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Residual Risk Assessment for the Off-Site Waste and Recovery 

Operations Source Category in Support of the February 2015 

Risk and Technology Review Final Rule

by

EPA’s Office of Air Quality Planning and Standards

Office of Air and Radiation

February 2015
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Table of Contents

1 Introduction........................................................................................................................ 4

2 Methods.............................................................................................................................. 4

2.1 Emissions and source data........................................................................................... 4

2.2 Dispersion modeling for inhalation exposure assessment........................................... 5

2.3 Estimating chronic human inhalation exposure .......................................................... 7

2.4 Acute Risk Screening and Refined Assessments ........................................................ 8

2.5 Multipathway and environmental risk screening ...................................................... 10

2.6 Dose-Response Assessment ...................................................................................... 13

2.6.1 Sources of chronic dose-response information .................................................. 13

2.6.2 Sources of acute dose-response information...................................................... 21

2.7 Risk Characterization ................................................................................................ 26

2.7.1 General ............................................................................................................... 26

2.7.2 Mixtures ............................................................................................................. 28

2.7.3 Facility-wide Risks ............................................................................................ 28

2.7.4 MACT-Allowable Risk...................................................................................... 29

3 Risk Results for the OSWRO Source Category............................................................... 29

3.1 Source Category Description and Results................................................................. 29

3.2 Risk Characterization ................................................................................................ 35

4 General Discussion of Uncertainties and How They Have Been Addressed................... 38

4.1 Exposure Modeling Uncertainties............................................................................. 38

4.2 Uncertainties in the Dose-Response Relationships................................................... 39

5 References........................................................................................................................ 49

Index of Tables

Table 2.2-1 AERMOD version 12345 model options for RTR modeling................................ 6

Table 2.6-1 Dose-Response Values for Chronic Inhalation Exposure to Carcinogens .......... 16

Table 2.6-2 Dose-Response Values for Chronic Inhalation Exposure to Noncarcinogens .... 18

Table 2.6-3 Dose-Response Values for Acute Exposure........................................................ 24

Table 3.1-1 Summary of Emissions from the OSWRO Source Category Used in the Residual 

Risk Assessment and Availability of Dose-Response Values ................................................. 30

Table 3.2-1 Summary of Source Category Level Inhalation Risks for OSWRO ................... 36

Table 3.2-2 Summary of Environmental Risks Screen Results for OSWRO.......................... 37

Table 3.2-3 Source Category Contribution to Facility-Wide Cancer Risks............................ 38

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 Technical Support Document for TRIM-Based Multipathway Screening 

Scenario for RTR: Summary of Approach and Evaluation
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Appendix 5 Detailed Risk Modeling Results

Appendix 6 Acute Impacts Refined Analysis

Appendix 7 Dispersion Model Receptor Revisions and Additions for OSWRO

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

CTE Central Tendency Estimate

ERPG Emergency Response Planning Guideline

HAP Hazardous Air Pollutant

HEM Human Exposure Model

HI Hazard index

HQ Hazard quotient

IRIS Integrated Risk Information System

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

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

RME Reasonable Maximum Exposure

RTR Risk and Technology

TOSHI Target-organ-specific hazard index

URE Unit risk estimate
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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 (HAP) 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 a memo entitled, “EPA’s Actions in Response to 

the Key Recommendations of the Science Advisory Board Review of Risk and Technology 

Review (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 off-site waste and recovery 

operations (OSWRO) source category. 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. 

2 Methods

2.1 Emissions and source data

The starting point for this assessment involved generating a list of facilities potentially subject 

to the OSWRO MACT. The initial list, which included over 400 facilities across the United 
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States, was developed from databases maintained for the Resource Conservation and 

Recovery Act (RCRA) and the EPA’s Office of Enforcement and Compliance Assurance 

(OECA), as well as from databases used in development of the original OSWRO MACT

standards. The facilities were checked against compliance databases, including the Air 

Facility System (AFS), and title V operating permit information to ensure that the facilities 

were still operating, were subject to the OSWRO MACT and were major sources of HAP 

emissions. The resulting facility list, which consisted of 53 facilities, was used to create a 

subset of the 2005 NEI that included facility, process and emissions data. Data from nine 

corporate entities were also gathered through a CAA section 114 questionnaire. Where data 

for a facility was available from the questionnaire, the questionnaire data was used; NEI data 

were used for the remaining facilities, where OSWRO emissions could be identified. These 

data underwent a QA/QC procedure, and revisions were made to the dataset as necessary. In 

addition, facility and industry representatives were contacted to clarify details and resolve 

issues with the data for several sources. In the end, data for 38 facilities were included in the 

modeling dataset. Details on the development of the emissions and source data for this source

category are discussed in a memorandum entitled, “Development of the RTR Emissions 

Dataset for the Off-Site Waste and Recovery Operations Source Category” available in the 

docket for this rule making. Section 3 below provides a summary of the emissions.

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 (HEM3). The approach used in applying this 

modeling system is outlined below, and further details are provided in Appendix 1 of this 

document, Technical Support document for HEM3 Modeling. The HEM3 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 HEM3 system, AERMOD version 12345, is a state-of-thescience Gaussian plume dispersion model that is preferred by EPA 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-by-hour 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.

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

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

summarized in Table 2.2-1 and are discussed further below.
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Table 2.2-1 AERMOD version 12345 model options for RTR modeling

Modeling Option Selected Parameter for chronic exposure

Type of calculations Hourly Ambient Concentration

Source type Point and Area

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 Included if source in urbanized area

Meteorology 1 year representative NWS from nearest site (over 800

stations)

In HEM3, meteorological data are ordinarily selected from a list of over 800 National 

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

Alaska, Hawaii, and Puerto Rico. 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 2011. While the 

selection of a single year may result in under-prediction of long-term ambient levels at some 

locations, likewise it may result in over-prediction at others. For each facility identified by its 

characteristic latitude and longitude coordinates, the closest meteorological station was used 

in the dispersion modeling. The average distance between a modeled facility and the 

applicable meteorological station was 23 miles (37 km). Appendix 2 of this document

(Meteorological Data Processing Using AERMET for HEM3) provides a complete listing of 

stations and assumptions along with further details used in processing the data through 

AERMET (the AERMOD meteorological data pre-processing program). 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 HEM3 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 7 to this document (Dispersion Model Receptor Revisions and Additions 

for the Off-Site Waste and Recovery Operations Source Category) for a discussion of user 

receptors and centroid location changes specific to this source category. The model accounts 

for the effects of multiple facilities when estimating concentration impacts at each block 

centroid. Typically we combined only the impacts of facilities within the same source 

category, and assessed chronic exposure and risk only for census blocks with at least one 
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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 emissions data contain 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. More description of our 

short-term assessment can be found in Section 2.4 of this document – Acute Risk Screening 

and Refined Assessments. 

Census block elevations for HEM3 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 1 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 the 

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 

particulate HAP; it will also reduce risk estimates for gaseous HAP, but typically by much 

less. Second, basing exposure estimates on average ambient concentrations at census block 
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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; 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 theoretical 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 Acute Risk Screening and Refined Assessments

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 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 more refined acute 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 are used 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 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. In some cases, this may 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 
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and boundaries of a facility 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 emission rate is defined as the total emissions for a year divided

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

screening was originally based on engineering judgement.

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 4 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.

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

our acute screening estimates to the extent possible. For the off-site waste and recovery 

operations source category, we conducted a review of the layout of the polar receptors around 

the facilities to determine the maximum off-site acute impact for the facilities that did not 

screen out during the initial run. Appendix 5 to this document (Draft Detailed Modeling 

Results) contains the initial acute results for the source category, while Appendix 6 to this 

document (Acute Impacts Refined Analysis) contains the refined acute results. 

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

location, and refined data where available. We used the following worst-case assumptions in 

our screening approach:
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 Peak 1-hour emissions were assumed to equal 10 times the average 1-hour emission 

rates.

 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 worst-case 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 

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

benchmarks are described in section 2.6 of this report.

2.5 Multipathway and environmental risk 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)1. The PB-HAP compounds or compound classes are identified 

for the screening from the EPA’s Air Toxics Risk Assessment Library [8]. Examples of PBHAP are cadmium compounds, chlordane, chlorinated dibenzodioxins and furans, DDE, 

heptachlor, hexachlorobenzene, hexachlorocyclohexane, lead compounds, mercury 

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

toxaphene, and trifluralin. 

Currently, the multipathway screen can be used to quantitatively estimate exposures and risks 

for the following five PB-HAP compounds: lead, cadmium compounds, chlorinated 

dibenzodioxins and furans, mercury compounds, and polycyclic organic matter (POM). 

These compounds are the focus of the screen because, based on current emissions 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 CAA. 

Potential impacts on human health from non-inhalation exposures to cadmium compounds, 

chlorinated dibenzodioxins and furans, mercury compounds, and polycyclic organic matter 

are evaluated using our screening scenario developed for use with the EPA’s peer-reviewed 

Total Risk Integrated Methodology – Fate, Transport, and Ecological Exposure 

 

1 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).
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(TRIM.FaTE2) model. With this approach, we first determine whether the facility-specific 

emission rates of each of the four PB-HAP listed above 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 thresholds 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 (of the four listed PB-HAP above) from each facility in the 

source category were compared to the emission rate threshold values for each of the four PBHAP 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 of the four PB-HAP listed above at which the maximum 

human health risk would be 1-in-1 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 I screen. 

See Appendix 4 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 

multi-pathway impacts. 

In evaluating the potential multi-pathway 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 

multi-pathway risks.

Emissions of polycyclic organic matter (analyzed as benzo(a)pyrene), polychlorinated 

biphenyls, lindane (gamma-hch), chlordane, hexachlorobenzene, methoxychlor, toxaphene, 

heptachlor, and trifluralin were identified in the emissions inventories for the OSWRO source 

category. 

 

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, 

polycyclic organic matter (POM), mercury (both inorganic mercury and methyl 

mercury) and lead.

 Two acid gases - hydrogen chloride (HCl), and hydrogen fluoride (HF). 

 

2 EPA’s Total Risk Integrated Methodology (General Information) http://www2.epa.gov/fera/total-riskintegrated-methodology-trim-general
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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 category 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-cast 

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

of each of the emitted environmental HAP exceeded ecological benchmarks represented as 

emissions threshold values. If off-site emissions from a facility do not exceed the Tier I

thresholds 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 I thresholds, we 

evaluate the facility further in Tier II.

In Tier II of the environmental screening analysis, the screening emission thresholds are 

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

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

the Tier II thresholds, the facility passes the screen, and is typically not evaluated further. If 

off-site emissions from a facility exceed the Tier II thresholds, the facility does not pass the 

screen and, therefore, may have the potential to cause adverse environmental effects. Such 

facilities are 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 HEM-3 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 
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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 

well-being.”3

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 inventory for the OSWRO

source category were based on the EPA Office of Air Quality Planning and Standards’ 

existing recommendations for HAP [9], also used for NATA [10]. 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 available science with 

respect to dose-response information. The recommendations are based on the following 

sources, in order of priority: 

1) US Environmental Protection Agency (EPA). 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.” 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/m3in air.” The 

SF is “an upper bound, approximating a 95% 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) [11] is an EPA database that contains scientific health 

 

3 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 [air quality] 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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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) [12] 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 

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 assessments4: “The 

guidelines for developing chronic inhalation exposure levels incorporate many 

recommendations of the U.S. EPA [13] and NAS [14].” The non-cancer information 

includes available inhalation health risk guidance values expressed as chronic inhalation 

reference exposure levels (RELs) [15]. 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 [16], defined similarly to EPA's URE. 

 

4 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 )
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In developing chronic risk estimates, we adjusted dose-response values for some HAP based 

on professional judgment, as follows: 

1) In the case of HAP categories such as glycol ethers and cyanide compounds, the most 

conservative dose-response value of the chemical category is used as a surrogate for other 

compounds in the group for which dose-response values are not available. This is done in 

order to examine, under conservative assumptions, whether these HAP that lack doseresponse values may pose an unacceptable risk and require further examination, or screen 

out from further assessment. 

2) 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. 

3) In 2004, the EPA determined that the Chemical Industry Institute of Toxicology (CIIT)

cancer dose-response value for formaldehyde (5.5 x 10-9per μg/m3) was based on better 

science than the IRIS cancer dose-response value (1.3 x 10-5per μg/m3), and we switched 

from using the IRIS value to the CIIT value in risk assessments supporting regulatory 

actions. Subsequent research published by EPA suggested that the CIIT model was not 

appropriate and in 2010 EPA returned to using 1991 IRIS value. EPA has been working 

on revising the formaldehyde IRIS assessment and the National Academy of Sciences 

(NAS) completed its review of the EPA’s draft assessment in April of 2011.

5 EPA will 

follow the NAS Report recommendations and will present results obtained by 

implementing the biologically-based dose-response (BBDR) model for formaldehyde. 

EPA will compare these estimates with those currently presented in the External Review 

draft of the assessment and will discuss their strengths and weaknesses. As recommended 

by the NAS committee, appropriated sensitivity and uncertainty analyses will be an 

integral component of implementing the BBDR model. The draft IRIS assessment will be 

revised in response to the NAS peer review and public comments and the final assessment 

will be posted on the IRIS database. In the interim, we will present findings using the 

1991 IRIS value as a primary estimate and EPA may also consider other information as 

the science evolves. 

4) A substantial proportion of POM reported to EPA’s National Emission Inventory (NEI) is

not speciated into individual compounds. As a result, it is necessary to apply the same 

simplifying assumptions to assessments that are used in NATA [17]. 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 OSWRO source category includes emissions of HAP with 

available chronic quantitative inhalation dose-response values. Of these, 56 are classified as 

known, probable, or possible carcinogens, with quantitative cancer dose-response values 

 

5 http://www.nap.edu/catalog.php?record_id=13142
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available. These 56 HAP, their quantitative inhalation chronic cancer dose-response values, 

and the source of each value are listed in Table 2.6-1. Eighty-two HAP have quantitative 

inhalation chronic noncancer threshold values available. These HAP, their threshold values, 

and the source of the value are listed in Table 2.6-2.

Table 2.6-1 Dose-Response Values for Chronic Inhalation Exposure to Carcinogens

URE (unit risk estimate for cancer)6 = cancer risk per μg/m3of average lifetime exposure. 

Sources: IRIS = EPA Integrated Risk Information System, EPA ORD = EPA Office of 

Research & Development, EPA OAQPS = EPA Office of Air Quality Planning & 

Standards, CAL = California EPA Office of Environmental Health Hazard Assessment.

HEAST = EPA Health Effects Assessment Tables, Conv. Oral = Oral unit risk converted to 

inhalation.

Pollutant CAS 

Number7

URE11

(1/μg/m3)

Source

1,1,2,2-Tetrachloroethane 79345 0.000058 IRIS

1,1,2-Trichloroethane 79005 0.000016 IRIS

1,2-Dibromo-3-chloropropane 96128 0.002 CAL

1,2-Diphenylhydrazine 122667 0.00022 IRIS

1,3-Butadiene 106990 0.00003 IRIS

1,3-Dichloropropene 542756 0.000004 IRIS

1,3-Propane sultone 1120714 0.00069 CAL

1,4-Dioxane 123911 0.000005 IRIS8

2,4,6-Trichlorophenol 88062 0.0000031 IRIS

2,4-Dinitrotoluene 121142 0.000089 CAL

2,4-Toluene diamine 95807 0.0011 CAL

2,4-Toluene diisocyanate 584849 0.000011 CAL

2-Nitropropane 79469 0.0000056 EPA OAQPS

3,3'-Dichlorobenzidine 91941 0.00034 CAL

4,4'-methylene Bis(2-chloroaniline) 101144 0.00043 CAL

4,4'-Methylenedianiline 101779 0.00046 CAL

4-Dimethylaminoazobenzene (pDimethylaminoazobenzene 60117 0.0013 CAL

Lindane (gamma-HCH) 58899 0.00031 CAL

Acetaldehyde 75070 0.0000022 IRIS

Acetamide 60355 0.00002 CAL

Acrylamide 79061 0.00016 IRIS

Acrylonitrile 107131 0.000068 IRIS

Allyl chloride 107051 0.000006 CAL

Aniline 62533 0.0000016 CAL

 

6 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/m3in air. URE’s are considered upper bound estimates meaning they represent 

a plausible upper limit to the true value. 

7 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 HAP.

8

IRIS external review draft value, September 2011. 
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Table 2.6-1 Dose-Response Values for Chronic Inhalation Exposure to Carcinogens

URE (unit risk estimate for cancer)6 = cancer risk per μg/m3of average lifetime exposure. 

Sources: IRIS = EPA Integrated Risk Information System, EPA ORD = EPA Office of 

Research & Development, EPA OAQPS = EPA Office of Air Quality Planning & 

Standards, CAL = California EPA Office of Environmental Health Hazard Assessment.

HEAST = EPA Health Effects Assessment Tables, Conv. Oral = Oral unit risk converted to 

inhalation.

Pollutant CAS 

Number7

URE11

(1/μg/m3)

Source

Benzene9 71432 0.0000078 IRIS

Benzidine 92875 0.1072 IRIS

Benzyl chloride 100447 0.000049 CAL

Bis(2-ethylhexyl)phthalate 117817 0.0000024 CAL

Bromoform 75252 0.0000011 IRIS

Carbon tetrachloride 56235 0.000006 IRIS

Chlordane 57749 0.0001 IRIS

Chlorobenzilate 510156 0.000078 HEAST

Dichloroethyl ether 111444 0.00033 IRIS

Epichlorohydrin (1-Chloro-2,3-epoxypropane) 106898 0.0000012 IRIS

Ethyl benzene 100414 0.0000025 CAL

Ethyl carbamate 51796 0.000464 CAL

Ethylene dibromide 106934 0.0006 IRIS

Ethylene dichloride 107062 0.000026 IRIS

Ethylene oxide 75218 0.000088 CAL

Ethylene thiourea 96457 0.000013 CAL

Ethylidene dichloride (1,1-Dichloroethane) 75343 0.0000016 CAL

Formaldehyde10 50000 0.000013 IRIS

Heptachlor 76448 0.0013 IRIS

Hexachlorobenzene 118741 0.00046 IRIS

Hexachlorobutadiene 87683 0.000022 IRIS

Hydrazine 302012 0.0049 IRIS

Methyl tert-butyl ether 1634044 0.00000026 CAL

Methylene chloride 75092 0.00000013 IRIS

Naphthalene 91203 0.000034 CAL

Nitrobenzene 98953 0.00004 IRIS

Nitrosodimethylamine 62759 0.022 IRIS

N-nitrosomorpholine 59892 0.0019 CAL

o-Toluidine 95534 0.000051 CAL

P-dichlorobenzene (1,4- dichlorobenzene) 106467 0.000011 CAL

Pentachlorophenol 87865 0.0000051 CAL

Polychlorinated biphenyls (PCB) 1336363 0.0001 IRIS

 

9 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.

10 The EPA has used the CIIT URE value, 5.5X10-9 per mg/m3

, to characterize formaldehyde cancer risk in 

some instances.
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Table 2.6-1 Dose-Response Values for Chronic Inhalation Exposure to Carcinogens

URE (unit risk estimate for cancer)6 = cancer risk per μg/m3of average lifetime exposure. 

Sources: IRIS = EPA Integrated Risk Information System, EPA ORD = EPA Office of 

Research & Development, EPA OAQPS = EPA Office of Air Quality Planning & 

Standards, CAL = California EPA Office of Environmental Health Hazard Assessment.

HEAST = EPA Health Effects Assessment Tables, Conv. Oral = Oral unit risk converted to 

inhalation.

Pollutant CAS 

Number7

URE11

(1/μg/m3)

Source

Polycyclic Organic Matter11 (POM)

 2-Acetylaminofluorene 53963 0.00208 CAL

Propylene oxide 75569 0.0000037 IRIS

Tetrachloroethene 127184 0.00000026 IRIS

Toxaphene 8001352 0.00032 IRIS

Trichloroethylene 79016 0.0000048 IRIS

Vinyl chloride 75014 0.0000088 IRIS

Table 2.6-2 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, EPA ORD = EPA Office of Research and Development

Pollutant CAS Number12 RfC 

(mg/m3)

Source12

1,1,1-Trichloroethane (methyl chloroform) 71556 5 IRIS - M

1,1,2-Trichloroethane 79005 0.4 CAL

1,2,4-Trichlorobenzene 120821 0.2 HEAST

1,2-Dibromo-3-chloropropane 96128 0.0002 IRIS - M

1,2-Epoxybutane 106887 0.02 IRIS - M

1,3-Butadiene 106990 0.002 IRIS - M

1,3-Dichloropropene 542756 0.02 IRIS - H

 

11 POM without a chemical-specific URE are assigned a URE associated with a mixture of POM compounds 

having similar characteristics. Details of this method, also used in the 2002 National Air Toxics Assessment, are 

available at http://www.epa.gov/ttn/atw/nata2002/02pdfs/pom_approach.pdf.

12 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.
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Table 2.6-2 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, EPA ORD = EPA Office of Research and Development

Pollutant CAS Number12 RfC 

(mg/m3)

Source12

1,4-Dichlorobenzene (P-dichlorobenzene) 106467 0.8 IRIS - M

1,4-Dioxane 123911 0.03 IRIS - D13

2,4-Dinitrotoluene 121142 0.007 CAL

2,4-Toluene diisocyanate 584849 0.00007 IRIS - M

2-Nitropropane 79469 0.02 IRIS - L

4,4'-Methylenedianiline 101779 0.02 CAL

4,4'-Methylenediphenyl diisocyanate 101688 0.0006 IRIS - M

Acetaldehyde 75070 0.009 IRIS - L

Acetonitrile 75058 0.06 IRIS - M

Acrolein 107028 0.00002 IRIS - M

Acrylamide 79061 0.006 IRIS - M

Acrylic acid 79107 0.001 IRIS - M

Acrylonitrile 107131 0.002 IRIS - M

Allyl chloride 107051 0.001 IRIS - L

Aniline 62533 0.001 IRIS - L

Benzene 71432 0.03 IRIS - M

Benzidine 92875 0.01 CAL

Bis(2-ethylhexyl)phthalate 117817 0.01 CAL

Carbon disulfide 75150 0.7 IRIS - M

Carbon tetrachloride 56235 0.1 IRIS - M

Chlordane 57749 0.0007 IRIS - L

Chlorine 7782505 0.00015 ATSDR

Chlorobenzene 108907 1 CAL

Chloroform 67663 0.098 ATSDR

Cresols (mixed) 1319773 0.6 CAL

 m-Cresol 108394 0.6 CAL

 o-Cresol 95487 0.6 CAL

 p-Cresol 106445 0.6 CAL

Cumene 98828 0.4 IRIS - M

Dichlorvos 62737 0.0005 IRIS - M

Diethanolamine 111422 0.003 CAL

Dimethyl formamide 68122 0.03 IRIS - M

 

13 IRIS external review draft value, September 2011. 
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Table 2.6-2 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, EPA ORD = EPA Office of Research and Development

Pollutant CAS Number12 RfC 

(mg/m3)

Source12

Epichlorohydrin (1-Chloro-2,3 

epoxypropane) 106898 0.001 IRIS - M

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

Ethylene glycol 107211 0.4 CAL

Ethylene oxide 75218 0.03 CAL

Ethylene thiourea 96457 0.003 CAL

Ethylidene dichloride (1,1-Dichloroethane) 75343 0.5 HEAST

Formaldehyde 50000 0.0098 ATSDR

Glycol Ethers14

 Ethylene glycol ethyl ether 110805 0.2 IRIS - M

 Ethylene glycol methyl ether 109864 0.02 IRIS - M

 Glycol ethers 181 0.02 IRIS - M

Hexachlorobenzene 118741 0.003 CAL

Hexachlorobutadiene 87683 0.09 CAL

Hexachlorocyclopentadiene 77474 0.0002 IRIS – M/H

Hexachloroethane 67721 0.03 IRIS - L

Hexane 110543 0.7 IRIS - M

Hydrazine 302012 0.0002 CAL

Hydrochloric acid (hydrogen chloride) 7647010 0.02 IRIS - L

Hydrofluoric acid (hydrogen fluoride) 7664393 0.014 CAL

Isophorone 78591 2 CAL

Lindane (gamma-HCH) 58899 0.0003 CAL

Maleic anhydride 108316 0.0007 CAL

Methanol 67561 4 CAL

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

 

14 The RfC value for ethylene glycol methyl ether was used as a surrogate for all glycol ethers without an RfC.
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Table 2.6-2 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, EPA ORD = EPA Office of Research and Development

Pollutant CAS Number12 RfC 

(mg/m3)

Source12

Naphthalene 91203 0.003 IRIS - M

Nitrobenzene 98953 0.009 IRIS - M

Pentachlorophenol 87865 0.1 CAL

Phenol 108952 0.2 CAL

Phosphine 7803512 0.0003 IRIS - L

Phthalic anhydride 85449 0.02 CAL

Propionaldehyde 123386 0.008 IRIS – L/M

Propylene dichloride 78875 0.004 IRIS - M

Propylene oxide 75569 0.03 IRIS - M

Styrene 100425 1 IRIS - M

Styrene oxide 96093 0.006 CAL

Tetrachloroethene 127184 0.04 IRIS -M

Titanium tetrachloride 7550450 0.0001 ATSDR

Toluene 108883 5 IRIS - H

Trichloroethylene 79016 0.002 IRIS - H

Triethylamine 121448 0.007 IRIS - L

Vinyl acetate 108054 0.2 IRIS - H

Vinyl chloride 75014 0.1 IRIS - M

Vinylidene chloride 75354 0.2 IRIS – H/M

Xylenes (mixed) 1330207 0.1 IRIS - M

 m-Xylene16 108383 0.1 IRIS - M

 o-Xylene15 95476 0.1 IRIS - M

 p-Xylene16 106423 0.1 IRIS - M

2.6.2 Sources of acute dose-response information 

Hazard identification and dose-response assessment information for preliminary acute 

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

HAP [18]. 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 

 

15 The RfC for mixed xylene was used as a surrogate.
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(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 HAP can be 

found in an EPA document of graphical arrays [19]. 

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 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 that “the primary purpose of the AEGL program and the NAC/AEGL 

Committee is to develop guideline levels for once-in-a-lifetime, short-term 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 
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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 

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 ( http://www.aiha.org/getinvolved/AIHAGuidelineFoundation/EmergencyResponsePlanningGuidelines/Pages/default.a

spx ) are described in their supporting documentation as follows: “ERPGs are air 

concentration guidelines for single exposures to agents and are intended for use as tools to 

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

transportation emergency planning, community emergency response plans and incident 

prevention and mitigation.”

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.”
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The emissions inventory for the OSWRO source category includes emissions of 57 HAP with 

relevant and available quantitative acute dose-response threshold values. These HAP, their 

acute threshold values, and the source of the value are listed below in Table 2.6-3.

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 

1,1-Dimethylhydrazine 57147 7.4

1,2-Epoxybutane 106887 210 410

1,3-Butadiene 106990 1500 12000 22 440

1,4-Dioxane 123911 61 1200 3

2,4-Toluene diisocyanate 584849 0.14 0.59 0.071 1.1

Acetaldehyde 75070 81 490 18 360 0.47

Acetonitrile 75058 22 540

Acrolein 107028 0.069 0.23 0.11 0.34 0.0025

Acrylic acid 79107 4.4 140 3 150 6

Acrylonitrile 107131 10 130 22 77

Allyl chloride 107051 8.8 170 9.4 130

Aniline 62533 30 46

Benzene 71432 170 2600 160 480 1.3

Benzyl chloride 100447 5.2 52 0.24

Biphenyl 92524 61

Carbon disulfide 75150 40 500 3.1 160 6.2

Carbon tetrachloride 56235 280 1200 130 630 1.9

Chlorine 7782505 1.5 5.8 2.9 8.7 0.21

Chlorobenzene 108907 46 690

Chloroform 67663 310 240 0.15

Chloromethyl methyl ether 107302 1.6 3.3

Cumene 98828 250 1500

Dichlorvos 62737 0.99 5.1

Dimethyl formamide 68122 270 6 300

Dimethyl sulfate 77781 0.12 0.62

Epichlorohydrin (1-Chloro-2,3-

epoxypropane) 106898 22 91 19 76 1.3

Ethyl acrylate 140885 34 150 0.041 120

Ethyl benzene 100414 140 4800

Ethylene dibromide 106934 130 180

Ethylene dichloride 107062 200 810

Ethylene oxide 75218 81 90

Formaldehyde 50000 1.1 17 1.2 12 0.055
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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 

Glycol Ethers16

 Ethylene glycol ethyl ether 110805 0.37

 Ethylene glycol methyl ether 109864 0.093

 Glycol ethers 181 0.093

Hexachlorobutadiene 87683 11 32

Hexane 110543 12000

Hydrazine 302012 0.13 17 0.65 6.5

Maleic anhydride 108316 0.8 8

Methanol 67561 690 2700 260 1300 28

Methyl bromide 74839 820 190 3.9

Methyl chloride 74873 1900 830

Methyl hydrazine 60344 3.2

Methyl iodide 74884 150 290

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

Phosphine 7803512 2.8 0.7

Propionaldehyde 123386 110 620

Propylene oxide 75569 170 690 120 590 3.1

Styrene 100425 85 550 210 1100 21

Tetrachloroethene 127184 240 1600 680 1400 20

Titanium tetrachloride 7550450 0.54 7.8 5 20

Toluene 108883 750 4500 190 1100 37

Trichloroethylene 79016 700 2400 540 2700

Triethylamine 121448 2.8

Vinyl acetate 108054 24 630 18 260

Vinyl chloride 75014 640 3100 1300 13000 180

Xylenes (mixed) 1330207 560 4000 22

 m-Xylene17 108383 22

 o-Xylene18 95476 22

 p-Xylene18 106423 22

 

16 The acute REL for ethylene glycol methyl ether was used as a surrogate for glycol ether compounds without 

an acute REL.

17 The REL for mixed xylenes was used as a surrogate.
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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 Analysis18, 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 the 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 HAP are upper-bound 

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

zero.

For EPA’s list of carcinogenic HAP 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 

 

18Memorandum 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/assets/omb/memoranda/fy2007/m07-24.pdf)
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small fraction of the total POM emissions may be reported as individual compounds, EPA 

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.

In the case of benzene, the high end of the reported cancer URE range was used in our 

assessments to provide a conservative estimate of potential cancer risks. Use of the high end 

of the range provides risk estimates that are approximately 3.5 times higher than use of the 

equally plausible low end value. If the estimated benzene–associated risks exceed 1 in a 

million, we also evaluated the impact of using the low end of the URE range on our risk 

results.

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 predicted 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.
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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.

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 

NEI data and modeling as described in Section 2.2. For the facilities in the OSWRO source

category, 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 this source category 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 the facility-wide assessment are 

summarized below in the risk characterization section of this document. The complete results 

of the facility-wide assessment are provided in Appendix 5 of this document, Detailed Risk 

Modeling Results.
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2.7.4 MACT-Allowable Risk

The emissions data in the data set for the OSWRO source category are estimates of actual 

emissions on an annual basis. The risk results presented in the following sections are based 

on these actual emissions. For the OSWRO source category, estimates of MACT-allowable 

emissions were also estimated, and the risk results based on those emissions are presented 

below as well. For more detail, please refer to the MACT-Allowable memo in docket for this 

action (MACT-Allowable Emissions for the Off-Site Waste and Recovery Operations Source 

Category, located in Docket EPA-HQ-OAR-2012-0360.)

3 Risk Results for the OSWRO Source Category

3.1 Source Category Description and Results

The OSWRO source category includes facilities that store, treat, recycle, or dispose of certain 

wastes, used oil, or used solvents from off-site locations. Specifically, facilities in the 

OSWRO source category handle off-site material, which is defined as any material generated 

from commercial, industrial, agricultural, and mining operations or community activities that 

is recycled, reused, reprocessed, discarded, or is being accumulated, stored, or treated 

(physically, chemically, biologically, or thermally) before being discarded, discharged, or 

recycled. The activities that take place at OSWRO sources vary widely depending on the 

specific treatment, storage, or other handling processes that are being conducted (e.g., 

treatment of wastewater, physical separation of waste mixtures, storage of wastes, 

reprocessing of wastes for recovery or recycling purposes, and other processes). Potential 

HAP emission sources at OSWRO operations include tanks, surface impoundments, 

containers, oil-water separators, process vents, equipment leaks, and individual drain systems 

and other material conveyance systems. 

The data set for the OSWRO source category contains 38 facilities identified with an 

OSWRO MACT code 806 in the 2005 National-scale Air Toxics Assessment (NATA) 

National Emissions Inventory (NEI), as explained in Section 2.1 of this document. The 2005 

NATA NEI was updated for this category with data from industry and enhanced quality 

assurance of facility and emission locations. In the NEI, thirty seven (37) of these facilities are 

identified as major sources one facility is identified as an area source, however we believe 

that all 38 facilities are major sources.

The emissions for the OSWRO source category data set (of 38 facilities) are summarized in 

Table 3.1-1. The total HAP emissions for the source category are approximately 80 tons per 

year. Based on these data, the HAP emitted in the largest quantities across these 38 facilities 

are toluene, methylene chloride, methanol, tetrachloroethene, xylenes (mixed), hydrochloric 

acid, methyl tert-butyl ether, acetonitrile, methyl isobutyl ketone, chlorine, 1,1,1-

trichloroethane, ethyl benzene, acetaldehyde, glycol ethers, hexane, trichloroethylene, 

formaldehyde, styrene, propionaldehyde, benzene, 2,4-toluene diamine, carbon tetrachloride, 

chloroform, ethylene dichloride, ethylene glycol, 1,2-epoxybutane, naphthalene, carbon 

disulfide, 1,1,2-trichloroethane, vinyl acetate, phenol, hydrofluoric acid, chlorobenzene,
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ethylene oxide, methylene diphenyl diisocyanate, cresols (mixed), nitrobenzene, acrylonitrile, 

vinylidene chloride, aniline, methyl methacrylate, propylene dichloride, phthalic anhydride, 

lindane (gamma-hch), 2,4-toluene diisocyanate, titanium tetrachloride, 2-nitropropane, mcresol (3-methylphenol), p-cresol (4-methy phenol), dimethyl formamide, acetamide, and 2,4-

d, salts and esters. These 52 chemicals account for 99 percent of the total emissions by mass. 

Persistent and bioaccumulative HAP (PB HAP)19 reported as emissions from these facilities 

include polycyclic organic matter (analyzed as benzo(a)pyrene), polychlorinated biphenyls, 

lindane (gamma-hch), chlordane, hexachlorobenzene, methoxychlor, toxaphene, heptachlor, 

and trifluralin. The following environmental HAP are emitted from the Off-Site Waste and 

Recovery Operations source category and are included in the environmental risk screen: 

POM, hydrogen chloride and hydrogen fluoride.

Table 3.1-1 Summary of Emissions from the OSWRO Source Category Used in the Residual Risk 

Assessment and Availability of Dose-Response Values

HAPa Emissions 

(tpy)

Number of 

Facilities 

Reporting 

HAP (38

facilities in 

data set)

Prioritized Inhalation Dose-Response 

Value Identified by OAQPSb

PBHAP?

Unit 

Risk 

Estimate 

for 

Cancer?

Reference 

Concentration 

for 

Noncancer?

Health 

Benchmark 

Values for 

Acute 

Noncancer?

Toluene 15 25  

Methylene Chloride 11 21   

Methanol 8 26  

Tetrachloroethene 8 22   

Xylenes (mixed) 7 24  

Hydrochloric Acid 6 7  

Methyl Tert-butyl Ether 5 11   

Acetonitrile 3 13  

Methyl Isobutyl Ketone 3 24 

Chlorine 2 3  

1,1,1-trichloroethane 2 9  

Ethyl Benzene 1 21   

Acetaldehyde 1 3   

Glycol Ethers  

 Glycol Ethers 1 8  

 Ethylene Glycol Ethyl Ether 0.002 2  

 Ethylene Glycol Methyl Ether 0.002 1  

Hexane 0.8 14  

Trichloroethylene 0.6 16   

Formaldehyde 0.4 12   

Styrene 0.4 17  

 

19 Persistent and bioaccumulative HAP are defined in the EPA’s Air Toxics Risk Assessment Library, Volume 1, 

EPA-453K-04-001A, as referenced in the NPRM 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-modelingair-toxics-risk-assessment-reference-library. 
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Table 3.1-1 Summary of Emissions from the OSWRO Source Category Used in the Residual Risk 

Assessment and Availability of Dose-Response Values

HAPa Emissions 

(tpy)

Number of 

Facilities 

Reporting 

HAP (38

facilities in 

data set)

Prioritized Inhalation Dose-Response 

Value Identified by OAQPSb

PBHAP?

Unit 

Risk 

Estimate 

for 

Cancer?

Reference 

Concentration 

for 

Noncancer?

Health 

Benchmark 

Values for 

Acute 

Noncancer?

Propionaldehyde 0.3 2  

Benzene 0.3 15   

2,4-toluene Diamine 0.3 2 

Carbon Tetrachloride 0.3 8   

Chloroform 0.3 11  

Ethylene Dichloride 0.3 8   

Ethylene Glycol 0.3 9 

1,2-epoxybutane 0.2 2  

Naphthalene 0.2 16  

Carbon Disulfide 0.2 6  

1,1,2-trichloroethane 0.2 6  

Vinyl Acetate 0.2 7  

Phenol 0.2 10  

Hydrofluoric Acid 0.1 4  

Chlorobenzene 0.1 7  

Ethylene Oxide 0.1 1   

Methylene Diphenyl Diisocyanate 0.1 1  

Cresols (mixed) 0.1 6 

Nitrobenzene 0.1 6  

Acrylonitrile 0.1 6   

Vinylidene Chloride 0.1 2 

Aniline 0.07 5   

Methyl Methacrylate 0.06 6  

Propylene Dichloride 0.05 4 

Phthalic Anhydride 0.05 4 

Lindane (gamma-hch) 0.05 3   

2,4-toluene Diisocyanate 0.05 3   

Titanium Tetrachloride 0.04 4  

2-nitropropane 0.04 3  

M-cresol (3-methylphenol) 0.04 2 

P-cresol (4-methy Phenol) 0.04 2 

Dimethyl Formamide 0.04 9  

Acetamide 0.04 2 

2,4-d, Salts And Esters 0.04 3

P-dichlorobenzene 0.03 4  

1,3-butadiene 0.03 2   

O-cresol 0.03 1 
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Table 3.1-1 Summary of Emissions from the OSWRO Source Category Used in the Residual Risk 

Assessment and Availability of Dose-Response Values

HAPa Emissions 

(tpy)

Number of 

Facilities 

Reporting 

HAP (38

facilities in 

data set)

Prioritized Inhalation Dose-Response 

Value Identified by OAQPSb

PBHAP?

Unit 

Risk 

Estimate 

for 

Cancer?

Reference 

Concentration 

for 

Noncancer?

Health 

Benchmark 

Values for 

Acute 

Noncancer?

Dichloroethyl Ether 0.03 2 

Chlordane 0.03 5   

Hexachlorobenzene 0.03 6   

Hexachlorobutadiene 0.03 4   

Hexachloroethane 0.02 5 

1,4-dioxane 0.02 3   

Epichlorohydrin 0.02 4   

Acetophenone 0.02 4

Methoxychlor 0.02 4 

Maleic Anhydride 0.02 4  

2,4-dinitrotoluene 0.02 2  

Dibutylphthalate 0.02 3

Ethyl Acrylate 0.02 6 

Cumene 0.02 8  

Isophorone 0.02 1 

Polychlorinated biphenyls 0.02 3  

1,1,2,2-tetrachloroethane 0.01 4 

Vinyl Chloride 0.01 4   

M-xylene 0.01 4  

Acrylamide 0.01 4  

Toxaphene 0.01 3  

Hexachlorocyclopentadiene 0.01 3 

Bis(2-ethylhexyl)phthalate 0.01 3  

1,2,4-trichlorobenzene 0.01 3 

P-xylene 0.01 2  

Dibenzofuran 0.009 1

Carbaryl 0.008 3

Dimethyl Phthalate 0.008 3

Ethyl Carbamate 0.008 3 

Acrylic Acid 0.008 4  

Methyl Chloride 0.008 3  

Triethylamine 0.008 4  

Pentachlorophenol 0.007 2  

Biphenyl 0.007 3 

4-nitrobiphenyl 0.007 1

N-nitrosomorpholine 0.007 1 

Nitrosodimethylamine 0.007 1 
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Table 3.1-1 Summary of Emissions from the OSWRO Source Category Used in the Residual Risk 

Assessment and Availability of Dose-Response Values

HAPa Emissions 

(tpy)

Number of 

Facilities 

Reporting 

HAP (38

facilities in 

data set)

Prioritized Inhalation Dose-Response 

Value Identified by OAQPSb

PBHAP?

Unit 

Risk 

Estimate 

for 

Cancer?

Reference 

Concentration 

for 

Noncancer?

Health 

Benchmark 

Values for 

Acute 

Noncancer?

Heptachlor 0.007 5  

Methyl Bromide 0.006 2  

Pentachloronitrobenzene 0.006 1

O-toluidine 0.006 3 

4-nitrophenol 0.005 1

O-xylene 0.005 2  

3,3'-dimethylbenzidine 0.005 1

4,4'-methylene Bis(2-chloroaniline) 0.005 1 

Dimethylcarbamoyl Chloride 0.005 1

Benzidine 0.005 2  

2,4-dinitrophenol 0.005 1

Ethyl Chloride 0.005 1 

Ethylene Dibromide 0.004 2   

3,3'-dimethoxybenzidine 0.004 1

2,4,6-trichlorophenol 0.004 1 

Hydrazine 0.004 3   

1,3-propane Sultone 0.004 1 

Captan 0.004 3

2,4,5-trichlorophenol 0.003 1

Chlorobenzilate 0.003 1 

Diethanolamine 0.003 2 

Dimethyl Sulfate 0.002 3 

Propylene Oxide 0.002 2   

4,4'-methylenedianiline 0.002 1  

Ethylene Thiourea 0.002 1  

Quinone 0.002 1

Allyl Chloride 0.002 1   

Methyl Iodide 0.002 1 

Benzyl Chloride 0.002 3  

1,3-dichloropropene 0.002 1  

P-dimethylaminoazobenzene 0.002 1 

Ethylidene Dichloride 0.002 2  

N,n-dimethylaniline 0.001 2

1,2-dibromo-3-chloropropane 0.001 1  

1,1-dimethylhydrazine 0.001 1 

1,2-diphenylhydrazine 0.001 1 

Benzotrichloride 0.001 1
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Table 3.1-1 Summary of Emissions from the OSWRO Source Category Used in the Residual Risk 

Assessment and Availability of Dose-Response Values

HAPa Emissions 

(tpy)

Number of 

Facilities 

Reporting 

HAP (38

facilities in 

data set)

Prioritized Inhalation Dose-Response 

Value Identified by OAQPSb

PBHAP?

Unit 

Risk 

Estimate 

for 

Cancer?

Reference 

Concentration 

for 

Noncancer?

Health 

Benchmark 

Values for 

Acute 

Noncancer?

Phosphorus, White 0.001 1

Hydroquinone 0.001 2

Polycyclic Organic Matter  

 2-acetylaminofluorene 0.001 1  

Dichlorvos 0.0008 1  

3,3'-dichlorobenzidine 0.0007 1 

Chloromethyl Methyl Ether 0.0006 1 

Bromoform 0.0004 1 

Styrene Oxide 0.0004 1 

Acrolein 0.0002 2  

Trifluralin 0.0001 2 

Methyl Hydrazine 0.00003 1 

Phosphine 0 1  

a Notes for how HAP were speciated for risk assessment:

 For emissions reported generically as “Glycol Ethers” or specific glycol ethers not found on EPA’s Technology 

Transfer Network for air toxics (see footnote b), emissions were treated as ethylene glycol methyl ether.

