphery of the industrial 

land. Generally, the coordinates listed for a Census block are near the center of the block. In 

these cases of mixed industrial and residential blocks, the coordinates may be on plant property. 

 The potential for overlap between an industrial facility and a Census block coordinate is 

illustrated in Figure 4-1. The figure shows Census blocks divisions around a large industrial 

facility, with the coordinates of emission sources at the facility illustrated by red squares. Census 

block boundaries are shown, and the coordinates given in the Census data base for populated 

blocks are indicated by blue circles. As the figure shows, the facility is spread over five Census 

blocks. A number of populated blocks can be seen to the Southwest of the facility, and most of 

the blocks covering the facility are not populated. However, the block containing most of the 

facility’s emission points also includes at least one residence (the total population given for the 

block is 5). The location of the residence is at the periphery of the block, near the one of the 

adjacent populated blocks. However, the coordinates given for the block are near its center, 

illustrated by the blue star, clearly between various emission sources at the facility. 

 In general, block coordinates were considered to be on plant property if they were located 

between the different emission source locations listed for the facility. In these situations, HEM3 

was rerun with an expanded overlap distance, in order to exclude the Census block coordinates 

that appeared to be located on plant property. The distance to the innermost polar receptor ring 

was also adjusted to ensure that this ring was not on plant property. 
23 

Figure 4-1. Illustration of a quality assurance test for Census block coordinates located on 

plant property. 

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24 

5. Uncertainties

 This analysis is subject to a number of uncertainties. For instance, model verification 

studies for AERMOD showed predicted maximum annual concentrations ranging from 0.3 to 1.6 

times measured values, with an average of 0.9. Predicted maximum short term (1 to 24 hours) 

concentrations were 0.25 to 2.5 times measured values, with an average of 1.0.16

 In addition, a number of simplifying assumptions have been made in this modeling 

analysis. First, the coordinates reported by the Department of Census for Census block “internal 

points” have been used as a surrogate for long-term population exposures. Locations of actual 

residences have not been modeled. In addition, the current version of HEM3 does not take into 

account the movement of people from one Census block to another during the course of their lives, 

or commuting patterns during a given day. Nor does the model take into account the attenuation 

of pollutant from outside emission sources in indoor air. Ideally, risks to individuals would be 

modeled as they move through their communities and undertake different activities. However, 

such modeling is time- and resource-intensive and can only capture a portion of the uncertainty 

associated with the full range of human activities. In general, it is expected that long-term 

exposures will be overstated for high-end estimates (as most individuals will not spend all their 

time at their highly affected residences), but may understate the total population exposed (as some 

individuals living outside the modeled area may regularly commute into the area for work or 

school). 

 When considering long-term or lifetime exposures, it should be noted that relatively few 

people in the United States reside in one place for their entire lives. For the purposes of this 

assessment, cancer risk estimates are based on a lifetime exposure at the 2000 Census-identified 

place of residence. While it is impossible to know how this assumption affects the risk 

experiences by a particular individual (as people can move into higher- or lower-risk areas), it is 

likely that this assumption will overstate the exposure to those most exposed (i.e., people already 

living in high exposure areas are unlikely to move to yet higher exposure areas). However, this 

assumption will also tend to underestimate the total number of people exposed and population risk 

(i.e., incidence) because population levels are generally increasing. 

 In the current analysis, only direct inhalation has been modeled. Other pathways such as 

the deposition of pollutants to drinking water, and to bioaccumulation of deposited pollutants in 

the food supply may be a significant source of exposure for persistent and bioaccumulative 

pollutants. These exposures will be modeled in subsequent analyses. Because this analysis is 

restricted to the inhalation pathway, the impacts of plume depletion due to deposition have not 

been into account. Thus, inhalation impacts may be overestimated for some pollutants, but 

exposures through other pathways would be underestimated. 

 A number of other simplifications have been made in the dispersion modeling 

analysis, as has been noted in Table 3-1. For instance, building wake effects have not been 

considered. Rural dispersion conditions have also been used for all facilities. This is viewed as a 

conservative assumption, since rural conditions result in less mixing close to the emission source. 
25 

In addition, meteorological observations are based on the closest station in the HEM3 

meteorological library (see Figure 2-1). Alternative meteorological stations may be more 

appropriate for some facilities. Ideally, facility-specific meteorological observations would be 

used. A single year of meteorological data (1991) was selected for dispersion modeling. When 

considering off-site meteorological data most site specific dispersion modeling efforts will employ 

up to five years of data to capture variability in weather patterns from year to year. However, 

because of the large number of facilities in the analysis and the extent of the dispersion modeling 

analysis (national scale), it was not practical to model five years of data. Other national studies 

such as NATA have also considered only a single year of meteorological data. A sensitivity 

analyses performed by the NATA assessment found that variability attributable to the selection of 

the meteorology location/time (both temporal and spatial) resulted in a 17-84% variation in 

predicted concentrations at a given station17. 

 Finally, risk and exposure factors are also subject to uncertainty. Not all individuals 

experience the same degree of exposure or internal dose of a given pollutant due to individualspecific parameters such as weight, age, and gender. While the health benchmarks used in the 

analysis crudely account for sensitive populations, a prototypical human (e.g., body weight, 

ventilation rate) is used to define the benchmark. Because of the variability of these parameters in 

the population, this factor will result in a degree of uncertainty in the resulting risk estimate. 

 Table 5-1 summarizes the general sources of uncertainty for the RTR modeling analysis. 

The table also gives a qualitative indication of the potential direction of bias on risk estimates. 

The sources of uncertainty in Table 5-1 are divided into four categories, based on the major 

components of the analysis: 

! emissions inventory 

! fate and transport modeling 

! exposure assessment 

! toxicity assessment 

It must also be noted that individual source categories may be subject to additional uncertainties. 

These are discussed in separate reports which have been prepared for each emission source 

category included in the RTR. 
26 

Table 5-1. Summary of General Uncertainties Associated with Risk and Technology Review Risk 

Parameter Assumption Uncertainty/Variability Discussion 

Poten

o

Emissions InventoryIndividual HAP emissions rates and facility characteristics (stack parameters, property boundaries) 

Emissions and facility characteristics from the NEI provide an accurate characterization of actual source emissions. 

Our emissions inventory is based on the 2002 NEI, our internal review, and public comments received. The degree to which the data in our inventory represents actual emissions is likely to vary across sources. Nearly half of the sources in the source category submitted a review of their emissions and facility characteristics data. Some detailed data, such as property boundary information was not available for most facility. This is an important consideration in determining acute impacts. 

Unbiavariab

Multiplier for short-term emission rates 

Maximum short term emission rates are estimated by applying a simple multiplier (a factor of 10) to average annual emissions. 

The ratio between short-term and long-term average emission rates may vary from among the different emission sources at a facility. In addition, the use of a simple multiplier means that impacts of maximum short term emissions are modeled for all meteorological conditions, including the worst-case conditions for population exposure. 

Potentthe facemissioccasiworst-Overeactual term e

Fate and Transport ModelingAtmospheric dispersion model choice 

AERMOD is one of EPA's recommended models for assessing pollutant concentrations from industrial facilities 

Field testing of these models have shown results to generally be within a factor of 2 of measured concentrations. 

Overe
Table 5-1. Summary of General Uncertainties Associated with Risk and Technology Review Risk 

 (continued) 

27 

Parameter Assumption Uncertainty/Variability Discussion 

Poten

o

Building downwash Not included in assessment 

Use of this algorithm in AERMOD could improve the dispersion calculations at individual facilities. However, data were not readily available to utilize this option. 

PotentmaximNo effout. 

Plume depletion and deposition Not included in assessment 

Ignoring these impacts for pollutants that deposit minimally and whose risks derive predominantly from inhalation should have minimal effect on risk estimates. 

Unbiaoveres

Meteorology 

One year of meteorological data from the nearest weather station (selected from 122 nationwide) is representative of long-term weather conditions at the facility. 

The use of one year of data rather than the five or more adds uncertainty based on whether that year is representative of each location’s climatology. Use of weather station data rather than on-site data can add to uncertainty. Additionally, the use of default surface parameters in the generation of the meteorological datasets imparts uncertainty to the results from any individual facility. 

Minimoveres

Reactivity Not included in the assessment. 

Chemical reactions and transformations of individual HAP into other compounds due to solar radiation and reactions with other chemicals happens in the atmosphere. However, in general, the HAP in this assessment do not react quickly enough for these transformations to be important near the sources, where the highest individual risks are estimated. Further, most of the HAP do not react quickly enough for these transformations to be important to risk estimates in the entire modeled domain (i.e., within 50 km of the source). 

No imestimaon popincide
Table 5-1. Summary of General Uncertainties Associated with Risk and Technology Review Risk 

 (continued) 

28 

Parameter Assumption Uncertainty/Variability Discussion 

Poten

o

Maximum modeling distance 

50 kilometers from center of facility 

This distance is considered to be the maximum downwind distance for a Gaussian plume model such as AERMOD. This is because, in general, winds cannot be considered to follow straight line trajectories beyond this distance. 

No effindiviundereincide

Exposure AssessmentLocations and shortterm movements of individuals 

Ambient concentration at centroid of each off-site census block is equal to the exposure concentration for all people living in that census block. Effect of human activity patterns on exposures is not included in the assessment. 

People live at different areas within block that may have higher or lower exposures than at the centroid. Individuals also move from outdoors to indoors and from home to school/work to recreation, etc., and this can affect their total exposure from these sources. 

Unbiafor moindivioveresexposfor lea

Long-term movements of individuals 

MIR individual is exposed continuously to the highest exposure concentration for a 70-year lifetime. Population moves into and out of exposure area consistent with national frequency distribution of residency. 

MIR (maximum individual risk) is defined in this way to be a maximum theoretical risk at a point where a person can actually reside. Individuals may move into or out of areas impacted by the facilities of interest in ways that don’t correspond exactly with national norms or local demographic patterns, and this could lead to localized biases. In general, source categories with larger numbers of facilities should carry less potential for bias. 

Unbiaindivioveresindivilikely least eremainpopulaincrea70 yea

Toxicity Assessment
Table 5-1. Summary of General Uncertainties Associated with Risk and Technology Review Risk 

 (continued) 

29 

Parameter Assumption Uncertainty/Variability Discussion 

Poten

o

Reference concentrations (RfC) 

Consistent with EPA guidance, RfCs are developed including uncertainty factors to be protective of sensitive subpopulations. Additionally, RfCs are developed based on the level producing an effect in the most sensitive target organ or system. 

While other organ systems may be impacted at concentrations above the RfC, these are not included in the calculation of target organspecific hazard indices. 

In genRfCs uwhose undereof uncvariabuncertpotentrisks. 

Unit Risk Estimate (URE) 

Use of unit risk estimates developed from dose-response models such as linear low-dose extrapolation. 

Uncertainty in extrapolating the impacts from short-duration, high-dose animal or work-related exposures to longer duration, lower-dose environmental impacts. 

Overenonlinfor linsparse generavalues aimed risks iand va

Toxicity of mixtures 

Cancer risks and noncancer hazard quotients were calculated for each HAP individually and then summed into a total risk or hazard index (assumption of additivity). 

Concurrent exposures to multiple chemicals may result in either increased or decreased toxicity due to chemical interactions but the data needed to quantify these effects are generally not available. 

Unbiamixtuundereoverescorrec
Table 5-1. Summary of General Uncertainties Associated with Risk and Technology Review Risk 

 (continued) 

30 

Parameter Assumption Uncertainty/Variability Discussion 

Poten

o

Surrogate doseresponse values for HAPs without values 

In the case of groups of HAPs such as glycol ethers, the most conservative dose-response value of the chemical group was used as a surrogate for missing doseresponse values in the group. For others, such as unspeciated metals, we have applied speciation profiles appropriate to the source category to develop a composite dose-response value for the group. For HAP which are not in a group and for which no URE’s or RfC’s are available from credible sources, no assessment of risk is made. 

Rather than neglecting the assessment of risks from some HAPs lacking dose response values, conservative assumptions allow the examination of whether these HAPs may pose an unacceptable risk and require further examination, or whether the conservative level examination with surrogates screens out the HAPs from further assessment. 

OvereconserUnbiaspecifThere underepollutinclud
31 

6. References

 

1. EPA. 2004. AERMOD: Description of Model Formulation. EPA-454/R-03-004, U.S. 

Environmental Protection Agency, Research Triangle Park, NC. 

www.epa.gov/scram001/dispersion_prefrec.htm#aermod

2. EPA. 2004. User's Guide for the AMS/EPA Regulatory Model - AERMOD. EPA454/B-03-001, U.S. Environmental Protection Agency, Research Triangle Park, NC. 

www.epa.gov/scram001/dispersion_prefrec.htm#aermod

3. EC/R Incorporated. 2006. The HEM-3 User’s Guide. Prepared for the U.S. 

Environmental Protection Agency, Sector-Based Assessment Group, Research Triangle 

Park, NC. 

4. EPA. 2007. OAQPS Prioritized Chronic Dose-Response Values (6/12/2007). U.S. 

Environmental Protection Agency, Research Triangle Park, NC. Available at: 

http://www.epa.gov/ttn/atw/toxsource/summary.html

5. Census. 2000. LandView® 5 on DVD. U.S. Census Bureau, Washington, DC. 

6. USGS. 2003. 1:250,000 Scale Digital Elevation Models (DEM). U.S. Geologic Survey, 

Washington, DC. http://edcsgs9.cr.usgs.gov/glis/hyper/guide/1_dgr_demfig/states.html

7. EPA. 2004. User's Guide for the AERMOD Terrain Preprocessor (AERMAP). EPA454/B-03-003, U.S. Environmental Protection Agency, Research Triangle Park, NC. 

http://www.epa.gov/scram001/dispersion_related.htm#aermap

8. EPA. 2004. Addendum: User's Guide for the AMS/EPA Regulatory Model - AERMOD

(related to deposition). U.S. Environmental Protection Agency, Research Triangle Park, 

NC. http://www.epa.gov/scram001/dispersion_prefrec.htm#aermod

9. Wesely, M.L., P.V. Doskey, and J.D. Shannon. 2002. Deposition Parameterizations for 

the Industrial Source Complex (ISC3) Model. Argonne National Laboratory, Argonne, 

Illinois. http://www.epa.gov/scram001/dispersion_prefrec.htm#aermod

10. EPA. 2006. 2002 National Emissions Inventory Data & Documentation. U.S. 

Environmental Protection Agency, Research Triangle Park, NC. 

http://www.epa.gov/ttn/chief/net/2002inventory.html

11. EPA. 2002. EMS-HAP Modeling System. Environmental Protection Agency, Research 

Triangle Park, NC. http://www.epa.gov/scram001/dispersion_related.htm#ems-hap
32 

 

12. Strum, Madeleine, Richard Mason, and Diane Linderman. 2002. User's Guide for the 

Emissions Modeling System for Hazardous Air Pollutants (EMS-HAP) Version 2.0. 

EPA-454/B-02-001, Environmental Protection Agency, Research Triangle Park, NC. 

http://www.epa.gov/scram001/dispersion_related.htm#ems-hap

13. MACTEC. 2006. Meteorological Data Processing Using Aermet for HEM-AERMOD. 

Prepared by MACTEC Federal Programs, Inc., Durham, NC for EC/R Incorporated, 

Chapel Hill, NC. 

14. Texas Commission on Environmental Quality. 2006. Reports on Air Emission Events. 

http://www.tceq.state.tx.us/compliance/field_ops/eer/

15. EPA. 2006. NEI Quality Assurance and Data Augmentation for Point Sources. U.S. 

Environmental Protection Agency, Emission Inventory Group, Research Triangle Park, 

NC. 

16. EPA. 2003. AERMOD: Latest Features and Evaluation Results. EPA-454/R-03-003, 

U.S. Environmental Protection Agency, Research Triangle Park, NC. 

http://www.epa.gov/scram001/7thconf/aermod/eval.pdf

17 EPA, 2001, National-Scale Air Toxics Assessment for 1996; EPA-453/R-01-003; EPA454/B-03-003, U.S. Environmental Protection Agency, Research Triangle Park, NC. 

http://www.epa.gov/ttn/atw/nata/peer.html 
Appendix 2 

Meteorological Data for HEM-3 Modeling 
 

2

DRAFT 

METEOROLOGICAL DATA PROCESSING 

USING AERMET 

FOR 

HEM-3 

Submitted by: 

MACTEC Federal Programs, Inc. 

5001 S. Miami Blvd., Suite 300 

Durham, NC 27703 

919-941-0333 

July 28, 2006 

Revisions May 14, 2012 
Meteorological Data Processing using AERMET July 28, 2006 

For HEM-AERMOD 

i 

TABLE OF CONTENTS 

PROJECT BACKGROUND ...............................................................................................1 

METEOROLOGICAL DATA PROCESSING ...................................................................1 

MISSING DATA .................................................................................................................5 

ANEMOMETER HEIGHTS ...............................................................................................6 

SURFACE CHARACTERISTICS ......................................................................................6 

MODIFICATIONS TO AERMET CODE ..........................................................................7 

RESULTS ............................................................................................................................7 

LIST OF TABLES 

Table

1 SURFACE STATIONS PAIRED WITH UPPER AIR STATION .........................1 

2 UPPER AIR STATIONS WITH MISSING 1200 GMT SOUNDINGS 

 AND STATION USED FOR DATA SUBSTITUTION .........................................5 

3 SURFACE CHARACTERISTICS ..........................................................................7 

4 PERCENT CALM AND MISSING BY STATION ...............................................8 

LIST OF FIGURES 

Figure

1 SURFACE AND UPPER AIR STATIONS ............................................................4 
Meteorological Data Processing using AERMET July 28, 2006 

Rev. 05/12 

For HEM-AERMOD 

1 

PROJECT DESCRIPTION AND BACKGROUND 

The AERMOD meteorological processor, AERMET, was used to process one year of 

hourly data from 122 National Weather Service (NWS) surface observation stations 

across the continental United States, Alaska, Hawaii, and Puerto Rico. 

METEOROLOGICAL DATA PROCESSING 

To estimate the boundary layer parameters required by AERMOD, AERMET requires 

hourly surface weather observations and the full (i.e., meteorological variables reported at 

all levels) twice-daily upper air soundings. The surface and upper air stations are paired 

to produce the required input data for AERMOD. 

One of the requirements of the project was to use surface observations prior to the 

introduction of the Automated Surface Observation System (ASOS). Installation and 

operation of ASOS equipment began in 1992; therefore, data for 1991 were processed 

with AERMET. 

A list of 86 previously paired surface and upper air stations was provided to MACTEC. 

On review of these stations, it was determined that data for 1991 for three of the stations 

– Galveston, TX, Houston-Hobby, TX and Boulder, CO – was not available. The 

remaining 83 stations did not provide adequate coverage for the MACT sources to be 

modeled with AERMOD. As a result, an additional 39 surface stations were identified to 

include for processing. These additional surface stations were paired with representative 

upper air stations. A list of the surface and paired upper air stations are in Table 1. 

Data were retrieved from products available from the National Climatic Data Center 

(NCDC). The surface data for 1991 were retrieved from the Hourly United States 

Weather Observation (HUSWO) CD. Upper air soundings were obtained from the 

Radiosonde Data of North America CDs produced by NCDC and the Forecast Systems 

Laboratory (FSL). 

TABLE 1 

SURFACE STATIONS WITH PAIRED UPPER AIR STATION 

SURFACE WBAN LAT LON UPPER AIR WBAN LAT LON 

Paducah, KY 03816 37.07 -88.77 Paducah, KY 03816 37.07 -88.77 

Huntsville, AL 03856 34.65 -86.78 Nashville, TN 13897 36.13 -86.68 

Huntington, WV 03860 38.37 -82.55 Huntington, WV 03860 38.37 -82.55 

Greenville, SC 03870 34.90 -82.22 Athens, GA 13873 33.95 -83.32 

Fort Worth, TX 03927 32.90 -97.02 Stephenville, TX 13901 32.22 -98.18 

Wichita, KS 03928 37.65 -97.45 Topeka, KS 13996 39.07 -95.63 

Lake Charles, LA 03937 30.12 -93.23 Lake Charles, LA 03937 30.12 -93.23 

Jackson, MS 03940 32.32 -90.08 Jackson, MS 03940 32.32 -90.08 
Meteorological Data Processing using AERMET July 28, 2006 

Rev. 05/12 

For HEM-AERMOD 

2 

SURFACE WBAN LAT LON UPPER AIR WBAN LAT LON 

Kansas City, MO 03947 39.30 -94.73 Topeka, KS 13996 39.07 -95.63 

San Juan, PR 11641 18.43 -66.02 San Juan, PR 11641 18.43 -66.00 

Orlando, FL 12815 28.45 -81.32 Tampa, FL 12842 27.70 -82.40 

Gainesville, FL 12816 29.68 -82.27 Waycross, GA 13861 31.25 -82.40 

Tampa, FL 12842 27.97 -82.53 Tampa Bay, FL 12842 27.97 -82.53 

W. Palm Beach, FL 12844 26.68 -80.10 W. Palm Beach, FL 12844 26.68 -80.12 

Victoria, TX 12912 28.85 -96.92 Corpus Christi, TX 12924 29.77 -97.50 

New Orleans, LA 12916 30.00 -90.25 Slidell, LA 53813 30.33 -89.82 

Port Arthur, TX 12917 29.95 -94.02 Lake Charles, LA 03937 30.12 -93.23 

Brownsville, TX 12919 25.90 -97.43 Brownsville, TX 12919 25.90 -97.43 

San Antonio, TX 12921 29.53 -98.47 Del Rio, TX 22010 29.37 -100.92 

Corpus Christi, TX 12924 27.77 -97.52 Corpus Christi, TX 12924 27.77 -97.52 

Houston, TX 12960 29.98 -95.37 Lake Charles, LA 03937 30.12 -93.23 

Raleigh, NC 13722 35.87 -78.78 Greensboro, NC 13723 36.10 -79.95 

Greensboro, NC 13723 36.08 -79.95 Greensboro, NC 13723 36.08 -79.95 

Norfolk, VA 13737 36.90 -76.20 Wallops Island, VA 93739 37.93 -75.47 

Philadelphia, PA 13739 39.87 -75.23 Atlantic City, NJ 93755 39.75 -74.67 

Richmond, VA 13740 37.50 -77.32 Sterling, VA 93734 38.98 -77.47 

Roanoke, VA 13741 37.32 -79.97 Greensboro, NC 13723 36.10 -79.95 

Wilmington, NC 13748 34.27 -77.90 Charleston, SC 13880 32.90 -80.03 

Wilmington, DE 13781 39.67 -75.60 Atlantic City, NJ 93755 39.75 -74.67 

Meridian, MS 13865 32.33 -88.75 Jackson, MS 03940 32.32 -90.08 

Charleston, WV 13866 38.38 -81.58 Huntington, WV 03860 38.37 -82.55 

Atlanta, GA 13874 33.63 -84.45 Athens, GA 13873 33.95 -83.33 

Birmingham, AL 13876 33.57 -86.75 Centerville, AL 03881 32.90 -87.25 

Bristol, TN 13877 36.47 -82.40 Huntington, WV 03860 38.37 -82.55 

Charleston, SC 13880 32.90 -80.03 Charleston, SC 13880 32.90 -80.03 

Charlotte, NC 13881 35.22 -80.93 Greensboro, NC 13723 36.08 -79.95 

Columbia, SC 13883 33.95 -81.12 Athens, GA 13873 33.95 -83.33 

Jacksonville, FL 13889 30.50 -81.70 Waycross, GA 13861 31.25 -82.40 

Memphis, TN 13893 35.05 -89.98 N. Little Rock, AR 03952 34.83 -92.27 

Mobile, AL 13894 30.68 -88.25 Slidell, LA 53813 30.33 -89.82 

Montgomery, AL 13895 32.30 -86.40 Centerville, AL 03881 32.90 -87.25 

Nashville, TN 13897 36.13 -86.68 Nashville, TN 13897 36.13 -86.68 

Shreveport, LA 13957 32.45 -93.82 Longview, TX 03951 32.35 -94.65 

Little Rock, AR 13963 34.73 -92.23 N. Little Rock, AR 03952 34.83 -92.27 

Wichita Falls, TX 13966 33.98 -98.50 Stephenville, TX 13901 32.22 -98.18 

Oklahoma City, OK 13967 35.40 -97.60 Norman, OK 03948 35.23 -97.47 

Baton Rouge, LA 13970 30.53 -91.15 Slidell, LA 53813 30.33 -89.82 

Dodge City, KS 13985 37.77 -99.97 Dodge City, KS 13985 37.77 -99.97 

St. Louis, MO 13994 38.75 -90.37 Peoria, IL 14842 40.67 -89.68 

Springfield, MO 13995 38.82 -92.27 Monet, MO 03946 36.87 -93.97 

Buffalo, NY 14733 42.93 -78.73 Buffalo, NY 14733 42.93 -78.73 

Newark, NJ 14734 41.72 -75.17 Atlantic City, NJ 93755 39.75 -74.67 

Albany, NY 14735 42.75 -73.82 Albany, NY 14735 42.75 -73.82 

Allentown, PA 14737 40.65 -75.45 Albany, NY 14735 42.75 -73.82 

Boston, MA 14739 42.37 -71.02 Chatham, MA 14684 41.67 -69.97 
Meteorological Data Processing using AERMET July 28, 2006 

Rev. 05/12 

For HEM-AERMOD 

3 

SURFACE WBAN LAT LON UPPER AIR WBAN LAT LON 

Hartford, CN 14740 41.93 -72.68 Albany, NY 14735 42.75 -73.80 

Burlington, VT 14742 44.47 -73.15 Albany, NY 14735 42.75 -73.82 

Portland, ME 14764 43.65 -70.30 Portland, ME 14764 43.65 -70.30 

Providence, RI 14765 41.73 -71.43 Chatham, MA 14684 41.67 -69.97 

Syracuse, NY 14771 43.12 -76.12 Buffalo, NY 14733 42.93 -78.73 

Williamsport, PA 14778 41.27 -77.05 Pittsburgh, PA 94823 40.53 -80.23 

Cleveland, OH 14820 41.40 -81.85 Pittsburgh, PA 94823 40.53 -80.23 

Columbus, OH 14821 39.98 -82.88 Dayton, OH 13840 39.83 -84.05 

Fort Wayne, IN 14827 41.00 -85.20 Dayton, OH 13840 39.83 -84.05 

Lansing, MI 14836 42.78 -84.58 Flint, MI 14826 42.97 -83.75 

Madison, WI 14837 43.13 -89.33 Green Bay, WI 14898 44.48 -88.13 

Erie, PA 14860 42.08 -80.18 Buffalo, NY 14733 42.93 -78.73 

Akron, OH 14895 40.92 -81.43 Pittsburgh, PA 94823 40.50 -80.23 

Green Bay, WI 14898 44.50 -88.12 Green Bay, WI 14898 44.50 -88.12 

Duluth, MN 14913 46.83 -92.22 Int'l Falls, MN 14918 48.57 -93.40 

Minneapolis, MN 14922 44.88 -93.23 St. Cloud, MN 14926 45.55 -94.05 

Moline, IL 14923 41.45 -90.52 Peoria, IL 14842 40.67 -89.68 

Rochester, MN 14925 43.92 -92.50 St. Cloud, MN 14926 45.55 -94.08 

Sioux Falls, SD 14944 43.57 -96.73 Huron, SD 14936 44.38 -98.22 

Eau Claire, WI 14991 44.87 -91.48 St. Cloud, MN 14926 45.55 -94.08 

Lihue, HI 22536 21.98 -159.33 Lihue, HI 22536 21.98 -159.33 

Midland, TX 23023 31.95 -102.18 Midland, TX 23023 31.95 -102.18 

El Paso, TX 23044 31.82 -106.38 El Paso, TX 23044 31.82 -106.38 

Amarillo, TX 23047 35.22 -101.70 Amarillo, TX 23047 35.22 -101.70 

Albuquerque, NM 23050 35.03 -106.62 Albuquerque, NM 23050 35.03 -106.62 

Denver, CO 23062 39.77 -104.87 Denver, CO 23062 39.77 -104.88 

Goodland, KS 23065 39.37 -101.70 Dodge City, KS 13985 37.77 -99.97 

Grand Junction, CO 23066 39.12 -108.53 Grand Junction, CO 23066 39.12 -108.53 

Tonopah, NV 23153 38.05 -117.08 Ely, NV 23154 39.30 -114.85 

Tucson, AZ 23160 32.12 -110.93 Tucson, AZ 23160 32.12 -110.93 

Daggett, CA 23161 34.87 -116.78 Desert Rock, NV 03160 36.62 -116.02 

Los Angeles, CA 23174 33.93 -118.38 Miramar, CA 03190 32.87 -117.15 

Prescott, AZ 23184 34.65 -112.43 Tucson, AZ 23160 32.12 -110.93 

San Diego, CA 23188 32.73 -117.17 Miramar, CA 03190 32.87 -117.15 

Sacramento, CA 23232 38.52 -121.50 Oakland, CA 23230 37.75 -122.22 

San Francisco, CA 23234 37.62 -122.40 Oakland, CA 23230 37.72 -122.22 

Bismarck, ND 24011 46.78 -100.75 Bismarck, ND 24011 46.78 -100.75 

Cheyenne, WY 24018 41.15 -104.77 Denver, CO 23062 39.77 -104.88 

Billings, MT 24033 45.80 -108.55 Great Falls, MT 24143 47.47 -111.38 

Casper, WY 24089 42.90 -106.47 Lander, WY 24021 42.82 -108.73 

Rapid City, SD 24090 44.05 -103.07 Rapid City, SD 24090 44.05 -103.07 

Salt Lake City, UT 24127 40.78 -111.97 Salt Lake City, UT 24127 40.78 -111.97 

Great Falls, MT 24143 47.47 -111.38 Great Falls, MT 24143 47.47 -111.38 

Spokane, WA 24157 47.62 -117.53 Spokane, WA 24157 47.62 -117.53 

Medford, OR 24225 42.37 -122.87 Medford, OR 24225 42.37 -122.87 

Portland, OR 24229 45.60 -122.60 Salem, OR 24232 44.90 -123.00 

Salem, OR 24232 44.90 -123.00 Salem, OR 24232 44.90 -123.00 
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SURFACE WBAN LAT LON UPPER AIR WBAN LAT LON 

Seattle, WA 24233 47.45 -122.32 Quillayute, WA 94240 47.93 -124.55 

Juneau, AK 25309 58.37 -134.58 Annette Is., AK 25308 55.03 -131.57 

Cold Bay, AK 25624 55.20 -162.72 Cold Bay, AK 25624 55.20 -162.72 

Fairbanks, AK 26411 64.80 -147.88 Fairbanks, AK 26411 64.80 -147.88 

Valdez, AK 26442 61.13 -146.35 Anchorage, AK 26409 61.22 -149.85 

Anchorage, AK 26451 61.20 -150.00 Anchorage, AK 26409 61.22 -149.85 

Nome, AK 26617 64.50 -165.43 Nome, AK 26617 64.50 -165.43 

Colo. Springs 93037 38.82 -104.72 Denver, CO 23062 39.77 -104.88 

Fresno, CA 93193 36.78 -119.72 Oakland, CA 23230 37.72 -122.22 

Baltimore, MD 93721 39.18 -76.67 Sterling, VA 93734 38.98 -77.47 

Sterling, VA 93738 38.95 -77.45 Sterling, VA 93734 38.98 -77.47 

Evansville, IN 93817 38.05 -87.52 Paducah, KY 03816 37.05 -88.77 

Indianapolis, IN 93819 39.73 -86.28 Dayton, OH 13840 39.83 -84.05 

Lexington, KY 93820 38.03 -84.60 Dayton, OH 13840 39.83 -84.05 

Louisville, KY 93821 38.18 -85.73 Dayton, OH 13840 39.83 -84.05 

Springfield, IL 93822 39.85 -89.68 Peoria, IL 14842 40.67 -89.68 

Pittsburgh, PA 94823 40.53 -80.23 Pittsburgh, PA 94823 40.50 -80.23 

Toledo, OH 94830 41.58 -83.80 Flint, MI 14826 42.97 -83.73 

Chicago, IL 94846 42.00 -87.93 Peoria, IL 14842 40.67 -89.68 

Detroit, MI 94847 42.23 -83.33 Flint, MI 14826 42.97 -83.75 

Figure 1 shows the surface stations (green circles) and upper air stations (red pentagons) 

used in this study. 

FIGURE 1. SURFACE AND UPPER AIR STATIONS 
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Meteorological Data Processing using AERMET July 28, 2006 

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MISSING DATA 

While AERMET will run if input observations are missing, the output will be incomplete, 

with missing data indicators in the output file. The input to and output from AERMET 

were examined for indications of missing input data. 

The 1200 Greenwich Mean Time (GMT) sounding is required for AERMET to calculate 

the convective boundary layer height and several associated parameters. There were 

many isolated days in which the 1200 GMT sounding was missing for many of the 

stations. However there also were several stations for which there were two or more 

consecutive days of missing 1200 GMT soundings. The longest period of consecutive 

missing 1200 GMT soundings was at Dayton, Ohio with seven days of missing 

soundings. To minimize the impact on the output from AERMET and not consume too 

much time and effort, MACTEC substituted upper air data from a representative upper air 

station for those periods for which the 1200 GMT sounding was missing for two or more 

consecutive days. Table 2 shows the station substitutions. 

TABLE 2 

UPPER AIR STATIONS WITH MISSING 1200 GMT SOUNDINGS AND STATION 

USED FOR DATA SUBSTITUTION 

Upper Air Station 

With Missing Data 

WBAN Station Used for Data 

Substitution 

WBAN 

Amarillo, TX 23047 Norman, OK 03948 

Athens, GA 13873 Greensboro, NC 13723 

Brownsville, TX 12929 Corpus Christi, TX 12924 

Centerville, AL 03881 Jackson, MS 03940 

Chatham, MA 14684 Portland, ME 14764 

Dayton, OH 13840 Paducah, KY 03826 

Ely, NV 23154 Winnemucca, NV 24128 

Grand Junction, CO 23066 Salt Lake City, UT 24127 

Lake Charles, LA 03937 Slidell, LA 53813 

Paducah, KY 03826 Nashville, TN 13897 

Peoria, IL 14842 Omaha, NE 94918 

Pittsburgh, PA 94823 Huntington, WV 03860 

Salt Lake City, UT 24127 Ely, NV 23154 

St. Cloud, MN 14926 Huron, SD 14936 
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ANEMOMETER HEIGHTS 

The file STNS.TXT located on the HUSWO CD was used for the anemometer heights 

required by AERMET. These heights are to the nearest meter and were deemed 

appropriate for use in this application. 

SURFACE CHARACTERISTICS 

One of the important requirements of applying the AERMOD model is the specification 

of surface characteristics for use in processing the meteorological data using AERMET. 

Application of AERMET requires specification of the surface roughness length, the 

Bowen ratio (an indicator of surface moisture), and the albedo (an indicator of surface 

reflectivity). These surface characteristics are used by AERMET to calculate the level of 

shear-induced mechanical turbulence generated by flow over the surface and for the 

energy balance calculations used in the determination of the Monin-Obukhov stability 

parameter and the convective velocity scale. 

The surface characteristics can be varied by wind direction sector (upwind) and by month 

or season. Current guidance specifies defining the characteristics for the meteorological 

measurement site and extending outward from the measurement location for three 

kilometers. In an application of this magnitude, it is impractical to evaluate and vary the 

surface characteristics for each of the 122 stations, therefore, MACTEC used annual 

values for one sector, as shown in Table 3. 

The albedo used here is representative of conditions for all seasons which includes winter 

without continuous snow cover. Most stations fall into this category and continuous 

snow coverage for several months even at northern stations in the continental United 

States is unlikely. 

Without a detailed and individual analysis for each of the 122 station locations and for 

this large an area for an entire year, a value representing an equal partition of the fluxes, 

or a Bowen ratio of 1.0, is the best representation to use in this type of analysis. 

The surface roughness not only includes runways and areas between runways, but usually 

terminal buildings and other airport structures. In addition, in urban environments, offairport structures often are within 3 kilometers of the measurement site. This 

combination of land covers suggests a value of 0.2 – 0.3 meters is appropriate. 

MACTEC used 0.25 for the roughness length. 
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TABLE 3 

SURFACE CHARACTERISTICS 

MODIFICATIONS TO AERMET CODE 

Recent use of AERMET identified two ‘bugs’ in the code that required fixing before 

running AERMET. The first had to do with the hourly surface observations. Incorrect 

missing indicators in the code for wind speed and direction were output when the data 

were extracted from the raw input file in Stage 1. When AERMET encountered these 

flags in Stage 3, AERMET simply stopped processing data, resulting in files with less 

data than requested. MACTEC modified the code to use correct missing indicators. 

MACTEC also checked other meteorological variables to be sure the correct missing 

indicators were used. No other missing indicators were in error. 

The second ‘bug’ was associated with the upper air soundings. AERMET would not 

process an entire station’s data if the surface pressure for the first sounding was less than 

850 millibars (mb). Surface pressures less than 850 mb is not uncommon for stations at 

high elevations such as Denver, CO and Albuquerque, NM. MACTEC modified the code 

to expand the allowable surface pressures. 

Both ‘bugs’ were fixed prior to running AERMET with the result that AERMET 

successfully ran to completion for all files. 

RESULTS 

AERMET successfully ran for all 122 stations. While the reports and output from 

AERMET provide an indication of the success of the runs, MACTEC ran AERMOD with 

a single source and single receptor to obtain information on the number of hours with 

calm winds or missing data. Table 4 shows these values by surface station. 

ADDENDUM – May 2012 

Since development of the original meteorological data set (122 stations) discussed above, 

additional sites have been added for Risk and Technology Review (RTR) analyses. 

These include meteorological data provided by state and local agencies, as well as 

additional sites processed by EPA risk staff to better represent dispersion conditions at 

Albedo 0.15 

Bowen Ratio 1.0 

Surface Roughness (m) 0.25 
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specific facility locations. In each case, data were processed using the latest available 

version of AERMET and processing approach outlined on the USEPA’s SCRAM 

website. A listing of these additional sites is provided in Table 5. 
Meteorological Data Processing using AERMET July 28, 2006 

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TABLE 4 

PERCENT CALM AND MISSING BY STATION 

WBAN Station Calms % Calm Missing % Missing Total % Total

03816 Paducah, KY 820 9.4% 66 0.75% 886 10.1%

03856 Huntsville, AL 719 8.2% 0 0.00% 719 8.2%

03860 Huntington, WV 340 3.9% 28 0.32% 368 4.2%

03870 Greenville, SC 810 9.2% 20 0.23% 830 9.5%

03927 Fort Worth, TX 508 5.8% 24 0.27% 532 6.1%

03928 Wichita, KS 215 2.5% 0 0.00% 215 2.5%

03937 Lake Charles, LA 616 7.0% 15 0.17% 631 7.2%

03940 Jackson, MS 1228 14.0% 58 0.66% 1286 14.7%

03947 Kansas City, MO 231 2.6% 0 0.00% 231 2.6%

11641 San Juan, PR 670 7.6% 11 0.13% 681 7.8%

12815 Orlando, FL 144 1.6% 0 0.00% 144 1.6%

12816 Gainesville, FL 952 10.9% 11 0.13% 963 11.0%

12842 Tampa, FL 572 6.5% 0 0.00% 572 6.5%

12844 W. Palm Beach, FL 566 6.5% 8 0.09% 574 6.6%

12912 Victoria, TX 320 3.7% 0 0.00% 320 3.7%

12916 New Orleans, LA 721 8.2% 8 0.09% 729 8.3%

12917 Port Arthur, TX 736 8.4% 10 0.11% 746 8.5%

12919 Brownsville, TX 418 4.8% 36 0.41% 454 5.2%

12921 San Antonio, TX 913 10.4% 11 0.13% 924 10.5%

12924 Corpus Christi, TX 371 4.2% 0 0.00% 371 4.2%

12960 Houston, TX 1097 12.5% 13 0.15% 1110 12.7%

13722 Raleigh, NC 810 9.2% 0 0.00% 810 9.2%

13723 Greensboro, NC 542 6.2% 24 0.27% 566 6.5%

13737 Norfolk, VA 598 6.8% 39 0.45% 637 7.3%

13739 Philadelphia, PA 154 1.8% 2 0.02% 156 1.8%

13740 Richmond, VA 605 6.9% 0 0.00% 605 6.9%

13741 Roanoke, VA 1287 14.7% 1 0.01% 1288 14.7%

13748 Wilmington, NC 976 11.1% 0 0.00% 976 11.1%

13781 Wilmington, DE 837 9.6% 0 0.00% 837 9.6%

13865 Meridian, MS 1567 17.9% 56 0.64% 1623 18.5%

13866 Charleston, WV 1257 14.3% 29 0.33% 1286 14.7%

13874 Atlanta, GA 309 3.5% 21 0.24% 330 3.8%

13876 Birmingham, AL 1228 14.0% 15 0.17% 1243 14.2%

13877 Bristol, TN 2541 29.0% 28 0.32% 2569 29.3%

13880 Charleston, SC 487 5.6% 0 0.00% 487 5.6%

13881 Charlotte, NC 985 11.2% 0 0.00% 985 11.2%

13883 Columbia, SC 1340 15.3% 20 0.23% 1360 15.5%

13889 Jacksonville, FL 1134 12.9% 12 0.14% 1146 13.1%

13893 Memphis, TN 809 9.2% 0 0.00% 809 9.2%

13894 Mobile, AL 527 6.0% 8 0.09% 535 6.1%

13895 Montgomery, AL 1027 11.7% 19 0.22% 1046 11.9%

13897 Nashville, TN 87 1.0% 0 0.00% 87 1.0%

13957 Shreveport, LA 1088 12.4% 11 0.13% 1099 12.5%
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WBAN Station Calms % Calm Missing % Missing Total % Total

13963 Little Rock, AR 1090 12.4% 0 0.00% 1090 12.4%

13966 Wichita Falls, TX 622 7.1% 23 0.26% 645 7.4%

13967 Oklahoma City, OK 158 1.8% 0 0.00% 158 1.8%

13970 Baton Rouge, LA 787 9.0% 9 0.10% 796 9.1%

13985 Dodge City, KS 30 0.3% 32 0.37% 62 0.7%

13994 St. Louis, MO 416 4.7% 26 0.30% 442 5.0%

13995 Springfield, MO 212 2.4% 39 0.45% 251 2.9%

14733 Buffalo, NY 181 2.1% 0 0.00% 181 2.1%

14734 Newark, NJ 197 2.2% 0 0.00% 197 2.2%

14735 Albany, NY 1217 13.9% 8 0.09% 1225 14.0%

14737 Allentown, PA 465 5.3% 9 0.10% 474 5.4%

14739 Boston, MA 36 0.4% 38 0.43% 74 0.8%

14740 Hartford, CN 204 2.3% 8 0.09% 212 2.4%

14742 Burlington, VT 155 1.8% 9 0.10% 164 1.9%

14764 Portland, ME 381 4.3% 25 0.29% 406 4.6%

14765 Providence, RI 215 2.5% 39 0.45% 254 2.9%

14771 Syracuse, NY 481 5.5% 0 0.00% 481 5.5%

14778 Williamsport, PA 1403 16.0% 24 0.27% 1427 16.3%

14820 Cleveland, OH 271 3.1% 27 0.31% 298 3.4%

14821 Columbus, OH 734 8.4% 0 0.00% 734 8.4%

14827 Fort Wayne, IN 440 5.0% 0 0.00% 440 5.0%

14836 Lansing, MI 632 7.2% 10 0.11% 642 7.3%

14837 Madison, WI 685 7.8% 30 0.34% 715 8.2%

14860 Erie, PA 286 3.3% 4 0.05% 290 3.3%

14895 Akron, OH 232 2.6% 28 0.32% 260 3.0%

14898 Green Bay, WI 414 4.7% 28 0.32% 442 5.0%

14913 Duluth, MN 336 3.8% 0 0.00% 336 3.8%

14922 Minneapolis, MN 311 3.6% 3 0.03% 314 3.6%

14923 Moline, IL 812 9.3% 24 0.27% 836 9.5%

14925 Rochester, MN 10 0.1% 0 0.00% 10 0.1%

14944 Sioux Falls, SD 492 5.6% 0 0.00% 492 5.6%

14991 Eau Claire, WI 1193 13.6% 0 0.00% 1193 13.6%

22536 Lihue, HI 6 0.1% 0 0.00% 6 0.1%

23023 Midland, TX 207 2.4% 19 0.22% 226 2.6%

23044 El Paso, TX 262 3.0% 0 0.00% 262 3.0%

23047 Amarillo, TX 113 1.3% 6 0.07% 119 1.4%

23050 Albuquerque, NM 606 6.9% 0 0.00% 606 6.9%

23062 Denver, CO 530 6.1% 0 0.00% 530 6.1%

23065 Goodland, KS 176 2.0% 41 0.47% 217 2.5%

23066 Grand Junction, CO 295 3.4% 15 0.17% 310 3.5%

23153 Tonopah, NV 341 3.9% 11 0.13% 352 4.0%

23160 Tucson, AZ 154 1.8% 24 0.27% 178 2.0%

23161 Daggett, CA 616 7.0% 11 0.13% 627 7.2%

23174 Los Angeles, CA 944 10.8% 21 0.24% 965 11.0%

23184 Prescott, AZ 376 4.3% 23 0.26% 399 4.6%

23188 San Diego, CA 473 5.4% 21 0.24% 494 5.6%

23232 Sacramento, CA 1324 15.1% 77 0.88% 1401 16.0%
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WBAN Station Calms % Calm Missing % Missing Total % Total

23234 San Francisco, CA 376 4.3% 77 0.88% 453 5.2%

24011 Bismarck, ND 676 7.7% 22 0.25% 698 8.0%

24018 Cheyenne, WY 110 1.3% 0 0.00% 110 1.3%

24033 Billings, MT 273 3.1% 25 0.29% 298 3.4%

24089 Casper, WY 138 1.6% 37 0.42% 175 2.0%

24090 Rapid City, SD 736 8.4% 33 0.38% 769 8.8%

24127 Salt Lake City, UT 581 6.6% 22 0.25% 603 6.9%

24143 Great Falls, MT 111 1.3% 26 0.30% 137 1.6%

24157 Spokane, WA 537 6.1% 36 0.41% 573 6.5%

24225 Medford, OR 1937 22.1% 0 0.00% 1937 22.1%

24229 Portland, OR 1180 13.5% 0 0.00% 1180 13.5%

24232 Salem, OR 1799 20.5% 0 0.00% 1799 20.5%

24233 Seattle, WA 66 0.8% 14 0.16% 80 0.9%

25309 Juneau, AK 1781 20.3% 6 0.07% 1787 20.4%

25624 Cold Bay, AK 119 1.4% 41 0.47% 160 1.8%

26411 Fairbanks, AK 1393 15.9% 0 0.00% 1393 15.9%

26442 Valdez, AK 661 7.5% 2 0.02% 663 7.6%

26451 Anchorage, AK 724 8.3% 0 0.00% 724 8.3%

26617 Nome, AK 358 4.1% 24 0.27% 382 4.4%

93037 Colo. Springs 195 2.2% 0 0.00% 195 2.2%

93193 Fresno, CA 1578 18.0% 76 0.87% 1654 18.9%

93721 Baltimore, MD 361 4.1% 3 0.03% 364 4.2%

93738 Sterling, VA 995 11.4% 0 0.00% 995 11.4%

93817 Evansville, IN 1434 16.4% 66 0.75% 1500 17.1%

93819 Indianapolis, IN 190 2.2% 0 0.00% 190 2.2%

93820 Lexington, KY 234 2.7% 0 0.00% 234 2.7%

93821 Louisville, KY 385 4.4% 0 0.00% 385 4.4%

93822 Springfield, IL 287 3.3% 25 0.29% 312 3.6%

94823 Pittsburgh, PA 493 5.6% 26 0.30% 519 5.9%

94830 Toledo, OH 898 10.3% 13 0.15% 911 10.4%

94846 Chicago, IL 343 3.9% 38 0.43% 381 4.3%

94847 Detroit, MI 468 5.3% 11 0.13% 479 5.5%

 
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13 

TABLE 5. 

ADDITIONAL METEOROLOGICAL STATIONS 

ADDED FOR RTR ANALYSES 

City State Year

Fairbanks AK 2001

Anaheim CA 2007

Azusa CA 2007

Banning Airport CA 2007

Burbank CA 2007

Central LA CA 2007

Costa Mesa CA 2007

Crestline CA 2007

Fontana CA 2007

Indio CA 2007

La Habra CA 2007

Lake Elsinore CA 2007

LAX CA 2007

Long Beach CA 2007

Lynnwood CA 2007

Mission Viejo CA 2007

Palm Springs CA 2007

Perris CA 2007

Pico Rivera CA 2007

Pomona CA 2007

Redlands CA 2007

Reseda CA 2007

Riverside CA 2007

San Bernardino CA 2007

Santa Clarita CA 2007

Santa Monica CA 2009

Upland CA 2007

Van Nuys Airport CA 2009

West LA CA 2007

Apalachicola FL 2005

Ft. Myers FL 2005

Gainesville FL 2005

Tallahassee FL 2005

Burlington IA 2004

Cedar Rapids IA 2004

Des Moines IA 2004

Dubuque IA 2004

Mason City IA 2004

Sioux City IA 2004
Meteorological Data Processing using AERMET July 28, 2006 

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City State Year

Waterloo IA 2004

Moline IL 2004

Iron Mountain MI 1999

Duluth MN 1999

Red Wing MN 1999

Herculaneum MO 1998

Omaha NE 2004

Sioux Falls SD 2004

Clintonville WI 1999

Eagle River WI 1999

Eau Claire WI 1999

Green Bay WI 1999

Hayward WI 1999

Juneau WI 1999

La Crosse WI 1999

Madison WI 1999

Manitowoc WI 1999

Marshfield WI 1999

Milwaukee WI 1999

Mineral Point WI 1999

Mosinee WI 1999

Oshkosh WI 1999

Rhinelander WI 1999

Sturgeon Bay WI 1999

Wausau WI 1999

Wisconsin Dells WI 1999

Wisconsin Rapids WI 1999

Parkersburg WV 2011
1

Appendix 3: 

Analysis of data on short-term emission rates 

relative to long-term emission rates 
2

Analysis of data on short-term emission rates 

relative to long-term emission rates 

Ted Palma 

Roy Smith 

EPA/OAQPS/SBAG 

1. Introduction 

1.1. The problem 

The process of listing hazardous air pollutants (HAPs) provided by the Clean Air Act (CAA, 

section 112(b)(2)) explicitly includes acute toxicity as a listing criterion. For this reason, in 

addition to chronic exposures, EPA considers acute exposures in risk-based decision-making for 

the HAP regulatory program. Estimating acute exposures via dispersion modeling requires input 

data on hourly meteorological conditions (available for most areas of the US) and short-term 

emission rates of individual facilities (almost universally absent from the National Emissions 

Inventory (NEI), the Toxic Release Inventory (TRI), and state emission databases). 

Lacking short-term emission rates, we must estimate peak short-term rates based on annual 

average rates, which are available. For Risk and Technology Review (RTR) rulemakings, we 

have assumed that the 1-hour emission rate for each facility could exceed the annual average 

hourly emission rate by as much as tenfold, and further assumed that this tenfold emission spike 

could coincide with worst-case meteorological conditions and the presence of a human receptor 

at the facility boundary, as a means of screening for potentially significant acute exposures. 

In a consultation on the “RTR Assessment Plan”, a panel of the EPA’s Science Advisory Board 

(SAB), several reviewers questioned the appropriateness of the factor of ten; some even 

suggested that this tenfold assumption may underestimate actual maximum short-term emissions 

for some facilities, and thereby also underestimate maximum acute risks. The SAB 

recommended an analysis of available short-term emissions data for HAP to test this assumption. 

This analysis responds to that SAB recommendation and attempts to test the protectiveness of the 

tenfold assumption using a database of “event emissions” collected from facilities in the 

Houston-Galveston area, to compare events representative of HAP releases to long-term release 

rates. We welcome comments from the public on the methods used and the conclusions reached 

by this analysis. 

2. Methods 

2.1. Texas Commission on Environmental Quality event emissions database 

The Texas Commission on Environmental Quality (TCEQ) collects emissions data using online 

reporting required of any facility releasing 100 pounds or more of a listed chemical (primarily 

ozone-forming VOCs) during a non-routine event. The TCEQ data are intended to improve the 

state’s knowledge of how short-term releases affect tropospheric ozone levels in that area. The 

database we utilized in our analysis was a subset of the TCEQ data covering emission events that 
3

occurred in an eight-county area in eastern Texas during a 756-day period between January 31, 

2003 and February 25, 2005. 

The complete emissions event data were obtained in April 2007 from Cynthia Folsom Murphy, a 

research scientists with the University of Texas at Austin (UTA) Center for Energy and 

Environmental Resources. The data were provided in four Excel spreadsheets generated from an 

original MS Access file. We used these Excel files to reconstruct a MS Access database in order 

to facilitate selection of a representative subset of records for this analysis. 

Although some of the released substances were HAPs, this was incidental to the database’s 

primary purpose of enhancing the TCEQ’s knowledge of photochemical activity. Thus, more 

than 80% of the released mass was ethene and propene, neither of which are HAPs. The 

database included release events caused by accidents, equipment failures, maintenance, startup, 

and shutdown. It also contained facility names, information on amounts of individual 

compounds released. To provide a basis for comparing the event releases with “typical” 

emissions, the UTA staff included total VOC emissions data for each facility for calendar year 

2004, obtained from the EPA Toxic Release Inventory (TRI). The database did not contain any 

records for facilities that did not experience any reportable events during this period. 

2.2. Data filtering 

Because the event release data were intended for modeling short-term releases of ozoneproducing VOCs, the database includes releases from accidents (which are regulated under 

section 112(r) of the CAA and are therefore not considered in residual risk assessments) and 

releases of light hydrocarbon compounds that are not HAPs and are much more volatile than 

most HAPs. This intent of this analysis, on the other hand, was to evaluate short-term releases of 

HAPs due to normal process variability or scheduled startups, shutdowns, and maintenance, 

relative to long-term release rates. Because the full emission events database was not 

representative of likely HAP emissions normally considered under the residual risk program, we 

filtered the release data as follows in an attempt to improve its representativeness: 

1. Hydrocarbons of C5 or less were dropped, except that all HAPs (including non-VOCs) 

were retained regardless of molecular structure; 

2. Accidental releases were dropped, but all others (including startup, shutdown, and 

maintenance) were retained; 

3. Only facilities whose long-term VOC releases exceeded 0.068 tons per day (25 tons per 

year) were retained, to approximate the population of facilities likely to be subject to 

residual risk standards (i.e., major facilities); 

4. A few release records had to be dropped because their facility numbers did not link to any 

facility in the database; 

5. A few facilities had to be dropped because the database did not include their 2004 TRI 

VOC release information. 

2.3. Analysis 
4

Annual VOC emissions and emission event release data were both converted to lb/hr. In order to 

conform to our atmospheric dispersion models, which estimate ambient concentrations for 

periods of 1 hour or more, amounts released during events shorter than 1 hour were assigned to 

the whole hour. For example, a release of 100 lb in ten minutes was converted to 100 lb/hr. 

Events longer than 1 hour were converted normally, e.g., a release of 100 lb in 120 minutes was 

converted to 50 lb/hr. The event release rates for individual compounds were summed, yielding 

a total release rate for each event. This total release rate for each event was divided by the 

annual VOC release rate for the facility to derive the ratio of peak-to-mean emission rate for the 

event. 

3. Results and Discussion 

3.1. Database filtering 

The original database contained 505 individual contaminants, including multiple redundancies. 

These redundancies did not affect this analysis, so we did not resolve them. After filtering out 

light, non-HAP, VOCs, 317 contaminants remained (Table 1). 

The database contained release records for 150 unique facilities. Of these, 48 facilities (Table 2) 

were major VOC emitters that reported releases of at least one of the contaminants in Table 1. 

The database contained 3641 individual release events reported by the original 150 facilities. Of 

these, 319 events involved a Table 1 contaminant released by a Table 2 facility during startup, 

shutdown, or maintenance. For evaluating short-term releases for residual risk assessments, 

these 319 events comprise the most representative subset of the full database. 

3.2. Descriptive statistics 

For this subset of emission events, ratios of event release rate to long-term release rate varied 

from 0.00000004 to 74. Distribution statistics appear in Tables 3 and 4. The 99th percentile ratio 

was 9 (i.e., an event release rate nine times the long-term average). Only 3 ratios exceeded our 

default assumption of 10, and of these only one exceeded 11. The full cumulative probability 

density of the ratios is shown in Figure 1. 

Figure 2 shows the relationship between ratio and event duration. As expected, the ratio 

declined as duration increased. Only 18 events lasted less than 2 hours, but these events 

produced the three highest ratios. Figure 3 is a similar ratio vs. duration plot, but with duration 

as a percentage of total time. Only 35 events exceeded 1% of the total period covered by the 

database. Figure 4 shows the relationship between ratio and total amount released, and suggests 

that the highest ratios were produced by facilities whose routine VOC emissions were relatively 

small. Thus, the events themselves also tended to be relatively small in absolute terms. 

3.3. Discussion 

These results suggest that the tenfold ratio assumption for short-term releases is protective, and 

that the facilities for which it may underestimate event releases may tend to be smaller emitters. 

However, this analysis is limited in the following ways by the nature of the database and the 

filtering that we applied: 
5

1. The only long-term release data available from the database were total VOC emissions 

for 2004. Ideally, we would have preferred to have routine release rates for each 

individual contaminant. However, retrieving these data from other sources and linking 

them to this database was not feasible. 

2. Removing VOCs that are not representative of HAPs, and comparing the releases against 

all VOCs, would tend to underestimate the true ratios. This effect could be quantitatively 

large. 

3. Retaining HAPs that are not VOCs (such as toxic metals), and including them in the total 

to be compared against all VOCs, would tend to overestimate the true ratios. The size of 

this effect is not known, but seems likely to be less than for (2) above. 

4. The database contains only facilities that had at least one release event during the 

reporting period. The number of facilities in the statistical population that did not 

experience an event is not known. The lack of data for these facilities (whose ratios in 

this analysis would have been zero) would cause the descriptive statistics to be skewed 

toward an overestimate. The size of this effect is unknown. 

Table 1. Event emissions in the Houston-Galveston area. 

Representative contaminants included in the analysis, selected because 

they are either HAPs or VOCs with more than 5 carbon atoms. (These 

data were retrieved directly from the original database, which included 

multiple redundancies that did not affect the analysis and were left 

intact.) 

Contaminant HAP CAS SAROAD 

2-Methyloctane No 3221-61-2 90008 

2-Methylpentane No 107-83-5 43229 

2-methylhexane No 591-76-4 43263 

2-Methylpentane No 107-83-5 43229 

2,2,3-Trimethylpentane No 564-02-3

2,2,4-Trimethylpentane Yes 540-84-1 43250 

dimethyl butane No 75-83-2 43291 

2,3-Dimethylbutane No 79-29-8 43276 

2,3,4-Trimethylpentane No 565-75-3 43252 

2,3-Dimethylbutane No 79-29-8 43276 

2,4-Dimethylpentane No 108-08-7 43247 

2-methylheptane No 592-27-8 43296 

2-methylhexane No 591-76-4 43263 

2-Methylpentane No 107-83-5 43229 

3-Methylhexane No 589-34-4 43295 

3-Methylpentane No 96-14-0 43230 

3-Methylhexane No 589-34-4 43295 

3-Methylpentane No 96-14-0 43230 

3-Methylheptane No 589-81-1 43253 

3-Methylhexane No 589-34-4 43295 

3-Methylpentane No 96-14-0 43230 
6

Table 1. Event emissions in the Houston-Galveston area. 

Representative contaminants included in the analysis, selected because 

they are either HAPs or VOCs with more than 5 carbon atoms. (These 

data were retrieved directly from the original database, which included 

multiple redundancies that did not affect the analysis and were left 

intact.) 

Contaminant HAP CAS SAROAD 

Acetaldehyde Yes 75-07-0 43503 

Acetic Acid No 64-19-7 43404 

Acetonitrile Yes 75-05-8 70016 

Acetophenone Yes 98-86-2

Acrolein Yes 107-02-8 43505 

Acrylic acid Yes 79-10-7 43407 

Acrylonitrile Yes 107-13-1 43704 

alkylphenol No none

Benzene Yes 71-43-2 45201 

Benzo[a]anthracene Yes 56-55-3 46716 

Benzo[a]pyrene Yes 50-32-8 46719 

Benzo[b]fluoranthene Yes 205-99-2 46717 

Biphenyl Yes 92-52-4 45226 

Butanol No 35296-72-1

Butyl Acrylate No 141-32-2 43440 

t-Butyl Alcohol No 75-65-0 43309 

butylcyclohexane No 1678-93-9 90101 

Butyraldehyde No 123-72-8 43510 

C9 Aromatics No none

Naphthalene Yes 91-20-3 46701 

Nonane No 111-84-2 43235 

C9+ No none

Carbon tetrachloride Yes 56-23-5 43804 

Carbonyl Sulfide Yes 463-58-1 43933 

Chloral No 75-87-6

Trichloromethane Yes 67-66-3 43803 

Chlorothalonil No 1897-45-6

Petroleum No 8002-05-9

Petroleum No 8002-05-9

Cumene Yes 98-82-8 45210 

Cyclohexane No 110-82-7 43248 

Cyclohexanol No 108-93-0 43317 

Cyclohexanone No 108-94-1 43561 

Cyclohexanone No 108-94-1 43561 

Decane No 124-18-5 43238 

Decane No 124-18-5 43238 

1,2-Dichloroethane No 107-06-2 43815 

Diethylbenzene (mixture) No 25340-17-4 45106 

Methyl Ether No 115-10-6 43350 

Dimethylcyclohexane No 27195-67-1 98059 

Dimethylcyclopentane No 28729-52-4 90064 
7

Table 1. Event emissions in the Houston-Galveston area. 

Representative contaminants included in the analysis, selected because 

they are either HAPs or VOCs with more than 5 carbon atoms. (These 

data were retrieved directly from the original database, which included 

multiple redundancies that did not affect the analysis and were left 

intact.) 

Contaminant HAP CAS SAROAD 

Dimethylcyclopentane No 28729-52-4 90064 

Dimethyl formamide Yes 68-12-2 43450 

Dimethylhexane No 28777-67-5 90067 

Dimethyl pentane No 38815-29-1 90063 

Epichlorohydrin Yes 106-89-8 43863 

Ethyl Alcohol No 64-17-5 43302 

Ethyl Acrylate Yes 140-88-5 43438 

Ethyl Alcohol No 64-17-5 43302 

Ethyl Benzene Yes 100-41-4 45203 

Ethyl Chloride Yes 75-00-3 43812 

Ethylcyclohexane No 1678-91-7 43288 

ethylacetylene No 107-00-6 43281 

Ethyl Benzene Yes 100-41-4 45203 

Ethylene Oxide Yes 75-21-8 43601 

ethylmethylbenzene No 25550-14-5 45104 

formaldehyde Yes 50-00-0 43502 

Furfural No 98-01-1 45503 

straight-run middle distillate No 64741-44-2

Gasoline No 86290-81-5

Gasoline No 86290-81-5

Heavy Olefins No none

n-Heptane No 142-82-5 43232 

n-Heptane No 142-82-5 43232 

Heptylene No 25339-56-4

hexane Yes 110-54-3 43231 

hexane Yes 110-54-3 43231 

2-Methylpentane No 107-83-5 43229 

hexane Yes 110-54-3 43231 

Hexene No 25264-93-1 43289 

Indeno[1,2,3-cd]pyrene Yes 193-39-5 46720 

Isobutyraldehyde No 78-84-2 43511 

2-Methyl-1-propanol No 78-83-1 43306 

2-Methyl-1-propanol No 78-83-1 43306 

Isobutyraldehyde No 78-84-2 43511 

Isoheptanes (mixture) No 31394-54-4 43106 

2-Methylpentane No 107-83-5 43229 

2,2,4-Trimethylpentane No 540-84-1 43250 

2,2,4-Trimethylpentane No 540-84-1 43250 

Isopar E No 

Isoprene No 78-79-5 43243 

2-Propanol No 67-63-0 43304 
8

Table 1. Event emissions in the Houston-Galveston area. 

Representative contaminants included in the analysis, selected because 

they are either HAPs or VOCs with more than 5 carbon atoms. (These 

data were retrieved directly from the original database, which included 

multiple redundancies that did not affect the analysis and were left 

intact.) 

Contaminant HAP CAS SAROAD 

2-Propanol No 67-63-0 43304 

Cumene Yes 98-82-8 45210 

Isopropylcyclohexane No 696-29-7 90128 

Diisopropyl ether No 108-20-3 85005 

Kerosene No 64742-81-0

Methyl ethyl ketone No 78-93-3 43552 

Methyl isobutenyl ketone Yes 141-79-7

Methanol Yes 67-56-1 43301 

Methyl Acetylene No 74-99-7 43209 

Cresol Yes 1319-77-3 45605 

Methyl Chloride Yes 74-87-3 43801 

methyl cyclohexane No 108-87-2 43261 

Methyl ethyl ketone No 78-93-3 43552 

Iodomethane No 74-88-4 86025 

Methyl Mercaptan No 74-93-1 43901 

methyl cyclohexane No 108-87-2 43261 

Methylcyclopentane No 96-37-7 43262 

2-Methyldecane No 6975-98-0 98155 

Methylheptane No 50985-84-7 90045 

2-methylheptane No 592-27-8 43296 

2-Methyl nonane No 871-83-0 90047 

Tert-butyl methyl ether No 1634-04-4 43376 

meta-xylene No 108-38-3 45205 

Nonane No 111-84-2 43235 

Naphtha No 8030-30-6 45101 

Naphthalene Yes 91-20-3 46701 

Naphtha No 8030-30-6 45101 

Naphthalene No 91-20-3 46701 

Butyl acetate No 123-86-4 43435 

Butyraldehyde No 123-72-8 43510 

Nonane No 111-84-2 43235 

Nonane No 111-84-2 43235 

Octadecene No 27070-58-2

n-Octane No 111-65-9 43233 

Octene (mixed isomers) No 25377-83-7

ortho-xylene No 95-47-6 45204 

Parathion Yes 56-38-2

4-Aminohippuric Acid No 61-78-9

Phenol Yes 108-95-2 45300 

Silicone No 63148-62-9

Naphtha No 8030-30-6 45101 
9

Table 1. Event emissions in the Houston-Galveston area. 

Representative contaminants included in the analysis, selected because 

they are either HAPs or VOCs with more than 5 carbon atoms. (These 

data were retrieved directly from the original database, which included 

multiple redundancies that did not affect the analysis and were left 

intact.) 

Contaminant HAP CAS SAROAD 

Naphtha No 8030-30-6 45101 

Polyethylene No 9002-88-4

Poly(Isobutylene) No 9003-27-4

Chloromethyl pivalate No 18997-19-8

Process fuel gas No none

Propionic Acid No 79-09-4 43405 

Propylene oxide No 75-56-9 43602 

para-xylene No 106-42-3 45206 

Styrene Yes 100-42-5 45220 

Sulfolane No 126-33-0

t-Butyl Alcohol No 75-65-0 43309 

t-Butyl Alcohol No 75-65-0 43309 

tert-butyl hydroperoxide No 75-91-2

Toluene Yes 108-88-3 45202 

Aqualyte(TM), LSC cocktail No 25551-13-7 45107 

1,3,4-Trimethylbenzene No 95-63-6 45208 

trimethylcyclopentane No 30498-64-7 98058 

trimethylpentane No 29222-48-8 90092 

Undecane No 1120-21-4 43241 

Vinyl acetate Yes 108-05-4 43453 

Vinyl acetate Yes 108-05-4 43453 

Vinyl chloride Yes 75-01-4 43860 

vinyl resin No none

Vinylcyclohexane No 695-12-5

xylenes Yes 1330-20-7 45102 

xylenes Yes 1330-20-7 45102 

meta-xylene Yes 108-38-3 45205 

ortho-xylene Yes 95-47-6 45204 

para-xylene Yes 106-42-3 45206 

Mineral spirits No 64475-85-0 43118 

Propylene glycol No 57-55-6 43369 

Vinyl chloride Yes 75-01-4 43860 

1-Decene No 872-05-9 90014 

2-Ethyl-1-hexanol No 104-76-7 43318 

2-Pyrrolidone No 616-45-5

Aromatic No none

Decene No 25339-53-1 90014 

2-N,N-Dibutylaminoethanol No 102-81-8 86007 

Diisopropanolamine No 110-97-4 86004 

N,N-Dimethylethanolamine No 108-01-0 84004 

trifluoroethane No 27987-06-0
10

Table 1. Event emissions in the Houston-Galveston area. 

Representative contaminants included in the analysis, selected because 

they are either HAPs or VOCs with more than 5 carbon atoms. (These 

data were retrieved directly from the original database, which included 

multiple redundancies that did not affect the analysis and were left 

intact.) 

Contaminant HAP CAS SAROAD 

2,2'-Oxybisethanol No 111-46-6 43367 

Hydrocarbons No none

Methyl Formate No 107-31-3 43430 

Isopropylamine No 75-31-0 86014 

n-Butanol No 71-36-3 43305 

Polypropylene glycol ether No 

N-Vinyl-2-Pyrrolidinone No 88-12-0

1,1-Di(t-Amylperoxy) 

Cyclohexane No 15667-10-4

1,2,3-Trimethyl-4-ethylbenzene No none

2-Methyldecane No 6975-98-0 98155 

2-methylheptane No 592-27-8 43296 

2-Methyl nonane No 871-83-0 90047 

2,5-Dimethylhexane-2,5-

dihydroperoxide No 3025-88-5

Butyl ether No 142-96-1 43372 

1,2-Dichloroethane Yes 107-06-2 43815 

Hydrindene No 496-11-7 98044 

Methylheptane No 50985-84-7 90045 

methyl methacrylate No 80-62-6 43441 

Naphtha No 8030-30-6 45101 

hexane Yes 110-54-3 43231 

tert-amyl hydroperoxide No 3425-61-4

1,3,4-Trimethylbenzene No 95-63-6 45208 

n-Butanol No 71-36-3 43305 

2-Butoxy ethanol Yes 111-76-2 43308 

hexane Yes 110-54-3 43231 

cycloheptane No 291-64-5 43115 

n-Heptane No 142-82-5 43232 

n-Octane No 111-65-9 43233 

Hexyl Carbitol No 112-59-4

Nonene No 27215-95-8

Silane, ethenyltrimethoxy No 2768-02-7

tetrahydrofuran No 109-99-9 70014 

Vinyl chloride Yes 75-01-4 43860 

Methyl Formate No 107-31-3 43430 

Phenyl ether No 101-84-8

phosgene Yes 75-44-5

1,2-Dichloroethane No 107-06-2 43815 

2-Butoxy ethanol Yes 111-76-2 43308 

Gasoline No 86290-81-5

1-Tridecanol No 112-70-9
11

Table 1. Event emissions in the Houston-Galveston area. 

Representative contaminants included in the analysis, selected because 

they are either HAPs or VOCs with more than 5 carbon atoms. (These 

data were retrieved directly from the original database, which included 

multiple redundancies that did not affect the analysis and were left 

intact.) 

Contaminant HAP CAS SAROAD 

1,2,4-Trichlorobenzene Yes 120-82-1 45208 

2-(2-Butoxyethoxy)ethanol Yes 112-34-5 43312 

2,3,4-trihydroxybenzophenone 

Ester No 1143-72-2

Methyl n-amyl ketone No 110-43-0 43562 

4,4-Cyclohexylidenebis[phenol] No 843-55-0

Anisole No 100-66-3

2-Butoxy ethanol Yes 111-76-2 43308 

Cresol-Formaldehyde novolac 

Resin No proprietary

Decane No 124-18-5 43238 

gamma-Butyrolactone No 96-48-0

Dimethyl pentane No 38815-29-1 90063 

Dodecyl Benzenesulfonic Acid No 27176-87-0

Ethanol Amine No 141-43-5 43777 

ethyl lactate No 687-47-8

Hexamethyldisilazane No 999-97-3

Methyl ethyl ketone No 78-93-3 43552 

Cresol Yes 1319-77-3 45605 

Naphthalene Sulfonic Acid Resin No 

Naphthalene Sulfonic Acid Resin No 

n-Butanol No 71-36-3 43305 

Decane No 124-18-5 43238 

1-Methyl-2-pyrrolidinone No 872-50-4 70008 

Pentyl Ester Acetic Acid No 

Phenol Formaldehyde Resin, 

Novolac No 

Phenol Formaldehyde Resin, 

Novolac No 

Propylene Glycol Monomethyl 

Ether No 107-98-2 70011 

Pyrocatechol No 120-80-9

Carbon Disulfide Yes 75-15-0 43934 

Hexene No 592-41-6 43245 

VOC No none

Methacrylic acid No 79-41-4 84009 

Methyl 3-hydroxybutyrate No 1487-49-6

t-Butyl Alcohol No 75-65-0 43309 

methyl valeraldehyde No 123-15-9

Butyl Methacrylate No 97-88-1 85008 

dipropyl ether No 111-43-3

n-Propanol No 71-23-8 43303 

Propyl propionate No 106-36-5 86052 
12

Table 1. Event emissions in the Houston-Galveston area. 

Representative contaminants included in the analysis, selected because 

they are either HAPs or VOCs with more than 5 carbon atoms. (These 

data were retrieved directly from the original database, which included 

multiple redundancies that did not affect the analysis and were left 

intact.) 

Contaminant HAP CAS SAROAD 

1,2-Epoxybutane Yes 106-88-7

Methylamine No 74-89-5

1,1-Dimethylcyclohexane No 590-66-9

1,1-Dimethylcyclopentane No 1638-26-2

2-Methylpentane No 107-83-5 43229 

dimethyl butane No 75-83-2 43291 

2,3,3-Trimethylpentane No 560-21-4

2,3-Dimethylhexane No 584-94-1

2,3-Dimethylpentane No 565-59-3

2,4-Dimethylhexane No 589-43-5

2,5-Dimethyl-hexane No 592-13-2

2-Butoxy ethanol Yes 111-76-2 43308 

2-mercaptoethanol No 60-24-2

Bisphenol A No 80-05-7

straight-run middle distillate No 64741-44-2

4-Vinylcyclohexene No 100-40-3

straight-run middle distillate No 64741-44-2

Allyl alcohol No 107-18-6

xylenes Yes 1330-20-7 45102 

Naphthalene Yes 91-20-3 46701 

3-Methylethylcyclohexane No 

VOC No none

Gasoline No 86290-81-5

Butyl ether No 142-96-1

dimethyl butane No 75-83-2

Dodecene No 25378-22-7

Styrene Yes 100-42-5 45220 

tetrahydrofuran No 109-99-9 70014 

hexane Yes 110-54-3 43231 

2-Propanol No 67-63-0 43304 

liquified petroleum gas No 68476-85-7

Methyl acetylene propadiene No 

methyl isobutyl ketone Yes 108-10-1

Methyl n-amyl ketone No 110-43-0 43562 

Methylpentane No 43133-95-5

Tert-butyl methyl ether Yes 1634-04-4 43376 

Toluene Yes 108-88-3 45202 

Mineral oil No 8012-95-1

Gasoline No 86290-81-5

2,2-Dimethylpropane No 463-82-1 43222 

n-propylbenzene No 103-65-1
13

Table 1. Event emissions in the Houston-Galveston area. 

Representative contaminants included in the analysis, selected because 

they are either HAPs or VOCs with more than 5 carbon atoms. (These 

data were retrieved directly from the original database, which included 

multiple redundancies that did not affect the analysis and were left 

intact.) 

Contaminant HAP CAS SAROAD 

propylcyclohexane No 1678-92-8

n-Octane No 111-65-9 43233 

ortho-xylene No 95-47-6 45204 

Gasoline No 86290-81-5

propylenimine No 75-55-8

Gasoline No 86290-81-5

Technical White Oil No 

Total Alkylate - non-speciated No 

Trichloroethylene Yes 79-01-6

Di(2-ethylhexyl) 

peroxydicarbonate No 16111-62-9

trimethylcyclopentane No 30498-64-7 98058 

Ultraformate No 

4-Vinylcyclohexene No 100-40-3
14

Table 2. Event emissions in the Houston-Galveston area. Major emitters 

reporting at least one release event of a representative substance. 

Company Name 

2004 VOC Emission 

Rate (lb/h) 

ATOFINA PETROCHEMICALS LA PORTE PLANT 47.88 

BALL METAL BEVERAGE CONTAINER CONROE 

FACILITY 

24.18 

BASF FREEPORT SITE 46.47 

BELVIEU ENVIRONMENTAL FUELS 112.3 

BOC GROUP CLEAR LAKE BOC GASES PLANT 9.52 

BP AMOCO CHEMICAL CHOCOLATE BAYOU PLANT 130.4 

BP AMOCO CHEMICAL PASADENA PLANT 36.92 

BP AMOCO POLYMERS 57.18 

BP PRODUCTS NORTH AMERICA TEXAS CITY 737.4 

BP TEXAS CITY CHEMICAL PLANT B 112.2 

CELANESE BAY CITY PLANT 17.12 

CELANESE CLEAR LAKE PLANT 53.11 

CELANESE PASADENA PLANT 5.934 

CHEVRON PHILLIPS CEDAR BAYOU PLANT 105.3 

CHEVRON PHILLIPS CHEMICAL SWEENY COMPLEX 106.7 

CHEVRON PHILLIPS HOUSTON CHEMICAL COMPLEX 215.7 

CROWN BEVERAGE PACKAGING 18.05 

CROWN CENTRAL PETROLEUM PASADENA PLANT 114.3 

CROWN CORK & SEAL 18.10 

DEER PARK LIQUID STORAGE TERMINAL 124.8 

DOW CHEMICAL LA PORTE SITE 5.902 

DOW TEXAS OPERATIONS FREEPORT 203.2 

E I DUPONT DE NEMOURS AND COMPANY - LA 

PORTE PLANT 

51.30 

EQUISTAR CHEMICALS CHANNELVIEW COMPLEX 275.4 

EQUISTAR CHEMICALS CHOCOLATE BAYOU 

COMPLEX 

84.87 

EQUISTAR CHEMICALS LA PORTE COMPLEX 90.97 

EXXON MOBIL CHEMICAL BAYTOWN OLEFINS PLANT 84.73 

EXXONMOBIL CHEMICAL BAYTOWN CHEMICAL 

PLANT 

313.7 

EXXONMOBIL CHEMICAL MONT BELVIEU PLASTICS 

PLANT 

40.64 

GOODYEAR HOUSTON CHEMICAL PLANT 85.68 

ISP TECHNOLOGIES TEXAS CITY PLANT 22.12 

KANEKA TEXAS CORPORATION 20.55 

KINDER MORGAN LIQUID TERMINALS PASADENA 913.9 

KINDER MORGAN LIQUIDS TERMINALS 132.7 

LBC HOUSTON BAYPORT TERMINAL 12.83 

LYONDELL CHEMICAL BAYPORT PLANT 30.04 

LYONDELL CHEMICAL CHANNELVIEW 74.15 

MARATHON ASHLAND PETROLEUM TEXAS CITY 

REFINERY 

111.8 

MOBIL CHEMICAL HOUSTON OLEFINS PLANT 26.29 

MORGANS POINT PLANT 31.03 

PASADENA PLANT 13.40 
15

Table 2. Event emissions in the Houston-Galveston area. Major emitters 

reporting at least one release event of a representative substance. 

Company Name 

2004 VOC Emission 

Rate (lb/h) 

SHELL OIL DEER PARK 405.2 

SOLUTIA CHOCOLATE BAYOU PLANT 53.09 

STOLTHAVEN HOUSTON TERMINAL 7.347 

SWEENY COMPLEX 157.1 

UNION CARBIDE TEXAS CITY OPERATIONS 174.4 

VALERO REFINING TEXAS CITY 260.1 

WHARTON GAS PLANT 7.552 

Table 3. Frequency distribution for ratio of event 

emission rate to long-term emission rate 

Bin Frequency 

Cumulative 

Frequency 

1.00E-08 0 0 

3.16E-08 0 0 

1.00E-07 2 2 

3.16E-07 1 3 

1.00E-06 0 3 

3.16E-06 2 5 

1.00E-05 1 6 

3.16E-05 2 8 

1.00E-04 5 13 

3.16E-04 9 22 

1.00E-03 15 37 

3.16E-03 28 65 

1.00E-02 33 98 

3.16E-02 41 139 

1.00E-01 59 198 

3.16E-01 38 236 

1.00E+00 33 269 

3.16E+00 31 300 

1.00E+01 16 316 

3.16E+01 2 318 

1.00E+02 1 319 

3.16E+02 0 319 
16

Table 4. Statistics for ratio of event emission 

rate to long-term emission rate 

Statistic for Ratio Value 

Median 0.043923 

75th %ile 0.342655 

90th %ile 2.204754 

95th %ile 3.344422 

96th %ile 3.400832 

97th %ile 3.8126 

98th %ile 4.790098 

99th %ile 8.973897 

Max 74.37138 

Average 0.815352 

Figure 1. Cumulative probability density for ratio of event to routine emission rates. 

Cumulative probability of event ratios

0

50

100

150

200

250

300

350

1.E-07 1.E-06 1.E-05 1.E-04 1.E-03 1.E-02 1.E-01 1.E+00 1.E+01 1.E+02 1.E+03 1.E+04

Ratio of event emission rate to long-term emission rate

n
17

Figure 2. Relationship between ratio of event to duration emission rate and emission 

duration.

Event ratio vs. duration

1.E-08

1.E-07

1.E-06

1.E-05

1.E-04

1.E-03

1.E-02

1.E-01

1.E+00

1.E+01

1.E+02

1 10 100 1,000 10,000 100,000

Event duration (min)

Ratio -- event emission rate to long-term emission rate

Figure 3. Relationship between ratio of event to duration emission rate and emission duration, as 

percentage of total time. 

Event ratio vs. duration

1.E-08

1.E-07

1.E-06

1.E-05

1.E-04

1.E-03

1.E-02

1.E-01

1.E+00

1.E+01

1.E+02

0% 1% 2% 3% 4% 5% 6% 7% 8%

Event duration (as % of total time)

Ratio -- event emission rate to long-term emission rate
18

Figure 4. Relationship between ratio of event to duration emission rate and total amount emitted 

during the event. 

Event ratio vs. 2004 VOC releases -- by event

1.E-08

1.E-07

1.E-06

1.E-05

1.E-04

1.E-03

1.E-02

1.E-01

1.E+00

1.E+01

1.E+02

1 10 100 1,000

Long-term VOC releases (lb/hr)

Ratio -- event emission rate to long-term emission rate
Appendix 4 Detailed Risk Modeling Results
Table 1 - Facility Identification Information

Source Category Facility NEI ID FacilityName Address City State

Phosphate Fertilizer Production andPhosphoric

Acid Manufacturing PHO_NEI23655 J.R. SIMPLOT COMPANY 16777 HOWLAND ROAD LATHROP CA

Phosphate Fertilizer Production andPhosphoric

Acid Manufacturing PHO_NEI26584 JR SIMPLOT COMPANYDON SIDING COMPLEX 1150 W. HIGHWAY 30 POCATELLO ID

Phosphate Fertilizer Production andPhosphoric

Acid Manufacturing PHO_NEI387 SF PHOSPHATES, INC 515 SOUTH HIGHWAY 430 ROCK SPRINGS WY

Phosphate Fertilizer Production andPhosphoric

Acid Manufacturing PHO_NEI45571 PCS PHOSPHATE COMPANY INC. - AURORA NC 306 NORTH AURORA NC

Phosphate Fertilizer Production andPhosphoric

Acid Manufacturing PHO_NEI7891 AGRIFOS L L C 2001 JACKSON RD PASADENA TX

Phosphate Fertilizer Production andPhosphoric

Acid Manufacturing PHO_NEIFL0570005 CF INDUSTRIES, INC., PLANT CITY PHOS 10608 PAUL BUCHMAN HWY PLANT CITY FL

Phosphate Fertilizer Production andPhosphoric

Acid Manufacturing PHO_NEIFL0570008 MOSAIC FERTILIZER, LLC 8813 HWY. 41 SOUTH RIVERVIEW FL

Phosphate Fertilizer Production andPhosphoric

Acid Manufacturing PHO_NEIFL1050052 CF INDUSTRIES, INC. 2501 BONNIE MINE RD BARTOW FL

Phosphate Fertilizer Production andPhosphoric

Acid Manufacturing PHO_NEIFL12286 CARGILL FERTILIZER, INC. 3600 HIGHWAY 60 WEST BARTOW FL

Phosphate Fertilizer Production andPhosphoric

Acid Manufacturing PHO_NEIFLT$4056 WHITE SPRINGS AGRICULTURAL CHEMICALS,INC E OF CR 137, E OF US 41 WHITE SPRINGS FL

Phosphate Fertilizer Production andPhosphoric

Acid Manufacturing PHO_NEIMS059305 MISSISSIPPI PHOSPHATES CORPORATION 601 HIGHWAY 611 PASCAGOULA MS

Phosphate Fertilizer Production andPhosphoric

Acid Manufacturing PHO_NEIRFL050053 CARGILL FERTILIZER, INC. 4390 CR 640 WEST BARTOW FL

Phosphate Fertilizer Production andPhosphoric

Acid Manufacturing PHO_NEIRFL050055 IMC PHOSPHATES COMPANY 7450 HIGHWAY 630 MULBERRY FL

Phosphate Fertilizer Production andPhosphoric

Acid Manufacturing PHO_NEIRFL050059 IMC PHOSPHATES COMPANY 3095 HIGHWAY 640 MULBERRY FL

Phosphoric Acid Manufacturing PHO_NEILA0930004 IMC PHOSPHATES CO/UNCLE SAM PLNT HIGHWAY 44 UNCLE SAM LA

Phosphate Fertilizer Production PHO_NEILA0930005 IMC PHOSPHATES CO/FAUSTINA PLNT HIGHWAY 44 UNCLE SAM LA

Phosphoric Acid Manufacturing PHO_NEI6147 PCS NITROGEN FERTILIZER,L.P./GEISMAR HWYS 3115 & 30 GEISMAR LA
Cancer 

MIR

Cancer

 Incidence

Noncancer 

Max HI

Target 

Organ

Cancer 

MIR

Noncancer 

Max HI

Target 

Organ

SC % of 

Facility-wide 

Cancer Risk

12057NEIFL0570005 4.6E-07 7.3E-04 1.6E-03 respiratory 5.4E-07 1.2E-01 skeletal 84%

12047NEIFLT$4056 1.8E-08 4.2E-06 7.8E-05 skeletal 1.1E-07 3.6E-02 skeletal 17%

12057NEIFL0570008 1.0E-07 1.8E-04 3.1E-03 skeletal 1.9E-07 2.4E-01 skeletal 54%

12105NEIFL12286 2.2E-08 4.2E-05 6.6E-04 skeletal 6.2E-08 6.4E-02 skeletal 36%

12105NEIRFL050059 6.4E-08 9.5E-05 2.1E-03 skeletal 9.1E-08 6.1E-02 skeletal 70%

16029NEIIDR029000 2.5E-09 2.4E-08 8.9E-04 skeletal 1.4E-08 3.1E-02 skeletal 18%

16077NEI26584 2.0E-07 6.8E-06 1.2E-03 kidney 3.1E-07 2.9E-02 skeletal 66%

22093NEILA0930005 1.2E-07 4.2E-05 5.4E-04 developmental 1.2E-07 5.4E-04 developmental 97%

28059NEIMS059305 4.0E-07 5.9E-05 3.4E-03 kidney 4.8E-07 1.8E-01 skeletal 83%

37013NEI45571 3.3E-08 8.2E-06 3.2E-04 kidney 2.0E-07 3.8E-02 skeletal 17%

56037NEI387 5.6E-09 7.7E-07 1.8E-05 skeletal 2.5E-08 1.0E-02 skeletal 22%

Cancer 

MIR

Cancer

 Incidence

Noncancer 

Max HI

Target 

Organ

Cancer 

MIR

Noncancer 

Max HI

Target 

Organ

SC % of 

Facility-wide 

Cancer Risk

12047NEIFLT$4056 9.5E-08 1.5E-05 3.6E-02 skeletal 1.1E-07 3.6E-02 skeletal 89%

37013NEI45571 8.9E-08 1.9E-05 3.8E-02 skeletal 2.0E-07 3.8E-02 skeletal 44%

28059NEIMS059305 8.3E-08 6.8E-06 1.7E-01 skeletal 4.8E-07 1.8E-01 skeletal 17%

12057NEIFL0570005 8.2E-08 7.4E-05 4.8E-02 skeletal 5.4E-07 1.2E-01 skeletal 15%

16077NEI26584 4.2E-08 9.6E-07 2.5E-02 skeletal 3.1E-07 2.9E-02 skeletal 14%

12105NEIRFL050059 2.8E-08 1.6E-05 5.8E-02 skeletal 9.1E-08 6.1E-02 skeletal 31%

22005NEI6147 2.3E-08 1.1E-05 3.7E-02 skeletal 2.4E-08 2.3E-01 respiratory 94%

56037NEI387 2.0E-08 2.67E-06 1.04E-02 skeletal 2.5E-08 1.0E-02 skeletal 78%

12057NEIFL0570008 1.1E-08 1.3E-05 2.3E-01 skeletal 1.9E-07 2.4E-01 skeletal 6%

12105NEIFL12286 1.0E-08 8.4E-06 6.4E-02 skeletal 6.2E-08 6.4E-02 skeletal 16%

16029NEIIDR029000 6.4E-09 4.9E-08 3.8E-02 skeletal 1.4E-08 3.1E-02 skeletal 46%

22093NEILA0930004 2.9E-09 4.1E-07 8.8E-02 skeletal 1.6E-08 9.0E-02 skeletal 18%

1. BOLD indicates a cancer risk greater than 1 in a million or a noncancer risk greater than 1

2. Facility cancer and non-cancer risks represent MIR at census block locations using Draft 2005 NATA. 

Table 2 – Maximum Predicted HEM-3 Chronic Risks

Facility NEI ID

Phosphate Fertilizer Production Chronic Risk 1 Facility-Wide Chronic Risk 1, 2

Facility NEI ID

Phosphoric Acid Manufacturing Chronic Risk 1 Facility-Wide Chronic Risk 1, 2
Appendix 5

Technical Support Document for TRIM-Based Multipathway Tiered Screening 

Scenario for RTR: Summary of Approach and Evaluation
Technical Support Document

for the TRIM-Based Multipathway

Tiered Screening Methodology for RTR

October 2014

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 Tiered Screening Methodology for RTR

CONTENTS

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 Overview ...................................................................................................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 Spatial 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 Exposure 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 of Approach .............................................................................................52

3.2 Selection of Site-Specific Characteristics for the Tier 2 Assessment........................55
TRIM-Based Tiered Screening Methodology for RTR

3.3 Estimation of Adjustment Factors for Selected Site-Specific 

Parameters..............................................................................................................55

3.3.1 Selection Values for Variables of Interest ..................................................56

3.3.2 Estimation of Adjustment Factors ..............................................................59

3.4 Preparing National Databases of Lake and Meteorological Data .............................64

3.4.1 Processing Lake Data for Tier 2 Assessment ............................................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 Assessment ....................................................................................................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

Attachment B. Description of Multimedia Ingestion Risk Calculator (MIRC) 

Used for RTR Exposure and Risk Estimates.................................................B-1

Attachment C. Dermal Risk Screening .................................................................................C-1

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

Tier 2 Assessment ........................................................................................D-1

Attachment E. Analysis of Lake Size and Sustainable Fish Population ...............................E-1
TRIM-Based Tiered Screening Methodology for RTR

Introduction and Background 1 October 2014

1. Introduction and Background

Section 112 of the Clean Air Act (CAA) directs the U.S. Environmental Protection Agency (EPA) 

to assess the risk remaining (i.e., residual risk) from emissions of 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 standards 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 resulting 

from exposures to persistent and bioaccumulative HAPs (PB-HAPs) via non-inhalation 

pathways, namely ingestion and dermal exposure. EPA’s assessment for RTR focuses on 

specific PB-HAPs that the Office of Air Quality Planning and Standards (OAQPS) has identified 

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 result 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.

To evaluate ingestion exposures and 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 noncancer 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 emission 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 the 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 the 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 can be evaluated further in Tier 2.