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. 
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3.2 Risk Characterization

This section presents the results of the risk assessment for the OSWRO source category based 

on the modeling methods described in the previous sections. All baseline risk results are 

developed using the best estimates of actual HAP emissions summarized in the previous 

section. 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, as well as a summary of the results of our facility-wide 

assessments and our analysis of risks associated with the maximum allowed emissions under 

the current MACT standards. A detailed summary of the facility-specific risk assessment 

results is available in Appendix 5 of this document, Draft Detailed Risk Modeling Results.

Table 3.2-1 summarizes the chronic and acute inhalation risk results for this source category. 

The results of the chronic inhalation cancer risk assessment for the OSWRO source category

estimate that the maximum lifetime individual cancer risks posed by these facilities could be 

as high as 9-in-1 million. The major contributors to this risk are benzidine, and 2,4-toluene 

diamine . Approximately 210,000 people were estimated to have cancer risks at or above 1-in1 in a million as a result of the emissions from this source category. The maximum chronic 

non-cancer target organ specific hazard index (TOSHI) value for the source category could be 

up to 0.6 associated with emissions of chlorine, indicating no significant potential for chronic 

non-cancer impacts. 

Analysis of potential differences between chronic inhalation risks based on actual emission 

levels and the maximum emissions allowed under the MACT standards (i.e., MACTallowable emissions) was conducted. See the memo, MACT-Allowable Emissions for the OffSite Waste and Recovery Operations Source Category, for more details on the calculation of 

these allowable risks. The maximum individual lifetime cancer risk based on allowable 

emissions is estimated to be 20-in-1 million, while the maximum chronic noncancer TOSHI 

value is estimated to be 1.

Worst-case acute hazard quotients (HQs) were calculated for every emitted HAP that has an 

acute benchmark, based on actual emissions. For cases where the screening acute HQ was 

greater than 1, we further refined the estimates by determining the highest HQ value that 

might occur outside facility boundaries. Analysis of potential off-site impacts for the 

OSWRO source category identified no facility with an acute value greater than 1. 

As presented above, the PB-HAP emitted by facilities in this category include polycyclic 

organic matter (POM) (2-acetylaminofluorene analyzed as benzo(a)pyrene TEQ) (1 facility), 

polychlorinated biphenyls (3 facilities), hexachlorobenzene (6 facilities), chlordane (5 

facilities), lindane (gamma hch) (3 facilities), methoxyclor (4 facilities), toxaphene (3 

facilities), heptachlor (5 facilities), and trifluralin (2 facilities). To identify potential 

multipathway health risks from PB-HAP other than lead, we first performed a screening 
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analysis (Tier I) that compared emissions of PB-HAP emitted from the OSWRO source 

category (based on actual emissions) to screening emission rates (see section 2.5), where 

those screening emission rates are available. Of the PB-HAP emitted by this category, the 

POM compound 2-acetylaminofluorene has a screening emission rate. The emissions of the 

POM 2-acetylaminofluorene were below the screening value for POM. The remaining PBHAP do not currently have multipathway screening values and so are not evaluated for 

potential non-inhalation risks. As shown in Table 3.1-1, these HAP are not emitted in 

appreciable quantities from off-site waste and recovery operations. 

We conducted a screening-level evaluation of the potential adverse environmental risks 

associated with emissions of the following environmental HAP from the Off-Site Waste and 

Recovery Operations source category: POM, hydrogen chloride, and hydrogen fluoride. The 

results of the environmental screening analysis are summarized in Table 3.2-2. In our Tier I

analysis, emissions of POM 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 II environmental screening analysis. For hydrogen 

fluoride and hydrogen chloride, the average modeled concentration around each facility (i.e., 

the average concentration of all off-site data points in the modeling domain) did not exceed 

the ecological benchmarks. In addition, each individual modeled concentration of hydrogen 

chloride and hydrogen fluoride (i.e., each off-site data point in the modeling domain) was 

below the ecological benchmarks for all facilities. 

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

Result HAP “Drivers”

Facilities in Source Category

Number of Facilities Estimated to be in Source 

Category20 38 n/a

Number of Facilities Identified in the NEI and 

Modeled in Risk Assessment 38 n/a

Cancer Risks

Maximum Individual Lifetime Cancer Risk (in 1 

million) 9 benzidine, 2,4-toluene diamine

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 n/a

Greater than or equal to 1 in 1 million 8

benzidine, 2,4-toluene diamine, 

nitrosodimethylamine, nnitrosomorpholine, ethylene oxide, 

lindane (gamma-HCH), 

hexachlorobenzene, heptachlor, 

formaldehyde, nitrobenzene, carbon 

tetrachloride, benzene, hydrazine, 

ethylene dichloride, ethyl carbamate, 

 

20 As explained in Section 2.1 of this document, the list of facilities began with 53 facilities and there are 38

facilities included in the modeling file used for this risk assessment.
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Table 3.2-1 Summary of Source Category Level Inhalation Risks for OSWRO

Result HAP “Drivers”

acetamide, naphthalene, acrylonitrile, 

2,4-toluene diisocyanate, chlordane, 

toxaphene, ethyl benzene

Chronic Noncancer Risks

Maximum Respiratory Hazard Index 0.6 chlorine

Number of Facilities with Maximum Respiratory Hazard Index:

Greater than 1 0 n/a

Acute NoncancerResults

Maximum Acute Hazard Quotient 1 glycol ethers (REL)

Number of Facilities With Potential for Acute 

Effects 0 n/a

Population Exposure

Number of People Living Within 50 Kilometers 

of Facilities Modeled 35,000,000 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 210,000 n/a

Number of People Exposed to Noncancer Respiratory Hazard Index:

Greater than 1 0 n/a

Estimated Cancer Incidence (excess cancer cases 

per year) 0.02 n/a

Contribution of HAP to Cancer Incidence:

Benzidine 73% n/a

2,4-Toluene diamine 19% n/a

Table 3.2-2 Summary of Environmental Risks Screen Results for OSWRO

Environmental 

HAP

Number of Facilities In Category 

Exceeding

Percent of Modeled 

Area in Category 

Exceeding2

Tier I Screen Tier II Screen1

NOAEL LOAEL NOAEL LOAEL NOAEL LOAEL

PBHAP POM None None - - 0% 0% 

Acid 

Gases3

HF NA None - - NA 0%

HCL4 NA None - - NA 0%

NA – Not Applicable. 

1- Tier II screen is performed for PB-HAP when there are exceedances of the Tier I 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 II results, if a Tier II analysis is performed. Otherwise, the percent area is based 

on the Tier I results. 
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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.

4 – For HCL, we evaluated one benchmark at the LOAEL level.

The facility-wide MIR and TOSHI, available in Appendix 5 of this document (Draft Detailed 

Risk Results), are based on emissions, as collected in the NEI, from all emissions sources at 

the identified facilities. The results of the facility-wide assessment are summarized in Table 

3.2-3. The results indicate that 17 facilities with OSWRO processes have facility-wide cancer

MIR greater than or equal to 1-in-1million. The maximum facility-wide MIR is 200 in a 

million due to emissions of beryllium compounds from cement manufacturing, with the 

OSWRO source category contributing less than 1 percent of the risk. The maximum facilitywide TOSHI is 4, and occurs at 2 facilities. At one, the HI is driven mainly by emissions of 

chlorine from industrial inorganic chemical manufacturing processes and synthetic organic 

chemical manufacturing processes, while the other is driven mainly by emissions of beryllium 

compounds from cement manufacturing. In each instance, the source category contributes 

less than 1 percent to the facility-wide TOSHI.

Table 3.2-3 Source Category Contribution to Facility-Wide Cancer Risks 

OSWRO Number of Facilities Binned by Facility-Wide 

MIR (in 1 million)

Source Category MIR 

Contribution to Facility-Wide 

MIR

<1 1≤ MIR<10 10≤ MIR<100 > 100 Total

> 90% 7* 4 0 0 11

50-90% 4 3 0 0 7

10-50% 1 0 0 0 1

< 10% 9 5 4 1 19

Total 21 * 12 4 1 38 *

* Includes one facility for which both source category and facility-wide modeled risk is 0.

4 General Discussion of Uncertainties and How They Have Been 

Addressed

4.1 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 

uncertainties in our emission inventory likely dominate the uncertainties in our exposure

modeling estimates. The chronic ambient 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 
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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% below the predicted value to as much as 84% higher than the 

predicted value. This variability translates directly to the predicted exposures and risks in our 

assessment, indicating that the actual risks could vary from 17% lower to 84% higher than the 

predicted 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. Furthermore, 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, 

we have refined our assessment by developing a better understanding of the geography of the 

facility relative to potential exposure locations. 

4.2 Uncertainties in the Dose-Response Relationships

In the sections that follow, separate discussions are provided on uncertainty associated with 

cancer potency factors and for 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 ( -

term (>24 hours up to 30 days), and subchronic (>30 days up to 10% 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 HAP

emitted by the source category 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 ingestion). Since exposures to these pollutants cannot be included in a quantitative risk 
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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).21 In some circumstances, the true risk 

could be as low as zero; however, in other circumstances the risk could also be greater.22 

When developing an upper bound estimate of risk and to provide risk values that do not 

 

21 IRIS glossary at 

http://ofmpub.epa.gov/sor_internet/registry/termreg/searchandretrieve/glossariesandkeywordlists/search.do?detai

ls=&glossaryName=IRIS%20Glossary

22 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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underestimate risk, EPA generally relies on conservative default approaches.23 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 is 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.24 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 predictions. 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 

 

23 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.

24 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% 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 values25 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 daily oral exposure (RfD) or of 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.26 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).

 

25

 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.

26 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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46

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.

There is no RfD or other comparable chronic health benchmark value for lead compounds. 

Thus, to address multipathway human health and environmental risks associated with 

emissions of lead from these facilities, ambient lead concentrations were compared to the 

NAAQS for lead. In developing the NAAQS for lead, EPA considered human health 

evidence reporting adverse health effects associated with lead exposure, as well as an EPA 

conducted multipathway risk assessment that applied models to estimate human exposures to 

air-related lead and the associated risk (73FR at 66979). EPA also explicitly considered the 

uncertainties associated with both the human health evidence and the exposure and risk 

analyses when developing the NAAQS for lead. For example, EPA considered uncertainties 

in the relationship between ambient air lead and blood lead levels (73FR at 66974), as well as 

uncertainties between blood lead levels and loss of IQ points in children (73FR at 66981). In 

considering the evidence and risk analyses and their associated uncertainties, the EPA 

Administrator noted his view that there is no evidence- or risk-based bright line that indicates 

a single appropriate level. Instead, he noted, there is a collection of scientific evidence and 

judgments and other information, including information about the uncertainties inherent in 

many relevant factors, which needs to be considered together in making this public health 

policy judgment and in selecting a standard level from a range of reasonable values (73FR at 

66998). In so doing, the Administrator decided that, a level for the primary lead standard of 
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47

0.15 μg/m3, in combination with the specified choice of indicator, averaging time, and form, 

is requisite to protect public health, including the health of sensitive groups, with an adequate 

margin of safety (73FR at 67006). A thorough discussion of the health evidence, risk and 

exposure analyses, and their associated uncertainties can be found in EPA’s final rule revising 

the lead NAAQS (73 FR 66970-66981, November 12, 2008). 

We also note the uncertainties associated with the health-based (i.e., primary) NAAQS are 

likely less than the uncertainties associated with dose-response values developed for many of 

the other HAP, particularly those HAP for which no human health data exist. In 1988, EPA’s 

IRIS program reviewed the health effects data regarding lead and its inorganic compounds 

and determined that it would be inappropriate to develop an RfD for these compounds, 

saying, “A great deal of information on the health effects of lead has been obtained through 

decades of medical observation and scientific research. This information has been assessed in 

the development of air and water quality criteria by the Agency's Office of Health and 

Environmental Assessment (OHEA) in support of regulatory decision-making by the Office 

of Air Quality Planning and Standards (OAQPS) and by the Office of Drinking Water 

(ODW). By comparison to most other environmental toxicants, the degree of uncertainty 

about the health effects of lead is quite low. It appears that some of these effects, particularly 

changes in the levels of certain blood enzymes and in aspects of children's neurobehavioral 

development, may occur at blood lead levels so low as to be essentially without a threshold. 

The Agency's RfD Work Group discussed inorganic lead (and lead compounds) at two 

meetings (07/08/1985 and 07/22/1985) and considered it inappropriate to develop an RfD for 

inorganic lead.” EPA’s IRIS assessment for Lead and compounds (inorganic) (CASRN 7439-

92-1), http://www.epa.gov/iris/subst/0277.htm. 

We note further that because of the multi-pathway, multi-media impacts of lead, the risk 

assessment supporting the NAAQS considered direct inhalation exposures and indirect airrelated multi-pathway exposures from industrial sources like primary and secondary lead 

smelting operations. It also considered background lead exposures from other sources (like 

contaminated drinking water and exposure to lead-based paints). In revising the NAAQS for 

lead, we note that the Administrator placed more weight on the evidence-based framework 

and less weight on the results from the risk assessment, although he did find the risk estimates 

to be roughly consistent with and generally supportive of the evidence-based framework 

applied in the NAAQS determination (73FR at 67004). Thus, when revising the NAAQS for 

lead to protect public health with an adequate margin of safety, EPA considered both the 

evidence-based framework and the risk assessment, albeit to different extents.

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 
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48

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 

lack of threshold values at different levels of severity should be factored into the risk 

characterization as potential uncertainties. 
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49

5 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 Docket EPA-HQ-OAR-2010-0600, 

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/scram001/guidance/guide/appw_05.pdf

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/risk-assessment-and-modeling-air-toxics-risk-assessmentreference-library

9. US EPA, 2005. Table 1. Prioritized Chronic Dose-Response Values. Office of Air 

Quality Planning and Standards. http://www2.epa.gov/fera/dose-response-assessmentassessing-health-risks-associated-exposure-hazardous-air-pollutants

10. US EPA, 2005. 1999 National Air Toxics Risk Assessment. 

http://www.epa.gov/ttn/atw/nata1999

11. US EPA, 2006. Integrated Risk Information System. http://www.epa.gov/iris/index.html.

12. US Agency for Toxic Substances and Disease Registry. 2006. Minimum Risk Levels (MRLs) 

for Hazardous Substances. http://www.atsdr.cdc.gov/mrls/index.html.

 
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13. 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.

14. NRC, 1994. National Research Council. Science and Judgment in Risk Assessment. 

Washington, DC: National Academy Press.

15. CA Office of Environmental Health Hazard Assessment, 2005. Chronic Reference 

Exposure Levels Adopted by OEHHA as of December 2008. 

http://oehha.ca.gov/air/hot_spots/rels_dec2008.html 

16. 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

17. 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.

18. US EPA, 2005. Table 2. Acute Dose-Response Values for Screening Risk Assessments 

(12/19/2011). Office of Air Quality Planning and Standards. http://www2.epa.gov/fera/doseresponse-assessment-assessing-health-risks-associated-exposure-hazardous-air-pollutants

19. U.S. EPA, 2009. Graphical Arrays of Chemical-Specific Health Effect Reference Values for 

Inhalation Exposures [Final Report]. EPA/600/R-09/061, 2009. 

http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=211003

20. CA Office of Environmental Health Hazard Assessment, 2000. All Acute Reference Exposure 

Levels developed by OEHHA as of May 2000. http://www.oehha.ca.gov/air/pdf/acuterel.pdf

21. American Industrial Hygiene Association, 2014. Current AIHA ERPG Values. 

http://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 
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14 June 2006. http://www.epa.gov/osa/spc/pdfs/CGIWGCommunication_II.pdf

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://www2.epa.gov/sites/production/files/2013-09/documents/cancer_guidelines_final_3-25-

05.pdf

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://www2.epa.gov/sites/production/files/2014-12/documents/rfd-final.pdf
Appendix 1

Technical Support Document for HEM-AERMOD Modeling 

Modeling for the Residual Risk and Technology Review 

Using the Human Exposure Model 3 – AERMOD Version

Updated 11/26/2013

Technical Support Document 

Prepared for: 

U. S. Environmental Protection Agency 

Office of Air and Radiation 

Office of Air Quality Planning and Standards 

Health and Environmental Impacts Division 

Air Toxics Assessment Group 

Research Triangle Park, NC 27711 

Prepared by: 

EC/R Incorporated 

501 Eastowne Drive, Suite 250 

Chapel Hill, North Carolina 27514
iii

Disclaimer

The research described in this document has been funded by the United 

States Environmental Protection Agency contracts 68-D-01-076, EP-D06-119, and currently EP-W-12-011 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. 
iv

Contents

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

2. Overview of the HEM-3 – 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 HEM-3 ...................................................................... 6

2.2.2 Use of Dilution Factors ............................................................................................................. 7

2.3 Postprocessing of AERMOD Results in HEM-3 ............................................................................. 7

2.3.1 Calculation of Impacts at Modeled Receptors .......................................................................... 8

2.3.2 Interpolation of Impacts at Outer Census Blocks ................................................................... 10

2.3.3 Calculation of Population Exposures and Incidence .............................................................. 10

2.3.4 Model Outputs ........................................................................................................................ 11

2.4 Data Libraries Used in HEM-3 ...................................................................................................... 13

2.4.1 Chemical Health Effects Information ..................................................................................... 13

2.4.2 Census Block Locations and Elevation Data .......................................................................... 13

2.4.3 Meteorological Data................................................................................................................ 14

2.4.4 Gaseous Deposition Parameters .............................................................................................. 14

3. Modeling for the Residual Risk Technology Review .......................................................................... 15

3.1 Emission Source Inputs.................................................................................................................. 15

3.2 Pollutant Cross-Referencing .......................................................................................................... 15

3.3 Meteorological Data....................................................................................................................... 16

3.4 Model Options Selected ................................................................................................................. 18

3.4.1 Urban or Rural Dispersion Characteristics ............................................................................. 18

3.4.2 Deposition and Plume Depletion ............................................................................................ 18

3.4.3 Cutoff Distance for Modeling of Individual Blocks ............................................................... 19

3.4.4 Facility Boundary Assumptions .............................................................................................. 19

3.5 Modeling of Multiple Facilities ..................................................................................................... 21

4. Quality Assurance ................................................................................................................................ 22

4.1 Engineering Review ....................................................................................................................... 22

4.2 Geographic Pre-Modeling Checks ................................................................................................. 22

4.3 Geographic Post-Modeling Checks ............................................................................................... 23

5. Uncertainties ........................................................................................................................................ 25

6. References ............................................................................................................................................ 32
v

Figures

Figure 3-1. AERMOD Meteorological Stations. ..........................................................................17

Tables

Table 2-1. Parameters Used to Delineate the Modeling Domain in HEM-3 ..................................4

Table 3-1. HEM-3 Domain and Set-Up Options As Used in the Residual Risk and Technology 

Review Assessments ....................................................................................................20

Table 5-1. Summary of General Uncertainties Associated with Risk and Technology Review 

 Risk Assessments ..........................................................................................................27
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 (RTR) currently being carried out by the U.S. 

Environmental Protection Agency (EPA). The model used in these risk assessments is the Human 

Exposure Model, Version 3 (HEM-3). HEM-3 incorporates 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 HEM-3-AERMOD 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 HEM-3 – AERMOD System

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

exposure, and estimation of human health risks. The state-of-the-art American Meteorological 

Society (AMS) EPA Regulatory Model (AERMOD)1,2is used for dispersion modeling.

AERMOD can handle a wide range of different source types which may be associated with an 

industrial source complex, including stack (point) sources, area and polygon sources, and volume 

sources. 

To prepare dispersion modeling inputs and carry out risk calculations, HEM-3 draws on 

four data libraries, which are provided with the model. The first is a library of meteorological 

data for over 824 stations, which are used for dispersion calculations. A second library of Census 

block (“centroid”) internal point locations and populations provides the basis of human exposure 

calculations. The Census library also includes the elevations of every Census block, which are 

used in the dispersion calculations for the RTR assessments. 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. The fourth data library, which 

provides deposition parameters for gaseous pollutants, is used only when the user opts to 

compute deposition and plume depletion (not computed for the RTR assessments to date). 

HEM-3 has been implemented in two versions: a single facility version, and a multiple 

facility version (“Multi HEM-3”). Multi HEM-3 is used in the RTR risk assessment modeling. 

Both versions operate under the same general principles. In essence, Multi HEM-3 provides a 

platform for running the single facility 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. Single HEM-3 includes a graphical user interface (GUI) for 

the selection of various dispersion modeling options. In Multi HEM-3, 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 (or “centroids”), at concentric rings 

surrounding the facility center, 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, HEM-3 estimates the total incremental cancer risks for people living within 

different distances of the modeled emission sources. 

Potential noncancer health effects due to chronic exposures are quantified using hazard 

quotients and hazard indices for various target organs. The “hazard quotient” (HQ) for a given 

chemical and receptor site is the ratio of the ambient concentration of the chemical to the 

reference concentration. The “hazard index” (HI) for a given organ is the sum of hazard 

quotients for substances that affect that organ. HEM-3 computes target-organ-specific hazard 
3 

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 (“acute”) 

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 HEM-3–AERMOD 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 HEM-3 to calculate risks 

and exposures, and Section 2.4 details the sources and methods used to produce HEM-3’s data 

libraries. The HEM-3 User’s Manuals – for single HEM-3 and Multi HEM-3 – provide 

additional details on the input data and algorithms used in the model.3

Specific model options

used in the RTR assessments are discussed in Chapter 3. 

2.1 Preparation of Dispersion Modeling Inputs 

HEM-3 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. HEM-3 can analyze point sources, 

area and polygon sources, and volume sources - configurations that are described in AERMOD's 

documentation.

1, 2

 For stack (point) 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 provide the 

terrain elevation at the base of each source. (The controlling hill height is also used in 

AERMOD’s flow calculations. Calculation of the controlling hill height by HEM-3 is discussed 

in Section 2.4.2.) If the terrain elevations are not provided by the user, HEM-3 will calculate 

elevations and controlling hill heights based on elevations and hill heights provided by the 

Census database for the Census blocks nearest to the facility. 

If particulate deposition and plume depletion are to be considered, then HEM-3 requires 

a third input file to specify the particle size distribution. This input file must include the 

average particle diameter, the mass fraction percentage, and the average particle density for 

each size range emitted. Another optional file can be used to specify building dimensions if

building wake effects are to be modeled. 
4

2.1.2 Defining the Modeling Domain and Receptors 

HEM-3 defines a modeling domain for each facility that is analyzed based on parameters 

specified by the model user or calculated by the model. 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). HEM-3 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 

generally set to 3 km for the RTR assessments, although it can be set differently 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 “user receptors” may 

include facility boundary locations, monitoring sites, individual residences, schools, or other 

locations of interest. 

For Census blocks in the outer group, beyond this modeling cutoff distance, emission 

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 facility 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 within 

AERMOD. The cutoff distance for modeling 

of individual Census blocks may be chosen by 

the user, but is typically 3000 meters for the 

RTR assessments. 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. 

Table 2-1. Parameters Used to Delineate 

the Modeling Domain in HEM-3

Parameter

Typical 

value

Modeling domain size – maximum 

radial distance to be modeled from 

facility center

50 km

Cutoff distance for modeling 

of individual blocks 1 3,000 m

Overlap distance – where receptors 

are considered on facility property 1 30 m

Polar receptor network:

Distance to the innermost ring 2≥ 100 m

Number of concentric rings 13

Number of radial directions 16

1Measured from each stack at the facility, and from 

the edges of each area or volume source

2Generally model-calculated to encompass all 

emission sources but not less than 100 meters from the 

facility center 
5

HEM-3 checks Census blocks that are very close to the facility in order to assess whether 

they overlap any point, area or volume emission sources. In addition, the user can specify an 

overlap distance, within which receptors will be considered to be on facility property. The 

default value for the overlap distance is 30 meters, or approximately equal to the width of a 

narrow buffer and a roadway. HEM-3 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 user-specified receptor locations. 

Polar receptor network 

The polar receptor network used in HEM-3 serves three functions. First, it is used to 

estimate default impacts if one or more Census locations are inside the overlap cutoff distance 

used to represent the facility boundary. Second, it is used to evaluate potential acute effects that 

may occur due to short-term exposures in locations outside the facility 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. 

Generally, the model calculates the inner radius (or first ring distance) for the polar 

receptor network to be just outside the emission source locations, but not less than 100 meters 

from the facility center. However, the user can override the default distance calculated by the 

model to fit the size and shape of the facility properties to be modeled. Likewise, the model will 

also use default values for the number of concentric rings to be analyzed (13 rings by default), 

and the number of radial directions (16 radials by default), although these default values can also 

be changed by the user to meet the needs of a specific modeling study. The inner radius of the 

polar network should be the minimum distance from the facility center that is generally outside 

of facility property. (For complex facility shapes, it is sometimes useful to specify an inner ring 

that encroaches on facility property in some directions.) HEM-3 will distribute the radial 

directions evenly around the facility. For the concentric rings, the model will generate a 

logarithmic progression of distances starting at the inner ring radium and ending at the outer 

radium of the modeling domain. 

Elevations and hill heights for model receptors 

HEM-3 includes terrain elevations by default for the RTR assessments, but the user can 

choose to exclude terrain effects when running AERMOD. If the default terrain option is used, 

HEM-3 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. 

Elevations and controlling hill heights for the polar grid receptors are also estimated 

based on values from the Census library. HEM-3 divides the modeling domain into sectors based 

on the polar receptor network, with each Census block assigned to the sector corresponding to 
6

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 HEM-3 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, HEM-3 will select the 

closest station to the center of the modeling domain, as is generally done for the RTR 

assessments. 

2.2 Running of AERMOD 

Based on the user input data and other data described in the previous section, HEM-3 

produces an input file suitable for AERMOD. HEM-3 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 HEM-3.

2.2.1 AERMOD Dispersion Options Used by HEM-3

AERMOD provides a wide array of options for controlling dispersion modeling 

calculations. In general, HEM-3 uses the regulatory default options when running AERMOD.1

These options include the following: 

 Use stack-tip downwash (except for Schulman-Scire downwash);

 Use buoyancy-induced dispersion (except for Schulman-Scire downwash);

 Do not use gradual plume rise (except for building downwash);

 Use the “calms processing” routines;

 Use upper-bound concentration estimates for sources influenced by building downwash

from super-squat buildings;

 Use default wind profile exponents;

 Use low wind speed threshold;

 Use default vertical potential temperature gradients;

 Use of missing-data processing routines; and

 Consider terrain effects.

The following additional AERMOD options are available to the HEM-3 user:

 Calculation of wet and dry deposition rates for vapor and particulate matter;

 Consideration of plume depletion (due to deposition) when calculating air concentrations;

 Consideration of building wake effects;
7

 Calculation of short term (acute) impacts; and

 Use of the FASTALL option, which conserves model runtime by simplifying the

AERMOD algorithms used to represent meander of the pollutant plume.

As noted in Section 2.1, the calculation of deposition or depletion and the consideration of 

building wake effects require additional user inputs. 

The user can opt to analyze short term impacts on a number of different time scales (i.e., 

1 hour, 6 hours, 8 hours, or 24 hours) 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. For RTR assessments, acute impacts are generally included in the modeling and the 

default multiplier of 10 is used, unless more source-specific information is available upon which 

to base the acute factor for the source category being modeled. 

2.2.2 Use of Dilution Factors 

To save computer run time when analyzing the impacts of multiple pollutants, HEM-3 

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 and plume depletion, the dilution factor does 

not vary from pollutant to pollutant. If deposition and depletion is chosen as a model option, 

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 HEM-3, 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 HEM-3 

HEM-3 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 (MIR) 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, HEM-3 calculates the maximum short term concentrations for all 

pollutants emitted by the facility. These maximum short term concentrations are compared with 

various threshold levels for acute health effects (e.g., the California EPA reference exposure 

level [REL] for no adverse effects). 

At the option of the model user, HEM-3 will also compute the long term and short term 

predicted ambient concentrations of all pollutants emitted by the facility 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, HEM-3 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. HEM-3 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 type j (particulate or gas)

Ei, k = emissions of pollutant k from source i and in pollutant type j
9

The above equations are equivalent to the following simpler equations: 

CRT = i, k ACi, k × UREk

HIT = i, k ACi, k  RCk

where: 

ACi, k = ambient concentration (g/m3

) for pollutant k at the given receptor. This is the

same as [Ei, k × DFi, j × CF] 

However, use of these simpler equations would require modeling all pollutants individually in 

AERMOD, and performing separate risk calculations for each pollutant. 

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. 

HEM-3 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 = Ei, k DFi, j  CF 

Short term concentrations: 

ACT = i ACi, k

ACi, k = Ei, k DFi, j  CF  M 

where: 

UREk = cancer unit risk factor for pollutant k

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 = non-cancer health effect reference concentration for pollutant k

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)
10

2.3.2 Interpolation of Impacts at Outer Census Blocks 

For Census blocks outside of the cutoff distance 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. If terrain elevation is part of the modeling, then an 

elevation is estimated for each polar receptor. HEM-3 estimates elevations and controlling hill 

heights for the polar grid receptors based on values from the census library. HEM-3 divides the 

modeling domain into sectors based on the polar grid receptor network, with each census block 

assigned to the sector corresponding to the closest polar grid receptor. 

HEM-3 then assigns each polar grid receptor 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. 

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 = IA1, r + (IA2, r – IA1, r) × (a – A1)  (A2 – A1) 

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: 

2.3.3 Calculation of Population Exposures and Incidence 

Using the predicted impacts for Census blocks, HEM-3 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: 

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 

receptor

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
11

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 

Furthermore, HEM-3 estimates the contributions of different chemicals and emission sources to 

total annual cancer incidence for the overall modeling domain using the following equations: 

TCRi, j = m [(k Ei, k × UREk) × DFi, j, m × CF  LT]

TCRi, k = TCRi, j × Ei, k × UREk  (k Ei, k × UREk)

where: 

2.3.4 Model Outputs 

The following is a summary of the outputs produced by HEM-3. These are written to a 

collection of files in ExcelTM and dBaseTM format (dbf). 

! Maximum long term impacts at populated locations 

" maximum lifetime individual cancer risk (MIR) 

" maximum TOSHI for the following health effects 

- respiratory system effects 

- liver effects 

- neurological system effects 

TCRi, j = the estimated total annual cancer incidence (cancers/year) to the population in the 

modeling domain due to emissions from pollutant type j (1 = particulate, 2 = gas) 

and emission source i

m = the sum over all Census blocks m within distance the modeling domain

k = the sum over all pollutant k, within pollutant type j

 Ei, k = emissions of pollutant k from source i (tons/year)

UREk = unit risk factor for pollutant k

DFi, j, m = dilution factor at receptor m, for emissions of pollutant type j (which includes 

pollutant k), from source i

CF = conversion factor, 0.02877 [(g/sec) / (ton/year)]

TCRi, k = the estimated annual cancer incidence (cancers/year) of the population in the 

modeling domain due to emissions of pollutant k (in pollutant type j) from emission 

source i
12

- 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 

- whole body 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 (often polar receptors) 

" 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 and polar grid receptor (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, and for each pollutant and source combination 
13

2.4 Data Libraries Used in HEM-3 

2.4.1 Chemical Health Effects Information 

HEM-3 includes a library of available health effects data for HAPs. For each pollutant, 

the library includes the following parameters, where available: 

! unit risk estimate (URE) for cancer 

! reference concentration (RfC) for chronic noncancer health effects 

! reference concentrations for acute health effects 

! target organs affected by the chemical for chronic noncancer health effects 

Unit risk estimates and reference concentrations included in the HEM-3 chemical library have 

been taken from EPA’s database of recommended dose-response factors for HAPs, which is 

updated periodically, consistent with continued research on these parameters.4The URE

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

exposure to an agent at a concentration of 1 microgram per cubic meter (μg/m3) in air (e.g., if the

URE = 1.5 x 10-6 per μg/m3

, 1.5 excess tumors are expected to develop per 1 million people if all

1 million people were exposed daily for a lifetime to 1 microgram of the chemical in 1 cubic 

meter of air).5

The RfC is a concentration estimate of a continuous inhalation exposure to the human 

population that is likely to be without an appreciable “risk” of deleterious non-cancer health 

effects during a lifetime. No adverse effects are expected as a result of exposure if the ratio of the 

potential exposure concentration to the RfC, defined as the hazard quotient (HQ), is less than 1.5

The reference benchmark concentration for acute health effects, similar to the chronic 

RfC, is the concentration below which no adverse health effects are anticipated when an 

individual is exposed to the benchmark concentration for 1 hour (or 8 hours, depending on the 

specific acute benchmark used and the formulation of that benchmark). Target organs are those 

organs (e.g., kidney) or organ systems (e.g., respiratory) which may be impacted with chronic 

non-cancer health effects by exposure to the chemical in question. A more in-depth discussion of 

the development and use of these parameters for estimating cancer risk and non-cancer hazard 

may be found in the EPA’s Air Toxics Risk Assessment Library.

6

The model user can add pollutants and associated health effects to HEM-3’s chemical 

health effects (dose-response) library, as needed. 

2.4.2 Census Block Locations and Elevation Data 

The HEM-3 Census library includes Census block identification codes, locations, 

populations, elevations, and controlling hill heights for all of the over 6 million Census blocks 

identified in the 2010 Census and the over 5 million Census blocks identified in the 2000 

Census. The model user may choose to use either Census database according to their modeling 

needs. The location coordinates reflect the internal “centroid” 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 
14

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 For 

the 2000 Census7and from the U.S. Census Bureau website for the 2010 Census.8 Locations and

populations for blocks in the Virgin Islands were obtained from the U.S. Census Bureau website. 

U.S. Geological Survey data was used to estimate the elevation of each census block in 

the continental U.S. and Hawaii. The data used for the 2000 Census elevations have a resolution 

of 3 arc seconds, or about 90 meters.9The data used for the 2010 Census elevations have a

resolution of 1/3 of an arc second, or about 10 meters.10 Using analysis tools (ArcGIS® 9.1

software application for the 2000 Census, and ArcGIS® 10 for the 2010 Census), elevation was 

estimated for each census block in Alaska and the U.S. Virgin Islands. The point locations of the 

census blocks in Alaska and the U.S. Virgin Islands were overlaid with a raster layer of North 

American Digital Elevation Model (DEM) elevations (in meters).9 An elevation value was

assigned to each census block point based on the closest point in the ArcGIS elevation raster file. 

An algorithm used in AERMAP, the AERMOD terrain processor, is used to determine

controlling hill heights.11, 12 These values are used for flow calculations within AERMOD. To

save run time and resources, the HEM-3 census block elevation database is 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 is projected away from the block centroid location, representing a 10% 

elevation grade. The controlling hill height is selected based on the highest elevation above that 

10% grade (in accordance with the AERMAP methodology). The distance cutoff for this 

calculation is 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 

HEM-3 includes an extensive library of meteorological data to support the AERMOD 

dispersion model. Currently 824 meteorological stations have been preprocessed for AERMOD 

as part of the RTR effort. Section 3.3 includes a depiction of these meteorological stations and 

Appendix 2 discusses the preparation of meteorological data for the RTR in more detail.

2.4.4 Gaseous Deposition Parameters 

HEM-3 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 the deposition and depletion option 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).13 HEM-3

includes a library of these parameters for most gaseous HAPs. This library is based on a 

compendium of gaseous deposition parameters developed by Argonne National Laboratories.14

It should be noted, however, that the deposition and depletion option of HEM-3 and AERMOD 

have not been used to date for the RTR assessments.
15

3. Modeling for the Residual Risk Technology Review

This section discusses the general approach used to implement the HEM-3 AERMOD 

system for the RTR modeling analyses. Separate reports have been prepared for all of the emission 

source categories analyzed to date. These reports provide information on the emissions inputs and 

results for specific emission categories. 

3.1 Emission Source Inputs 

HEM-3 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 and other area sources); 

! stack diameter (or configuration of fugitive and other area sources); 

! emission velocity; and 

! emission temperature. 

Currently, emissions data for the RTR assessments are taken from the 2005 National 

Emissions Inventory (NEI).15 These data include HAP emission rates, emission source

coordinates, stack heights, stack diameters, flow rates, and exit temperatures. EPA performs 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 is 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 in 

the docket prepared for the specific RTR source category proposed or final rule. 

As noted in the previous section, industrial emission sources can be characterized in 

AERMOD as point, area, polygon, or volume sources. 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. 

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). HEM-3 requires an exact match in the 
16 

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 HEM-3 reference file. 

Pollutant-specific dose response values are used in the HEM-3 modeling whenever 

available, including when modeling POM pollutants. Pollutant groupings, such as POM 

groupings, are used for POMs without a chemical-specific unit URE’s. These POMs are assigned 

a URE associated with various POM compounds having similar characteristics. “An Overview of 

Methods for EPA’s National-Scale Air Toxics Assessment” 2011 document16 provides more

details regarding POM modeling, including: 

[S]ome emissions of POM were reported in [the] NEI as “7-PAH” or “16-PAH,”

representing subsets of certain POM, or simply as “total PAH” or “polycyclic organic 

matter.” In other cases, individual POM compounds are reported for which no 

quantitative cancer dose-response value has been published in the sources used for 

NATA. As a result, simplifying assumptions that characterize emissions reported as 

POM are applied so that cancer risk can be quantitatively evaluated for these 

chemicals without substantially under- or overestimating risk (which can occur if all 

reported emissions of POM are assigned the same URE). To accomplish this, POM 

emissions as reported in NEI are grouped into categories. EPA assigns dose-response 

values based on the known or estimated toxicity for POM within each group and on 

information for the POM speciation of emission sources, such as wood fires and 

industrial processes involving combustion. 

Emissions of metal compounds are 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.17, 18

3.3 Meteorological Data 

Nationwide meteorological data files are accessed by HEM-3 and used for the RTR 

modeling. The current HEM-3 AERMOD Meteorological Library includes over 800 

nationwide locations, depicted in Figure 3-1. This library contains surface and upper air 

meteorological data from National Weather Service (NWS) observation stations, which are

named beginning with the state abbreviation for the state in which the station is located. 

AERMOD requires surface and upper air meteorological data that meet specific format 

requirements.19, 20 Appendix 2 discusses the preprocessing performed on the meteorological 

data used by AERMOD and includes a detailed listing of the 824 meteorological station pairs.
17

Figure 3-1. AERMOD Meteorological Stations
18

3.4 Model Options Selected 

HEM-3 presents a number of options for characterizing the modeling domain and data 

sources. As many sources are generally modeled in RTR assessments, established defaults and 

common practices are relied on to make these choices. The choices available to a HEM-3 user and 

the selections that are made in most RTR assessments are presented in Table 3-1. Some of the key 

selections are discussed in more detail in the paragraphs below. 

It should be noted that 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,21 but

no facility-specific analyses of these data were performed to characterize short-term emissions 

from these emission sources, and upset emissions are generally not modeled for the RTR risk 

assessments. 

3.4.1 Urban or Rural Dispersion Characteristics 

Current RTR source category assessments which use the 2010 Census are based on either 

urban or rural dispersion characteristics, depending on the land characteristics surrounding each 

modeled facility. The EPA provides guidance on whether to select urban or rural dispersion 

coefficients in its Guideline on Air Quality Models.22 In general, the urban option is used if (1) the

land use is classified as urban for more than 50% of the land within a 3-kilometer radius of the 

emission source, or (2) the population density within a 3-kilometer radius is greater than 750 

people per square kilometer. Of these two criteria, the land use criterion is more definitive. 

Using the 2010 Census, the HEM-3 model determines, by default, whether to use rural or 

urban dispersion characteristics. HEM-3 will find the nearest census block to the facility center 

and determine whether that census block is in an urban area, as designated by the 2010 Census.23

The population of the designated urban area will be used to specify the population input for 

AERMOD's urban mode. (Alternatively, a user may select the rural or urban option to override 

determination by the model. If a user selects an urban dispersion environment, then the user must 

provide the urban population as well.) 

For the 2008 and prior screening-level RTR assessments of 51 source categories, the rural 

option was chosen to be most conservative (i.e., more likely to overestimate risk results). The rural 

option is also chosen by default by the HEM-3 model whenever the 2000 Census is selected by the 

user. 

3.4.2 Deposition and Plume Depletion 

The RTR modeling analysis to date has not taken into account the depletion of pollutant 

concentrations in the plume due to wet or dry deposition, although HEM-3 can model deposition 
19

and depletion using AERMOD. In addition, reactivity and decay have not been considered. 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 (the maximum individual risk, or MIR). 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 may 

consider deposition and depletion. 

3.4.3 Cutoff Distance for Modeling of Individual Blocks 

The cutoff distance for modeling individual Census blocks is initially set to 3 km by 

default. This distance generally ensures that the maximum individual cancer risk and the 

maximum TOSHI are modeled explicitly and not interpolated. Following a modeling run, the 

results for each facility are checked to determine whether the maximum impacts are located inside 

the modeling cutoff distance. If the maximum impacts are outside the cutoff distance, and if any of 

the impacts are significant, then HEM-3 is rerun for the facility with a cutoff distance greater than 

3 km. In general, this is done if the cancer risk exceeds 1 in 1 million or any TOSHI exceeds 1. 

However, the risks for such facilities are generally very low, since the maximum impacts are in 

most cases only interpolated when the nearest Census block is more than 3 km from the facility 

(i.e., in sparsely populated areas). 

3.4.4 Facility Boundary Assumptions 

The main input mechanisms for incorporating facility boundary information in HEM-3 are 

the overlap distance, the distance to the innermost polar receptor ring, and user-specified receptor 

locations. The NEI does not provide information on facility boundaries. However, satellite/aerial 

images are used to locate residential populations that are closer to a facility than the Census block 

centroid. User-specified receptor locations are used in such assessments to avoid underestimating 

risk. Conservative default assumptions are used for the overlap distance and the innermost polar 

receptor ring. However, these are adjusted for some categories where facility sites are known to be 

large. In addition, satellite imagery is used to check the facility boundary assumptions for facilities 

with large projected impacts. These checks are discussed further in the section on Quality 

Assurance (Section 4). 
20

Table 3-1. HEM-3 Domain and Set-Up Options As Used in the Residual Risk and 

Technology Review Assessments

Option Selection

Dispersion model AERMOD

Census database: 2010 or 2000 Based on date of 

analysis

Type of analysis: chronic, acute, or both Both

Averaging time for short term impacts 1-hour

Multiplier for short term emissions 10 generally, 

although source 

type-specific 

factors are used if 

available

Dispersion characteristics: urban or rural, as determined by model, based on 

closest 2010 Census block to each facility (when using 2010 Census). Rural by 

default, when using the 2000 Census.

Urban or Rural 

based on facility 

location; (Rural 

for 2000 Census)

Include terrain impacts Yes

Include building wake effects No

Calculate deposition (wet, dry, or both) & include impacts of plume depletion No

User-specified receptor locations (for residential population locations, facility

boundary sites, or other sites of interest)

Yes, for some 

facilities

Modeling domain size – maximum distance to be modeled 50 km

Cutoff distance for modeling of individual blocks 3 kma

Overlap distance where receptors are considered to be on facility property –

measured from each source

30 mb

Polar receptor network specifications:

Distance from the facility center to the innermost ring ≥ 100 mc

Number of rings 13

Number of directions 16

Meteorology data Closest site

a The individual block modeling cutoff is increased for categories and for some facilities to ensure 

that the maximum individual risk values are not interpolated.

b The overlap distance is adjusted for some facilities to avoid modeling locations that are on facility

property (see section 4.2).

c HEM-3 sets the innermost ring distance to be just outside the emission sources but not < 100 m.
21

3.5 Modeling of Multiple Facilities 

HEM-3 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 are 

summed at each Census block for all facilities affecting the Census block. 

As described earlier (Section 2.3.4), HEM-3 produces detailed output tables containing the 

risk and population for every Census block in the modeling domain. These detailed tables are 

combined for all facilities in a source category and the risk for each Census block is summed, 

using the RTR Summary Program add-on module to the Multi HEM-3 model.3Thus, the effect of

multiple facilities in the same source category on the same receptor are estimated. The resulting 

“combined facility” or “cluster-effect” Census block risks are used to calculate population 

exposure to different cancer risk levels, noncancer hazard indices, and source category incidence. 
22

4. Quality Assurance

The National Emissions Inventory (NEI) 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.24

 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 performs an 

engineering review of NEI data for the emission source categories included in the RTR analysis. 

This engineering review includes two main components. The first component addresses the list of 

facilities included in each source category. EPA engineers review independent sources of 

information to identify all sources in the category that are included in the NEI. In addition, EPA 

reviews 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 focuses on the appropriateness of facility 

emissions. EPA reviews the list of HAPs reportedly emitted by each facility to make sure that the 

pollutants are appropriate to the source category. In addition, EPA engineers review the magnitude 

of those HAP emissions. In cases where new or better data are known to exist for a particular 

source category, that information is 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 Pre-Modeling 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.25 In addition, coordinates for all emission sources at a given facility are

checked to ensure that they are within 3 km of one another. These QA checks happen prior to 

HEM-3 modeling and the results of such checks are reflected in the HEM-3 input files.

Another pre-modeling geographic QA check regards the location of the census block 

receptors. As noted above, to estimate ambient concentrations for evaluating long-term exposures, 

the HEM-3 model uses the census block centroids as dispersion model receptors. The census block 

centroids are often 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 facility 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. 
23

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 is within 300 meters of any 

emission source, aerial images of the facility are reviewed to determine whether the block centroid 

is 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 very 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. Block 

centroids confirmed to be located on facility property are moved 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, block centroids are relocated in some cases, or additional user-specified receptors are 

added to a block. Aerial images of all large census blocks within one kilometer of any emission 

source are examined. 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 does not 

represent the residential locations, it is relocated in the HEM-3 input files to better represent them. 

If residential locations cannot be represented by a single receptor (that is, the residences are spread 

out over the block), additional user-specified receptors are included in the HEM-3 input files to 

represent residences nearer to the facility than the centroid. 

4.3 Geographic Post-Modeling Checks 

As part of the RTR modeling analysis, additional geographical QA checks are made for 

some facilities, after initial HEM-3 modeling results are reviewed. Facilities subjected to these 

additional checks include: 

! cases where the initial estimates of maximum risks are particularly high 

" maximum individual cancer risk of over 1 in 10,000 

" any maximum TOSHI above 10 

! cases where no Census blocks are identified by the model within 3 km of the facility 

HEM-3 produces a detailed Google EarthTM map of the modeled point and area emission 

sources and surrounding receptors (including Census block centroids, polar receptors and userspecified receptors) overlaying Google EarthTM’s satellite imagery. This map allows a QA check 

of the specific source locations, as well as an approximate check of the facility boundaries. The 

emission source coordinates are reviewed for each of these facilities and compared with the 

address reported for the facility. If the address and the coordinates represent the same location, 

then the coordinates are taken to be correct. For more recent modeling of source categories, the 

emission coordinates initially modeled by HEM-3 tend to be correct, as they undergo premodeling scrutiny and QA checks (as discussed in Section 4.2). 
24

More rarely, the modeled emission coordinates will be determined post initial modeling 

not to be located on facility property. If the facility and emission coordinate locations are different, 

then the satellite imagery for the address and the coordinate location are reviewed to determine 

whether either photograph includes an industrial facility. Generally for the 2008 and prior 

screening-level RTR assessments of 51 source categories, where the two locations were different, 

the facility address was found to be correct (and the emission source coordinates required 

correction). 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 are found to be incorrect, HEM-3 is 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 are also reviewed. This review draws on detailed Census block boundary maps and 

satellite imagery. 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 centroid 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 

facility property. 

In general, block coordinates are considered to be on facility property if they are located 

between the different emission source locations listed for the facility. In these situations, HEM-3

is rerun with an expanded overlap distance, in order to exclude the Census block coordinates that 

appear to be located on facility property. The distance to the innermost polar receptor ring is also 

adjusted to ensure that this ring is not on facility property, but as close to the apparent facility 

boundaries as possible.
25

5. Uncertainties

The RTR risk assessments using HEM-3 and AERMOD are subject to a number of 

uncertainties. For instance, model verification studies for AERMOD show 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 1.0.25

In addition, a number of simplifying assumptions are made in these modeling analyses. 

First, the coordinates reported by the Department of Census for Census block internal points 

(“centroids”) 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 HEM-3 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 longterm 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 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 analyses, only direct inhalation is 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 hazardous 

air pollutants (PB HAP). Screening level evaluations of the potential human health risks 

associated with emissions of PB HAP from the modeled facilities are used to determine if 

additional analyses are needed, but these analyses are outside the scope of this document. Because 

the HEM-3 AERMOD analyses are restricted to the inhalation pathway, the impacts of plume 

depletion due to deposition are not taken into account. Thus, inhalation impacts may be 

overestimated for some pollutants, but exposures through other pathways would be 

underestimated. 
26

A number of other simplifications are made in the dispersion modeling analyses, as noted 

in Table 3-1. For instance, building wake effects are not considered. In addition, meteorological 

observations are based on the closest station in the HEM-3 meteorological library (see Figure 3-1). 

Alternative meteorological stations may be more appropriate for some facilities. Ideally, facilityspecific meteorological observations would be used. A single year of meteorological data (2011) is 

currently used for AERMOD’s dispersion modeling. (The 2008 and prior screening-level RTR 

assessments of 51 source categories used meteorological data based on the year 1991.) 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 analyses and the extent of the dispersion modeling 

analysis (national scale), it is not practical to model five years of data. Other national studies such 

as NATA also consider 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 station.26

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 

analyses 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 analyses. 

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 analyses:

! emissions inventory; 

! fate and transport modeling; 

! exposure assessment; and 

! toxicity assessment. 

It must also be noted that individual source categories may be subject to additional uncertainties. 

These are discussed in separate reports which are prepared for each emission source category 

included in the RTR assessments.
27

Table 5-1. Summary of General Uncertainties Associated with Risk and Technology Review Risk Assessments

Parameter Assumption Uncertainty/Variability Discussion

Potential Direction of Bias 

on Risk Estimates

Emissions Inventory

Individual 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 current emissions inventory is based on the 

2005 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. For the 2008 

screening level assessments, nearly half of the 

sources in a given source category submitted a 

review of their emissions and facility 

characteristics data. Some detailed data, such as 

property boundary information is not available 

for most facilities. This is an important 

consideration in determining acute impacts.

Unbiased overall, magnitude 

variable

Multiplier for short-term 

emission rates

Generally, 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 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.

Potential overestimate due to 

the fact that worst-case 

emissions are assumed to 

occasionally coincide with 

worst-case meteorology.

Overestimate due to lack of 

actual information on shortterm emission rates.

Fate and Transport Modeling

Atmospheric dispersion 

model choice

AERMOD is EPA's recommended 

dispersion model for assessing 

pollutant concentrations from 

industrial facilities

Field testing of dispersion models, including 

AERMOD, have shown results to generally be 

within a factor of 2 of measured concentrations.

Unbiased overall
Table 5-1. Summary of General Uncertainties Associated with Risk and Technology Review Risk Assessments

(continued) 

28

Parameter Assumption Uncertainty/Variability Discussion

Potential Direction of Bias 

on Risk Estimates

Building downwash Not included in assessments

Use of this algorithm in AERMOD could 

improve the dispersion calculations at individual 

facilities. However, data are not readily 

available to utilize this option.

Potential underestimate of 

maximum risks near facility. 

No effect on risks further 

out.

Plume deposition and 

depletion Not included in assessments

Ignoring these impacts for pollutants that 

deposit minimally, and whose risks derive 

predominantly from inhalation, should have 

minimal effect on risk estimates.

Unbiased or minimal 

overestimate.

Meteorology

One year of meteorological data 

from the nearest weather station 

(selected from 824 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.

Minimal underestimate or 

overestimate.

Reactivity Not included in the assessments

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 impact on maximum risk 

estimates. Minimal impact 

on population risks and 

incidence.
Table 5-1. Summary of General Uncertainties Associated with Risk and Technology Review Risk Assessments

(continued) 

29

Parameter Assumption Uncertainty/Variability Discussion

Potential Direction of Bias 

on Risk Estimates

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 effect on maximum 

individual risks. Minimal 

underestimation of 

incidence.

Exposure Assessment

Locations 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.

Unbiased across population 

for most pollutants and 

individuals, likely 

overestimate for most 

exposed and underestimate 

for least exposed persons.

Long-term movements 

of individuals

MIR individual is exposed 

continuously to the highest 

exposure concentration for a 70-

year lifetime.

Maximum individual risk (MIR) is defined in 

this way to be a maximum theoretical risk at a 

point where a person can actually reside.

Unbiased for most 

individuals, likely 

overestimate for the actual 

individual most exposed and 

likely underestimate for the 

least exposed. Incidence 

remains unbiased unless 

population around facilities 

increases or decreases over 

70 years.
Table 5-1. Summary of General Uncertainties Associated with Risk and Technology Review Risk Assessments

(continued) 

30

Parameter Assumption Uncertainty/Variability Discussion

Potential Direction of Bias 

on Risk Estimates

Toxicity Assessment

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 general, EPA derives 

RfCs using procedures 

whose goal is to avoid 

underestimating risks in light 

of uncertainty and 

variability. The greater the 

uncertainties, the greater the 

potential for overestimating 

risks.

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.

Overestimate of risks for 

nonlinear carcinogens and 

for linear carcinogens with 

sparse health effects data. In 

general, EPA derives URE 

values using procedures 

aimed at overestimating 

risks in light of uncertainty

and variability.

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.

Unbiased overall. Some 

mixtures may have 

underestimated risks, some 

overestimated, and some 

correctly estimated.
Table 5-1. Summary of General Uncertainties Associated with Risk and Technology Review Risk Assessments

(continued) 

31

Parameter Assumption Uncertainty/Variability Discussion

Potential Direction of Bias 

on Risk Estimates

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.

Overestimate where most 

conservative values used. 

Unbiased where categoryspecific profiles applied.

There is the potential to 

underestimate risks for 

pollutants which are not 

included in the assessment.
32

6. References

1. EPA. 2004. AERMOD: Description of Model Formulation. EPA-454/R-03-004, U.S.

Environmental Protection Agency, Research Triangle Park, NC.

http://www.epa.gov/ttn/scram/7thconf/aermod/aermod_mfd.pdf

2. EPA. 2004. User's Guide for the AMS/EPA Regulatory Model - AERMOD. EPA-454/B03-001, U.S. Environmental Protection Agency, Research Triangle Park, NC.

www.epa.gov/scram001/dispersion_prefrec.htm#aermod

3. EPA. 2013. The Human Exposure Model (HEM): Single-facility HEM-3 User’s Guide;

and Multi-facility HEM-3 and RTR Summary Programs User’s Guide. Prepared for the

U.S. Environmental Protection Agency, Research Triangle Park, NC, by EC/R Inc.,

Chapel Hill, NC. February 2013. http://www.epa.gov/ttn/fera/human_hem.html

4. EPA. 2012. Dose-Response Assessment for Assessing Health Risks Associated With

Exposure to Hazardous Air Pollutants. U.S. Environmental Protection Agency, Research

Triangle Park, NC. Available at: http://www.epa.gov/ttn/atw/toxsource/summary.html

5. EPA. 2011. Glossary of Key Terms. Technology Transfer Network Air Toxics, 2005

National-Scale Air Toxics Assessment. U.S. Environmental Protection Agency.

http://www.epa.gov/ttn/atw/natamain/gloss1.html.

6 . EPA. 2004. Air Toxics Risk Assessment Reference Library, EPA-453-K-04-001A, U.S.

Environmental Protection Agency, Research Triangle Park, NC. April 2004.

www.epa.gov/ttn/fera/risk_atra_vol1.html

7. Census. 2000. LandView® 5 on DVD. U.S. Census Bureau, Washington, DC.

8. Census. 2010. Census Summary File 1 – United States:

http://www2.census.gov/census_2010/04-Summary_File_1/ prepared by the U.S. Census

Bureau, Washington, DC, 2011. See also Technical Documentation for the 2010 Census

Summary File 1.

9. USGS. 2006. US GeoData Digital Elevation Models – Fact Sheet 040-00 (April 2000).

U.S. Department of the Interior - U.S. Geological Survey, Washington, DC.

http://data.geocomm.com/sdts/fs04000.pdf

10. USGS. 2010. USGS Seamless Data Warehouse. U.S. Department of the Interior - U.S.

Geological Survey, Washington, DC. http://nationalmap.gov/viewer.html
33

11. 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

12. EPA. 2011. Addendum to the User’s Guide for the AERMOD Terrain Preprocessor

(AERMAP) (EPA-454/B-03-003, October 2004), U.S. Environmental Protection Agency,

Research Triangle Park, NC. March 2011.

http://www.epa.gov/scram001/dispersion_related.htm#aermap

13. EPA. 2012. Addendum: User's Guide for the AMS/EPA Regulatory Model – AERMOD

(EPA-454/B-03-001, September 2004) (Section 2.2 related to deposition). U.S.

Environmental Protection Agency, Research Triangle Park, NC. December 2012. http://

www.epa.gov/scram001/dispersion_prefrec.htm#aermod. See also the AERMOD

Deposition Algorithms – Science Document (Revised Draft)” at

http://www.epa.gov/ttn/scram/7thconf/aermod/aer_scid.pdf

14. Wesely, M.L., P.V. Doskey, and J.D. Shannon. 2002. Deposition Parameterizations for

the Industrial Source Complex (ISC3) Model. ANL/ER/TR–01/003, Argonne National

Laboratory, Argonne, Illinois 60439. See “AERMOD Deposition Parameterizations

Document” pdf link under “Model Supporting Documents” section of TTN’s

Preferred/Recommended Models webpage at

http://www.epa.gov/scram001/dispersion_prefrec.htm#aermod

15. EPA. 2012. 2005 National Emissions Inventory Data & Documentation. U.S.

Environmental Protection Agency, Research Triangle Park, NC.

http://www.epa.gov/ttn/chief/net/2005inventory.html#inventorydata

16. EPA. 2011. An Overview of Methods for EPA’s National-Scale Air Toxics Assessment.

Prepared for EPA’s Office of Air Quality, Planning, and Standards, Research Triangle

Park, NC, by ICF International , Durham, NC. January 2011, p 55.

http://www.epa.gov/ttn/atw/nata2005/05pdf/nata_tmd.pdf.

17. EPA. 2004. EMS-HAP Modeling System. Environmental Protection Agency, Research

Triangle Park, NC. http://www.epa.gov/scram001/dispersion_related.htm#ems-hap

18. Strum, Madeleine, Richard Mason, and James Thurman. 2004. User's Guide for the

Emissions Modeling System for Hazardous Air Pollutants (EMS-HAP) Version 3.0.

EPA-454/B-03-006, Environmental Protection Agency, Research Triangle Park, NC.

August 2004. http://www.epa.gov/ttn/scram/userg/other/emshapv3ug.pdf

19. EPA. 2004. User’s Guide for the AERMOD Meteorological Preprocessor (AERMET),

EPA-454/B-03-002, U.S. Environmental Protection Agency, Research Triangle Park,

NC. http://www.epa.gov/scram001/7thconf/aermod/aermetugb.pdf
34

20. EPA. 2012. Addendum to the User’s Guide for the AERMOD Meteorological

Preprocessor (AERMET) (EPA-454/B-03-002, November 2004), U.S. Environmental

Protection Agency, Research Triangle Park, NC. December 2012.

http://www.epa.gov/ttn/scram/metobsdata_procaccprogs.htm#aermet

21. Texas Commission on Environmental Quality. 2006. Reports on Air Emission Events.

http://www.tceq.texas.gov/field/eventreporting

22. 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.

Appendix W of 40 CFR Part 51.

http://www.epa.gov/ttn/scram/guidance/guide/appw_05.pdf

23. Federal Register, 2012. Qualifying Urban Areas for the 2010 Census. FR 77:59 (27

March 2012). p.18652. http://www.gpo.gov/fdsys/pkg/FR-2012-03-27/pdf/2012-6903.pdf

24. EPA. 2006. NEI Quality Assurance and Data Augmentation for Point Sources. U.S.

Environmental Protection Agency, Emission Inventory Group, RTP, NC.

http://www.epa.gov/ttn/atw/nata2005/05pdf/2002nei_qa_augmentation_report0206.pdf

25. 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

26. EPA, 2001, National-Scale Air Toxics Assessment for 1996; EPA-453/R-01-003; EPA

454/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 HEM3 Modeling
2

DRAFT

METEOROLOGICAL DATA PROCESSING 

USING AERMET

FOR

HEM3
Meteorological Data Processing using AERMET December 28, 2012

For HEM3

1 

BACKGROUND

The AERMOD meteorological processor, AERMET, and its supporting modeling system 

(AERSURFACE and AERMINUTE) were used to process one year of meteorological 

data for over 800 observation stations across the continental United States, Alaska, 

Hawaii, and Puerto Rico.

METEOROLOGICAL DATA

To estimate the boundary layer parameters required by AERMOD, AERMET requires 

hourly surface weather observations (which may include hourly values calculated from 1-

minute data) 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. 

USEPA meteorologists obtained calendar year 2011 Integrated Surface Hourly Data 

(ISHD) for over 800 Automated Surface Observation System (ASOS) stations spanning 

the entire US, as well as Puerto Rico and the US Virgin Islands, from the National 

Climatic Data Center (NCDC). To support AERMET, ASOS 1-minute data for each 

surface station were also obtained from NCDC in a DSI 6405 format.

Further, upper air sounding data for the same time period for over 80 observation sites 

were obtained from the “NOAA/ESRL” online Radiosonde Database. These datasets 

were produced by ESRL in Forecast Systems Laboratory (FSL) format. Appendix 1 lists 

the surface stations, as well as the location, ground elevation, and anemometer height for 

each station. Figure 1 is a map that shows the locations of all the surface stations. 

Appendix 2 lists the upper air stations and their locations. 

AERMET PROCESSING

Utilizing the AERMET meteorological data preprocessor, and the ASOS surface and FSL 

upper air stations, surface and profile files for input into AERMOD were generated 

nationwide. The surface stations were paired with representative upper air stations by 

taking the upper air station closest to each surface station. The AERSURFACE tool was 

used to estimate the surface characteristics for input into AERMET utilizing land cover 

data surrounding the surface station. In addition, the AERMINUTE preprocessor was 

used to process 1-minute ASOS wind data for input into AERMET. Table 1 outlines the 

approach/inputs each of the data preprocessors and tools used to generate the AERMOD

meteorological data.
Meteorological Data Processing using AERMET December 28, 2012

For HEM3

1 

Figure 1. Surface Stations
Meteorological Data Processing using AERMET December 28, 2012

For HEM3

1 

Table 1. AERMET Processing Options

AERMET Options Version 11059

ASOS Site Yes

Surface Data Format NCDC TD-3505 (ISHD)

Upper Air Data Format FSL, all levels, tenths m/s

AERMINUTE Options Version 11325

Include 1 minute ASOS 

Data

Yes, where available TD-6405 

format

AERSURFACE Options Version 08009

Landcover data USGS NLCD92 GeoTIFF format 

(except Alaska and Hawaii which 

used 2001 landcover data)

Radius for Surface 

Roughness Calculations

1 km

Site Characteristics Airport Site (where applicable)

Arid Regions for all sites in AZ, 

NM, UT only

Temporal resolution Monthly, 12 sectors

Site Surface Moisture Average

Snow Cover Late Winter/Winter without 

continuous snow cover – all states 

(except Alaska where we used 

continuous snow)

RESULTS

To assure that each surface and profile file would run properly, USEPA meteorologists 

ran AERMOD using a model plant. Further, the surface files were examined for 

completeness. If more than 10 percent of the data were missing, the station was not 

considered suitable for the HEM-3 meteorological database. In all, 824 met station pairs 

ran successfully and will be included in the HEM-3 meteorological library. A summary

of these stations, along with the missing and calm hours for each, are presented in 

Appendix 3
1 of 18

State City WBAN # Latitude Longitude

Base 

Elevation 

(M)

Anemometer Ht 

(M)

AK ANNETTE 25308 55.04333 -131.57000 33 10.05

AK JUNEAU 25309 58.35500 -134.57500 7 10.05

AK HAINES 25323 59.24500 -135.52000 7 10.05

AK KETCHIKAN 25325 55.35667 -131.71167 26 10.05

AK PALMER 25331 61.59611 -149.09167 70 10.05

AK SITKA 25333 57.04833 -135.36000 5 10.05

AK SKAGWAY 25335 59.46000 -135.31333 6 10.05

AK YAKUTAT 25339 59.51028 -139.62778 12 10.05

AK KALWOCK 25367 55.58000 -133.07500 20 10.05

AK KODIAK 25501 57.75000 -152.49167 21 10.05

AK KING SALMON 25503 58.68361 -156.65389 14 10.05

AK ILIAMNA 25506 59.75333 -154.91500 52 10.05

AK HOMER 25507 59.64667 -151.47667 17 10.05

AK SELDOVIA 25516 59.44333 -151.70167 9 10.05

AK COLD BAY 25624 55.20667 -162.72167 24 10.05

AK ST PAUL ISLAND 25713 57.16333 -170.22000 11 10.05

AK ANCHORAGE 26409 61.21694 -149.85500 42 10.05

AK CORDOVA 26410 60.48889 -145.45111 13 10.05

AK FAIRBANKS 26411 64.81667 -147.85500 132 10.05

AK NORTHWAY 26412 62.96139 -141.94583 522 10.05

AK DELTA JUNCTION/FT GREELY 26415 63.99500 -145.71833 391 10.05

AK EAGLE 26422 64.77667 -141.14833 273 10.05

AK GULKANA 26425 62.16028 -145.45750 479 10.05

AK NENANA 26435 64.55000 -149.07167 109 10.05

AK SEWARD 26438 60.12833 -149.41667 4 10.05

AK ANCHORAGE 26451 61.17500 -149.99333 37 10.05

AK ANCHORAGE 26491 61.17861 -149.96139 27 10.05

AK PORTAGE GLACIER 26492 60.78500 -148.83889 31 10.05

AK KALTAG 26502 64.32667 -158.74167 45 10.05

AK MC GRATH 26510 62.95333 -155.60333 101 10.05

AK KENAI 26523 60.57972 -151.23917 28 10.05

AK TALKEETNA 26528 62.32167 -150.09167 107 10.05

AK TANANA 26529 65.17444 -152.10694 67 10.05

AK BETTLES 26533 66.91611 -151.50889 196 10.05

AK KOTZEBUE 26616 66.88500 -162.59667 2 10.05

AK NOME 26617 64.51333 -165.44333 6 10.05

AK KIVALINA 26642 67.73167 -164.54833 4 10.05

AK DEADHORSE 27406 70.19167 -148.47722 16 10.05

AK BARROW 27502 71.28667 -156.76333 12 10.05

AK WAINWRIGHT 27503 70.63917 -159.99500 8 10.05

AK NUIQSUT 27515 70.21167 -151.00167 16 10.05

AL HUNTSVILLE 03856 34.64361 -86.78556 190 10.05

AL TROY 03878 31.86056 -86.01222 117 10.05

Appendix 1

ASOS Surface Stations used for AERMET (2011)
2 of 18

State City WBAN # Latitude Longitude

Base 

Elevation 

(M)

Anemometer Ht 

(M)

AL MOBILE 13838 30.62639 -88.06806 5 10.05

AL DOTHAN 13839 31.32139 -85.44972 113 10.05

AL ANNISTON 13871 33.58806 -85.85806 182 10.05

AL BIRMINGHAM 13876 33.56389 -86.75444 187 10.05

AL MOBILE 13894 30.68833 -88.24556 66 10.05

AL MONTGOMERY 13895 32.30056 -86.39417 38 10.05

AL MUSCLE SHOALS 13896 34.74528 -87.61028 166 10.05

AL EVERGREEN 53820 31.41556 -87.04417 77 10.05

AL ALABASTER 53864 33.17833 -86.78167 172 10.05

AL TUSCALOOSA 93806 33.21194 -87.61583 48 10.05

AR JONESBORO 03953 35.83111 -90.64639 79 10.05

AR HOT SPRINGS 03962 34.47806 -93.09611 155 10.05

AR LITTLE ROCK 13963 34.74667 -92.23306 77 10.05

AR FORT SMITH 13964 35.33361 -94.36500 136 10.05

AR HARRISON 13971 36.26139 -93.15472 415 10.05

AR TEXARKANA 13977 33.45361 -94.00750 116 10.05

AR BLYTHEVILLE 53869 35.94028 -89.83083 77 10.05

AR MOUNTAIN HOME 53918 36.36889 -92.47028 281 10.05

AR MONTICELLO 53919 33.63833 -91.75083 85 10.05

AR RUSSELLVILLE 53920 35.25778 -93.09472 117 10.05

AR FAYETTEVILLE/SPRINGDALE 53922 36.28167 -94.30667 388 10.05

AR De QUEEN 53925 34.04694 -94.39944 105 10.05

AR WEST MEMPHIS 53959 35.13500 -90.23444 73 10.05

AR PINE BLUFF 93988 34.17500 -91.93472 62 10.05

AR EL DORADO 93992 33.22083 -92.81333 80 10.05

AR FAYETTEVILLE 93993 36.00500 -94.17000 379 10.05

AZ WINDOW ROCK 03029 35.65750 -109.06139 2052 7.92

AZ FLAGSTAFF 03103 35.14028 -111.67222 2135 10.05

AZ PAGE 03162 36.92611 -111.44778 1308 10.05

AZ PHOENIX 03184 33.68833 -112.08167 453 10.05

AZ SCOTTSDALE 03192 33.62278 -111.91056 449 10.05

AZ GRAND CANYON 03195 35.94611 -112.15472 1991 10.05

AZ NOGALES 03196 31.42083 -110.84583 1191 10.05

AZ TUCSON 23160 32.13139 -110.95528 777 10.05

AZ PHOENIX 23183 33.44306 -111.99028 337 10.05

AZ PRESCOTT 23184 34.65167 -112.42083 1525 10.05

AZ WINSLOW 23194 35.02194 -110.72194 1489 10.05

AZ DOUGLAS BISBEE 93026 31.46917 -109.60361 1251 10.05

AZ ST. JOHNS 93027 34.51833 -109.37917 1744 10.05

AZ SAFFORD 93084 32.85472 -109.63528 966 10.05

AZ KINGMAN 93167 35.25944 -113.93722 1042 10.05

CA ONTARIO 03102 34.05611 -117.60028 281 10.05

CA PALM SPRINGS 03104 33.62778 -116.16000 -37 10.05

CA SAN DIEGO 03131 32.81583 -117.13944 128 10.05

CA IMPERIAL 03144 32.83417 -115.57861 -18 10.05

CA LANCASTER 03159 34.74083 -118.21889 713 10.05

CA FULLERTON 03166 33.87194 -117.97889 26 10.05

CA HAWTHORNE 03167 33.92278 -118.33417 18 10.05
3 of 18

State City WBAN # Latitude Longitude

Base 

Elevation 

(M)

Anemometer Ht 

(M)

CA RIVERSIDE 03171 33.95194 -117.43861 229 10.05

CA CARLSBAD 03177 33.12806 -117.27944 94 10.05

CA SAN DIEGO 03178 32.57222 -116.97944 158 10.05

CA CHINO 03179 33.97528 -117.63611 193 10.05

CA LONG BEACH 23129 33.82833 -118.16306 9 10.05

CA VAN NUYS 23130 34.20972 -118.48917 239 10.05

CA CAMARILLO 23136 34.21667 -119.08333 84 10.05

CA BURBANK 23152 34.20056 -118.35861 222 10.05

CA BAKERSFIELD 23155 35.43361 -119.05583 149 10.05

CA BISHOP 23157 37.37306 -118.36278 1250 10.05

CA BLYTHE 23158 33.61917 -114.71694 120 10.05

CA DAGGETT 23161 34.85361 -116.78667 584 10.05

CA LOS ANGELES 23174 33.93806 -118.40556 34 10.05

CA NEEDLES 23179 34.76611 -114.62333 271 10.05

CA PALMDALE 23182 34.62944 -118.08361 764 10.05

CA SANDBERG 23187 34.74361 -118.72444 1375 10.05

CA SAN DIEGO 23188 32.73472 -117.16861 4 10.05

CA SANTA BARBARA 23190 34.42611 -119.84361 3 10.05

CA AVALON 23191 33.40500 -118.41583 487 10.05

CA SANTA ROSA 23213 38.50917 -122.81167 36 10.05

CA EMIGRANT GAP 23225 39.29167 -120.70833 1612 10.05

CA OAKLAND 23230 37.75472 -122.22083 1 10.05

CA SACRAMENTO 23232 38.51250 -121.49250 5 10.05

CA SALINAS 23233 36.66333 -121.60528 23 10.05

CA SAN FRANCISCO 23234 37.61972 -122.39806 2 10.05

CA STOCKTON 23237 37.89417 -121.23722 8 10.05

CA MOUNTAIN VIEW 23244 37.41472 -122.04750 10 10.05

CA CONCORD 23254 37.99167 -122.05194 5 10.05

CA MERCED 23257 37.28472 -120.51278 46 10.05

CA MODESTO 23258 37.62583 -120.95333 22 10.05

CA MONTEREY 23259 36.58806 -121.84528 50 10.05

CA UKIAH 23275 39.12583 -123.20083 184 10.05

CA WATSONVILLE 23277 36.93583 -121.78861 49 10.05

CA LIVERMORE 23285 37.69389 -121.81722 120 10.05

CA SAN JOSE 23293 37.36167 -121.92750 16 10.05

CA MOUNT SHASTA 24215 41.33250 -122.33278 1077 10.05

CA RED BLUFF 24216 40.15194 -122.25361 108 10.05

CA REDDING 24257 40.51500 -122.31333 148 10.05

CA MONTAGUE 24259 41.78139 -122.46806 803 10.05

CA ARCATA/EUREKA 24283 40.97806 -124.10861 61 10.05

CA CRESCENT CITY 24286 41.78028 -124.23667 17 10.05

CA HANFORD 53119 36.31861 -119.62889 74 10.05

CA RAMONA 53120 33.03333 -116.91667 423 10.05

CA OCEANSIDE 53121 33.21944 -117.34944 7 10.05

CA OXNARD 93110 34.20083 -119.20639 11 10.05

CA LOS ANGELES 93134 34.02778 -118.29583 54 5.79

CA PALM SPRINGS 93138 33.82806 -116.50528 124 10.05

CA SANTA ANA 93184 33.68000 -117.86639 12 10.05
4 of 18

State City WBAN # Latitude Longitude

Base 

Elevation 

(M)

Anemometer Ht 

(M)

CA FRESNO 93193 36.78000 -119.71944 101 10.05

CA SANTA MONICA 93197 34.01583 -118.45139 43 10.05

CA MARYSVILLE 93205 39.09778 -121.56972 19 10.05

CA SAN LUIS OBISPO 93206 35.23722 -120.64139 61 10.05

CA PASO ROBLES 93209 35.67278 -120.62694 247 10.05

CA OROVILLE 93210 39.49000 -121.61833 55 10.05

CA SACRAMENTO 93225 38.69556 -121.58972 7 10.05

CA NAPA 93227 38.21333 -122.27972 4 10.05

CA HAYWARD 93228 37.65944 -122.12139 10 10.05

CA SOUTH LAKE TAHOE 93230 38.89389 -119.99528 1908 10.05

CA VACAVILLE 93241 38.37694 -121.96139 33 10.05

CA MADERA 93242 36.98778 -120.11056 75 10.05

CA ALTURAS 94299 41.48333 -120.56667 1336 10.05

CO LAMAR 03013 38.07000 -102.68806 1123 10.05

CO DENVER 03017 39.83278 -104.65750 1657 10.05

CO BURLINGTON 03026 39.24472 -102.28417 1278 10.05

CO ALAMOSA 23061 37.43611 -105.86556 2296 10.05

CO GRAND JUNCTION 23066 39.13417 -108.53750 1469 10.05

CO LA JUNTA 23067 38.05139 -103.52694 1280 10.05

CO TRINIDAD 23070 37.25917 -104.34056 1749 10.05

CO CRAIG 24046 40.49528 -107.52111 1886 10.05

CO DURANGO 93005 37.14306 -107.75972 2017 10.05

CO LEADVILLE 93009 39.22806 -106.31611 3027 10.05

CO LIMON 93010 39.18944 -103.71583 1631 10.05

CO MONTROSE 93013 38.50500 -107.89750 1740 10.05

CO COLORADO SPRINGS 93037 38.81194 -104.71111 1884 10.05

CO PUEBLO 93058 38.29000 -104.49833 1424 10.05

CO DENVER 93067 39.57028 -104.84889 1788 10.05

CO CORTEZ 93069 37.30306 -108.62750 1796 10.05

CO ASPEN 93073 39.22306 -106.86833 2341 10.05

CO MEEKER 94050 40.04889 -107.88528 1939 10.05

CT GROTON NEW LONDON 14707 41.32750 -72.04944 2 10.05

CT WINDSOR LOCKS 14740 41.93806 -72.68250 52 10.05

CT HARTFORD 14752 41.73611 -72.65056 4 10.05

CT NEW HAVEN 14758 41.26389 -72.88722 2 10.05

CT DANBURY 54734 41.37139 -73.48278 139 10.05

CT WILLIMANTIC 54767 41.74194 -72.18361 74 10.05

CT MERIDEN 54788 41.50972 -72.82778 32 10.05

CT BRIDGEPORT 94702 41.17500 -73.14556 2 10.05

DC WASHINGTON 13743 38.86500 -77.03417 3 10.05

DC WASHINGTON 93738 38.93472 -77.44750 88 10.05

DE GEORGETOWN 13764 38.68917 -75.35917 16 10.05

DE WILMINGTON 13781 39.67278 -75.60083 23 10.05

FL MARIANNA 03818 30.83556 -85.18389 33 10.05

FL PUNTA GORDA 12812 26.91722 -81.99139 7 10.05

FL ORLANDO 12815 28.43389 -81.32500 27 10.05

FL GAINESVILLE 12816 29.68972 -82.27194 41 10.05

FL BROOKSVILLE 12818 28.47361 -82.45444 20 10.05
5 of 18

State City WBAN # Latitude Longitude

Base 

Elevation 

(M)

Anemometer Ht 

(M)

FL LEESBURG 12819 28.82083 -81.80972 23 10.05

FL APALACHICOLA 12832 29.73333 -85.03333 6 10.05

FL DAYTONA BEACH 12834 29.17722 -81.06000 9 10.05

FL FORT MYERS 12835 26.58639 -81.86361 4 10.05

FL KEY WEST 12836 24.55333 -81.75361 2 10.05

FL MELBOURNE 12838 28.10278 -80.64583 8 10.05

FL MIAMI 12839 25.82389 -80.29972 2 10.05

FL ORLANDO 12841 28.54528 -81.33306 33 10.05

FL TAMPA 12842 27.96139 -82.54028 2 10.05

FL VERO BEACH 12843 27.65556 -80.41806 6 10.05

FL WEST PALM BEACH 12844 26.68472 -80.09944 5 10.05

FL FORT LAUDERDALE 12849 26.07194 -80.15361 3 10.05

FL ORLANDO 12854 28.77972 -81.24361 16 10.05

FL SARASOTA/BRADENTON 12871 27.40139 -82.55861 6 10.05

FL ST PETERSBURG/ CLEARWATER 12873 27.91056 -82.68750 1 10.05

FL WINTER HAVEN 12876 28.06222 -81.75417 44 10.05

FL MIAMI 12882 25.90694 -80.28028 2 10.05

FL FORT LAUDERDALE 12885 26.19694 -80.17083 3 10.05

FL MIAMI 12888 25.64750 -80.43306 2 10.05

FL FORT MYERS 12894 26.53611 -81.75500 8 10.05

FL FORT PIERCE 12895 27.49806 -80.37667 7 10.05

FL MARATHON 12896 24.72583 -81.05167 2 10.05

FL NAPLES 12897 26.15250 -81.77500 2 10.05

FL CRESTVIEW 13884 30.77972 -86.52250 49 10.05

FL JACKSONVILLE 13889 30.49444 -81.69333 8 10.05

FL PENSACOLA 13899 30.47306 -87.18750 34 10.05

FL DESTIN 53853 30.40000 -86.47167 6 7.92

FL JACKSONVILLE 53860 30.33611 -81.51472 14 10.05

FL POMPANO BEACH 92805 26.25000 -80.10833 5 10.05

FL ST PETERSBURG 92806 27.76472 -82.62750 2 10.05

FL HOLLYWOOD 92809 25.99889 -80.24111 3 10.05

FL TALLAHASSEE 93805 30.39306 -84.35333 17 10.05

GA MACON 03813 32.68778 -83.65444 104 10.05

GA AUGUSTA 03820 33.36972 -81.96472 40 10.05

GA SAVANNAH 03822 32.11889 -81.20222 8 10.05

GA ATLANTA 03888 33.77917 -84.52139 244 10.05

GA AUGUSTA 13837 33.46694 -82.03861 126 10.05

GA ALBANY 13869 31.53556 -84.19444 58 10.05

GA ALMA 13870 31.53611 -82.50667 59 10.05

GA ATHENS 13873 33.94833 -83.32667 244 10.05

GA ATLANTA 13874 33.64028 -84.42694 304 10.05

GA BRUNSWICK 13878 31.25167 -81.39139 6 10.05

GA ATLANTA 53819 33.35528 -84.56694 243 10.05

GA GAINESVILLE 53838 34.27194 -83.83028 386 10.05

GA ATLANTA 53863 33.87500 -84.30222 298 10.05

GA CARTERSVILLE 53873 34.12306 -84.84861 230 10.05

GA ROME 93801 34.34778 -85.16111 211 10.05
6 of 18

State City WBAN # Latitude Longitude

Base 

Elevation 

(M)

Anemometer Ht 

(M)

GA COLUMBUS 93842 32.51611 -84.94222 119 10.05

GA VALDOSTA 93845 30.78250 -83.27667 60 10.05

HI HILO 21504 19.72333 -155.05139 9 10.05

HI KAILUA/KONA 21510 19.73556 -156.04889 12 10.05

HI KAHULUI 22516 20.90194 -156.43306 16 10.05

HI HONOLULU 22521 21.32750 -157.94306 2 10.05

HI KAUNAKAKAI 22534 21.15722 -157.09861 134 10.05

HI KAPOLEI 22551 21.31667 -158.06667 7 10.05

IA BURLINGTON 14931 40.78333 -91.12528 207 10.05

IA DES MOINES 14933 41.53778 -93.66611 277 10.05

IA IOWA CITY 14937 41.63278 -91.54306 198 10.05

IA MASON CITY 14940 43.15778 -93.33139 362 10.05

IA SIOUX CITY 14943 42.39139 -96.37917 332 10.05

IA OTTUMWA 14950 41.10667 -92.44806 255 10.05

IA SPENCER 14972 43.16444 -95.20167 408 10.05

IA WATERLOO 94910 42.55444 -92.40111 264 10.05

IA ESTHERVILLE 94971 43.40750 -94.74611 401 10.05

IA DAVENPORT 94982 41.61389 -90.59139 226 10.05

IA MARSHALLTOWN 94988 42.11278 -92.91750 296 10.05

IA LAMONI 94991 40.63306 -93.90194 84 10.05

ID JEROME 04110 42.72667 -114.45639 1224 10.05

ID CHALLIS 04114 41.52278 -114.21500 1536 10.05

ID BOISE 24131 43.56500 -116.22000 858 10.05

ID BURLEY 24133 42.54250 -113.77167 1261 10.05

ID IDAHO FALLS 24145 43.51639 -112.06722 1442 10.05

ID LEWISTON 24149 46.37472 -117.01444 434 10.05

ID MULLAN PASS 24154 47.45694 -115.64500 1833 7.92

ID POCATELLO 24156 42.92028 -112.57111 1353 10.05

ID TWIN FALLS 94178 42.48194 -114.48694 1267 10.05

ID McCALL 94182 44.88889 -116.10167 1526 10.05

ID REXBURG 94194 43.83389 -111.88111 1481 10.05

IL DECATUR 03887 39.98444 -88.86556 205 10.05

IL CAHOKIA/ST.LOUIS 03960 38.57139 -90.15722 124 10.05

IL CHICAGO/AURORA 04808 41.77000 -88.48139 214 10.05

IL

CHICAGO/PROSPECT 

HEIGHTS/WHEELING 04838 42.12083 -87.90472 194 10.05

IL LAWRENCEVILLE 13809 38.76417 -87.60556 131 10.05

IL CHICAGO 14819 41.78611 -87.75222 187 10.05

IL PEORIA 14842 40.66750 -89.68389 199 10.05

IL CHICAGO/WAUKEGAN 14880 42.42194 -87.86778 217 10.05

IL MOLINE 14923 41.46528 -90.52333 180 10.05

IL MATTOON/CHARLESTON 53802 39.47806 -88.28028 216 10.05

IL BLOOMINGTON/NORMAL 54831 40.47778 -88.91583 259 10.05

IL CARBONDALE/MURPHYBORO 93810 37.77972 -89.24972 124 10.05

IL SPRINGFIELD 93822 39.84528 -89.68389 180 10.05

IL QUINCY 93989 39.94250 -91.19444 231 10.05

IL ROCKFORD 94822 42.19611 -89.09250 223 10.05
7 of 18

State City WBAN # Latitude Longitude

Base 

Elevation 

(M)

Anemometer Ht 

(M)