• In Tier 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. The environmental scenario assumptions are refined by incorporating 

binned site-specific meteorological data and and locations of fishable lakes near the 

facility (see below). The risk-based threshold for each PB-HAP is then adjusted for that 
TRIM-Based Tiered 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. Facilities having emissions 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 screening 

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 lake 

assessment. Facilities 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 assessments, 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 rates 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 this 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 attachments provide a comprehensive record of the 

characteristics and methods associated 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 facilities 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 throughout 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 multipathway 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

• Polycyclic organic matter (POM).
TRIM-Based Tiered Screening Methodology for RTR

Introduction and Background 3 October 2014

Exhibit 1. Conceptual Decision Tree for Evaluating Non-Inhalation Exposures

for PB-HAPs
TRIM-Based Tiered 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 CAA1

 Thus, although 

EPA has identified nine other PB-HAPs that should be evaluated as part of residual risk 

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 threshold 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-cancer HQ threshold level of concern. For the screening scenario, threshold

emission rates 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 nature 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. 

Important 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 among

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, the approach used to assess ingestion exposures and resulting risks for RTR has 

four components (Exhibit 2): 

1. Fate and transport modeling of PB-HAPs emitted to air that partition into soil, water, and 

other environmental media (including fish uptake); 

2. Modeling of uptake of PB-HAPs by farm food chain media from soil and air; 

3. Estimating ingestion exposures in terms of average daily dose for consumption of farm 

food items by a hypothetical exposed human; and

4. Calculating lifetime cancer risk estimates or chronic non-cancer HQs for each HAP

and corresponding 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 exposures 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 was 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 complete 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.gov/ttn/fera/).
TRIM-Based Tiered 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 Ingestion 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 the 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 properties governing fate and transport of 

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

The inclusion of central-tendency values where warranted is intended to minimize the number of 

false positives.

Based on sensitivity analyses and model testing it is generally 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 more 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 

Estimation
TRIM-Based Tiered Screening Methodology for RTR

Introduction and Background 6 October 2014

1.1.2.2 Modeling Transfer and Uptake 

MIRC was 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,

• Air-to-surface deposition rates for PB-HAPs in both 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 the transfer and uptake of PB-HAPs through the farm food 

chain using algorithms based on those included in EPA’s Human Health Risk Assessment 

Protocol for Hazardous Waste Combustion 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 exposure 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

old; 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 characteristics 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 (99th percentile for fish and 

90th percentile for all other food types) based on EPA’s Exposure Factors Handbook (EPA 

2011a) or other sources as appropriate. All media were assumed to be obtained from a location 

impacted by the modeled source. This approach results in an overestimate of total chemical 

exposure for a hypothetical exposure scenario. For example, the resulting total food ingestion 

rate is extremely high for a hypothetical consumer, with ingestion rates at the 99th percentile for 

fish and the 90th 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).

Dermal absorption of chemicals that are originally airborne is generally relatively minor 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 Section 2.4.2.3 and Section 3.4 of 

Attachment B for full discussions of infant exposures via breast milk ingestion. 
TRIM-Based Tiered 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 cancer 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 these age-group specific risks. Similarly, HQs 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 screening threshold emission rate.

1.1.2.5 Determining Threshold Emission Rates

Tier 1 emission thresholds 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 emitting 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 risk potential. Once 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 screen out of the Tier 1 assessment for some source categories. However, 

conducting a full site-specific assessment of all facilities that cannot be screened out in Tier 1 

would not be practical.

Site-specific values for some influential variables, however, 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 can 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 the 

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 these 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 Tiered Screening Methodology for RTR

Introduction and Background 8 October 2014

1.2.1.1 Site Specific Considerations

For Tier 2, location-specific data on two types of variables are taken into account:

• Meteorological 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 

• Locations of fishable lake(s) relative to the facility5 (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 systematically 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 farms and lakes, 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, Tier 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 TRIM.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 

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 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 searches 

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 than 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, consists 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 fishing of upper 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 Tiered Screening Methodology for RTR

Introduction and Background 9 October 2014

their lake dynamics are too complex to realistically 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 additional 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 

evaluated 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; for example, the Gavin Fly Ash Impooundment would not be included in the screening 

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. These rankings are then 

used to refine the Tier 2 risk metric for the fisher, as discussed in the next section.

To perform Tier 2 assessments, 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 tool, 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 in 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 emission 

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 Fisher Assessment

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 to 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 the 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 up 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 screening result for the fisher can be expressed 

as the sum of the screening result 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 highestconcentration 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 acres of lakes, the risk reflects a reduced ingestion rate 

based on the cumulative lake acreage. 
TRIM-Based Tiered 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 the Tier 3 

assessment introduces additional site-specificity 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 the 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 assessments) 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 assessment after 

evaluating their validity using aerial imagery and other available data. These additional data 

sources are not used in the Tier 2 screening. 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 using the revised lake database

and the Tier 2 methods described in Section 1.2. If removing a lake(s) causes the originallyfished lakes to sum to less than 373 total acres, then in the rescreening, the fisher will catch and 

consume 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 assessment. 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 substantially 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 

with 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 chemical deposition or exposure for 

that hour. If 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-rise assessment above) along 

with the hourly 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 meteorology statistics used in Tier 2. Using these data in 
TRIM-Based Tiered 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 

characterization 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 assessment is converted to a 

time-series adjustment factor—the revised screening result divided by the screening result after 

the Tier 3 lake assessment. This ratio can then be multiplied by the screening result after the 

Tier 3 lake assessment, yielding the 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 2014a), petroleum refinery facility (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 uses detailed sitespecific data to parameterize more accurately (to the extent possible) each important parameter 

that affects pollutant fate and transport. These site-specific properties are incorporated into 

model scenarios configured in TRIM.FaTE and MIRC. 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 and 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 

environmental 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 the media concentrations, exposure 

estimates, and risk estimates for the various ingested media using a range 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 Tiered Screening Methodology 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) implements a 

tiered approach to evaluate multipathway exposures and human health risks for the Risk and 

Technology Review (RTR) program. EPA’s assessment for RTR focuses on persistent and 

bioaccumulative hazardous air pollutants (PB-HAPs) that the Office of Air Quality Planning and 

Standards (OAQPS) has identified 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 

“screened 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-specific 

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 scenarios, 

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 hypothetical 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 assessed. 

The Tier 2 and Tier 3 screening methods are discussed in Sections 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 noninhalation 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 (see Exhibit 4 for threshold screening emission rates). 
TRIM-Based Tiered Screening Methodology 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-bycase 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 PBHAP exceed Tier 

1 threshold?

Diagram Key

Outcome

Process

Decision 

Point

TIER 1 EVALUATION

Proceed to Tier 2

YES
TRIM-Based Tiered Screening Methodology for RTR

Tier 1 Methodology 14 October 2014

The TRIM-based multipathway modeling configuration, 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 described herein, 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 modeling 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 screen); or 

• Risks above the levels of concern from non-inhalation exposures 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, the 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 minimal, resources can 

be targeted toward those 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 changed 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 model using

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 threshold emission rates were calculated 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 

results 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 from 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 values 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-HAPs 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 cancer endpoint.
TRIM-Based Tiered Screening Methodology 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 estimated 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 

Emission Rate

(TPY)

Basis of Threshold

(Type of Health Endpoint)

POM (as benzo(a)pyrene equivalents)a 2.58E-03 Cancer

Dioxins (as 2,3,7,8-TCDD equivalents)a 2.81E-09 Cancer

Cadmium 1.18E-02 Non-cancer

Mercury (as divalent mercury emissions) 3.16E-04 Non-cancer

TPY = 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 threshold risk quantities in nearly all circumstances, given the health protective

and hypothetical nature of the Tier 1 screening configuration. It is considered very unlikely that 

the estimated risk at a real site would be greater than the estimated risk for the simulated Tier 1 

scenario at equivalent emission rates. This is because the Tier 1 scenario assumes, for many of 

the most risk influential parameters in the model, parameter values that result in high-end risk 

estimates. In the real world, the probability 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. Additional 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 associated 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 the 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 assessment of multipathway human exposures for RTR focuses on PB-HAPs 8 that the 

Office of Air Quality Planning and Standards (OAQPS) has identified 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

Although 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 refers 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). The terms are used interchangeably throughout this text.
TRIM-Based Tiered Screening Methodology 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 identification 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 the 14 chemicals and 

groups that are PB-HAPs.

Exhibit 5. OAQPS PB-HAP Compounds

PB-HAP Compounda Addressed by Screening Scenario?

Cadmium compounds Yes

Chlordane No

Chlorinated dibenzodioxins and furans Yes

DDE (1,1-dichloro-2,2-bis(p-chlorophenyl) ethylene) 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 Tiered Screening Methodology 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 exposure 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 various 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 assess risks from hazardous waste combustion facilities, EPA identified 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 scenarios 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, Resident 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 including 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 homegrown 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 children 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 for 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 Tiered Screening Methodology for RTR

Tier 1 Methodology 18 October 2014

Other non-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 water 

exposure pathway is not likely for the modeling scenario developed for this assessment

because the likelihood that humans would use a lake as a drinking water source was assumed 

to be low.11 Dermal 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-HAPs, 

presented in Attachment C, showed that 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 the potential for non-inhalation risks above the levels of concern for PBHAPs emissions from facilities in the context of residual risk assessments conducted as part of 

RTR. The Tier 1 scenario used to derive the threshold emission rates is not intended to 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 levels 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, meteorology, types of exposures, and ingestion rates. The overall result is a 

scenario that is unlikely 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 development and application of the Tier 1 scenario for residual risk evaluations considered 

EPA’s technical and policy guidelines presented in the Residual Risk Report to Congress (EPA

1999); Volumes I and II of the Air Toxics Risk Assessment Reference Library (EPA 2004a,

2005a); and other EPA publications. The scenario described herin is the culmination of 

assessments completed since 2005; it provides the basis for an 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 Sections 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 

reviewers, 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:

 11An exception to this generality would be reservoirs used for drinking water supplies. This situation might be worthy 

of additional analysis, if warranted by the characteristics of a given assessment (e.g., to estimate PB-HAP 

concentrations in treated drinking water derived from reservoirs).
TRIM-Based Tiered Screening Methodology for RTR

Tier 1 Methodology 19 October 2014

1. Fate and transport modeling of PB-HAPs emitted to air by the source that partition into soil, 

water, and other environmental media (including fish12); 

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 chronic 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 fate 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. Modeling software, however, is not currently available for these modules. 

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

ExcelTM-based computing framework, was constructed to complete the calculations required for 

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

 12As 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. Uptake of PB-HAPs into all other biotic media assumed to be ingested is modeled in the 

second step of the modeling framework.

13Information 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 Quotient
TRIM-Based Tiered Screening Methodology 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 Configuration and Parameterization

The 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 lake 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 values, 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 temporal 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 data (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 changes in soil 

characteristics 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 the 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 modeling step depicted in the first box in Exhibit 6 is implemented for 

RTR using the Fate, Transport, and Ecological Exposure module of the TRIM modeling system 

(TRIM.FaTE).

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

 14TRIM.FaTE is a spatially explicit, compartmental mass balance model that describes movement and transformation 

of pollutants over time, through a user-defined, bounded system that includes both biotic and abiotic compartments. 

Outputs include pollutant concentrations in multiple environmental media and biota.
TRIM-Based Tiered Screening Methodology 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 available 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 

comprehensive evaluation of the performance of TRIM.FaTE for modeling mercury was 

documented in Volumes I and II of the TRIM.FaTE Evaluation Report (EPA 2002b, 2005b). 

Algorithms specific to the fate and transport of chlorinated dibenzo-dioxin and -furan congeners 

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 technical 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) are 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 Waste 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 carries

out and the types of input data it requires is presented in Exhibit 7. This exhibit demonstrates 

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

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

calculations carried out using MIRC. 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.

 15The farm food chain calculations 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 Tiered Screening Methodology 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 behavior 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 for POM and dioxins are benzo[a]pyrene and 

2,3,7,8-TCDD, respectively. These compounds were selected because they are relatively wellstudied among the members of the two groups and are also among the most toxic species 

within each group. 

Derivation of appropriate 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 

emissions 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 

Chemicalspecific uptake/

transfer factors

Ingestion 

dose-response 

values

FFC media 

concentrations

Average daily 

doses

Cancer risks and 

hazard quotients

Access db 

process

Tool 

output Input Key to symbols: data
TRIM-Based Tiered Screening Methodology 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 scaling can be expressed through an 

equation as follows:

EmissPAHi:BaP = EmissPAHi × EEFPAHi:BaP × TEFPAHi:BaP

where:

EmissPAHi:BaP = Risk-weighted emissions of PAHi (weighted according to cancer risk

relative to BaP for oral exposures)

EmissPAHi = Emission rate of PAHi

EEFPAHi:BaP = Exposure equivalency (weighting) factor accounting for difference in

relative oral exposure between PAHi and BaP

TEFPAHi:BaP = Toxicity equivalency (weighting) factor accounting for difference in relative toxicity via 

oral route between PAHi 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 scenario.

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 species 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 Organization (van den 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 

TRIM.FaTE and MIRC. TRIM.FaTE is configured for 14 POM congeners 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. Several 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 average 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 Congeners

The calculated EEFs, TEFs, and total REFs for the 14 POM congeners 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 between chemical-specific properties (e.g., Kow and Henry’s law 

constant, kh) 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 youngest 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 

Kow and exposure is stronger than for any other chemical-specific property. Exposure 
TRIM-Based Tiered Screening Methodology for RTR

Tier 1 Methodology 24 October 2014

surrogates were thus identified for each congener by calculating Total Lifetime Average Daily 

Dose (Age Adjusted) for each based on the congener’s Kow and the power regression of the 

modeled PAHs. Exhibit 9 shows that as Kow increases, so too does exposure.

Exhibit 8. Exposure, Toxicity, and Risk Equivalency Factors Relative to BaP

for POM Congeners Currently Evaluated in Tiers 1 and 2 Assessments

Chemical

Fully 

Parameterized 

in 

TRIM.FaTE?

Exposure 

Equivalency 

Factor (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 0.07 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 0b 0

Phenanthrene n 0.06 0b 0

Pyrene n 0.15 0b 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 2010a),

For POMs reported as unspeciated groups (i.e., “PAH, total” and “Polycyclic Organic Matter”) 

EPA assigned surrogates with Kow values near the upper end of the range of all of the Kow

values, corresponding to an exposure near the upper end of the range (log Kow = 6.5). This 

assignment is assumed to be health protective and likely will not under predict exposure.
TRIM-Based Tiered Screening Methodology for RTR

Tier 1 Methodology 25 October 2014

Exhibit 9. Relationship between Exposure and Kow

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 shortlived 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 moderate 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 believes 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 congeners 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 Individual Isomers Reported,” “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 TCDD. 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 Tiered Screening Methodology 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 ratio of the IRISbased 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 long-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 process, 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 Attachment A.

2.3.1 Chemical Properties

The general chemical/physical properties that TRIM.FaTE requires, such as Henry’s law 

constant, molecular weight, and melting point, were obtained from peer-reviewed and standard 

reference sources. Numerous other chemical-specific properties are related more specifically to 
TRIM-Based Tiered Screening Methodology 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 purpose 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 presented 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 size of a relatively small-to-medium facility at the fence 

line. With a predominant wind direction 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 

scenario. 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 height 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 within 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

 16Mass 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 effect 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 unaffected by deposition to the far reaches of the watershed.) 
TRIM-Based Tiered Screening Methodology 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 lateral spread of the plume (σy, 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 to 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 layers (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 those of the surface parcel 

layout. A single air parcel (N2) overlies surface Parcels N6 and N7, and the air over the lake is 

divided into air Parcels 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 that in turn affect chemical transport via water runoff, ground infiltration, 

deposition of suspended sediments in the water column, 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-specific 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 so that a simplified water balance is achieved. 

Although TRIM.FaTE maintains a chemical mass balance, the model does not calculate or 

maintain media mass balances (e.g., for water) except where specified in certain formulas. For 

the Tier 1 scenario, the parameters 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:

 17http://www.epa.gov/scram001/userg/regmod/isc3v2.pdf
TRIM-Based Tiered Screening Methodology for RTR

Tier 1 Methodology 29 October 2014

[total precipitation] = [evapotranspiration] + [total runoff]

In this equation, total 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 the Tier 1

scenario are meant to represent a relatively wet and moderately warm location in the United 

States (USGS 1987). Following 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 m3

/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 balance 

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 (removal 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 concentration is 
TRIM-Based Tiered Screening Methodology for RTR

Tier 1 Methodology 30 October 2014

assumed to remain constant in TRIM.FaTE, and the flushing rate of the lake (calculated via the 

water balance approach described above) was then used to estimate the removal of sediment 

from the modeling domain via lake water outflow. The difference 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 returned 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 velocity 

(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 mass 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/m2

-day, based on calculations using the USLE. The 

HHRAP documentation notes that using the USLE to calculate sediment load to a lake from the 

surrounding watershed sometimes leads to overestimates (EPA 2005a). For the Tier 1

scenario, however, this approach was considered to be appropriate in that health protective 

assumptions are a goal of the screening scenario.18 Surface soil compartments adjacent to the 

lake are linked directly to the lake for the purposes of estimating erosion and 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 assumed 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, the total average daily sediment 

load to the lake from the watershed is about 16,600 kg/day. About 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 properties, 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 applicable 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) 

the 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 

 18Based 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 Tiered Screening Methodology for RTR

Tier 1 Methodology 31 October 2014

source). Ensuring that 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 basis). 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-HAPs 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 5th percentile of the annual averages 

among 239 stations; by comparison, the mean annual average wind speed is approximately 4 

m/s in the contiguous United States). The mixing height (mean heights from 4 states) used was 

710 m (the 5th percentile of annual averages among all 40 states in the SCRAM database).

Exhibit 12. Summary of Key Meteorological 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 5th 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 western 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 pattern 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 5th percentile of annual average speed for 239 stations 

across the contiguous United States (about 50 years of data per 

station). Value is similar to the annual average wind speeds of 
TRIM-Based Tiered Screening Methodology 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 

domain 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 (Holzworth 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 Climatic 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 modeling 

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 simulated as sediment particles). Zooplankton 

feed on algae in the water column, while benthic invertebrates, 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 zooplankton 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 support 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 

abiotic 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 species and food webs across 

U.S. aquatic ecosystems is substantial, reflecting the wide range of sizes, locations, and

physical/chemical attributes of both flowing (rivers, streams) and low-flow water bodies (ponds, 

lakes, reservoirs). In general, lentic bodies of water (lakes and ponds) can accumulate higher 

levels of contaminants in both sediments and biota than lotic systems (rivers, streams). Also,

that initial research (ICF 2005) suggests that a lake of at least 60 hectares (ha) or 150 acres 

could support higher trophic level predatory fish, with some fraction of their diet comprising

smaller fish. 

The RTR Tier 1 scenario includes a generic aquatic ecosystem with a 47-hectare (116-acre) 

lake. Although slightly smaller than the size suggested by the previous review (ICF 2005), a 47-

ha lake is large 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 Tiered Screening Methodology for RTR

Tier 1 Methodology 33 October 2014

Also, this size was compatible with the overall size of the defined watershed 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. Biomass is based on an 

assumption that the total fish biomass (wet-weight) for the aquatic ecosystem is 5.7 grams per 

square meter (gw/m2

, 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 fish 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 

(gw/m2)

Fraction of 

Total Fish 

Biomass

Algae

green algae,

diatoms, bluegreen algae7.95 – Autotrophic –

Zooplankton

crustaceans, 

rotifers, 

protozoans

6.36 – 100% algaea 5.7 E−8

Macrophyte hydrilla, milfoil 500 – – –

Water column 

planktivore

young-of-theyear, 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

largemouth bass, 

walleye 0.2 3.5% 100% water column omnivore 2.0

Benthic 

invertebrate

aquatic insect 

larvae, 

crustaceans, 

mollusksb

20 – detritus in sediments 0.000255

Benthic 

omnivore

small catfish, 

rock bass 2.0 35.1% 100% benthic invert. 0.25

Benthic 

carnivore

large catfish, 

sculpins 1.0 17.5% 50% benthic invert.50% benthic omniv. 2.0

Total Fish Biomassc 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, caddisflies, 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 invertebrates.

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 exposure 
TRIM-Based Tiered Screening Methodology 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 very 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 

summarized 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 

Calculations 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)

Concentration 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 chemical 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 year of emissions. 

For the chemicals modeled in this scenario, long-term concentrations in environmental media 

will be relatively constant at 50 years. Aside from mercury, chemicals modeled for RTR 

approach steady state before 50 years. And, although mercury 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 consumption 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 Tiered Screening Methodology 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 

foodstuffs 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 in 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-HAPs in FFC media using biotransfer factors. 

Environmental media 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 concentrations 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 general, plant- 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 parameter 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, consumption 

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 summarized 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 

that 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 for 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 Tiered Screening Methodology for RTR

Tier 1 Methodology 36 October 2014

Exhibit 15. Summary of Ingestion Exposure Pathways and Routes of Uptake

Ingestion 

Exposure 

Pathway

Medium Ingested

Intermediate 

Exposure 

Pathway – Farm 

Animalsa

Environmental Uptake Route

Medium Processb

Incidental 

ingestion of soil Untilled surface soil N/A Surface soil Deposition; transfer via erosion and runoff 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.FaTEc

Consumption of 

breast milkd Breast milk N/A Breast milk

Contaminant ingested by 

mother partitions to 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 

produce 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 Ingestion 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 uptake

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 ingestion 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 90th percentile values for all food types except for fish, 

which was set at 99th percentile values). All media were assumed to be obtained from locations 

impacted by the modeled source. Although this approach could result in an overestimate of 
TRIM-Based Tiered Screening Methodology for RTR

Tier 1 Methodology 37 October 2014

total chemical exposure for a hypothetical exposure scenario (for example, note that 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 characteristics selected for the Tier 1

scenario are summarized in Exhibit 16. 

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

Exposure Factor Selection for Screening Assessment

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 distribution or 

recommended value

Ingestion rate (IR) for farm produce and animal 

products other than fish (varies by age and medium)

90th percentile of distribution of consumers who 

produce own food

Ingestion rate for fish For adults, 99th percentile as-prepared ingestion 

rate representative of subsistence fisher woman. 

For children, based on 99th percentile, asprepared, 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 lossd Assumed to be “typical”; varies depending on food 

product (see Attachment B). Cooking losses were 

not considered for fish consumption because 

intake rates represent “as prepared” values.

Food preparation/cooking adjustment factor for fishe Mercury = 1.5

Cadmium = 1.5

Dioxin = 0.7

PAH = 1.0

a

Data for exposure characteristics are presented in Attachment B. Exposure parameter values were based on data obtained

primarily from the Exposure Factors Handbook (EPA 2011). See Attachment B for 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 preparation or 

cooking, or after cooking.

e

Because “as consumed,” fish consumption rates are used with whole-fish concentrations, adjustments 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 algorithms 

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 data 
TRIM-Based Tiered Screening Methodology for RTR

Tier 1 Methodology 38 October 2014

from the Exposure Factors Handbook (EPA 2011). The ingestion exposure 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 Infant 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 chemical 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 contaminants 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, cadmium, 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 partition 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 milk 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 2005a).

Exposure via the breast milk consumption pathway is estimated in MIRC for dioxins 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 (CSFs) 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 in Attachment B. Equations used to estimate 

cancer risk and non-cancer hazard also are provided in Attachment B. 
TRIM-Based Tiered Screening Methodology 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 compounds (as Cd) not available 1E−3 IRIS

Elemental mercury not available not available

Divalent mercury not available 3E−4 IRIS

Methyl mercury not available 1E−4 IRIS

Organics

Benzo[a]pyrenea 7.3 IRIS not available

2,3,7,8-TCDD 1.5E+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 supplemental 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 timeweighted 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 screening 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 dictates 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 Tiered Screening Methodology for RTR

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Characteristic Value

Neutral or 

Health 

Protective?

Comments on Assumptions

Surface soil 

properties

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

Health 

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 direction based on data from 

Yakima, Washington, where wind is 

predominantly from the west.

Wind speed 2.8 m/sec Health 

Protective

Low wind speed (5th percentile of long-term 

averages for contiguous United States); 

increases net deposition to lake/watershed.

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 (5th percentile of long-term averages 

for contiguous United States); 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. 

Surface 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 year

Neutral Consistent with calculated water balance; 

reasonable in light of published values for 

small lakes. Might overestimate flushing

rate if water inputs are also overestimated. 

Note that after evapotranspiration, 

remaining water volume added via 

precipitation is assumed to flow into or 

through lake.
TRIM-Based Tiered Screening Methodology for RTR

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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/m2

-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 fish) but decreases levels in 

surface soil (and farm products). 

Aquatic food web 

structure and 

components

Multilevel; includes 

large, upper 

trophic-level fish

Health 

Protective

Inclusion of upper trophic-level fish and 

absence of large-bodied 

herbivore/detritivore 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 from 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.

Biotransfer 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 (for 

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

Ingestion rates for all 

farm 

produce/livestock 

types

Person obtains all 

food sources from 

local farm; 

ingestion rate is 

90th percentile of 

rates for homeproduced 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: 159 g/day

6 to 11: 268 g/day

12 to 19: 331 g/day

Health Protective The adult rate is the 99th percentile value 

for adult females from Burger (2002) and is

considered representative of subsistence 

fishers. 

Rates for children are based on the 99th

percentile, consumer-only fish ingestion

rates from EPA 2002. Rates were adjusted 
TRIM-Based Tiered Screening Methodology for RTR

Tier 1 Methodology 42 October 2014

Characteristic Value

Neutral or 

Health 

Protective?

Comments on Assumptions

to be representative 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 meals from local farm products.

Body weight Mean of national 

distribution

Neutral Note that this does not affect the bodyweight-normalized rates for produce and 

animal products.

Other Chemical-Specific Characteristics

General chemical 

properties used in

fate and transport 

modeling (Henry’s 

law, Kow, 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, 

algal 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 subjected to a series of evaluations. As described previously 

the 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

Some 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 particles can be transported over long 

distances before being deposited. Cadmium has been observed to partition primarily to soil 

when released to the environment (ATSDR 2008). The mobility of cadmium in soil depends 
TRIM-Based Tiered Screening Methodology for RTR

Tier 1 Methodology 43 October 2014

strongly on soil pH, clay content, and availability of organic matter—factors that determine 

whether the cadmium is dissolved or sorbed in surface soil. In general, cadmium adsorbs to soil 

particles in the surface layers of the soil profile, but to a lesser degree than many other heavy 

metals (HSDB 2005). Cadmium also binds 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). 

Cadmium also enters surface waters, which can occur via atmospheric deposition, runoff and 

erosion, or wastewater streams. Most cadmium compounds entering the water column are 

quickly removed through adsorption to organic matter in sediment or to other suspended 

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. 2000; 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 (BMFs) 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, the 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). Actual 

cadmium levels in ingestible products, however, varies based on type of food, 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 concentrated in certain organ meats, such as 

kidney and liver (ATSDR 2008). In a study of cadmium concentrations in 14 food groups 

(including prepared foods), meat, cheese, and fruits generally contained low levels of cadmium

(ATSDR 2008). 

For the RTR screening 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 higher 

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 particular medium does not necessarily mean higher risk). In Exhibit 19, the contributions 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 ADD for all age groups. Most of the additional exposure was from 
TRIM-Based Tiered Screening Methodology 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 cadmium. 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 Contributions of Modeled Food Types to 

Cadmium Ingestion Exposures and Hazard Quotients

2.5.3 Mercury Compounds

Some 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 production 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 to 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 the 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 

revolatilized back into the atmosphere. Most divalent mercury from atmospheric deposition will 
TRIM-Based Tiered Screening Methodology 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 significantly 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, divalent 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 to 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 (ATSDR 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 bioaccumulates and efficiently biomagnifies in aquatic organisms. A considerable

amount (25–60 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 elemental mercury produced 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 organisms, 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 and 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). Concentrations 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. Relative to divalent mercury, methyl mercury concentrations in fish were very 

high (approximately 95 percent of total mercury). 
TRIM-Based Tiered Screening Methodology for RTR

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Exhibit 20. Estimated Contributions of Modeled Food Types 

to Methyl Mercury Ingestion Exposures

2.5.3.3 Average Daily Dose

In Exhibit 20, the contributions 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 

dominant 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 

bioaccumulates 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 determine the emission threshold 

for mercury.

2.5.4 Dioxins

Incineration and combustion processes are believed to be the primary emission sources for 

chlorinated dioxins (ATSDR 1998). The five stationary source 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, 

secondary aluminum smelting, and biological incineration. 
TRIM-Based Tiered Screening Methodology for RTR

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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 adsorb 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 

particles 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. Although 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). Because most dioxins in the 

aquatic environment are adsorbed to suspended particles, however, 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 appears 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 metabolize certain congeners, such as 2,3,7,8-TCDD, and release 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 behavior 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 multiple ingestion exposure pathways. Actual 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 anthropogenic pollutants. Dioxins appear to enter the terrestrial food chain primarily 

through vapor-phase deposition onto surfaces of plants, which are then consumed by larger 

animals. Another major source of exposure to dioxins is through ingestion of contaminated 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 screening 
TRIM-Based Tiered Screening Methodology 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 have 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 fishers, 

however, might 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)

The contributions of ingestion exposure pathways to the lifetime average daily dose (LADD)

(and thus lifetime cancer risk) for the modeled dioxin congeners are presented in Exhibit 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 chain 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., Kow, 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. Stationary emission sources account for approximately 80 percent of total 

annual PAH emissions. Although the primary source of stationary source PAH emissions is 

thought to be residential wood burning, 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 Tiered Screening Methodology for RTR

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Exhibit 21. Estimated Contributions of Modeled Food

Types to Dioxin Ingestion Exposures

2.5.5.1 Behavior in the Environment

PAHs emitted to the atmosphere can travel long distances in vapor form or attached to particles, 

or they can deposit relatively close to an emission source via wet or dry deposition onto water, 

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 (ATSDR 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 carbon 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 

sediment 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 contaminated 

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 increased 
TRIM-Based Tiered Screening Methodology for RTR

Tier 1 Methodology 50 October 2014

exposure to PAHs through association (e.g., direct uptake, 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 non-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 values 

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. These compounds are readily metabolized by higher trophic 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 meat 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 higher than those output by MIRC. 

For the RTR screening scenario, concentrations output by MIRC were generally lower than the 

reported ranges for benzo(a)pyrene in ingestible products. This trend is likely the result of 

background exposure in reported measurements 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

The contributions of ingestion exposure pathways to the LADD (and thus lifetime cancer risk) for 

various PAHs are presented in Exhibit 22. As shown, the variability in the driving exposure 

pathways across 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 environmental fate and transport processes of these PAHs (e.g., Kow, molecular 

weight, 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 exposure 

pathways is consistent with information provided in the literature.

2.5.6 Summary

This assessment provides 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 reveals 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 LADD.
TRIM-Based Tiered Screening Methodology for RTR

Tier 1 Methodology 51 October 2014

Exhibit 22. Estimated Contributions of Modeled Food 

Types to PAH Ingestion Exposures
TRIM-Based Tiered Screening Methodology for RTR

Tier 2 Methodology 52 October 2014

3. Tier 2 Methodology

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 negatives 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 be 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 sitespecific 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 

• Locations 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 based 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 assumption 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 variables 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 facilityby-facility 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 fish 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, the 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 facilities that need to be evaluated in Tier 2, the 

implementation focuses on estimating refined risk using pre-calculated databases (discussed 
TRIM-Based Tiered Screening Methodology 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 meteorological and lake 

data are discussed in more detail in Section 3.5. The meteorological database includes annualaverage summary statistics on wind direction, wind speed, and precipitation for more than 800

surface stations paired with 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. Lakes 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 or 

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 combination 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 Factors
TRIM-Based Tiered Screening Methodology for RTR

Tier 2 Methodology 54 October 2014

(and the subsequent exposure and risk 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 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.

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 factors. 

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 facility, 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 currently 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 factors 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® AccessTM 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 direction, 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 facility-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 that PB-HAP 

at that facility. A facility then screens out in Tier 2 if the emissions are below the Tier 2 threshold

for the fisher scenario and for the farmer scenario. 
TRIM-Based Tiered Screening Methodology for RTR

Tier 2 Methodology 55 October 2014

3.2 Selection of Site-Specific Characteristics for the Tier 2 Assessment

The screening scenario 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 

generally 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 characteristics 

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 plausible 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 uncertainty 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 lakes 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 sitespecific values with a relatively high level of confidence.

3.3 Estimation of Adjustment Factors 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. 

 19Only 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 Methodology 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 the 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 the 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 parameter 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 assessment, 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 frequencies, 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 

interest 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 below. 

Wind Direction: Within the context of the hypothetical exposure scenario used in Tier 1 (and 

under actual conditions), when the frequency with which the wind blows 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. The 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 estimate 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 patterns 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). 

In 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 present 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 relationship 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 threshold 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 the time on average).
TRIM-Based Tiered Screening Methodology 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 to 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, corresponding to the 5th 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

stations nationwide).20 This value is similar to the annual average wind speeds of the U.S. 

Deep South.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 88th 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 modeled 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 precipitation 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 

Coast22. Though the rate was an estimate, it does correspond to approximately the 95th

percentile (i.e., higher rate) precipitation in the National Climatic Data Center’s (NCDC) 30-year 

(1981–2010) data from U.S. stations.

23 To estimate adjustment factors in the Tier 2 

assessment, model simulations were conducted with the parameter value set to three lower 

values (i.e., 1,187 mm/year, 924 mm/year and 512 mm/year; values also shown in Exhibit 24), 

corresponding to the 75th, 50th and 25th percentiles, respectively, of the NCDC data. Locations 

with lower precipitation levels were assumed to have a minimum 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 precipitation 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.

 20http://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 speeds for screening analyses. 21National Climatic Data Center CliMaps (NCDC-CliMaps) (2007). 22National Climatic Data Center Historical Climate Series (NCDC-HCS) (2007). 23http://www.ncdc.noaa.gov/oa/climate/normals/usnormals.html
TRIM-Based Tiered Screening Methodology for RTR

Tier 2 Methodology 58 October 2014

Exhibit 24. Lake Distance and Meteorological Parameter Values for which Adjustment 

Factors were Developed in Tier 2a,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 value is the 5th percentile (i.e., lower) of annual average mixing 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 

different 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 maintaining 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 which 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 

farther 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 

 24Support Center for Regulatory Atmospheric Modeling; http://www.epa.gov/scram001/tt24.htm
TRIM-Based Tiered Screening Methodology for RTR

Tier 2 Methodology 59 October 2014

the fish consumption pathway, which is an important pathway of exposure for several chemicals 

(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 classify 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 healthprotective, 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 land 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 

controlling 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 of land area to lake area 

in each domain resulted in scenarios with increasingly large lakes, 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 requirements 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 

configurations 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 applied 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 actual 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. Although 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. This 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 at a risk level of 1E−6 incremental lifetime cancer 

risk or an incremental HQ of 1, depending on the toxic effect of the chemical in question).
TRIM-Based Tiered Screening Methodology for RTR

Tier 2 Methodology 60 October 2014

Exhibit 25. Layouts for Tier 2 TRIM.FaTE Simulations Using Alternate Distances 

Between the Facility and the Fishable Lakea

a

The no-lake scenario is not shown.
TRIM-Based Tiered Screening Methodology for 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 combination 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 assessment 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 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. 

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 lake 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 adjustment 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 Incorporation 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 as the product of exposure equivalency factors (EEFs) and 

toxicity equivalency factors (TEFs). For the PAHs, this can be expressed as:

REFPAH = EEFPAH × TEFPAH

The EEFs for PAHs represent the ratio of the exposure to a particular species to the exposure 

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 particular 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, moving 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, others 15 based on Kow-based regression estimates) and the dioxin species currently 
TRIM-Based Tiered Screening Methodology for 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 factors 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 EPAH at a facility, the risk-equivalent BaP emission 

level can be expressed as: 

EBAP EQIV = EPAH SPECIES × EEFPAH SPECIES × TEFPAH SPECIES

Then, the Tier 1 screening result is estimated by comparing the risk-equivalent BaP emissions 

to the BaP emission threshold:

RatioTIER 1_PAH SPECIES = ETIER 1_BAP EQUIV

ThresholdTIER 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:

RatioTIER 2_PAH SPECIES = ETIER 2_BAP EQUIV

ThresholdTIER 2_BAP

Using the definition of the risk-equivalent BaP emissions, this can be re-expressed for a given 

PAH species as:

RatioTIER 2_PAH SPECIES = EPAH SPECIES × EEFTIER 2_PAH SPECIES × TEFPAH SPECIES

ThresholdTIER 2_BAP

This expression may be further reconfigured, after some algebraic rearrangement, in terms of 

the Tier 1 ratio as:

RatioTIER 2_PAH SPECIES = RatioTIER 1_PAH SPECIES × 

ThresholdTIER 1_BAP

ThresholdTIER 2_BAP

× 

EEFTIER 2_PAH SPECIES

EEFTIER 1_PAH SPECIES

Tier 2 Adjustment FactorPAH SPECIES = ThresholdTIER 1_BAP

ThresholdTIER 2_BAP

× 

EEFTIER 2_PAH SPECIES

EEFTIER 1_PAH SPECIES

These equations simply state that to adjust the Tier 1 threshold to a Tier 2 threshold 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 Methodology for 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 may be expressed, by summing the above expression, as: 

RatioTIER 2_ALL PAHs = � RatioTIER 1_PAH SPECIES × 

ThresholdTIER 1_BAP

ThresholdTIER 2_BAP

× 

EEFTIER 2_PAH SPECIES

EEFTIER 1_PAH SPECIES

ALL PAHs

If the ratio of total BaP equivalents 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 fisher 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 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 (see Attachment E for further discussion on lake 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 is 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

compartments. 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 deposition 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 substantially 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 order of highest 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 acreage is smaller than 373 acres, then the fisher 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 T2 Fish risk = T2 Fish risk × �

Lake SA(acs)

373 acs �

Equation 1: Scaled fish risk for each lake 1 to n-1
TRIM-Based Tiered Screening Methodology for RTR

Tier 2 Methodology 64 October 2014

(2) Scaled T2 risk = T2 Fish risk × �

373 acs − ∑(SAs of Previously Visited 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 T2 Fish risk = (Eq. 2) +�(Eq. 1)

Equation 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) Total Scaled T2 Fish 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 development 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 ArcGISTM products.25 Because this 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 

estimated 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 as “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 processing 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 tools, 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 assessment 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 Data Update for ArcGISTM 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 Methodology for 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 five 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 (WCCs) 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 are 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 environment. For the screening scenario, we 

assume that fishers 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, comprise 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 compartment 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 adult 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 consumption.

5. A total fish standing biomass of 40 g wet weight/m2 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 maximum fish 

ingestion rates as a function of standing biomass and lake size were estimated. Exhibit 26

presents these findings, where the grey shading indicating when WCC fish would probably not 

be present, the white (unshaded) cells 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 size 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/m2

, this corresponds 

to 25 acres (the first “white” box when moving from smaller 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/m2

, 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 to ensure that 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 near 
TRIM-Based Tiered Screening Methodology for RTR

Tier 2 Methodology 66 October 2014

a facility to be included in the Tier 2 assessment. In addition, larger 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 inside the lake.

It should be noted that an individual lake 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, the 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 approximately 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 the 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 Methodology for RTR

Tier 2 Methodology 67 October 2014

Exhibit 26. Maximum Fish Ingestion Rate (g/day) Associated with Sustainable Fishinga

Standing 

Biomass 

(g ww/m2)

b Size of Pond or Lake (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 83 117 166 250 333 499 666 1332

a

Dark gray shading indicates insufficient population size for TL4 (WCC) fish (<50 adults) to be sustainable for more than a decade; yellow-shaded cells indicate the likelihood to provide long-term

sustainable fish populations with at least 500 TL4 adult fish present; white area indicates medium-term sustainability. b

Represents the standing biomass of TL4 fish. At the upper-limit standing biomass of 40 g ww/ m2estimated 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 Methodology 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 data 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 Advisory 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 NATA (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 onsite 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, the SAB 

accepted this matching as standard practice when modeling large numbers of sources, although 

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; EPA 2010b). 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 stations 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 1996 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 

areas 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 data 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 proximity 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 density 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 Methodology 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 lakes (see 

Introduction and Section 3.5). EPA divided the periphery around 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 degrees

SW: >202.5 to 247.5 degrees

W: >247.5 to 292.5 degrees

NW: >292.5 to 337.5 degrees
TRIM-Based Tiered Screening Methodology for RTR

Tier 2 Methodology 70 October 2014

Exhibit 27. The Locations of Meteorological Stations and Point Source Facilitiesa

a

The 2005 NATA was the most recent, comprehensive, finalized 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 developed 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 height 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 available).

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 values 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 to 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 screening 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 levels. 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 disposal 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 purposes, 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 and 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). Although the distance 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 lakedistance scenario. The tool identifies the largest farm screening result from all scenarios 

evaluated for a facility. The tool presents the final Tier 2 screening results for the fisher and 

farmer 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 tables, 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 screening 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 Toola

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” means 

“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 Toola, b

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” 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 Toola,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 shown. The headers 

“Stationary Fisher” and “Traveling Fisher” are shorthand references to the initial fisher scenario (i.e., catches and consumes 373 g/d 

per lake, regardless of lake area) and the refined fisher scenario (i.e., assumes a 1 g/acre/d lake productivity), respectively. “SV” 

means “screening value”, which is the ratio of emissions to emission screening threshold.

b The table displayed reflects a Tier 3 Screening Assesment having been conducted. In a Tier 2 Screening Assesssment, the Add’l 

Lake Assessment, 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 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 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).

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 

conducted (beyond the Tier 2 methods) is to assess the 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. Aerial 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 it 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 dataset 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 qualified, 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 fisher catches 

and consumes fish according to the refined Tier 2 fisher methods discussed in Section 3. All 

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 were 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 assessment 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 the 
TRIM-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 screening 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 groundlevel exposure. In reality, the physical height of an emission source, in combination 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 upper-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 availability 

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 atmospheric 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. 

For 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. The mass of chemical 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 height and plume rise. This 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 results 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 average 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 screening results 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 facility 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 facility’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 risk 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. The screening-level risk associated with each lake is multiplied by the percent of daily 

ingested fish 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 results are compared to 

the Tier 2 screening results calculated previously. 
TRIM-Based Tiered Screening Methodology for RTR

Tier 3 Methodology 78 October 2014

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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. 

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EPA 2004b. Evaluation of TRIM.FaTE Volume III: Model Comparison Focusing on Dioxin Test 

Case. EPA-453/R-04-002. Available at: 

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Background Exposures. Dioxin Reassessment, NAS Review Draft. U.S. Environmental 

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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.

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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: 

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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) 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=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 Cement Manufacturing.” EPA 

Science Advisory Board. EPA-SAB-10-007. 05/07/2010.

EPA 2011a Exposure Factors Handbook: 2011 edition. National Center for Environmental 

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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 

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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 Office of Air Quality Planning and 

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EPA Office of Air Quality Planning and Standards. 01/31/2014.

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Hazardous Substances Data Bank (HSDB). 2005. Bethesda, MD: National Library of Medicine, 

U.S. [Last Revision Date 06/23/2005]. Cadmium Compounds; Hazardous Substances 

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Web Analysis; submitted to Deirdre Murphy and Terri Hollingsworth, U.S. EPA, from 

Margaret McVey and Rebecca Kauffman, ICF Consulting. October 18.

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mercury, 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 

landscape data sets for multimedia source-to-dose models. University of California at 

Berkeley. Supported by U.S. Environmental Protection Agency (Sustainable Technology 
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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.

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(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) Quality 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 element 

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 in 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-Interscience, New York, pp. 931–933.

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Turner, D.B. 1970. Workbook of Atmospheric Dispersion Estimates. PHS Publication No. 999-

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Administration, Cincinnati, Ohio. 

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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, Washington, D.C. 58 

pp.
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TRIM-Based Tiered Screening Methodology for RTR

Attachment A i October 2014

TRIM.FaTE Inputs

Attachment A. TRIM.FaTE Inputs
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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 for 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 Screening 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 Compartments 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 Parameters 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-35
TRIM-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 Chemical-Specific Parameters for Aquatic Species in the 

TRIM.FaTE Screening Scenario .....................................................................A-43
TRIM-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 presents 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-27 present 

parameters specific to the chemicals modeled in the scenario.

Exhibit A-1. TRIM.FaTE Simulation Parameters 

for the TRIM.FaTE Screening Scenario

Parameter Name Units Value Used Reference

Start of simulation date/time 1/1/1990, midnight Consistent with met data.

End of simulation date/time 1/1/2040, midnight Consistent with met data set; selected to 

provide a 50-year modeling period.

Simulation time step hr 1 Selected value.

Output time stepa hr 4 Selected value.

a

Output time step is set in TRIM.FaTE using the scenario properties "simulationStepsPerOutputStep" and "simulationTimeStep."

.
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 United States, 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 was concluded that a 

conservative estimate would be approximately 42% of the time.

Rainfall rate m3[rain]/m2

[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 Island 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 Holzworth, 

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 unitless -- Value not used in current dynamic runs (would need to be reevaluated if steadystate 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 Parameters for the TRIM.FaTE Screening Scenario

Parameter Name Units Value Used Reference

Atmospheric dust load kg[dust]/m3[air] 6.15E-08 Bidleman 1988

Density of air g/cm3 0.0012 USEPA 1997b

Dust density kg[dust]/m3[dust] 1,400 Bidleman 1988

Fraction organic matter 

on particulates

unitless 0.2 Harner and Bidleman 1998
TRIM-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 Value Used Reference

Surface Soil Compartment 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 layer thickness above 

surface soil

m 0.005 Thibodeaux 1996; McKone et al. 2001 (Table 

3).

Density of soil solids (dry weight) kg[soil]/m3[soil] 2600 Default in McKone et al. 2001 (Table 3).

Thickness - untilleda m 0.01 McKone et al. 2001 (p. 30).

Thickness - tilleda m 0.20 USEPA 2005.

Erosion fraction unitless variesb See Exhibit 5.

Fraction of area available for 

erosion

m2[area available]/m2[total] 1 Assumption ; area assumed rural.

Fraction of area available for runoff m2[area available]/m2[total] 1 Assumption ; area assumed rural.

Fraction of area available for vertical 

diffusion

m2[area available]/m2[total] 1 Assumption ; area assumed rural.

Fraction sand 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 variesb See Exhibit A-5.

Total erosion rate kg [soil]/m2/day variesb See Exhibit A-6.

Total runoff rate m3[water]/m2/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[compartment] 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]/m3[soil] 2,600 McKone et al. 2001 (Table 3).

Fraction sand 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 Compartment Type, continued

Thickness - untilleda m 0.79 McKone et al. 2001 (Table 16 - U.S. average).

Thickness - tilleda m 0.6 Adjusted from McKone et al. 2001 (Table 16).

Organic carbon fraction unitless 0.008 McKone et al. 2001 (Tables 16 and A-3, U.S. 

average).

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 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]/m3[soil] 2,600 Default in McKone et al. 2001 (Table 3).

Fraction sand unitless 0.35 Assumption.

Thicknessa m 1.4 McKone et al. 2001 (Table 17).

Organic carbon fraction unitless 0.003 McKone et al. 2001 (Tables 16 and A-3, U.S. 

average).

pH unitless 6.8 Assumption.

Water content volume[water]/volume[compartment] 0.21 McKone et al. 2001 (Table 17 - national 

average).

Groundwater Compartment Type

Thicknessa m 3 McKone et al. 2001 (Table 3).

Fraction sand 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 solid material in aquifer kg[soil]/m3[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 Screening Scenario

Originating Compartment Destination Compartment Runoff/Erosion Fraction

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

SurfSoil_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_N5a SW_Pond 0.0

SurfSoil_N4 0.5

SurfSoil_S5 0.5

sink 0.0

SurfSoil_S5a SW_Pond 0.0

SurfSoil_N5 0.0

SurfSoil_S4 1.0

sink 0.0 a

Assumes that N5 is higher ground than S5, and half of the runoff flows into N4, and the other half into S5. Assumes all runoff from S5 

flows into S4. 
TRIM-Based Tiered Screening Methodology for RTR

Attachment A A-7 October 2014

TRIM.FaTE Inputs

Exhibit A-6. USLE Erosion Parameters for the TRIM.FaTE Screening Scenario

Soil 

Parcel Area Rainfall/Erosivity Index

Soil 

Erodibility 

Index

LengthSlope 

Factor

Land 

Use

Cover 

Mgmt 

Factor

Supporting 

Practices 

Factor

Unit Soil Loss

Sediment

Delivery 

Ratioa

Calculated 

(Adjusted) 

Erosion 

Rate

Code m2 R (100 ft-ton/ac)

K 

(ton/ac/(100 

ft-ton/acre))

LS 

(USCS) type C

(USCS) P A 

(ton/ac/yr)

A 

(kg/m2

/d) SDRa kg/m2

/d

N1 5.8E+04 300 0.39 1.5 grass 0.1 1 17.55 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 forest 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 SDR = a * (AL)-b; where a is the empirical intercept coefficient (based on the size of the watershed), AL is the total watershed area receiving deposition (m2), and b is the 

empirical slope coefficient (always 0.125).
TRIM-Based Tiered Screening Methodology 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 x
TRIM-Based Tiered Screening Methodology 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

Coniferousa Grass/Herba

Value 

Used Reference Value Used Reference

Leaf Compartment Type

Allow exchange 1=yes, 0=no 1 - Seasonalb -

Average leaf area index m2[leaf]/ 

m2[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.

Correction exponent, octanol 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/m3 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 elements, 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

Seasonalc -

Stomatal area normalized 

effective diffusion path length

1/m 200 Wilmer and Fricker 1996. 200 Wilmer and Fricker 1996.

Vegetation attenuation factor m2/kg 2.9 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 years 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]/m2[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 - Seasonalb -
TRIM-Based Tiered Screening Methodology for RTR

Attachment A A-10 October 2014

TRIM.FaTE Inputs

Exhibit A-8. Terrestrial Plant Parameters for the TRIM.FaTE Screening Scenario

Parameter Name Units

Coniferousa Grass/Herba

Value 

Used Reference Value Used Reference

Particle on Leaf Compartment Type, continued

Volume particle per area leaf m3[leaf 

particles]/m2[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 Only

Allow exchange 1=yes, 0=no Seasonalb -

Correction exponent, octanol to 

lipid

unitless 0.76 Trapp 1995.

Lipid content of root kg/kg wet weight 0.011 Calculated.

Water content of root kg/kg wet weight 0.8 Assumption.

Wet density of root kg/m3 820 Soybean, Paterson et al. 1991.

Wet mass per soil area kg/m2 1.4 Temperate grassland, Jackson et al. 

1996.

Stem Compartment Type – Nonwoody Only

Allow exchange 1=yes, 0=no Seasonalb -

Correction exponent, octanol to 

lipid

unitless 0.76 Trapp 1995.

Density of phloem fluid kg/m3 1,000 Assumption.

Density of xylem fluid kg/cm3 900 Assumption.

Flow rate of transpired water per 

leaf area

m3[water]/m2[leaf

]

0.0048 Crank et al. 1981.

Fraction of transpiration flow 

rate that is phloem rate

unitless 0.05 Paterson et al. 1991.

Lipid content of stem kg/kg wet weight 0.00224 Leaves of European beech, Riederer 

1995.

Water content of stem unitless 0.8 Paterson et al. 1991

Wet density of stem kg/m3 830 Assumption.

Wet mass per soil area kg/m2 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-Based Tiered Screening Methodology for RTR

Attachment A A-11 October 2014

TRIM.FaTE Inputs

Exhibit A-9. Surface 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 volume cell, not water)

g[algae]/m3[algae] 1,000,000 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.

Deptha m 3.18 WI DNR 2007 - calculation based 

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 

dimensions 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 1997b.

Total suspended sediment 

concentration

kg[sediment]/m3[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-Based Tiered Screening Methodology 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

Deptha m 0.05 McKone et al. 2001 (Table 

3).

Fraction sand unitless 0.25 Assumption.

Organic carbon fraction unitless 0.02 McKone et al. 2001 (Table 

3).

Porosity of the sediment 

zone

volume[total pore 

space]/volume[sediment 

compartment]

0.6 USEPA 2005.

Solid material density in 

sediment

kg[sediment]/m3[sediment] 2,600 McKone et al. 2001 (Table 

3).

pH unitless 7.3 Assumption.

Sediment 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 Methodology for RTR

Attachment A A-13 October 2014

TRIM.FaTE Inputs

Exhibit A-11. Aquatic Animals Food Chain, Density, and Mass for the TRIM.FaTE Screening Scenario

Aquatic Biota 

(Consuming 

Organism)

Fraction Diet

Biomass

(kg/m2)

Body 

Weight 

(kg)

Reference

Algae

Zooplankto

n

Benthic 

Invertebrate

Water 

Column 

Herbivore

Benthic 

Omnivore

Water 

Column 

Omnivore

Benthic 

Carnivore

Water 

Column 

Carnivore

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 Screening Methodology 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 Namea Units Value Reference

CAS numberb unitless 7440-43-9 -

Diffusion coefficient in pure 

air

m2[air]/day 0.71 USEPA 1999 (Table A-2-

35).

Diffusion coefficient in pure 

water

m2[water]/day 8.16E-05 USEPA 1999 (Table A-2-

35).

Henry's Law constant Pa-m3/mol 1.00E-37 USEPA 1999 (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 (Koa)

m3[air]/m3[octanol] - -

Octanol-carbon partition 

coefficient (Koc)

- -

Octanol-water partition 

coefficient (Kow)

L[water]/kg[octanol] - -

a

All parameters in this table are TRIM.FaTE chemical properties.

b

This CAS numbers applies to elemental Cd; however, the cations of cadmium are being modeled.
TRIM-Based Tiered Screening Methodology 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 MHgb

CAS number unitless 7439-97-6 14302-87-5 22967-92-6 -

Diffusion coefficient 

in pure air

m2[air]/day 0.478 0.478 0.456 USEPA 1997b.

Diffusion coefficient 

in pure water

m2[water]/day 5.54E-05 5.54E-05 5.28E-05 USEPA 1997b.

Henry's Law 

constant

Pa-m3/mol 719 7.19E-05 0.0477 USEPA 1997b.

Melting point degrees K 234 5.50E+02 443 CARB 1994.

Molecular weight g/mol 201 201 216 USEPA 1997b.

Octanol-water 

partition coefficient 

(Kow)

L[water]/kg[octanol] 4.15 3.33 1.7 Mason et al. 1996.

Vapor washout ratio m3[air]/m3[rain] 1,200 1.6E+06 0 USEPA 1997b, 

based on Petersen 

et al. 1995.

a

All parameters in this table are TRIM.FaTE chemical properties.

b

On this and all following tables, Hg(0) = elemental mercury, Hg(2) = divalent mercury, and MHg = methyl mercury.
TRIM-Based Tiered Screening Methodology 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

m2/day 0.451 0.691 0.009 0.388 0.441 0.372 0.009 0.190

Diffusion coefficient 

in pure water

m2/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-m3/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 

(Kow)

L[water]/L[octanol] 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 pure air

m2/day 0.009 0.009 0.009 0.009 0.009 0.009

Diffusion coefficient 

in pure water

m2/day 8.64E-05 8.64E-05 8.64E-05 8.64E-05 8.64E-05 8.64E-05

Henry's Law 

constant

Pa-m3/mol 0.04 0.53 0.0076 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 

(Kow)

L[water]/L[octanol] 8.71E+05 5.37E+05 3.16E+06 1.45E+05 1.51E+04 5.25E+0

6
TRIM-Based Tiered Screening Methodology for RTR

Attachment A A-17 October 2014

TRIM.FaTE Inputs

Exhibit A-14. PAH Chemical-Specific Parameters for the TRIM.FaTE Screening Scenario

Parameter Name Units Reference

CAS number unitless -

Diffusion coefficient in pure 

air

m2/day USEPA 2005. Exceptions include USEPA 1997a (7,12-Dimethylbenz(a)anthracene), and 

USEPA 2007 (2-Methylnaphthalene, Acenaphthylene, and Benzo(g,h,i)perylene)

Diffusion coefficient in pure 

water

m2/day USEPA 2005. Exceptions include USEPA 1997a (7,12-Dimethylbenz(a)anthracene), and 

USEPA 2007 (2-Methylnaphthalene, Acenaphthylene, and Benzo(g,h,i)perylene)

Henry's Law constant Pa-m3/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 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)

Octanol-water partition 

coefficient (Kow)

L[water]/L[octanol] 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 Methodology 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

Value

1,2,3,4,6,7,8,9-

OCDD

1,2,3,4,6,7,8,9-

OCDF

1,2,3,4,6,7,8-

HpCDD

1,2,3,4,6,7,8-

HpCDF

1,2,3,4,7,8,9-

HpCDF

1,2,3,4,7,8-

HxCDD

1,2,3,4,7,8-

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

m2/day 0.751 0.168 0.782 0.176 0.176 0.816 0.183

Diffusion coefficient in 

pure water

m2/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-m3/mol 0.684 0.19 1.22 1.43 1.42 1.08 1.45

Melting point 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-water partition 

coefficient (Kow)

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

Value

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

1,2,3,7,8-

PeCDD

1,2,3,7,8-

PeCDF

2,3,4,6,7,8-

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

m2/day 0.816 0.183 0.816 0.183 0.854 0.192 0.183

Diffusion coefficient in 

pure water

m2/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-m3/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 (Kow)

L[water]/L[octanol] 1.62E+08 8.24E+07 1.62E+08 3.80E+07 1.86E+07 6.17E+06 8.31E+07
TRIM-Based Tiered Screening Methodology for RTR

Attachment A A-19 October 2014

TRIM.FaTE Inputs

Exhibit A-15. Dioxin Chemical-Specific Parameters for the TRIM.FaTE Screening Scenario

Parameter Name Units

Value

Reference

2,3,4,7,8-

PeCDF

2,3,7,8-

TCDD

2,3,7,8-

TCDF

CAS number unitless 57117-31-4 1746-01-6 51207-31-9 -

Diffusion coefficient in 

pure air

m2/day 0.192 0.899 0.203 US EPA 2005

Diffusion coefficient in 

pure water

m2/day 6.91E-05 4.84E-05 5.19E-05 US EPA 2005

Henry's Law constant Pa-m3/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 (Kow)

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 

2000a (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 Methodology 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 

Muhlbaier and Tissue 

1980.

Washout ratio m3[air]/m3[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, Else = 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 water

L[water]/g[algae 

wet wt]

1.87 McGeer et al. 2003.
TRIM-Based Tiered Screening Methodology 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

Parameter 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 Assumption.

Oxidation rate 1/day 0.00385 0 0 Low end of half-life range (6 months to 2 years) in 

USEPA 1997b.

Reduction rate 1/day 0 0 0 Assumption.

Washout ratio m3[air]/m3[rain] 200,000 200,000 200,000 Assumption.

Surface 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 1997b.

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 1997b)].

Demethylation rate 1/day N/A N/A 0.06 Range reported in Porvari and Verta 1995 is 3E-2 

to 6E-2 /day; value is average maximum potential 

demethylation rate constant under anaerobic 

conditions.

Methylation rate 1/day 0 0.001 0 Range reported in Porvari and Verta 1995 is 2E-4 

to 1E-3 /day; value is average maximum potential 

methylation rate constant under anaerobic 

conditions.

Oxidation rate 1/day 0 0 0 Value assumed in USEPA 1997b.

Reduction rate 1/day 0 1.25E-05 0 Value used for untilled surface soil (2cm), 10% 

moisture content, in USEPA 1997b; general range 

is (0.0013/day)*moisture content to 

(0.0001/day)*moisture content for forested region 

(Lindberg 1996; Carpi and Lindberg 1997).
TRIM-Based Tiered Screening Methodology 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 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 1997b.

Demethylation rate 1/day N/A N/A 0.06 Range reported in Porvari and Verta 1995 is 3E-2 

to 6E-2 /day; value is average maximum potential 

demethylation rate constant under anaerobic 

conditions.

Methylation rate 1/day 0 0.001 0 Range reported in Porvari and Verta 1995 is 2E-4 

to 1E-3 /day; value is average maximum potential 

methylation rate constant under anaerobic 

conditions.

Oxidation rate 1/day 0 0 0 Value assumed in USEPA 1997b.

Reduction rate 1/day 0 3.25E-06 0 Value used for tilled surface soil (20cm), 10% 

moisture content, in USEPA 1997b (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 1997b.

Demethylation rate 1/day N/A N/A 0.06 Range reported in Porvari and Verta 1995 is 3E-2 

to 6E-2 /day; value is average maximum potential 

demethylation rate constant under anaerobic 

conditions.

Methylation rate 1/day 0 0.001 0 Range reported in Porvari and Verta 1995 is 2E-4 

to 1E-3 /day; value is average maximum potential 

methylation rate constant under anaerobic 

conditions.

Oxidation rate 1/day 0 0 0 Value assumed in USEPA 1997b.

Reduction rate 1/day 0 3.25E-06 0 Value used for tilled surface soil (20cm), 10% 

moisture content, in USEPA 1997b (Lindberg 

1996; Carpi and Lindberg 1997).
TRIM-Based Tiered Screening Methodology 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 1997b.

Demethylation rate 1/day N/A N/A 0.06 Range reported in Porvari and Verta 1995 is 3E-2 

to 6E-2 /day; value is average maximum potential 

demethylation rate constant under anaerobic 

conditions.

Methylation rate 1/day 0 0.001 0 Range reported in Porvari and Verta 1995 is 2E-4 

to 1E-3 /day; value is average maximum potential 

methylation rate constant under anaerobic 

conditions.

Oxidation rate 1/day 1.00E-08 0 0 Small default nonzero value (0 assumed in USEPA 

1997b).

Reduction rate 1/day 0 3.25E-06 0 Value used for tilled surface soil (20cm), 10% 

moisture content, in USEPA 1997b (Lindberg 

1996; Carpi and Lindberg 1997).

Surface Water Compartment Type

Algal surface area-specific uptake rate 

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).

Dow ("overall Kow") 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 1997b.

Vapor dry deposition velocity m/day N/A 2500 USEPA 1997b (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 rate 1/day 0 0.001 0 Value used in EPA 1997; range is 1E-4 to 3E4/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 1997b; reported values 

range from less than 5E-3/day for depths greater 

than 17m, up to 3.5/day (Xiao et al. 1995; Vandal 

et al. 1995; Mason et al. 1995a; Amyot et al. 

1997).
TRIM-Based Tiered Screening Methodology 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

Reference 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 1997b.

Demethylation rate 1/day N/A N/A 0.0501 Average range of 2E-4 to 1E-1/day from Gilmour 

and Henry 1991.

Methylation rate 1/day 0 1.00E-04 0 Value used in EPA 1997b; 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 1997b; 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 Methodology 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 Scenario

Parameter Name Units

Value

2Methyl 712DMB Acenaphthene

Acenaphthylene 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 ratio 200000 200000 200000 200000 200000 200000 200000 200000 200000

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 Compartment 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 2290
TRIM-Based Tiered Screening Methodology for RTR

Attachment A A-26 October 2014

TRIM.FaTE Inputs

Exhibit A-18. PAH Chemical-Specific Parameters for Abiotic Compartments in the TRIM.FaTE Screening Scenario

Parameter Name Units

Value

Reference

Chr DahA Fluoranthene

Fluorene

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. Exceptions 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 ratio 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. Exceptions 

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 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 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) / 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 Methodology for RTR

Attachment A A-27 October 2014

TRIM.FaTE Inputs

Exhibit A-18. PAH Chemical-Specific Parameters for Abiotic Compartments in the TRIM.FaTE Screening Scenario

Parameter Name Units

Value

Reference Chr DahA Fluora

n-thene

Fluorene

IcdP

Vadose Zone Soil Compartment 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 / 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.

Surface Water Compartment Type

RatioOfConcInAlgaeToConcD

issolvedInWater

(g[chem]/kg[algae])/

(g[chem]/L[water])

280 1388 67.4 5.8 1653 Kow from Del Vento and Dachs 2002.

Half-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 

HSDB 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. assigned for 

sediment, except for BbF and IcdP, which were not in 

Table 2.3 of Mackay et. al.
TRIM-Based Tiered Screening Methodology for RTR

Attachment 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

Value

1,2,3,4,6,7,8,9

-OCDD

1,2,3,4,6,7,8,9

-OCDF

1,2,3,4,6,7,8-

HpCDD

1,2,3,4,6,7,8-

HpCDF

1,2,3,4,7,8,9-

HpCDF

1,2,3,4,7,8-

HxCDD

Air Compartment Type

Deposition Velocity m/day 500 500 500 500 500 500

Half-life day 162 321 64 137 122 42

Washout Ratio m3[air]/m3[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 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[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

Half-life day 1095 1095 1095 1095 1095 1095
TRIM-Based Tiered Screening Methodology for RTR

Attachment A A-29 October 2014

TRIM.FaTE Inputs

Exhibit A-19. Dioxin Chemical-Specific Parameters for Abiotic

Compartments in the TRIM.FaTE Screening Scenario

Parameter Name Units

Value

1,2,3,4,7,8-

HxCDF

1,2,3,6,7,8-

HxCDD

1,2,3,6,7,8-

HxCDF

1,2,3,7,8,9-

HxCD

D

1,2,3,7,8,9-

HxCDF

1,2,3,7,8-

PeCDD

Air Compartment Type

Deposition velocity m/day 500 500 500 500 500 500

Half-life day 78 28 55 28 51 18

Washout ratio m3[air]/m3[rain] 10000 9000 10000 9000 10000 18000

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

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

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 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[water])

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 1095
TRIM-Based Tiered Screening Methodology for RTR

Attachment A A-30 October 2014

TRIM.FaTE Inputs

Exhibit A-19. Dioxin Chemical-Specific Parameters for Abiotic

Compartments in the TRIM.FaTE Screening Scenario

Parameter Name Units

Value

1,2,3,7,8-

PeCDF

2,3,4,6,7,8-

HxCDF

2,3,4,7,8-

PeCDF

2,3,7,8-TCDD

2,3,7,8-TCDF

Air Compartment Type

Deposition velocity m/day 500 500 500 500 500

Half-life day 31 59 33 12 19

Washout ratio m3[air]/m3[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 characteristic 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 characteristic 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 1095
TRIM-Based Tiered Screening Methodology for RTR

Attachment A A-31 October 2014

TRIM.FaTE Inputs

Exhibit A-19. Dioxin Chemical-Specific Parameters for Abiotic

Compartments in the TRIM.FaTE Screening Scenario

Parameter Name Reference

Air Compartment Type

Deposition velocity McKone et al. 2001.

Half-life Atkinson 1996 as cited in USEPA 2000b; vapor phase 

reaction with hydroxyl radical.

Washout ratio Vulykh et al. 2001.

Surface Soil Compartment 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 Zone Soil Compartment Type

Input characteristic depth Not 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 Zone 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 aerobic 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.

Surface Water Compartment Type

Ratio Of Conc In Algae To Conc Dissolved 

In Water

Estimated from Kow value using model from DelVento and 

Dachs 2002

Half-life Kim and O’Keefe 1998, as cited in USEPA 2000b. 

Sediment Compartment Type

Half-life Estimation based on Adriaens and Grbic-Galic 1992,1993 

and Adriaens et al. 1995 as cited in USEPA 2000b.
TRIM-Based Tiered Screening Methodology for RTR

Attachment A A-32 October 2014

TRIM.FaTE Inputs

Exhibit A-20. Cadmium Chemical-Specific Parameters for

Plant Compartments in the 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 Herbsa

Root to Root Soil PartitionAlpha of Steady Stateunitless 0.95 Henning et al. 2001.

Root to Root Soil PartitionPartitioning Coefficientm3[bulk root 

soil]/m3[root]

0.23 Nriagu 1980; based on average 

value calculated from various 

agricultural plant species.

Root to Root Soil PartitionTime to Reach Alphaday 28 Henning et al. 2001.

Stem Compartment Type - Grasses and Herbsa

Transpiration stream 

concentration factor (TSCF)

m3[soil pore 

water]/m3[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 Methodology for RTR

Attachment A A-33 October 2014

TRIM.FaTE Inputs

Exhibit A-21. Mercury Chemical-Specific Parameters for 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 Assumed 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 Assumed to be nearly instantaneous

Reduction rate 1/day 0 0 0 Assumption.

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 Assumption.

Root Compartment Type - Grasses and Herbsa

Alpha for root-root zone bulk soil unitless 0.95 0.95 0.95 Selected value.

Root/root-zone-soil-water partition 

coefficient

m3[bulk root soil]/ m3[root] 0 0.18 1.2 Hg2-geometric mean Leonard et al. 1998, 

John 1972, Hogg et al. 1978; MHgassumed, 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 Assumption.

Reduction rate 1/day 0 0 0 Assumption.

Stem Compartment Type - Grasses and Herbsa

Transpiration stream concentration 

factor (TSCF)

m3[soil pore 

water]/m3[xylem fluid]

0 0.5 0.2 Calculation from Norway spruce, Scots 

pine, Bishop et al. 1998.

Demethylation rate 1/day N/A N/A 0.03 Calculated from Bache et al. 1973.

Methylation rate 1/day 0 0 0 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 Methodology 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 Acenaphthene

Acenaphthylene 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 Herbsa

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 Herbsa

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 Methodology for RTR

Attachment A A-35 October 2014

TRIM.FaTE Inputs

Exhibit A-23. PAH Chemical-Specific Parameters for Plant Compartments in the TRIM.FaTE Screening Scenario

Parameter Name Units

Value

Reference

Chr DahA Fluoranthene 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; Benzo(ghi)perylene; 

Fluoranthene; and Fluorene).

Root Compartment Type - Grasses and Herbsa

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 Herbsa

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 TRIM.FaTE.
TRIM-Based Tiered Screening Methodology for RTR

Attachment A A-36 October 2014

TRIM.FaTE Inputs

Exhibit A-23. 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 as 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/day 0.3 Assumption based on USEPA 2000b (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 - Grasses and Herbsa

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 soil-water interaction - alpha unitless 0.95 Assumption.

Stem Compartment Type - Grasses and Herbsa

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 Methodology 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

Absorption 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 partitioning - alpha of 

equilibrium

unitless 0.95 Assumption.

Sediment partitioning - partition 

coefficient

kg[bulk sed/kg[invertebrate wet wt] 0.27 Assumption.

Sediment partitioning - time to reach 

alpha of equilibrium

day 21 Hare et al. 2001.

Benthic Omnivore Compartment Type

Assimilation efficiency from food 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 2001.

Elimination rate constant unitless 1.73E-02 Assumption.

Benthic Carnivore Compartment Type

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 2001.

Elimination rate constant 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 Yan and Wang 2002.

Absorption rate constant unitless 2.46 Calculated based on body weight from regression 

in Hendriks and Heikens 2001.

Elimination rate constant unitless 1.73E-02 Assumption.
TRIM-Based Tiered Screening Methodology for RTR

Attachment A A-38 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

Water-column Omnivore Compartment Type

Assimilation efficiency from food unitless 0.1 Assumption based 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 2001.

Elimination rate constant unitless 1.73E-02 Assumption.

Water-column Carnivore Compartment Type

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 2001.

Elimination rate constant unitless 1.73E-02 Assumption
TRIM-Based Tiered Screening Methodology 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) MHg

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 value (i.e., proportion 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 equilibrium for sediment 

partitioning

day 14 14 14 Experiment duration from 

Saouter et al. 1991.

All Fish Compartments Typesa

Elimination adjustment factor unitless 3 3 1 Trudel and Rasmussen 1997.

Assimilation efficiency from food unitless 0.06 0.06 0.5 Williams 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 efficiency 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 Methodology 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 Acenaphthene

Acenaphthylene BaA BaP BbF BghiP BkF

Zooplankton Compartment 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/day 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

Vd (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 Typesa

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 Water-column Carnivore.
TRIM-Based Tiered Screening Methodology for RTR

Attachment A A-41 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 Fluoranthene Fluorene IcdP

Zooplankton Compartment Type

Absorption rate 

constant

L[water]/kg[fish 

wet wt]-day

42649.95 42655.48 142000 15000 42655.93 Kow 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 Kow 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).

Elimination rate 

constant

1/day 2.3746 0.4331 8.678 81.87 0.269 Kow 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.

Vd (ratio of 

concentration

in benthic 

invertebrates to

concentration in 

water)

ml/g 7235 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 Methodology 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 Fluoranthene Fluorene IcdP

All Fish Compartment Typesa

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 Omnivore, and Water-column Carnivore.
TRIM-Based Tiered Screening Methodology 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 Scenario

Parameter Name Units

Value

1,2,3,4,6,7,8,9-

OCDD

1,2,3,4,6,7,8,9-

OCDF

1,2,3,4,6,7,8-

HpCDD

1,2,3,4,6,7,8-

HpCDF

1,2,3,4,7,8,9-

HpCDF

1,2,3,4,7,8-

HxCDD

1,2,3,4,7,8-

HxCDF

1,2,3,6,7,8-

HxCDD

1,2,3,6,7,8-

HxCDF

Zooplankton Compartment

Absorption rate constant L[water]/kg[fish 

wet wt]-day

8640 8640 8640 8640 8640 8640 8640 8640 8640

Assimilation efficiency from algae unitless 0.08 0.05 0.21 0.09 0.2 0.31 0.31 0.31 0.31

Elimination rate constant 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 constant unitless 0 0 0 0 0 0 0 0 0

Sediment partitioning partition 

coefficient

kg/kg 0.0013 0.0017 0.0055 0.0012 0.042 0.033 0.0081 0.013 0.02

Sediment partitioning alpha of 

equilibrium

unitless 0.95 0.95 0.95 0.95 0.95 0.95 0.95 0.95 0.95

Sediment partitioning time to

reach alpha of equilibrium

days 120 42 120 42 42 120 42 120 42

Vd (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 Compartmentsa

Assimilation efficiency from food unitless 0.08 0.05 0.21b 0.09 0.2 0.31c 0.31 0.31 0.31

Chemical uptake rate via gill 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 70
TRIM-Based Tiered Screening Methodology for RTR

Attachment A A-44 October 2014

TRIM.FaTE Inputs

Exhibit A-27. Dioxin Chemical-Specific Parameters for Aquatic Species in the TRIM.FaTE Screening Scenario

Parameter Name Units

Value

1,2,3,7,8,9-

HxCDD

1,2,3,7,8,9-

HxCDF

1,2,3,7,8-

PeCDD

1,2,3,7,8-

PeCDF

2,3,4,6,7,8-

HxCDF

2,3,4,7,8-

PeCDF

2,3,7,8-TCDD

2,3,7,8-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 Equilibrium

days 120 42 120 42 42 42 120 42

Vd (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 Compartmentsa

Assimilation efficiency from food unitless 0.31 0.31 0.42 0.42 0.31 0.42 0.41 0.51

Chemical uptake rate via gill 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 70
TRIM-Based Tiered Screening Methodology for RTR

Attachment A A-45 October 2014

TRIM.FaTE Inputs

Exhibit A-27. Dioxin Chemical-Specific Parameters for Aquatic Species in the TRIM.FaTE Screening Scenario

Parameter Name Units Reference

Zooplankton Compartment

Absorption rate constant L[water]/kg[fish 

wet wt]-day

Zhang et al. 2011; used copepod ku value.

Assimilation efficiency from algae unitless Morrison et al. 1999. Exceptions include Niimi and Oliver 1986 (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 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).

Elimination rate constant 1/day Arnot and Gobas 2004; used Kow value.

Half-life day Morrison et al. 1999; used metabolic rates for invertebrates.

Benthic Invertebrate Compartment

Clearance constant unitless Assumption.

Sediment partitioning partition coefficient kg/kg Rubinstein et al. 1990; used TCDD data for sandworm.

Sediment partitioning alpha of equilibrium unitless Rubinstein et al. 1990.

Sediment Partitioning Time to Reach 

Alpha of Equilibrium

days Rubinstein et al. 1990.

Vd (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 Compartmentsa

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, Water-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 column omnivore, benthic carnivore, and water column carnivore; 0.37 used for water column herbivore.
TRIM-Based Tiered Screening Methodology for RTR

Attachment A A-46 October 2014

TRIM.FaTE Inputs

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TRIM-Based Tiered Screening Methodology for RTR

Attachment B i October 2014

Description of MIRC

Attachment B. Description of Multimedia Ingestion Risk Calculator 

(MIRC) Used for RTR Exposure and Risk Estimates
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TRIM-Based Tiered Screening Methodology 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 Vegetable 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-45
TRIM-Based Tiered Screening Methodology 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 Adjustment Factor (FPCAF) for 

Fish B-81

6.5. Breast-Milk Infant Exposure Pathway Parameter Values .....................................B-82

6.5.1. Receptor-specific Parameters................................................................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 Ingestion ...................................................................B-90

7.2. Default Screening-Level Population-Specific Parameter Values .......................... B-92

7.3. Default Chemical-Specific Parameter Values for Screening Analysis .................. B-93

7.4. Screening-Level Parameter Values for Nursing Infant Exposure.......................... B-94

7.4.1. DioxinsB-94

7.4.2. Methyl Mercury......................................................................................B-95

8. References ...................................................................................................................B-96
TRIM-Based Tiered Screening Methodology for RTR

Attachment B v October 2014

Description of MIRC

Exhibits, Attachment B

Exhibit B-1. Overview of MIRC Software Application for Performing Farm-FoodChain 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 Aboveground Produce............................B-10

Exhibit B-5. Chemical Transfer Pathways for Animal Products..............................................B-16

Exhibit B-6. Oral Dose-response Values Used to Calculate RTR Screening 

Threshold Emission Rates for PB-HAP Chemicalsa ........................................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) 

Groupsa ..........................................................................................................B-41

Exhibit B-9. MIRC Parameters Used to Estimate Chemical Concentrations in 

Farm Foods ....................................................................................................B-48

Exhibit B-10. Chemical-Specific Inputs for Produce Parameters for Chemicals 

Included in MIRC ............................................................................................B-49

Exhibit B-11. Chemical-Specific Inputs by Plant Type for Chemicals in MIRC......................B-51

Exhibit B-12. Non-Chemical-Specific Produce Inputs...........................................................B-61

Exhibit B-13. Animal Product Chemical-specific Inputs for Chemicals Included in 

MIRC ..............................................................................................................B-62

Exhibit B-14. Soil and Plant Ingestion Rates for Animals......................................................B-64

Exhibit 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 Adultsa ........................................................................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 (IRCO,y)

a ......................................................................B-74

Exhibit B-20. Fraction of Population Consuming Freshwater/Estuarine Fish on a 

Single Day (FPC,y)............................................................................................B-75

Exhibit B-21. Calculated Long-term Mean and Percentile per capita Fish 

Ingestion Rates for Children and Adults (IRPC,y) ..............................................B-75

Exhibit B-22. Calculated Mean and 90th Percentile Per capita Fish Ingestion 

Rates for Populations of Recreational Fishers (IRPC,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 Total Food Intake for 

Children and Adults.........................................................................................B-78
TRIM-Based Tiered Screening Methodology 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-26. 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 Scenario for Farming Households..................................................B-91

Exhibit B-32. Mean Body Weight Estimates for Adults and Childrena ...................................B-92

Exhibit B-33. Chemical-Specific Parameter Values for Input to MIRCa .................................B-93

Exhibit B-34. Chemical and Animal-Type Specific Biotransfer Factor (Ba) Values 

for Input to MIRC ............................................................................................B-94
TRIM-Based Tiered Screening Methodology for RTR

Attachment B vii October 2014

Description of MIRC

Equations, Attachment B

Equation B-1. Chemical Concentration in Aboveground Produce..........................................B-11

Equation B-2. Chemical Concentration in Aboveground Produce Due to Root 

Uptake ............................................................................................................B-11

Equation B-3. Chemical Concentration in Aboveground Produce Due to 

Deposition of Particle-phase Chemical............................................................B-12

Equation B-4. Chemical Concentration in Aboveground Produce Due to Air-toPlant Transfer of Vapor-phase Chemical ........................................................B-12

Equation B-5. Conversion of Aboveground Produce Chemical Concentration from 

Dry- 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 Soil 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-27
TRIM-Based Tiered Screening Methodology 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 Nonlactating 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 Hydrophilic Chemicals ..................B-35

Equation B-45. Calculation of Infant Average Daily Absorbed Dose of Methyl 

Mercury...........................................................................................................B-36

Equation B-46. Calculation 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

Equation 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-73
TRIM-Based Tiered Screening Methodology 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 Ingestion 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 ingestion 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 the 

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 home- 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 concentrations, 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 

appropriate 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 milk 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, many 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 predicted 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 

 27Fully functional versions of MIRC have been developed in both AccessTM-based and ExcelTM-based formats; 

however, MIRC currently is not publicly available. 
TRIM-Based Tiered Screening Methodology 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. Households 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, therefore, 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 2005a).

1.3. Use in EPA’s Air Toxics Program

For PB-HAPs, indirect exposure pathways, 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 

Integrated 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 dry 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 bioaccumulation 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 various 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, ingestion 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.ExpoIngestion, and TRIM.Risk.

28

1.4. MIRC Highlights

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 also 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 Methodology 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 Guidelines),

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 and 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 other inputs that determine concentrations in farm food stuffs.

• Value options for receptor characteristics in the database include the mean and 50th, 

90th, 95th, and 99th percentile values where data permit.

• For carcinogens with a mutagenic mode of action, MIRC 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 values for children issued September 30, 2008, in the Agency’s ChildSpecific Exposure Factors Handbook (CSEFH) (EPA 2008a).

1.5. Organization of This Attachment

Sections 2 through 5 of this attachment describe the exposure and risk models implemented in 

MIRC. Section 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 used 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, and 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. These 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 populations 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 water 
TRIM-Based Tiered Screening Methodology 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) products: 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 an 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 (Sections 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 environmental 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 risks 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 Methodology 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 Concern

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 Risks
TRIM-Based Tiered Screening Methodology for RTR

Attachment B B-6 October 2014

Description of MIRC

A form within the graphical user interface enables the user to construct specific scenarios by 

choosing ingestion sources, receptor ages, and other input choices (e.g., diet composition, body 

weight percentiles). This feature facilitates the assessment of various exposure scenarios. To 

begin an assessment, the user must supply values for the following chemical-specific 

parameters for the scenario being evaluated:

• Air concentration of total chemical,

• Fraction chemical in air in vapor-phase,

• Wet and dry deposition 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 concentrations 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 database) 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 of EPA’s HHRAP (EPA 2005a). 

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 across 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 individual 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 the concentrations of chemicals in FFC food categories grown in an area of 

airborne chemical deposition using algorithms and parameter values provided in HHRAP (EPA 

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 summarizes 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 Methodology 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)

• Ingestion of forage, silage, and graina

• Soil ingestion

Pork • Ingestion of silage and graina

• Soil ingestion

Poultry and eggs • Ingestion of graina

• Soil ingestion

a

Chemical concentrations in forage, silage, and grain 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 concentration 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 

concentration 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 other 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 accumulate chemical via direct deposition of particle-bound 

chemical and vapor transfer. 

The 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 values. For secondary animal 

products (dairy products and eggs), chemical concentrations 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, 2008a), exposures of children are 

expected to differ from exposures of adults due to differences in body weights, ingestion rates, 

dietary preferences, 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 Methodology 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 values across exposure 

parameters, in 2005, EPA recommended 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 been calculated. In Chapter 13 

of both its Exposure Factors Handbook (EFH; EPA 2011a) and its Child-Specific Exposure 

Factors Handbook (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 because 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 EPA 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) remains 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 recently published major

analysis of those data. Because ingestion of home-grown produce and animal products are the 

primary exposure pathways 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, in 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 years); 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 effects, respectively, for the above age groupings. Exposure 

and risks to infants under 1 year of age are estimated only for the breast-milk-ingestion 

pathway. 
TRIM-Based Tiered Screening Methodology 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 adults. 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 lifestages 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 mutagenic 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:

• children 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 with other or unknown MOAs. Currently in MIRC, 

the 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 chemical concentrations 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. 

As 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

The algorithms and parameters used to estimate chemical concentrations in produce and 

animal products are described in Sections 3.1.1 and 3.1.2 of this attachment, respectively. 

Discussions of the parameter value options and the values selected as defaults 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 

difference 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 emission 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 multipathway 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 Methodology for RTR

Attachment B B-10 October 2014

Description of MIRC

3.1.1. Estimating Chemical Concentrations in Produce

Produce (vegetables and fruits) can become contaminated directly by deposition of airborne 

chemicals to foliage and fruits or indirectly by uptake of chemicals deposited to the soil. Given 

these two contamination processes, produce is divided into two main groups: aboveground 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 chemicals 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 PBHAPs 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 Media 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 soil

Belowground 

Produce

Root vegetables Root uptake from soil

3.1.1.1. Aboveground Produce

For aboveground 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). The 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 concentration 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 (PrAG-produce) is 

estimated using an empirical bioconcentration factor (BrAG-produce ) that relates the chemical 

Exhibit B-4. Estimating Chemical 

Concentration in Aboveground Produce

Deposition 

of Particles 

(Pd)

Root Uptake 

from Soil 

(PrAG-produce)

Vapor 

Transfer 

(Pv)

Chemical Concentration in 

Aboveground Produce
TRIM-Based Tiered Screening Methodology 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 (Csroot-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 through which aboveground protected produce becomes contaminated. As 

shown below, the chemical concentration in the aboveground plant due to root uptake from soil

(PrAG-produce- DW) is estimated using an empirical bioconcentration factor (BrAG-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 (Csroot-zone_produce). These equations all 

assume measurements on a dry-weight (DW) basis.

Equation B-1. Chemical Concentration in Aboveground Produce 

where:

CAG-produce-DW(i) = Concentration of chemical in edible portion of aboveground produce type i, 

exposed or protected, on 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

PrAG-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 produce 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: 

PrAG-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)

Csroot-zone_produce = Average chemical concentration in soil at root-zone depth in produce-growing 

area (mg/kg soil DW)

BrAG-produce-DW(i) =

Chemical-specific plant/soil chemical bioconcentration factor for edible portion 

of aboveground produce type i, exposed or protected (g soil DW / g produce 

DW)

AG produce DW (i ) P AG produce DW (i ) Pd(i ) Pv(i ) C − − = r − − + +

P AG produce DW (i ) Csroot-zone_produce BrAG produce DW (i ) r − − = × − −
TRIM-Based Tiered Screening Methodology 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/m2

-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/m2

-yr)

Rp(i) = Interception fraction of the edible portion 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 edible portion of plant type i (kg produce 

DW/m2

)

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/sec, 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/m2

-yr. 

With MIRC, the user inputs both the dry and wet particle-phase deposition rates, Drdp and 

Drwp, respectively, in units of g/m2

-yr for a specific location relative to an emissions source. 

Those deposition rates 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 dryand wet-particle-phase deposition rates, respectively, are in units of g/m2

-yr. Users of 

TRIM.FaTE should note that the dry- and wet-particle-deposition rates output from TRIM.FaTE 

are in units of g/m2

-day; therefore, users must adjust the TRIM.FaTE output values to units of

g/m2

-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

kp i Tp i

i

i Yp kp

Drdp Fw Drwp Rp e

Pd

×

× + × × × − =

−

a

AG i AG i

i

Ca Fv Bv VG

Pv

ρ

( ) ( )

( )

× × × =
TRIM-Based Tiered Screening Methodology for RTR

Attachment B B-13 October 2014

Description of MIRC

where:

Pv(i) =

Concentration of chemical in edible portion of aboveground produce type 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/m3

) 

Fv = Fraction of airborne chemical in vapor phase (unitless)

BvAG(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)

VGAG(i) =

Empirical correction factor for aboveground exposed produce 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)

ρa = Density of air (g/m3

)

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 source 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 vaporphase chemical in units of μg-sec/g-m3

. For MIRC, the user inputs the average annual total air 

concentration of the chemical, Ca, for a specific location relative to the source in units of g/m3

; 

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 note 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 μg/m3

; therefore, the user must adjust the value 

to units of g/m3 (i.e., divide by 1,000 μg/g) before entering it in MIRC.

The calculations of chemical concentration in aboveground produce, (CAG-produce-DW), shown 

above, are on a dry-weight (DW) 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, CAG-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

Dry- to Wet-Weight Basis

where:

CAG-produce-WW(i) = Chemical concentration in edible portion of aboveground produce type i on a wetweight (WW) basis (mg/kg produce WW)

CAG-produce-DW(i) = Chemical concentration in edible portion of aboveground produce type i on a dryweight (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

AG produce WW i AG produce DW i

MAF

C C
TRIM-Based Tiered Screening Methodology 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 produce, 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 rootzone depth in the produce-growing area (Csroot-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 soil (Csroot-zone_produce) on a dry-weight basis. Belowground 

produce (i.e., tubers or root vegetables) are protected from the deposition and vapor 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:

CBG-produce-WW = Concentration of chemical in belowground (BG) produce (i.e., tuber or root 

vegetable) on a wet-weight (WW) basis (mg chemical/kg produce WW)*

Csroot-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)*

VGrootveg =

Empirical correction factor for belowground produce (i.e., tuber or root 

vegetable) to account for possible 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 = Units 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-weight 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 concentrations 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 an estimate of the chemical’s Kow from empirically 

Kds UCF

Csroot zone produce RCF VGrootveg

×

× × = − _ CBG-produce-WW
TRIM-Based Tiered Screening Methodology 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 calculated for many of the chemicals in HHRAP are included in the MIRC database

(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

values from Kow . For inorganic chemicals, chemical specific empirical values for the root/soil 

bioconcentration factor must be used. The root/soil bioconcentration factor, now specified as 

BrBG-produce-DW, must be obtained from the literature for each inorganic chemical on a DW basis. 

For inorganic chemicals, therefore, Equation B-7 is used instead of Equation B-6.

Equation B-7. Chemical Concentration in Belowground Produce: Inorganic Chemicals

where:

CBG-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 dryweight (DW) basis (mg/kg produce DW)

Csroot-zone_produce = Average chemical concentration in soil at root-zone depth in produce-growing 

area (mg/kg soil DW)

BrBG-produce-DW = Chemical-specific root/soil chemical bioconcentration factor for edible portion 

of belowground produce (g soil DW / g produce DW)

VGrootveg = 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 

vegetables must be transformed to a WW estimate, as shown in Equation B-8. 

Equation B-8. Conversion of Belowground Produce Chemical Concentration from 

Dry- to Wet-Weight Basis

where:

CBG-produce-WW = Chemical concentration in edible portion of belowground produce on a weightweight (WW) basis (mg/kg produce WW)

CBG-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 dryweight (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

root zone _ produce BG produce DW rootveg

BG-produce-DW

Cs Br VG

C

× × = − − −





 



 − − − = − − ×

100

(100 ) BG

BG produce WW BG produce DW

MAF C C
TRIM-Based Tiered Screening Methodology 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 (PlantCh-Intake(i,m)) and

incidental ingestion of soil for ground-foraging animals (SoilCh-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 PBHAPs (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 MIRC for chemical intake with plant feeds (PlantCh-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)

• Ingestion of forage, silage, and graina

• Incidental soil ingestion

Pork • Ingestion of silage and graina

• Incidental soil ingestion

Poultry and eggs • Ingestion of graina

• Incidental soil ingestion

a

Chemical concentrations in plant feed (i.e., forage, silage, and grain) are estimated via intermediate 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 chemicals via direct deposition of 

particle-bound chemical and transfer of vapor-phase chemical, while all 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 accumulate 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 calculate the 

concentration of chemical in beef, pork, or total dairy and Equation B-10 to calculate 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 of 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 other 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

Cmammal m Ba m MF SoilCh Intake m PlantCh Intake i m

1

( ) ( ) ( ) ( , )
TRIM-Based Tiered Screening Methodology for RTR

Attachment B B-17 October 2014

Description of MIRC

where:

Cmammal(m) = Concentration of chemical in mammalian animal product m, where m = beef, 

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 intake/day] or day/kg WW)

MF = Chemical-specific mammalian metabolism factor that accounts for endogenous 

degradation of the chemical (unitless)

SoilCh-Intake(m) =

Incidental ingestion of chemical in surface soils by livestock type m during 

grazing or consumption of foods placed on the ground (mg/day); see Equation 

B-11 below

PlantCh-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:

Cpoultry(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 food product m, where m = poultry or 

eggs (day/kg animal product WW) 

SoilCh-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

PlantCh-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, the incidental ingestion of the chemical in soils by livestock during grazing or 

consumption of feed placed on the ground (SoilCh-Intake(m)) is estimated using empirical soil 

ingestion rates (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., the 

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 chemical is deposited. MIRC 

allows the user to enter a surface soil concentration for areas where livestock forage, CsS-livestock, 

that is distinct from the surface soil concentration input for areas where produce may be grown 

and where humans might incidentally ingest soils (see Section 6.1 of this attachment). 