IL CHICAGO 94846 41.98611 -87.91417 202 10.05

IL CHAMPAIGN/URBANA 94870 40.03972 -88.27778 226 10.05

IL CHICAGO/WEST CHICAGO 94892 41.91444 -88.24639 228 10.05

IN TERRE HAUTE 03868 39.45194 -87.30889 174 10.05

IN BLOOMINGTON 03893 39.14444 -86.61667 257 10.05

IN VALPARAISO 04846 41.45250 -87.00583 235 10.05

IN FORT WAYNE 14827 41.00611 -85.20556 252 7.92

IN GOSHEN 14829 41.52722 -85.79222 251 10.05

IN LAFAYETTE 14835 40.41222 -86.93694 182 10.05

IN SOUTH BEND 14848 41.70722 -86.33306 237 10.05

IN INDIANAPOLIS 53842 39.82500 -86.29583 249 10.05

IN SHELBYVILLE 53866 39.57806 -85.80333 245 10.05

IN EVANSVILLE 93817 38.04306 -87.53694 122 10.05

IN INDIANAPOLIS 93819 39.71000 -86.27222 241 10.05

IN MUNCIE 94895 40.23417 -85.39361 285 10.05

KS SALINA 03919 38.81333 -97.66083 387 10.05

KS WICHITA 03928 37.64722 -97.42944 402 10.05

KS MANHATTAN 03936 39.13528 -96.67778 321 10.05

KS OLATHE 03967 38.85000 -94.73917 329 10.05

KS WICHITA 03974 37.74972 -97.21889 430 10.05

KS LAWRENCE 03997 39.00833 -95.21167 252 10.05

KS PARSONS 03998 37.32778 -95.50417 265 10.05

KS TOPEKA 13920 38.95028 -95.66389 315 10.05

KS CHANUTE 13981 37.67028 -95.48417 300 10.05

KS CONCORDIA 13984 39.54917 -97.65194 448 10.05

KS DODGE CITY 13985 37.77278 -99.96972 785 10.05

KS HUTCHINSON 13986 38.06806 -97.86056 464 7.92

KS EMPORIA 13989 38.33056 -96.18972 367 10.05

KS TOPEKA 13996 39.07250 -95.62583 269 7.92

KS GARDEN CITY 23064 37.92722 -100.72472 877 10.05

KS GOODLAND 23065 39.36750 -101.69306 1112 10.05

KS OLATHE 93909 38.83167 -94.88972 325 10.05

KS COFFEYVILLE 93967 37.09111 -95.56639 228 7.92

KS HILL CITY 93990 39.37556 -99.82972 669 10.05

KS RUSSELL 93997 38.87222 -98.82806 568 10.05

KY PADUCAH 03816 37.05639 -88.77389 123 10.05

KY LONDON 03849 37.08722 -84.07694 360 10.05

KY JACKSON 03889 37.59139 -83.31444 405 10.05

KY LOUISVILLE 13810 38.22806 -85.66361 158 10.05

KY FRANKFORT 53841 38.18472 -84.90333 236 10.05

KY BOWLING GREEN 93808 36.98111 -86.43639 160 10.05

KY LEXINGTON 93820 38.04083 -84.60583 294 10.05

KY LOUISVILLE 93821 38.17722 -85.72972 146 10.05

LA LAKE CHARLES 03937 30.12472 -93.22833 3 10.05

LA BOOTHVILLE 12884 29.34972 -89.40750 0 10.05

LA NEW ORLEANS 12916 29.99278 -90.25083 0 10.05

LA ALEXANDRIA 13935 31.39472 -92.29556 28 10.05

LA MONROE 13942 32.51083 -92.03750 23 10.05
8 of 18

State City WBAN # Latitude Longitude

Base 

Elevation 

(M)

Anemometer Ht 

(M)

LA SHREVEPORT 13957 32.44694 -93.82417 70 10.05

LA BATON ROUGE 13970 30.53722 -91.14694 20 10.05

LA LAFAYETTE 13976 30.20500 -91.98750 12 7.92

LA SLIDELL 53865 30.34500 -89.82083 8 10.05

LA SHREVEPORT 53905 32.53972 -93.74444 53 10.05

LA NEW IBERIA 53915 30.05278 -91.88750 5 10.05

LA NEW ORLEANS 53917 30.04250 -90.02806 2 7.92

LA ALEXANDRIA 93915 31.33472 -92.55861 24 10.05

MA FITCHBURG 04780 42.55194 -71.75583 102 10.05

MA BEDFORD 14702 42.47000 -71.28944 41 10.05

MA BOSTON 14739 42.36056 -71.01056 6 10.05

MA NANTUCKET 14756 41.25306 -70.06083 12 10.05

MA PITTSFIELD 14763 42.42722 -73.28917 349 10.05

MA WESTFIELD/SPRINGFIELD 14775 42.15778 -72.71611 81 10.05

MA NORWOOD 54704 42.19083 -71.17361 14 10.05

MA BEVERLY 54733 42.58417 -70.91750 26 10.05

MA ORANGE 54756 42.57000 -72.29111 168 10.05

MA NORTH ADAMS 54768 42.69622 -73.17021 198 1.21

MA PLYMOUTH 54769 41.90972 -70.72944 44 10.05

MA TAUNTON 54777 41.87556 -71.02111 8 10.05

MA CHATHAM 94624 41.68750 -69.99333 19 10.05

MA HYANNIS 94720 41.66861 -70.28000 12 10.05

MA LAWRENCE 94723 42.71722 -71.12389 41 10.05

MA VINEYARD HAVEN 94724 41.39306 -70.61500 18 10.05

MA NEW BEDFORD 94726 41.67639 -70.95833 21 7.92

MA WORCESTER 94746 42.26722 -71.87611 306 10.05

MD HAGERSTOWN 93706 39.70778 -77.72972 211 10.05

MD SALISBURY 93720 38.34056 -75.51028 15 10.05

MD BALTIMORE 93721 39.17222 -76.68389 44 10.05

MD OCEAN CITY 93786 38.30833 -75.12389 2 10.05

ME FRENCHVILLE 04836 47.28556 -68.30722 299 10.05

ME AUGUSTA 14605 44.32056 -69.79722 107 10.05

ME BANGOR 14606 44.80750 -68.82417 45 10.05

ME CARIBOU 14607 46.86694 -68.03278 189 10.05

ME HOULTON 14609 46.12306 -67.79194 145 10.05

ME MILLINOCKET 14610 45.64778 -68.68611 123 10.05

ME PORTLAND 14764 43.64222 -70.30444 15 10.05

ME FRYEBURG 54772 43.99056 -70.94750 137 10.05

ME WISCASSET 94623 43.96361 -69.71167 16 10.05

MI HOLLAND 04839 42.74611 -86.09667 207 10.05

MI ADRIAN 04847 41.86778 -84.07944 243 7.92

MI GAYLORD 04854 45.01333 -84.70139 406 10.05

MI BATTLE CREEK 14815 42.30750 -85.25111 283 10.05

MI DETROIT 14822 42.40917 -83.01000 190 10.05

MI FLINT 14826 42.96667 -83.74944 235 10.05

MI JACKSON 14833 42.25972 -84.45944 302 10.05

MI LANSING 14836 42.78028 -84.57889 262 10.05

MI MUSKEGON 14840 43.17111 -86.23667 191 10.05
9 of 18

State City WBAN # Latitude Longitude

Base 

Elevation 

(M)

Anemometer Ht 

(M)

MI PELLSTON 14841 45.57083 -84.79611 215 10.05

MI SAGINAW 14845 43.53306 -84.07972 201 10.05

MI SAULT STE MARIE 14847 46.46667 -84.36667 220 10.05

MI TRAVERSE CITY 14850 44.74083 -85.58250 186 10.05

MI DETROIT 14853 42.23667 -85.52611 216 10.05

MI HANCOCK 14858 47.16861 -88.50556 323 7.92

MI HOUGHTON LAKE 94814 44.36778 -84.69083 351 10.05

MI KALAMAZOO 94815 42.23472 -85.55194 262 10.05

MI PONTIAC 94817 42.66500 -83.41806 296 10.05

MI DETROIT 94847 42.21528 -83.34861 192 10.05

MI GRAND RAPTIDS 94860 42.88222 -85.52306 241 10.05

MI BENTON HARBOR 94871 42.12917 -86.42222 191 10.05

MI ANN ARBOR 94889 42.22300 -83.74400 253 10.05

MI IRON MOUNTAIN/KINGSFORD 94893 45.81833 -88.11444 343 10.05

MN ALEXANDRIA 14910 45.88306 -95.39306 433 10.05

MN DULUTH 14913 46.84389 -92.19417 433 10.05

MN INTERNATIONAL FALLS 14918 48.56639 -93.40306 361 10.05

MN MINNEAPOLIS 14922 44.88306 -93.22889 248 10.05

MN ROCHESTER 14925 43.90417 -92.49167 397 10.05

MN ST CLOUD 14926 45.54472 -94.05194 310 10.05

MN ST PAUL 14927 44.93028 -93.04806 214 10.05

MN REDWOOD FALLS 14992 44.54722 -95.08222 312 10.05

MN HIBBING 94931 47.38667 -92.83889 408 10.05

MN BRAINERD 94938 46.40472 -94.13083 372 10.05

MN MINNEAPOLIS 94960 45.06250 -93.35083 264 10.05

MN BAUDETTE 94961 48.72750 -94.61028 330 7.92

MN MINNEAPOLIS 94963 44.83222 -93.47028 276 10.05

MN PARK RAPIDS 94967 46.90056 -95.06778 438 10.05

MO CAPE GIRARDEAU 03935 37.22528 -89.57056 102 10.05

MO COLUMBIA 03945 38.81694 -92.21833 272 10.05

MO KANSAS CITY 03947 39.29917 -94.71778 297 10.05

MO JEFFERSON CITY 03963 38.59111 -92.15583 168 10.05

MO ST LOUIS 03966 38.65722 -90.65583 140 10.05

MO POPLAR BLUFF 03975 36.77250 -90.32472 100 10.05

MO SEDALIA 03994 38.70417 -93.18333 273 10.05

MO JOPLIN 13987 37.14944 -94.49833 296 10.05

MO KANSAS CITY 13988 39.12333 -94.59250 226 7.92

MO ST LOUIS 13994 38.75250 -90.37361 199 10.05

MO SPRINGFIELD 13995 37.23972 -93.38972 385 10.05

MO ROLLA/VICHY 13997 38.12750 -91.76944 336 10.05

MO KIRKSVILLE 14938 40.09722 -92.54333 293 10.05

MO LEE'S SUMMIT 53879 38.95972 -94.37139 304 10.05

MO WEST PLAINS 53901 36.87806 -91.90250 375 10.05

MO ST CHARLES 53904 38.92861 -90.42806 134 10.05

MS JACKSON 03940 32.31972 -90.07750 91 10.05

MS TALLULAH/VICKSBURG 03996 32.34806 -91.03000 31 10.05

MS HATTIESBURG 13833 31.26500 -89.25306 45 10.05
10 of 18

State City WBAN # Latitude Longitude

Base 

Elevation 

(M)

Anemometer Ht 

(M)

MS MERIDIAN 13865 32.33306 -88.75111 88 10.05

MS JACKSON 13927 32.33472 -90.22250 103 10.05

MS GREENVILLE 13939 33.48278 -90.98556 38 10.05

MS GREENWOOD 13978 33.49556 -90.08417 45 10.05

MS PASCAGOULA 53858 30.46361 -88.53194 4 10.05

MS TUPELO 93862 34.26083 -88.77111 104 10.05

MS GULFPORT 93874 30.40722 -89.07000 7 10.05

MS MCCOMB 93919 31.17833 -90.47194 126 10.05

MT BILLINGS 24033 45.80806 -108.54306 1091 10.05

MT LEWISTOWN 24036 47.04917 -109.46667 1254 10.05

MT MILES 24037 46.42806 -105.88611 800 10.05

MT BOZEMAN 24132 45.79361 -111.15222 1349 10.05

MT BUTTE 24135 45.95333 -112.51250 1678 10.05

MT CUT BANK 24137 48.60833 -112.37611 1170 10.05

MT DILLON 24138 45.25500 -112.55167 1585 10.05

MT GREAT FALLS 24143 47.47333 -111.38222 1117 10.05

MT HELENA 24144 46.60556 -111.96361 1167 10.05

MT KALISPELL 24146 48.30417 -114.26361 901 10.05

MT LIVINGSTON 24150 45.69944 -110.44833 1415 10.05

MT MISSOULA 24153 46.92083 -114.09250 973 10.05

MT GLASGOW 94008 48.21389 -106.62139 696 10.05

MT HAVRE 94012 48.55944 -109.78000 788 10.05

MT WOLF POINT 94017 48.09444 -105.57444 604 10.05

MT BAKER 94055 46.35833 -104.25000 903 10.05

NC HICKORY 03810 35.74111 -81.38972 357 7.92

NC ASHEVILLE 03812 35.43194 -82.53750 645 10.05

NC RALEIGH/DURHAM 13722 35.87056 -78.78639 121 10.05

NC GREENSBORO 13723 36.09750 -79.94361 277 10.05

NC WILMINGTON 13748 34.26833 -77.90611 7 7.92

NC LUMBERTON 13776 34.61000 -79.05944 37 10.05

NC ELIZABETH CITY 13786 36.26056 -76.17500 2 7.92

NC CHARLOTTE 13881 35.21444 -80.94361 213 10.05

NC GASTONIA 53870 35.19667 -81.15583 241 10.05

NC MONROE 53872 35.01694 -80.62056 204 10.05

NC NEW BERN 93719 35.06750 -77.04722 3 10.05

NC CAPE HATTERAS 93729 35.23222 -75.62250 3 7.92

NC FAYETTEVILLE 93740 34.99139 -78.88028 55 7.92

NC ROCKY MOUNT 93759 35.85500 -77.89306 46 10.05

NC BEAUFORT 93765 34.73361 -76.66056 2 10.05

NC MAXTON 93782 34.79167 -79.36611 65 10.05

NC BURLINGTON 93783 36.04667 -79.47694 183 7.92

NC CHAPEL HILL 93785 35.93333 -79.06417 153 10.05

NC WINSTON SALEM 93807 36.13361 -80.22222 290 10.05

ND FARGO 14914 46.92528 -96.81111 273 10.05

ND GRAND FORKS 14916 47.94917 -97.17583 256 10.05

ND JAMESTOWN 14919 46.92972 -98.67833 455 10.05

ND BISMARCK 24011 46.77417 -100.74750 503 10.05

ND DICKINSON 24012 46.79722 -102.80194 786 10.05
11 of 18

State City WBAN # Latitude Longitude

Base 

Elevation 

(M)

Anemometer Ht 

(M)

ND MINOT 24013 48.25944 -101.28111 505 10.05

ND WILLISTON 94014 48.19472 -103.64194 580 10.05

ND HETTINGER 94038 46.01389 -102.65472 822 7.92

NE GRAND ISLAND 14935 40.95833 -98.31250 564 7.92

NE LINCOLN 14939 40.83111 -96.76444 357 10.05

NE NORFOLK 14941 41.98056 -97.43694 476 10.05

NE OMAHA 14942 41.31028 -95.89917 299 10.05

NE CHADRON 24017 42.83750 -103.09528 1001 10.05

NE NORTH PLATTE 24023 41.12194 -100.66833 842 7.92

NE SCOTTSBLUFF 24028 41.87417 -103.59528 1203 7.92

NE SIDNEY 24030 41.10139 -102.98472 1306 10.05

NE VALENTINE 24032 42.85861 -100.55139 789 7.92

NE ALLIANCE 24044 42.05722 -102.80000 1196 10.05

NE IMPERIAL 24091 40.51000 -101.62000 996 10.05

NE MC COOK 94040 40.20639 -100.59139 780 10.05

NE BROKEN BOW 94946 41.43667 -99.63889 770 10.05

NE HASTINGS 94949 40.60444 -98.42722 590 10.05

NE FALLS CITY 94957 40.07889 -95.59194 299 10.05

NE ORD 94958 41.62361 -98.95167 628 10.05

NE TEKAMAH 94978 41.76361 -96.17778 312 10.05

NH MANCHESTER 14710 42.93333 -71.43833 69 10.05

NH CONCORD 14745 43.19528 -71.50111 104 10.05

NH WHITEFIELD 54728 44.36750 -71.54500 318 7.92

NH JAFFREY 54770 42.80500 -72.00361 309 7.92

NH ROCHESTER 54791 43.27806 -70.92222 101 10.05

NH BERLIN 94700 44.57611 -71.17861 343 10.05

NH LEBANON 94765 43.62639 -72.30472 84 10.05

NJ MILLVILLE 13735 39.36611 -75.07833 18 7.92

NJ NEWARK 14734 40.71583 -74.16944 2 10.05

NJ TRENTON 14792 40.27667 -74.81389 58 7.92

NJ CALDWELL 54743 40.87639 -74.28306 53 10.05

NJ SOMERVILLE 54785 40.62389 -74.66944 84 10.05

NJ ATLANTIC CITY 93730 39.45750 -74.45667 20 7.92

NJ MOUNT HOLLY 93780 39.94056 -74.84111 15 10.05

NJ TETERBORO 94741 40.85000 -74.06139 2 7.92

NM CLINES CORNERS 03027 35.00278 -105.66278 2160 10.05

NM ROSWELL 23009 33.30806 -104.54111 1105 7.92

NM TUCUMCARI 23048 35.18222 -103.60306 1230 10.05

NM SANTA FE 23049 35.61694 -106.08889 1915 7.92

NM ALBUQUERQUE 23050 35.04194 -106.61556 1618 10.05

NM CLAYTON 23051 36.44583 -103.15417 1512 10.05

NM RATON 23052 36.74139 -104.50167 1935 10.05

NM LAS VEGAS 23054 35.65417 -105.14250 2093 10.05

NM DEMING 23078 32.26222 -107.72056 1312 10.05

NM GALLUP 23081 35.51111 -108.78944 1970 10.05

NM FARMINGTON 23090 36.74361 -108.22917 1675 10.05

NM CARLSBAD 93033 32.33750 -104.26333 992 10.05

NM TRUTH OR CONSEQUENCES 93045 33.23667 -107.26806 1469 7.92
12 of 18

State City WBAN # Latitude Longitude

Base 

Elevation 

(M)

Anemometer Ht 

(M)

NV MERCURY 03160 36.62056 -116.02778 985 10.05

NV TONOPAH 23153 38.06028 -117.08722 1644 10.05

NV ELY 23154 39.29500 -114.84528 1904 10.05

NV LAS VEGAS 23169 36.07889 -115.15528 648 10.05

NV RENO 23185 39.48389 -119.77111 1344 7.92

NV ELKO 24121 40.82500 -115.79167 1539 7.92

NV WINNEMUCCA 24128 40.90194 -117.80722 1309 10.05

NV LOVELOCK 24172 40.06639 -118.56528 1189 10.05

NV LAS VAGAS 53123 36.21167 -115.19583 666 10.05

NY NIAGARA FALLS 04724 43.10722 -78.94528 178 10.05

NY BINGHAMTON 04725 42.20778 -75.98139 486 10.05

NY ISLIP 04781 40.79389 -73.10167 26 10.05

NY MONTGOMERY 04789 41.50917 -74.26500 108 10.05

NY WESTHAMPTON BEACH 14719 40.84361 -72.63222 13 10.05

NY NEW YORK 14732 40.77889 -73.88083 3 10.05

NY BUFFALO 14733 42.94083 -78.73583 216 10.05

NY ALBANY 14735 42.74806 -73.80333 85 10.05

NY DUNKIRK 14747 42.49333 -79.27222 203 7.92

NY ELMIRA/CORNING 14748 42.15944 -76.89194 285 7.92

NY GLEN FALLS 14750 43.34111 -73.61028 98 7.92

NY POUGHKEEPSIE 14757 41.62667 -73.88417 48 7.92

NY ROCHESTER 14768 43.11667 -77.67667 164 10.05

NY SYRACUSE 14771 43.10917 -76.10333 125 10.05

NY WELLSVILLE 54757 42.10944 -77.99194 636 10.05

NY FULTON 54773 43.34972 -76.38472 143 7.92

NY PENN YAN 54778 42.64250 -77.05639 268 10.05

NY FARMINGDALE 54787 40.73417 -73.41694 23 10.05

NY SHIRLEY 54790 40.82167 -72.86889 18 10.05

NY DANSVILLE 94704 42.57083 -77.71306 197 10.05

NY MASSENA 94725 44.93583 -74.84556 62 7.92

NY NEW YORK 94728 40.78333 -73.96667 48 10.05

NY SARANAC LAKE 94740 44.38528 -74.20667 505 10.05

NY WHITE PLAINS 94745 41.06694 -73.70750 116 10.05

NY NEW YORK 94789 40.65528 -73.79556 3 10.05

NY WATERTOWN 94790 43.99222 -76.02167 94 7.92

OH WOOSTER 04842 40.87472 -81.88694 338 10.05

OH TOLEDO 04848 41.56306 -83.47639 189 10.05

OH LORAIN/ELYRIA 04849 41.17944 -82.17944 241 7.92

OH LIMA 04850 40.70833 -84.02667 297 7.92

OH DEFIANCE 04851 41.33750 -84.42889 214 10.05

OH NEW PHILADELPHIA 04852 40.47000 -81.42000 271 10.05

OH CLEVELAND 04853 41.51750 -81.68361 177 10.05

OH MARION 04855 40.61611 -83.06361 300 10.05

OH ASHTABULA 04857 41.77806 -80.69583 280 7.92

OH NEWARK 04858 40.02278 -82.46250 268 7.92

OH WILMINGTON 13841 39.42028 -83.82167 322 10.05

OH AKRON 14813 41.03750 -81.46417 318 10.05

OH CLEVELAND 14820 41.40500 -81.85278 237 10.05
13 of 18

State City WBAN # Latitude Longitude

Base 

Elevation 

(M)

Anemometer Ht 

(M)

OH COLUMBUS 14821 39.99139 -82.88083 247 10.05

OH FINDLAY 14825 41.01361 -83.66861 247 10.05

OH YOUNGSTOWN/WARREN 14852 41.25444 -80.67389 357 10.05

OH MANSFIELD 14891 40.82028 -82.51778 394 10.05

OH AKRON 14895 40.91806 -81.44250 368 10.05

OH LANCASTER 53844 39.75556 -82.65722 258 10.05

OH HAMILTON 53855 39.36444 -84.52472 185 10.05

OH DAYTON 53859 39.59361 -84.22639 290 7.92

OH CINCINNATI 93812 39.10333 -84.41889 145 10.05

OH COVINGTON/CINCINNATI 93814 39.04306 -84.67167 262 10.05

OH DAYTON 93815 39.90611 -84.21861 303 10.05

OH ZANESVILLE 93824 39.94444 -81.89222 269 10.05

OH TOLEDO 94830 41.58861 -83.80139 205 10.05

OK GUYMON 03030 36.68167 -101.50528 949 7.92

OK CLINTON 03932 35.33972 -99.20028 586 10.05

OK LAWTON 03950 34.56778 -98.41639 328 10.05

OK OKLAHOMA CITY 03954 35.53417 -97.64694 390 7.92

OK STILLWATER 03965 36.16028 -97.08583 294 10.05

OK FREDERICK 03981 34.35222 -98.98417 380 10.05

OK OKLAHOMA CITY 13967 35.38861 -97.60028 388 10.05

OK TULSA 13968 36.19750 -95.88639 195 10.05

OK PONCA CITY 13969 36.73056 -97.09972 307 10.05

OK GAGE 13975 36.29667 -99.77472 668 10.05

OK TULSA 53908 36.03944 -95.98444 190 10.05

OK GUTHRIE 53913 35.85028 -97.41556 328 10.05

OK MC ALESTER 93950 34.89889 -95.78333 233 10.05

OK MUSKOGEE 93953 35.65667 -95.36139 185 7.92

OK HOBART 93986 35.00806 -99.05111 475 7.92

OR HERMISTON 04113 45.82583 -119.26111 193 10.05

OR SCAPPOOSE 04201 45.77278 -122.86111 13 10.05

OR BAKER CITY 24130 44.83806 -117.80972 1024 10.05

OR PENDLETON 24155 45.69833 -118.83417 451 10.05

OR ONTARIO 24162 44.02056 -117.01278 666 10.05

OR THE DALLES 24219 45.61861 -121.16722 72 10.05

OR EUGENE 24221 44.13333 -123.21444 108 7.92

OR MEDFORD 24225 42.38917 -122.87139 398 10.05

OR PORTLAND 24229 45.59083 -122.60028 6 10.05

OR REDMOND 24230 44.25417 -121.15000 928 10.05

OR ROSEBURG 24231 43.23889 -123.35472 151 10.05

OR SALEM 24232 44.90778 -122.99500 62 7.92

OR SEXTON SUMMIT 24235 42.61694 -123.38083 1168 10.05

OR PORTLAND 24242 45.54944 -122.40000 7 10.05

OR BURNS 94185 43.59222 -118.95389 1262 10.05

OR ASTORIA 94224 46.15806 -123.87750 3 10.05

OR KLAMATH FALLS 94236 42.14694 -121.72417 1244 7.92

OR PORTLAND 94261 45.54056 -122.94861 59 7.92

OR MC MINNVILLE 94273 45.19472 -123.13389 47 7.92

OR AURORA 94281 45.24861 -122.76861 59 7.92
14 of 18

State City WBAN # Latitude Longitude

Base 

Elevation 

(M)

Anemometer Ht 

(M)

PA JOHNSTOWN 04726 40.30139 -78.83389 694 7.92

PA BRADFORD 04751 41.80306 -78.64028 643 7.92

PA DU BOIS 04787 41.17833 -78.89889 551 10.05

PA MEADVILLE 04843 41.62639 -80.21500 429 10.05

PA PHILADELPHIA 13739 39.86833 -75.23111 2 10.05

PA HARRISBURG 14711 40.19361 -76.76333 91 7.92

PA READING 14712 40.37333 -75.95944 101 7.92

PA ALTOONA 14736 40.30000 -78.31694 447 10.05

PA ALLENTOWN 14737 40.65083 -75.44917 117 7.92

PA HARRISBURG 14751 40.21722 -76.85139 102 7.92

PA PITTSBURGH 14762 40.35472 -79.92167 378 7.92

PA SELINSGROVE 14770 40.82056 -76.86417 137 10.05

PA WILKES-BARRE/SCRANTON 14777 41.33889 -75.72667 290 10.05

PA WILLIAMSPORT 14778 41.24333 -76.92167 160 7.92

PA ERIE 14860 42.08000 -80.18250 222 7.92

PA LANCASTER 54737 40.12028 -76.29444 122 7.92

PA POTTSTOWN 54782 40.23833 -75.55722 89 7.92

PA DOYLESTOWN 54786 40.33000 -75.12250 116 10.05

PA MOUNT POCONO 54789 41.13889 -75.37944 577 10.05

PA CLEARFIELD 54792 41.04667 -78.41167 461 10.05

PA YORK 93778 39.91806 -76.87417 144 10.05

PA PHILADELPHIA 94732 40.08194 -75.01111 31 7.92

PA PITTSBURGH 94823 40.50139 -80.23111 341 10.05

PR SAN JUAN 11641 18.43000 -66.00000 2 5.79

RI PROVIDENCE 14765 41.72194 -71.43250 16 10.05

RI NEWPORT 14787 41.53000 -71.28361 44 7.92

RI WESTERLY 14794 41.34972 -71.79889 20 10.05

SC GREER 03870 34.89944 -82.21917 285 7.92

SC FLORENCE 13744 34.18778 -79.73083 43 7.92

SC CHARLESTON 13880 32.89861 -80.04083 12 10.05

SC COLUMBIA 13883 33.94194 -81.11806 69 7.92

SC GREENVILLE 13886 34.84611 -82.34611 307 10.05

SC CLEMSON 53850 34.67194 -82.88639 271 7.92

SC ORANGEBURG 53854 33.46167 -80.85806 60 7.92

SC COLUMBIA 53867 33.97056 -80.99583 55 7.92

SC ROCK HILL 53871 34.98694 -81.05750 196 7.92

SC GREENWOOD 53874 34.24861 -82.15917 192 10.05

SC NORTH MYRTLE BEACH 93718 33.81556 -78.72056 8 7.92

SC ANDERSON 93846 34.49500 -82.70917 234 10.05

SD ABERDEEN 14929 45.44972 -98.42139 395 10.05

SD HURON 14936 44.38528 -98.22889 391 10.05

SD SIOUX FALLS 14944 43.57694 -96.75361 433 10.05

SD WATERTOWN 14946 44.93083 -97.15444 532 7.92

SD PHILIP 24024 44.05111 -101.60111 673 7.92

SD PIERRE 24025 44.38278 -100.28583 524 7.92

SD RAPID CITY 24090 44.04556 -103.05389 963 10.05

SD CUSTER 94032 43.73056 -103.62806 1699 7.92

SD PINE RIDGE 94039 43.02056 -102.51833 1003 7.92
15 of 18

State City WBAN # Latitude Longitude

Base 

Elevation 

(M)

Anemometer Ht 

(M)

SD MOBRIDGE 94052 45.54639 -100.40778 515 7.92

SD MITCHELL 94950 43.77694 -98.03750 375 7.92

SD WINNER 94990 43.39056 -99.84222 617 10.05

TN JACKSON 03811 35.59306 -88.91667 131 7.92

TN CROSSVILLE 03847 35.95139 -85.08500 566 7.92

TN

BRISTOL/JOHNSON 

CITY/KINGSPORT 13877 36.47972 -82.39889 457 10.05

TN CHATTANOOGA 13882 35.03333 -85.20000 205 7.92

TN KNOXVILLE 13891 35.81806 -83.98583 293 7.92

TN MEMPHIS 13893 35.06111 -89.98500 93 7.92

TN NASHVILLE 13897 36.11889 -86.68917 183 10.05

TX BORGER 03024 35.70000 -101.39361 927 10.05

TX LONGVIEW 03901 32.38472 -94.71139 106 10.05

TX COLLEGE STATION 03904 30.58833 -96.36361 93 7.92

TX DALLAS-FT WORTH 03927 32.89639 -97.04111 167 7.92

TX DALLAS 03971 32.68083 -96.86806 198 7.92

TX DENTON 03991 33.20611 -97.19889 198 10.05

TX BURNET 03999 30.74056 -98.23528 389 10.05

TX HARLINGEN 12904 26.22806 -97.65417 10 7.92

TX VICTORIA 12912 28.86250 -96.92972 34 7.92

TX BEAUMONT/PORT ARTHUR 12917 29.95056 -94.02056 4 7.92

TX HOUSTON 12918 29.64528 -95.27861 13 10.05

TX BROWNSVILLE 12919 25.90639 -97.42556 6 10.05

TX SAN ANTONIO 12921 29.53278 -98.46361 236 10.05

TX GALVESTON 12923 29.26500 -94.86028 2 7.92

TX CORPUS CHRISTI 12924 27.77306 -97.51278 12 10.05

TX ALICE 12932 27.74083 -98.02722 52 7.92

TX PALACIOS 12935 28.72750 -96.25083 5 7.92

TX COTULLA 12947 28.45806 -99.22000 140 10.05

TX PORT ISABEL 12957 26.16583 -97.34583 4 7.92

TX MC ALLEN 12959 26.17528 -98.23833 30 7.92

TX HOUSTON 12960 29.99250 -95.36389 29 7.92

TX HONDO 12962 29.35944 -99.17417 280 10.05

TX SAN ANTONIO 12970 29.33667 -98.47083 174 7.92

TX NEW BRAUNFELS 12971 29.70861 -98.04528 197 7.92

TX ROCKPORT 12972 28.08361 -97.04639 8 7.92

TX HOUSTON 12975 29.52111 -95.24028 12 7.92

TX ANGLETON/LAKE JACKSON 12976 29.10972 -95.46194 7 7.92

TX HOUSTON 12977 29.62222 -95.65639 23 10.05

TX AUSTIN/BERGSTROM 13904 30.17944 -97.68056 148 7.92

TX AUSTIN/CITY 13958 30.31667 -97.76667 200 10.05

TX WACO 13959 31.61139 -97.22861 152 7.92

TX DALLAS 13960 32.84694 -96.85139 145 7.92

TX FORT WORTH 13961 32.81917 -97.36139 187 7.92

TX ABILENE 13962 32.41111 -99.68167 544 7.92

TX WICHITA FALLS 13966 33.97861 -98.49278 308 7.92

TX TYLER 13972 32.35417 -95.40222 161 7.92

TX JUNCTION 13973 30.51083 -99.76639 522 7.92
16 of 18

State City WBAN # Latitude Longitude

Base 

Elevation 

(M)

Anemometer Ht 

(M)

TX DEL RIO 22010 29.36694 -100.92167 307 7.92

TX CHILDRESS 23007 34.43361 -100.28778 593 10.05

TX MIDLAND 23023 31.93222 -102.20806 872 7.92

TX SAN ANGELO 23034 31.35139 -100.49389 582 7.92

TX WINK 23040 31.77972 -103.20139 858 7.92

TX LUBBOCK 23042 33.66750 -101.82139 989 10.05

TX EL PASO 23044 31.81111 -106.37583 1200 7.92

TX AMARILLO 23047 35.21944 -101.70556 1095 7.92

TX GUADALUPE PASS 23055 31.83111 -104.80889 1661 5.79

TX FORT STOCKTON 23091 30.91500 -102.91194 917 10.05

TX CONROE 53902 30.35167 -95.41417 72 7.92

TX HUNTSVILLE 53903 30.74667 -95.58694 104 7.92

TX ARLINGTON 53907 32.66361 -97.09389 187 7.92

TX FORT WORTH 53909 32.97333 -97.31806 201 7.92

TX HOUSTON 53910 30.06167 -95.55250 47 7.92

TX TERRELL 53911 32.71000 -96.26722 143 7.92

TX CORSICANA 53912 32.02722 -96.39778 135 7.92

TX MC KINNEY 53914 33.18028 -96.59028 169 10.05

TX DALHART 93042 36.02333 -102.54722 1216 7.92

TX MINERAL WELLS 93985 32.78222 -98.06111 287 10.05

TX LUFKIN 93987 31.23389 -94.75000 86 7.92

UT BRYCE CANYON 23159 37.70639 -112.14556 2312 10.05

UT MILFORD 23176 38.44333 -113.02833 1532 7.92

UT OGDEN 24126 41.19611 -112.01139 1355 10.05

UT SALT LAKE CITY 24127 40.78694 -111.96806 1286 10.05

UT MOAB 93075 38.75500 -109.75417 1390 7.92

UT CEDAR CITY 93129 37.70167 -113.09722 1703 7.92

UT PRICE 93141 39.54528 -110.74972 1777 7.92

UT VERNAL 94030 40.44111 -109.50917 1603 10.05

UT LOGAN 94128 41.78722 -111.85333 1355 10.05

VA DANVILLE 13728 36.57278 -79.33611 169 10.05

VA LYNCHBURG 13733 37.33750 -79.20667 273 10.05

VA NORFOLK 13737 36.90361 -76.19194 4 7.92

VA RICHMOND 13740 37.51111 -77.32333 50 10.05

VA ROANOKE 13741 37.31694 -79.97417 346 10.05

VA CHARLOTTESVILLE 93736 38.13861 -78.45306 187 7.92

VA WALLOPS ISLAND 93739 37.94056 -75.49639 11 7.92

VA NEWPORT NEWS 93741 37.13194 -76.49306 12 7.92

VA WAKEFIELD 93773 36.98389 -77.00722 32 7.92

VA RICHMOND/ASHLAND 93775 37.70806 -77.43444 62 10.05

VT BURLINGTON 14742 44.46806 -73.15028 101 7.92

VT SPRINGFIELD 54740 43.34361 -72.51778 175 7.92

VT MORRISVILLE 54771 44.53444 -72.61444 225 7.92

VT BENNINGTON 54781 42.89139 -73.24694 244 10.05

VT BARRE/MONTPELIER 94705 44.20333 -72.57944 336 7.92

WA MOSES LAKE 24110 47.20778 -119.31917 355 7.92

WA EPHRATA 24141 47.30444 -119.51361 381 7.92

WA SPOKANE 24157 47.62139 -117.52778 717 10.05
17 of 18

State City WBAN # Latitude Longitude

Base 

Elevation 

(M)

Anemometer Ht 

(M)

WA WALLA WALLA 24160 46.09472 -118.28694 356 7.92

WA PASCO 24163 46.26472 -119.11806 122 7.92

WA BELLINGHAM 24217 48.79389 -122.53722 47 7.92

WA ELLENSBURG 24220 47.03389 -120.53028 531 7.92

WA EVERETT 24222 47.90778 -122.28028 166 7.92

WA OLYMPIA 24227 46.97333 -122.90333 57 7.92

WA SEATTLE 24233 47.46139 -122.31361 113 7.62

WA SEATTLE 24234 47.53028 -122.30083 5 7.92

WA YAKIMA 24243 46.56417 -120.53361 320 7.92

WA DEER PARK 94119 47.96861 -117.42139 670 10.05

WA PULLMAN/MOSCOW 94129 46.74389 -117.10861 769 1.21

WA SPOKANE 94176 47.68306 -117.32139 591 10.05

WA OMAK 94197 48.46444 -119.51694 394 7.92

WA HOQUIAM 94225 46.97111 -123.93667 4 7.92

WA SHELTON 94227 47.23806 -123.14750 83 7.92

WA WENATCHEE 94239 47.39889 -120.20667 375 7.92

WA QUILLAYUTE 94240 47.93417 -124.56083 56 10.05

WA RENTON 94248 47.49333 -122.21444 6 7.92

WA PORT ANGELES 94266 48.12028 -123.49833 79 7.92

WA TACOMA 94274 47.26750 -122.57611 88 7.92

WA FRIDAY HARBOR 94276 48.52222 -123.02306 33 7.92

WA VANCOUVER 94298 45.62083 -122.65722 8 7.92

WI RHINELANDER 04803 45.63083 -89.46528 493 7.92

WI WISCONSIN RAPTIDS 04826 44.35917 -89.83694 309 7.92

WI FOND DU LAC 04840 43.77000 -88.48639 240 7.92

WI SHEBOYGAN 04841 43.76944 -87.85139 226 7.92

WI KENOSHA 04845 42.59500 -87.93806 223 7.92

WI MADISON 14837 43.14056 -89.34528 262 7.92

WI MILWAUKEE 14839 42.94667 -87.89694 206 7.92

WI WAUSAU 14897 44.92861 -89.62667 366 7.92

WI GREEN BAY 14898 44.51278 -88.12000 210 7.92

WI LA CROSSE 14920 43.87600 -91.25890 199 7.92

WI LONE ROCK 14921 43.21194 -90.18139 218 7.92

WI EAU CLAIRE 14991 44.86528 -91.48500 270 7.92

WI RACINE 94818 42.76111 -87.81361 203 5.79

WI OSHKOSH 94855 43.98444 -88.55694 246 10.05

WI ASHLAND 94929 46.54861 -90.91889 251 7.92

WI HAYWARD 94973 46.02611 -91.44417 367 10.05

WI MARSHFIELD 94985 44.63694 -90.18917 383 7.92

WI B0SCOBEL 94994 43.15611 -90.67750 205 10.05

WV CLARKSBURG 03802 39.29556 -80.22889 360 7.92

WV PARKERSBURG 03804 39.34500 -81.43917 247 10.05

WV BLUEFIELD 03859 37.29583 -81.20778 873 7.92

WV HUNTINGTON 03860 38.38167 -82.55500 251 7.92

WV BECKLEY 03872 37.79500 -81.12472 757 7.92

WV ELKINS 13729 38.88528 -79.85278 603 7.92

WV MARTINSBURG 13734 39.40194 -77.98444 164 7.92

WV MORGANTOWN 13736 39.64278 -79.91639 372 7.92
18 of 18

State City WBAN # Latitude Longitude

Base 

Elevation 

(M)

Anemometer Ht 

(M)

WV CHARLESTON 13866 38.37944 -81.59139 277 7.92

WV WHEELING 14894 40.17639 -80.64722 366 7.92

WY EVANSTON 04111 41.27306 -111.03056 2176 7.92

WY CHEYENNE 24018 41.15778 -104.80694 1864 7.92

WY LANDER 24021 42.81667 -108.73333 1704 10.05

WY LARAMIE 24022 41.31250 -105.67444 2217 10.05

WY ROCK SPRINGS 24027 41.59417 -109.06528 2055 4.87

WY SHERIDAN 24029 44.77389 -106.97639 1209 7.92

WY GREYBULL 24048 44.51694 -108.08222 1191 7.92

WY RAWLINS 24057 41.80639 -107.20000 2065 7.92

WY RIVERTON 24061 43.06417 -108.45889 1660 7.92

WY WORLAND 24062 43.96583 -107.95083 1269 7.92

WY CASPER 24089 42.89750 -106.47306 1621 7.92

WY BIG PINEY 24164 42.58444 -110.10750 2116 7.92

WY GILLETTE 94023 44.33944 -105.54194 1327 7.92

WY TORRINGTON 94053 42.06472 -104.15278 1279 10.05

WY BUFFALO 94054 44.38139 -106.72111 1502 10.05

WY DOUGLAS 94057 42.79722 -105.38556 1500 7.92
1 of 2

State City WBAN # Latitude Longitude

AK ANNETTE ISLAND 25308 55.03000 -131.57000

AK YAKUTAT 25339 59.52000 -139.67000

AK ANCHORAGE IAP/PT. CAMPBE 26409 61.17000 -150.02000

AK KODIAK 25501 57.75000 -152.48000

AK KING SALMON 25503 58.68000 -156.65000

AK COLD BAY 25624 55.20000 -162.72000

AK ST PAUL ISLAND 25713 57.15000 -170.22000

AK FAIRBANKS 26411 64.82000 -147.87000

AK MCGRATH 26510 62.97000 -155.62000

AK KOTZEBUE 26616 66.87000 -162.63000

AK NOME AP 26617 64.50000 -165.43000

AK POINT BARROW 27502 71.30000 -156.78000

AL BIRMINGHAM (SHELBY APT) 53823 34.60000 -86.62000

LA SLIDELL 53813 30.33000 -89.82000

FL TALLAHASEE 93805 30.52000 -86.58000

AR N LITTLE ROCK 03952 34.83000 -92.27000

MO SPRINGFIELD REGIONAL AP 13995 37.23000 -93.40000

LA SHREVEPORT REGIONAL AP 13957 32.45000 -93.83000

NM ALBUQUERQUE 23050 35.05000 -106.62000

AZ FLAGSTAFF/BELLEMT (ARMY) 53103 35.23000 -111.82000

AZ TUCSON 23160 32.23000 -110.96000

NV LAS VEGAS 03120 36.05000 -115.18000

CA MIRAMAR NAS 03190 32.87000 -117.15000

CA VANDENBERG 93214 34.75000 -120.57000

NV RENO 03198 39.57000 -119.80000

CA OAKLAND INT AP 23230 37.75000 -122.22000

OR MEDFORD 24225 42.37000 -122.87000

KS DODGE CITY 13985 37.77000 -99.97000

CO DENVER/STAPLETON ARPT 23062 39.77000 -104.88000

CO GRAND JUNCTION 23066 39.12000 -108.53000

NY BROOKHAVEN 94703 40.87000 -72.87000

VA STERLING(WASH DULLES) 93734 38.98000 -77.47000

VA WALLOPS ISLAND 93739 37.93000 -75.48000

FL TAMPA BAY/RUSKIN 12842 27.70000 -82.40000

FL JACKSONVILLE 13889 30.43000 -81.70000

FL KEY WEST INT AP 12836 24.55000 -81.75000

FL MIAMI/FL INTL UNIV 92803 25.75000 -80.38000

GA PEACHTREE CITY 53819 33.35000 -84.56000

SC CHARLESTON 13880 32.90000 -80.03000

HI HILO 21504 19.72000 -155.07000

HI LIHUE/KAUAI 22536 21.98000 -159.35000

IA DAVENPORT MUNICIPAL AP 94982 41.60000 -90.57000

NE OMAHA/VALLEY 94980 41.32000 -96.37000

MN MINNEAPOLIS 94983 44.83000 -93.55000

ID BOISE 24131 43.57000 -116.22000

Appendix 2

Upper Air Stations used for AERMET (2011)
2 of 2

State City WBAN # Latitude Longitude

NV ELKO 04105 40.87000 -115.73000

WY RIVERTON 24061 43.06000 -108.47000

WA SPOKANE INTNL APT 04106 47.68000 -117.63000

UT SALT LAKE CITY 24127 40.77000 -111.97000

IL LINCOLN-LOGAN COUNTY AP 04833 40.15000 -89.33000

WI GREEN BAY 14898 44.48000 -88.13000

OH WILMINGTON 13841 39.42000 -83.82000

MI DETROIT/PONTIAC 04830 42.70000 -83.47000

TN NASHVILLE 13897 34.60000 -86.62000

KS TOPEKA 13996 39.07000 -95.62000

NE NORTH PLATTE 24023 41.13000 -100.68000

LA LAKE CHARLES 03937 30.12000 -93.22000

NY ALBANY 54775 42.69000 -73.83000

MA CHATHAM 14684 41.67000 -69.97000

ME GRAY 54762 43.89000 -70.25000

ME CARIBOU 14607 46.87000 -68.02000

MI GAYLORD / ALPENA 04837 44.55000 -84.43000

MN INTERNATIONAL FALLS 14918 48.57000 -93.38000

MS JACKSON/THOMPSON FLD 03940 32.32000 -90.07000

MT GREAT FALLS 04102 47.45000 -111.38000

MT GLASGOW 94008 48.20000 -106.62000

SD RAPID CITY 94043 44.07000 -103.21000

NC GREENSBORO 13723 36.08000 -79.95000

NC MOREHEAD CITY/NEWPORT 93768 34.70000 -76.80000

SD ABERDEEN 14929 45.45000 -98.42000

ND BISMARCK 24011 46.77000 -100.75000

NM SANTA TERESA 03020 31.90000 -106.70000

TX AMARILLO 23047 35.23000 -101.70000

TX MIDLAND 23023 31.93000 -102.20000

NY BUFFALO/GRTR ARPT 14733 42.93000 -78.73000

PA PITTSBURGH/MOON TOWNSHIP 94823 40.53000 -80.23000

OK NORMAN 03948 35.23000 -97.47000

OR SALEM 24232 44.92000 -123.02000

PR SAN JUAN 11641 18.43000 -66.00000

VA ROANOKE/BLACKSBURG 53829 37.20000 -80.41000

TX FT WORTH 03990 32.80000 -97.30000

TX BROWNSVILLE 12919 25.90000 -97.43000

TX CORPUS CHRISTI 12924 27.77000 -97.50000

TX DEL RIO 22010 29.37000 -100.92000

WA QUILLAYUTE 94240 47.95000 -124.55000
1 of 21

State City WBAN # State City WBAN #

 % 

Missing % Calms

AK ANNETTE 25308 AK ANNETTE ISLAND 25308 1.05 11

AK JUNEAU 25309 AK YAKUTAT 25339 0.29 15

AK HAINES 25323 AK YAKUTAT 25339 0.24 4

AK KETCHIKAN 25325 AK ANNETTE ISLAND 25308 0.19 8

AK PALMER 25331 AK

ANCHORAGE IAP/PT. 