Equation B-11. Incidental Ingestion of Chemical in Soil by Livestock

( ) Cpoultry (m) = Ba(m) × SoilCh−Intake(m) + PlantCh−Intake(i,m)

SoilCh−Intake(m) = Qs ×Css−livestock ×Bs
TRIM-Based Tiered Screening Methodology for RTR

Attachment B B-18 October 2014

Description of MIRC

where:

SoilCh-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)

Css-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 calculated 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 chemical intake for poultry is based on grain consumption only. The intake of 

chemical with each feed type, i, PlantCh-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) = Ingestion 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

Cfeed(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 aboveground 

produce in Equation B-1, with 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:

Cfeed(i) = Concentration of chemical in plant feed type i on a dry-weight (DW) basis (mg 

chemical/kg plant feed DW), where i = forage, silage, or grain

PlantCh−Intake(i,m) = F(i,m) ×Qp(i,m) ×Cfeed(i )

Cfeed(i) = Prfeed(i) + Pd(i) + Pv(i)
TRIM-Based Tiered Screening Methodology for RTR

Attachment B B-19 October 2014

Description of MIRC

Prfeed(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) =

Concentration 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-plant 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 area 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 kernels, soy beans) 

are protected from the air (i.e., by husks, pods).

Equation B-14. Chemical Concentration in Livestock Feed Due to Root Uptake

where:

Prfeed(i) =

Concentration 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 

Csroot-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

Brfeed(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 used 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, respectively). 

There are no conversions of DW feed to WW feed, because all feed ingestion rates for livestock 

are based on DW feed.

3.2. Chemical Intake Calculations for Adults and Non-Infant Children

MIRC calculates human chemical intake rates from the ingestion of home-grown foods as 

average daily doses (ADDs) normalized to body weight for each age group, 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 feed(i ) Csroot zone _ feed(i ) Brfeed(i ) r = − ×

































×

× × × = 365 days

(y)

(y) (y)

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

(y,i)

EF

BW AT

C IR FC ED

ADD
TRIM-Based Tiered Screening Methodology for RTR

Attachment B B-20 October 2014

Description of MIRC

where: 

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 home 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 (CSFs) 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 related to the dose absorbed by the 

mother as discussed 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 

group 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 weights 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 (ATy) is equal to the exposure duration for that group 

(EDy); therefore these 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 Methodology 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 first six exposure pathways 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:

ADDSoil(y) = Average daily chemical intake from incidental ingestion of soil or ingestion by child in 

age group y (mg chemical/kg body weight-day)

CSoil = Concentration of chemical in soil from contaminated area on a dry-weight (DW) basis 

(μg/g soil DW)

IRSoil(y) = Soil ingestion rate for age group y (g DW/day)

FCSoil = 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 than 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: trophic 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.









































× × ×

= 365 days

EF

BW

μg

mg C IR FC 0.001 

ADD

(y)

Soil Soil(y) Soil

Soil(y)
TRIM-Based Tiered Screening Methodology 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

CFish = (CFishTL3 × FTL3) + (CFishTL4 × FTL4)

where:

ADDFish(y) = Average daily chemical intake from ingestion of local fish for age group y (mg/kgday)

L1Fish* = Weight of fish brought into home that is discarded during preparation (e.g., head, 

bones, liver, other viscera, belly fat, skin with fat) (unitless)

L2Fish* = Loss of weight during cooking, such as evaporation and loss of fluids into pan 

(unitless)

CFishTL3 = Chemical concentration in whole fish for trophic level 3.5 (TL3) fish on a wetweight (WW) basis (mg/kg WW)

CFishTL4 = Chemical concentration in whole fish for trophic level 4 (TL4) fish on a wet-weight 

(WW) basis (mg/kg WW)

FTL3 = Fraction of fish intake that is from TL3 (unitless)

FTL4 = Fraction of fish intake that is from TL4 (unitless)

CFish = Consumption-weighted mean chemical concentration in total fish (i.e., as 

specified by Equation B-18) (mg/kg WW)

FCFish = Fraction of local fish consumed derived from contaminated area (unitless)

BW(y) = Body weight for age y (kg)

IRFish(y)* = Local fish ingestion rate for 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 options 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 fish (L1Fish and L2Fish in 

Equation B-17). 

( ) ( ) 365 days

g

kg 0 001

1 1 1 2 















×

















× × ×

= − × − ×

EF

BW

C IR . FC

ADD L L

(y)

Fish Fish(y) Fish

Fish(y) Fish Fish
TRIM-Based Tiered Screening Methodology 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 (Equation 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:

ADDExpFruit(y)

ADDProFruit(y)

= Average daily chemical intake from ingestion of exposed fruit or protected fruit 

(depending on subscript) (mg chemical/kg body weight-day)

L1ExpFruit =

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)

L1ProFruit = Mean reduction in fruit weight that results from paring or other preparation 

techniques for protected fruits (unitless)

L2ExpFruit = Mean reduction in fruit weight that results from draining liquids from cooked 

forms of the fruit (unitless)

CExpFruit

CProFruit

=

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 scenario (≤ 365 days)

FCExpFruit

FCProFruit

= Fraction of exposed fruits or protected fruits (depending on subscript) obtained 

from contaminated area (unitless)

IRExpFruit(y)

IRProFruit(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 (L1ExpFruit and L1ProFruit; e.g., coring 

apples and pears; peeling apples; pitting cherries). Cooking of exposed fruit (e.g., berries, 

apples, peaches) often results in further weight loss that results from liquids lost during cooking 

and drained from the cooking vessel (L2ExpFruit). 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 separate 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 

( ) ( ) 















×













 = − × − × × × ×

g 365 days

kg 1 1 1 2 0 001 EF ADDExpFruit(y) L ExpFruit L ExpFruit CExpFruit IRExpFruit(y) . FCExpFruit

( ) 















×













 = − × × × ×

g 365 days

kg 1 1 0 001 EF ADDProFruit(y) L ProFruit CProFruit IRProFruit(y) . FCProFruit
TRIM-Based Tiered Screening Methodology for RTR

Attachment B B-24 October 2014

Description of MIRC

such as corn, cabbage, 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:

ADDExpVeg(y)

ADDProVeg(y)

ADDRootVeg(y)

=

Average chemical intake from ingestion of exposed vegetables, protected 

vegetables, or root vegetables (depending on subscript) for age group y (mg 

chemical/kg body weight-day)

L1ExpVeg = Mean net preparation and cooking weight loss for exposed vegetables (unitless); 

includes removing stalks, paring skins, discarding damaged leaves

L1ProVeg = Mean net cooking weight loss for protected vegetables (unitless); includes 

removing husks, discarding pods of beans and peas, removal of outer leaves

L1RootVeg = Mean net cooking weight loss for root vegetables (unitless); includes losses from 

removal of tops and paring skins

L2RootVeg = Mean net post cooking weight loss for root vegetables from draining cooking 

liquids and removal of skin after cooking (unitless)

CExpVeg

CProVeg

CRootVeg

=

Chemical concentration in exposed vegetables, protected 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)

FCExpVeg

FCProVeg

FCRootVeg

= Fraction of exposed vegetables, protected vegetables, or root vegetables 

(depending on subscript) obtained from contaminated area (unitless)

IRExpVeg(y)

IRProVeg(y)

IRRootVeg(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)

( ) 















×













 = − × × × ×

g 365 days

kg 1 1 0 001 EF ADDExpVeg(y) L ExpVeg CExpVeg IRExpVeg(y) . FCExpVeg

( ) 















×













 = − × × × ×

g 365 days

kg 1 1 0 001 EF ADDProVeg(y) L ProVeg CProVeg IRProVeg(y) . FCProVeg

( ) ( ) 















×













 = − × − × × × ×

g 365 days

kg 1 1 1 2 0 001 EF ADDRootVeg(y) L RootVeg L RootVeg CRootVeg IRRootVeg(y) . FCRootVeg
TRIM-Based Tiered Screening Methodology 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 in Equation B-24 through Equation 2-28 for homegrown beef, dairy (milk), 

pork, poultry, and eggs, respectively.

Equation B-24. Chemical Intake from Ingestion of Beef

where:

ADDBeef(y) = Average daily chemical intake from ingestion of beef for age group y (mg/kgday)

L1Beef = Mean net cooking loss for beef (unitless)

L2Beef = Mean net post cooking loss for beef (unitless)

CBeef = Concentration of contaminant in beef (mg/kg WW))

EF = Exposure frequency; number of days per year of exposure for family(ies) as 

specified for scenario (≤ 365 days)

IRBeef(y) = Ingestion rate of contaminated beef for age group y (g WW/kg-day)

FCBeef = Fraction of beef consumed raised on contaminated area or fed contaminated 

silage and grains (unitless)

Equation B-25. Chemical Intake from Dairy Ingestion

where:

ADDDairy(y) = Average daily chemical intake from ingestion of total dairy for age group y

(mg/kg-day)

CDairy = 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 days)

IRDairy(y) = Ingestion rate of contaminated total dairy for age group y (g WW/kg-day)

FCDairy = Fraction of total dairy products from contaminated area (unitless)

Equation B-26. Chemical Intake from Pork Ingestion

( ) ( ) 















×













 = − × − × × × ×

g 365 days

kg 1 1 1 2 0 001 EF ADDBeef(y) L Beef L Beef CBeef IRBeef(y) . FCBeef

















×













 = × × ×

g 365 days

kg 0 001 EF ADDDairy(y) CDairy IRDairy(y) . FCDairy

( ) ( ) 















×













 = − × − × × × ×

g 365 days

kg 1 1 1 2 0 001 EF ADDPork(y) L Pork L Pork CPork IRPork(y) . FCPork
TRIM-Based Tiered Screening Methodology for RTR

Attachment B B-26 October 2014

Description of MIRC

where:

ADDPork(y) = Average daily chemical intake from ingestion of pork for age group y (mg/kgday)

L1Pork = Mean net cooking loss for pork (unitless); includes dripping and volatile losses 

during cooking; averaged over various cuts and preparation methods

L2Pork =

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

CPork = Concentration 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)

IRPork(y) = Ingestion rate of contaminated pork for age y (g WW/kg-day)

FCPork = Fraction of pork obtained 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 exposure for family(ies) as 

specified for scenario (≤365 days)

IRPoultry(y) = Ingestion rate of poultry for age group y (g WW/kg-day)

FCPoultry = Fraction of poultry from contaminated area or fed contaminated grains 

(unitless)

The reduction in the weight 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:

ADDEgg(y) = Average daily chemical intake from ingestion of eggs for age group y (mg/kgday)

( ) ( ) 















×













 = − × − × × × ×

g 365 days

kg 1 1 1 2 0 001 EF ADDPoultry(y) L Poultry L Poultry CPoultry IRPoultry(y) . FCPoultry

















×













 = × × ×

g 365 days

kg 0 001 EF ADDEgg(y) CEgg IREgg(y) . FCEgg
TRIM-Based Tiered Screening Methodology for RTR

Attachment B B-27 October 2014

Description of MIRC

CEgg = 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)

IREgg(y) = Ingestion rate of contaminated eggs for age group y (g WW/kg-day)

FCEgg = Fraction of eggs obtained from contaminated area (unitless)

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 concentration 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). 

Equation B-29. Chemical Intake from Drinking Water Ingestion

where:

ADDDW(y) = Average daily chemical intake from ingestion of drinking water from local 

residential water source for age group y (mg/kg-day)

CDW = Concentration of contaminant in drinking water (g/L)

IRDW(y) = Drinking water ingestion rate for age group y (mL/day)

FCDW = 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 

individual in each age group is expected to contact (e.g., soil, local fish, five types of homegrown 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 exposure 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.

















×













 × × = 365 days

EF

BW

C IR FC

ADD

(y)

DW DW(y) DW

DW(y)

ADD(<1) = ADDbreastmilk

∑ = − = −

n

i

ADD ADD i 1 (1 2) (1 2, )

∑ = − = −

n

i

ADD ADD i 1 (3 5) (3 5, )
TRIM-Based Tiered Screening Methodology 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) = Total 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 average 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) = Total 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, assumed to be 70 years. 

3.4. Chemical Intake Calculations for Nursing Infants

The scientific literature indicates that infants can be exposed to some chemicals via their 

mothers’ breast milk. The magnitude of the exposure can be estimated from information on the 

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 chemical. To add this exposure pathway to the MIRC 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. 

Note 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

ADD ADD i 1 (6 11) (6 11, )

∑ = − = −

n

i

ADD ADD i 1 (12 19) (12 19, )

∑ = = n

i

ADD adult ADD adult i 1 ( ) ( , )





 



  +



 



  +



 



  +



 



  +



 



  +



 



 = < 70

50

70

8

70

6

70

3

70

2

70

1 LADD ADD( 1) ADD(1-2) ADD(3-5) ADD(6-11) ADD(12-19) ADD(adult)
TRIM-Based Tiered Screening Methodology 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 (DAIinf) is estimated 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 (IRmilk), the absorption efficiency of the 

chemical by the oral route of exposure (AEinf), the bodyweight of the infant (BWinf), 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 (1987) and includes variables for both 

the concentration of the chemical in the breast milk fat (Cmilkfat) and the concentration of the 

chemical in the aqueous phase of breast milk (Caqueous). The remainder of the DAIinf-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 Cmilkfat or Caqueous 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 available 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

where:

DAIinf = Average daily dose of chemical absorbed by infant (mg chemical/kg body 

weight-day)

Cmilkfat = Concentration of chemical in lipid phase of maternal milk (mg chemical/kg milk 

lipid; calculated using Equation B-38)

fmbm = Fraction of fat in breast milk (unitless) 

Caqueous = Concentration of chemical in aqueous phase of maternal milk (mg chemical/kg 

aqueous phase milk; calculated using Equation B-42)

IRmilk = Infant milk ingestion rate over the duration of nursing (kg milk/day) 

AEinf =

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) 

BWinf = Body weight of infant averaged over the duration of nursing (kg) 

AT = Averaging time 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 nonlipid phases of milk. The remaining equations, however, assume that a chemical of concern will 

partition to the lipid or aqueous phase of breast milk, but not to both. Different models are used 

to estimate Cmilkfat (described in Section 3.4.2 of this attachment) and Caqueous (described in 

Section 3.4.3 of this attachment).

[( ) ( ( ))]

BW AT

C f C f IR AE ED

DAI

inf

milkfat mbm aqueous mbm milk inf

inf

×

× + × − × × × = 1
TRIM-Based Tiered Screening Methodology for RTR

Attachment B B-30 October 2014

Description of MIRC

3.4.2. Chemical 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 three firstorder 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) approach 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 maternal 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 breast 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 of breast feeding and 

then decrease over time (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 breast 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 assumption 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. Chemical Concentration in Breast Milk Fat

where:

Cmilkfat = Concentration of chemical in lipid phase of maternal milk (mg chemical/kg lipid)

DAImat = Daily absorbed maternal chemical dose (mg chemical/kg maternal body weightday; calculated using Equation B-39)

ff =

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)

kelim =

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)

ffm = Fraction of maternal body weight that is fat stores (unitless)

kfat_elac =

Chemical-specific rate constant for total elimination of chemical in the lipid 

phase of milk during nursing (per day; value from literature or calculated using 

Equation B-41)

tbf = Duration of breast feeding (days) 

tpn = Duration of mother’s exposure prior to parturition and initiation of breast feeding 

(days) 

( )















 − 













 − − ×

× +

×

× = − elim pn − fat elac bf k t

fat elac

k t elim

fat elac fat elac bf

elim

elim fm

mat f

milkfat e

k

k

e

k k t

k

k f

DAI f C _ 1 1 1

_ _ _
TRIM-Based Tiered Screening Methodology for RTR

Attachment B B-31 October 2014

Description of MIRC

Equation B-38 relies on the daily maternal absorbed intake (DAImat) to determine the 

concentration of the chemical in the breast milk fat. DAImat is multiplied by the fraction of the 

chemical that is stored in maternal fat (ff) to determine the amount (i.e., mass) of chemical in the 

fat. This product, divided by the chemical-specific elimination rate constant (kelim) for nonlactating adult women and the fraction of the mother’s weight that is fat (ffm), 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 (DAImat ff / kelim ffm) 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 duration, 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 chemical 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, kelim and the elimination constant for a lipophilic chemical in 

lactating women via the lipid phase of breast milk (kfat_elac), the duration of the mother’s 

chemical exposure prior to nursing (tpn), and the duration of breast feeding (tbf). The whole body 

concentration (DAImat ff / kelim ffm), the maximum theoretical 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 Appendix B of MPE (EPA 1998).

To estimate an average daily dose absorbed by an infant’s mother, or DAImat, 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 (AEmat) or fraction of the chemical absorbed by the oral route of exposure, 

as shown in Equation B-39. The value for AEmat 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 AEmat or to use the higher AEmat

of the food and water AEmat values if available; hence, a single AEmat parameter is included in 

Equation B-39.

Equation B-39. Daily Maternal Absorbed Intake

where:

DAImat = 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 attachment, Equation B-35)

AEmat =

Absorption efficiency of the chemical by the oral exposure route (i.e., chemicalspecific fraction of ingested chemical that is absorbed) by the mother (unitless) 

(value from literature or EPA default – see Section 6.4 of this attachment) 

DAImat = ADD(adult) × AEmat
TRIM-Based Tiered Screening Methodology 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 which 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 intakes 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 chemical 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 convert 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:

kelim = Chemical-specific elimination rate constant for elimination of the chemical for 

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 lactating 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. Where 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, kfat_elac (EPA 1998). 

Equation B-41. Biological Elimination Constant for Lipophilic Chemicals

for Lactating Women

where:

h

kelim

ln2 =

fm mat

milk f mbm

fat elac elim f BW

IR f f k k

×

× × _ = +
TRIM-Based Tiered Screening Methodology for RTR

Attachment B B-33 October 2014

Description of MIRC

kfat_elac =

Rate constant for total elimination of chemical during nursing (per day); accounts 

for both elimination by adults in general and the additional chemical elimination 

via the lipid phase of milk in nursing women

kelim =

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 literature or calculated from half-life using Equation B-40)

IRmilk = Infant milk ingestion rate over the duration of nursing (kg/d)

ff =

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)

fmbm = Fraction of fat in breast milk (unitless) 

ffm = Fraction of maternal body weight that is fat stores (unitless)

BWmat = 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 Kfat_elac is 

estimated by adding an estimate of the first-order elimination constant for breast feeding losses 

to kelim, which is the chemical-specific total elimination rate constant for non-lactating women. 

The breast feeding losses are estimated from the infant’s intake rate of breast milk (IRmilk), the 

fraction of the total maternal body burden of the chemical that is stored in maternal body fat (ff), 

the fraction of the mother’s breast milk that consists of fat (lipids) (fmbm), the mother’s body 

weight (BWmat), and the fraction of the mother’s weight that is body fat (ffm). In Equation B-41, 

the value for the mother’s body weight 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 (Caqueous). EPA adapted Smith’s (1987) 

steady state concentration model for estimating Cmilkfat and developed the Caqueous model shown 

in Equation B-42 (EPA 1998). Chemicals 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 Caqueous

equation 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

aq elac pm

mat pl bm

aqueous k f

DAI f Pc

C

×

× × =

_
TRIM-Based Tiered Screening Methodology for RTR

Attachment B B-34 October 2014

Description of MIRC

where:

Caqueous = Concentration of chemical in aqueous phase of maternal milk (mg/kg)

DAImat = Daily absorbed maternal chemical dose (mg/kg-day; calculated by Equation B-39)

fpl =

Fraction of chemical in the body (based on absorbed intake) that is in the blood 

plasma compartment (unitless; value from literature or calculated by Equation 

B-43)

Pcbm = Partition coefficient for chemical between the plasma and breast milk in the 

aqueous phase (unitless); assumed to equal 1.0

kaq_elac =

Chemical-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)

fpm = Fraction of maternal weight that is blood plasma (unitless)

Equation B-42 is a steady-state concentration model that, like the Equation B-38 for Cmilkfat, is 

dependent on the maternal absorbed daily intake (DAImat). In Equation B-42, DAImat is multiplied 

by the fraction of the absorbed chemical that is circulating in the blood plasma compartment (fpl) 

and a partitioning coefficient for the chemical between plasma and the aqueous phase of breast 

milk (Pcbm). For highly water-soluble 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 Pcbm would equal 1.0. The denominator includes the 

biological elimination constant for the chemical in the aqueous phase of breast milk in lactating 

women (kaq_elac) and the fraction of the mother’s weight that is plasma (fpl). 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 blood 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 (fpl) is required. Ideally, an empirical value for fpl should be used. If 

empirical values are not available, fpl 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 (fbl; EPA 1998).

Equation B-43. Fraction of Total Chemical in Body in the Blood Plasma Compartment

where:

fpl = Fraction of chemical in body (based on absorbed intake) that is in the blood 

plasma compartment (unitless); chemical-specific

( ) bp RBC bp

bl bp

pl f Pc f

f f

f

+ −

× = 1
TRIM-Based Tiered Screening Methodology for RTR

Attachment B B-35 October 2014

Description of MIRC

fbl = Fraction of chemical in body (based on absorbed intake) in the whole blood 

compartment (unitless); chemical-specific

fbp = Fraction of whole blood that is plasma (unitless)

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 (fbl) is 

known for a given chemical, then the fraction 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 

(PcRBC) and the fraction of whole blood that is plasma (fbp).

Another parameter for which a value is needed to solve Equation B-42 is the total chemical 

elimination rate for lactating women for hydrophilic chemicals, kaq_elac. As for kfat_elac for lipophilic 

chemicals, kaq_elac for hydrophilic chemicals would be equal to kelim 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 basic 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 chemicals 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 

simply kelim, the elimination of the chemical from a non-lactating woman, as shown in Equation 

B-40. The extent to which kelim is an underestimate of kaq_elac for a given chemical will determine 

the extent of health protective bias in kaq_elac. 

Equation B-44. Biological Elimination Rate Constant for Hydrophilic Chemicals

where:

kaq_elac = Chemical-specific rate constant for total elimination of chemical by lactating 

women for hydrophilic chemicals (per day)

kelim =

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-life 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 steady-state fraction of total hydrophilic 

chemical body burden in the mother that is in the blood plasma (fpl, see Exhibit B-29). This 

parameter could be estimated using Equation B-43 if a suitable chemical-specific fraction of 

chemical in the body that is in the whole blood (fbl) could be found. However, the value found for 

fbl 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 (PBTK) models of lactational transfer of methylmercury (MeHg) in humans. Most 

PBTK models that we identified focused on gestational transfer of mercury between mother and 

kaq _ elac = kelim
TRIM-Based Tiered Screening Methodology 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 lactational 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. 1976) and from 34 mother-nursinginfant 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 

coefficients 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 MeHg 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 maternal 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 DAIinf 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: 

DAIinf_MeHg = Average daily dose of MeHg absorbed by infant from breast milk (mg/kg-day)

DAImat_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 include carcinogenic potency slope factors

for ingestion and non-cancer oral reference doses (RfDs) for chronic exposures. The doseresponse 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 (IRIS); 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-HAP 

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).

DAIinf_MeHg = DAImat_MeHg
TRIM-Based Tiered Screening Methodology 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 Chemicalsa

Chemical CAS No.

Cancer Slope Factor Reference Dose

Value 

(mg/kg-day)

-1 Source

Value 

(mg/kgday)Source

Inorganics

Cadmium compounds in foodb 7440439 not available 1.0E-03 IRIS

Mercury (elemental) 7439976 not available not available

Mercuric chloride 7487947 not available 3.0E-04 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 TEFsc not available

1,2,3,4,6,7,8-

Heptachlorodibenzofuran 67562394 1.50E+03 Derived from WHO 2005 TEFsc not available

1,2,3,4,7,8,9-

Heptachlorodibenzofuran 55673897 1.50E+03 Derived from WHO 2005 TEFsc not available

1,2,3,4,7,8-

Hexachlorodibenzo-p-dioxin 39227286 1.50E+04 Derived from WHO 2005 TEFsc not available

1,2,3,4,7,8-

Hexachlorodibenzofuran

70648269 1.50E+04 Derived from WHO 

2005 TEFsc not available

1,2,3,6,7,8-

Hexachlorodibenzo-p-dioxin 57653857 1.50E+04 Derived from WHO 2005 TEFsc not available

1,2,3,6,7,8-

Hexachlorodibenzofuran 57117449 1.50E+04 Derived from WHO 2005 TEFsc not available

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 TEFsc not available

2,3,4,6,7,8-

Hexachlorodibenzofuran 60851345 1.50E+04 Derived from WHO 2005 TEFsc not available

1,2,3,4,6,7,8,9-

Octachlorodibenzo-p-dioxin 3268879 4.50E+01 Derived from WHO 2005 TEFsc not available

1,2,3,4,6,7,8,9-

Octachlorodibenzofuran 39001020 4.50E+01 Derived from WHO 2005 TEFsc not available

1,2,3,7,8-

Pentachlorodibenzo-p-dioxin 40321764 1.50E+05 Derived from WHO 2005 TEFsc not available

1,2,3,7,8-

Pentachlorodibenzofuran 57117416 4.50E+03 Derived from WHO 2005 TEFsc not available

2,3,4,7,8-

Pentachlorodibenzofuran 57117314 4.50E+04 Derived from WHO 2005 TEFsc not available

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 TEFsc not available
TRIM-Based Tiered Screening Methodology 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 Chemicalsa

Chemical CAS No.

Cancer Slope Factor Reference Dose

Value 

(mg/kg-day)

-1 Source

Value 

(mg/kgday)Source

Polycyclic Organic Matter

1-Methylnaphthalene 90120 5.0E-01 EPA 1999, POM 

Group 72002d 7.0E-02 ATSDR

2-Acetylaminofluorene 53963 1.0E+01 EPA 1999, POM 

Group 75002d not available

2-Chloronaphthalene 91587 5.0E-01 EPA 1999, POM 

Group 72002d 8.0E-02 IRIS

2-Methylnaphthalene 91576 5.0E-01 EPA 1999, POM 

Group 72002d 5.0E-02 ATSDR

3-Methylcholanthrene 56495 2.2E+1 CalEPA not available

7,12-Dimethylbenz(a)anthracene 57976 2.5E+02 CalEPA not available

Acenaphthene 83329 5.0E-01 EPA 1999, POM 

Group 72002d 6.0E-02 IRIS

Acenaphthylene 208968 5.0E-01 EPA 1999, POM 

Group 72002d not available

Anthracene 120127 0e IRIS 3.0E-01 IRIS

Benz(a)anthracene 56553 1.2E+00 CalEPA not available

Benzo(a)pyrene 50328 7.3E+00 IRIS not available

Benzo(b)fluoranthene 205992 1.2E+00 CalEPA not available

Benzo(e)pyrene 192972 5.0E-01 EPA 1999, POM 

Group 72002d not available

Benzo(g,h,i)perylene 191242 5.0E-01 EPA 1999, POM 

Group 72002d 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

Dibenzo(a,h)anthracene 53703 4.1E+00 CalEPA not available

Dibenzo(a,i)pyrene 189559 1.2E+02 CalEPA not available

Dibenzo(a,j)acridine 224420 1.2E+00 CalEPA not available

Fluoranthene 206440 5.0E-01 EPA 1999, POM 

Group 72002d 4.0E-02 IRIS

Fluorene 86737 5.0E-01 EPA 1999, POM 

Group 72002d 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 71002d not available

Perylene 198550 5.0E-01 EPA 1999, POM 

Group 72002d not available

Phenanthrene 85018 0e IRIS not available

Polycyclic Organic Matter 246 5.0E-01 EPA 1999, POM 

Group 71002d not available
TRIM-Based Tiered Screening Methodology 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 Chemicalsa

Chemical CAS No.

Cancer Slope Factor Reference Dose

Value 

(mg/kg-day)

-1 Source

Value 

(mg/kgday)Source

Pyrene 129000 0e IRIS 3.0E-02 IRIS

Retene 483658 5.0E-01 EPA 1999, POM 

Group 72002d not available

ATSDR = Agency for Toxic Substances and Disease Registry, 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 equivalency 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 available 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 available 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://www.epa.gov/ttn/atw/nata1999/99pdfs/pomapproachjan.pdf and is summarized in Section 4.4 of this attachment. e

Weight of evidence evaluations indicated that the available data were adequate to determine that this chemical was not carcinogenic 

(EPA 2010a),

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). Users 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 first 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 during 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 comparison 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 concentrations 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 Methodology 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 Factora

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-dioxinb 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 39001020 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

Source: 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, approximately 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 for 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 methodology 

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 associated with the individual 

POM species and POM groups reported in the National Emissions Inventory (NEI). Individual 

POMs 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 member 
TRIM-Based Tiered Screening Methodology 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) Groupsa

Individual POM or POM Group CAS No. Cancer Slope Factorb

(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)pyrene 192972

Benzo(c)phenanthrene 195197

Perylene 198550

Benzo(g,h,i)fluoranthene 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 91587

POM Group 73002

7,12-Dimethylbenz(a)anthracene 57976 1000

POM Group 74002

Dibenzo(a,i)pyrene 189559

100 Dibenzo(a,h)pyrene 189640
TRIM-Based Tiered Screening Methodology for RTR

Attachment B B-42 October 2014

Description of MIRC

Exhibit B-8. Oral Dose-response Values for

Polycyclic Organic Matter (POM) Groupsa

Individual POM or POM Group CAS No. Cancer Slope Factorb

(mg/kg-day)-1

POM Group 75002

3-Methylcholanthrene 56495

10

Dibenzo(a,e)pyrene 192654

5-Methylchrysene 3697243

Benzo(a)pyrene 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)anthracene 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 factors 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 characterization 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 excess lifetime cancer risk (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 with 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 Methodology for RTR

Attachment B B-43 October 2014

Description of MIRC

Equation B-46. Calculation of Excess Lifetime Cancer Risk

ELCR = LADD × CSF

where: 

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/kgday)

CSF = Oral carcinogenic potency slope factor for chemical (per mg/kg-day)

As described in Section 3.3 of this attachment, the LADD (in mg/kg-day) for a chemical is 

calculated to reflect age-related differences in exposure rates that are experienced by a 

hypothetical individual throughout his or her lifetime of exposure. The total chemical intake is 

normalized to a lifetime, which for the purposes of this assessment is assumed to be 70 years. 

EPA considers 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 CSFs. 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

2005b,c). For carcinogens with a mutagenic MOA for cancer, EPA concluded 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 2005c). These conclusions are represented by agedependent 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 

rates, 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 cancer risk for each of the three lifestages separately and then adding the risks

for i = 1 to 6 age groups. 

( ) ( )

3

6 5

2

10 1 yr 3 1 yr

10

3 5

1 2

=

= × + × =

< =

( - )

( - )

( 1)

ADAF

ADAF .

ADAF

( ) ( )

1

2

8

3 4 yrs 1 4 yrs

3

12 19

6 11

=

= × + × =

=

(adult)

( - )

( - )

ADAF

ADAF

ADAF
TRIM-Based Tiered Screening Methodology for RTR

Attachment B B-44 October 2014

Description of MIRC

Equations B-47 to B-53. Lifetime Cancer Risk: 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)

Equation 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) = Risk from chemical ingestion in first year of life

Risk(1-2) = Risk from chemical ingestion from first birthday through age 2 years

Risk(3-5) = Risk 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) = Average 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) = Risk from chemical ingestion for the i

th age group 

ELCR = Total 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 that there is no appreciable risk that non-cancer health 

effects will occur. If 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

Risk i 1 ( )
TRIM-Based Tiered Screening Methodology 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 the potential for adverse health effects associated with chronic 

exposure via ingestion pathways. The 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 

threshold 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 lifestage for 

exposure may need to be estimated from the information provided in the critical developmental 

study(ies) from which the RfD was derived (e.g., in consultation with the RfD documentation in 

EPA’s IRIS or in a toxicological profile developed for the chemical). For 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 evaluated 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 RfDs

When 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 assumptions 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 highest HQs to develop 

an appropriate approach to assessing their potential joint action. 

RfD

ADD HQ =
TRIM-Based Tiered Screening Methodology for RTR

Attachment B B-46 October 2014

Description of MIRC

Equation B-55. Hazard Index Calculation

HI = HQ1 + HQ2 ... HQn

where:

HI = Hazard index (unitless)

HQ1 = Hazard Quotient for chemical 1 (unitless)

HQ2 = Hazard Quotient for chemical 2 (unitless)

HQn = 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 different 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 chemical 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 Supplementary 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 to 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-foodchain (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 values 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 currently 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 other 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 Methodology 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 dioxin 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 single air concentration (in g/m3);

• 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/m2

-yr);

• two fish tissue concentrations, one each for forage and game 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., groundwater 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, the drinking water exposure pathway is 

not modeled for the scenario developed 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 concentrations 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® ExcelTM

routine written in Visual Basic. 

6.2. Farm-Food-Chain Parameter Values 

Using the chemical information specified in Section 6.1 above as inputs, MIRC calculates

chemical concentrations in foods that are commonly grown or raised on family farms: exposed 

and protected fruits; 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. Parameter values

required for these HHRAP algorithms, including chemical-specific media transfer factors (e.g., 
TRIM-Based Tiered Screening Methodology for RTR

Attachment B B-48 October 2014

Description of MIRC

soil-to-plant transfer coefficients) 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-recommended parameter values are the 

default values in MIRC; however, these 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

BrAG-produceDW(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)

BvAG(i) Chemical-specific air-to-plant biotransfer factor for aboveground 

produce type i for vapor-phase chemical in air

Unitless ([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

Kds Chemical-specific soil/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) Moisture adjustment factor for aboveground produce type i to 

convert the chemical concentration estimated for dry-weight 

produce to the corresponding chemical concentration for fullweight fresh produce

Percent water

RCF Chemical-specific root concentration factor for tubers and root 

produce on a wet-weight (WW) basis

L soil pore water/ kg root 

WW

Rp(i) Plant-specific interception fraction for the edible portion of 

aboveground exposed produce or animal forage and silage 

Unitless

Tp(i) Length of plant exposure to deposition per harvest of the edible 

portion of aboveground exposed produce or animal forage and 

silage

Year

VGAG(i) Empirical correction factor for aboveground exposed produce 

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

VGrootveg Empirical correction factor for belowground produce (i.e., tuber or 

root vegetable) to account for possible overestimate of the 

diffusive transfer of chemicals from the outside to the inside of 

bulky tubers or roots (based on carrots and potatoes)

Unitless

Yp(i) Plant-specific yield or standing crop biomass of the edible portion 

of produce or animal feed

kg produce DW/m2

Animal Products

Bs Soil bioavailability factor for livestock Unitless

MF Chemical-specific mammalian metabolism factor that accounts 

for endogenous degradation of the chemical

Unitless
TRIM-Based Tiered Screening Methodology 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(beef) Chemical-specific biotransfer factor for chemical in diet of cow to 

chemical in beef on a fresh-wet (FW; equivalent to WW) basis

mg chemical/kg FW 

tissue/mg chemical/day 

or day/kg FW tissue

Ba(dairy) Biotransfer factor in dairy day/kg FW tissue

Ba(pork) Biotransfer factor in pork day/kg FW tissue

Ba(poultry) Biotransfer factor in poultry day/kg FW tissue

Ba(eggs) 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 2005a

DW = dry weight; FW = fresh 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 presents additional non-chemicalspecific 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. Options for other parameter values are not included in MIRC at this time; 

however, the user can overwrite values if appropriate. Refer to HHRAP (EPA 2005a, 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-toPlant 

Biotransfer 

Factor (BvAG(i))

(unitless)d

Inorganics

Cadmium compounds 0.6 NA 7.5E+01 NAe

Mercury (elemental) 0.6 NA 1.0E+03 0f

Mercuric chloride 0.6 NA 5.8E+04 1.8E+03

Methyl mercury 0.6 NA 7.0E+03 0f

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+05
TRIM-Based Tiered Screening Methodology 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-toPlant 

Biotransfer 

Factor (BvAG(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+04
TRIM-Based Tiered Screening Methodology 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-toPlant 

Biotransfer 

Factor (BvAG(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 gammaemitting radionuclides 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 particles 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/tsd/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 chemicalspecific 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 compound between soil pore-water and 

soil particles and strongly influences the release and movement of a compound into the subsurface soils and underlying aquifer. 

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 mercuric 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), then 

reducing this value by a factor of 100, as suggested by Lorber (1995), who concluded 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 (2000). e

It is assumed that metals, with the exception of vapor-phase elemental mercury, do not transfer significantly from air into leaves.

fSpeciation 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 BioConcentration 

Factor 

(BrAG-produce-DW(i))

(unitless)a

Empirical Correction 

Factor- Belowground 

Produce 

(VGrootveg) (unitless)b

Empirical 

Correction FactorAboveground 

Produce 

(VGAG(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. Fruit 1.3E-01 - -

Prot. Veg. 1.3E-01 - -

Root 6.4E-02 1.0E+00 -

Silage 3.6E-01 - 5.0E-01
TRIM-Based Tiered Screening Methodology 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 BioConcentration 

Factor 

(BrAG-produce-DW(i))

(unitless)a

Empirical Correction 

Factor- Belowground 

Produce 

(VGrootveg) (unitless)b

Empirical 

Correction FactorAboveground 

Produce 

(VGAG(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 -

Silage - - 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 mercury

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 2.3E-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-01
TRIM-Based Tiered Screening Methodology 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 BioConcentration 

Factor 

(BrAG-produce-DW(i))

(unitless)a

Empirical Correction 

Factor- Belowground 

Produce 

(VGrootveg) (unitless)b

Empirical 

Correction FactorAboveground 

Produce 

(VGAG(i)) (unitless)c

Acenaphthene

Exp. Fruit 2.1E-01 - 1.0E+00

Exp. Veg. 2.1E-01 - 1.0E+00

Forage 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-01
TRIM-Based Tiered Screening Methodology 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 BioConcentration 

Factor 

(BrAG-produce-DW(i))

(unitless)a

Empirical Correction 

Factor- Belowground 

Produce 

(VGrootveg) (unitless)b

Empirical 

Correction FactorAboveground 

Produce 

(VGAG(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. Fruit 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-01
TRIM-Based Tiered Screening Methodology 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 BioConcentration 

Factor 

(BrAG-produce-DW(i))

(unitless)a

Empirical Correction 

Factor- Belowground 

Produce 

(VGrootveg) (unitless)b

Empirical 

Correction FactorAboveground 

Produce 

(VGAG(i)) (unitless)c

Dibenz(a,h)anthracene

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 1.6E+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

Forage 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-01
TRIM-Based Tiered Screening Methodology 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 BioConcentration 

Factor 

(BrAG-produce-DW(i))

(unitless)a

Empirical Correction 

Factor- Belowground 

Produce 

(VGrootveg) (unitless)b

Empirical 

Correction FactorAboveground 

Produce 

(VGAG(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 - -

Prot. 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-01
TRIM-Based Tiered Screening Methodology 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 BioConcentration 

Factor 

(BrAG-produce-DW(i))

(unitless)a

Empirical Correction 

Factor- Belowground 

Produce 

(VGrootveg) (unitless)b

Empirical 

Correction FactorAboveground 

Produce 

(VGAG(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-01
TRIM-Based Tiered Screening Methodology 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 BioConcentration 

Factor 

(BrAG-produce-DW(i))

(unitless)a

Empirical Correction 

Factor- Belowground 

Produce 

(VGrootveg) (unitless)b

Empirical 

Correction FactorAboveground 

Produce 

(VGAG(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-01
TRIM-Based Tiered Screening Methodology 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 BioConcentration 

Factor 

(BrAG-produce-DW(i))

(unitless)a

Empirical Correction 

Factor- Belowground 

Produce 

(VGrootveg) (unitless)b

Empirical 

Correction FactorAboveground 

Produce 

(VGAG(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-01
TRIM-Based Tiered Screening Methodology 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 BioConcentration 

Factor 

(BrAG-produce-DW(i))

(unitless)a

Empirical Correction 

Factor- Belowground 

Produce 

(VGrootveg) (unitless)b

Empirical 

Correction FactorAboveground 

Produce 

(VGAG(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 BrAG-produce-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 basis 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 hectare 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 2005a), Appendix B, VGrootveg 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 Kow

greater than 4) to the center of the produce is not likely. In addition, typical preparation techniques, 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 2005a) recommends using a VGrootveg value of 0.01 for PB-HAP 

with a log Kow greater than 4 and a value of 1.0 for PB-HAP with a log Kow 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 discussed in HHRAP (EPA 2005a), Appendix B, VGag represents an empirical correction factor that reduces aboveground 

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 

Kow greater than 4) to the center of the produce is not likely. In addition, typical preparation techniques, such as washing, peeling, 

and cooking, further reduces residues. For aboveground produce, HHRAP (EPA 2005a) recommends using a VGag value of 0.01 

for PB-HAP with a log Kow greater than 4 and a value of 1.0 for PB-HAP with a log Kow less than 4 based on information provided in 

EPA 1994b. In developing these values, EPA (1994b) 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 the 

concentration of PB-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 VGag value of 1.0, also based on information provided in EPA 1994b. 

A VGag value for silage is not provided in EPA 1994b; 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 sitespecific silage, this value may under- or overestimate the actual value.
TRIM-Based Tiered Screening Methodology 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

Plant 

Surface 

Loss 

Coefficient 

(kp(i))

(1/year)b

Length of 

Plant 

Exposure to 

Deposition 

(Tp(i))

(year)c

Yield or 

Standing 

Crop 

Biomass 

(Yp(i))

(kg/m2)

d

Plant TissueSpecific 

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 aboveground 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-specific 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 does 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 using 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 constant 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 hay 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 sitespecific value of Tp is more than 60 days. d

Yp values for aboveground produce and forage were calculated using an equation presented in Baes et al. (1984) and Shor et al. 

(1982): Yp = Yhi / Ahi, where Yhi = Harvest yield of i

th crop (kg DW) and Ahi = Area planted to ith crop (m2), and using values for Yh

and Ah 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 Methodology 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 BioAvailability 

Factor (Bs) 

(unitless)

Biotransfer Factors (Bam) (day/kg FW tissue)a

and Metabolism Factors (MF) (unitless)b 

Mammal Non-mammal

Beef 

(Babeef)

Dairy 

(Badairy)

Pork 

(Bapork) MF Eggs (Baeggs)

Poultry 

(Bapoultry) 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

Acenaphthene 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 NA

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 5.8E-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 NA
TRIM-Based Tiered Screening Methodology 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 BioAvailability 

Factor (Bs) 

(unitless)

Biotransfer Factors (Bam) (day/kg FW tissue)a

and Metabolism Factors (MF) (unitless)b 

Mammal Non-mammal

Beef 

(Babeef)

Dairy 

(Badairy)

Pork 

(Bapork) MF Eggs (Baeggs)

Poultry 

(Bapoultry) 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

HexaCDF, 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 otherwise indicated. NA = not applicable. a

As discussed in HHRAP (EPA 2005a), Appendix A, biotransfer factors for mercury compounds were obtained from EPA 1997b. Considering 

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 animal 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 (Kow 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 recommended 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. (2001). 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 Methodology 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 cattleb 0.5

Silage 2.5

Forage 8.8

Grain 0.47

Dairy cattlec 0.4

Silage 4.1

Forage 13.2

Grain 3.0

Swined 0.37 Silage 1.4

Grain 3.3

Chicken (eggs)e 0.022 Grain 0.2

Source: EPA 2005a HHRAP (Chapter 5). a

Beef cattle: NC DEHNR (1997) and EPA (1994b) recommended a soil ingestion 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 (1994b) 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 weight 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 dairy cattle and the assumption of uniform contamination of soil ingested by cattle.

Swine: NC DEHNR (1997) recommended a soil ingestion rate for swine of 0.37, estimated 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 assumption of uniform contamination of the soil ingested by swine.

Chicken: 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 

empirical data to support soil ingestion rates for chicken and the assumption of uniform contamination of soil ingested by chicken. 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 (1994b and 1990), and Boone et al. (1981). The principal uncertainty 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 1992) 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 animals and are assumed not to eat forage (EPA 1998). EPA (1994b 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 NAS (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 include

variability of the proportion of grain and silage in the diet, 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 exposure parameters included in MIRC and their 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), timeweighted average values were estimated for the MIRC age groups from the available data. 
TRIM-Based Tiered Screening Methodology for RTR

Attachment B B-65 October 2014

Description of MIRC

In MIRC, ingestion rates for home-produced farm food items are included for exposed fruit, 

protected fruit, exposed vegetables, protected vegetables, root vegetables, beef, total dairy, 

pork, poultry, and eggs. Those ingestion rates are already normalized to body weight (i.e., gwet 

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, ingestion rates also are included for drinking water (mL/day), soil (mg/day), and fish 

(g/day). These ingestion rates, however, are on a per 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) options included in MIRC include mean, 5th, 10th, 50th, 90th, and 95th

percentile values 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 for 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 5th 10th 50th 90th 95th

Adulta (20-70) 50 80.0a 52.9 56 69.3 89.7 97.6

Child < 1b 1 7.83 6.03 6.38 7.76 9.24 9.66

Child 1-2c 2 12.6 9.9 10.4 12.5 14.9 15.6

Child 3-5d 3 18.6 13.5 14.4 17.8 23.6 26.2

Child 6-11e 6 36.0 22.1 24.0 33.5 51.2 58.6

Child 12-19f 8 64.2 39.5 45 64.2 83.5 89

[Child 12-19g 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 2008 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 sizes 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 estimate was calculated 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 age groups 11 to <16 years and 16 to <21 years from Table 

8-3 of the 2008 CSEFH. Estimated 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 comparison purposes only and are not included in MIRC.
TRIM-Based Tiered Screening Methodology 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 children 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 tap. 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 (2004b), 

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 Exhibit 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 Ingestion Rates for 

Children and Adultsa

Lifestage (years) Ingestion Rates, Community Water (mL/day)

Mean 50th 90th 95th 99th

Child <1b 504 482 969 1113 1440

Child 1-2c 332 255 687 903 1318

Child 3-5d 382 316 778 999 1592

Child 6-11e 532 417 1149 1499 2274

Child 12-19f 698 473 1641 2163 3467

Adultg 1219 981 2534 3087 4567

Sources: EPA 2004, 2008a

*The sample size does not meet minimum reporting requirements as described in EPA 2008a. 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. One 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 2008a). 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 2008 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 represents 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 through age 20, which 

contributes to uncertainty in the estimates for 12 to 19 years.

g

Adult drinking water ingestion rates were obtained from EPA (2004b), Appendix E, Part I, Table A1 for community water, both 

sexes (ages 20+), direct plus indirect water ingestion.