CAMPBE 26409 0.30 11

AK SITKA 25333 AK ANNETTE ISLAND 25308 0.10 5

AK SKAGWAY 25335 AK YAKUTAT 25339 0.19 11

AK YAKUTAT 25339 AK YAKUTAT 25339 0.41 17

AK KALWOCK 25367 AK ANNETTE ISLAND 25308 2.32 17

AK KODIAK 25501 AK KODIAK 25501 0.11 3

AK KING SALMON 25503 AK KING SALMON 25503 0.14 4

AK ILIAMNA 25506 AK KING SALMON 25503 0.18 3

AK HOMER 25507 AK

ANCHORAGE IAP/PT. 

CAMPBE 26409 0.38 9

AK SELDOVIA 25516 AK KODIAK 25501 1.29 32

AK COLD BAY 25624 AK COLD BAY 25624 0.18 2

AK ST PAUL ISLAND 25713 AK ST PAUL ISLAND 25713 0.26 2

AK ANCHORAGE 26409 AK

ANCHORAGE IAP/PT. 

CAMPBE 26409 0.42 15

AK CORDOVA 26410 AK

ANCHORAGE IAP/PT. 

CAMPBE 26409 1.02 30

AK FAIRBANKS 26411 AK FAIRBANKS 26411 0.22 23

AK NORTHWAY 26412 AK FAIRBANKS 26411 0.25 33

AK

DELTA JUNCTION/FT 

GREELY 26415 AK FAIRBANKS 26411 0.22 6

AK EAGLE 26422 AK FAIRBANKS 26411 2.16 40

AK GULKANA 26425 AK

ANCHORAGE IAP/PT. 

CAMPBE 26409 0.23 15

AK NENANA 26435 AK FAIRBANKS 26411 0.39 18

AK SEWARD 26438 AK

ANCHORAGE IAP/PT. 

CAMPBE 26409 0.27 10

AK ANCHORAGE 26451 AK

ANCHORAGE IAP/PT. 

CAMPBE 26409 0.41 6

AK ANCHORAGE 26491 AK

ANCHORAGE IAP/PT. 

CAMPBE 26409 0.50 12

AK PORTAGE GLACIER 26492 AK

ANCHORAGE IAP/PT. 

CAMPBE 26409 2.58 13

AK KALTAG 26502 AK MCGRATH 26510 1.54 23

AK MC GRATH 26510 AK MCGRATH 26510 0.94 28

AK KENAI 26523 AK

ANCHORAGE IAP/PT. 

CAMPBE 26409 0.15 7

AK TALKEETNA 26528 AK

ANCHORAGE IAP/PT. 

CAMPBE 26409 0.42 25

Surface Upper Air

Appendix 3

Processed Stations used for AERMET (2011)