6.3.3. Local Food Ingestion Rates

MIRC includes mean, median, 90th, 95th, and 99th percentile food-specific ingestion rates (IRs) 

for consumers-only of farm food chain (FFC) media for adults and children. The mean and 
TRIM-Based Tiered Screening Methodology for RTR

Attachment B B-67 October 2014

Description of MIRC

percentile values are from EPA’s analysis 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 means 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 

options 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 

second 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). 

The food-specific 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)

Beefa N/A 4.14 4.00 3.77 1.72 1.93

Dairyb N/A 91.6 50.9 27.4 13.6 2.96

Eggsa N/A 2.46 1.42 0.86 0.588 0.606

Exposed Fruita N/A 6.14 2.60 2.52 1.33 1.19

Exposed Vegetablea N/A 3.48 1.74 1.39 1.07 1.38

Porka N/A 2.23 2.15 1.50 1.28 1.10

Poultrya N/A 3.57 3.35 2.14 1.50 1.37

 29Note that EPA’s 2008 CSEFH does not distinguish 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 Methodology 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 Fruita N/A 16.6 12.4 8.50 2.96 5.19

Protected Vegetablea N/A 2.46 1.30 1.10 0.78 0.862

Root Vegetablea 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)

Beefa N/A 2.51 2.49 2.11 1.51 1.55

Dairyb N/A 125 66.0 34.4 15.5 2.58

Eggsa N/A 1.51 0.83 0.561 0.435 0.474

Exposed Fruita N/A 1.82 1.11 0.61 0.62 0.593

Exposed Vegetablea N/A 1.89 1.16 0.64 0.66 0.812

Porka N/A 1.80 1.49 1.04 0.89 0.802

Poultrya N/A 3.01 2.90 1.48 1.30 0.922

Protected Fruita N/A 7.59 5.94 3.63 1.23 2.08

Protected Vegetablea N/A 1.94 1.04 0.79 0.58 0.564

Root Vegetablea N/A 0.46 0.52 0.57 0.56 0.59

Water (mL/day)c N/A 255 316 417 473 981

90th percentile ingestion rates (g/kg-day)d

Beefa N/A 9.49 8.83 11.4 3.53 4.41

Dairyb N/A 185 92.5 57.4 30.9 6.16

Eggsa N/A 4.90 3.06 1.90 1.30 1.31

Exposed Fruita N/A 12.7 5.41 6.98 3.41 2.37

Exposed Vegetablea N/A 10.7 3.47 3.22 2.35 3.09

Porka N/A 4.90 4.83 3.72 3.69 2.23

Poultrya N/A 7.17 6.52 4.51 3.13 2.69

Protected Fruita N/A 44.8 32.0 23.3 7.44 15.1

Protected Vegetablea N/A 3.88 2.51 2.14 1.85 1.81

Root Vegetablea N/A 7.25 4.26 3.83 2.26 2.49

Water (mL/day)c N/A 687 778 1149 1640 2534

95th percentile ingestion rates (g/kg-day)

Beefa N/A 12.9 12.5 12.5 3.57 5.83

Dairyb N/A 167 89.9 56.0 32.3 7.80

Eggsa N/A 5.38 3.62 2.37 1.43 1.59

Exposed Fruita N/A 14.6 6.07 11.7 4.78 3.38

Exposed Vegetablea N/A 11.9 6.29 5.47 3.78 4.46

Porka N/A 6.52 6.12 4.73 6.39 2.60

Poultrya N/A 8.10 7.06 5.07 3.51 3.93

Protected Fruita N/A 48.3 35.1 26.9 11.4 19.2

Protected Vegetablea N/A 9.42 5.10 3.12 2.20 2.83

Root Vegetablea N/A 10.4 4.73 5.59 3.32 3.37
TRIM-Based Tiered Screening Methodology 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

99th percentile ingestion rates (g/kg-day)

Beefa N/A 20.9 19.8 13.3 4.28 6.84

Dairyb N/A 180 87.2 54.8 34.7 9.20

Eggsa N/A 16.2 11.2 8.19 4.77 1.83

Exposed Fruita N/A 25.2 32.5 15.7 5.9 13.0

Exposed Vegetablea N/A 12.1 7.36 13.3 5.67 8.42

Porka N/A 8.71 9.74 6.61 4.29 3.87

Poultrya N/A 9.63 10.24 6.12 4.60 4.93

Protected Fruita N/A 109 71.2 58.2 19.1 34.4

Protected Vegetablea N/A 9.42 5.31 5.40 2.69 5.56

Root Vegetablea 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, 2011a). 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 CSFII. b

Primary source for values was 1987–1988 NFCS survey, compiled results presented in Chapter 13 of 2011 Exposure Factors 

Handbook (EPA, 2011a). 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 Kahn and Stralka (2008) analysis of CSFII data, and from EPA’s 

analysis of NHANES 2003–2006 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 assessments 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). The HHRAP 

methodology, Section 6.2.2.2, recommends a method by which to calculate the “missing” agespecific consumer-only ingestion rates, as explained below. Food-specific intake rates (IRs) for 

those child age groups and food items not included in Chapter 13 of the 2011 EFH, that is 

IRage_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 Methodology for RTR

Attachment B B-70 October 2014

Description of MIRC

The ratio of IRPC, age_group_x to IRPC_total 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 IRCO, age_group_x to IRCO_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 same. 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:

IRCO, 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

IRCO_total = Mean or percentile-specific consumer-only intake of the farm food item, as 

brought into the home, for the total NFCS survey population 

IRPC, age_group_x = Mean or percentile-specific per capita intake of the food type from all sources, 

as consumed, for the specific child age group X from the CSFII 

IRPC_total = Mean or percentile-specific per capita intake of the farm food item for the total

CSFII survey population

In this discussion, per capita (as opposed to consumer-only) indicates the intake rates are 

based on 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 rate 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 the 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 90th

percentile ingestion estimates, which are likely more reliable than the 95th or 99th percentile 

estimates in this particular calculation.

6.3.4. Local Fish Ingestion Rates

6.3.4.1. Screening 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

CO_total PC, age_group_x

CO, age_group_x

× =
TRIM-Based Tiered Screening Methodology for RTR

Attachment B B-71 October 2014

Description of MIRC

Various Home Produced Food Items) (EPA 2011a), includes family-caught 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 years 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). Finally, 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. Therefore, 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 99th percentile of fish ingestion rates for 

woman fishers as reported by Burger (2002). This rate is based upon ingestion 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 species 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 fishers 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.

Because 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 ingestion 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 99th

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 Continuing 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 represents consumption of 

finfish 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 estimate ingestion rates for this age group, 
TRIM-Based Tiered Screening Methodology 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. Exhibit B-18 provides the 

fish ingestion rates used in the screening assessment. 

Exhibit B-18. Fish Ingestion Rates 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 107.7a 159.0b 268.2c 331.0c 373d

a

A fish ingestion rate for ages 1-2 years was not available. The 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]), 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 rates based on ingestion rates from EPA

(2002), Section 4.2.1.1 Table 5 (freshwater/estuarine habitat). d

This value represents the 99th 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, commercial 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 fish 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 children, MIRC also includes values for the mean and the 90th, 95th, and 99th percentile 

fish per-capita ingestion rates (freshwater and estuarine fish only) based on EPA’s analysis of 

1994-96 and 1998 CSFII data (EPA 2002, 2008a). Those rates include individuals who eat fish 

and those who do not eat fish. As shown in EPA’s 2008 CSEFH, Table 10-7, the 90th percentile 

per capita ingestion rates estimated from the two-day CSFII recall period are zero for some child 

age groups. Although not presented in CSEFH Table 10-7, median ingestion rates 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 rates that are zero result 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 daily ingestion of other types of food products (e.g., dairy, produce, 

meat). Use of zero for fish ingestion rates, however, is not useful in MIRC. As a result, an 

alternative method was used to estimate fish ingestion rates for children and adults that could 

provide reasonable, non-zero values for all age groups and percentiles. 
TRIM-Based Tiered Screening Methodology for RTR

Attachment B B-73 October 2014

Description of MIRC

The alternative, age-group-specific fish ingestion rates were derived using values for each age 

group, y: 

• Mean or other appropriate percentile consumer-only fish ingestion rates 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 

consumer-only sample size / U.S. population sample for age group y. The data to 

calculate those fractions are available in the 2008 CSEFH and EPA 2002.

Equation B-57 was used to calculate the alternative, per capita fish ingestion rates by age group 

(IRPC,y): 

Equation B-57. Calculation of Alternative Age-Group-Specific Fish Ingestion Rates

where:

IRPC,y = Per capita fish ingestion rate for age group y (g/day)

IRCO,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)

FPC,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 that 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 ingestion 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 ingestion 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 data 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 marine), 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 screening 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. 

 30Most 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.

PC,y CO,y FPC,y IR = IR ×
TRIM-Based Tiered Screening Methodology for RTR

Attachment B B-74 October 2014

Description of MIRC

As noted in Section 6.4.3 of this attachment, 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 loss factors L1 and L2 in 

Equation B-15. Suggested values are presented in Section 6.4.3.

Exhibit B-19. Daily Mean and Percentile Consumer-Only Fish Ingestion Rates

for Children and Adults (IRCO,y)

a

Lifestage (years) Ingestion Rates, All Fish (g/day)

Mean 50th 90th 95th 99th

Child <1 NA NA NA NA NA

Child 1-2b 27.31 15.61 64.46 87.60 138.76*

Child 3-5c 40.31 23.04 95.16 129.31 204.84*

Child 6-11d 61.49 28.46 156.86* 247.69* 385.64*

Child 12-19e 79.07 43.18 181.40* 211.15* 423.38*

Adultf 81.08 47.39 199.62* 278.91 505.65*

Sources: EPA 2002, 2008a

NA = not applicable; it is assumed that children < 1 year of age 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 children 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 provide reasonable, non-zero values for all age groups and percentiles. b

A fish IR for ages 1-2 years was not available. The value represents the consumer-only fish ingestion rate for ages 3 to 5 from 

EPA (2002) (Section 5.2.1.1 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. 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 fish 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 represent the consumer-only 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 Methodology 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 (FPC,y)

Lifestage (years) Fraction Consuming Fish

Child 3-5 0.0503a

Child 6-11 0.0440b

Child 12-19 0.0493c

Adult 0.08509d

Sources: EPA 2002, 2008a

a

This value was calculated 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 for 

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 the 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 Rates

for Children and Adults (IRPC,y)

Lifestage (years) Ingestion Rates, All Fish (g/day)

Mean 50th 90th 95th 99th

Child <1 NA NA NA NA NA

Child 1-2a 1.37 0.79 3.24 4.41 6.98

Child 3-5b 2.03 1.16 4.79 6.51 10.3

Child 6-11c 2.71 1.25 6.90 10.9 17.0

Child 12-19d 3.90 2.13 8.95 10.4 20.9

Adulte 6.90 4.03 16.99 23.73 43.02

Sources: EPA 2002, 2008a

NA = not applicable; it is assumed that children < 1 year of age do not consume fish. a

Values were calculated as (consumer-only IR for Child 1-2) x (fraction of population consuming fish for Child 3-5). b

Values were calculated as (consumer-only IR for Child 3-5) x (fraction of population consuming fish for Child 3-5). c

Values were calculated as (consumer-only IR for Child 6-11) x (fraction of population consuming fish for Child 6-11). d

Values were calculated as (consumer-only IR estimated for Child 12-19) x (fraction of population estimated to consume fish for 

Child 12-19). e

Values were calculated as (consumer-only IR for Adults) x (fraction of population consuming fish for Adults).
TRIM-Based Tiered Screening Methodology 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 fishers, black and female recreational fishers, and fishers 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 than the general population. 

Recreational fisher values are from the EFH (EPA, 2011a). Black and female recreational 

fishers ingestion rates are presented in Burger (2002). The fish ingestion rates for Hispanic, 

Laotian, and Vietnamese populations were derived 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 95th percentile ingestion rates for each subpopulation. In part due to the low 

sample size in the Shilling study (n of 30 to 45), 95th percentile values were believed to be 

unrealistically high. The 90th percentile ingestion rate estimates presented in Exhibit B-22 for 

Hispanic, Laotian, and Vietnamese fishers were derived by EPA using information from Shilling 

et al. (2010; EPA, 2010).

Exhibit B-22. Calculated Mean and 90th Percentile Per capita Fish Ingestion Rates for 

Populations of Recreational Fishers (IRPC,y)

Subpopulation

Percentile Units

Recreational 

Fishera

Female 

Recreational 

Fisherb

Black 

Recreational 

Fisherb

Hispanic 

Recreational 

Fisherc

Laotian 

Recreational 

Fisherc

Vietnamese 

Recreational 

Fisherc

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, 1997a) b

Burger (2002) weights are “as consumed” for locally caught fish. c

Shilling, F., A. White, L. Lippert, and M. Lubell. 2010. Contaminated 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 assumed 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-weighted 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 Kow 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 concentration 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 Methodology 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 

gardening 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 for children 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 2008a). 

Data on soil ingestion rates are sparse; the soil ingestion rates listed in Exhibit 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 garden or farm. The default ingestion rates in 

MIRC are the 90th percentile values, as for other ingestion rate parameters.

Exhibit B-23. Daily Mean and Percentile Soil Ingestion Rates 

for Children and Adults

Age Group

(years)

Soil Ingestion Rate (mg/day)

Meana 50th a 90th 95th 99th

Child < 1 NA

Child 1-2 50 50 200b 200b 200b

Child 3-5 50 50 200b 200b 200b

Child 6-11 50 50 201c 331d 331d

Child 12-19 50 50 201c 331d 331d

Adult 20-70 20 20 201c 331d 331d

Sources: EPA 2008a, EPA 2011a a

For mean and 50th 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 mean value for adults from EPA’s 2011 EFH, Chapter 5, Table 5-1. b

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. c

Values are 90th percentile adult ingestion rates calculated in Stanek et al. 1997; used to represent older children and adults. d

Values are 95th percentile adult ingestion rates calculated in Stanek et al. 1997; used to represent 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 would be included in any probabilistic module developed for MIRC. 

The 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 multiple 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 for one or any of the other food categories.
TRIM-Based Tiered Screening Methodology 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 Group 

Consuming Food Mean 50th 90th 95th 99th

Total Food Intake (g/day, as consumed)

Child < 1a 67.0% - 99.7% h 322 270 599 779 1152

Child 1-2b 100% 1,032 996 1537 1703 2143

Child 3-5c 100% 1,066 1,020 1,548 1,746 2,168

Child 6-11d 100% 1,118 1,052 1,642 1,825 2,218

Child 12-19e 100% 1,197 1,093 1,872 2,231 2,975

Adultf 100% 1,100 1,034 1,738 2,002 2,736

Total Food Intake (g/kg-day, as consumed)

Child < 1a 67.0% - 99.7% h 39 34 72 95 147

Child 1-2b 100% 82 79 125 144 177

Child 3-5c 100% 61 57 91 102 132

Child 6-11d 100% 40 38 61 70 88

Child 12-19e 100% 21 19 34 40 51

Adultg 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 to <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 obtained from Table 14-3 of the 2008 CSEFH (age group 3 to <6 years, N=4,112). d

These values were obtained from Table 14-3 of the 2008 CSEFH (age group 6 to <11 years, N=1,553). These values represents 

a health protective (i.e., slightly low) estimate for ages 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 values 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 include: 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 “nonconsumers” of food.

6.4. Other Exposure Factor Values

The other exposure parameters 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 food 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 days 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 and 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 products every day of the year. 
TRIM-Based Tiered Screening Methodology 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 product 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 consumed (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 produced 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 cooking losses 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 during 

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 1987 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 into 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 losses (e.g., non-consumption of bones, draining cooking liquid [e.g., spinach]). 

MIRC includes mean values for these three types of preparation and cooking losses 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. Therefore, 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 were 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 Methodology for RTR

Attachment B B-80 October 2014

Description of MIRC

Exhibit B-25. Fraction Weight Losses 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])

(unitless)b

Exposed Fruitc 0.244 0.305

Exposed Vegetable 0.162d NA

Protected Fruit 0.29e NA

Protected Vegetable 0.088f NA

Root Vegetableg 0.075 0.22

Beef 0.27 0.24

Pork 0.28 0.36

Poultry 0.32 0.295h

Fishi 0.0 0.0

Source: EPA 1997a and 2011a

NA = Not Available

a

For fruits, includes losses from draining cooked forms. For 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, fat, or other 

ingredients. For meats, includes dripping and volatile 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 from draining or removal of skin. 

For meats, includes losses from cutting, shrinkage, excess fat, bones, scraps, and juices. c

These values represent averages of means for all fruits with available data (except oranges) (Table 13-6). d

This value represents 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 value for oranges (Table 13-6). f

This value represents 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 represent averages of means for all root vegetables with available data (Table 13-7). Root vegetables include 

beets, carrots, onions, and potatoes.

h

This value represents 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 brought into the home from the field (divided 

by the consumers of the fish), an appropriate value for L1 would be 0.31 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, the L2 factor does not distinguish between 

weight loss during cooking by water evaporation, which might leave the chemical 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 the skin, which often is discarded, and lower in the consumed portion of many bulky 

fruits and vegetables. 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, 1994a) as 

reported in EPA’s EFH, which 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 fish 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 consuming the fish), the 

user should also set the L1 and L2 parameter to non-zero values. 
TRIM-Based Tiered Screening Methodology for RTR

Attachment B B-81 October 2014

Description of MIRC

6.4.4. Food Preparation/Cooking Adjustment Factor (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 concentration in cooked fish. The following sections discuss FPCAFs for each of the 4 PBHAPs. 

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 Freshwater Fish (EPA 2011b), 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). Cooking fish typically increases MeHg levels 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 to 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 trout 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 uncooked 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 percent 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 skin on and 57 percent for chinook salmon cooked with 
TRIM-Based Tiered Screening Methodology 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 that 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 assume 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 

acknowledges 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 Exposure 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 empirically derived or estimated by an appropriate model. For parameters that are scenariospecific or for which empirical values are required, the default values provided in MIRC are 

listed. For parameters for which MIRC calculates values, the appropriate equation is listed. 

Scenario- and receptor-specific parameters are discussed in Section 6.5.1 and chemicalspecific 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 DAIinf. For parameter values that can be 

estimated when empirical values are not available, see the equation description in Section 3.4

of this attachment.
TRIM-Based Tiered Screening Methodology 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 Milk

Parameter Description Default Value 

AT Averaging time for infant’s exposure via breast milk, i.e., duration of 

nursing (days) =ED

BWinf Body weight of infant (kg) averaged over duration of nursing exposure 7.8

BWmat

Body weight of mother (kg) averaged over duration of mother’s 

exposure

66

DAImat 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

fbp Fraction of mother’s whole blood that is plasma (unitless) 0.65

ffm Fraction of mother’s body weight that is fat (unitless) 0.30

fmbm Fraction of fat in mother’s breast milk (unitless) 0.04

fpm Fraction of mother’s body weight that is plasma (unitless) 0.046

IRmilk Mean infant milk ingestion rate over duration of nursing (kg/day) 0.709

tbf Duration of breast feeding (days) 365

tpn Duration of maternal chemical 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 exposure 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 (BWinf). The user selects a value for BWinf , 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 that 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 (tbf) 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 2008a). 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. Average 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 7.8a 9.0 9.6

5th percentile 5.0 6.0 7.0 7.5

10th percentile 5.3 6.4 7.4 7.8

15th percentile 5.5 6.7 7.7 8.2

25th percentile 5.8 7.0 8.1 8.7

50th percentile 6.4 7.8 8.9 9.5

75th percentile 7.1 8.6 9.9 10.5

85th percentile 7.4 9.0 10.3 11.0
TRIM-Based Tiered Screening Methodology for RTR

Attachment B B-84 October 2014

Description of MIRC

Exhibit B-27. Average 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 7.8a 9.0 9.6

90th percentile 7.7 9.2 10.6 11.3

95th percentile 8.0 9.7 11.1 11.8

Source: EPA (2008a); each value is the time-weighted average from the data summaries presented in the CSEFH, Table 8-3. a

MIRC default

Maternal body weight (BWmat). This parameter represents the body weight of the mother 

averaged over the entire duration of the mother’s exposure to the chemical of concern. The 

maternal body weight is needed to calculate the biological elimination constant for the lipophilic 

chemical in lactating women (kfat_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 BWmat of 66 kg is based on CSFII data compiled by EPA for non-lactating and nonpregnant 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 BWmat value is not the value that MIRC uses to estimate the mother’s absorbed daily intake 

(DAImat). 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 rates, 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 66.0a

5th 47.1

10th 50.2

25th 54.3

50th 62.0

75th 72.0

90th 85.7

95th 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 (fbp). Steinbeck (1954) reported that plasma 

volume accounts for approximately 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 Methodology 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 (ffm). 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 (tbf), 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-pdioxins 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 establish 

a health protective screening scenario, the MIRC default value for ffm is 0.30.

Fraction of fat in mother’s breast milk (fmbm). The Cmilkfat 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 fmbm of 0.04 (i.e., 4 percent) is the value EPA recommended for MPE

(EPA 1998). 

Fraction of maternal weight that is plasma (fpm). 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 volume 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 fpm therefore is 0.046.

Infant breast milk ingestion rate (IRmilk). 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 recommended 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 screening-level default of 980 mL/day is an upperbound 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 Methodology 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 980a 1.01a EPA 2011a†

0 to < 1 month 510 0.525 950 0.979 EPA 2008a††

1 to < 3 months 690 0.711 980 1.01 EPA 2008a†

3 to < 6 months 770 0.793 1,000 1.03 EPA 2008a†

6 to < 12 

months 620 0.639 1,000 1.03 EPA 2008a†

a

MIRC default; † Based on review of multiple studies; †† Based on a single study

Duration of breast feeding (tbf). 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 protective 

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 tbf is 365 

days.

Duration of the mother’s exposure to the chemical of concern prior to nursing (tpn). 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 chemical 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 meet 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 tpn

term results in an over-estimate of the concentration of the chemical in breast milk by a factor of 

28.1 compared with the prediction using Equation B-38 with the tpn 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 tpn 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 days 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 lipophilic 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 Methodology 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

AEinf 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)

AEmat Maternal absorption 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)

fbl 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

ff Fraction of steady-state lipophilic chemical body 

burden in mother that is stored in body fat (unitless)

≥0.90 (ATSDR 

1992)

NA

fpl Fraction of steady-state total hydrophilic chemical 

body burden in mother that is in the blood plasma 

compartment (unitless)

NA Not yet identifiedb

h Biological half-life for chemical in non-lactating 

women (days)

3650 (EPA 

1994c)

50 (Sherlock et al. 

1984)

kaq_elac Rate constant for total elimination of hydrophilic 

chemicals by lactating women (per day)

NA = kelim

kelim Rate constant for elimination of chemical for nonlactating women (per day; related to chemical halflife)1.9E-04b 1.4E-02 c

kfat_elac Rate constant for total elimination of lipophilic 

chemicals by lactating women (per day)

Est. using 

Equation B-41

NA

Pcbm Partition coefficient for hydrophilic chemical 

between maternal blood plasma and aqueous 

phase of breast milk (g milk/g plasma; model 

assumption)

NA 1.0 (model 

assumption)

PcRBC 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 determined 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 half-life (h) using Equation B-40. 

Absorption efficiency of the chemical by the oral route of exposure for the infant (AEinf). The 

models included in MIRC assume that the AEinf from the lipid phase of breast milk is equal to the 

AEinf from the aqueous phase of the milk. Reviewers of the model stated that this assumption 

may not be valid and that ideally, the equation DAIinf would include variables for the AEinf from 

the breast milk fat and the AEinf from the aqueous phase of breast 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 AEinf values for a given chemical. If data on 

the AE from the mother or an adult but not for the infant are available, data for the adult may be 

used for AEinf. Reviewers also recommended that chemical-specific values come from studies 

that account for absorption of the chemical from milk, because absorption from other matrices 

(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 AEinf for a screening level assessment 

is 1.0, which assumes 100 percent absorption (EPA 1998). 
TRIM-Based Tiered Screening Methodology for RTR

Attachment B B-88 October 2014

Description of MIRC

The default value for AEinf 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 of 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 high 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 (AEmat). The 

default value for both dioxins and MeHg is 1.0, as described in the previous paragraph. 

Fraction of total maternal chemical body burden that is in the whole blood (fbl). The default 

value for MeHg in MIRC, 0.059, is from 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 (ff). Based on ATSDR’s 

Toxicological Profile for Selected PCBs (ATSDR 1992) and Sullivan et al. (1991), EPA 

concluded that the “fraction of 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 

dioxins at steady state. 

Fraction of total maternal chemical body burden that is in blood plasma (fpl). 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 fpl 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 fbl, 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 to 

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 health 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 lactating 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-life for 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 (kaq_elac). The parameter 

kaq_elac is equal to kelim 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 assumption for water soluble chemicals is that kaq_elac is equal to kelim as discussed 

for Equation B-43. The extent to which kelim is an underestimate of kaq_elac for a given chemical 

will determine the extent of health protective bias in kaq_elac. 
TRIM-Based Tiered Screening Methodology for RTR

Attachment B B-89 October 2014

Description of MIRC

Chemical elimination rate constant for non-lactating women (kelim). Although values for this 

parameter often are reported directly in the literature, MIRC estimates kelim from chemical halflife assuming first-order kinetics as shown in Equation B-40. For example, for a biological halflife of 3,650 days for dioxins, kelim is estimated to be 1.9E-04 per day. Assuming a biological 

half-life of 50 days for MeHg, the value for kelim is estimated to be 0.014 per day.

Rate constant for total elimination of lipophilic chemicals by lactating women (kfat_elac). Although 

values for this parameter might be found in the scientific literature for some chemicals, in MIRC, 

kfat_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 

kfat_elac.

Partition coefficient for chemical between maternal blood plasma and aqueous phase of breast 

milk (Pcbm). 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 (PcRBC). 

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). 

For MeHg, MIRC includes a value of 40 based on Hollins et al. (1975) study of cats exposed 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

The 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 PB-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 90th percentile ingestion rates for soil, breast milk, and 

farm food items and 99th 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 nature. 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 chemicals. 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 Rates

The screening-level (or default) values for ingestion rates for soil, breast milk, and for each farm 

food item are equal to the 90th 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 Methodology for RTR

Attachment B B-90 October 2014

Description of MIRC

7.1.1. Fish Ingestion Rates

The adult fish ingestion rate was obtained from Burger (2002), a study that examined daily 

consumption of wild-caught fish for high-end recreationalists (white, black 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 (90th percentile), 

172 g/day (95th percentile), and 373 g/day (99th percentile). As shown in Exhibit B-31 and 

discussed in Section 6.3.4, for adults, the rate of fish ingestion assumed in the screening 

scenario is 373 g/day, which corresponds to the 99th percentile value estimated by Burger for 

adult females. This 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 99th 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 both consumers and nonconsumers), 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 directly correspond to the age groups in the EPA 

(2002) report. As described in Section 6.3.4, these adjustments convert the available agespecific data on fish ingestion rates to the age-specific values needed for MIRC.

For the screening-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 those 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 presented here are representative of the 99th percentile of the 

evaluated data set, the use of these inputs (compared with 90th percentile values used for other 

food types) is not considered to be inconsistent. This is due to the idiosyncrasies of the survey 

data 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 rates 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 and 

is applicable for national rulemakings given that it is very likely that subsistence woman fishers 

of child bearing age are located throughout the United States. Using a high-end subsistence fish 

ingestion 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 all food types are obtained from the area of chemical 

deposition specified 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 Methodology 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 homegrown 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 Food: Homegrown Only). 

The second row from the bottom presents the 90th 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 90th percentile 

individual total food ingestion 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 values 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 yrs

Child 

12–19 yrs

Adult

20–70 yrs

Farm Food Item

Beefa NA 9.49 8.83 11.4 3.53 4.41 g/kg-day

Dairyb NA 185 92.5 57.4 30.9 6.16 g/kg-day

Eggsa NA 4.90 3.06 1.90 1.30 1.31 g/kg-day

Exposed Fruita NA 12.7 5.41 6.98 3.41 2.37 g/kg-day

Exposed Vegetablea NA 10.7 3.47 3.22 2.35 3.09 g/kg-day

Porka NA 4.90 4.83 3.72 3.69 2.23 g/kg-day

Poultrya NA 7.17 6.52 4.51 3.13 2.69 g/kg-day

Protected Fruita NA 44.8 32.0 23.3 7.44 15.1 g/kg-day

Protected Vegetablea NA 3.88 2.51 2.14 1.85 1.81 g/kg-day

Root Vegetablea NA 7.25 4.26 3.83 2.26 2.49 g/kg-day

Other

Breast milkc 1.01 NA NA NA NA NA kg/day

Soil (dry) NA 200d 200d 201e 201e 201e mg/day

Fish (per individual)f NA 107.7g 159.0g 268.2h 331.0h 373 g/day
TRIM-Based Tiered Screening Methodology 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 yrs

Child 

12–19 yrs

Adult

20–70 yrs

Total Food Ingestion Rates for Comparison Only (not in MIRC; excludes soil and water)

Total Food: Homegrown 

onlyi NA 259 142 99 51 35.5 g/kg-day

Total Food: All Sourcesj 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 2011a, EPA 2008a, 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, 2011a). 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, 2011a). 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 an NHANES 2003–2006 analysis in Chapter 11 of the 

Exposure Factors Handbook.

c

Infants are 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 99th percentile value for adult females considered high-end 

recreationists; this value is believed to be representative of subsistence fishers. The 99th percentile values for children were derived 

based on EPA’s Estimated Per Capita Fish Consumption in the United 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, for children aged 1–2 years, the fish 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 rates were calculated using the 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 estimate is calculated by multiplying the food ingestion rates on previous rows (excluding 

soil and water) by (1-L1)x(1-L2), where L1 and L2 are the loss rates from Exhibit B-25. The rows are then summed to get the total postcooking ingestion rate.

j

90th 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 Childrena

Lifestage (years) Duration (years) Mean Body Weight (kg)

Adultb (20-70) 50 80.0

Child < 1c 1 7.83

Child 1-2c 2 12.6

Child 3-5d 3 18.6
TRIM-Based Tiered Screening Methodology for RTR

Attachment B B-93 October 2014

Description of MIRC

Exhibit B-32. Mean Body Weight Estimates for Adults and Childrena

Lifestage (years) Duration (years) Mean Body Weight (kg)

Child 6-11e 6 36.0

Child 12-19f 8 64.2

a

Sources: EPA 1997, 2008a

b EPA-recommended value (EPA 2011a). d

These values were obtained directly from Table 8-3 of the 2008 CSEFH. e

Each BW represents a time-weighted average of BWs 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

These values were calculated as time-weighted average BW for age groups 11 to <16 years and 16 to <21 years from 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 Analysis

Exhibit B-33 presents chemical-specific parameter values for input to MIRC for the screeninglevel analysis. Values for bioavailability when ingested in soil (Bs), mammalian metabolism 

factors (MF), correction factors for belowground produce (VGrootveg), wet deposition fractions 

(Fw), air to plant transfer factors (BvAG), 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 MIRCa

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 

from 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 unitless
TRIM-Based Tiered Screening Methodology for RTR

Attachment B B-94 October 2014

Description of MIRC

Exhibit B-33. Chemical-Specific Parameter Values for Input to MIRCa

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 vaporphase 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 parameters 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-typespecific, 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 overor 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 chloride 1.1E-04 1.4E-06 3.4E-05 2.4E-02 2.4E-02

Methyl mercury 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 values 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 1998). The assumption that lactational 

transfer of dioxins to the infant occurs via the lipid-phase of milk appears reasonable. The 
TRIM-Based Tiered Screening Methodology for RTR

Attachment B B-95 October 2014

Description of MIRC

following screening-level assumptions used in that model should bias the results toward healthprotective 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 

overestimate 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 ATSDR (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 been 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 sensitive 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 

duration 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 partially 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 (DAIinf) is indistinguishable from the 

dose of MeHg absorbed by its mother from her food (DAImat). The data are limited, and the 

model includes various assumptions; however, there is no known directional bias in the 

estimates. 
TRIM-Based Tiered Screening Methodology for RTR

Attachment B B-96 October 2014

Description of MIRC

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TRIM-Based Tiered Screening Methodology for RTR

Attachment B B-97 October 2014

Description of MIRC

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Attachment B B-99 October 2014

Description of MIRC

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TRIM-Based Tiered Screening Methodology for RTR

Attachment B B-100 October 2014

Description of MIRC

USDA. 1993. Food and Nutrient Intakes by Individuals in the United States, 1 Day, 1987-88.

Nationwide Food Consumption Survey 1987-88, NFCS Report No. 87-I-1. (As cited in EPA

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EPA (US. Environmental Protection Agency). 1990. Interim Final Methodology for Assessing 

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Volume V. External Review Draft. U.S. EPA Region 5, Chicago, Illinois.
TRIM-Based Tiered Screening Methodology 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 

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EPA. 1997b. Mercury Study Report to Congress. Volume III: Fate and Transport of Mercury in 

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EPA. 1997c. Parameter Guidance Document. National Center for Environmental Assessment, 

NCEA-0238.

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Update. Office of Water, Office of Science and Technology, Washington, D.C. EPA-822-R-
TRIM-Based Tiered Screening Methodology for RTR

Attachment B B-102 October 2014

Description of MIRC

00-001. October. Available at: 

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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 Hazardous Waste Combustion 

Facilities. Office of Solid Waste and Emergency Response, Washington, DC. EPA-530-R05-006. 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 

Exposures 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.

EPA. 2005c. Guidelines for Carcinogen Risk Assessment. Risk Assessment Forum, 

Washington, DC. EPA/630/P-03/001F. March. Available from:

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). Office 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. EPA/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 Board; 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=194584
TRIM-Based Tiered Screening Methodology 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 of 

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. December.

Van den Berg, M., L.S. Birnbaum, M. Denison, M. De vito, 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.

Zabik, ME; Zabik, MJ. 1995. Tetra-chlorodibenzo-p-dioxin residue reduction by 

cooking/processing of fish fillets harvested from the Great Lakes. Bulletin of Environmental 

Contamination and Toxicology. 55:264-269.
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TRIM-Based Tiered Screening Methodology for RTR

Attachment C i October 2014

Dermal Risk Screening

Attachment C. Dermal Risk Screening
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TRIM-Based Tiered Screening Methodology 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 .......................................................................................................................10
TRIM-Based Tiered Screening Methodology for RTR

Attachment C iv October 2014

Dermal Risk Screening

Exhibits, 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 

Chemicals.........................................................................................................C-4

Exhibit C-3. Scenario-Specific Exposure Values for Water and Soil Contact..........................C-5

Exhibit C-4. Chemical-Specific Dermal Exposure 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-9
TRIM-Based Tiered Screening Methodology for RTR

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. However, dermal absorption of chemicals that are 

originally airborne is generally a relatively minor pathway of exposure compared to other 

exposure pathways (EPA 2006, Cal/EPA 2000). This section demonstrates that for the 

conservative tiered screening scenario developed for RTR multipathway evaluation, the dermal 

exposure route is not 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, we 

evaluated the potential for cancer and chronic non-cancer effects for the four PB-HAPs currently 

assessed in the multipathway screening evaluation for RTR: cadmium, divalent mercury, 

2,3,7,8-TCDD, and benzo(a)pyrene. EPA has not developed carcinogenic potency slope 

factors (CSFs) 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 extrapolation 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) oral absorption is assumed, and no 

adjustment is made (EPA 2004). 

The absorbed cancer slope factor (CSFABS) is based on the oral cancer slope factor (CSFO) and 

the fraction of the contaminant absorbed in the gastrointestinal track (ABSGI), as follows: 

where:

CSFABS = Absorbed slope factor (mg/kg-day)-1

CSFo = Oral slope factor (mg/kg-day)-1

ABSGI = Fraction of chemical absorbed in gastrointestinal tract (unitless)

The absorbed reference dose (RfDABS) is based on the oral reference dose (RFDO) and the 

fraction of the contaminant absorbed in the gastrointestinal tract (ABSGI), as shown below.

where:

RfDABS = Absorbed reference dose (mg/kg-day)

RfDo = Oral reference dose (mg/kg-day)

ABSGI = Fraction of chemical absorbed in gastrointestinal tract (unitless)

GI

o

ABS ABS

CSF CSF =

RfDABS = RfDo × ABSGI
TRIM-Based Tiered Screening Methodology for RTR

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

Fraction of Contaminant 

Absorbed in GI Tract (ABSGI)

(unitless)

Absorbed Cancer 

Slope Factor (CSFABS)

a

(mg/kg-day)-1

Absorbed Reference 

Dose (RfDABS)

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 

required 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 risks resulting from absorption of the chemical through the skin from 

contact with contaminated water and soil were 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 be 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 dose (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.

where:

DAevent = Absorbed dose per event; chemical-specific; equation for DAevent also differs 

depending on water or soil contact (mg/cm2-event)

EV = Event frequency (events/day)

ED = Exposure duration (years)

BW AT

DA EV ED EF SA DAD event

×

× × × × =
TRIM-Based Tiered Screening Methodology for RTR

Attachment C C-3 October 2014

Dermal Risk Screening

EF = Exposure frequency (days/year)

SA = Skin surface area available for contact (cm2

)

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)

DAevent is estimated to be the total dose absorbed through 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 DAevent 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 DAevent values is provided in RAGS Part E, Chapter 3 (see 

Equation 3.12).

Water – Organic Chemicals:

Water – Inorganic Chemicals:

Soil – All Chemicals:

where:

DAevent = Absorbed dose per event (mg/cm2

-event)

Cw

Cs

= Chemical concentration in water (mg/cm3) or soil (mg/kg)

Kp = 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)

τevent = Chemical-specific lag time per event (hr/event)

tevent = Receptor-specific event duration (hr/event)

AF = Receptor- and activity-specific adherence factor of soil to skin (mg/cm2

-event)

ABS = Chemical-specific dermal absorption fraction (unitless)

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-specific 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

event w p

6 t DA C 2 FA K × × = × × ×

event w p event DA = C ×K × t

DAevent = Cs × AF × ABS ×CF
TRIM-Based Tiered Screening Methodology for RTR

Attachment C C-4 October 2014

Dermal Risk Screening

2.3. Receptor-Specific Parameters

Dermal exposures and 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 Tables 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 individuals 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 ratio 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 Groupa

(years)

Surface Area for

Water Exposure (cm2)

Surface Area for

Soil Exposure (cm2)

Adult 20-70 18,150g 6,878h

Child <1b 3,992 1,772

Child 1-2c 5,700 2,405

Child 3-5d 7,600 3,354

Child 6-11e 10,800 4,501

Child 12-19f 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 time-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 surface 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 summarizes 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 Methodology for RTR

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 (tevent) 

(hr/event)

Child 1 Reasonable maximum exposure 

scenario for showering/bathing Adult 0.58 from RAGS Part E, Exhibit 3-2 

Soil Contact

Soil Adherence Factor (AF) 

(mg/cm2)

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 350

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) (days) 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 receptor 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 water concentration in the dermal analysis, the 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 soil 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 

(Kp), which measures the rate that a chemical penetrates the skin. Dermal absorption of soilbound 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 Methodology for RTR

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 

Mercury

2,3,7,8-

TCDD

Benzo[a]

pyrene

Source

Chemical concentration in 

Water (Cw) (mg/cm3)

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 TRIM.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 B4 (inorganics)

Fraction absorbed water 

(FA) (unitless)

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 0.045a 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 were used in conjunction with exposure information to evaluate the potential for 

cancer risks and non-cancer 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 DADs and the 

absorbed CSF for each chemical, as follows:

Dermal Cancer Risk = DAD × CSFABS

where:

DAD = Dermal Absorbed Dose (mg/kg-day)

CSFABS = Absorbed cancer slope factor (mg/kg-day)-1

Lifetime dermal cancer risks were calculated for 2,3,7,8-TCDD and benzo[a]pyrene. The total 

risk accounts for dermal exposures that an individual might receive from these PB-HAPs in 

water plus soil over his or her lifetime (70 years). 
TRIM-Based Tiered Screening Methodology for RTR

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 DADs to the absorbed 

RfD for each chemical, as shown below:

Dermal HQ = DAD / RfDABS

where:

DAD = Dermal Absorbed Dose (mg/kg-day)

RfDABS = Absorbed reference dose (mg/kg-day)

The aggregate HQ accounts for exposures that an 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 was 0.006 (representing divalent mercury exposure for children less than 1 year of age) or 

less. This is approximately 170 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 4.1E-09 for benzo[a]pyrene; this 

value is approximately 240 times smaller than the ingestion risk (i.e., 1E-06) estimated for the 

same screening threshold emission rate. 
TRIM-Based Tiered Screening Methodology for RTR

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

20-70

Hazard 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

20-70

Hazard 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

20-70

Hazard Quotient

Receptor

Divalent Mercury Water

Soil

Total
TRIM-Based Tiered Screening Methodology for RTR

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 Differencea

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 >300

Total 4.12E-09 >200 a

Represents the magnitude of difference between the estimated dermal risk and

the potential ingestion risk associated with the screening scenario.

Based on these results and taking into consideration the extremely conservative nature 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 children 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 350 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 Methodology for RTR

Attachment C 10 October 2014

Dermal Risk Screening

5. References

Cal/EPA (California Environmental Protection Agency) Office 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. Available 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 publishers.

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. Available at: 

http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=199243 .
TRIM-Based Tiered Screening Methodology for RTR

Attachment D i October 2014RIM.FaTE Parameters

Attachment D. Summary of TRIM.FaTE Parameters Considered for 

Inclusion in Tier 2 Assessment
[This page intentionally left blank.]
TRIM-Based Tiered Screening Methodology for RTR

Attachment B, Addendum 1 iii October 2014

Tier 2 TRIM.FaTE Parameters

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 Methodology for RTR

Attachment D D-1 October 2014

Tier 2 TRIM.FaTE Parameters

Exhibit 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 chemical 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 

blows 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 

confirmed 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:

This variable is relatively 

straightforward to vary in the 

Tier 2 screening scenarios.

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 to 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 

sensitivity 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 

scenarios.

Low to Moderate: 

The annuallyaveraged wind 

speed can be 

estimated for 

any surface 

meteorological 

station. Then, 

facilities can be 

linked to the 

closest surface 

meteorological 

station.

High
TRIM-Based Tiered Screening Methodology for RTR

Attachment D D-2 October 2014

Tier 2 TRIM.FaTE Parameters

Exhibit 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 PBHAPs (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 

annuallyaveraged 

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.

High
TRIM-Based Tiered Screening Methodology for RTR

Attachment D D-3 October 2014

Tier 2 TRIM.FaTE Parameters

Exhibit 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 dispersion 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 scenarios.

Moderate to 

High: Mixing 

height estimates 

are available for 

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.

High
TRIM-Based Tiered Screening Methodology for RTR

Attachment D D-4 October 2014

Tier 2 TRIM.FaTE Parameters

Exhibit 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

Distance 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 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 scenarios than the 

meteorological parameters.

Low: The lakes 

within a given 

radius of each 

facility can be 

found using 

ArcGISTM (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 as 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 scenarios 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 farming now 

or in the future. 

Medium
TRIM-Based Tiered Screening Methodology for RTR

Attachment D D-5 October 2014

Tier 2 TRIM.FaTE Parameters

Exhibit 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 particleattached chemicals into the 

lake. But the associated 

higher flush rate will likely 

reduce 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 

capture 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 lower 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 requires updates to 

the layout coordinates and 

requires more effort to vary in 

the Tier 2 screening scenarios

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. 

Medium
TRIM-Based Tiered Screening Methodology for RTR

Attachment D D-6 October 2014

Tier 2 TRIM.FaTE Parameters

Exhibit 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 constant 

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 parameter 

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 

fraction for any 

lake near a 

facility cannot be 

readily 

determined.

Medium
TRIM-Based Tiered Screening Methodology for RTR

Attachment D D-7 October 2014

Tier 2 TRIM.FaTE Parameters

Exhibit 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 scenarios.

High: The 

specific 

methylation / 

demethylation 

rates for mercury 

in the vicinity of a 

specific facility 

cannot be readily 

determined. 

Low

Total 

phosphorus 

levels in the 

lake 

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 potentially 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 scenarios.

High: The total 

phosphorus 

levels in lakes 

near a specific 

facility cannot be 

readily 

determined.

Low
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TRIM-Based Tiered Screening Methodology for RTR

Attachment E i October 2014

Lake Size Analysis

Attachment E. Analysis of Lake Size and 

Sustainable Fish Population
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TRIM-Based Tiered Screening Methodology 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-8
TRIM-Based Tiered Screening Methodology for RTR

Attachment E iv October 2014

Lake Size Analysis

[This page intentionally left blank.]
TRIM-Based Tiered Screening Methodology for RTR

Attachment E E-1 October 2014

Lake Size Analysis

1. Introduction

Identifying the smallest 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 fishers 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 threshold 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 fishers (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 

invertebrates 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 fish 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 fishers might prefer to catch and consume the pelagic (TL4) game fish 

species, their generally lower abundance compared with fish that also consume benthic 

invertebrates dictates that fishers 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 fishers 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 weight 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 assumption 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 rate 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 estimate, and models to predict sustainable harvests of 

different fisheries are numerous and complex. Species-specific parameters of key to such 
TRIM-Based Tiered Screening Methodology 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 that a 

typical loss from fishing by birds, mammals, and humans would be approximately 10 percent of 

the fish biomass in the prey fish compartment (TL3) and 10 percent of the biomass in the 

predator fish compartment (TL4). The authors also found that as overall 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 fishers can harvest 10 percent of the 

biomass of pelagic WCC fish without diminishing 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 ponds 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 fishing: 

the general productivity of a lake (expressed as grams of fish wet weight per meter squared, g 

ww/m2

); 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 supporting 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 31 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/m2

Meso-eutro (TP = 30 μg/L) = 3.7 g ww/m2

Eutro-hypereutro (TP = 100 μg/L) = 8.5 g ww/m2

Nürnberg (1996) also summarized total fish biomass limits from Bachmann et al. (1996) for the 

same lake trophic status categories based on a sample of 60 lakes in Florida:

Oligo-meso (TP = 10 μg/L) = 7.4 g ww/m2

Meso-eutro (TP = 30 μg/L) = 10.6 g ww/m2

Eutro-hypereutro (TP = 100 μg/L) = 15.6 g ww/m2

As might be expected, for the same TP concentrations, standing fish biomass 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 between TP and standing stock of fish 

using a regression model based on samples from 21 lakes ranging in surface area from 

0.1–25,000 hectares (~0.25–62,000 acres) and located between 0° and 56° N latitude and 
TRIM-Based Tiered Screening Methodology 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 + 0.072 (TP)

where:

B = total fish biomass (kg/hectare)

TP = total phosphorous (μg/L)

The regression model of Hanson and Legget (1982) predicted total fish biomass densities in 

lakes of 3.0–9.5 g ww/m2 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/m2 over the same range of TP 

concentrations.