Processed
2 of 21

State City WBAN # State City WBAN #

 % 

Missing % Calms

Surface Upper Air Processed

AK TANANA 26529 AK FAIRBANKS 26411 0.63 12

AK BETTLES 26533 AK FAIRBANKS 26411 1.08 15

AK KOTZEBUE 26616 AK KOTZEBUE 26616 0.14 3

AK NOME 26617 AK NOME AP 26617 0.22 8

AK KIVALINA 26642 AK KOTZEBUE 26616 0.15 2

AK DEADHORSE 27406 AK POINT BARROW 27502 0.10 2

AK BARROW 27502 AK POINT BARROW 27502 0.10 1

AK WAINWRIGHT 27503 AK POINT BARROW 27502 0.26 2

AK NUIQSUT 27515 AK POINT BARROW 27502 2.08 3

AL HUNTSVILLE 03856 AL

BIRMINGHAM (SHELBY 

APT) 53823 0.25 7

AL TROY 03878 AL

BIRMINGHAM (SHELBY 

APT) 53823 0.38 25

AL MOBILE 13838 LA SLIDELL 53813 1.34 5

AL DOTHAN 13839 FL TALLAHASEE 93805 0.27 5

AL ANNISTON 13871 AL

BIRMINGHAM (SHELBY 

APT) 53823 0.71 16

AL BIRMINGHAM 13876 AL

BIRMINGHAM (SHELBY 

APT) 53823 0.25 18

AL MOBILE 13894 LA SLIDELL 53813 0.35 7

AL MONTGOMERY 13895 AL

BIRMINGHAM (SHELBY 

APT) 53823 0.32 11

AL MUSCLE SHOALS 13896 AL

BIRMINGHAM (SHELBY 

APT) 53823 0.38 14

AL EVERGREEN 53820 AL

BIRMINGHAM (SHELBY 

APT) 53823 0.81 27

AL ALABASTER 53864 AL

BIRMINGHAM (SHELBY 

APT) 53823 0.46 27

AL TUSCALOOSA 93806 AL

BIRMINGHAM (SHELBY 

APT) 53823 0.26 20

AR JONESBORO 03953 AR N LITTLE ROCK 03952 0.29 8

AR HOT SPRINGS 03962 AR N LITTLE ROCK 03952 0.64 12

AR LITTLE ROCK 13963 AR N LITTLE ROCK 03952 0.15 7

AR FORT SMITH 13964 AR N LITTLE ROCK 03952 0.13 6

AR HARRISON 13971 MO

SPRINGFIELD REGIONAL 

AP 13995 0.31 4

AR TEXARKANA 13977 LA

SHREVEPORT REGIONAL 

AP 13957 0.19 4

AR BLYTHEVILLE 53869 AR N LITTLE ROCK 03952 0.81 27

AR MOUNTAIN HOME 53918 MO

SPRINGFIELD REGIONAL 

AP 13995 0.57 8

AR MONTICELLO 53919 AR N LITTLE ROCK 03952 2.63 12

AR RUSSELLVILLE 53920 AR N LITTLE ROCK 03952 0.65 27

AR

FAYETTEVILLE/SPRING

DALE 53922 MO

SPRINGFIELD REGIONAL 

AP 13995 0.56 6

AR De QUEEN 53925 LA

SHREVEPORT REGIONAL 

AP 13957 0.68 19

AR WEST MEMPHIS 53959 AR N LITTLE ROCK 03952 2.34 7

AR PINE BLUFF 93988 AR N LITTLE ROCK 03952 0.99 10
3 of 21

State City WBAN # State City WBAN #

 % 

Missing % Calms

Surface Upper Air Processed

AR EL DORADO 93992 LA

SHREVEPORT REGIONAL 

AP 13957 0.78 15

AR FAYETTEVILLE 93993 MO

SPRINGFIELD REGIONAL 

AP 13995 0.53 16

AZ WINDOW ROCK 03029 NM ALBUQUERQUE 23050 1.32 24

AZ FLAGSTAFF 03103 AZ

FLAGSTAFF/BELLEMT 

(ARMY) 53103 0.76 24

AZ PAGE 03162 AZ

FLAGSTAFF/BELLEMT 

(ARMY) 53103 0.86 10

AZ PHOENIX 03184 AZ TUCSON 23160 0.59 9

AZ SCOTTSDALE 03192 AZ TUCSON 23160 0.32 21

AZ GRAND CANYON 03195 AZ

FLAGSTAFF/BELLEMT 

(ARMY) 53103 0.59 17

AZ NOGALES 03196 AZ TUCSON 23160 1.03 11

AZ TUCSON 23160 AZ TUCSON 23160 0.26 2

AZ PHOENIX 23183 AZ TUCSON 23160 0.31 4

AZ PRESCOTT 23184 AZ

FLAGSTAFF/BELLEMT 

(ARMY) 53103 0.24 3

AZ WINSLOW 23194 AZ

FLAGSTAFF/BELLEMT 

(ARMY) 53103 0.39 3

AZ DOUGLAS BISBEE 93026 AZ TUCSON 23160 3.77 10

AZ ST. JOHNS 93027 AZ

FLAGSTAFF/BELLEMT 

(ARMY) 53103 0.53 8

AZ SAFFORD 93084 AZ TUCSON 23160 0.24 3

AZ KINGMAN 93167 NV LAS VEGAS 03120 0.24 4

CA ONTARIO 03102 CA MIRAMAR NAS 03190 0.61 13

CA PALM SPRINGS 03104 CA MIRAMAR NAS 03190 0.31 3

CA SAN DIEGO 03131 CA MIRAMAR NAS 03190 0.39 11

CA IMPERIAL 03144 CA MIRAMAR NAS 03190 0.40 5

CA LANCASTER 03159 CA VANDENBERG 93214 0.25 6

CA FULLERTON 03166 CA MIRAMAR NAS 03190 1.03 21

CA HAWTHORNE 03167 CA MIRAMAR NAS 03190 0.43 26

CA RIVERSIDE 03171 CA MIRAMAR NAS 03190 0.89 15

CA CARLSBAD 03177 CA MIRAMAR NAS 03190 0.78 14

CA SAN DIEGO 03178 CA MIRAMAR NAS 03190 0.39 2

CA CHINO 03179 CA MIRAMAR NAS 03190 0.51 18

CA LONG BEACH 23129 CA MIRAMAR NAS 03190 0.58 15

CA VAN NUYS 23130 CA MIRAMAR NAS 03190 0.18 16

CA CAMARILLO 23136 CA VANDENBERG 93214 0.79 11

CA BURBANK 23152 CA MIRAMAR NAS 03190 0.47 11

CA BAKERSFIELD 23155 CA VANDENBERG 93214 0.41 8

CA BISHOP 23157 NV RENO 03198 1.05 2

CA BLYTHE 23158 CA MIRAMAR NAS 03190 0.75 5

CA DAGGETT 23161 NV LAS VEGAS 03120 0.41 2

CA LOS ANGELES 23174 CA MIRAMAR NAS 03190 0.35 4

CA NEEDLES 23179 NV LAS VEGAS 03120 0.63 3

CA PALMDALE 23182 CA MIRAMAR NAS 03190 0.33 4

CA SANDBERG 23187 CA VANDENBERG 93214 3.34 1

CA SAN DIEGO 23188 CA MIRAMAR NAS 03190 0.09 9
4 of 21

State City WBAN # State City WBAN #

 % 

Missing % Calms

Surface Upper Air Processed

CA SANTA BARBARA 23190 CA VANDENBERG 93214 0.23 16

CA AVALON 23191 CA MIRAMAR NAS 03190 0.50 4

CA SANTA ROSA 23213 CA OAKLAND INT AP 23230 0.27 18

CA EMIGRANT GAP 23225 NV RENO 03198 1.00 6

CA OAKLAND 23230 CA OAKLAND INT AP 23230 0.13 4

CA SACRAMENTO 23232 CA OAKLAND INT AP 23230 0.41 13

CA SALINAS 23233 CA OAKLAND INT AP 23230 0.08 5

CA SAN FRANCISCO 23234 CA OAKLAND INT AP 23230 0.07 3

CA STOCKTON 23237 CA OAKLAND INT AP 23230 0.19 6

CA MOUNTAIN VIEW 23244 CA OAKLAND INT AP 23230 0.46 16

CA CONCORD 23254 CA OAKLAND INT AP 23230 0.73 11

CA MERCED 23257 CA OAKLAND INT AP 23230 0.37 4

CA MODESTO 23258 CA OAKLAND INT AP 23230 0.46 11

CA MONTEREY 23259 CA OAKLAND INT AP 23230 0.32 6

CA UKIAH 23275 CA OAKLAND INT AP 23230 0.27 37

CA WATSONVILLE 23277 CA OAKLAND INT AP 23230 0.32 14

CA LIVERMORE 23285 CA OAKLAND INT AP 23230 0.38 14

CA SAN JOSE 23293 CA OAKLAND INT AP 23230 0.14 8

CA MOUNT SHASTA 24215 OR MEDFORD 24225 2.73 24

CA RED BLUFF 24216 NV RENO 03198 0.23 4

CA REDDING 24257 OR MEDFORD 24225 0.56 15

CA MONTAGUE 24259 OR MEDFORD 24225 0.30 12

CA ARCATA/EUREKA 24283 OR MEDFORD 24225 0.21 9

CA CRESCENT CITY 24286 OR MEDFORD 24225 0.40 6

CA HANFORD 53119 CA VANDENBERG 93214 0.79 11

CA RAMONA 53120 CA MIRAMAR NAS 03190 0.96 25

CA OCEANSIDE 53121 CA MIRAMAR NAS 03190 1.22 24

CA OXNARD 93110 CA VANDENBERG 93214 0.50 8

CA LOS ANGELES 93134 CA MIRAMAR NAS 03190 2.84 47

CA PALM SPRINGS 93138 CA MIRAMAR NAS 03190 0.42 6

CA SANTA ANA 93184 CA MIRAMAR NAS 03190 0.62 14

CA FRESNO 93193 CA VANDENBERG 93214 0.32 10

CA SANTA MONICA 93197 CA MIRAMAR NAS 03190 0.19 10

CA MARYSVILLE 93205 CA OAKLAND INT AP 23230 0.40 9

CA SAN LUIS OBISPO 93206 CA VANDENBERG 93214 0.38 10

CA PASO ROBLES 93209 CA VANDENBERG 93214 0.55 11

CA OROVILLE 93210 NV RENO 03198 0.50 9

CA SACRAMENTO 93225 CA OAKLAND INT AP 23230 0.14 6

CA NAPA 93227 CA OAKLAND INT AP 23230 0.42 7

CA HAYWARD 93228 CA OAKLAND INT AP 23230 0.57 6

CA SOUTH LAKE TAHOE 93230 NV RENO 03198 0.81 15

CA VACAVILLE 93241 CA OAKLAND INT AP 23230 0.70 16

CA MADERA 93242 CA OAKLAND INT AP 23230 2.49 8

CA ALTURAS 94299 OR MEDFORD 24225 0.80 22

CO LAMAR 03013 KS DODGE CITY 13985 0.87 3

CO DENVER 03017 CO DENVER/STAPLETON ARPT 23062 0.26 1
5 of 21

State City WBAN # State City WBAN #

 % 

Missing % Calms

Surface Upper Air Processed

CO BURLINGTON 03026 CO DENVER/STAPLETON ARPT 23062 0.94 1

CO ALAMOSA 23061 CO DENVER/STAPLETON ARPT 23062 0.58 6

CO GRAND JUNCTION 23066 CO GRAND JUNCTION 23066 1.22 4

CO LA JUNTA 23067 CO DENVER/STAPLETON ARPT 23062 0.37 1

CO TRINIDAD 23070 CO DENVER/STAPLETON ARPT 23062 0.24 1

CO CRAIG 24046 CO GRAND JUNCTION 23066 0.27 18

CO DURANGO 93005 CO GRAND JUNCTION 23066 0.65 8

CO LEADVILLE 93009 CO DENVER/STAPLETON ARPT 23062 0.48 5

CO LIMON 93010 CO DENVER/STAPLETON ARPT 23062 0.34 3

CO MONTROSE 93013 CO GRAND JUNCTION 23066 0.27 4

CO COLORADO SPRINGS 93037 CO DENVER/STAPLETON ARPT 23062 0.24 1

CO PUEBLO 93058 CO DENVER/STAPLETON ARPT 23062 0.31 4

CO DENVER 93067 CO DENVER/STAPLETON ARPT 23062 0.30 2

CO CORTEZ 93069 CO GRAND JUNCTION 23066 1.22 4

CO ASPEN 93073 CO GRAND JUNCTION 23066 0.43 6

CO MEEKER 94050 CO GRAND JUNCTION 23066 1.48 13

CT

GROTON NEW 

LONDON 14707 NY BROOKHAVEN 94703 0.29 5

CT WINDSOR LOCKS 14740 NY BROOKHAVEN 94703 0.34 7

CT HARTFORD 14752 NY BROOKHAVEN 94703 0.53 13

CT NEW HAVEN 14758 NY BROOKHAVEN 94703 0.32 10

CT DANBURY 54734 NY BROOKHAVEN 94703 0.92 15

CT WILLIMANTIC 54767 NY BROOKHAVEN 94703 0.64 21

CT MERIDEN 54788 NY BROOKHAVEN 94703 0.29 24

CT BRIDGEPORT 94702 NY BROOKHAVEN 94703 0.30 5

DC WASHINGTON 13743 VA STERLING(WASH DULLES) 93734 0.34 5

DC WASHINGTON 93738 VA STERLING(WASH DULLES) 93734 0.29 9

DE GEORGETOWN 13764 VA WALLOPS ISLAND 93739 0.43 13

DE WILMINGTON 13781 VA STERLING(WASH DULLES) 93734 0.29 4

FL MARIANNA 03818 FL TALLAHASEE 93805 1.58 17

FL PUNTA GORDA 12812 FL TAMPA BAY/RUSKIN 12842 0.82 4

FL ORLANDO 12815 FL TAMPA BAY/RUSKIN 12842 0.34 6

FL GAINESVILLE 12816 FL JACKSONVILLE 13889 0.42 16

FL BROOKSVILLE 12818 FL TAMPA BAY/RUSKIN 12842 0.90 16

FL LEESBURG 12819 FL TAMPA BAY/RUSKIN 12842 0.34 6

FL APALACHICOLA 12832 FL TALLAHASEE 93805 0.22 11

FL DAYTONA BEACH 12834 FL JACKSONVILLE 13889 0.27 11
6 of 21

State City WBAN # State City WBAN #

 % 

Missing % Calms

Surface Upper Air Processed

FL FORT MYERS 12835 FL TAMPA BAY/RUSKIN 12842 0.19 7

FL KEY WEST 12836 FL KEY WEST INT AP 12836 0.39 2

FL MELBOURNE 12838 FL TAMPA BAY/RUSKIN 12842 0.14 3

FL MIAMI 12839 FL MIAMI/FL INTL UNIV 92803 0.27 7

FL ORLANDO 12841 FL TAMPA BAY/RUSKIN 12842 0.54 5

FL TAMPA 12842 FL TAMPA BAY/RUSKIN 12842 0.18 4

FL VERO BEACH 12843 FL TAMPA BAY/RUSKIN 12842 0.08 6

FL WEST PALM BEACH 12844 FL MIAMI/FL INTL UNIV 92803 0.15 4

FL FORT LAUDERDALE 12849 FL MIAMI/FL INTL UNIV 92803 0.13 4

FL ORLANDO 12854 FL TAMPA BAY/RUSKIN 12842 0.16 4

FL

SARASOTA/BRADENTO

N 12871 FL TAMPA BAY/RUSKIN 12842 0.30 2

FL

ST PETERSBURG/ 

CLEARWATER 12873 FL TAMPA BAY/RUSKIN 12842 0.18 3

FL WINTER HAVEN 12876 FL TAMPA BAY/RUSKIN 12842 4.29 14

FL MIAMI 12882 FL MIAMI/FL INTL UNIV 92803 0.25 6

FL FORT LAUDERDALE 12885 FL MIAMI/FL INTL UNIV 92803 0.53 7

FL MIAMI 12888 FL MIAMI/FL INTL UNIV 92803 0.19 10

FL FORT MYERS 12894 FL TAMPA BAY/RUSKIN 12842 0.43 6

FL FORT PIERCE 12895 FL TAMPA BAY/RUSKIN 12842 0.21 11

FL MARATHON 12896 FL KEY WEST INT AP 12836 0.37 3

FL NAPLES 12897 FL MIAMI/FL INTL UNIV 92803 0.25 5

FL CRESTVIEW 13884 FL TALLAHASEE 93805 0.32 23

FL JACKSONVILLE 13889 FL JACKSONVILLE 13889 0.26 13

FL PENSACOLA 13899 LA SLIDELL 53813 0.26 3

FL DESTIN 53853 FL TALLAHASEE 93805 1.70 9

FL JACKSONVILLE 53860 FL JACKSONVILLE 13889 0.22 15

FL POMPANO BEACH 92805 FL MIAMI/FL INTL UNIV 92803 0.13 3

FL ST PETERSBURG 92806 FL TAMPA BAY/RUSKIN 12842 0.21 2

FL HOLLYWOOD 92809 FL MIAMI/FL INTL UNIV 92803 0.19 8

FL TALLAHASSEE 93805 FL TALLAHASEE 93805 0.10 19

GA MACON 03813 GA PEACHTREE CITY 53819 0.35 12

GA AUGUSTA 03820 SC CHARLESTON 13880 0.18 21

GA SAVANNAH 03822 SC CHARLESTON 13880 0.24 8

GA ATLANTA 03888 GA PEACHTREE CITY 53819 0.32 21

GA AUGUSTA 13837 SC CHARLESTON 13880 0.67 6

GA ALBANY 13869 FL TALLAHASEE 93805 0.32 17

GA ALMA 13870 FL JACKSONVILLE 13889 0.51 17

GA ATHENS 13873 GA PEACHTREE CITY 53819 0.40 8

GA ATLANTA 13874 GA PEACHTREE CITY 53819 0.10 2

GA BRUNSWICK 13878 FL JACKSONVILLE 13889 1.08 6

GA ATLANTA 53819 GA PEACHTREE CITY 53819 0.32 32

GA GAINESVILLE 53838 GA PEACHTREE CITY 53819 0.65 6

GA ATLANTA 53863 GA PEACHTREE CITY 53819 0.29 14

GA CARTERSVILLE 53873 GA PEACHTREE CITY 53819 0.53 35

GA ROME 93801 GA PEACHTREE CITY 53819 1.78 26
7 of 21

State City WBAN # State City WBAN #

 % 

Missing % Calms

Surface Upper Air Processed

GA COLUMBUS 93842 GA PEACHTREE CITY 53819 0.30 15

GA VALDOSTA 93845 FL TALLAHASEE 93805 0.47 21

HI HILO 21504 HI HILO 21504 0.19 2

HI KAILUA/KONA 21510 HI HILO 21504 0.18 1

HI KAHULUI 22516 HI HILO 21504 0.14 1

HI HONOLULU 22521 HI LIHUE/KAUAI 22536 0.14 1

HI KAUNAKAKAI 22534 HI LIHUE/KAUAI 22536 1.50 2

HI KAPOLEI 22551 HI LIHUE/KAUAI 22536 0.14 1

IA BURLINGTON 14931 IA

DAVENPORT MUNICIPAL 

AP 94982 0.22 3

IA DES MOINES 14933 NE OMAHA/VALLEY 94980 0.15 2

IA IOWA CITY 14937 IA

DAVENPORT MUNICIPAL 

AP 94982 0.39 8

IA MASON CITY 14940 MN MINNEAPOLIS 94983 0.10 3

IA SIOUX CITY 14943 NE OMAHA/VALLEY 94980 0.08 3

IA OTTUMWA 14950 IA

DAVENPORT MUNICIPAL 

AP 94982 0.14 1

IA SPENCER 14972 NE OMAHA/VALLEY 94980 0.07 2

IA WATERLOO 94910 IA

DAVENPORT MUNICIPAL 

AP 94982 0.21 5

IA ESTHERVILLE 94971 MN MINNEAPOLIS 94983 0.40 1

IA DAVENPORT 94982 IA

DAVENPORT MUNICIPAL 

AP 94982 0.40 2

IA MARSHALLTOWN 94988 IA

DAVENPORT MUNICIPAL 

AP 94982 1.13 2

IA LAMONI 94991 NE OMAHA/VALLEY 94980 0.26 1

ID JEROME 04110 ID BOISE 24131 0.53 1

ID CHALLIS 04114 NV ELKO 04105 0.38 14

ID BOISE 24131 ID BOISE 24131 0.15 5

ID BURLEY 24133 ID BOISE 24131 0.16 4

ID IDAHO FALLS 24145 WY RIVERTON 24061 0.26 3

ID LEWISTON 24149 WA SPOKANE INTNL APT 04106 0.42 6

ID MULLAN PASS 24154 WA SPOKANE INTNL APT 04106 3.38 2

ID POCATELLO 24156 UT SALT LAKE CITY 24127 0.15 3

ID TWIN FALLS 94178 ID BOISE 24131 0.06 1

ID McCALL 94182 ID BOISE 24131 0.22 22

ID REXBURG 94194 WY RIVERTON 24061 0.09 5

IL DECATUR 03887 IL

LINCOLN-LOGAN COUNTY 

AP 04833 0.27 4

IL CAHOKIA/ST.LOUIS 03960 IL

LINCOLN-LOGAN COUNTY 

AP 04833 0.27 9

IL CHICAGO/AURORA 04808 IA

DAVENPORT MUNICIPAL 

AP 94982 0.10 6

IL

CHICAGO/PROSPECT 

HEIGHTS/WHEELING 04838 IA

DAVENPORT MUNICIPAL 

AP 94982 0.07 9

IL LAWRENCEVILLE 13809 IL

LINCOLN-LOGAN COUNTY 

AP 04833 0.68 5
8 of 21

State City WBAN # State City WBAN #

 % 

Missing % Calms

Surface Upper Air Processed

IL CHICAGO 14819 IL

LINCOLN-LOGAN COUNTY 

AP 04833 0.11 2

IL PEORIA 14842 IL

LINCOLN-LOGAN COUNTY 

AP 04833 0.30 6

IL CHICAGO/WAUKEGAN 14880 WI GREEN BAY 14898 1.11 5

IL MOLINE 14923 IA

DAVENPORT MUNICIPAL 

AP 94982 0.35 8

IL

MATTOON/CHARLEST

ON 53802 IL

LINCOLN-LOGAN COUNTY 

AP 04833 0.23 4

IL

BLOOMINGTON/NORM

AL 54831 IL

LINCOLN-LOGAN COUNTY 

AP 04833 1.88 2

IL

CARBONDALE/MURPH

YBORO 93810 IL

LINCOLN-LOGAN COUNTY 

AP 04833 0.16 9

IL SPRINGFIELD 93822 IL

LINCOLN-LOGAN COUNTY 

AP 04833 0.06 4

IL QUINCY 93989 IL

LINCOLN-LOGAN COUNTY 

AP 04833 0.13 1

IL ROCKFORD 94822 IA

DAVENPORT MUNICIPAL 

AP 94982 0.08 5

IL CHICAGO 94846 IA

DAVENPORT MUNICIPAL 

AP 94982 0.15 2

IL CHAMPAIGN/URBANA 94870 IL

LINCOLN-LOGAN COUNTY 

AP 04833 0.14 2

IL

CHICAGO/WEST 

CHICAGO 94892 IA

DAVENPORT MUNICIPAL 

AP 94982 0.40 6

IN TERRE HAUTE 03868 IL

LINCOLN-LOGAN COUNTY 

AP 04833 0.23 9

IN BLOOMINGTON 03893 OH WILMINGTON 13841 0.16 13

IN VALPARAISO 04846 IL

LINCOLN-LOGAN COUNTY 

AP 04833 0.19 5

IN FORT WAYNE 14827 OH WILMINGTON 13841 0.14 5

IN GOSHEN 14829 MI DETROIT/PONTIAC 04830 0.19 6

IN LAFAYETTE 14835 IL

LINCOLN-LOGAN COUNTY 

AP 04833 0.74 9

IN SOUTH BEND 14848 MI DETROIT/PONTIAC 04830 0.07 5

IN INDIANAPOLIS 53842 OH WILMINGTON 13841 1.56 9

IN SHELBYVILLE 53866 OH WILMINGTON 13841 0.13 3

IN EVANSVILLE 93817 TN NASHVILLE 13897 0.32 11

IN INDIANAPOLIS 93819 OH WILMINGTON 13841 0.15 2

IN MUNCIE 94895 OH WILMINGTON 13841 0.31 5

KS SALINA 03919 KS TOPEKA 13996 0.05 2

KS WICHITA 03928 KS TOPEKA 13996 0.11 2

KS MANHATTAN 03936 KS TOPEKA 13996 0.49 12

KS OLATHE 03967 KS TOPEKA 13996 0.32 2

KS WICHITA 03974 KS TOPEKA 13996 0.24 1

KS LAWRENCE 03997 KS TOPEKA 13996 0.35 5

KS PARSONS 03998 MO

SPRINGFIELD REGIONAL 

AP 13995 0.81 10
9 of 21

State City WBAN # State City WBAN #

 % 

Missing % Calms

Surface Upper Air Processed

KS TOPEKA 13920 KS TOPEKA 13996 0.53 3

KS CHANUTE 13981 KS TOPEKA 13996 0.38 2

KS CONCORDIA 13984 KS TOPEKA 13996 0.48 2

KS DODGE CITY 13985 KS DODGE CITY 13985 0.34 0

KS HUTCHINSON 13986 KS DODGE CITY 13985 0.37 4

KS EMPORIA 13989 KS TOPEKA 13996 0.84 1

KS TOPEKA 13996 KS TOPEKA 13996 0.37 9

KS GARDEN CITY 23064 KS DODGE CITY 13985 0.22 0

KS GOODLAND 23065 NE NORTH PLATTE 24023 0.38 1

KS OLATHE 93909 KS TOPEKA 13996 0.17 2

KS COFFEYVILLE 93967 MO

SPRINGFIELD REGIONAL 

AP 13995 0.14 3

KS HILL CITY 93990 KS DODGE CITY 13985 0.26 2

KS RUSSELL 93997 KS DODGE CITY 13985 0.49 0

KY PADUCAH 03816 TN NASHVILLE 13897 0.33 10

KY LONDON 03849 TN NASHVILLE 13897 0.38 23

KY JACKSON 03889 OH WILMINGTON 13841 1.67 19

KY LOUISVILLE 13810 OH WILMINGTON 13841 0.34 14

KY FRANKFORT 53841 OH WILMINGTON 13841 0.26 12

KY BOWLING GREEN 93808 TN NASHVILLE 13897 0.21 15

KY LEXINGTON 93820 OH WILMINGTON 13841 1.48 11

KY LOUISVILLE 93821 OH WILMINGTON 13841 0.09 4

LA LAKE CHARLES 03937 LA LAKE CHARLES 03937 0.46 7

LA BOOTHVILLE 12884 LA SLIDELL 53813 0.64 4

LA NEW ORLEANS 12916 LA SLIDELL 53813 0.19 5

LA ALEXANDRIA 13935 LA LAKE CHARLES 03937 1.53 24

LA MONROE 13942 LA

SHREVEPORT REGIONAL 

AP 13957 0.66 12

LA SHREVEPORT 13957 LA

SHREVEPORT REGIONAL 

AP 13957 0.07 6

LA BATON ROUGE 13970 LA SLIDELL 53813 0.50 11

LA LAFAYETTE 13976 LA LAKE CHARLES 03937 0.66 13

LA SLIDELL 53865 LA SLIDELL 53813 0.41 23

LA SHREVEPORT 53905 LA

SHREVEPORT REGIONAL 

AP 13957 0.22 8

LA NEW IBERIA 53915 LA LAKE CHARLES 03937 0.21 8

LA NEW ORLEANS 53917 LA SLIDELL 53813 2.03 2

LA ALEXANDRIA 93915 LA LAKE CHARLES 03937 0.58 13

MA FITCHBURG 04780 NY ALBANY 54775 0.55 14

MA BEDFORD 14702 MA CHATHAM 14684 0.29 13

MA BOSTON 14739 MA CHATHAM 14684 0.08 1

MA NANTUCKET 14756 MA CHATHAM 14684 0.09 1

MA PITTSFIELD 14763 NY ALBANY 54775 0.68 19

MA

WESTFIELD/SPRINGFIE

LD 14775 NY ALBANY 54775 0.99 14

MA NORWOOD 54704 MA CHATHAM 14684 0.59 21

MA BEVERLY 54733 MA CHATHAM 14684 0.17 7

MA ORANGE 54756 NY ALBANY 54775 0.59 28

MA NORTH ADAMS 54768 NY ALBANY 54775 1.14 23
10 of 21

State City WBAN # State City WBAN #

 % 

Missing % Calms

Surface Upper Air Processed

MA PLYMOUTH 54769 MA CHATHAM 14684 0.88 11

MA TAUNTON 54777 MA CHATHAM 14684 0.84 19

MA CHATHAM 94624 MA CHATHAM 14684 1.66 5

MA HYANNIS 94720 MA CHATHAM 14684 0.23 10

MA LAWRENCE 94723 ME GRAY 54762 1.72 9

MA VINEYARD HAVEN 94724 MA CHATHAM 14684 0.23 4

MA NEW BEDFORD 94726 MA CHATHAM 14684 0.83 13

MA WORCESTER 94746 NY ALBANY 54775 0.41 1

MD HAGERSTOWN 93706 VA STERLING(WASH DULLES) 93734 0.22 6

MD SALISBURY 93720 VA WALLOPS ISLAND 93739 0.25 12

MD BALTIMORE 93721 VA STERLING(WASH DULLES) 93734 0.21 11

MD OCEAN CITY 93786 VA WALLOPS ISLAND 93739 1.66 8

ME FRENCHVILLE 04836 ME CARIBOU 14607 0.71 1

ME AUGUSTA 14605 ME GRAY 54762 0.47 5

ME BANGOR 14606 ME GRAY 54762 0.19 10

ME CARIBOU 14607 ME CARIBOU 14607 0.11 5

ME HOULTON 14609 ME CARIBOU 14607 0.70 13

ME MILLINOCKET 14610 ME CARIBOU 14607 1.26 18

ME PORTLAND 14764 ME GRAY 54762 0.19 3

ME FRYEBURG 54772 ME GRAY 54762 2.32 28

ME WISCASSET 94623 ME GRAY 54762 1.84 31

MI HOLLAND 04839 MI DETROIT/PONTIAC 04830 0.42 7

MI ADRIAN 04847 MI DETROIT/PONTIAC 04830 0.14 7

MI GAYLORD 04854 MI GAYLORD / ALPENA 04837 0.78 10

MI BATTLE CREEK 14815 MI DETROIT/PONTIAC 04830 0.15 4

MI DETROIT 14822 MI DETROIT/PONTIAC 04830 0.51 5

MI FLINT 14826 MI DETROIT/PONTIAC 04830 0.26 5

MI JACKSON 14833 MI DETROIT/PONTIAC 04830 0.22 8

MI LANSING 14836 MI DETROIT/PONTIAC 04830 0.16 7

MI MUSKEGON 14840 WI GREEN BAY 14898 0.26 6

MI PELLSTON 14841 MI GAYLORD / ALPENA 04837 0.19 16

MI SAGINAW 14845 MI DETROIT/PONTIAC 04830 0.17 2

MI SAULT STE MARIE 14847 MI GAYLORD / ALPENA 04837 1.18 7

MI TRAVERSE CITY 14850 MI GAYLORD / ALPENA 04837 0.19 13

MI DETROIT 14853 MI DETROIT/PONTIAC 04830 0.27 5

MI HANCOCK 14858 WI GREEN BAY 14898 0.19 3

MI HOUGHTON LAKE 94814 MI GAYLORD / ALPENA 04837 0.24 8

MI KALAMAZOO 94815 MI DETROIT/PONTIAC 04830 0.51 7

MI PONTIAC 94817 MI DETROIT/PONTIAC 04830 0.15 6

MI DETROIT 94847 MI DETROIT/PONTIAC 04830 0.16 4

MI GRAND RAPTIDS 94860 MI DETROIT/PONTIAC 04830 0.11 3

MI BENTON HARBOR 94871 MI DETROIT/PONTIAC 04830 1.21 9

MI ANN ARBOR 94889 MI DETROIT/PONTIAC 04830 0.33 9

MI

IRON 

MOUNTAIN/KINGSFOR

D 94893 WI GREEN BAY 14898 0.54 12

MN ALEXANDRIA 14910 MN MINNEAPOLIS 94983 3.12 2
11 of 21

State City WBAN # State City WBAN #

 % 

Missing % Calms

Surface Upper Air Processed

MN DULUTH 14913 MN INTERNATIONAL FALLS 14918 0.13 3

MN

INTERNATIONAL 

FALLS 14918 MN INTERNATIONAL FALLS 14918 0.31 11

MN MINNEAPOLIS 14922 MN MINNEAPOLIS 94983 0.17 3

MN ROCHESTER 14925 MN MINNEAPOLIS 94983 0.05 1

MN ST CLOUD 14926 MN MINNEAPOLIS 94983 0.25 7

MN ST PAUL 14927 MN MINNEAPOLIS 94983 0.15 6

MN REDWOOD FALLS 14992 MN MINNEAPOLIS 94983 0.13 2

MN HIBBING 94931 MN INTERNATIONAL FALLS 14918 0.43 15

MN BRAINERD 94938 MN MINNEAPOLIS 94983 0.32 8

MN MINNEAPOLIS 94960 MN MINNEAPOLIS 94983 0.15 10

MN BAUDETTE 94961 MN INTERNATIONAL FALLS 14918 0.64 8

MN MINNEAPOLIS 94963 MN MINNEAPOLIS 94983 0.17 3

MN PARK RAPIDS 94967 MN INTERNATIONAL FALLS 14918 0.34 5

MO CAPE GIRARDEAU 03935 TN NASHVILLE 13897 0.25 8

MO COLUMBIA 03945 MO

SPRINGFIELD REGIONAL 

AP 13995 0.67 1

MO KANSAS CITY 03947 KS TOPEKA 13996 0.17 1

MO JEFFERSON CITY 03963 MO

SPRINGFIELD REGIONAL 

AP 13995 0.29 8

MO ST LOUIS 03966 IL

LINCOLN-LOGAN COUNTY 

AP 04833 0.11 14

MO POPLAR BLUFF 03975 MO

SPRINGFIELD REGIONAL 

AP 13995 0.55 12

MO SEDALIA 03994 MO

SPRINGFIELD REGIONAL 

AP 13995 0.65 1

MO JOPLIN 13987 MO

SPRINGFIELD REGIONAL 

AP 13995 0.23 2

MO KANSAS CITY 13988 KS TOPEKA 13996 0.39 5

MO ST LOUIS 13994 IL

LINCOLN-LOGAN COUNTY 

AP 04833 0.07 3

MO SPRINGFIELD 13995 MO

SPRINGFIELD REGIONAL 

AP 13995 0.02 2

MO ROLLA/VICHY 13997 MO

SPRINGFIELD REGIONAL 

AP 13995 1.14 2

MO KIRKSVILLE 14938 IA

DAVENPORT MUNICIPAL 

AP 94982 0.22 3

MO LEE'S SUMMIT 53879 KS TOPEKA 13996 0.79 1

MO WEST PLAINS 53901 MO

SPRINGFIELD REGIONAL 

AP 13995 0.22 6

MO ST CHARLES 53904 IL

LINCOLN-LOGAN COUNTY 

AP 04833 0.16 6

MS JACKSON 03940 MS JACKSON/THOMPSON FLD 03940 0.70 17
12 of 21

State City WBAN # State City WBAN #

 % 

Missing % Calms

Surface Upper Air Processed

MS TALLULAH/VICKSBURG 03996 MS JACKSON/THOMPSON FLD 03940 0.82 8

MS HATTIESBURG 13833 LA SLIDELL 53813 1.00 23

MS MERIDIAN 13865 MS JACKSON/THOMPSON FLD 03940 0.38 17

MS JACKSON 13927 MS JACKSON/THOMPSON FLD 03940 1.16 13

MS GREENVILLE 13939 MS JACKSON/THOMPSON FLD 03940 0.18 4

MS GREENWOOD 13978 MS JACKSON/THOMPSON FLD 03940 0.51 10

MS PASCAGOULA 53858 LA SLIDELL 53813 1.52 28

MS TUPELO 93862 AL

BIRMINGHAM (SHELBY 

APT) 53823 0.25 10

MS GULFPORT 93874 LA SLIDELL 53813 0.51 13

MS MCCOMB 93919 LA SLIDELL 53813 2.75 17

MT BILLINGS 24033 MT GREAT FALLS 04102 0.21 1

MT LEWISTOWN 24036 MT GREAT FALLS 04102 0.59 2

MT MILES 24037 MT GLASGOW 94008 0.35 1

MT BOZEMAN 24132 MT GREAT FALLS 04102 0.55 6

MT BUTTE 24135 MT GREAT FALLS 04102 0.89 9

MT CUT BANK 24137 MT GREAT FALLS 04102 0.30 2

MT DILLON 24138 MT GREAT FALLS 04102 0.34 1

MT GREAT FALLS 24143 MT GREAT FALLS 04102 0.55 2

MT HELENA 24144 MT GREAT FALLS 04102 0.68 10

MT KALISPELL 24146 MT GREAT FALLS 04102 0.68 19

MT LIVINGSTON 24150 MT GREAT FALLS 04102 1.54 2

MT MISSOULA 24153 MT GREAT FALLS 04102 0.45 17

MT GLASGOW 94008 MT GLASGOW 94008 0.23 2

MT HAVRE 94012 MT GREAT FALLS 04102 0.27 3

MT WOLF POINT 94017 MT GLASGOW 94008 0.41 8

MT BAKER 94055 SD RAPID CITY 94043 0.38 5

NC HICKORY 03810 NC GREENSBORO 13723 0.86 17

NC ASHEVILLE 03812 NC GREENSBORO 13723 0.33 17

NC RALEIGH/DURHAM 13722 NC GREENSBORO 13723 0.18 12

NC GREENSBORO 13723 NC GREENSBORO 13723 0.18 6

NC WILMINGTON 13748 NC

MOREHEAD 

CITY/NEWPORT 93768 0.22 10

NC LUMBERTON 13776 NC GREENSBORO 13723 1.11 15

NC ELIZABETH CITY 13786 NC

MOREHEAD 

CITY/NEWPORT 93768 0.38 3

NC CHARLOTTE 13881 NC GREENSBORO 13723 0.31 5

NC GASTONIA 53870 NC GREENSBORO 13723 1.61 22

NC MONROE 53872 NC GREENSBORO 13723 0.94 13

NC NEW BERN 93719 NC

MOREHEAD 

CITY/NEWPORT 93768 0.30 9

NC CAPE HATTERAS 93729 NC

MOREHEAD 

CITY/NEWPORT 93768 0.32 1

NC FAYETTEVILLE 93740 NC GREENSBORO 13723 0.19 4
13 of 21

State City WBAN # State City WBAN #

 % 

Missing % Calms

Surface Upper Air Processed

NC ROCKY MOUNT 93759 NC

MOREHEAD 

CITY/NEWPORT 93768 0.95 16

NC BEAUFORT 93765 NC

MOREHEAD 

CITY/NEWPORT 93768 1.24 4

NC MAXTON 93782 NC GREENSBORO 13723 0.17 11

NC BURLINGTON 93783 NC GREENSBORO 13723 0.19 15

NC CHAPEL HILL 93785 NC GREENSBORO 13723 1.92 24

NC WINSTON SALEM 93807 NC GREENSBORO 13723 0.62 14

ND FARGO 14914 SD ABERDEEN 14929 0.71 2

ND GRAND FORKS 14916 MN INTERNATIONAL FALLS 14918 0.17 2

ND JAMESTOWN 14919 ND BISMARCK 24011 0.15 2

ND BISMARCK 24011 ND BISMARCK 24011 0.11 4

ND DICKINSON 24012 ND BISMARCK 24011 0.11 1

ND MINOT 24013 ND BISMARCK 24011 0.13 2

ND WILLISTON 94014 MT GLASGOW 94008 0.42 5

ND HETTINGER 94038 ND BISMARCK 24011 0.40 6

NE GRAND ISLAND 14935 NE OMAHA/VALLEY 94980 0.19 1

NE LINCOLN 14939 NE OMAHA/VALLEY 94980 0.10 5

NE NORFOLK 14941 NE OMAHA/VALLEY 94980 0.18 4

NE OMAHA 14942 NE OMAHA/VALLEY 94980 0.18 3

NE CHADRON 24017 SD RAPID CITY 94043 0.35 4

NE NORTH PLATTE 24023 NE NORTH PLATTE 24023 0.19 4

NE SCOTTSBLUFF 24028 SD RAPID CITY 94043 0.22 1

NE SIDNEY 24030 NE NORTH PLATTE 24023 0.29 0

NE VALENTINE 24032 NE NORTH PLATTE 24023 0.14 2

NE ALLIANCE 24044 NE NORTH PLATTE 24023 0.18 1

NE IMPERIAL 24091 NE NORTH PLATTE 24023 0.51 2

NE MC COOK 94040 NE NORTH PLATTE 24023 0.61 2

NE BROKEN BOW 94946 NE NORTH PLATTE 24023 0.16 7

NE HASTINGS 94949 NE OMAHA/VALLEY 94980 0.16 3

NE FALLS CITY 94957 KS TOPEKA 13996 1.50 4

NE ORD 94958 NE NORTH PLATTE 24023 0.16 7

NE TEKAMAH 94978 NE OMAHA/VALLEY 94980 0.82 2

NH MANCHESTER 14710 ME GRAY 54762 0.29 11

NH CONCORD 14745 ME GRAY 54762 0.30 21

NH WHITEFIELD 54728 ME GRAY 54762 0.96 37

NH JAFFREY 54770 NY ALBANY 54775 0.64 19

NH ROCHESTER 54791 ME GRAY 54762 1.38 14

NH BERLIN 94700 ME GRAY 54762 0.49 37

NH LEBANON 94765 NY ALBANY 54775 0.53 28

NJ MILLVILLE 13735 VA WALLOPS ISLAND 93739 1.19 18

NJ NEWARK 14734 NY BROOKHAVEN 94703 0.45 3

NJ TRENTON 14792 NY BROOKHAVEN 94703 0.32 8

NJ CALDWELL 54743 NY BROOKHAVEN 94703 0.57 26

NJ SOMERVILLE 54785 NY BROOKHAVEN 94703 1.07 31

NJ ATLANTIC CITY 93730 VA WALLOPS ISLAND 93739 0.22 7

NJ MOUNT HOLLY 93780 NY BROOKHAVEN 94703 0.50 18

NJ TETERBORO 94741 NY BROOKHAVEN 94703 0.22 9
14 of 21

State City WBAN # State City WBAN #

 % 

Missing % Calms

Surface Upper Air Processed

NM CLINES CORNERS 03027 NM ALBUQUERQUE 23050 2.47 2

NM ROSWELL 23009 NM SANTA TERESA 03020 0.39 3

NM TUCUMCARI 23048 TX AMARILLO 23047 0.99 1

NM SANTA FE 23049 NM ALBUQUERQUE 23050 0.22 1

NM ALBUQUERQUE 23050 NM ALBUQUERQUE 23050 0.15 3

NM CLAYTON 23051 TX AMARILLO 23047 1.55 1

NM RATON 23052 NM ALBUQUERQUE 23050 0.11 1

NM LAS VEGAS 23054 NM ALBUQUERQUE 23050 0.15 1

NM DEMING 23078 NM SANTA TERESA 03020 0.16 2

NM GALLUP 23081 NM ALBUQUERQUE 23050 0.46 16

NM FARMINGTON 23090 NM ALBUQUERQUE 23050 0.63 2

NM CARLSBAD 93033 TX MIDLAND 23023 0.61 2

NM

TRUTH OR 

CONSEQUENCES 93045 NM SANTA TERESA 03020 0.21 2

NV MERCURY 03160 NV LAS VEGAS 03120 1.16 5

NV TONOPAH 23153 NV LAS VEGAS 03120 0.95 1

NV ELY 23154 NV ELKO 04105 0.54 2

NV LAS VEGAS 23169 NV LAS VEGAS 03120 0.31 1

NV RENO 23185 NV RENO 03198 0.19 13

NV ELKO 24121 NV ELKO 04105 0.32 11

NV WINNEMUCCA 24128 NV ELKO 04105 0.80 4

NV LOVELOCK 24172 NV RENO 03198 1.06 10

NV LAS VAGAS 53123 NV LAS VEGAS 03120 0.43 4

NY NIAGARA FALLS 04724 NY BUFFALO/GRTR ARPT 14733 0.13 4

NY BINGHAMTON 04725 NY ALBANY 54775 0.18 2

NY ISLIP 04781 NY BROOKHAVEN 94703 0.11 6

NY MONTGOMERY 04789 NY ALBANY 54775 1.71 18

NY

WESTHAMPTON 

BEACH 14719 NY BROOKHAVEN 94703 2.93 12

NY NEW YORK 14732 NY BROOKHAVEN 94703 0.15 1

NY BUFFALO 14733 NY BUFFALO/GRTR ARPT 14733 0.09 1

NY ALBANY 14735 NY ALBANY 54775 0.13 12

NY DUNKIRK 14747 NY BUFFALO/GRTR ARPT 14733 0.68 4

NY ELMIRA/CORNING 14748 NY BUFFALO/GRTR ARPT 14733 0.58 19

NY GLEN FALLS 14750 NY ALBANY 54775 0.62 23

NY POUGHKEEPSIE 14757 NY ALBANY 54775 0.54 27

NY ROCHESTER 14768 NY BUFFALO/GRTR ARPT 14733 0.11 4

NY SYRACUSE 14771 NY ALBANY 54775 0.11 6

NY WELLSVILLE 54757 NY BUFFALO/GRTR ARPT 14733 0.34 4

NY FULTON 54773 NY BUFFALO/GRTR ARPT 14733 1.46 11

NY PENN YAN 54778 NY BUFFALO/GRTR ARPT 14733 0.75 7

NY FARMINGDALE 54787 NY BROOKHAVEN 94703 0.74 5

NY SHIRLEY 54790 NY BROOKHAVEN 94703 0.53 11

NY DANSVILLE 94704 NY BUFFALO/GRTR ARPT 14733 0.15 7

NY MASSENA 94725 NY ALBANY 54775 0.34 13

NY NEW YORK 94728 NY BROOKHAVEN 94703 3.48 5

NY SARANAC LAKE 94740 NY ALBANY 54775 0.48 21

NY WHITE PLAINS 94745 NY BROOKHAVEN 94703 0.33 7

NY NEW YORK 94789 NY BROOKHAVEN 94703 0.11 1
15 of 21

State City WBAN # State City WBAN #

 % 

Missing % Calms

Surface Upper Air Processed

NY WATERTOWN 94790 NY ALBANY 54775 0.80 7

OH WOOSTER 04842 PA

PITTSBURGH/MOON 

TOWNSHIP 94823 0.17 9

OH TOLEDO 04848 MI DETROIT/PONTIAC 04830 0.10 4

OH LORAIN/ELYRIA 04849 PA

PITTSBURGH/MOON 

TOWNSHIP 94823 0.58 4

OH LIMA 04850 OH WILMINGTON 13841 0.49 4

OH DEFIANCE 04851 MI DETROIT/PONTIAC 04830 0.78 11

OH NEW PHILADELPHIA 04852 PA

PITTSBURGH/MOON 

TOWNSHIP 94823 0.50 18

OH CLEVELAND 04853 PA

PITTSBURGH/MOON 

TOWNSHIP 94823 0.11 2

OH MARION 04855 OH WILMINGTON 13841 0.46 3

OH ASHTABULA 04857 PA

PITTSBURGH/MOON 

TOWNSHIP 94823 0.71 10

OH NEWARK 04858 OH WILMINGTON 13841 0.21 19

OH WILMINGTON 13841 OH WILMINGTON 13841 0.35 4

OH AKRON 14813 PA

PITTSBURGH/MOON 

TOWNSHIP 94823 0.47 9

OH CLEVELAND 14820 PA

PITTSBURGH/MOON 

TOWNSHIP 94823 0.10 2

OH COLUMBUS 14821 OH WILMINGTON 13841 0.07 5

OH FINDLAY 14825 OH WILMINGTON 13841 0.11 2

OH

YOUNGSTOWN/WARR

EN 14852 PA

PITTSBURGH/MOON 

TOWNSHIP 94823 0.10 3

OH MANSFIELD 14891 OH WILMINGTON 13841 0.23 2

OH AKRON 14895 PA

PITTSBURGH/MOON 

TOWNSHIP 94823 0.07 2

OH LANCASTER 53844 OH WILMINGTON 13841 0.42 2

OH HAMILTON 53855 OH WILMINGTON 13841 1.12 19

OH DAYTON 53859 OH WILMINGTON 13841 0.21 4

OH CINCINNATI 93812 OH WILMINGTON 13841 0.56 2

OH

COVINGTON/CINCINNA

TI 93814 OH WILMINGTON 13841 0.13 4

OH DAYTON 93815 OH WILMINGTON 13841 0.10 2

OH ZANESVILLE 93824 PA

PITTSBURGH/MOON 

TOWNSHIP 94823 0.13 13

OH TOLEDO 94830 MI DETROIT/PONTIAC 04830 0.94 7

OK GUYMON 03030 TX AMARILLO 23047 0.18 1

OK CLINTON 03932 OK NORMAN 03948 0.42 0

OK LAWTON 03950 OK NORMAN 03948 0.65 6

OK OKLAHOMA CITY 03954 OK NORMAN 03948 0.14 1

OK STILLWATER 03965 OK NORMAN 03948 0.16 3

OK FREDERICK 03981 OK NORMAN 03948 0.92 1

OK OKLAHOMA CITY 13967 OK NORMAN 03948 0.26 1

OK TULSA 13968 OK NORMAN 03948 0.10 3

OK PONCA CITY 13969 OK NORMAN 03948 0.47 5

OK GAGE 13975 KS DODGE CITY 13985 1.10 2

OK TULSA 53908 OK NORMAN 03948 0.43 15
16 of 21

State City WBAN # State City WBAN #

 % 

Missing % Calms

Surface Upper Air Processed

OK GUTHRIE 53913 OK NORMAN 03948 2.92 2

OK MC ALESTER 93950 OK NORMAN 03948 2.16 8

OK MUSKOGEE 93953 OK NORMAN 03948 0.55 2

OK HOBART 93986 OK NORMAN 03948 2.32 0

OR HERMISTON 04113 WA SPOKANE INTNL APT 04106 0.41 5

OR SCAPPOOSE 04201 OR SALEM 24232 0.88 19

OR BAKER CITY 24130 ID BOISE 24131 1.12 11

OR PENDLETON 24155 WA SPOKANE INTNL APT 04106 0.30 3

OR ONTARIO 24162 ID BOISE 24131 0.78 10

OR THE DALLES 24219 OR SALEM 24232 0.24 11

OR EUGENE 24221 OR SALEM 24232 0.25 7

OR MEDFORD 24225 OR MEDFORD 24225 0.45 23

OR PORTLAND 24229 OR SALEM 24232 0.13 7

OR REDMOND 24230 OR SALEM 24232 1.24 10

OR ROSEBURG 24231 OR MEDFORD 24225 0.42 32

OR SALEM 24232 OR SALEM 24232 0.34 13

OR SEXTON SUMMIT 24235 OR MEDFORD 24225 0.98 3

OR PORTLAND 24242 OR SALEM 24232 0.43 14

OR BURNS 94185 ID BOISE 24131 0.74 7

OR ASTORIA 94224 OR SALEM 24232 0.26 5

OR KLAMATH FALLS 94236 OR MEDFORD 24225 0.42 18

OR PORTLAND 94261 OR SALEM 24232 0.34 20

OR MC MINNVILLE 94273 OR SALEM 24232 0.66 14

OR AURORA 94281 OR SALEM 24232 0.64 20

PA JOHNSTOWN 04726 PA

PITTSBURGH/MOON 

TOWNSHIP 94823 0.16 2

PA BRADFORD 04751 NY BUFFALO/GRTR ARPT 14733 1.83 12

PA DU BOIS 04787 PA

PITTSBURGH/MOON 

TOWNSHIP 94823 0.35 4

PA MEADVILLE 04843 PA

PITTSBURGH/MOON 

TOWNSHIP 94823 1.31 5

PA PHILADELPHIA 13739 VA STERLING(WASH DULLES) 93734 0.15 3

PA HARRISBURG 14711 VA STERLING(WASH DULLES) 93734 0.34 12

PA READING 14712 VA STERLING(WASH DULLES) 93734 0.80 15

PA ALTOONA 14736 VA STERLING(WASH DULLES) 93734 0.24 10

PA ALLENTOWN 14737 NY BROOKHAVEN 94703 0.22 9

PA HARRISBURG 14751 VA STERLING(WASH DULLES) 93734 0.23 11

PA PITTSBURGH 14762 PA

PITTSBURGH/MOON 

TOWNSHIP 94823 0.65 2

PA SELINSGROVE 14770 VA STERLING(WASH DULLES) 93734 1.03 29

PA

WILKESBARRE/SCRANTON 14777 NY ALBANY 54775 0.30 8
17 of 21

State City WBAN # State City WBAN #

 % 

Missing % Calms

Surface Upper Air Processed

PA WILLIAMSPORT 14778 NY BUFFALO/GRTR ARPT 14733 0.46 16

PA ERIE 14860 NY BUFFALO/GRTR ARPT 14733 0.21 3

PA LANCASTER 54737 VA STERLING(WASH DULLES) 93734 0.22 8

PA POTTSTOWN 54782 VA STERLING(WASH DULLES) 93734 1.42 23

PA DOYLESTOWN 54786 NY BROOKHAVEN 94703 1.21 18

PA MOUNT POCONO 54789 NY BROOKHAVEN 94703 0.35 5

PA CLEARFIELD 54792 PA

PITTSBURGH/MOON 

TOWNSHIP 94823 0.29 8

PA YORK 93778 VA STERLING(WASH DULLES) 93734 0.43 23

PA PHILADELPHIA 94732 NY BROOKHAVEN 94703 0.23 8

PA PITTSBURGH 94823 PA

PITTSBURGH/MOON 

TOWNSHIP 94823 0.13 9

PR SAN JUAN 11641 PR SAN JUAN 11641 0.62 14

RI PROVIDENCE 14765 MA CHATHAM 14684 0.02 3

RI NEWPORT 14787 MA CHATHAM 14684 0.74 6

RI WESTERLY 14794 NY BROOKHAVEN 94703 0.29 12

SC GREER 03870 NC GREENSBORO 13723 0.31 9

SC FLORENCE 13744 SC CHARLESTON 13880 0.95 8

SC CHARLESTON 13880 SC CHARLESTON 13880 0.15 6

SC COLUMBIA 13883 SC CHARLESTON 13880 0.81 11

SC GREENVILLE 13886 NC GREENSBORO 13723 0.27 8

SC CLEMSON 53850 GA PEACHTREE CITY 53819 0.37 10

SC ORANGEBURG 53854 SC CHARLESTON 13880 0.54 16

SC COLUMBIA 53867 SC CHARLESTON 13880 0.81 31

SC ROCK HILL 53871 NC GREENSBORO 13723 0.27 17

SC GREENWOOD 53874 GA PEACHTREE CITY 53819 1.64 13

SC

NORTH MYRTLE 

BEACH 93718 SC CHARLESTON 13880 0.22 8

SC ANDERSON 93846 GA PEACHTREE CITY 53819 1.82 14

SD ABERDEEN 14929 SD ABERDEEN 14929 0.13 2

SD HURON 14936 SD ABERDEEN 14929 0.33 4

SD SIOUX FALLS 14944 SD ABERDEEN 14929 0.11 5

SD WATERTOWN 14946 SD ABERDEEN 14929 0.14 3

SD PHILIP 24024 SD RAPID CITY 94043 0.19 3

SD PIERRE 24025 SD ABERDEEN 14929 0.22 2

SD RAPID CITY 24090 SD RAPID CITY 94043 0.09 5

SD CUSTER 94032 SD RAPID CITY 94043 1.38 5

SD PINE RIDGE 94039 SD RAPID CITY 94043 0.18 6

SD MOBRIDGE 94052 ND BISMARCK 24011 0.23 3

SD MITCHELL 94950 SD ABERDEEN 14929 0.48 2

SD WINNER 94990 SD ABERDEEN 14929 0.82 1

TN JACKSON 03811 TN NASHVILLE 13897 0.86 13

TN CROSSVILLE 03847 TN NASHVILLE 13897 0.76 16

TN

BRISTOL/JOHNSON 

CITY/KINGSPORT 13877 VA ROANOKE/BLACKSBURG 53829 0.26 28
18 of 21

State City WBAN # State City WBAN #

 % 

Missing % Calms

Surface Upper Air Processed

TN CHATTANOOGA 13882 TN NASHVILLE 13897 0.22 24

TN KNOXVILLE 13891 TN NASHVILLE 13897 0.19 9

TN MEMPHIS 13893 AR N LITTLE ROCK 03952 0.18 4

TN NASHVILLE 13897 TN NASHVILLE 13897 3.57 19

TX BORGER 03024 TX AMARILLO 23047 0.47 1

TX LONGVIEW 03901 LA

SHREVEPORT REGIONAL 

AP 13957 0.41 6

TX COLLEGE STATION 03904 TX FT WORTH 03990 0.10 4

TX DALLAS-FT WORTH 03927 TX FT WORTH 03990 0.10 2

TX DALLAS 03971 TX FT WORTH 03990 0.53 1

TX DENTON 03991 TX FT WORTH 03990 0.33 3

TX BURNET 03999 TX FT WORTH 03990 0.99 6

TX HARLINGEN 12904 TX BROWNSVILLE 12919 0.30 1

TX VICTORIA 12912 TX CORPUS CHRISTI 12924 0.26 2

TX

BEAUMONT/PORT 

ARTHUR 12917 LA LAKE CHARLES 03937 0.11 4

TX HOUSTON 12918 LA LAKE CHARLES 03937 0.09 5

TX BROWNSVILLE 12919 TX BROWNSVILLE 12919 0.08 3

TX SAN ANTONIO 12921 TX CORPUS CHRISTI 12924 0.07 2

TX GALVESTON 12923 LA LAKE CHARLES 03937 2.19 3

TX CORPUS CHRISTI 12924 TX CORPUS CHRISTI 12924 0.15 1

TX ALICE 12932 TX CORPUS CHRISTI 12924 0.15 2

TX PALACIOS 12935 TX CORPUS CHRISTI 12924 0.16 1

TX COTULLA 12947 TX CORPUS CHRISTI 12924 1.34 2

TX PORT ISABEL 12957 TX BROWNSVILLE 12919 0.10 1

TX MC ALLEN 12959 TX BROWNSVILLE 12919 0.13 2

TX HOUSTON 12960 LA LAKE CHARLES 03937 0.16 6

TX HONDO 12962 TX DEL RIO 22010 0.29 1

TX SAN ANTONIO 12970 TX CORPUS CHRISTI 12924 0.47 8

TX NEW BRAUNFELS 12971 TX CORPUS CHRISTI 12924 0.11 2

TX ROCKPORT 12972 TX CORPUS CHRISTI 12924 0.07 1

TX HOUSTON 12975 LA LAKE CHARLES 03937 0.34 13

TX

ANGLETON/LAKE 

JACKSON 12976 LA LAKE CHARLES 03937 0.38 10

TX HOUSTON 12977 LA LAKE CHARLES 03937 0.08 6

TX AUSTIN/BERGSTROM 13904 TX CORPUS CHRISTI 12924 0.19 7

TX AUSTIN/CITY 13958 TX FT WORTH 03990 4.25 11

TX WACO 13959 TX FT WORTH 03990 0.13 2

TX DALLAS 13960 TX FT WORTH 03990 0.31 4

TX FORT WORTH 13961 TX FT WORTH 03990 0.13 2

TX ABILENE 13962 TX FT WORTH 03990 0.99 1

TX WICHITA FALLS 13966 OK NORMAN 03948 0.10 1

TX TYLER 13972 LA

SHREVEPORT REGIONAL 

AP 13957 0.08 3

TX JUNCTION 13973 TX DEL RIO 22010 0.46 8

TX DEL RIO 22010 TX DEL RIO 22010 0.23 3

TX CHILDRESS 23007 TX AMARILLO 23047 1.00 1
19 of 21

State City WBAN # State City WBAN #

 % 

Missing % Calms

Surface Upper Air Processed

TX MIDLAND 23023 TX MIDLAND 23023 0.31 1

TX SAN ANGELO 23034 TX MIDLAND 23023 0.27 2

TX WINK 23040 TX MIDLAND 23023 0.57 1

TX LUBBOCK 23042 TX AMARILLO 23047 0.14 1

TX EL PASO 23044 NM SANTA TERESA 03020 0.18 3

TX AMARILLO 23047 TX AMARILLO 23047 0.10 0

TX GUADALUPE PASS 23055 NM SANTA TERESA 03020 4.93 0

TX FORT STOCKTON 23091 TX MIDLAND 23023 0.33 1

TX CONROE 53902 LA LAKE CHARLES 03937 0.18 18

TX HUNTSVILLE 53903 LA LAKE CHARLES 03937 0.42 11

TX ARLINGTON 53907 TX FT WORTH 03990 0.15 2

TX FORT WORTH 53909 TX FT WORTH 03990 0.13 7

TX HOUSTON 53910 LA LAKE CHARLES 03937 0.37 10

TX TERRELL 53911 TX FT WORTH 03990 0.13 6

TX CORSICANA 53912 TX FT WORTH 03990 0.25 1

TX MC KINNEY 53914 TX FT WORTH 03990 0.46 7

TX DALHART 93042 TX AMARILLO 23047 0.26 1

TX MINERAL WELLS 93985 TX FT WORTH 03990 0.41 3

TX LUFKIN 93987 LA

SHREVEPORT REGIONAL 

AP 13957 0.19 9

UT BRYCE CANYON 23159 AZ

FLAGSTAFF/BELLEMT 

(ARMY) 53103 0.35 2

UT MILFORD 23176 UT SALT LAKE CITY 24127 0.47 2

UT OGDEN 24126 UT SALT LAKE CITY 24127 0.61 4

UT SALT LAKE CITY 24127 UT SALT LAKE CITY 24127 0.06 3

UT MOAB 93075 CO GRAND JUNCTION 23066 0.32 14

UT CEDAR CITY 93129 NV LAS VEGAS 03120 0.21 6

UT PRICE 93141 UT SALT LAKE CITY 24127 0.30 5

UT VERNAL 94030 CO GRAND JUNCTION 23066 0.82 8

UT LOGAN 94128 UT SALT LAKE CITY 24127 0.56 15

VA DANVILLE 13728 NC GREENSBORO 13723 0.50 11

VA LYNCHBURG 13733 VA ROANOKE/BLACKSBURG 53829 2.60 22

VA NORFOLK 13737 VA WALLOPS ISLAND 93739 0.22 5

VA RICHMOND 13740 VA STERLING(WASH DULLES) 93734 0.09 6

VA ROANOKE 13741 VA ROANOKE/BLACKSBURG 53829 0.17 12

VA CHARLOTTESVILLE 93736 VA STERLING(WASH DULLES) 93734 0.53 27

VA WALLOPS ISLAND 93739 VA WALLOPS ISLAND 93739 0.51 5

VA NEWPORT NEWS 93741 VA WALLOPS ISLAND 93739 0.15 10

VA WAKEFIELD 93773 VA WALLOPS ISLAND 93739 1.82 29

VA RICHMOND/ASHLAND 93775 VA STERLING(WASH DULLES) 93734 1.12 22

VT BURLINGTON 14742 NY ALBANY 54775 0.08 7

VT SPRINGFIELD 54740 NY ALBANY 54775 0.32 36

VT MORRISVILLE 54771 ME GRAY 54762 1.66 33

VT BENNINGTON 54781 NY ALBANY 54775 1.47 22
20 of 21

State City WBAN # State City WBAN #

 % 

Missing % Calms

Surface Upper Air Processed

VT BARRE/MONTPELIER 94705 ME GRAY 54762 0.97 20

WA MOSES LAKE 24110 WA SPOKANE INTNL APT 04106 0.35 4

WA EPHRATA 24141 WA SPOKANE INTNL APT 04106 0.29 3

WA SPOKANE 24157 WA SPOKANE INTNL APT 04106 0.24 4

WA WALLA WALLA 24160 WA SPOKANE INTNL APT 04106 0.43 3

WA PASCO 24163 WA SPOKANE INTNL APT 04106 0.19 8

WA BELLINGHAM 24217 WA QUILLAYUTE 94240 0.41 13

WA ELLENSBURG 24220 WA SPOKANE INTNL APT 04106 0.50 9

WA EVERETT 24222 WA QUILLAYUTE 94240 1.52 6

WA OLYMPIA 24227 WA QUILLAYUTE 94240 0.86 18

WA SEATTLE 24233 WA QUILLAYUTE 94240 0.08 3

WA SEATTLE 24234 WA QUILLAYUTE 94240 0.55 12

WA YAKIMA 24243 WA SPOKANE INTNL APT 04106 0.59 7

WA DEER PARK 94119 WA SPOKANE INTNL APT 04106 0.45 13

WA PULLMAN/MOSCOW 94129 WA SPOKANE INTNL APT 04106 0.64 14

WA SPOKANE 94176 WA SPOKANE INTNL APT 04106 0.25 16

WA OMAK 94197 WA SPOKANE INTNL APT 04106 1.02 9

WA HOQUIAM 94225 WA QUILLAYUTE 94240 0.48 5

WA SHELTON 94227 WA QUILLAYUTE 94240 0.17 18

WA WENATCHEE 94239 WA SPOKANE INTNL APT 04106 0.43 7

WA QUILLAYUTE 94240 WA QUILLAYUTE 94240 4.29 13

WA RENTON 94248 WA QUILLAYUTE 94240 0.42 10

WA PORT ANGELES 94266 WA QUILLAYUTE 94240 1.54 8

WA TACOMA 94274 WA QUILLAYUTE 94240 0.21 11

WA FRIDAY HARBOR 94276 WA QUILLAYUTE 94240 0.40 19

WA VANCOUVER 94298 OR SALEM 24232 0.51 20

WI RHINELANDER 04803 WI GREEN BAY 14898 0.26 9

WI WISCONSIN RAPTIDS 04826 WI GREEN BAY 14898 0.23 11

WI FOND DU LAC 04840 WI GREEN BAY 14898 0.19 5

WI SHEBOYGAN 04841 WI GREEN BAY 14898 0.32 6

WI KENOSHA 04845 WI GREEN BAY 14898 0.11 2

WI MADISON 14837 WI GREEN BAY 14898 0.38 10

WI MILWAUKEE 14839 WI GREEN BAY 14898 0.11 4

WI WAUSAU 14897 WI GREEN BAY 14898 0.26 6

WI GREEN BAY 14898 WI GREEN BAY 14898 0.50 6

WI LA CROSSE 14920 MN MINNEAPOLIS 94983 0.24 5

WI LONE ROCK 14921 IA

DAVENPORT MUNICIPAL 

AP 94982 0.24 14

WI EAU CLAIRE 14991 MN MINNEAPOLIS 94983 0.39 11

WI RACINE 94818 WI GREEN BAY 14898 0.27 3

WI OSHKOSH 94855 WI GREEN BAY 14898 0.24 5

WI ASHLAND 94929 MN MINNEAPOLIS 94983 0.13 6

WI HAYWARD 94973 MN MINNEAPOLIS 94983 0.89 22

WI MARSHFIELD 94985 WI GREEN BAY 14898 0.61 5

WI B0SCOBEL 94994 IA

DAVENPORT MUNICIPAL 

AP 94982 1.26 21
21 of 21

State City WBAN # State City WBAN #

 % 

Missing % Calms

Surface Upper Air Processed

WV CLARKSBURG 03802 PA

PITTSBURGH/MOON 

TOWNSHIP 94823 0.17 21

WV PARKERSBURG 03804 PA

PITTSBURGH/MOON 

TOWNSHIP 94823 2.05 18

WV BLUEFIELD 03859 VA ROANOKE/BLACKSBURG 53829 0.35 4

WV HUNTINGTON 03860 OH WILMINGTON 13841 0.54 11

WV BECKLEY 03872 VA ROANOKE/BLACKSBURG 53829 0.16 7

WV ELKINS 13729 PA

PITTSBURGH/MOON 

TOWNSHIP 94823 0.32 34

WV MARTINSBURG 13734 VA STERLING(WASH DULLES) 93734 0.26 17

WV MORGANTOWN 13736 PA

PITTSBURGH/MOON 

TOWNSHIP 94823 0.68 14

WV CHARLESTON 13866 VA ROANOKE/BLACKSBURG 53829 0.26 24

WV WHEELING 14894 PA

PITTSBURGH/MOON 

TOWNSHIP 94823 0.68 5

WY EVANSTON 04111 UT SALT LAKE CITY 24127 0.78 1

WY CHEYENNE 24018 CO DENVER/STAPLETON ARPT 23062 0.30 1

WY LANDER 24021 WY RIVERTON 24061 0.56 4

WY LARAMIE 24022 CO DENVER/STAPLETON ARPT 23062 0.63 1

WY ROCK SPRINGS 24027 WY RIVERTON 24061 0.42 2

WY SHERIDAN 24029 WY RIVERTON 24061 0.11 8

WY GREYBULL 24048 WY RIVERTON 24061 0.33 8

WY RAWLINS 24057 WY RIVERTON 24061 0.92 4

WY RIVERTON 24061 WY RIVERTON 24061 0.32 2

WY WORLAND 24062 WY RIVERTON 24061 0.57 6

WY CASPER 24089 WY RIVERTON 24061 0.21 1

WY BIG PINEY 24164 WY RIVERTON 24061 0.40 6

WY GILLETTE 94023 SD RAPID CITY 94043 0.54 3

WY TORRINGTON 94053 SD RAPID CITY 94043 3.93 1

WY BUFFALO 94054 WY RIVERTON 24061 0.37 2

WY DOUGLAS 94057 SD RAPID CITY 94043 1.00 4
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
Technical Support Document

for the TRIM-Based Multipathway

Tiered Screening Methodology for RTR

March, 2013

Prepared For:

U.S. Environmental Protection Agency

Office of Air Quality Planning and Standards

Research Triangle Park, NC 27711

Prepared By:

ICF International

2222 East NC-54

Suite 480

Durham, NC 27713
[This page intentionally left blank.]
TRIM-Based Tiered Screening Methodology for RTR

Overview iii 6-29-2012

CONTENTS

1. Introduction and Background ............................................................................................. 1

2. Tier 1 ................................................................................................................................. 2

2.1 Chemicals of Concern ...............................................................................................2

2.2 Development of Emission Thresholds........................................................................4

2.2.1 Modeling Fate and Transport...................................................................... 5

2.2.2 Modeling Transfer and Uptake.................................................................... 6

2.2.3 Estimating Ingestion Exposure ................................................................... 6

2.2.4 Calculating Lifetime Cancer Risk and Non-Cancer HQs ............................. 7

2.2.5 Determining Threshold Emission Rates...................................................... 7

3. Tier 2 ................................................................................................................................. 7

4. Refined Multipathway Assessment .................................................................................... 9

5. References ........................................................................................................................ 9

Attachment A. Tier 1 Screening Methodology.................................................................A-1

A.1 Introduction............................................................................................................ A-6

A.2 Summary of Approach ........................................................................................... A-6

A.2.1 Overview .................................................................................................A-6

A.2.2 Chemicals of Potential Concern...............................................................A-9

A.2.3 Conceptual Exposure Scenario .............................................................A-10

A.2.4 Approach to Development of the Tier 1 Scenario...................................A-12

A.2.5 Fate and Transport Modeling (TRIM.FaTE) ...........................................A-14

A.2.6 Exposure Modeling and Risk Characterization (MIRC) ..........................A-15

A.2.7 Implementation of Risk-based Emission Scaling Factors for 

POM and Dioxin Emissions...................................................................A-16

A.3 Description of Environmental Modeling Scenario................................................. A-20

A.3.1 Chemical Properties ..............................................................................A-20

A.3.2 Spatial Layout........................................................................................A-21

A.3.3 Watershed and Water Body Parameterization .......................................A-22

A.3.4 Meteorology...........................................................................................A-24

A.3.5 Aquatic Food Web.................................................................................A-26

A.3.6 Using TRIM.FaTE Media Concentrations ..............................................A-27

A.4 Description of Exposure and Risk Modeling Scenario.......................................... A-28

A.4.1 Calculating Concentrations in Farm Food Chain Media .........................A-29

A.4.2 Ingestion Exposure................................................................................A-29

A.4.3 Calculating Risk.....................................................................................A-32

A.4.4 Summary of Tier 1 Assumptions............................................................A-33

A.5 Evaluation of Screening Scenario........................................................................ A-37

A.5.1 Introduction............................................................................................A-37

A.5.2 Cadmium Compounds...........................................................................A-37

A.5.3 Mercury Compounds .............................................................................A-38

A.5.4 DioxinsA-40

A.5.5 Polycyclic Aromatic Hydrocarbons.........................................................A-42

A.5.6 Summary...............................................................................................A-44
A.6 References .......................................................................................................... A-45

Addendum 1. TRIM.FaTE Inputs................................................................................... 1-1

Addendum 2. Description of Multimedia Ingestion Risk Calculator (MIRC) 

Used for RTR Exposure and Risk Estimates................................................................... 2-1

Addendum 3. Dermal Risk Screening............................................................................ 3-1

Attachment B. Tier 2 Screening Methodology.................................................................B-1

B.1 Overview of Approach ........................................................................................... B-5

B.2 Selection of Site-Specific Characteristics to Include in the Tier 2 

Analysis................................................................................................................. B-7

B.3 Estimation of Adjustment Factors for Selected Site-Specific 

Parameters............................................................................................................ B-8

B.3.1 Selection Values for Variables of Interest ................................................B-9

B.3.2 Estimation of Adjustment Factors ..........................................................B-12

B.4 Preparing National Databases of Lake and Meteorological Data ......................... B-16

B.4.1 Processing Lake Data for Tier 2 Analysis ..............................................B-16

B.4.2 Processing Meteorological Data for Tier 2 Analysis ...............................B-19

B.5 Implementation of Tier 2 Analysis ........................................................................ B-23

B.5.1 Facility List for Tier 2 Screen (Step 3)....................................................B-26

B.5.2 Facility/Lake Distance Table (Step 4) ....................................................B-26

B.5.3 Finding the Closest Meteorological Station (Step 5) ..............................B-27

B.5.4 Assembling Threshold Adjustment Factors (Step 6) ..............................B-29

B.5.5 Finalizing Threshold Adjustment Factors and Assembling 

Screening Results (Step 7) ...................................................................B-30

B.5.6 Assembling Results (Step 8) .................................................................B-30

B.6 References .......................................................................................................... B-33

Addendum 1. Summary of TRIM.FaTE Parameters Considered for 

Inclusion in Tier 2 Analysis ...................................................................... 1-1

Addendum 2. Analysis of Lake Size and Sustainable Fish Population.......................... 2-1
[This page intentionally left blank.]
TRIM-Based Tiered Screening Methodology for RTR

Overview 1 6-29-2012

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 5.A.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 below and Addendum 3). 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 emitting PB-HAPs is used to refine some of the 

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 
TRIM-Based Tiered Screening Methodology for RTR

Overview 2 6-29-2012

then adjusted for that facility based on an understanding of how exposure concentrations 

estimated for the screening scenario change with meteorology and lake location. PBHAP emissions that do not exceed the adjusted threshold are assumed to pose risks 

below levels of concern and no additional multipathway assessment for RTR is required. 

Facilities having emissions that exceed the adjusted thresholds for Tier 2 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 analyses, 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 Tier 4 

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 overview, each of the tiers in the multipathway assessment approach is 

described in additional detail. Attachments to this appendix provide a comprehensive record of 

the characteristics and methods associated with Tier 1 (Attachment A) and Tier 2 (Attachment 

B). If a site-specific analysis is conducted, a separate report detailing that analysis will be 

prepared.

2. Tier 1

The methods used in Tier 1 are intended to enable EPA to evaluate PB-HAP emissions from 

multiple sources in a particular 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”). 

2.1 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

Overview 3 6-29-2012

Exhibit 1. Conceptual Decision Tree for Evaluating Non-Inhalation Exposures

for PB-HAPs

Diagram Key

Decision point

Outcome

Tools / Data

Tier 2 Facility-specific Adjusted Threshold Emission Rates

TIER 1 TIER 2

Facility screens out – no further analysis necessary NO

Facility proceeds to Tier 2

NO

Prioritize facilities for site- specific analysis

Does the emission rate for any PB-HAP exceed Tier 1 threshold?

Does the emission rate for any PB-HAP exceed Tier 2 adjusted threshold?

Health-protective Configuration & Meteorological Data

Emissions Inventory Data

Health Protective Exposure Assumptions

Tier 1 Configuration with Local Meteorology and Site- specific Lake Location Health Protective Exposure Assumptions Facility screens out – no further analysis necessary

Estimates of Site Specific Risk

Site-Specific

Site-specific Configuration $ Meteorological Data Vary Exposure Assumptions

Evaluate PB-HAP emissions by facility

YES

Tier 1 Threshold Emission Rates

YES

Process Risk Characterization
TRIM-Based Tiered Screening Methodology for RTR

Overview 4 6-29-2012

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 PAH for example, 

does not imply a resulting cancer risk of 60 in 1 million. Rather, exceeding the threshold 

emissions rate by 60 times for PAH 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 Attachment A—Technical Support Document for TRIM-Based Multipathway Tier 1 Scenario 

for RTR—to this appendix.

2.2 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 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

Overview 5 6-29-2012

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

2.2.1 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. (See Attachment A and Addenda 1 and 2 for additional discussion on parameter 

values and their selection.) 

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 

 

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

Overview 6 6-29-2012

layout used to develop the threshold emission levels in Tier 1 and other details of the Tier 1 

methodology are presented in Attachment A to this appendix.

2.2.2 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.

2.2.3 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 of this analysis, 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 of the 

distribution of national data (99th percentile for fish and 90th percentile for all other food types) for

consumers of that food type recommended by EPA in the Exposure Factors Handbook (U.S. 

EPA 2011a) or in 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).

 

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 A.4.2.3 of Attachment A 

and Section 3.4 of Addendum 2 for full discussions of infant exposures via breast milk ingestion. 
TRIM-Based Tiered Screening Methodology for RTR

Overview 7 6-29-2012

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 Attachment A and Addendum 3). 

2.2.4 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 

Addendum 2, 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 

Addendum 2, the HQ for the most sensitive age group is used to determine the screening 

threshold emission rate.

2.2.5 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.

3. 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. Once the initial screen is complete, however, facilities that 

“fail” for any PB-HAPs can be scrutinized further. Based on screening analyses conducted for 

RTR to date, many facilities could “fail” the Tier 1 screen for some source categories. However, 

conducting a full site-specific analysis 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 these site-specific values instead of the generic 

values used in Tier 1 can be used to justify adjusting the screening threshold for a given PBHAP at that facility, potentially eliminating the facility from concern while maintaining a high 

degree of confidence that risks above levels of concern have not been overlooked. Specifically, 

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 the 

farm and lake (using wind direction), wind speed, precipitation rate, and mixing height;

and 

 Location of the nearest fishable lake(s) relative to the facility5(including the absence of a 

fishable lake).

These variables affect the PB-HAP concentrations in environmental media estimated by 

TRIM.FaTE, but they are not related to specific exposure assumptions. The exposure 

 

5

The lake size also was changed for each new facility lake distance, which allowed for the simulations to maintain a 

constant ratio between watershed and erosion area compared with lake area.
TRIM-Based Tiered Screening Methodology for RTR

Overview 8 6-29-2012

assumptions, such as ingestion rate and fraction of diet derived from the lake and farm remain 

at fixed, health-protective values in Tier 2. In selecting the fate and transport variables to 

include in Tier 2, a balance was struck between the degree of impact on the risk estimate, the 

ease of implementation in TRIM.FaTE, and the ease of obtaining relatively certain site-specific 

values for all facilities that might be evaluated under the RTR program.

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 farm and lake, so the effect of site-specific wind directions can be evaluated 

outside 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 screening scenario values) were selected using statistics on U.S. meteorological data or 

professional judgment to capture the expected range in the facility data. Four to six values were 

selected to result in a total number of runs that was reasonable. This set of values was used to 

develop “bins” for each variable.

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 baseline 

Tier 1 screening scenario and the risk metric obtained from the adjusted run. For a given facility

and PB-HAP, an adjusted Tier 2 emission threshold can be estimated by multiplying the Tier 1 

emission threshold 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.

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 Attachment B. The meteorological database includes 

annual-average summary statistics on wind direction, wind speed, and precipitation for more 

than 1,500 surface stations located throughout the United States (data available from the 

National Climatic Data Center). The mixing heights for each surface station were estimated 

using the data from the upper-air station closest to the surface station. The mixing height data 

are available for more than 50 stations (data available from EPA). The lake database, based on 

U.S. Geological Survey (USGS) data and including location and size information, consisted 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 angling of 

upper trophic level fish, only lakes greater than 25 acres were included. These databases are 

described in more detail in Attachment B.

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, using GIS software, each relevant lake witnin a 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 screened to remove lakes whose names 

indicate uses related to disposal, evaporation, or treatment (sometimes the name indicates one 

of these uses while the USGS designations do not). Third, the lakes around the facility that 

remain after the first two processing steps are screened to include only the closest lake for each 

octant. 
TRIM-Based Tiered Screening Methodology for RTR

Overview 9 6-29-2012

To access these databases, a Microsoft® Excel tool was created that merges the TRIM.FaTE 

Tier 2 adjustment factors with the lake and meteorology information relevant to a specific facility 

from the databases. In the tool, each facility is matched with the closest meteorological station, 

and the values for the four relevant meteorological parameters at that station are recorded. The 

distances from the facility to the nearest lakes estimated using GIS are also imported. These 

five values become the set of facility-specific parameters. Then, the adjustment factors for each 

chemical for the combination of these five variables are determined. As described above, the 

Tier 1 screening emission threshold is then multiplied by the appropriate adjustment factor to 

obtain an updated, Tier 2 emission threshold for that PB-HAP. More information about Tier 2 

assessment methods can be found in Attachment B.

4. Refined Multipathway Assessment

If, based on results of the screening analyses, 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 analysis might be performed. Examples of recent refined 

multipthway analyses include assessments of two coal-fired electric utility units conducted in 

support of EPA’s utility rule (U.S. EPA 2011c), residual risk assessments of two secondary lead 

smelting facilities (U.S. EPA 2011b), 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 U.S. EPA 2009).

Whereas a Tier 2 analysis incorporates some binned site-specific and regional information on 

meteorology and water bodies, a refined multipathway analysis uses detailed site-specific 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 properties 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, erosion, runoff, surface water and 

sediment, water transfer, and aquatic ecosystems.

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.

5. References

U.S. EPA (U.S. Environmental Protection Agency) (2005) Human Health Risk Assessment 

Protocol for Hazardous Waste Combustion Facilities. EPA Office of Solid Waste. EPA530/R-05-006. 09/2005.

U.S. 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.

U.S. EPA. (2011a) Exposure Factors Handbook: 2011 edition. National Center for 

Environmental Assessment, Washington, DC; EPA/600/R-09/052F. Available from the 
TRIM-Based Tiered Screening Methodology for RTR

Overview 10 6-29-2012

National Technical Information Service, Springfield, VA, and online at 

http://www.epa.gov/ncea/efh.

U.S. EPA. (2011b) Human Health Multipathway Residual Risk Assessment for the Secondary 

Lead Smelting Source Category. Draft Report. Prepared by ICF International for EPA Risk 

and Exposure Assessment Group. 02/15/2011.

U.S. EPA. (2011c) Technical Support Document: Case Study Analyses of Potential Local-scale 

Human Health Risks Associated with Mercury Emissions from Electric Utility Steamgenerating Units. Draft. Prepared by ICF International for EPA Office of Air Quality Planning 

and Standards. 02/17/2011.
TRIM-Based Tiered Screening Methodology for RTR

Attachment A – Tier 1 A-1 6-29-2012

Attachment A. Tier 1 Screening Methodology
[This page intentionally left blank.]
TRIM-Based Tiered Screening Methodology for RTR

Attachment A – Tier 1 A-3 6-29-2012

CONTENTS, ATTACHMENT A 

Attachment A. Tier 1 Screening Methodology.................................................................A-1

A.1 Introduction.....................................................................................................................A-6

A.2 Summary of Approach ....................................................................................................A-6

A.2.1 Overview ............................................................................................................... A-6

A.2.2 Chemicals of Potential Concern............................................................................. A-9

A.2.3 Conceptual Exposure Scenario............................................................................ A-10

A.2.4 Approach to Development of the Tier 1 Scenario................................................. A-12

A.2.4.1 Modeling Framework .............................................................................A-13

A.2.4.2 Model Configuration and Parameterization............................................A-14

A.2.5 Fate and Transport Modeling (TRIM.FaTE) ......................................................... A-14

A.2.6 Exposure Modeling and Risk Characterization (MIRC) ........................................ A-15

A.2.7 Implementation of Risk-based Emission Scaling Factors for POM and 

Dioxin Emissions ................................................................................................. A-16

A.2.7.1 Calculation of Scaling Factors for POM Congeners ...............................A-17

A.2.7.2 Calculation of Scaling Factors for Dioxin Congeners .............................A-19

A.3 Description of Environmental Modeling Scenario ..........................................................A-20

A.3.1 Chemical Properties ............................................................................................ A-20

A.3.2 Spatial Layout...................................................................................................... A-21

A.3.3 Watershed and Water Body Parameterization ..................................................... A-22

A.3.3.1 Water Balance.......................................................................................A-22

A.3.3.2 Sediment Balance .................................................................................A-23

A.3.4 Meteorology......................................................................................................... A-24

A.3.5 Aquatic Food Web ............................................................................................... A-25

A.3.6 Using TRIM.FaTE Media Concentrations............................................................. A-27

A.4 Description of Exposure and Risk Modeling Scenario ...................................................A-28

A.4.1 Calculating Concentrations in Farm Food Chain Media ....................................... A-29

A.4.2 Ingestion Exposure.............................................................................................. A-29

A.4.2.1 Exposure Scenarios and Corresponding Inputs.....................................A-29

A.4.2.2 Calculating Average Daily Doses...........................................................A-32

A.4.2.3 Infant Ingestion of Breast Milk................................................................A-32

A.4.3 Calculating Risk................................................................................................... A-32

A.4.4 Summary of Tier 1 Assumptions .......................................................................... A-33

A.5 Evaluation of Screening Scenario .................................................................................A-37

A.5.1 Introduction.......................................................................................................... A-37

A.5.2 Cadmium Compounds ......................................................................................... A-37

A.5.2.1 Behavior in the Environment..................................................................A-37
TRIM-Based Tiered Screening Methodology for RTR

Attachment A – Tier 1 A-4 6-29-2012

A.5.2.2 Concentrations in Ingestible Products....................................................A-37

A.5.2.3 Average Daily Dose (ADD)....................................................................A-38

A.5.3 Mercury Compounds ........................................................................................... A-38

A.5.3.1 Behavior in the Environment..................................................................A-38

A.5.3.2 Concentrations in Ingestible Products....................................................A-40

A.5.3.3 Average Daily Dose...............................................................................A-40

A.5.4 Dioxins................................................................................................................. A-40

A.5.4.1 Behavior in the Environment..................................................................A-40

A.5.4.2 Concentrations in Ingestible Products....................................................A-42

A.5.4.3 Lifetime Average Daily Dose (LADD).....................................................A-42

A.5.5 Polycyclic Aromatic Hydrocarbons....................................................................... A-42

A.5.5.1 Behavior in the Environment..................................................................A-43

A.5.5.2 Concentrations in Ingestible Products....................................................A-44

A.5.5.3 Lifetime Average Daily Dose .................................................................A-44

A.5.6 Summary ............................................................................................................. A-44

A.6 References ...................................................................................................................A-45

Addendum 1. TRIM.FaTE Inputs................................................................................... 1-1

Addendum 2. Description of Multimedia Ingestion Risk Calculator (MIRC) 

Used for RTR Exposure and Risk Estimates................................................................... 2-1

Addendum 3. Dermal Risk Screening............................................................................ 3-1
TRIM-Based Tiered Screening Methodology for RTR

Attachment A – Tier 1 A-5 6-29-2012

EXHIBITS, ATTACHMENT A

Exhibit_Att A-1. Conceptual Decision Tree for Evaluation of Non-Inhalation 

Exposures of PB-HAPs.....................................................................................A-7

Exhibit_Att A-2. Emission Thresholds for Screening of Multipathway ExposuresError! Bookmark not defined.

Exhibit_Att A-3. OAQPS PB-HAP Compounds.....................................................................A-10

Exhibit_Att A-4. Overview of Ingestion Exposure and Risk Screening Evaluation 

Method............................................................................................................A-13

Exhibit_Att A-5. Overview of Process Carried Out in the Multimedia Ingestion 

Risk Calculator................................................................................................A-16

Exhibit_Att A-6. Exposure, Toxicity, and Risk Equivalency Factors Relative to 

BaP for Modeled POM Congeners..................................................................A-18

Exhibit_Att A-7. Relationship between Exposure and Kow for POM Congeners...................A-19

Exhibit_Att A-8. Exposure and Toxicity Equivalency Factors Relative to TCDD 

for Modeled Dioxin Congeners........................................................................A-20

Exhibit_Att A-9. TRIM.FaTE Surface Parcel Layout .............................................................A-21

Exhibit_Att A-10. Summary of Key Meteorological Inputs.....................................................A-25

Exhibit_Att A-11. Aquatic Biota Parameters for the TRIM.FaTE Screening 

Scenario .........................................................................................................A-27

Exhibit_Att A-12. Spatial Considerations – TRIM.FaTE Results Selected for 

Calculating Farm Food Chain Media Concentrations and Receptor 

Exposures.......................................................................................................A-28

Exhibit_Att A-13. Summary of Ingestion Exposure Pathways and Routes of 

Uptake ............................................................................................................A-30

Exhibit_Att A-14. Overview of Exposure Factors Used for RTR Multipathway 

Screeninga,b....................................................................................................A-31

Exhibit_Att A-15. Dose-response Values for PB-HAPs Addressed by the 

Screening Scenario.........................................................................................A-33

Exhibit_Att A-16. Summary of RTR Screening Scenario Assumptions .................................A-33

Exhibit_Att A-17. Estimated Contributions of Modeled Food Types to Cadmium 

Ingestion Exposures and Hazard Quotients ....................................................A-39

Exhibit_Att A-18. Estimated Contributions of Modeled Food Types to Methyl 

Mercury Ingestion Exposures..........................................................................A-41

Exhibit_Att A-19. Estimated Contributions of Modeled Food Types to Dioxin 

Ingestion Exposures .......................................................................................A-43

Exhibit_Att A-20. Estimated Contributions of Modeled Food Types to PAH 

Ingestion Exposures .......................................................................................A-45
TRIM-Based Tiered Screening Methodology for RTR

Attachment A – Tier 1 A-6 6-29-2012

A.1 Introduction

As discussed in the Overview Document, the U.S. Environmental Protection Agency (EPA) will 

implement 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 A.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 analysis are assumed to pose no risks to human health

above levels of concern and and are not considered in further analyses. 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 document 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 A.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 A.3 and A.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 A.5 provides a brief discussion of the screening threshold emissions for each of 

the chemicals assessed. References cited in this report are listed in Section A.6. 

More refined multipathway risk screening methods, may be used for facilities that do not screen 

out of the Tier 1 analysis. The Tier 2 screen is discussed separately in Attachment B.

A.2 Summary of Approach 

A.2.1 Overview

The Tier 1 approach for evaluating non-inhalation, multipathway exposures to PB-HAPs for RTR 

is diagrammed in Exhibit_Att A-1. Air toxics emitted by a source under consideration are 

reviewed to determine, first, whether emissions are reported for any of the four PB-HAPs of 

concern for non-inhalation pathways. If such emissions are reported, the emission rates are 

compared to Tier 1 threshold-screening emission rates that have been derived using the TRIMbased Tier 1 scenario described in this document (see Exhibit_Att A-2 for threshold screening 

emission rates). 
TRIM-Based Tiered Screening Methodology for RTR

Attachment A – Tier 1 A-7 6-29-2012

Exhibit_Att A-1. Conceptual Decision Tree for Evaluation of Non-Inhalation

Exposures of PB-HAPs

Facility screens out

Non-inhalation risks not likely, but evaluate on a case-by-case basis

Facility screens out; no concern for multipathway risk (check emissions of PB-HAPs other than Cd, dioxins, Hg, POM, Pb on a case-by- case basis)

Are any PB-HAPs emitted?

Are PB-HAPs of primary concern for RTR emitted? (Cd, dioxins, Hg, POM)?

Evaluate HAP emissions by facility

YES

YES

NO

NO

NO

Does the emission rate for any PB- HAP exceed Tier 1 threshold?

Diagram Key

Outcome

Process

Decision Point

TIER 1 EVALUATION Proceed to Tier 2

YES
TRIM-Based Tiered Screening Methodology for RTR

Attachment A – Tier 1 A-8 6-29-2012

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_Att A-2.

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

Attachment A – Tier 1 A-9 6-29-2012

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_Att A-2. 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

3.09E−03 Cancer

Dioxins (as 2,3,7,8-TCDD equivalents)a

6.38E-09 Cancer

Cadmium 7.00E−02 Non-cancer

Divalent mercury 1.94E−03 Non-cancer

TPY = U.S. short tons per year

a

See Section A.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 each 

risk influential parameter 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 greater than 50 km 

away. Additional conservative assumptions used in the Tier 1 screen are described in Section

A.4.4 of this Attachment.

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 A.5.3 for a detailed discussion of mercury. 

A.2.2 Chemicals of Potential Concern

EPA’s assessment of multipathway human exposures for RTR focuses on PB-HAPs 8that 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 

 

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

Attachment A – Tier 1 A-10 6-29-2012

that are PB-HAPs based on a two-step process taking into account the following (U.S. EPA 

2004a):

 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 (U.S. EPA 2004a). Exhibit_Att A-3 presents the 14 

chemicals and groups that are PB-HAPs.

Exhibit_Att A-3. 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

aSource of list: U.S. EPA (2004a). Compounds in bold text can be evaluated using the current version of the TRIMbased screening scenario.

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_Att A-3). 

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

A.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 analysis. 

 

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

Attachment A – Tier 1 A-11 6-29-2012

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);

 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 (U.S. 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 analysis 

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

 

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 A.4.2.3 of this attachment 

and Section 3.4 of Addendum 2 to this attachment for full discussions of infant exposures via breast milk ingestion.
TRIM-Based Tiered Screening Methodology for RTR

Attachment A – Tier 1 A-12 6-29-2012

As discussed in detail in Section A.4.2, exposure via these pathways is assessed for adults, 

various age categories for children, and nursing infants (for dioxins only).

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 analysis 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 (U.S. EPA 2006,

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

presented in Addendum 3 to this attachment, showed that the dermal exposure route is not a 

significant risk pathway relative to ingestion exposures.

A.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

(U.S. EPA 1999); Volumes I and II of the Air Toxics Risk Assessment Reference Library (U.S. 

EPA 2004a, 2005a); and other EPA publications. The scenario described in this appendix is the 

culmination of analyses 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 analyses, as described in Attachment B

and Error! Reference source not found., 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.

A.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

Attachment A – Tier 1 A-13 6-29-2012

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; 

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_Att A-4.

As shown in Exhibit_Att A-4, 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_Att A-4—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 Excel-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 cancer risks and chronic non-cancer HQs. This framework is 

 

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_Att A-4. 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

Attachment A – Tier 1 A-14 6-29-2012

conceptually identical to the ingestion exposure and risk analyses that TRIM is intended to 

cover.

A.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 (U.S. 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.

A.2.5 Fate and Transport Modeling (TRIM.FaTE)

The fate and transport modeling step depicted in the first box in Exhibit_Att A-4 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 model the fate and transport of emitted PB-HAPs and to estimate concentrations in 

 

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

Attachment A – Tier 1 A-15 6-29-2012

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 (U.S. 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 (U.S. EPA 2002b, 2005b). 

Algorithms specific to the fate and transport of 14 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 (U.S. EPA 2004b). More recently (largely as part of this current

project), 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 chemicalspecific properties used by TRIM.FaTE to model cadmium (as well as PAHs, mercury, and 

dioxins) are documented in Addendum 1 to this attachment. 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 A.3.

A.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 (U.S. 

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_Att A-5. 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 A.4 and Addendum 2 to this attachment, and all 

inputs required by these calculations are documented in Addendum 2.

 

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

Attachment A – Tier 1 A-16 6-29-2012

Exhibit_Att A-5. Overview of Process Carried Out in the 

Multimedia Ingestion Risk Calculator

A.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 Chemical- specific uptake/ transfer factors Ingestion dose-response values

FFC media concentrations Average daily doses Cancer risks and hazard quotients

Access db process Tool output Input Key to symbols: data
TRIM-Based Tiered Screening Methodology for RTR

Attachment A – Tier 1 A-17 6-29-2012

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 der Berg et al. 2006). Refer to 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.

A.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_Att A-6. 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
TRIM-Based Tiered Screening Methodology for RTR

Attachment A – Tier 1 A-18 6-29-2012

property. Exposure 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_Att A-7 shows that as Kow increases, so too 

does exposure.

Exhibit_Att A-6. Exposure, Toxicity, and Risk Equivalency Factors Relative to BaP

for POM Congeners Currently Evaluated in Tiers 1 and 2 Analyses

Chemical

Fully 

Parameterized 

in 

TRIM.FaTE?

Exposure 

Equivalency 

Factor 

(EEF)

Toxicity 

Equivalency 

Factor 

(TEF)a

Risk 

Equivalency 

Factor 

(REF)

Dibenzo(a,i)pyrene n 22.06 16.44 362.61

7,12-Dimethylbenz(a)anthracene Y 1.91 34.25 65.31

3-Methylcholanthrene n 2.75 3.01 8.28

Dibenz(a,h)anthracene Y 4.56 0.56 2.56

Benzo(k)fluoranthene Y 7.00 0.16 1.15

Benzo(a)pyrene Y 1 1 1

Benzo(b)flouranthene Y 4.38 0.16 0.72

Indeno(1,2,3-cd)pyrene Y 3.10 0.16 0.51

PAH, total n 3.34 0.068 0.23

Polycyclic Organic Matter n 3.34 0.068 0.23

Benzo(g,h,i)perylene Y 2.91 0.068 0.20

Benzo(e)pyrene n 2.88 0.068 0.20

Retene n 2.32 0.068 0.16

Dibenzo(a,j)acridine n 0.41 0.16 0.067

Perylene n 0.64 0.068 0.044

Benzo(a)anthracene Y 0.075 0.16 0.012

Chrysene Y 0.18 0.016 0.0030

2-Acetylaminofluorene n 0.0014 1.37 0.0019

Fluoranthene Y 0.019 0.068 0.0013

Acenaphthylene Y 0.012 0.068 0.00083

2-Chloronaphthalene n 0.011 0.068 0.00075

Fluorene Y 0.010 0.068 0.00067

Acenaphthene Y 0.0087 0.068 0.00060

1-Methylnaphthalene n 0.0057 0.068 0.00039

2-Methylnaphthalene Y 0.0052 0.068 0.00036

Carbazole n 0.0040 0.0027 0.000011

Anthracene n 0.023 0 0

Phenanthrene n 0.024 0 0

Pyrene n 0.066 0 0

aTEFs 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 Addendum 2 to this 

attachment. 
TRIM-Based Tiered Screening Methodology for RTR

Attachment A – Tier 1 A-19 6-29-2012

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.

Exhibit_Att A-7. Relationship between Exposure and Kow 

for POM Congeners

A.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_Att A-8. 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

Attachment A – Tier 1 A-20 6-29-2012

Exhibit_Att A-8. 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- 3.96 1.0 3.96

TetraCDD, 2,3,7,8- 1.0 1.0 1.0

Dioxins, Total, w/o Indiv. Isomers Rptd. 1.0 1.0 1.0

Dioxins 1.0 1.0 1.0

2,3,7,8-TCDD TEQ 1.0 1.0 1.0

HexaCDD, 1,2,3,4,7,8- 1.65 0.10 0.17

PentaCDF, 2,3,4,7,8- 0.50 0.30 0.15

HexaCDD, 1,2,3,6,7,8- 1.10 0.1 0.11

HexaCDF, 1,2,3,7,8,9- 0.64 0.1 0.064

HexaCDF, 1,2,3,6,7,8- 0.62 0.10 0.062

HexaCDD, 1,2,3,7,8,9 - 1.10 0.041 0.046

HexaCDF, 1,2,3,4,7,8- 0.42 0.1 0.042

HexaCDF, 2,3,4,6,7,8- 0.58 0.1 0.058

PentaCDF, 1,2,3,7,8- 0.61 0.03 0.018

TetraCDF, 2,3,7,8- 0.16 0.1 0.016

HeptaCDD, 1,2,3,4,6,7,8- 1.19 0.01 0.012

HeptaCDF, 1,2,3,4,6,7,8- 0.33 0.01 0.0033

HeptaCDF, 1,2,3,4,7,8,9- 0.30 0.01 0.0030

OctaCDD, 1,2,3,4,6,7,8,9- 0.45 0.0003 0.00014

OctaCDF, 1,2,3,4,6,7,8,9- 0.37 0.0003 0.00011

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 Addendum 2 to this attachment.

A.3 Description of Environmental Modeling Scenario

As described in Section A.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 Addendum 1 to this attachment.

A.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

Attachment A – Tier 1 A-21 6-29-2012

a particular abiotic or biotic compartment type; these properties are discussed generally in the 

sections that follow and are documented in Addendum 1 to this attachment.

A.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_Att A-9. 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_Att A-9. 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

Attachment A – Tier 1 A-22 6-29-2012

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), for example. 

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).

A.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.

A.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

Attachment A – Tier 1 A-23 6-29-2012

[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.

 65 percent of total precipitation remains in the modeled system and contributes to total 

runoff.

 Total runoff is divided between overland runoff and seepage to groundwater as follows:

 40 percent of total precipitation contributes to overland runoff.

 25 percent of total precipitation infiltrates into the groundwater and eventually flows into 

the lake. 

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 

Addendum 1 to this attachment. 

A.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. 
TRIM-Based Tiered Screening Methodology for RTR

Attachment A – Tier 1 A-24 6-29-2012

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 

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 (U.S. 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_Att A-9). 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.

A.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) 

 

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

Attachment A – Tier 1 A-25 6-29-2012

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 

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_Att A-10 but an artificial 

data set was compiled for this analysis (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_Att A-10. 

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_Att A-10. Summary of Key Meteorological Inputs

Meteorologica

l Property Selected Value Justification

Air temperature Constant at 298 Kelvin Recommended default value listed in HHRAP (U.S. 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.bValue 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.
TRIM-Based Tiered Screening Methodology for RTR

Attachment A – Tier 1 A-26 6-29-2012

Meteorologica

l Property Selected Value Justification

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 

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

aNational Climatic Data Center CliMaps (NCDC-CliMaps) (2007). http://cdo.ncdc.noaa.gov/cgi-bin/climaps/climaps.pl. 

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

cNational Climatic Data Center 1981-2010 Climate Normals; http://www.ncdc.noaa.gov/oa/climate/normals/usnormals.html

A.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). The scenario 

includes a benthic invertebrate compartment to represent the primary invertebrate consumers 

(second trophic level) in the benthic environment, and the fish compartments represent the 

higher tropic levels in the aquatic system. For TRIM.FaTE to provide reasonable predictions of 

the distribution of a chemical across biotic and abiotic compartments in aquatic systems, the 

biomass of the aquatic biotic compartments 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 in 

support of developing and parameterizing aquatic biotic compartments for TRIM.FaTE (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,

the previous 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 temperature). 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 
TRIM-Based Tiered Screening Methodology for RTR

Attachment A – Tier 1 A-27 6-29-2012

Tier 1 scenario are listed in Exhibit_Att A-11. 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).

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_Att A-11. 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% algaea5.7 E−8

Macrophyte hydrilla, milfoil 500 – – –

Water column 

planktivore

young-of-theyear, minnows 2.0 35.1% 100% zooplankton 0.025

Water column 

omnivore sucker, carp 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 Biomassc5.7

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

bBenthic 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.

A.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 

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 (see Exhibit_Att A-4). 
TRIM-Based Tiered Screening Methodology for RTR

Attachment A – Tier 1 A-28 6-29-2012

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_Att A-12. 

Exhibit_Att A-12. 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 angler 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).

A.4 Description of Exposure and Risk Modeling Scenario 

This section describes the approach for modeling chemical concentrations in farm food chain 

(FFC) media (Section A.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 A.4.2); and calculating human health screening risk metrics associated with these 

exposure pathways (Section A.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 Addendum 2 to this attachment.
TRIM-Based Tiered Screening Methodology for RTR

Attachment A – Tier 1 A-29 6-29-2012

A.4.1 Calculating Concentrations in Farm Food Chain Media

As was shown in Exhibit_Att A-5, 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; U.S. 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 2.2.2, 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 (U.S. EPA 2005a). Addendum 2 to this attachment provides parameter 

values used in MIRC for the Tier 1 assessment.

A.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_Att A-13.

A.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 (U.S. EPA 2011).
TRIM-Based Tiered Screening Methodology for RTR

Attachment A – Tier 1 A-30 6-29-2012

Exhibit_Att A-13. 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

aCalculation of intermediate exposure concentrations were required only for the farm animal/animal product ingestion pathways.

bProcess by which HAP enters medium ingested by humans.

cModeled in TRIM.FaTE.

d

The consumption of breast milk exposure scenario is discussed in Section A.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 of the distribution of national
TRIM-Based Tiered Screening Methodology for RTR

Attachment A – Tier 1 A-31 6-29-2012

data for for all food types except for fish, which was set at the 99th percentile). All media were 

assumed to be obtained from locations impacted by the modeled source. Although this 

approach results in an overestimate of 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 farm 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_Att A-14. 

Exhibit_Att A-14. 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

Intake rate and ingestion rate (IR) for farm produce 

and animal products other than fish (varies by age 

and media consumed)

90th percentile of distribution of consumers who 

produce own food

Ingestion rate for fish 105.1 g/day (represents the 99

th percentile of fish 

consumption in the adult U.S. general population

including consumers and non-consumersc; this 

estimate is considered within the range of average 

values for subsistence anglersd); lower values 

were assumed for younger age groups, also based 

on the 99th percentile of the U.S. general 

population. Value reflects “as prepared” 

consumption rate so cooking and preparation 

losses did not need to be considered.

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)e

1

Cooking lossfAssumed to be “typical”; varies depending on food 

product (see Addendum 2 to this attachment). 

Cooking losses were not considered for fish 

consumption because intake rates represent “as 

prepared” values.

aData for exposure characteristics are presented in Addendum 2 to this attachment. Exposure parameter values were based on 

data obtained from the Exposure Factors Handbook (U.S. EPA 2011). See Addendum 2 to this attachment for details.

bExposure 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 Addendum 2.

c

Fish consumption rates for adult and child consumers were derived based on Estimated Per Capita Fish Consumption in the United 

States (U.S. EPA 2002c). Consumption rates were normalized to body weights that are representative of the age groups used in 

the screening scenario.

d

The relationship equating the 99th percentile of fish consumption in the general population with the range of average fish 

consumption rates for subsistence anglers was obtained from Methodology for Deriving Ambient

Water Quality Criteria for the Protection of Human Health (U.S. EPA 2000). The 105.1-g/day consumption rate is similar to the 

“default” value of 142.4 g/day EPA’s Office of Water recommends for use in estimating exposures to subsistence fishers.

e

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. 

fCooking 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.
TRIM-Based Tiered Screening Methodology for RTR

Attachment A – Tier 1 A-32 6-29-2012

A.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 (U.S. EPA 2003), which derived much of its input 

data from the Exposure Factors Handbook (U.S. 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 Addendum 2 to this attachment.

A.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 2005).

Exposure to TCDD via breast milk consumption during the first year of life was estimated to 

account for approximately 30 percent of an adult’s lifetime ADD. The 30 percent ratio was 

established by performing multiple modeling iterations using different scenarios. In the absence 

of congener-specific data, dioxin congeners were assumed to manifest the same tendency to 

accumulate in breast milk as TCDD. As a consequence, exposure to all dioxin congeners via 

breast milk consumption during the first year of life were estimated to account for approximately 

30 percent of an adult’s lifetime exposure to those congeners. Exposure via the breast milk 

pathway during the first year of life was accounted for, in this way, in developing the threshold 

screening level for dioxins. 

A.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_Att A-15 and are discussed in more detail in Addendum 2 to this attachment. 

Equations used to estimate cancer risk and non-cancer hazard also are provided in Addendum 

2 to this attachment. 
TRIM-Based Tiered Screening Methodology for RTR

Attachment A – Tier 1 A-33 6-29-2012

Exhibit_Att A-15. 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

10 IRIS not available

2,3,7,8-TCDD 1.5E+5 EPA ORD 7E−10 IRIS

Source: U.S. EPA (2007).

CSF = cancer slope factor; RfD = reference dose; IRIS = 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 Addendum 2 to this attachment).

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 (U.S. 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 Addendum 2 to this attachment.

A.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_Att A-16 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_Att A-16. Summary of RTR 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

Attachment A – Tier 1 A-34 6-29-2012

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.

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). 
TRIM-Based Tiered Screening Methodology for RTR

Attachment A – Tier 1 A-35 6-29-2012

Characteristic Value

Neutral or 

Health 

Protective?

Comments on Assumptions

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 

Addendum 2 to 

this attachment 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 

Addendum 2 to 

this attachment 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.
TRIM-Based Tiered Screening Methodology for RTR

Attachment A – Tier 1 A-36 6-29-2012

Exhibit_Att A-16. Summary of RTR Screening Scenario Assumptions

Characteristic Value

Neutral or 

Health 

Protective?

Comments on Assumptions

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 1.47 g/kg-day for 

adults; between 

1.27 and 2.08

g/kg-day for 

children ages 1–19

years

Health Protective Rates are based on the 99th percentile of 

general population fish consumption from 

EPA’s 2002 analysis of freshwater and 

estuarine fish consumption derived from the 

1994–96 and 1998 USDA Continuing 

Survey of Food Intake by Individuals

(CSFII; USDA 2002). Consumption rates 

were normalized to body weights that are

representative of the age groups used in 

the screening scenario. These ingestion 

rates likely overestimate long-term fish 

consumption rates for the general 

population. See Addendum 2 to this 

attachment 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.
TRIM-Based Tiered Screening Methodology for RTR

Attachment A – Tier 1 A-37 6-29-2012

A.5 Evaluation of Screening Scenario

A.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. The major PB-HAP 

categories of concern for this analysis are cadmium compounds (Section A.5.2), mercury 

compounds (Section A.5.3), dioxins (Section A.5.4), and POM (Section A.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.

A.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). 

A.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 

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 2005a). 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.

A.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 
TRIM-Based Tiered Screening Methodology for RTR

Attachment A – Tier 1 A-38 6-29-2012

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. 

A.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_Att A-17, 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 as much as 98 percent of 

the ADD for some groups. The combined contribution from all other exposure pathways 

accounts for less than 5 percent of the total ADD for all age groups. Most of the additional 

exposure was from 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. 

A.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 (U.S. EPA 1997). 

A.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 (U.S. 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 

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. 
TRIM-Based Tiered Screening Methodology for RTR

Attachment A – Tier 1 A-39 6-29-2012

Exhibit_Att A-17. Estimated Contributions of Modeled Food Types to 

Cadmium Ingestion Exposures and Hazard Quotients

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 (U.S. 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 (U.S. 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. 

0

0.2

0.4

0.6

0.8

1

0

0.0001

0.0002

0.0003

0.0004

0.0005

Child 1-2 Child 3-5 Child 6-11 Child 12-19 Adult Hazard Quotient ADD (mg/kg/day)

Fish Meat, Dairy, & Eggs

Fruits & Vegetables Soil
TRIM-Based Tiered Screening Methodology for RTR

Attachment A – Tier 1 A-40 6-29-2012

A.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 (U.S. 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 (U.S. 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_Att A-18. 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). 

A.5.3.3 Average Daily Dose

In Exhibit_Att A-18, 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.

A.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. 

A.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 
TRIM-Based Tiered Screening Methodology for RTR

Attachment A – Tier 1 A-41 6-29-2012

Exhibit_Att A-18. Estimated Contributions of Modeled Food Types 

to Methyl Mercury Ingestion Exposures

sediment, which appears to be the primary environmental sink for this chemical group (U.S. 

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 (U.S. 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.

0

0.2

0.4

0.6

0.8

1

0

0.00002

0.00004

0.00006

0.00008

0.0001

Child 1-2 Child 3-5 Child 6-11 Child 12-19 Adult Hazard Quotient ADD (mg/kg/day)

Fish Meat, Dairy, & Eggs

Fruits & Vegetables Soil
TRIM-Based Tiered Screening Methodology for RTR

Attachment A – Tier 1 A-42 6-29-2012

A.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 

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 nearly 94 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 anglers, 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 analysis.

A.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_Att

A-19. 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. 

A.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

Attachment A – Tier 1 A-43 6-29-2012

Exhibit_Att A-19. Estimated Contributions of Modeled Food

Types to Dioxin Ingestion Exposures

A.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 
TRIM-Based Tiered Screening Methodology for RTR

Attachment A – Tier 1 A-44 6-29-2012

expected to be especially high. Sediment-dwelling organisms can experience increased 

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. 

A.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.

A.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_Att A-20. As shown, the variability in the driving 

exposure pathways across PAHs is significant, with fish, beef, dairy, fruits, and vegetables 

comprising between 86 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.

A.5.6 Summary

This analysis 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 Risk and Technology Review Program,

as modeled in TRIM.FaTE. In general, the modeled behavior of the compounds is consistent 

with data found in the literature. 

This analysis 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

Attachment A – Tier 1 A-45 6-29-2012

Exhibit_Att A-20. Estimated Contributions of Modeled Food 

Types to PAH Ingestion Exposures

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TRIM-Based Tiered Screening Methodology for RTR

Attachment A – Tier 1 A-47 6-29-2012

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TRIM-Based Tiered Screening Methodology for RTR

Attachment A – Tier 1 A-48 6-29-2012

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of Hexachlorodibenzo-p-dioxin (HxCDD), mixture of 1,2,3,6,7,8-HxCDD and 1,2,3,7,8,9-

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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.
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 1 1-1 6-29-2012

TRIM.FaTE Inputs

Addendum 1. TRIM.FaTE Inputs
[This page intentionally left blank.]
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 1 1-3 6-29-2012

TRIM.FaTE Inputs

Exhibits, Addendum 1

Exhibit_Add A1-1. TRIM.FaTE Simulation Parameters for the TRIM.FaTE 

Screening Scenario........................................................................................... 1-5

Exhibit_Add A1-2. Meteorological Inputs for the TRIM.FaTE Screening Scenario.................. 1-6

Exhibit_Add A1-3. Air Parameters for the TRIM.FaTE Screening Scenario............................ 1-7

Exhibit_Add A1-4. Soil and Groundwater Parameters for the TRIM.FaTE 

Screening Scenario........................................................................................... 1-8

Exhibit_Add A1-5. Runoff Assumptions for the TRIM.FaTE Screening Scenario.................. 1-10

Exhibit_Add A1-6. USLE Erosion Parameters for the TRIM.FaTE Screening 

Scenario ......................................................................................................... 1-11

Exhibit_Add A1-7. Terrestrial Plant Placement for the TRIM.FaTE Screening 

Scenario ......................................................................................................... 1-12

Exhibit_Add A1-8. Terrestrial Plant Parameters for the TRIM.FaTE Screening 

Scenario ......................................................................................................... 1-13

Exhibit_Add A1-9. Surface Water Parameters for the TRIM.FaTE Screening 

Scenario ......................................................................................................... 1-15

Exhibit_Add A1-10. Sediment Parameters for the TRIM.FaTE Screening 

Scenario ......................................................................................................... 1-16

Exhibit_Add A1-11. Aquatic Animals Food Chain, Density, and Mass for the 

TRIM.FaTE Screening Scenario ..................................................................... 1-17

Exhibit_Add A1-12. Cadmium Chemical-Specific Parameters for the TRIM.FaTE 

Screening Scenario......................................................................................... 1-18

Exhibit_Add A1-13. Mercury Chemical-Specific Parameters for the TRIM.FaTE 

Screening Scenario......................................................................................... 1-19

Exhibit_Add A1-14. PAH Chemical-Specific Parameters for the TRIM.FaTE 

Screening Scenario......................................................................................... 1-20

Exhibit_Add A1-15. Dioxin Chemical-Specific Parameters for the TRIM.FaTE 

Screening Scenario......................................................................................... 1-22

Exhibit_Add A1-16. Cadmium Chemical-Specific Parameters for Abiotic 

Compartments in the TRIM.FaTE Screening Scenario................................... 1-24

Exhibit_Add A1-17. Mercury Chemical-Specific Parameters for Abiotic 

Compartments in the TRIM.FaTE Screening Scenario.................................... 1-25

Exhibit_Add A1-18. PAH Chemical-Specific Parameters for Abiotic 

Compartments in the TRIM.FaTE Screening Scenario.................................... 1-29

Exhibit_Add A1-19. Dioxin Chemical-Specific Parameters for Abiotic 

Compartments in the TRIM.FaTE Screening Scenario.................................... 1-32

Exhibit_Add A1-20. Cadmium Chemical-Specific Parameters for Plant 

Compartments in the TRIM.FaTE Screening Scenario.................................... 1-36

Exhibit_Add A1-21. Mercury Chemical-Specific Parameters for Plant 

Compartments in the TRIM.FaTE Screening Scenario.................................... 1-37
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 1 1-4 6-29-2012

TRIM.FaTE Inputs

Exhibit_Add A1-22. PAH Chemical-Specific Parameters for Plant Compartments 

in the TRIM.FaTE Screening Scenario............................................................ 1-38

Exhibit_Add A1-23. PAH Chemical-Specific Parameters for Plant Compartments 

in the TRIM.FaTE Screening Scenario............................................................ 1-39

Exhibit_Add A1-24. Cadmium Chemical-Specific Parameters for Aquatic 

Species in the TRIM.FaTE Screening Scenario .............................................. 1-41

Exhibit_Add A1-25. Mercury Chemical-Specific Parameters for Aquatic Species 

in the TRIM.FaTE Screening Scenario............................................................ 1-43

Exhibit_Add A1-26. PAH Chemical-Specific Parameters for Aquatic Species in 

the TRIM.FaTE Screening Scenario ............................................................... 1-44

Exhibit_Add A1-27. Dioxin Chemical-Specific Parameters for Aquatic Species in 

the TRIM.FaTE Screening Scenario ............................................................... 1-47
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 1 1-5 6-29-2012

TRIM.FaTE Inputs

This attachment provides tables of the modeling inputs for the TRIM.FaTE screening scenario. 

Exhibit_Add A1-1 presents runtime settings for TRIM.FaTE. Exhibit_Add A1-2 and Exhibit_Add 

A1-3 present meteorological and air parameters, respectively, entered into the model. 

Exhibit_Add A1-4, Exhibit_Add A1-5, and Exhibit_Add A1-6 present the parameters for soil and 

groundwater, runoff assumptions, and the USLE (universal soil loss equation) erosion 

parameters, respectively, for the screening scenario. Exhibit_Add A1-7 and Exhibit_Add A1-8

present terrestrial parameters. Exhibit_Add A1-9 through Exhibit 1-11 present lake parameters, 

and Exhibit_Add A1-12 through Exhibit 1-27 present parameters specific to the chemicals 

modeled in the scenario.

Exhibit_Add A1-1. 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.

aOutput time step is set in TRIM.FaTE using the scenario properties "simulationStepsPerOutputStep" and "simulationTimeStep."

.
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 1 1-6 6-29-2012

TRIM.FaTE Inputs

Exhibit_Add A1-2. 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 Professional judgment; 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 m

3

[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, Addendum 1 1-7 6-29-2012

TRIM.FaTE Inputs

Exhibit_Add A1-3. 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/cm30.0012 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, Addendum 1 1-8 6-29-2012

TRIM.FaTE Inputs

Exhibit_Add A1-4. 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 variesbSee Exhibit 5.

Fraction of area available for 

erosion

m

2

[area available]/m2[total] 1 Professional judgment; area assumed rural.

Fraction of area available for runoff m

2

[area available]/m2[total] 1 Professional judgment; area assumed rural.

Fraction of area available for vertical 

diffusion

m

2

[area available]/m2[total] 1 Professional judgment; area assumed rural.

Fraction sand unitless 0.25 Professional judgment.

Organic carbon fraction unitless 0.008 U.S. average in McKone et al. 2001 (Table 16 

and A-3).

pH unitless 6.8 Professional judgment.

Runoff fraction unitless variesbSee Exhibit_Add A1-5.

Total erosion rate kg [soil]/m2/day variesbSee Exhibit_Add A1-6.

Total runoff rate m

3

[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 Professional judgment.
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 1 1-9 6-29-2012

TRIM.FaTE Inputs

Exhibit_Add A1-4. 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 Professional judgment.

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 Professional judgment.

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 Professional judgment.

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 Professional judgment.

Organic carbon fraction unitless 0.004 Professional judgment.

pH unitless 6.8 Professional judgment.

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).

aSet using the volume element properties file.

bSee separate tables (Exhibit_Add A1-5 and Exhibit_Add A1-6) for erosion/runoff fractions and total erosion rates.
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 1 1-10 6-29-2012

TRIM.FaTE Inputs

Exhibit_Add A1-5. 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_N5aSW_Pond 0.0

SurfSoil_N4 0.5

SurfSoil_S5 0.5

sink 0.0

SurfSoil_S5aSW_Pond 0.0

SurfSoil_N5 0.0

SurfSoil_S4 1.0

sink 0.0

aAssumes 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, Addendum 1 1-11 6-29-2012

TRIM.FaTE Inputs

Exhibit_Add A1-6. 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 m

2 R (100 ft-ton/ac)

K 

(ton/ac/(100 

ft-ton/acre))

LS 

(USCS) type C(USCS) P

A 

(ton/ac/yr)

A 

(kg/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

aCalculated 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, Addendum 1 1-12 6-29-2012

TRIM.FaTE Inputs

Exhibit_Add A1-7. 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, Addendum 1 1-13 6-29-2012

TRIM.FaTE Inputs

Exhibit_Add A1-8. 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 m

2

[leaf]/ 

m

2

[area]

5.0 Harvard Forest, dom. red oak and 

red maple, CDIAC website

5.0 Mid-range of 4-6 for old fields, R.J. 

Luxmoore, ORNL.

Calculate wet deposition

interception fraction (Boolean)

1=yes, 0=no 0 Professional judgment. 0 Professional judgment.

Correction exponent, octanol to 

lipid

unitless 0.76 From roots, Trapp 1995. 0.76 From roots, Trapp 1995.

Degree stomatal opening unitless 1 Set to 1 for daytime based on 

professional judgment (stomatal 

diffusion is turned off at night using 

a different property, IsDay).

1 Set to 1 for daytime based on 

professional judgment (stomatal diffusion 

is turned off at night using a different 

property, IsDay).

Density of wet leaf kg/m3820 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 Professional judgment. 0.05 Professional judgment.

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 m

2

/kg 2.9 Grass/hay, Baes et al. 1984. 2.9 Grass/hay, Baes et al. 1984.

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

Wet deposition interception 

fraction

unitless 0.2 Calculated based on 5 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, Addendum 1 1-14 6-29-2012

TRIM.FaTE Inputs

Exhibit_Add A1-8. 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 m

3

[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 Professional judgment.

Wet density of root kg/m3820 Soybean, Paterson et al. 1991.

Wet mass per soil area kg/m21.4 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/m31,000 Professional judgment.

Density of xylem fluid kg/cm3900 Professional judgment.

Flow rate of transpired water per 

leaf area

m

3

[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/m3830 Professional judgment.

Wet mass per soil area kg/m20.24 Calculated from leaf and root biomass 

density based on professional judgment.

aSee Exhibit_Add A1-7 for assignment of plant types to surface soil compartments.

bBegins March 9 (set to 1), ends November 7 (set to 0). Nationwide 80th percentile.

cBegins 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, Addendum 1 1-15 6-29-2012

TRIM.FaTE Inputs

Exhibit_Add A1-9. 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 Professional judgment.

Organic carbon fraction in 

suspended sediments

unitless 0.02 Professional judgment.

pH unitless 7.3 Professional judgment.

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.

aSet using the volume element properties named "top" and "bottom."
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 1 1-16 6-29-2012

TRIM.FaTE Inputs

Exhibit_Add A1-10. 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 Professional judgment.

Organic carbon fraction unitless 0.02 McKone et al. 2001 (Table 

3).

Porosity of the sediment 

zone

volume[total pore 

space]/volume[sediment 

compartment]

0.6 USEPA 2005.

Solid material density in 

sediment

kg[sediment]/m3[sediment] 2,600 McKone et al. 2001 (Table 

3).

pH unitless 7.3 Professional judgment.

Sediment resuspension 

velocity

m/day 6.69E-05 Calculated from water 

balance model.

aSet using the volume element properties named "top" and "bottom."
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 1 1-17 6-29-2012

TRIM.FaTE Inputs

Exhibit_Add A1-11. 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 Professional 

judgment.

Water Column 

Herbivore

0% 100% 0% 0% 0% 0% 0% 0% 0.002 0.025 Professional 

judgment.

Benthic Omnivore 0% 0% 100% 0% 0% 0% 0% 0% 0.002 2.50E-01 Professional 

judgment.

Water Column 

Omnivore

0% 0% 0% 100% 0% 0% 0% 0% 0.001 0.25 Professional 

judgment.