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/m2 in two lakes of 27 and 

22 acres (11 and 9 hectares), respectively, in south-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 piscivorous 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 relationship 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 r2 of 0.75:

Log10 (B) = 1.07 + 1.14 × Log10 (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/m2 (Yurk and Ney, 1989) and 1.6 g ww/m2 

(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/m2

, respectively.

Ideally, one would have data indicating TP levels in lakes in the vicinity of facilities 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 included 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 exceeding 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/m2

. The minimum 
TRIM-Based Tiered Screening Methodology for RTR

Attachment E E-4 October 2014

Lake Size Analysis

and maximum total fish biomass densities were 3.2 and 133.2 g ww/m2, respectively, and the 

median value was 30.9 g ww/m2

. 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 

discussed above. The 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 samples of natural 

lakes in the United States. For the purpose of estimating the minimum lake size that would 

support a sustainable WCC fishery, we rounded that value down to a single significant digit of 

40 g ww/m2 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 high 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 Trophic 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 lakes, 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 efficiencies 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 biomass) 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) comprise 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 Håkanson 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-Based Tiered Screening Methodology for RTR

Attachment E E-5 October 2014

Lake Size Analysis

Håkanson and Boulion (2004) created a “distribution coefficient” 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 balanced 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 total 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 sustainable 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 duration (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, 

variability 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 books 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 variation (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 populationlevel ecological risk assessment has 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, 1987). Several 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., several 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, Ne, 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). Ne 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 ratios, the effective population size is essentially the same 

as the actual breeding adult population size (Ewens et al., 1987). One of the most extensive 
TRIM-Based Tiered Screening Methodology for RTR

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 across 

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 assumed 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 lakes. 

4. Summary of Assumptions for the Lake Size Analysis

The following assumptions were used in processing lake data for the Tier 2 assessment and in 

estimating 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 standing 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. Humans can harvest 10 percent of the biomass of a fish compartment without threatening 

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. Only 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]/m2

) in Lake X

Total SB = Total standing biomass of fish (g ww/m2

) in Lake X

Fraction WCC = Fraction of WCC fish in Lake 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

BWa (Equation 2)

where:
TRIM-Based Tiered Screening Methodology for RTR

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/m2

; from Equation 1)

CF = 4047 (unit conversion factor m2

/acre)

BWa = Body weight of adult TL4 fish (2000 g; assumed)

The likely annual productivity of WCC fish (kg/year) in Lake X can be estimated using

Equation 3.

Productivity WCC = Lake Size x WCC SB x CF1

CF2 (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 fish (g ww/m2

; from Equation 1)

CF1 = 4047 (unit conversion factor 1, m2

/acre)

CF2 = 1000 (unit conversion factor 2, g/kg)

The maximum fish ingestion rate (g/day) for WCCs plus BCs associated with sustainable fishing 

can be predicted using Equation 4. It assumes that the fishers 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 CF1

CF2 (Equation 4)

where:

Max Sustain IR (BC + WCC) = Predicted maximum sustainable ingestion rate for BC and WCCfish (g/day)

Productivity WCC = Likely 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-Based Tiered Screening Methodology for RTR

Attachment E E-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 analysis: 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. Minimum 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. Can. 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, and 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 prepared 
TRIM-Based Tiered Screening Methodology 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, Mississippi, 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 Theory 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 and 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 Allendorf, 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.) Conservation 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 viable 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, Cambridge: 

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. Phosphorus-fish community biomass relationships in southern 

Appalachian reservoirs: can lakes be too clean for fish? Lake and Reservoir Management 

5(2): 83-90.
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Appendix 6

Dispersion Model Receptor Revisions and Additions
Dispersion Model Receptor Revisions and Additions for the Phosphoric Acid 

Manufacturing and Phosphate Fertilizer Production Source Categories

To estimate ambient concentrations for evaluating long-term exposures, the HEM-3 

model uses the geographic centroids of census blocks (currently utilizing the 2010 Census) as 

dispersion model receptors. The census block centroids are generally good surrogates for where 

people live within a census block. A census block generally encompasses about 40 people or 10-

15 households. However, in cases where a block centroid is located on industrial property, or 

where a census block is large and the centroid less likely to be representative of the block’s 

residential locations, the block centroid may not be an appropriate surrogate.

Census block centroids that are on facility property can sometimes be identified by their 

proximity to emission sources. In cases where a census block centroid was within 300 meters of 

any emission source, we viewed aerial images of the facility to determine whether the block 

centroid was likely located on facility property. The selection of the 300-meter distance reflects a 

compromise between too few and too many blocks identified as being potentially on facility 

property. Distances smaller than 300 meters would identify only block centroids near the 

emission sources and could exclude some block centroids that are still within facility boundaries, 

particularly for large facilities. Distances significantly larger than 300 meters would identify 

many block centroids that are outside facility boundaries, particularly for small facilities. Where 

we confirmed a block centroid on facility property, we moved the block centroid to a location 

that best represents the residential locations in the block.

In addition, census block centroids for blocks with large areas may not be representative 

of residential locations. Risk estimates based on such centroids can be understated if there are 

residences nearer to a facility than the centroid, and overstated if the residences are farther from 

the facility than the centroid. To avoid understating the maximum individual risk associated 

with a facility, in some cases we relocated block centroids, or added dispersion model receptors 

other than the block centroid. We examined aerial images of all large census blocks within one

kilometer of any emission source. Experience from previous risks characterizations show that in 

most cases the MIR is generally located within 1 km of the facility boundary. If the block 

centroid did not represent the residential locations, we relocated it to better represent them. If 

residential locations could not be represented by a single receptor (that is, the residences were

spread out over the block), we added additional receptors for residences nearer to the facility 

than the centroid. 

For these source categories, the table below contains each census block for which we 

changed the centroid location because it was on facility property or was otherwise not 

representative of the residential locations in the block. The table also contains the locations of 

additional receptors that were included to represent residential locations nearer to the facility 

than the block centroid.
Revised Census Block Centroid Locations and Additional Receptors 

for Phosphate Fertilizer Production and Phosphoric Acid Manufacturing 

Centroid Revisions

Additional Receptors

Source Category NEI ID

HEM Census 

Block Latitude Longitude Note

Phosphoric Acid and Fertilizer 

Produuction 16029NEIIDR029000 160299602002082 42.692691 -111.551232

Change centroid to a 

population area

Phosphoric Acid and Fertilizer 

Produuction 12057NEIFL0570008 120570136041053

27.865795 -82.380258

Change centroid to a 

population area

Phosphoric Acid and Fertilizer 

Produuction 12057NEIRFL050059 120570139032000 27.846196 -82.084055

Change centroid to a 

population area

Phosphoric Acid and Fertilizer 

Produuction 12105NEIFL12286 121050148043024 27.932274 -81.893307

Change centroid to a 

population area

Phosphoric Acid 

Manufacturing 22047NEI6147

220479532003048 30.21858 -91.09597

Change centroid to a 

population area

Phosphoric Acid and Fertilizer 

Produuction 37013NEI45571 370139308001152 35.356689 -76.734247

Change centroid to a 

population area

Phosphoric Acid and Fertilizer 

Produuction 37013NEI45571 370139308001023 35.338224 -76.860086

Change centroid to a 

population area

Phosphoric Acid and Fertilizer 

Produuction 12047NEIFLT#4056 120479601001112 30.476025 -82.860864

Change centroid to a 

population area

Phosphoric Acid and Fertilizer 

Produuction 12057NEIFL0570008 120570136041073 27.869574 -82.395876

Make Zero Population

Recommended Corr.

Source Category NEI ID

HEM Census 

Block Latitude Longitude Note

Phosphoric Acid and Fertilizer 

Produuction 12101NEIFL0570005

28.172136 -82.153143 Add Receptor

Phosphoric Acid and Fertilizer 

Produuction 12057NEIFL0570008 27.865253 -82.38053

Add Receptor

Phosphoric Acid and Fertilizer 

Produuction 12057NEIRFL050059

27.84420 -82.07470 Add Receptor

Phosphoric Acid and Fertilizer 

Produuction 12057NEIRFL050059

27.827050 -82.087423 Add Receptor

Phosphoric Acid 

Manufacturing 22047NEI6147

30.218330 -91.083472 Add Receptor

Recommended Corr.
1

Appendix 7 – Environmental Risk Screen
Table 1 – PB-HAP Benchmarks Included in the Environmental Risk Screen

EcoHAP Assessment Endpoint Benchmark Effects Level Benchmark Value

Benchmark 

Source

Cadmium Fish - Avian Piscivores NOAEL (merganser) 0.7 (mg/kg BW/day) d

Cadmium Fish - Avian Piscivores LOAEL (merganser) 1 (mg/kg BW/day) d

Cadmium Fish - Mammalian Piscivores NOAEL (mink) 0.742 (mg/kg BW/day) e

Cadmium Fish - Mammalian Piscivores LOAEL (mink) 7.42 (mg/kg BW/day) e

Cadmium Sediment Community No-effect Level 0.6 (mg/kg dry wt) b

Cadmium Sediment Community Threshold Level 1.2 (mg/kg dry wt) a

Cadmium Sediment Community Probable-effect Level 3.5 (mg/kg dry wt) b

Cadmium Surface Soil - Dist. 1 - 312 m Threshold - Mammalian Insectivores (shrew) 0.36 (mg/kg dry wt) a

Cadmium Surface Soil - Dist. 1 - 312 m

Threshold - Avian Ground Insectivores 

(woodcock) 0.77 (mg/kg dry wt)

a

Cadmium Surface Soil - Dist. 1 - 312 m Threshold Level - Plant Community 32 (mg/kg dry wt) a

Cadmium Surface Soil - Dist. 1 - 312 m Threshold Level - Invertebrate Community 140 (mg/kg dry wt) a

Cadmium Surface Soil - Dist. 2 - 850 m Threshold - Mammalian Insectivores (shrew) 0.36 (mg/kg dry wt) a

Cadmium Surface Soil - Dist. 2 - 850 m

Threshold - Avian Ground Insectivores 

(woodcock) 0.77 (mg/kg dry wt)

a

Cadmium Surface Soil - Dist. 2 - 850 m Threshold Level - Plant Community 32 (mg/kg dry wt) a

Cadmium Surface Soil - Dist. 2 - 850 m Threshold Level - Invertebrate Community 140 (mg/kg dry wt) a

Cadmium Surface Soil - Dist. 3 - 1,500 m Threshold - Mammalian Insectivores (shrew) 0.36 (mg/kg dry wt) a

Cadmium Surface Soil - Dist. 3 - 1,500 m

Threshold - Avian Ground Insectivores 

(woodcock) 0.77 (mg/kg dry wt)

a

Cadmium Surface Soil - Dist. 3 - 1,500 m Threshold Level - Plant Community 32 (mg/kg dry wt) a

Cadmium Surface Soil - Dist. 3 - 1,500 m Threshold Level - Invertebrate Community 140 (mg/kg dry wt) a

Cadmium Surface Soil - Dist. 4 - 3,500 m Threshold - Mammalian Insectivores (shrew) 0.36 (mg/kg dry wt) a

Cadmium Surface Soil - Dist. 4 - 3,500 m

Threshold - Avian Ground Insectivores 

(woodcock) 0.77 (mg/kg dry wt)

a

Cadmium Surface Soil - Dist. 4 - 3,500 m Threshold Level - Plant Community 32 (mg/kg dry wt) a

Cadmium Surface Soil - Dist. 4 - 3,500 m Threshold Level - Invertebrate Community 140 (mg/kg dry wt) a

Cadmium Surface Soil - Dist. 5 - 7,500 m Threshold - Mammalian Insectivores (shrew) 0.36 (mg/kg dry wt) a

Cadmium Surface Soil - Dist. 5 - 7,500 m

Threshold - Avian Ground Insectivores 

(woodcock) 0.77 (mg/kg dry wt)

a

Cadmium Surface Soil - Dist. 5 - 7,500 m Threshold Level - Plant Community 32 (mg/kg dry wt) a

Cadmium Surface Soil - Dist. 5 - 7,500 m Threshold Level - Invertebrate Community 140 (mg/kg dry wt) a

Cadmium Water-column Community Threshold Level 0.25 (ug/L) c

Cadmium Water-column Community Frank-effect Level 2 (ug/L) c

Dioxin Fish - Avian Piscivores NOAEL (merganser) 0.0000014 (mg/kg BW/day) f

Dioxin Fish - Avian Piscivores LOAEL (merganser) 0.000014 (mg/kg BW/day) f

Dioxin Fish - Mammalian Piscivores NOAEL (mink)

0.000000771 (mg/kg 

BW/day) f
Table 1 – PB-HAP Benchmarks Included in the Environmental Risk Screen

EcoHAP Assessment Endpoint Benchmark Effects Level Benchmark Value

Benchmark 

Source

Dioxin Fish - Mammalian Piscivores LOAEL (mink) 0.00000771 (mg/kg BW/day) f

Dioxin Sediment Community Threshold Level 0.00000116 (mg/kg dry wt) g

Dioxin Surface Soil - Dist. 1 - 312 m Threshold - Mammalian Insectivores (shrew) 0.0000002 (mg/kg dry wt) h

Dioxin Surface Soil - Dist. 2 - 850 m Threshold - Mammalian Insectivores (shrew) 0.0000002 (mg/kg dry wt) h

Dioxin Surface Soil - Dist. 3 - 1,500 m Threshold - Mammalian Insectivores (shrew) 0.0000002 (mg/kg dry wt) h

Dioxin Surface Soil - Dist. 4 - 3,500 m Threshold - Mammalian Insectivores (shrew) 0.0000002 (mg/kg dry wt) h

Dioxin Surface Soil - Dist. 5 - 7,500 m Threshold - Mammalian Insectivores (shrew) 0.0000002 (mg/kg dry wt) h

Dioxin Water-column Community Threshold Level 0.000012 (ug/L) i

Dioxin Water-column Community Frank-effect Level 0.1 (ug/L) i

Mercuric Chloride Sediment Community Threshold Level 0.15 (mg/kg dry wt) a

Mercuric Chloride Sediment Community Probable-effect Level 0.7 (mg/kg dry wt) j

Mercuric Chloride Surface Soil - Dist. 1 - 312 m Threshold Level - Plant Community 0.3 (mg/kg dry wt) k

Mercuric Chloride Surface Soil - Dist. 1 - 312 m Threshold Level - Invertebrate Community 0.1 (mg/kg dry wt) k

Mercuric Chloride Surface Soil - Dist. 2 - 850 m Threshold Level - Plant Community 0.3 (mg/kg dry wt) k

Mercuric Chloride Surface Soil - Dist. 2 - 850 m Threshold Level - Invertebrate Community 0.1 (mg/kg dry wt) k

Mercuric Chloride Surface Soil - Dist. 3 - 1,500 m Threshold Level - Plant Community 0.3 (mg/kg dry wt) k

Mercuric Chloride Surface Soil - Dist. 3 - 1,500 m Threshold Level - Invertebrate Community 0.1 (mg/kg dry wt) k

Mercuric Chloride Surface Soil - Dist. 4 - 3,500 m Threshold Level - Plant Community 0.3 (mg/kg dry wt) k

Mercuric Chloride Surface Soil - Dist. 4 - 3,500 m Threshold Level - Invertebrate Community 0.1 (mg/kg dry wt) k

Mercuric Chloride Surface Soil - Dist. 5 - 7,500 m Threshold Level - Plant Community 0.3 (mg/kg dry wt) k

Mercuric Chloride Surface Soil - Dist. 5 - 7,500 m Threshold Level - Invertebrate Community 0.1 (mg/kg dry wt) k

Mercuric Chloride Water-column Community Threshold Level 0.77 (ug/L) c

Mercuric Chloride Water-column Community Frank-effect Level 1.4 (ug/L) c

Mercury (methyl) Fish - Avian Piscivores NOAEL (merganser) 0.013 (mg/kg BW/day) f

Mercury (methyl) Fish - Avian Piscivores LOAEL (merganser) 0.078 (mg/kg BW/day) f

Mercury (methyl) Fish - Mammalian Piscivores NOAEL (mink) 0.0247 (mg/kg BW/day) e

Mercury (methyl) Fish - Mammalian Piscivores LOAEL (mink) 0.123 (mg/kg BW/day) e

Mercury (methyl) Sediment Community Threshold Level 0.2 (mg/kg dry wt) l

Mercury (methyl) Sediment Community Probable-effect Level 1 (mg/kg dry wt) l

Mercury (methyl) Surface Soil - Dist. 1 - 312 m Threshold Level - Invertebrate Community 0.67 (mg/kg dry wt) I

Mercury (methyl) Surface Soil - Dist. 2 - 850 m Threshold Level - Invertebrate Community 0.67 (mg/kg dry wt) i

Mercury (methyl) Surface Soil - Dist. 3 - 1,500 m Threshold Level - Invertebrate Community 0.67 (mg/kg dry wt) i

Mercury (methyl) Surface Soil - Dist. 4 - 3,500 m Threshold Level - Invertebrate Community 0.67 (mg/kg dry wt) i

Mercury (methyl) Surface Soil - Dist. 5 - 7,500 m Threshold Level - Invertebrate Community 0.67 (mg/kg dry wt) i

Mercury (methyl) Water-column Community Threshold Level 0.0028 (ug/L) c

Mercury (methyl) Water-column Community Frank-effect Level 0.099 (ug/L) c

PAH Fish - Mammalian Piscivores NOAEL (mink) 0.417 (mg/kg BW/day) e

PAH Fish - Mammalian Piscivores LOAEL (mink) 4.17 (mg/kg BW/day) e
Table 1 – PB-HAP Benchmarks Included in the Environmental Risk Screen

EcoHAP Assessment Endpoint Benchmark Effects Level Benchmark Value

Benchmark 

Source

PAH Sediment Community No-effect Level 0.032 (mg/kg dry wt) b

PAH Sediment Community Threshold Level 0.15 (mg/kg dry wt) l, h

PAH Sediment Community Probable-effect Level 1.45 (mg/kg dry wt) l

PAH Surface Soil - Dist. 1 - 312 m Threshold - Mammalian Insectivores (shrew) 1.52 (mg/kg dry wt) h

PAH Surface Soil - Dist. 2 - 850 m Threshold - Mammalian Insectivores (shrew) 1.52 (mg/kg dry wt) h

PAH Surface Soil - Dist. 3 - 1,500 m Threshold - Mammalian Insectivores (shrew) 1.52 (mg/kg dry wt) h

PAH Surface Soil - Dist. 4 - 3,500 m Threshold - Mammalian Insectivores (shrew) 1.52 (mg/kg dry wt) h

PAH Surface Soil - Dist. 5 - 7,500 m Threshold - Mammalian Insectivores (shrew) 1.52 (mg/kg dry wt) h

PAH Water-column Community Threshold Level 0.014 (ug/L) a, h, m

PAH Water-column Community Frank-effect Level 0.24 (ug/L) a

Lead Ambient Air NAAQS Secondary Standard 0.15 ug/m3n

Benchmark Sources:

a. U.S. EPA OSWER

b. Environment Canada

c. U.S. EPA OW

d. CA DTSC HERD (2009)

e. Sample, et. al, 1996

f. U.S. EPA 1995

g. U.S. EPA Region 3, 4, and 5.

h. U.S. EPA Region 5

i. U.S. EPA Region 4

j. Florida DEP, NOAA

k. ORNL and EPA Region 4 and 6

l. MacDonald, et. al. 2000

m. U.S. EPA Region 6

n. NAAQS for Lead, Secondary Standard
Table 2 – Acid Gas Benchmarks Included in the Environmental Risk Screen

a- Note that the human health RfC is 20 μg/m3, which is lower than the ecological benchmark.

Acid Gas Chronic 90-day Benchmark in μg/m3

Hydrochloric acid - LOEL 50a

Hydrofluoric acid – Plant Community LOEL 0.5

Hydrofluoric acid – Plant Community LOEL 0.4
Table 3 – Hydrofluoric Acid Ecological Screening Data for Each

Phosphoric Acid Manufacturing Facility

NA – Not applicable, this is a facility level metric.

1 – If the average concentration of all offsite data points in the modeling domain does not exceed the benchmark then “No” will appear in the 

column. If the average concentration of all offsite data points in the modeling domain exceeds the benchmark, then the number of times the 

concentration exceeds the benchmark will appear in the column. 

2- The percent of the offsite modeled area that exceeded the benchmark. A value of 0% indicates that none of the individual modeled data 

points exceeded the benchmark. 

3 –The average HF concentration for all offsite points that exceeded the benchmark divided by the benchmark. 

4- The highest offsite HF concentration for a single point at each facility divided by the benchmark.

Facility 

NEI ID

Canadian LOEL of 0.4 ug/m3 Washington State LOEL of 0.5 ug/m3

Does Average Facility 

Concentration Exceed 

Benchmark?1

% of Area Modeled 

Exceeding2

Does Average Facility 

Concentration Exceed 

Benchmark?1

% of Area Modeled 

Exceeding2

570008 No 2% No 2%

4056 No 6% No 3%

45571 No 3% No 2%

93004 No <1% No <1%

50059 No 1% No 1%

12286 No 1% No 1%

59305 No <1% No <1%

570005 No <1% No <1%

6147 No 0% No 0%

26584 No 0% No 0%

387 No 0% No 0%

Category NA 1% NA 1%
Appendix 8

Acute Noncancer Risk Analysis for Phosphate Fertilizer and Phosphoric Acid 

Manufacturing
Acute Modeling Approach

Initial acute screening risk calculations were performed with the HEM-3 model. 

HEM-3 estimates acute (1-hour) impacts at both polar and census block receptors, refer 

to Table 1. It is assumed for this short period of time that an exposed individual could be 

located at any off-site location. The lack of readily available detailed property boundary 

information for many of the facilities evaluated made it difficult to determine whether 

receptors were on- or off-site. In the absence of such information, the first ring of polar 

receptors was placed 100 meters from the plant center for many facilities. However, 

these polar rings often transected on-site locations, restricting public access to exposures 

at these levels and thereby overestimating exposures.

The screening approach used by HEM-3 to estimate maximum 1-hour exposures 

also likely overestimates. To estimate maximum 1-hour concentrations at each receptor, 

HEM-3 sums the maximum concentrations attributed to each source, regardless of 

whether those maximum concentrations occurred during the same hour. In other words, 

HEM-3 assumes that the maximum impact from each source at each receptor occurs at 

the same time. In actuality, maximum impacts from different sources may occur at 

different times. 

This appendix includes the results of refined acute assessments performed for 

facilities that exceeded short-term health benchmarks using the HEM-3 approach, refer to 

Table 2. The refinements can address both areas of conservatism described above. The 

simplest refinement is to plot the HEM-3 polar grid results on aerial photographs of the 

facilities; This allows the assessment of off-site locations that may be accessible to the 

public (e.g., roadways and public buildings.). In cases where this refinement results in 

lower acute risks no further refinement was conducted. However, if acute risk still 

remain at or near levels of concern a further refinement is conducted. This second 

refinement includes remodeling with the AERMOD model directly (instead of using 

HEM) to estimate maximum 1-hour concentrations at each receptor by pairing individual 

source impacts in time and space. The AERMOD results are then plotted on aerial 

photographs as noted above to determine off-site impacts. Modeling with AERMOD 

also allows the estimation of the frequency of higher impacts at or above the health levels 

of concern. The attached figures present the estimated hazard quotient values (the 

modeled 1-hour concentration of a pollutant divided by its short-term health benchmark) 

at the polar receptors near the facilities. Each set of figures is prefaced with a description 

of the maximum off-site hazard quotient value and where that value occurs. Refer to 

below for a brief summary;

• NEI45571; maximum offsite acute HQ(REL) for hydrofluoric acid of 2; east of 

facility along Pamlico Sound (waterbody)

• NEILA0930004; maximum offsite acute HQ(REL) for hydrofluoric acid of 2, 

southeast of the facility
REL AEGL1 AEGL2 ERPG1 ERPG2

22093NEILA0930005 Elemental Gaseous Mercury 4.2E-01 0.0E+00 1.5E-04 0.0E+00 1.2E-04

12057NEIFL0570008 Hydrofluoric acid 2.9E-01 8.5E-02 3.5E-03 4.4E-02 4.4E-03

12105NEIRFL050059 Hydrofluoric acid 1.6E-01 4.5E-02 1.9E-03 2.3E-02 2.3E-03

28059NEIMS059305 Arsenic compounds 1.2E-01 0.0E+00 0.0E+00 0.0E+00 0.0E+00

16029NEIIDR029000 Hydrofluoric acid 1.1E-01 3.3E-02 1.4E-03 1.7E-02 1.7E-03

12105NEIFL12286 Hydrofluoric acid 7.6E-02 2.2E-02 9.1E-04 1.1E-02 1.1E-03

28059NEIMS059305 Hydrofluoric acid 5.7E-02 1.7E-02 6.8E-04 8.6E-03 8.6E-04

22093NEILA0930005 Arsenic compounds 4.2E-02 0.0E+00 0.0E+00 0.0E+00 0.0E+00

37013NEI45571 Hydrofluoric acid 3.4E-02 9.9E-03 4.1E-04 5.1E-03 5.1E-04

12057NEIFL0570008 Arsenic compounds 2.5E-02 0.0E+00 0.0E+00 0.0E+00 0.0E+00

12057NEIFL0570005 Arsenic compounds 2.3E-02 0.0E+00 0.0E+00 0.0E+00 0.0E+00

12057NEIFL0570005 Hydrofluoric acid 2.2E-02 6.4E-03 2.6E-04 3.3E-03 3.3E-04

12047NEIFLT$4056 Hydrofluoric acid 2.1E-02 6.3E-03 2.6E-04 3.2E-03 3.2E-04

12057NEIFL0570005 Elemental Gaseous Mercury 2.0E-02 0.0E+00 6.9E-06 0.0E+00 5.9E-06

12105NEIRFL050059 Arsenic compounds 1.2E-02 0.0E+00 0.0E+00 0.0E+00 0.0E+00

16077NEI26584 Hydrofluoric acid 1.2E-02 3.5E-03 1.4E-04 1.8E-03 1.8E-04

16077NEI26584 Arsenic compounds 1.1E-02 0.0E+00 0.0E+00 0.0E+00 0.0E+00

12105NEIFL12286 Arsenic compounds 6.5E-03 0.0E+00 0.0E+00 0.0E+00 0.0E+00

12047NEIFLT$4056 Arsenic compounds 5.0E-03 0.0E+00 0.0E+00 0.0E+00 0.0E+00

37013NEI45571 Arsenic compounds 2.8E-03 0.0E+00 0.0E+00 0.0E+00 0.0E+00

37013NEI45571 Elemental Gaseous Mercury 2.5E-03 0.0E+00 8.9E-07 0.0E+00 7.5E-07

56037NEI387 Hydrofluoric acid 2.2E-03 6.5E-04 2.7E-05 3.3E-04 3.3E-05

22093NEILA0930005 Hydrofluoric acid 2.0E-03 5.9E-04 2.4E-05 3.0E-04 3.0E-05

12047NEIFLT$4056 Elemental Gaseous Mercury 1.7E-03 0.0E+00 5.9E-07 0.0E+00 5.0E-07

56037NEI387 Arsenic compounds 1.6E-03 0.0E+00 0.0E+00 0.0E+00 0.0E+00

16077NEI26584 Elemental Gaseous Mercury 1.5E-03 0.0E+00 5.4E-07 0.0E+00 4.6E-07

28059NEIMS059305 Elemental Gaseous Mercury 1.3E-03 0.0E+00 4.7E-07 0.0E+00 4.0E-07

12057NEIFL0570008 Elemental Gaseous Mercury 1.3E-03 0.0E+00 4.5E-07 0.0E+00 3.9E-07

12105NEIRFL050059 Elemental Gaseous Mercury 1.3E-03 0.0E+00 4.4E-07 0.0E+00 3.8E-07

12105NEIFL12286 Elemental Gaseous Mercury 3.3E-04 0.0E+00 1.2E-07 0.0E+00 1.0E-07

16029NEIIDR029000 Arsenic compounds 2.1E-04 0.0E+00 0.0E+00 0.0E+00 0.0E+00

56037NEI387 Elemental Gaseous Mercury 1.4E-04 0.0E+00 4.8E-08 0.0E+00 4.1E-08

16029NEIIDR029000 Elemental Gaseous Mercury 8.8E-05 0.0E+00 3.1E-08 0.0E+00 2.6E-08

12047NEIFLT$4056 Beryllium compounds 0.0E+00 0.0E+00 0.0E+00 0.0E+00 1.3E-04

12057NEIFL0570005 Beryllium compounds 0.0E+00 0.0E+00 0.0E+00 0.0E+00 9.8E-05

Table 1 – Maximum Predicted Acute Risks (HEM-3)

Pollutant

Maximum Hazard Quotient1

Facility NEI ID

Phosphate Fertilizer Production

Screen with Emissions Multiplier 10(x)

 1 of 5
REL AEGL1 AEGL2 ERPG1 ERPG2

Table 1 – Maximum Predicted Acute Risks (HEM-3)

Pollutant

Maximum Hazard Quotient1

Facility NEI ID

Phosphate Fertilizer Production

Screen with Emissions Multiplier 10(x)

12057NEIFL0570008 Beryllium compounds 0.0E+00 0.0E+00 0.0E+00 0.0E+00 9.4E-06

12105NEIFL12286 Beryllium compounds 0.0E+00 0.0E+00 0.0E+00 0.0E+00 2.5E-06

12105NEIRFL050059 Beryllium compounds 0.0E+00 0.0E+00 0.0E+00 0.0E+00 4.4E-06

16029NEIIDR029000 Beryllium compounds 0.0E+00 0.0E+00 0.0E+00 0.0E+00 2.7E-07

16077NEI26584 Beryllium compounds 0.0E+00 0.0E+00 0.0E+00 0.0E+00 9.2E-06

22093NEILA0930005 Beryllium compounds 0.0E+00 0.0E+00 0.0E+00 0.0E+00 4.7E-05

28059NEIMS059305 Beryllium compounds 0.0E+00 0.0E+00 0.0E+00 0.0E+00 3.1E-05

37013NEI45571 Beryllium compounds 0.0E+00 0.0E+00 0.0E+00 0.0E+00 3.5E-06

56037NEI387 Beryllium compounds 0.0E+00 0.0E+00 0.0E+00 0.0E+00 2.7E-06

12047NEIFLT$4056 Gaseous Divalent Mercury 0.0E+00 0.0E+00 0.0E+00 0.0E+00 0.0E+00

12057NEIFL0570005 Gaseous Divalent Mercury 0.0E+00 0.0E+00 0.0E+00 0.0E+00 0.0E+00

12057NEIFL0570008 Gaseous Divalent Mercury 0.0E+00 0.0E+00 0.0E+00 0.0E+00 0.0E+00

12105NEIFL12286 Gaseous Divalent Mercury 0.0E+00 0.0E+00 0.0E+00 0.0E+00 0.0E+00

12105NEIRFL050059 Gaseous Divalent Mercury 0.0E+00 0.0E+00 0.0E+00 0.0E+00 0.0E+00

16029NEIIDR029000 Gaseous Divalent Mercury 0.0E+00 0.0E+00 0.0E+00 0.0E+00 0.0E+00

16077NEI26584 Gaseous Divalent Mercury 0.0E+00 0.0E+00 0.0E+00 0.0E+00 0.0E+00

22093NEILA0930005 Gaseous Divalent Mercury 0.0E+00 0.0E+00 0.0E+00 0.0E+00 0.0E+00

28059NEIMS059305 Gaseous Divalent Mercury 0.0E+00 0.0E+00 0.0E+00 0.0E+00 0.0E+00

37013NEI45571 Gaseous Divalent Mercury 0.0E+00 0.0E+00 0.0E+00 0.0E+00 0.0E+00

56037NEI387 Gaseous Divalent Mercury 0.0E+00 0.0E+00 0.0E+00 0.0E+00 0.0E+00

 Note: BOLD indicates acute risks greater than 1

REL AEGL1 AEGL2 ERPG1 ERPG2

37013NEI45571 Hydrofluoric acid 22.43E+00 7.10E-01 2.91E-02 3.64E-01 3.64E-02

22093NEILA0930004 Hydrofluoric acid 22.20E+00 6.44E-01 2.64E-02 3.30E-01 3.30E-02

12105NEIRFL050059 Hydrofluoric acid 22.20E+00 6.43E-01 2.64E-02 3.29E-01 3.29E-02

16029NEIIDR029000 Hydrofluoric acid 21.75E+00 5.14E-01 2.11E-02 2.63E-01 2.63E-02

12057NEIFL0570008 Hydrofluoric acid 21.42E+00 4.16E-01 1.71E-02 2.13E-01 2.13E-02

12105NEIFL12286 Hydrofluoric acid 21.10E+00 3.21E-01 1.32E-02 1.65E-01 1.65E-02

28059NEIMS059305 Hydrofluoric acid 21.06E+00 3.10E-01 1.27E-02 1.59E-01 1.59E-02

12057NEIFL0570005 Hydrofluoric acid 29.73E-01 2.85E-01 1.17E-02 1.46E-01 1.46E-02

12047NEIFLT$4056 Hydrofluoric acid 26.00E-01 1.76E-01 7.20E-03 9.00E-02 9.00E-03

Phosporic Acid Manufacturing

Facility NEI ID Pollutant

Maximum Hazard Quotient1

1 Some maximum acute impacts may be at onsite locations.

 2 of 5
REL AEGL1 AEGL2 ERPG1 ERPG2

Table 1 – Maximum Predicted Acute Risks (HEM-3)

Pollutant

Maximum Hazard Quotient1

Facility NEI ID

Phosphate Fertilizer Production

Screen with Emissions Multiplier 10(x)

37013NEI45571 Elemental Gaseous Mercury 4.43E-01 0.00E+00 1.56E-04 0.00E+00 1.33E-04

22005NEI6147 Hydrofluoric acid 24.03E-01 1.18E-01 4.84E-03 6.05E-02 6.05E-03

12047NEIFLT$4056 Arsenic compounds 2.09E-01 0.00E+00 0.00E+00 0.00E+00 0.00E+00

12105NEIFL12286 Arsenic compounds 1.92E-01 0.00E+00 0.00E+00 0.00E+00 0.00E+00

16077NEI26584 Hydrofluoric acid 2, 3 1.84E-01 5.37E-02 2.20E-03 2.75E-02 2.75E-03

56037NEI387 Hydrofluoric acid 27.28E-02 2.13E-02 8.74E-04 1.09E-02 1.09E-03

28059NEIMS059305 Arsenic compounds 6.65E-02 0.00E+00 0.00E+00 0.00E+00 0.00E+00

12105NEIRFL050059 Arsenic compounds 4.86E-02 0.00E+00 0.00E+00 0.00E+00 0.00E+00

37013NEI45571 Hydrochloric acid 1.94E-02 1.51E-02 1.23E-03 9.05E-03 1.36E-03

12057NEIFL0570008 Arsenic compounds 8.92E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00

37013NEI45571 Arsenic compounds 7.58E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00

16077NEI26584 Arsenic compounds 7.58E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00

12057NEIFL0570005 Arsenic compounds 6.99E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00

12047NEIFLT$4056 Elemental Gaseous Mercury 6.08E-03 0.00E+00 2.15E-06 0.00E+00 1.82E-06

37013NEI45571 Acrolein 3.93E-03 1.43E-04 4.28E-05 8.94E-05 2.89E-05

16029NEIIDR029000 Arsenic compounds 3.93E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00

16077NEI26584 Elemental Gaseous Mercury 2.58E-03 0.00E+00 9.10E-07 0.00E+00 7.74E-07

12057NEIFL0570005 Elemental Gaseous Mercury 2.02E-03 0.00E+00 7.14E-07 0.00E+00 6.07E-07

12105NEIFL12286 Elemental Gaseous Mercury 1.95E-03 0.00E+00 6.88E-07 0.00E+00 5.85E-07

56037NEI387 Arsenic compounds 1.88E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00

22093NEILA0930004 Arsenic compounds 1.80E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00

22005NEI6147 Arsenic compounds 1.20E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00

12105NEIRFL050059 Elemental Gaseous Mercury 1.05E-03 0.00E+00 3.71E-07 0.00E+00 3.16E-07

28059NEIMS059305 Elemental Gaseous Mercury 1.02E-03 0.00E+00 3.60E-07 0.00E+00 3.06E-07

37013NEI45571 Formaldehyde 9.99E-04 5.00E-05 3.23E-06 4.58E-05 4.58E-06

22093NEILA0930004 Elemental Gaseous Mercury 4.28E-04 0.00E+00 1.51E-07 0.00E+00 1.28E-07

12057NEIFL0570008 Elemental Gaseous Mercury 3.97E-04 0.00E+00 1.40E-07 0.00E+00 1.19E-07

16029NEIIDR029000 Elemental Gaseous Mercury 2.73E-04 0.00E+00 9.64E-08 0.00E+00 8.19E-08

22005NEI6147 Elemental Gaseous Mercury 1.28E-04 0.00E+00 4.52E-08 0.00E+00 3.84E-08

37013NEI45571 Benzyl chloride 9.90E-05 0.00E+00 0.00E+00 4.57E-06 4.57E-07

56037NEI387 Elemental Gaseous Mercury 9.81E-05 0.00E+00 3.46E-08 0.00E+00 2.94E-08

37013NEI45571 Acetaldehyde 4.12E-05 2.39E-07 3.95E-08 1.07E-06 5.37E-08

37013NEI45571 Benzene 3.41E-05 2.61E-07 1.71E-08 2.77E-07 9.24E-08

37013NEI45571 Chloroform 1.34E-05 0.00E+00 6.46E-09 0.00E+00 8.35E-09

37013NEI45571 Methyl bromide 1.39E-06 0.00E+00 6.63E-09 0.00E+00 2.86E-08

37013NEI45571 Carbon disulfide 7.13E-07 1.11E-07 8.84E-09 1.43E-06 2.76E-08

37013NEI45571 Methylene chloride 7.03E-07 1.43E-08 5.18E-09 9.84E-09 3.78E-09

 3 of 5
REL AEGL1 AEGL2 ERPG1 ERPG2

Table 1 – Maximum Predicted Acute Risks (HEM-3)

Pollutant

Maximum Hazard Quotient1

Facility NEI ID

Phosphate Fertilizer Production

Screen with Emissions Multiplier 10(x)

37013NEI45571 Toluene 4.58E-07 2.26E-08 3.77E-09 8.92E-08 1.54E-08

37013NEI45571 Phenol 9.37E-08 9.37E-09 6.11E-09 1.43E-08 2.86E-09

37013NEI45571 Xylenes (mixed) 5.71E-08 2.25E-09 3.14E-10 0.00E+00 0.00E+00

37013NEI45571 Tetrachloroethene 5.06E-08 4.21E-09 6.32E-10 1.49E-09 7.22E-10

37013NEI45571 Styrene 4.04E-08 9.98E-09 1.54E-09 4.04E-09 7.71E-10

37013NEI45571 o-Xylene 7.03E-09 0.00E+00 0.00E+00 0.00E+00 0.00E+00

12047NEIFLT$4056 Beryllium compounds 0.00E+00 0.00E+00 0.00E+00 0.00E+00 4.42E-05

12057NEIFL0570005 Beryllium compounds 0.00E+00 0.00E+00 0.00E+00 0.00E+00 1.87E-05

12057NEIFL0570008 Beryllium compounds 0.00E+00 0.00E+00 0.00E+00 0.00E+00 1.88E-06

12105NEIFL12286 Beryllium compounds 0.00E+00 0.00E+00 0.00E+00 0.00E+00 2.02E-05

12105NEIRFL050059 Beryllium compounds 0.00E+00 0.00E+00 0.00E+00 0.00E+00 6.83E-06

16029NEIIDR029000 Beryllium compounds 0.00E+00 0.00E+00 0.00E+00 0.00E+00 1.79E-06

16077NEI26584 Beryllium compounds 0.00E+00 0.00E+00 0.00E+00 0.00E+00 9.56E-06

22005NEI6147 Beryllium compounds 0.00E+00 0.00E+00 0.00E+00 0.00E+00 4.88E-07

22093NEILA0930004 Beryllium compounds 0.00E+00 0.00E+00 0.00E+00 0.00E+00 2.59E-06

28059NEIMS059305 Beryllium compounds 0.00E+00 0.00E+00 0.00E+00 0.00E+00 2.73E-06

37013NEI45571 Beryllium compounds 0.00E+00 0.00E+00 0.00E+00 0.00E+00 8.21E-06

56037NEI387 Beryllium compounds 0.00E+00 0.00E+00 0.00E+00 0.00E+00 3.21E-06

37013NEI45571 Chlorobenzene 0.00E+00 1.62E-08 1.08E-09 0.00E+00 0.00E+00

37013NEI45571 Cumene 0.00E+00 7.20E-10 1.20E-10 0.00E+00 0.00E+00

37013NEI45571 Dimethyl sulfate 0.00E+00 1.36E-05 2.63E-06 0.00E+00 0.00E+00

37013NEI45571 Ethyl benzene 0.00E+00 2.34E-08 6.84E-10 0.00E+00 0.00E+00

37013NEI45571 Ethylene dibromide 0.00E+00 3.13E-10 2.26E-10 0.00E+00 0.00E+00

37013NEI45571 Ethylene dichloride 0.00E+00 0.00E+00 0.00E+00 6.78E-09 1.67E-09

37013NEI45571 Methyl chloride 0.00E+00 0.00E+00 9.47E-09 0.00E+00 2.17E-08

37013NEI45571 Methyl hydrazine 0.00E+00 0.00E+00 1.80E-06 0.00E+00 0.00E+00

37013NEI45571 Methyl methacrylate 0.00E+00 9.72E-09 1.39E-09 0.00E+00 0.00E+00

37013NEI45571 Methyl tert-butyl ether 0.00E+00 6.60E-09 5.66E-10 0.00E+00 0.00E+00

37013NEI45571 n-Hexane 0.00E+00 0.00E+00 1.89E-10 0.00E+00 0.00E+00

37013NEI45571 Propionaldehyde 0.00E+00 1.17E-07 2.08E-08 0.00E+00 0.00E+00

37013NEI45571 Vinyl acetate 0.00E+00 1.07E-08 4.09E-10 1.43E-08 9.92E-10

 Note: BOLD indicates acute risks greater than 1

2 An acute hourly multiplier of 1 was used vs 10

1 Some maximum acute impacts may be at onsite locations.

 4 of 5
REL AEGL1 AEGL2 ERPG1 ERPG2

Table 1 – Maximum Predicted Acute Risks (HEM-3)

Pollutant

Maximum Hazard Quotient1

Facility NEI ID

Phosphate Fertilizer Production

Screen with Emissions Multiplier 10(x)

3 An acute hourly multiplier of 2 was used for hydrofluoric acid emissions vs 10 based upon site specific data, refer to 

memorandum entitled, “Emissions Data Used in Residual Risk Modeling: Phosphoric Acid and Phosphate Fertilizer 

Production Source Categories” available in the docket for this action. 

 5 of 5
37013NEI45571 Hydrofluoric acid REL 2.4 1.6 Max off-site HQ with emission specific multipliers

22093NEILA0930004 Hydrofluoric acid REL 2.2 2.2 Max off-site HQ with emission specific multipliers

12105NEIRFL050059 Hydrofluoric acid REL 2.2 0.6 Max off-site HQ with emission specific multipliers

16029NEIIDR029000 Hydrofluoric acid REL 1.8 0.6 Max off-site HQ with emission specific multipliers

Phosphoric Acid Manufacturing

1

 Indicates modeling technique used to refined estimates

Table 2: Maximum Predicted Acute Risks Greater than 1 (Refined Approach)

Facility NEI ID Pollutant Criteria

HEM-3

(Screening)

Refined

Results Refined Modeling Approach 1
Phosphoric Acid Manufacturing 

(Refined Acute Risk Assessment)

• Figure 1 – NEI4571; maximum offsite acute 

HQ(REL) for hydrofluoric acid of 2; east of facility

• Figure 2 – NEILA0930004 maximum offsite acute 

HQ(REL) for hydrofluoric acid of 2; southeast of 

facility
Copyrighted page(s) removed. Copyrighted 

material is not available in Regulations.gov since 

it may not be reproduced without consent of the 

copyright holder. 

Removed pages can be viewed at the USEPA 

Docket Center, Public Reading Room. 

Address: 1301 Constitution Ave, NW 

Room 3334 

Washington, DC 20004 

Telephone: 202-566-1744 

Email: docket-customerservice@epa.gov
Appendix 9

Technical Support Document Human Health Multipathway Residual Risk Screening Assessment 

for the Phosphate Fertilizer Production and Phosphoric Acid Manufacturing Source Categories
Technical Support Document: Human Health Multipathway Residual 

Risk Screening Assessment for the Phosphate Fertilizers Production 

and Phosphoric Acid Manufacturing Source Categories

REVISED DRAFT

July 7, 2014

Prepared For:

U.S. Environmental Protection Agency

Office of Air Quality Planning and Standards

Research Triangle Park, NC 27711

Under Contract No. EP-12-010, Work Assignment 2-20

Prepared By:

ICF International

2635 Meridian Pkwy.

Suite 200

Durham, NC 27713
[This page intentionally left blank.]
Human Health Multipathway Residual Risk Screening Assessment for the Phosphate Fertilizers 

Production and Phosphoric Acid Manufacturing Source Categories

CONTENTS

Tables........................................................................................................................................iii

Figures.......................................................................................................................................iv

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

2. Tier 1 Emission Screening Assessment............................................................................. 2

2.1 Tier 1 Emission Screening Results for these Source Categories ...............................3

3. Tier 2 Emission Screening Assessment............................................................................. 4

3.1 Adjustment Factors Applied to Tier 1 Screening Values ............................................4

3.2 Calculating Refined Tier 2 Fisher Screening Values ..................................................5

3.3 Finalizing Tier 2 Farmer Screening Values ................................................................7

3.4 Tier 2 Screening Results for these Source Categories...............................................7

4. Tier 3 Emission Screening Assessment............................................................................. 9

4.1 Lake Assessment ......................................................................................................9

4.2 Tier 3 Lake Assessments Results for these Source Categories...............................11

4.3 Plume-rise Assessment ...........................................................................................12

4.4 Tier 3 Plume-rise Assessment Results for these Source Categories .......................13

4.5 Assessment of Time-series Meteorology and Effective Release 

Heights....................................................................................................................14

4.6 Tier 3 Time-series Assessment Results for these Source Categories......................14

5. Comparison to Tier 1 Screening Results for this Source Category....................................16

6. References .......................................................................................................................18

TABLES

Table 2-1. Tier 1 Emission Screening Results for these Source Categories............................... 3

Table 3-1. Tier 2 Screening Results for these Source Categories .............................................. 8

Table 4-1. Results of Tier 3 Lake Assessments for Mercury Emissions ....................................11

Table 4-2. Results of Tier 3 Plume-rise Assessments for Mercury Emissions ...........................13

Table 4-3. Tier 3 SVs Before and After Additional Lake and Plume-rise 

Assessments ......................................................................................................14

Table 4-4. Tier 3 Fisher SVs Before and After Time-series Assessments .................................16

Table 5-1. Comparison of Tier 1 and Tier 3 Screening Results for these Source 

Categories ..........................................................................................................18

Draft iii July 2014
Human Health Multipathway Residual Risk Screening Assessment for the Phosphate Fertilizers 

Production and Phosphoric Acid Manufacturing Source Categories

FIGURES

Figure 4-1. Example of Lake Removed from Screening—Likely Evaporated or 

Drained...............................................................................................................10

Figure 4-2. Example of Lake Removed from Screening—Likely an Industrial Lake...................10

Figure 4-3. Lakes Assessed for NEIMS059305 (Phosphate Fertilizers Production 

Source Category)................................................................................................12

Figure 4-4. The Modeling Spatial Scenario Used for the Unnamed Lake and for 

“Crane Lakes”.....................................................................................................15

Figure 4-5. The Modeling Spatial Scenario Used for “Black Creek Cooling Pond”.....................16

Draft iv July 2014
Human Health Multipathway Residual Risk Screening Assessment for the Phosphate Fertilizers 

Production and Phosphoric Acid Manufacturing Source Categories

1. Introduction

This report provides a discussion on a screening-level assessment of human health risks, via 

the ingestion pathway, from exposure to persistent and bioaccumulative hazardous air 

pollutants (PB-HAPs) emitted to the air from phosphate fertilizers production facilities and 

phosphoric acid manufacturing facilities. While this report contains a discussion on some of the 

screening methods, a more detailed discussion on the development, methods, and 

implementation of the screening assessment can be found in Appendix 5 of the Risk Report.1

Where appropriate, this discussion provides references to particular sections of Appendix 5 to 

the Risk Report, where further details on the subject matter can be found.

Cadmium, dioxins, mercury, and polycyclic aromatic hydrocarbons (PAHs) were the evaluated 

PB-HAP groups,

2 selected from a larger list of PB-HAPs based in part on ranking their toxicityweighted air emissions across all source categories. The evaluated exposure scenarios were a 

fisher scenario (i.e., consumption of fish from a lake) and a farmer scenario (i.e., consumption of 

farm food chain [FFC] media such as animal products, fruits and vegetables, and incidental 

ingestion of soil). EPA evaluates the two scenarios together (i.e., the sum of fisher and farmer 

exposures and risks) in the first tier of the assessment, and EPA evaluates them separately in 

the second and third tiers. EPA designed the screening scenarios to be health-protective in 

estimating exposures and risks. Specifically, they are intended to avoid underestimating 

ingestion exposure and risk due to the air emissions of these PB-HAPs from facilities nationwide 

evaluated in the Risk and Technology Review (RTR) program.

EPA uses the TRIM.FaTE3 model to evaluate the environmental transport, transfer, and fate of 

PB-HAP air emissions. The MIRC4 program then calculates FFC chemical uptake along with 

human exposure, human health cancer risks, and human health non-cancer hazard quotients

(HQs) resulting from ingestion of fish and FFC media. For screening assessments of 

multipathway risk across the RTR program, EPA used these models with fate and transport 

parameter values, idealized spatial layouts, and generalized meteorology that overall create 

ingestion exposure situations that are unlikely to be exceeded at any actual facility evaluated in 

the RTR program. ICF (hereafter “we”) conducted these model runs to identify threshold 

emission rates (hereafter “thresholds”) that correspond to levels of concern for cancer risks and 

HQs. The levels of concern are incremental, lifetime, human cancer risks of 1-in-one million (for 

PAHs and dioxins) and chronic, non-cancer HQs of 1 (for cadmium and mercury).

For the iterative (“tiered”) screening-level ingestion assessment described in this report, we

assessed the potential for ingestion risks above levels of concern in the phosphate fertilizers

production source category and the phosphoric acid manufacturing source category by 

comparing PB-HAP emissions from facilities within the source categories to the thresholds. We 

use the term “screening values” (SVs) to refer to the ratio of a facility’s emissions of a PB-HAP 

1

Appendix 5 to the Risk Report is the Technical Support Document for the TRIM-Based Multipathway Tiered 

Screening Methodology for RTR.

2

The phrase “PB-HAP group” is used to distinguish the individual PB-HAP chemicals and congeners from the overall 

family (grouping) of those chemicals. For example, EPA’s ingestion risk methods in its Risk and Technology Review 

(RTR) program evaluate emissions of 17 individual congeners of chlorinated dibenzo-p-dioxins and chlorinated 

dibenzofurans in the “dioxin” PB-HAP group. 

3

TRIM.FaTE is the Fate, Transport, and Ecological Exposure module of the Total Risk Integrated Methodology 

modeling system. Additional TRIM.FaTE information can be found at: http://www2.epa.gov/fera/total-risk-integratedmethodology-trim-trimfate. 

4

MIRC is the Multimedia Ingestion Risk Calculator.

Draft 1 July 2014

 
Human Health Multipathway Residual Risk Screening Assessment for the Phosphate Fertilizers 

Production and Phosphoric Acid Manufacturing Source Categories

to that PB-HAP’s threshold. When rounded to one significant figure, an SV of 1 or smaller

indicates that the emissions did not exceed the threshold and are unlikely to pose health risks 

above levels of concern (i.e., the emissions “screened out”). An SV of 2 or greater indicates that 

the emissions exceeded the threshold and potentially pose health risks above levels of concern.

The phosphate fertilizers production source category contains 11 facilities, and the phosphoric 

acid manufacturing source category contains 12 facilities. As described in this report, most 

emissions of the evaluated PB-HAPs screened out in the Tier 1 assessment. For the phosphoric 

acid manufacturing source category, one facility’s cadmium and mercury emissions exceeded

their respective Tier 1 thresholds but screened out in the Tier 2 assessment. For the phosphate 

fertilizers production source category, one facility’s mercury emissions exceeded the Tier 1

threshold, but the emissions screened out in Tier 3, which included additional assessments of 

its emissions and corresponding meteorology and lake data.

In Sections 2, 3, and 4, we briefly describe the Tiers 1, 2, and 3 screening methods and discuss

the results of those screening assessments. We provide in Section 5 a summary comparison of 

the Tier 1 screening results with the screening results after the Tiers 2 and 3 assessments.

2. Tier 1 Emission Screening Assessment

The thresholds developed for Tier 1 are intended to apply to any U.S. facility; that is, EPA

designed them to be applicable to every facility regardless of the facility’s characteristics and 

surrounding environment and the population exposed in its vicinity. The approach to develop the 

thresholds5 assumes ingestion exposure situations that are unlikely to be exceeded at any 

actual facility evaluated in the RTR program. This health-protectiveness is achieved by several 

methods, including (but not limited to): (1) an idealized spatial layout where the evaluated lake 

and farming area are within 2 to 3 km of the emission source and, thus, receive high levels of 

direct deposition; (2) a watershed design where the lake receives substantial indirect chemical 

deposition through large magnitudes of runoff and erosion; (3) a meteorology scenario that 

enhances air concentrations and chemical deposition; and, (4) relatively high ingestion rates 

representing subsistence-level consumption of fish and FFC media. 

Each evaluated PB-HAP group has a single Tier 1 threshold, as shown in Table 2-1 (column e). 

For Tier 1, EPA normalizes the emissions of all dioxin congeners to 2,3,7,8-tetrachlorodibenzop-dioxin-equivalent emissions, and EPA normalizes the emissions of all PAH congeners to 

benzo(a)pyrene-equivalent emissions.

6 These normalization factors (i.e., exposure equivalency 

factors [EEFs]) are based on oral toxicity factors and factors related to the congeners’

environmental fate and transport characteristics as analyzed in the TRIM.FaTE model.7 For 

mercury, EPA established the threshold for emissions of divalent mercury, although the 

threshold is based on hazards associated with ingestion of methyl mercury from contaminated 

fish (with relatively minimal contribution from ingestion of divalent mercury from FFC media).

8

5

See Appendix 5 of the Risk Report (Sections 1 and 2, and Attachment A) for additional information.

6

We sometimes use 2,3,7,8-TCDD and BaP this report as shorthand terms for 2,3,7,8-tetrachlorodibenzo-p-dioxin

and benzo(a)pyrene, respectively.

7

See Appendix 5 to the Risk Report (Section A.2.7 of Attachment A) for additional information on Tier 1 screening 

methods.

8

TRIM models mercury transformation in the environment, and exposure in the screening scenario associated with 

ingestion of fish contaminated with methyl mercury (related to emissions of divalent mercury and transformation to 

methyl mercury) results in much larger hazards than exposure associated with ingestion of fish or FFC contaminated 

with divalent mercury. (See Section A.2 and A.5.3 in Appendix 5 to the Risk Report.)

Draft 2 July 2014

 
Human Health Multipathway Residual Risk Screening Assessment for the Phosphate Fertilizers 

Production and Phosphoric Acid Manufacturing Source Categories

2.1 Tier 1 Emission Screening Results for these Source Categories

As shown in Table 2-1, emissions of dioxins and PAHs (emitted by one facility in the phosphoric 

acid manufacturing source category) screened out because the emissions were approximately a 

factor of 5 below the respective thresholds.

In the phosphate fertilizers production source category, cadmium emissions screened out. 

Mercury emissions exceeded the threshold by approximately a factor of 20 at one (out of 11) 

mercury-emitting facilities. This facility was NEIMS059305, a Mississippi Phosphates Corp.

facility in Pascagoula, MS.

In the phosphoric acid manufacturing source category, cadmium and mercury emissions 

exceeded their respective thresholds by approximately factors of 2 and 7, respectively, at one 

(out of 12) facilities emitting cadmium and mercury. This facility was NEI45571, a PCS 

Phosphates Co. facility in Aurora, NC. PAH and dioxin emissions screened out. When PAH and 

dioxin SVs were summed together per facility (i.e., calculating the total PAH+dioxin screeninglevel potential cancer risk), the highest total “cancer SV” was 0.4 and screened out (not shown 

in table).

Table 2-1. Tier 1 Emission Screening Results for these Source Categories

Source Cat.

PB-HAP

Group

(a)

Num. Facil. 

Exceeding 

Threshold

(b)

Largest 

SV

[(d)/(e)]

(c)

Facil. with Largest SV

(d)

Emiss. at 

Facil. with 

Largest 

SV (TPY)a

(e)

Emiss. 

Screening 

Thresh.

(TPY)

Phosphate 

Fertilizers 

Production

Mercuryb

(non-cancer)

1 20 NEIMS059305

(Mississippi Phosphates Corp.;

Pascagoula, MS)

6.74E-03 3.16E-04

Cadmium

(non-cancer)

0 0.9 NEIMS059305

(Mississippi Phosphates Corp.;

Pascagoula, MS)

1.11E-02 1.18E-02

PAHs as BaP

(cancer)

N/E N/E N/E N/E 2.58E-03

Dioxins as 

2,3,7,8-TCDD

(cancer)

N/E N/E N/E N/E 2.81E-09

Phosphoric 

Acid 

Manufacturing

Mercuryb

(non-cancer)

1 7 NEI45571

(PCS Phosphate Co. Inc. - Aurora;

Aurora, NC)

2.23E-03 3.16E-04

Cadmium

(non-cancer)

1 2 NEI45571

(PCS Phosphate Co. Inc. - Aurora;

Aurora, NC)

2.13E-02 1.18E-02

PAHs as BaP

(cancer)

0 0.2 NEI45571

(PCS Phosphate Co. Inc. - Aurora;

Aurora, NC)

5.54E-04 2.58E-03

Dioxins as 

2,3,7,8-TCDD

(cancer)

0 0.2 NEI45571

(PCS Phosphate Co. Inc. - Aurora;

Aurora, NC)

5.63E-10 2.81E-09

Draft 3 July 2014
Human Health Multipathway Residual Risk Screening Assessment for the Phosphate Fertilizers 

Production and Phosphoric Acid Manufacturing Source Categories

Source Cat.

PB-HAP

Group

(a)

Num. Facil. 

Exceeding 

Threshold

(b)

Largest 

SV

[(d)/(e)]

(c)

Facil. with Largest SV

(d)

Emiss. at 

Facil. with 

Largest 

SV (TPY)a

(e)

Emiss. 

Screening 

Thresh.

(TPY)

Notes: N/E = not emitted. TPY = tons per year. SVs rounded to one significant figure. Cell shadings indicate records corresponding 

to SVs of 2 or greater (i.e., the emissions exceeded the threshold). a

We normalized PAH and dioxin emissions in this column to BaP and 2,3,7,8-TCDD, respectively, for oral toxicity and Tier 1 

modeled environmental fate and transport.

b

The emission screening threshold for mercury applies to emissions of divalent mercury, although EPA derived it based on hazards 

associated with ingestion of fish contaminated with methyl mercury (i.e., emissions of divalent mercury, which transforms to methyl 

mercury within the environment, accumulates in fish tissue, and is consumed by the fisher). The contribution from hazards 

associated ingestion of divalent mercury from contaminated FFC or fish media is relatively minimal.

3. Tier 2 Emission Screening Assessment

The generic site characteristics used in Tier 1 can differ from those actually present at many 

facilities. In Tier 2, EPA takes into account some site-specific characteristics—specifically, 

meteorological conditions and locations of potentially fishable lakes—to apply adjustments to

the Tier 1 SVs, creating Tier 2 SVs.

9

For a facility undergoing Tier 2 screening, EPA partitions the 50-km area around the facility into

eight “wedges” or “octants” centered on the facility and compartmentalizing areas north of the 

facility, northeast, east, and so on. As described in Section 3.1, the lake locations and some 

meteorological conditions related to a facility can vary by octant in Tier 2. Though the distance 

from facility to modeled farm remains fixed for each octant, EPA adjusts the location of the 

modeled lake based on actual lake locations. This affects chemical concentrations in some FFC 

media due to modeled erosion and runoff patterns that are directed mostly toward the lake. 

Therefore, for every relevant PB-HAP emitted by a facility, EPA calculates the fisher SV of each 

lake, using the distance from facility to lake and the meteorology conditions of the octant the 

lake is in; and, since lake location can affect the farm SV, EPA calculates a corresponding 

farmer SV for each lake scenario.

Based on the Tier 2 SVs and other considerations with respect to the source category being 

assessed, EPA might conduct a Tier 3 assessment on some facilities, leading to adjustments to 

the Tier 2 SVs for those facilities. The results we present in Section 3.4 are prior to this Tier 3 

assessment; we discuss the Tier 3 assessment and its effects on SVs in Section 4.

3.1 Adjustment Factors Applied to Tier 1 Screening Values

In developing the adjustment factors used in Tier 2, we conducted hundreds of TRIM.FaTE and 

MIRC model runs corresponding to ranges of meteorological characteristics expected to be 

found at most U.S. locations, and corresponding to lake locations ranging from 2 km to 40 km 

away from the facility. As in Tier 1, the model runs provided emission rates corresponding to risk 

and hazard levels of concern. Because each modeled scenario has different environmental fate 

and transport characteristics, each scenario also has different EEFs to relate PAH and dioxin 

congeners to BaP and 2,3,7,8-TCDD, respectively. Unlike in Tier 1, EPA calculates EEFs and 

thresholds separately in Tier 2 for the fisher and farmer scenarios. The Tier 2 adjustment factors 

are the ratios of the Tier 2 EEFs and thresholds to those of Tier 1. EPA applies the Tier 2 

9

See Appendix 5 to the Risk Report (Sections 1 and 3, and Attachment B) for additional information on Tier 2 

screening methods.

Draft 4 July 2014

 
Human Health Multipathway Residual Risk Screening Assessment for the Phosphate Fertilizers 

Production and Phosphoric Acid Manufacturing Source Categories

adjustment factors to Tier 1 SVs in order to calculate Tier 2 fisher SVs and Tier 2 farmer SVs.

We discuss below an additional adjustment factor based on wind direction.

In order to match an assessed facility in Tier 2 with its appropriate meteorology- and lakerelated Tier 2 adjustment factors, EPA matches the facility with the same meteorology station to 

which it is matched in the RTR inhalation risk assessment (i.e., typically the closest station), and 

EPA also identifies qualifying lakes around the facility. Some of a facility’s meteorology patterns

vary by octant, and qualifying lakes (discussed below) can be scattered around multiple octants 

and at multiple distances from the facility. Therefore, for each lake in each octant around a 

facility, EPA matches its combination of meteorology data and lake distance to its appropriate 

scenario in the Tier 2 model runs and, thus, to the appropriate Tier 2 adjustment factors.

EPA bases the meteorology data used in Tier 2 on the same dataset used in RTR inhalation risk 

assessments, consisting of more than 800 meteorology stations nationwide. We processed 

hourly data from the meteorology stations to derive typical or total station-specific values of wind 

speed, wind direction, mixing height, and precipitation. For each station, we then matched the 

combination of these values with the most representative meteorological scenario used in the 

Tier 2 model runs. The matching method is health-protective (i.e., wind speeds and mixing 

heights match to their closest, smaller value in the Tier 2 model runs, and a precipitation value

to its closest, larger value). We also calculated the frequency of winds blowing toward each of 

the octants around each station. EPA compares the frequency of winds blowing into an octant to 

the frequency used in Tier 1, creating a Tier 2 wind frequency adjustment factor.

EPA analyzes all qualifying lakes within 50 km of the facility. A qualifying lake (1) is at least 25 

acres in surface area but no larger than 100,000 acres;10 (2) is classified by USGS as a lake or 

reservoir but not used for disposal, treatment, or evaporator purposes; (3) does not have a 

name suggesting it is not intended for public use (e.g., “Gavin Fly Ash Impoundment”); and, (4) 

has not been removed in previous source category assessments based on these and other 

inspection methods. EPA assigns each lake to its respective octant around the assessed facility 

and to the closest lake distance value used in the Tier 2 model runs, where the matching 

method is health-protective (i.e., a lake’s distance matches to its closest, smaller value in the 

Tier 2 model runs). In cases where there are no qualifying lakes around the facility, there is no 

exposure or risk to the fisher.

The Tier 2 fisher and farmer SVs calculated at this point in the assessment are “initial SVs”; this 

is because subsequent steps in Tier 2 refine the fisher SVs (see Section 3.2) and select one of 

the potentially many farmer SVs (see Section 3.3).

3.2 Calculating Refined Tier 2 Fisher Screening Values

The subsistence fisher in Tier 1 consumes fish from one lake at the subsistence ingestion rates, 

regardless of the lake’s ability to support a fish population that allows for that level of 

consumption across many years. In Tier 2, EPA considers the lake’s capacity to accommodate 

the assumed fish ingestion rates. For any particular lake, the fisher in Tier 2 catches and 

consumes fish from that lake at an amount proportional to the area of the lake, which serves as 

an approximation of the lake’s biological productivity.

11 In order to consume fish at the ingestion 

rate used in Tier 2, the fisher might need to fish multiple lakes (and be exposed to several 

different chemical concentration levels in fish).

10Discussed further in Attachment B of Appendix 5 to the Risk Report.

11See Appendix 5 to the Risk Report (Attachment B) for additional information on potential lake productivity.

Draft 5 July 2014

 
Human Health Multipathway Residual Risk Screening Assessment for the Phosphate Fertilizers 

Production and Phosphoric Acid Manufacturing Source Categories

The initial Tier 2 fisher SVs and the Tier 1 fisher SVs use a fixed set of fish ingestion rates 

covering a range of ages. The highest fish ingestion rate among the age groups is 373 g fish/d 

for adults, which for simplicity EPA uses to estimate a lake’s capacity to support subsistence 

fishing. Smaller lakes are unlikely to support this level of fish consumption over time (i.e., the 

lake would be “fished out”), although the fisher could catch and consume that amount of fish

collectively from multiple small lakes. In Tier 2, EPA assumes that a fisher can catch and 

consume no more than 1 g fish/d/acre lake without impacting the lake’s long-term sustainability. 

In order to reach the assumed ingestion rate, EPA assumes that the fisher catches and 

consumes fish from one or more lakes until he is able to fish a total of 373 acres of lake and 

consume a total of 373 g fish/d (i.e., the screening scenario adult fish ingestion rate) or until 

there are no more qualifying lakes to fish to reach 373 total acres.

EPA uses the initial Tier 2 fisher SVs calculated for all qualifying lakes around a facility, along 

with the acreage values of those lakes, to identify which lake(s) the fisher catches and 

consumes fish from and the amount of fish caught from each lake. Using this information, EPA

refines the Tier 2 fisher SV based on a scenario assuming consumption of fish from multiple 

lakes. EPA conducts the steps below for each PB-HAP group emitted by a facility.

1. Identify the lake with the highest chemical concentration in fish12 for a given PB-HAP group, 

according to the initial Tier 2 fisher SVs.

2. If the lake with the highest concentration is at least 373 acres in area, then the fisher

consumes fish from only that lake, at the subsistence-level ingestion rates (the adult rate 

being 373 g fish/d). That is, the refined Tier 2 fisher SV equals the initial Tier 2 fisher SV.

3. If the lake with the highest concentration is smaller than 373 acres, then the fisher catches 

only the amount of fish consistent with the assumption of 1 g fish/d/acre lake. That is, 

multiply the initial Tier 2 fisher SV for that lake by [its acreage]÷[373 acres]; put another way, 

multiply the SV by [the daily mass of fish consumed from the lake]÷[373 g fish/d]. Apply the 

following logic to subsequent lakes. 

a. The fisher catches and consumes fish from one or more additional lakes, in order 

of highest to lowest concentration. If multiple lakes have the same concentration, 

then he fishes them in order of largest to smallest acreage. He stops fishing 

when he fishes lakes totaling 373 acres or when there are no more qualifying 

lakes to fish to reach 373 total acres. Scale each additional lake’s initial Tier 2 

fisher SV by acreage (i.e., by fish consumption rate) in the same way as the first 

fished lake (see above).

b. If the cumulative area of all qualifying lakes is smaller than 373 acres (i.e., if all 

lakes together cannot support a 373 g fish/d adult ingestion rate, assuming 1 g 

fish/d/acre lake), then the fisher does not consume the full 373 g fish/d.

c. If the cumulative area of all qualifying lakes is larger than 373 acres, the fisher

consumes only the amount of fish from the final lake necessary to reach 373 g 

fish/day. That is, scale the initial Tier 2 fisher SV for that lake by [the remaining 

acreage necessary to reach 373 acres, after fishing the prior lake(s)]÷[373 

acres].

12In the screening assessments, for a given ingestion rate, SVs are directly and proportionally related to chemical 

concentrations in the exposure media. For simplicity and to more clearly relate the potential health impacts of 

consuming a lake’s fish to the chemical concentrations in those fish, EPA discusses fish concentrations in describing 

the methods of calculating the refined Tier 2 angler SVs.

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d. The refined Tier 2 fisher SV (reflecting the refined fisher methods) is the sum of 

the adjusted Tier 2 fisher SVs of all fished lakes as calculated above. 

3.3 Finalizing Tier 2 Farmer Screening Values

For a given PB-HAP group emitted by a facility, EPA identifies the largest Tier 2 farmer SV from 

all the relevant meteorology and lake-distance scenarios. This helps to avoid underestimating 

exposure and risk.

3.4 Tier 2 Screening Results for these Source Categories

In this section, we discuss the Tier 2 screening results. The results in this section use the 

refined Tier 2 fisher SVs (discussed in Section 3.2) and the largest Tier 2 farmer SVs (discussed 

in Section 3.3).

As shown in Table 3-1, all emissions screened out except for mercury emissions from 

NEIMS059305 (phosphate fertilizers production source category). The fisher SV at that facility 

was 3, while the farmer SV was 0.002. Mercury and cadmium emissions from NEI45571 

(phosphoric acid production source category), which exceeded their respective Tier 1 thresholds 

by factors of 7 and 2, screened out in Tier 2—its mercury and cadmium Tier 2 fisher SVs were 1 

and 0.3, respectively, and the Tier 2 farmer SVs were 0.0007 and 0.02, respectively.

When PAH and dioxin SVs were summed together per facility in the phosphoric acid 

manufacturing source category (i.e., calculating the total PAH+dioxin screening-level potential 

cancer risk), the highest total “cancer SVs” screened out (SV = 0.04 for fisher, 0.07 for farmer; 

not shown in table).

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Table 3-1. Tier 2 Screening Results for these Source Categories

Facil. Type

PB-HAP

Group

Num. Facil. 

Exceeding

Fisher

Threshold

Num. Facil. 

Exceeding

Farmer 

Threshold

Num. Facil. 

Exceeding 

Threshold,

Either Fisher

or Farmer,

Both Fisher

and Farmer

Largest 

Fisher

SV

Largest 

Farmer 

SV Facil. with Largest Fisher SV

Facil. with Largest Farmer 

SV

Phosphate 

Fertilizers 

Production

Mercurya

(non-cancer)

1 1 1

0

3 0.002 NEIMS059305

(Mississippi Phosphates Corp.;

Pascagoula, MS)

NEIMS059305

(Mississippi Phosphates Corp.;

Pascagoula, MS)

Cadmium

(non-cancer)

0 0 0

0

0.1 0.008 NEIMS059305

(Mississippi Phosphates Corp.;

Pascagoula, MS)

NEIMS059305

(Mississippi Phosphates Corp.;

Pascagoula, MS)

PAHs as BaP

(cancer)

N/E N/E N/E N/E N/E N/E N/E

Dioxins as 

2,3,7,8-TCDD

(cancer)

N/E N/E N/E N/E N/E N/E N/E

Phosphoric 

Acid 

Manufacturing

Mercurya

(non-cancer)

0 0 0

0

1 0.0007 NEI45571

(PCS Phosphate Co. Inc. - Aurora;

Aurora, NC)

NEI45571

(PCS Phosphate Co. Inc. - Aurora;

Aurora, NC)

Cadmium

(non-cancer)

0 0 0

0

0.3 0.02 NEI45571

(PCS Phosphate Co. Inc. - Aurora;

Aurora, NC)

NEI45571

(PCS Phosphate Co. Inc. - Aurora;

Aurora, NC)

PAHs as BaP

(cancer)

0 0 0

0

0.01 0.06 NEI45571

(PCS Phosphate Co. Inc. - Aurora;

Aurora, NC)

NEI45571

(PCS Phosphate Co. Inc. - Aurora;

Aurora, NC)

Dioxins as 

2,3,7,8-TCDD

(cancer)

0 0 0

0

0.03 0.01 NEI45571

(PCS Phosphate Co. Inc. - Aurora;

Aurora, NC)

NEI45571

(PCS Phosphate Co. Inc. - Aurora;

Aurora, NC)

Notes: N/E = not emitted. TPY = tons per year. SVs rounded to one significant figure. Cell shadings indicate records corresponding to SVs of 2 or greater (i.e., the emissions exceeded 

the threshold). Emissions and screening thresholds not shown for space purposes. a

The emission screening threshold for mercury applies to emissions of divalent mercury, although EPA derived it based on hazards associated with ingestion of fish contaminated with 

methyl mercury (i.e., emissions of divalent mercury, which transforms to methyl mercury within the environment, accumulates in fish tissue, and is consumed by the fisher). The 

contribution from hazards associated ingestion of divalent mercury from contaminated FFC or fish media is relatively minimal.

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4. Tier 3 Emission Screening Assessment

Based on the Tier 2 SVs and other considerations with respect to the source category being 

assessed, EPA may conduct Tier 3 assessments on some facilities. We discuss in this section

the Tier 3 assessment conducted for mercury hazards for NEIMS059305 in the phosphate 

fertilizers production source category, whose Tier 2 fisher SV for mercury was 3.

4.1 Lake Assessment

Other than verifying a facility’s emission rates, one of the simplest assessments EPA can 

conduct (beyond the Tier 2 methods) is assessing the 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 EPA uses 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., it has evaporated or been drained) or it uses a classification that 

might not accurately reflect the lake’s purpose or type. EPA uses aerial and street-view imagery 

and internet searches to quickly ascertain if an assessed lake actually exists, if it is probably 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 it 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 Figure 4-1 identifies a lake from the USGS dataset 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 Figure 4-2 identifies a 

lake that originally qualified, 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, EPA evaluates all lakes from which the fish catches 

and consumes fish according to the refined Tier 2 fisher methods discussed in Section 3.2. EPA 

permanently removes, from the screening assessment, all lakes meeting the criteria discussed 

in the previous paragraphs, so that they are no longer used in any screening assessment. If 

EPA removes a lake 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, EPA evaluates additional lakes that were not fished, using the methods discussed in 

Section 3.2, in descending order of initial Tier 2 fisher SV and lake area.

If EPA removes any lakes from the screening, EPA conducts the screening again (using the Tier 

2 methods) with the revised lake dataset. Although EPA conducts this additional lake 

assessment based on the Tier 2 SVs of a particular set of facilities, lakes removed during that 

assessment could affect the SVs of other facilities in the 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 SVs of both facilities. EPA does not sum SVs across facilities.

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Figure 4-1. Example of Lake 

Removed from Screening—Likely 

Evaporated or Drained

Note: Aerial imagery from ESRI World Imagery (2014).

Figure 4-2. Example of Lake Removed from Screening—Likely an 

Industrial Lake

Note: Aerial imagery from ESRI World Imagery (2014).

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4.2 Tier 3 Lake Assessments Results for these Source Categories

The information in Table 4-1 is related to the lakes evaluated in the screening of NEIMS059305 

in the phosphate fertilizers production source category. The table includes one lake we removed 

during the assessment (see gray shaded row). For lakes remaining in the screening after the 

lake assessment, the table contains information on how much of the subsistence fisher’s daily 

fish consumption came from each lake, and how much of the corresponding fisher SV was 

attributable to each lake. 

Table 4-1. Results of Tier 3 Lake Assessments for Mercury Emissions

Facil. NEI ID Lake Name

Lake 

Area

(acres)

Selection

Order

Percent 

of 

Ingested 

Fish 

Coming 

From 

This Lake

Percent of 

Tier 3

Fisher SV

Attributed 

To This 

Lake

Reason for 

Removal

NEIMS059305

(phosphate 

fertilizers 

production source 

category)

Name Not Provided 49 1 9% 16% --

Crane Lakes 82 2 10% 14% --

Black Creek Cooling 

Ponda 59 3 81% 70% --

Presley Pond 30 RMV-1 -- -- Evaporated 

or drained

Notes: 

The lakes shown in this table, and their corresponding selection order and percentages of ingestion and SV, are related only to the 

mercury emissions from this facility. For other PB-HAP groups emitted by this facility, the fisher might fish a different set of lakes 

and/or with a different selection order.

“RMV” indicates that we removed the lake during Tier 3. The number beside the “RMV” designation was for map labeling purposes 

(see the figure in this subsection). The percentages of ingestion and SV reflect the assessment after we removed this lake. a

Black Creek Cooling Pond appears to have been created by a dam and, despite its name, we found no reliable information 

suggesting it is restricted access, unfishable, or industrial.

Figure 4-3 is an aerial image delineating the lakes in Table 4-1. The labels of the lakes in the 

map correspond to the data in the “Selection Order” column of the table. Lakes labeled in the 

map with negative numbers (e.g., -1, -2, and so on) are qualifying lakes that the fisher did not 

fish because the fish ingestion rate was already met by fishing the first set of lakes (i.e., by 

fishing those labeled 1, 2, and so on). The map is focused on the fished and removed lakes, so 

it does not show all unfished lakes. Table 4-1, for space purposes, does not contain information 

on these unfished lakes. NEIMS059305 is close to the Gulf of Mexico and has many water 

bodies of varying types within 50 km; the red outlines and text in the map call attention to water 

bodies within the map extent that did not qualify for the Tier 2 because they appeared to be 

estuarine.

As discussed in Section 3.4, this facility’s mercury emissions exceeded the Tier 2 thresholds for 

fishers by approximately a factor of 3. Removing one lake from this facility’s screening did not 

effectively decrease its Tier 3 fisher SV, so we conducted a plume-rise assessment on the 

mercury emissions, as discussed in Sections 4.3 and 4.4.

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 Figure 4-3. Lakes Assessed for NEIMS059305 

(Phosphate Fertilizers Production Source 

Category)

Notes: Aerial imagery from ESRI (2014). Lake labels correspond to the 

“Selection Order” column in Table 4-1. The “lake” shown in Figure 4-1

corresponds to “RMV-1” here.

4.3 Plume-rise Assessment

In Tiers 1 and 2, all chemicals are emitted inside the mixing layer and are available for groundlevel exposure. In reality, the physical height of an emission source, in combination 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 upper-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.

EPA uses methods summarized by Seinfeld and Pandis (1998) to estimate how often a facility’s 

emissions reach the upper-air sink, which decreases availability for ground-level exposure. The 

methods to estimate the amount of chemical lost to the upper-air sink require use of the facility’s 

Draft 12 July 2014
Human Health Multipathway Residual Risk Screening Assessment for the Phosphate Fertilizers 

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corresponding meteorology data (e.g., air temperature and vertical temperature gradient, wind 

speed, and atmospheric 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. 

For each relevant emission source, EPA estimates the hourly effective release height (i.e., sum

of actual release height and plume rise) and compares it to the hourly mixing height to 

determine the mass of chemical remaining in the mixing layer. EPA compares the mass of 

chemical remaining in the mixing layer, summed across the sources, 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 height and plume rise. EPA applies 

this final plume-rise adjustment factor to the Tier 2 SVs.

4.4 Tier 3 Plume-rise Assessment Results for these Source Categories

The Tier 3 lake assessment did not effectively lower the Tier 3 fisher SV for mercury emissions 

from NEIMS059305 (phosphate fertilizers production source category; see Section 4.2). We

also conducted a plume-rise assessment on the emissions. Table 4-2 contains information on 

the mercury emissions and physical characteristics of each unique emission source, along with 

the fraction of mercury emitted from each source that remained in the mixing layer (i.e., is not 

deposited into the upper-air sink). We show in the last column the total fraction of emissions 

from all sources together that remained in the mixing layer. We calculated this value by 

weighting the value in the previous column (fraction remaining in the mixing layer, by source) by 

the fraction of mercury emissions coming from each source (i.e., the third column), and 

summing those weighted values. To account for these plume-rise data, we scaled the Tier 2 

fisher and farmer SVs by the fractions shown in the last column.

Table 4-2. Results of Tier 3 Plume-rise Assessments for Mercury Emissions

Facil. NEI ID

Source 

Num.

Fraction of 

Emiss. From 

This Source

Physical Characteristics of Emiss.

Source

Fraction of 

Emiss.

Remaining in 

Mixing Layer

Height 

(m)

Exit 

Gas 

Temp.

(K)

Inside 

Diameter 

(m)

Exit 

Gas 

Vel.

(m/s)

By 

Source

Facility 

Total

NEIMS059305

(phosphate 

fertilizers 

production source 

category)

1 0.0121 24.7 350 1.8 13.1 0.806 0.7679

2 0.9879 48.8 355 1.8 12.5 0.7675

Table 4-3 contains the previously calculated Tier 2 SVs for mercury (reflecting the assessment 

described in Section 3), along with the SVs after the Tier 3 lake and plume-rise assessments, 

for NEIMS059305 (phosphate fertilizers production source category).

While the Tier 3 lake assessments alone did not effectively decrease the SVs, the combined 

effect of the lake and plume-rise assessments was a decrease in the SVs from 3 to 2 for the 

fisher scenario, and from 0.002 to 0.001 for the farmer scenario. The mercury emissions still 

exceeded the threshold for the fisher scenario. These assessments did not impact SVs at any 

other facilities in the phosphate fertilizers production or phosphoric acid manufacturing source 

categories. We discuss in Sections 4.5 and 4.6 an additional assessment that we conducted on 

the emissions using time-series meteorology and release-height data.

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Table 4-3. Tier 3 SVs Before and After Additional Lake and Plume-rise Assessments

Facil. NEI ID Facil. Name Facil. City

Facil. 

State

Tier 2 and Tier 3 Mercury SVa

Before Tier 3 

Assessments

After Tier 3 

Lake 

Assessments

After Tier 3

Lake AND 

Plume-rise 

Assessments

Fisher Farmer Fisher Farmer Fisher Farmer

NEIMS059305

(phosphate 

fertilizers 

production 

source 

category)

Mississippi 

Phosphates 

Corp.

Pascagoula MS 3 0.002 3 0.002 2 0.001

a

SVs rounded to one significant figure. Cell shadings indicate SVs of 2 or greater (i.e., the emissions exceeded the threshold).

4.5 Assessment of Time-series Meteorology and Effective Release Heights

As discussed in Section 3, EPA bases the Tier 2 SVs 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 average 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. 

EPA also bases Tier 2 SVs on a constant emission release height that is always within the 

mixing layer. Including hourly effective release heights (discussed in Sections 4.3 and 4.4) along 

with hourly meteorology data further increases the accuracy of the estimates of potential risk.

For a facility undergoing a time-series assessment, EPA develops a time series of hourly 

effective release heights for sources emitting the assessed PB-HAP, and EPA identifies the Tier 

2 spatial scenario that best matches each lake from which the hypothetical fisher catches and 

consumes fish. Using this data along with the facility’s matching hourly meteorology data, the 

facility’s emission rates, and the subsistence ingestion rates, EPA models chemical fate and 

transport in TRIM.FaTE and calculates the corresponding screening-level risks using MIRC. 

This modeling results in a screening-level risk value for each lake, corresponding to ingestion of 

fish exposed to the evaluated PB-HAP. If the farmer scenario is specifically being assessed, the 

modeling will also result in a screening-level risk value for each farm. EPA multiplies the 

screening-level risk associated with each lake by the percent of daily ingested fish caught from

the lake (see Sections 3.2 and 4.2); summing those products across all modeled lakes results in 

the screening-level fisher risk (i.e., fisher SV) associated with the evaluated PB-HAP emitted by 

the facility. The time-series-adjusted Tier 3 SVs are compared to the Tier 2 SVs calculated 

previously.

4.6 Tier 3 Time-series Assessment Results for these Source Categories

In order to account for the potential effects on exposure and risk modeling from using timeseries meteorology and effective release heights in TRIM.FaTE, we ran TRIM.FaTE three times, 

corresponding to each of the three assessed lakes for the mercury assessment for 

NEIMS059305 (phosphate fertilizers production source category). We used the “lake at 10 km” 

spatial scenario, shown in Figure 4-4, to model concentrations in the unnamed lake and Crane 

Lakes (see Table 4-1). In reality, these two lakes were respectively 12 and 19 km from the 

facility. We used the “lake at 20 km” spatial scenario, shown in Figure 4-5, to model 

Draft 14 July 2014
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Production and Phosphoric Acid Manufacturing Source Categories

concentrations in Black Creek Cooling Pond (see Table 4-1), which in reality was 23 km from 

the facility.

 Figure 4-4. The Modeling Spatial Scenario Used for the 

Unnamed Lake and for “Crane Lakes”

Notes: Labels = surface parcel names for modeling. Shadings = ground cover type for

modeling. Arrows within scenario = direction of runoff and erosion. 

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Figure 4-5. The Modeling Spatial Scenario Used for “Black 

Creek Cooling Pond”

Notes: Labels = surface parcel names for modeling. Shadings = ground cover type for 

modeling. Arrows within scenario = direction of runoff and erosion.

Table 4-4 contains the Tier 3 fisher SVs for mercury emissions from NEIMS059305 (phosphate 

fertilizers production source category), both before and after the time-series assessments. Both 

SVs reflect the Tier 3 lake assessments (see Section 4.2) but not the plume-rise assessments 

(see Section 4.4); this is because the time-series assessment includes chemical loss due to 

plume rise into the upper-air sink. Beside the previous results, the table contains the fisher SV

after the time-series assessments. We did not assess farmer SVs with these time-series runs.

The time-series assessments decreased the fisher SV to below levels of concern (i.e., the 

emissions screened out). 

Table 4-4. Tier 3 Fisher SVs Before and After Time-series Assessments

Facil. NEI ID

Facil. 

Name Facil. City

Facil. 

State

Tier 3 Mercury Fisher SVa

Before Tier 3 Timeseries AssessmentsAfter Tier 3 Timeseries Assessments

NEIMS059305

(phosphate fertilizers 

production source 

category)

Mississippi 

Phosphates 

Corp.

Pascagoula MS 3 1

a

SVs rounded to one significant figure. Cell shadings indicate SVs of 2 or greater (i.e., the emissions exceeded the threshold). We 

only assessed the fisher scenario during the time-series assessments.

5. Comparison to Tier 1 Screening Results for this Source Category

In Table 5-1, we present a high-level comparison between the Tier 1 SVs and Tier 3 SVs. The 

Tier 3 SVs reflect the Tier 3 screening of mercury emissions from one facility; otherwise, the 

Draft 16 July 2014
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Tier 3 SVs are unchanged from the Tier 2 screening. The remainder of this section provides an 

overview of the results for each of the four PB-HAPs. 

• Mercury: 

– Emissions at one facility in each source category exceeded the Tier 1 threshold but 

screened out in Tier 3.

– The largest Tier 3 fisher and farmer SVs were respectively approximately 20 times 

and 20,000 times smaller than the largest Tier 1 SV (which combined fish- and FFCrelated exposure).

– When the largest Tier 3 fisher SV was combined with the largest Tier 3 farmer SV (to 

directly compare to the largest Tier 1 SV), the combined Tier 3 SV was approximately

20 times smaller (not shown in the table). This is because nearly all mercury 

exposure occurs through fish ingestion.

• Cadmium: 

– Emissions at one phosphoric acid manufacturing facility exceeded the Tier 1 

threshold but screened out in Tier 3.

– The largest Tier 3 fisher and farmer SVs were respectively approximately nine times 

and 100 times smaller than the largest Tier 1 SV (which combined fish- and FFCrelated exposure).

– When the largest Tier 3 fisher SV was combined with the largest Tier 3 farmer SV (to 

directly compare to the largest Tier 1 SV), the combined Tier 3 SV was approximately

three times smaller than the Tier 1 SV (not shown in the table). This is because nearly 

all cadmium exposure occurs through fish ingestion.

• PAHs:

– All emissions screened out.

– The largest Tier 3 fisher and farmer SVs were respectively approximately 20 times 

and three times smaller than the largest Tier 1 SV (which combined fish- and FFCrelated exposure). 

– When the largest Tier 3 fisher SV was combined with the largest Tier 3 farmer SV (to 

directly compare to the largest Tier 1 SV), the combined Tier 3 SV was approximately

three times smaller than the largest Tier 1 SV (not shown in the table).

• Dioxins:

– All emissions screened out. 

– The largest Tier 3 fisher and farmer SVs were respectively approximately seven times 

and 20 times smaller than the largest Tier 1 SV (which combined fish- and FFCrelated exposure). 

– When the largest Tier 3 fisher SV was combined with the largest Tier 3 farmer SV (to 

directly compare to the largest Tier 1 SV), the combined Tier 3 SV was approximately

five times smaller than the largest Tier 1 SV (not shown in the table).

• PAHs + Dioxins: 

– The facility-level sum of PAH and dioxin SVs screened out in all screening 

assessments (not shown in table). 

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Table 5-1. Comparison of Tier 1 and Tier 3 Screening Results for these Source Categories

Source Cat.

PB-HAP

Group

Num. Facil. Exceeding Threshold Largest SV

Tier 1, Combined 

Fisher and Farmer

Tier 3, 

Fisher

Tier 3, 

Farmer

Tier 1, Combined 

Fisher and Farmer

Tier 3, 

Fisher

Tier 3, 

Farmer

Phosphate 

Fertilizers 

Production

Mercurya

(non-cancer)

1 0 0 20 1 0.001

Cadmium

(non-cancer)

0 0 0 0.9 0.1 0.008

PAHs as BaP

(cancer)

N/E N/E N/E N/E N/E N/E

Dioxins as 

2,3,7,8-TCDD

(cancer)

N/E N/E N/E N/E N/E N/E

Phosphoric 

Acid 

Manufacturing

Mercurya

(non-cancer)

1 N/E N/E 7 1 0.0007

Cadmium

(non-cancer)

1 N/E N/E 2 0.3 0.02

PAHs as BaP

(cancer)

0 0 0 0.2 0.01 0.06

Dioxins as 

2,3,7,8-TCDD

(cancer)

0 0 0 0.2 0.03 0.01

Notes: N/E = not emitted. SVs rounded to one significant figure. Cell shadings indicate records corresponding to SVs of 2 or 

greater (i.e., the emissions exceeded the Tier 1 threshold). Emissions and screening thresholds not shown for space purposes. a

The emission screening threshold for mercury applies to emissions of divalent mercury, although EPA derived it based on 

hazards associated with ingestion of fish contaminated with methyl mercury (i.e., emissions of divalent mercury, which transforms 

to methyl mercury within the environment, accumulates in fish tissue, and is consumed by the fisher). The contribution from 

hazards associated ingestion of divalent mercury from contaminated FFC or fish media is relatively minimal.

6. References

ESRI (Environmental Systems Research Institute). 2014. World Imagery. Accessed June 12, 

2014. Available at: 

http://www.arcgis.com/home/item.html?id=10df2279f9684e4a9f6a7f08febac2a9.

Seinfeld, J.H., and S.N. Pandis. 1998. Atmospheric Chemistry and Physics: From Air Pollution 

to Climate Change, Wiley-Interscience, New York, pp. 931–933.

Draft 18 July 2014