Benthic Carnivore 0% 0% 50% 0% 50% 0% 0% 0% 0.001 2.0 Professional 

judgment.

Water Column 

Carnivore

0% 0% 0% 0% 0% 100% 0% 0% 0.0002 2.0 Professional 

judgment.

Zooplankton 100% 0% 0% 0% 0% 0% 0% 0% 0.0064 5.70E-08 Professional 

judgment.
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 1 1-18 6-29-2012

TRIM.FaTE Inputs

Exhibit_Add A1-12. Cadmium Chemical-Specific Parameters

for the TRIM.FaTE Screening Scenario

Parameter Namea Units Value Reference

CAS numberbunitless 7440-43-9 -

Diffusion coefficient in pure 

air

m

2

[air]/day 0.71 USEPA 1999 (Table A-2-

35).

Diffusion coefficient in pure 

water

m

2

[water]/day 8.16E-05 USEPA 1999 (Table A-2-

35).

Henry's Law constant Pa-m

3

/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)

m

3

[air]/m3[octanol] - -

Octanol-carbon partition 

coefficient (Koc)

- -

Octanol-water partition 

coefficient (Kow)

L[water]/kg[octanol] - -

aAll 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, Addendum 1 1-19 6-29-2012

TRIM.FaTE Inputs

Exhibit_Add A1-13. 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

m

2

[air]/day 0.478 0.478 0.456 USEPA 1997b.

Diffusion coefficient 

in pure water

m

2

[water]/day 5.54E-05 5.54E-05 5.28E-05 USEPA 1997b.

Henry's Law 

constant

Pa-m

3

/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 m

3

[air]/m3[rain] 1,200 1.6E+06 0 USEPA 1997b, 

based on Petersen 

et al. 1995.

aAll parameters in this table are TRIM.FaTE chemical properties.

bOn 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, Addendum 1 1-20 6-29-2012

TRIM.FaTE Inputs

Exhibit_Add A1-14. PAH Chemical-Specific Parameters for the TRIM.FaTE Screening Scenario

Parameter Name Units

Value

2Methyl 712DMB Acenaphthene Acenaphthylene BaA BaP BbF BghiP

CAS number unitless 91-57-6 57-97-6 83-32-9 208-96-8 56-55-3 50-32-8 205-99-2 191-24-2

Diffusion coefficient 

in pure air

m

2

/day 0.451 0.691 0.009 0.388 0.441 0.372 0.009 0.190

Diffusion coefficient 

in pure water

m

2

/day 6.70E-05 6.91E-05 8.64E-05 6.03E-05 7.78E-05 7.78E-05 8.64E-05 4.54E-05

Henry's Law 

constant

Pa-m

3

/mol 50.56 0.20 18.50 12.70 1.22 0.07 0.05 0.03

Melting point degrees K 307.75 396.65 366.15 365.65 433 452 441 550.15

Molecular weight g/mol 142.20 256.35 154.21 152.20 228.29 252.32 252.32 276.34

Octanol-water 

partition coefficient 

(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

m

2

/day 0.009 0.009 0.009 0.009 0.009 0.009

Diffusion coefficient 

in pure water

m

2

/day 8.64E-05 8.64E-05 8.64E-05 8.64E-05 8.64E-05 8.64E-05

Henry's Law 

constant

Pa-m

3

/mol 0.04 0.53 0.01 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, Addendum 1 1-21 6-29-2012

TRIM.FaTE Inputs

Exhibit_Add A1-14. PAH Chemical-Specific Parameters for the TRIM.FaTE Screening Scenario

Parameter Name Units Reference

CAS number unitless -

Diffusion coefficient in pure 

air

m

2

/day USEPA 2005. Exceptions include USEPA 1997a (7,12-Dimethylbenz(a)anthracene), and 

USEPA 2007 (2-Methylnaphthalene, Acenaphthylene, and Benzo(g,h,i)perylene)

Diffusion coefficient in pure 

water

m

2

/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-m

3

/mol USEPA 2005. Exceptions include USEPA 2003 (2-Methylnaphthalene) HSDB 2001a 

(7,12-Dimethylbenz(a)anthracene), HSDB 2001b (Acenaphthylene), and HSDB 2001c 

(Benzo(g,h,i)perylene)

Melting point degrees K Budavari 1996. Exceptions include USEPA 2003 (2-Methylnaphthalene), HSDB 2001a 

(7,12-Dimethylbenz(a)anthracene), HSDB 2001b (Acenaphthylene), HSDB 2001c 

(Benzo(g,h,i)perylene), and USEPA 2005 (Acenaphthene, Fluoranthene, and Fluorene)

Molecular weight g/mol Budavari 1996. Exceptions include 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, Addendum 1 1-22 6-29-2012

TRIM.FaTE Inputs

Exhibit_Add A1-15. 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

m

2

/day 0.751 0.168 0.782 0.176 0.176 0.816 0.183

Diffusion coefficient in 

pure water

m

2

/day 6.91E-05 6.91E-05 6.91E-05 6.91E-05 6.91E-05 6.91E-05 6.91E-05

Henry's Law constant Pa-m

3

/mol 0.684 0.19 1.22 1.43 1.42 1.08 1.45

Melting 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

m

2

/day 0.816 0.183 0.816 0.183 0.854 0.192 0.183

Diffusion coefficient in 

pure water

m

2

/day 6.91E-05 6.91E-05 6.91E-05 6.91E-05 6.91E-05 6.91E-05 6.91E-05

Henry's Law constant Pa-m

3

/mol 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, Addendum 1 1-23 6-29-2012

TRIM.FaTE Inputs

Exhibit_Add A1-15. 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

m

2

/day 0.192 0.899 0.203 US EPA 2005

Diffusion coefficient in 

pure water

m

2

/day 6.91E-05 4.84E-05 5.19E-05 US EPA 2005

Henry's Law constant Pa-m

3

/mol 0.505 3.33 1.46 US EPA 2005

Melting point degrees K 469.3 578.0 500.0 Mackay et al. 2000. Exceptions include USEPA 2000a 

(1,2,3,6,7,8-HxCDD, 1,2,3,7,8,9-HxCDF, and 1,2,3,7,8-

PeCDD), ATSDR 1998 (1,2,3,6,7,8-HxCDF, 1,2,3,7,8-

PeCDF, and 2,3,4,6,7,8-HxCDF), and NLM 2002 

(1,2,3,7,8,9-HxCDD)

Molecular weight g/mol 340.42 322 306 Mackay et al. 2000. Exceptions include ATSDR 1998 

(1,2,3,6,7,8-HxCDF, 1,2,3,7,8,9-HxCDF, 1,2,3,7,8-PeCDD, 

1,2,3,7,8-PeCDF, and 2,3,4,6,7,8-HxCDF) and NLM 2002 

(1,2,3,6,7,8-HxCDD and 1,2,3,7,8,9-HxCDD)

Octanol-water partition 

coefficient (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, Addendum 1 1-24 6-29-2012

TRIM.FaTE Inputs

Exhibit_Add A1-16. 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 m

3

[air]/m3[rain] 200,000 MacKay et al. 1986.

Surface Soil Compartment Type

Use input characteristic depth 

(Boolean)

0 = no, Else = yes 0 Professional judgment.

Root Zone Soil Compartment Type

Use input characteristic depth 

(Boolean)

0 = no, Else = yes 0 Professional judgment.

Vadose Zone Soil Compartment Type

Use input characteristic depth 

(Boolean)

0 = no, Else = yes 0 Professional judgment.

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, Addendum 1 1-25 6-29-2012

TRIM.FaTE Inputs

Exhibit_Add A1-17. 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 Professional judgment.

Methylation rate 1/day 0 0 0 Professional judgment.

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 Professional judgment.

Washout ratio m

3

[air]/m3[rain] 200,000 200,000 200,000 Professional judgment.

Surface Soil Compartment Type

Use input characteristic depth 

(Boolean)

0 = no, Else = yes 0 0 0 Professional judgment.

Soil-water partition coefficient L[water]/kg[soil 

wet wt]

1,000 58,000 7,000 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, Addendum 1 1-26 6-29-2012

TRIM.FaTE Inputs

Exhibit_Add A1-17. 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 Professional judgment.

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 Professional judgment.

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, Addendum 1 1-27 6-29-2012

TRIM.FaTE Inputs

Exhibit_Add A1-17. 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 Professional judgment.

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, Addendum 1 1-28 6-29-2012

TRIM.FaTE Inputs

Exhibit_Add A1-17. 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 Professional judgment.

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, Addendum 1 1-29 6-29-2012

TRIM.FaTE Inputs

Exhibit_Add A1-18. 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, Addendum 1 1-30 6-29-2012

TRIM.FaTE Inputs

Exhibit_Add A1-18. 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 Professional judgment.

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 Professional judgment.

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 Professional judgment.
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 1 1-31 6-29-2012

TRIM.FaTE Inputs

Exhibit_Add A1-18. 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, Addendum 1 1-32 6-29-2012

TRIM.FaTE Inputs

Exhibit_Add A1-19. 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 m

3

[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, Addendum 1 1-33 6-29-2012

TRIM.FaTE Inputs

Exhibit_Add A1-19. 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-

HxCDD

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 m

3

[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, Addendum 1 1-34 6-29-2012

TRIM.FaTE Inputs

Exhibit_Add A1-19. 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 m

3

[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, Addendum 1 1-35 6-29-2012

TRIM.FaTE Inputs

Exhibit_Add A1-19. 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) Professional judgment.

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 Professional judgment.

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) Professional judgment.

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, Addendum 1 1-36 6-29-2012

TRIM.FaTE Inputs

Exhibit_Add A1-20. 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 Professional judgment.

Particle on Leaf Compartment Type

Transfer factor to leaf 1/day 0.200 Professional judgment.

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 Coefficientm

3

[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)

m

3

[soil pore 

water]/m3[xylem 

fluid]

0.45 Tsiros et al. 1999.

aRoots 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, Addendum 1 1-37 6-29-2012

TRIM.FaTE Inputs

Exhibit_Add A1-21. 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 Professional judgment (assumed 1% of 

transfer factor from leaf particle to leaf).

Demethylation rate 1/day N/A N/A 0.03 Calculated from Bache et al. 1973.

Methylation rate 1/day 0 0 0 Assumed from Gay 1975, Bache et al. 

1973.

Oxidation rate 1/day 1.0E+06 0 0 Professional judgment; assumed close to 

instantaneous

Reduction rate 1/day 0 0 0 Professional judgment.

Particle on Leaf Compartment Type

Transfer factor to leaf 1/day 0.2 0.2 0.2 Professional judgment.

Demethylation rate 1/day N/A N/A 0 Professional judgment.

Methylation rate 1/day 0 0 0 Professional judgment.

Oxidation rate 1/day 0 0 0 Professional judgment.

Reduction rate 1/day 0 0 0 Professional judgment.

Root Compartment Type - Grasses and Herbsa

Alpha for root-root zone bulk soil unitless 0.95 0.95 0.95 Selected value.

Root/root-zone-soil-water partition 

coefficient

m

3

[bulk root soil]/ 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 Professional judgment.

Demethylation rate 1/day N/A N/A 0 Professional judgment.

Methylation rate 1/day 0 0 0 Professional judgment.

Oxidation rate 1/day 0 0 0 Professional judgment.

Reduction rate 1/day 0 0 0 Professional judgment.

Stem Compartment Type - Grasses and Herbsa

Transpiration stream concentration 

factor (TSCF)

m

3

[soil pore 

water]/m3[xylem fluid]

0 0.5 0.2 Calculation from Norway spruce, Scots 

pine, Bishop et al. 1998.

Demethylation rate 1/day N/A N/A 0.03 Calculated from Bache et al. 1973.

Methylation rate 1/day 0 0 0 Professional judgment.

Oxidation rate 1/day 0 0 0 Professional judgment.

Reduction rate 1/day 0 0 0 Professional judgment.

aRoots 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, Addendum 1 1-38 6-29-2012

TRIM.FaTE Inputs

Exhibit_Add A1-22. 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

aRoots 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, Addendum 1 1-39 6-29-2012

TRIM.FaTE Inputs

Exhibit_Add A1-23. 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 Professional judgment.

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 Professional judgment.

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 Professional judgment.

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).

aRoots 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, Addendum 1 1-40 6-29-2012

TRIM.FaTE Inputs

Exhibit_Add A1-23. 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 Professional judgment 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 Professional judgment.

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.

aRoots 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, Addendum 1 1-41 6-29-2012

TRIM.FaTE Inputs

Exhibit_Add A1-24. 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 Professional judgment.

Sediment partitioning - partition 

coefficient

kg[bulk sed/kg[invertebrate wet wt] 0.27 Professional judgment.

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 Professional judgment 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 Professional judgment.

Benthic Carnivore Compartment Type

Assimilation efficiency from food unitless 0.1 Professional judgment 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 Professional judgment.

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 Professional judgment.
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 1 1-42 6-29-2012

TRIM.FaTE Inputs

Exhibit_Add A1-24. 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 Professional judgment based on Yan and Wang 

2002.

Assimilation efficiency from plants unitless 0.1 Professional judgment 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 Professional judgment.

Water-column Carnivore Compartment Type

Assimilation efficiency from food unitless 0.1 Professional judgment 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 Professional judgment.
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 1 1-43 6-29-2012

TRIM.FaTE Inputs

Exhibit_Add A1-25. 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 Professional judgment.

How Much Faster Hg Elimination Is Than

For MHg

unitless 3 3 1 Professional judgment.

Methylation rate 1/day 0 0 0 Professional judgment.

Oxidation rate 1/day 0 1.0E+06 0 Professional judgment.

Reduction rate 1/day 0 0 0 Professional judgment.

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 Professional judgment.

Methylation rate 1/day 0 0 0 Professional judgment.

Oxidation rate 1/day 1.0E+06 0 0 Professional judgment.

Reduction rate 1/day 0 0 0 Professional judgment.

Water-column Herbivore Compartment Type

Assimilation efficiency from plankton unitless 0.06 0.06 0.5 Williams et al. 2010.

aScreening 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, Addendum 1 1-44 6-29-2012

TRIM.FaTE Inputs

Exhibit_Add A1-26. 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

aScreening 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, Addendum 1 1-45 6-29-2012

TRIM.FaTE Inputs

Exhibit_Add A1-26. 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, Addendum 1 1-46 6-29-2012

TRIM.FaTE Inputs

Exhibit_Add A1-26. 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.

aScreening 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, Addendum 1 1-47 6-29-2012

TRIM.FaTE Inputs

Exhibit_Add A1-27. 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 0.21 0.09 0.2 0.37 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, Addendum 1 1-48 6-29-2012

TRIM.FaTE Inputs

Exhibit_Add A1-27. 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, Addendum 1 1-49 6-29-2012

TRIM.FaTE Inputs

Exhibit_Add A1-27. 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 Professional judgment.

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 Professional judgment.

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.

aScreening 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, Addendum 1 1-50 6-29-2012

TRIM.FaTE Inputs

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simulation of organic chemical processes. Boca Raton, FL: Lewis Publishers. pp. 153-190.

Rubinstein, N.I., R.J. Pruell, B.K. Taplin, J.A. LiVolsi, and C.B. Norwood. 1990. Bioavailability of 2,3,7,8-

TCDD, 2,3,7,8-TCDF and PCBs to marine benthos from Passaic river sediments. Chemosphere

20:1097-1102.

Sangster, J. (1993) LOGKOW, A Databank of Evaluated Octanol-Water Partition Coefficients. 1st 

Edition, Montreal, Quebec, Canada.

Saouter, E., F. Ribeyre, A. Boudou, and R. Maurybrachet. 1991. Hexagenia rigida (Ephemeroptera) as

a biological model in aquatic ecotoxicology - Experimental studies on mercury transfers from

sediment. Environmental Pollution 69:51-67.

Sijm, D.T.H.M., H. Wever, P.J. de Vries, and A. Opperhuizen. 1989. Octan-1-ol/water partition 

coefficients of polychlorinated dibenzo-p-dioxins and dibenzofurans: experimental values

determined with a stirring method. Chemosphere 19(1-6):263-266.

Simonich, S.L., and R.A. Hites. 1994. Importance of vegetation in removing polycyclic aromatic 

hydrocarbons from the atmosphere. Nature 370:49-51.

Spero, J., B. DeVito, and L. Theodore. [eds.]. 2000. Regulatory chemicals handbook. New York, NY:

CRC Press, p. 1072.
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 1 1-56 6-29-2012

TRIM.FaTE Inputs

Stehly, G.R., P.F. Landrum, M.G. Henry, and C. Klemm. 1990. Toxicokinetics of PAHs in Hexagenia

Environmental Toxicology and Chemistry 9:167-174.

Thibodeaux, L.J. 1996. Environmental chemodynamics: Movement of chemicals in air, water, and soil. 

New York, NY: John Wiley and Sons, Inc.

Thomann, R.V. 1989. Bioaccumulation model of organic-chemical distribution in aquatic food-chains. 

Environmental Science and Technology 23(6):699-707.

Trapp, S. 1995. Model for uptake of xenobiotics into plants. In: Trapp, S. and J. C. McFarlane, eds.

Plant contamination: Modeling and simulation of organic chemical processes. Boca Raton, FL:

Lewis Publishers. pp. 107-151.

Trudel, M., and J.B. Rasmussen. 1997. Modeling the elimination of mercury by fish. Environmental 

Science and Technology 31:1716-1722.

Tsiros, I.X.; R.B. Ambrose, and A. Chronopoulou-Sereli. 1999. Air-vegetation partitioning of toxic 

chemicals in environmental simulation modeling. Global NEST: The International Journal 1(3):177-

184.

USEPA (U.S. Environmental Protection Agency). 2007. Draft risk assessment for the Siemens Water 

Technologies Corp. carbon reactivation facility Parker, Arizona. Appendix F. Chemical-physical 

parameters for compounds not in USEPA's HHRAP. Available at:

http://www.epa.gov/region9/waste/siemens/pdf/RiskAssessment/siemens-riskassessAppxF.pdf.

USEPA. 2005. Human Health Risk Assessment Protocol for Hazardous Waste Combustion Facilities. 

Companion Database. Available at: http://www.epa.gov/region6/6pd/rcra_c/protocol/protocol.htm.

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http://www.epa.gov/iris/toxreviews/1006tr.pdf. 

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(OPPT). Available at: http://www.epa.gov/oppt/exposure/docs/episuitedl.htm.

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TCDD) and related compounds, Volume 3: Properties, environmental levels, and background 

exposures, Chapter 2: Physical and chemical properties and fate and Appendix A. EPA/600/P00/001Bc. Available at: http://www.epa.gov/ncea/pdfs/dioxin/part1/volume3/chap-2.pdf.

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TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 1 1-57 6-29-2012

TRIM.FaTE Inputs

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polychlorinated dibenzo-p-dioxins (PCDDs) and dibenzofurans (PCDFs) and their relevance for 

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cycle in Pallette Lake, Wisconsin, USA. Water, Air, and Soil Pollution 80:789-798.

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Environmental Media: Selection of Congeners for Modeling. Technical Note 6/2001. Meteorological 

Synthesizing Center - East.

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marine food chain. Marine Ecology Progress Series 312:101-111.

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bioaccumulation from water and food by the benthic amphipod Leptocherius plumulosus. 

Environmental Toxicology and Chemistry 29(8):1755-1761.

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divalent mercury in aqueous solution. Water, Air, and Soil Pollution 80:789-798.

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marine fish Lutjanus argentimaculatus. Marine Ecology Progress Series 238: 173-186.

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copepods and fish. Environmental Pollution 159(12):3390-3397.
[This page intentionally left blank]
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-1 6-29-2012

Description of MIRC

Addendum 2. Description of Multimedia Ingestion Risk Calculator 

(MIRC) Used for RTR Exposure and Risk Estimates
[This page intentionally left blank.]
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-3 6-29-2012

Description of MIRC

CONTENTS, ADDENDUM 2

1. Introduction................................................................................................................... 2-11

1.1. Purpose and Overview .........................................................................................2-11

1.2. Scope of MIRC .....................................................................................................2-11

1.3. Use in EPA’s Air Toxics Program..........................................................................2-12

1.4. MIRC Highlights....................................................................................................2-12

1.5. Organization of This Addendum............................................................................2-13

2. MIRC Overview............................................................................................................. 2-13

2.1. Software ...............................................................................................................2-14

2.2. Exposure Pathways ..............................................................................................2-16

2.3. Receptor Groups ..................................................................................................2-17

3. Exposure Algorithms..................................................................................................... 2-19

3.1. Farm Food Chain Algorithms ................................................................................2-19

3.1.1. Estimating Chemical Concentrations in Produce ................................... 2-20

3.1.2. Estimating Chemical Concentrations in Animal Products....................... 2-26

3.2. Chemical Intake Calculations for Adults and Non-Infant Children .........................2-29

3.2.1. Chemical Intake from Soil Ingestion....................................................... 2-31

3.2.2. Chemical Intake from Fish Ingestion...................................................... 2-31

3.2.3. Chemical Intake from Fruit Ingestion ..................................................... 2-32

3.2.4. Chemical Intake from Vegetable Ingestion............................................. 2-33

3.2.5. Chemical Intake from Animal Product Ingestion .................................... 2-34

3.2.6. Chemical Intake from Drinking Water Ingestion ..................................... 2-37

3.3. Total Chemical Intake...........................................................................................2-37

3.4. Chemical Intake Calculations for Nursing Infants..................................................2-38

3.4.1. Infant Average Daily Absorbed Dose..................................................... 2-38

3.4.2. Chemical Concentration in Breast Milk Fat ............................................ 2-39

3.4.3. Chemical Concentration in Aqueous Phase of Breast Milk .................... 2-43

3.4.4. Alternative Model for Infant Intake of Methyl Mercury ............................ 2-45

4. Dose-Response Values Used for Assessment.............................................................. 2-46

4.1. Cadmium ..............................................................................................................2-49

4.2. Dioxins (2,3,7,8-TCDD) ........................................................................................2-49

4.3. Mercury ................................................................................................................2-50

4.4. Polycyclic Organic Matter .....................................................................................2-50

5. Risk Estimation ............................................................................................................. 2-52

5.1. Cancer Risks ........................................................................................................2-52

5.2. Non-cancer Hazard Quotients ..............................................................................2-54

5.2.1. Hazard Quotients for Chemicals with a Chronic RfD.............................. 2-55
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-4 6-29-2012

Description of MIRC

5.2.2. Hazard Quotients for Chemicals with RfD Based on 

Developmental Effects .......................................................................... 2-55

5.2.3. Hazard Index for Chemicals with RfDs .................................................. 2-55

6. Model Input Options...................................................................................................... 2-56

6.1. Environmental Concentrations..............................................................................2-57

6.2. Farm-Food-Chain Parameter Values ....................................................................2-57

6.2.1. List of Farm-Food-Chain (FFC) Parameters .......................................... 2-57

6.2.2. Produce Parameter Values.................................................................... 2-59

6.2.3. Animal Product Parameter Values......................................................... 2-72

6.3. Adult and Non-Infant Exposure Parameter Values................................................2-74

6.3.1. Body Weights ........................................................................................ 2-75

6.3.2. Water Ingestion Rates ........................................................................... 2-76

6.3.3. Local Food Ingestion Rates ................................................................... 2-77

6.3.4. Local Fish Ingestion Rates .................................................................... 2-81

6.3.5. Soil Ingestion Rates............................................................................... 2-86

6.3.6. Total Food Ingestion Rates.................................................................... 2-87

6.4. Other Exposure Factor Values..............................................................................2-87

6.4.1. Exposure Frequency ............................................................................. 2-88

6.4.2. Fraction Contaminated .......................................................................... 2-88

6.4.3. Preparation and Cooking Losses........................................................... 2-88

6.5. Breast-Milk Infant Exposure Pathway Parameter Values ......................................2-90

6.5.1. Receptor-specific Parameters................................................................ 2-90

6.5.2. Chemical-Specific Parameter Values..................................................... 2-94

7. Summary of MIRC Default Exposure Parameter Settings ............................................. 2-97

7.1. Default Ingestion Rates ........................................................................................2-98

7.1.1. Fish Ingestion Rates.............................................................................. 2-98

7.1.2. Farm Food Chain Ingestion ................................................................... 2-98

7.2. Default Screening-Level Population-Specific Parameter Values .........................2-100

7.3. Default Chemical-Specific Parameter Values for Screening Analysis .................2-101

7.4. Screening-Level Parameter Values for Nursing Infant Exposure.........................2-103

7.4.1. Dioxins2-103

7.4.2. Methyl Mercury.................................................................................... 2-103

8. References ................................................................................................................. 2-104

1. Hazard Identification and Dose Response Assessment .................................................. 3-5

2. Dermal Exposure Estimation........................................................................................... 3-6

2.1. Equations for Estimating Dermal Exposure.............................................................3-6

2.2. Exposure Factors and Assumptions .......................................................................3-7

2.3. Receptor-Specific Parameters ................................................................................3-8

2.4. Scenario-Specific Parameters ................................................................................3-8

2.5. Chemical-Specific Parameters................................................................................3-9
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-5 6-29-2012

Description of MIRC

3. Screening-Level Cancer Risks and Non-Cancer Hazard Quotients............................... 3-10

3.1. Dermal Cancer Risk..............................................................................................3-10

3.2. Dermal Hazard Quotient .......................................................................................3-11

4. Dermal Screening Results ............................................................................................ 3-11

5. References ................................................................................................................... 3-14

6. Introduction..................................................................................................................... 2-4

6.1. Angler Behavior......................................................................................................2-4

6.2. Fish Biology............................................................................................................2-5

6.2.1. Lake Productivity ..................................................................................... 2-5

6.2.2. Proportion of Fish Biomass by Trophic Level........................................... 2-7

6.2.3. Minimum Viable Population Size ............................................................. 2-8

6.3. Summary of Assumptions for the Lake Size Analysis .............................................2-9

6.4. Equations Used to Determine Lake Fish Populations .............................................2-9

6.5. References ...........................................................................................................2-11
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-6 6-29-2012

Description of MIRC

Exhibits, Addendum 2

Exhibit_Add A1-1. TRIM.FaTE Simulation Parameters for the TRIM.FaTE 

Screening Scenario........................................................................................... 1-5

Exhibit_Add A1-2. Meteorological Inputs for the TRIM.FaTE Screening Scenario.................. 1-6

Exhibit_Add A1-3. Air Parameters for the TRIM.FaTE Screening Scenario............................ 1-7

Exhibit_Add A1-4. Soil and Groundwater Parameters for the TRIM.FaTE 

Screening Scenario........................................................................................... 1-8

Exhibit_Add A1-5. Runoff Assumptions for the TRIM.FaTE Screening Scenario.................. 1-10

Exhibit_Add A1-6. USLE Erosion Parameters for the TRIM.FaTE Screening 

Scenario ......................................................................................................... 1-11

Exhibit_Add A1-7. Terrestrial Plant Placement for the TRIM.FaTE Screening 

Scenario ......................................................................................................... 1-12

Exhibit_Add A1-8. Terrestrial Plant Parameters for the TRIM.FaTE Screening 

Scenario ......................................................................................................... 1-13

Exhibit_Add A1-9. Surface Water Parameters for the TRIM.FaTE Screening 

Scenario ......................................................................................................... 1-15

Exhibit_Add A1-10. Sediment Parameters for the TRIM.FaTE Screening 

Scenario ......................................................................................................... 1-16

Exhibit_Add A1-11. Aquatic Animals Food Chain, Density, and Mass for the 

TRIM.FaTE Screening Scenario ..................................................................... 1-17

Exhibit_Add A1-12. Cadmium Chemical-Specific Parameters for the TRIM.FaTE 

Screening Scenario......................................................................................... 1-18

Exhibit_Add A1-13. Mercury Chemical-Specific Parameters for the TRIM.FaTE 

Screening Scenario......................................................................................... 1-19

Exhibit_Add A1-14. PAH Chemical-Specific Parameters for the TRIM.FaTE 

Screening Scenario......................................................................................... 1-20

Exhibit_Add A1-15. Dioxin Chemical-Specific Parameters for the TRIM.FaTE 

Screening Scenario......................................................................................... 1-22

Exhibit_Add A1-16. Cadmium Chemical-Specific Parameters for Abiotic 

Compartments in the TRIM.FaTE Screening Scenario................................... 1-24

Exhibit_Add A1-17. Mercury Chemical-Specific Parameters for Abiotic 

Compartments in the TRIM.FaTE Screening Scenario.................................... 1-25

Exhibit_Add A1-18. PAH Chemical-Specific Parameters for Abiotic 

Compartments in the TRIM.FaTE Screening Scenario.................................... 1-29

Exhibit_Add A1-19. Dioxin Chemical-Specific Parameters for Abiotic 

Compartments in the TRIM.FaTE Screening Scenario.................................... 1-32

Exhibit_Add A1-20. Cadmium Chemical-Specific Parameters for Plant 

Compartments in the TRIM.FaTE Screening Scenario.................................... 1-36

Exhibit_Add A1-21. Mercury Chemical-Specific Parameters for Plant 

Compartments in the TRIM.FaTE Screening Scenario.................................... 1-37
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-7 6-29-2012

Description of MIRC

Exhibit_Add A1-22. PAH Chemical-Specific Parameters for Plant Compartments 

in the TRIM.FaTE Screening Scenario............................................................ 1-38

Exhibit_Add A1-23. PAH Chemical-Specific Parameters for Plant Compartments 

in the TRIM.FaTE Screening Scenario............................................................ 1-39

Exhibit_Add A1-24. Cadmium Chemical-Specific Parameters for Aquatic 

Species in the TRIM.FaTE Screening Scenario .............................................. 1-41

Exhibit_Add A1-25. Mercury Chemical-Specific Parameters for Aquatic Species 

in the TRIM.FaTE Screening Scenario............................................................ 1-43

Exhibit_Add A1-26. PAH Chemical-Specific Parameters for Aquatic Species in 

the TRIM.FaTE Screening Scenario ............................................................... 1-44

Exhibit_Add A1-27. Dioxin Chemical-Specific Parameters for Aquatic Species in 

the TRIM.FaTE Screening Scenario ............................................................... 1-47

Exhibit_Add A2-1. Overview of MIRC Software Application for Performing FarmFood-Chain Ingestion Exposure and Risk Calculations................................... 2-15

Exhibit_Add A2-2. Transfer Pathways for Modeled Farm Food Chain (FFC) 

Media.............................................................................................................. 2-17

Exhibit_Add A2-3. Chemical Transfer Pathways for Produce............................................... 2-20

Exhibit_Add A2-4. Estimating Chemical Concentration in Aboveground Produce ................. 2-20

Exhibit_Add A2-5. Chemical Transfer Pathways for Animal Products ................................... 2-26

Exhibit_Add A2-6. Oral Dose-response Values Used to Calculate RTR Screening 

Threshold Emission Rates for PB-HAP Chemicalsa........................................ 2-47

Exhibit_Add A2-7. WHO 2005 Toxic Equivalency Factors (TEFs) for Dioxin 

Congeners ...................................................................................................... 2-50

Exhibit_Add A2-8. Oral Dose-response Values for Polycyclic Organic Matter 

(POM) Groupsa............................................................................................... 2-51

Exhibit_Add A2-10. Chemical-Specific Inputs for Produce Parameters for 

Chemicals Included in MIRC........................................................................... 2-59

Exhibit_Add A2-11. Chemical-Specific Inputs by Plant Type for Chemicals in 

MIRC .............................................................................................................. 2-61

Exhibit_Add A2-12. Non-Chemical-Specific Produce Inputs................................................. 2-71

Exhibit_Add A2-13. Animal Product Chemical-specific Inputs for Chemicals 

Included in MIRC ............................................................................................ 2-72

Exhibit_Add A2-14. Soil and Plant Ingestion Rates for Animals ........................................... 2-74

Exhibit_Add A2-15. Mean and Percentile Body Weight Estimates for Adults and 

Children .......................................................................................................... 2-76

Exhibit_Add A2-16. Estimated Daily Per Capita Mean and Percentile Water 

Ingestion Rates for Children and Adultsa......................................................... 2-77

Exhibit_Add A2-17. Summary of Age-Group Specific Food Ingestion Rates for 

Farm Food Items ............................................................................................ 2-78

Exhibit_Add A2-18. Daily Mean and Percentile Consumer-Only Fish Ingestion 

Rates for Children and Adults (IRCO,y)

a

............................................................ 2-84
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-8 6-29-2012

Description of MIRC

Exhibit_Add A2-19. Fraction of Population Consuming Freshwater/Estuarine 

Fish on a Single Day (FPC,y) ............................................................................ 2-84

Exhibit_Add A2-20. Calculated Long-term Mean and Percentile per capita Fish 

Ingestion Rates for Children and Adults (IRPC,y) .............................................. 2-85

Exhibit_Add A2-21. Calculated Mean and 90th Percentile Per capita Fish 

Ingestion Rates for Populations of Recreational Fishers (IRPC,y) .................... 2-85

Exhibit_Add A2-22. Daily Mean and Percentile Soil Ingestion Rates for Children 

and Adults....................................................................................................... 2-86

Exhibit_Add A2-23. Daily Mean and Percentile Per Capita Total Food Intake for 

Children and Adults......................................................................................... 2-87

Exhibit_Add A2-24. Fraction Weight Losses from Preparation of Various Foods.................. 2-89

Exhibit_Add A2-25. Scenario- and Receptor-Specific Input Parameter Values 

Used to Estimate Infant Exposures via Breast Milk ......................................... 2-91

Exhibit_Add A2-26. Average Body Weight for Infants........................................................... 2-91

Exhibit_Add A2-27. Time-weighted Average Body Weight for Mothers ................................ 2-92

Exhibit_Add A2-28. Infant Breast Milk Intake Rates ............................................................. 2-94

Exhibit_Add A2-29. Chemical-specific Input Parameter Values for Breast Milk 

Exposure Pathway.......................................................................................... 2-95

Exhibit_Add A2-30. Farm Food Category Ingestion Rates for Health Protective 

Screening Scenario for Farming Households.................................................. 2-99

Exhibit_Add A2-31. Mean Body Weight Estimates for Adults and Childrena....................... 2-100

Exhibit_Add A2-32. Chemical-Specific Parameter Values for Input to MIRCa..................... 2-101

Exhibit_Add A2-33. Chemical and Animal-Type Specific Biotransfer Factor (Ba) 

Values for Input to MIRC............................................................................... 2-102

Exhibit_Add A3-1. Cancer Slope Factors and Reference Doses Based on 

Absorbed Dose................................................................................................. 3-6

Exhibit_Add A3-2. Receptor-Specific Body Surface Area Assumed to be 

Exposed to Chemicals ...................................................................................... 3-8

Exhibit_Add A3-3. Scenario-Specific Exposure Values for Water and Soil 

Contact ............................................................................................................. 3-9

Exhibit_Add A3-4. Chemical-Specific Dermal Exposure Values for Water and 

Soil Contact .................................................................................................... 3-10

Exhibit_Add A3-5. Summary of Dermal Non-Cancer Hazards.............................................. 3-12

Exhibit_Add A3-6. Summary of Dermal Cancer Risks .......................................................... 3-13
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-9 6-29-2012

Description of MIRC

Equations, Addendum 2

Equation 2-1. Chemical Concentration in Aboveground Produce .......................................... 2-21

Equation 2-2. Chemical Concentration in Aboveground Produce Due to Root 

Uptake ............................................................................................................ 2-21

Equation 2-3. Chemical Concentration in Aboveground Produce Due to 

Deposition of Particle-phase Chemical............................................................ 2-22

Equation 2-4. Chemical Concentration in Aboveground Produce Due to............................... 2-22

Equation 2-5. Conversion of Aboveground Produce Chemical Concentration from 

Dry- to Wet-Weight Basis................................................................................ 2-23

Equation 2-6. Chemical Concentration in Belowground Produce: Nonionic 

Organic Chemicals.......................................................................................... 2-24

Equation 2-7. Chemical Concentration in Belowground Produce: Inorganic 

Chemicals....................................................................................................... 2-25

Equation 2-8. Conversion of Belowground Produce Chemical Concentration from 

Dry- to Wet-Weight Basis................................................................................ 2-25

Equation 2-9. Chemical Concentration in Beef, Pork, or Total Dairy...................................... 2-26

Equation 2-10. Chemical Concentration in Poultry or Eggs ................................................... 2-27

Equation 2-11. Incidental Ingestion of Chemical in Soil by Livestock..................................... 2-27

Equation 2-12. Ingestion of Chemical in Feed by Livestock................................................... 2-28

Equation 2-13. Chemical Concentration in Livestock Feed (All Aboveground) ...................... 2-28

Equation 2-14. Chemical Concentration in Livestock Feed Due to Root Uptake................... 2-29

Equation 2-15. Average Daily Dose for Specified Age Group and Food Type ...................... 2-29

Equation 2-16. Chemical Intake from Soil Ingestion ............................................................. 2-31

Equation 2-17. Chemical Intake from Fish Ingestion ............................................................ 2-31

Equation 2-18. Consumption-weighted Chemical Concentration in Fish .............................. 2-32

Equation 2-19. Chemical Intake from Consumption of Exposed Fruits ................................. 2-33

Equation 2-20. Chemical Intake from Consumption of Protected Fruits................................ 2-33

Equation 2-21. Chemical Intake from Exposed Vegetables.................................................. 2-34

Equation 2-22. Chemical Intake from Protected Vegetables................................................. 2-34

Equation 2-23. Chemical Intake from Root Vegetables ........................................................ 2-34

Equation 2-24. Chemical Intake from Ingestion of Beef........................................................ 2-35

Equation 2-25. Chemical Intake from Dairy Ingestion........................................................... 2-35

Equation 2-26. Chemical Intake from Pork Ingestion............................................................ 2-35

Equation 2-27. Chemical Intake from Poultry Ingestion ........................................................ 2-36

Equation 2-28. Chemical Intake from Egg Ingestion............................................................. 2-36

Equation 2-29. Chemical Intake from Drinking Water Ingestion............................................ 2-37
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-10 6-29-2012

Description of MIRC

Equations 2-30 to 2-35. Total Average Daily Dose of a Chemical for Different 

Age Groups .................................................................................................... 2-37

Equation 2-31........................................................................................................................ 2-37

Equation 2-32........................................................................................................................ 2-37

Equation 2-33........................................................................................................................ 2-37

Equation 2-34........................................................................................................................ 2-37

Equation 2-35........................................................................................................................ 2-37

Equation 2-36. Lifetime Average Daily Dose (LADD) ........................................................... 2-38

Equation 2-37. Average Daily Dose of Chemical to the Nursing Infant ................................. 2-39

Equation 2-38. Chemical Concentration in Breast Milk Fat................................................... 2-40

Equation 2-39. Daily Maternal Absorbed Intake ................................................................... 2-41

Equation 2-40. Biological Elimination Rate Constant for Chemicals for Nonlactating Women............................................................................................. 2-42

Equation 2-41. Biological Elimination Constant for Lipophilic Chemicals for 

Lactating Women............................................................................................ 2-42

Equation 2-42. Chemical Concentration in Aqueous Phase of Breast Milk ........................... 2-43

Equation 2-43. Fraction of Total Chemical in Body in the Blood Plasma 

Compartment.................................................................................................. 2-44

Equation 2-44. Biological Elimination Rate Constant for Hydrophilic Chemicals................... 2-45

Equation 2-45. Calculation of Infant Average Daily Absorbed Dose of Methyl 

Mercury........................................................................................................... 2-46

Equation 2-46. Calculation of Excess Lifetime Cancer Risk ................................................. 2-53

Equations 2-47 to 2-53. Lifetime Cancer Risk: Chemicals with a Mutagenic MOA 

for Cancer....................................................................................................... 2-54

Equation 2-48........................................................................................................................ 2-54

Equation 2-49........................................................................................................................ 2-54

Equation 2-50........................................................................................................................ 2-54

Equation 2-51........................................................................................................................ 2-54

Equation 2-52........................................................................................................................ 2-54

Equation 2-53. ELCR ................................................................................. 2-54

Equation 2-54. Hazard Quotient for Chemicals with a Chronic RfD...................................... 2-55

Equation 2-55. Hazard Index Calculation ............................................................................. 2-56

Equation 2-56. Calculation of Age-Group-Specific and Food-Specific Ingestion 

Rates .............................................................................................................. 2-80

Equation 2-57. Calculation of Alternative Age-Group-Specific Fish Ingestion 

Rates .............................................................................................................. 2-83



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Risk i 1

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TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-11 6-29-2012

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.

19

 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 

 

19Fully functional versions of MIRC have been developed in both Access-based and Excel-based formats; however, 

MIRC currently is not publicly available. 
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-12 6-29-2012

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.

20

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. 

 

20 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 A, Addendum 2 2-13 6-29-2012

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 Addendum

Sections 2 through 5 of this addendum 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 A, Addendum 2 2-14 6-29-2012

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 

addendum) 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 addendum). 

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_Add A2-1 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 A, Addendum 2 2-15 6-29-2012

Description of MIRC

Exhibit_Add A2-1. 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 A, Addendum 2 2-16 6-29-2012

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 analysis of various exposure scenarios. To 

begin an analysis, 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 pan fish and in game 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 addendum). 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_Add A2-2 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 A, Addendum 2 2-17 6-29-2012

Description of MIRC

Exhibit_Add A2-2. 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

aChemical 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 addendum.

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 addendum.

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 A, Addendum 2 2-18 6-29-2012

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 2011) 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 A, Addendum 2 2-19 6-29-2012

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 

addendum 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 addendum, 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 A, Addendum 2 2-20 6-29-2012

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_Add A2-3 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_Add A2-3. 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_Add A2-4. 

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_Add A2-4. 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 A, Addendum 2 2-21 6-29-2012

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 2-1. 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 2-2. 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_produc e BrAG produce DW(i)r      
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-22 6-29-2012

Description of MIRC

Equation 2-3. 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 2-3 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 2-3. 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 2-4. Chemical Concentration in Aboveground Produce Due to 

Air-to-Plant Transfer of Vapor-phase Chemical

 

where:

( ) ( )

( ( )* ( ))

( )

( )

1,000 ( ( )) (1 )

i i

k p i Tp i

i

i

Yp kp

Drdp Fw Drwp Rp e

Pd



     





a

AG i AG i

i

Ca Fv Bv VG

Pv



( ) ( )

( )

  


TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-23 6-29-2012

Description of MIRC

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 2-4 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-m

3

. 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 2-5 and the moisture 

content (MAF) of the FFC food category.

Equation 2-5. 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 A, Addendum 2 2-24 6-29-2012

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 2-6. 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 2-6. 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 A, Addendum 2 2-25 6-29-2012

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 2-7 is used instead of Equation 2-6.

Equation 2-7. 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 2-6) 

(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 2-8. 

Equation 2-8. 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 2-5, 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 A, Addendum 2 2-26 6-29-2012

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_Add A2-5

summarizes the pathways by which chemicals are transferred to these home- or farm-raised 

animal food products. Note that for a general screening-level assessment, all of the pathways 

can be modeled, as is done for EPA’s RTR calculation of screening threshold emission rates for 

PB-HAPs (EPA 2008b). 

The feed options for farm animals in MIRC include forage (plants grown on-site for animal 

grazing, such as grass), silage (wet forage grasses, fresh-cut hay, or other fresh plant material 

that has been stored and fermented), and grain products grown on the farm. As seen in

Exhibit_Add A2-5, 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_Add A2-5. 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

aChemical concentrations in plant feed (i.e., forage, silage, and grain) are estimated via intermediate calculations (see 

Equation 2-13, Equation 2-14, Equation 2-3, and Equation 2-4). 

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 2-9 to calculate the 

concentration of chemical in beef, pork, or total dairy and Equation 2-10 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 2-9). 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 2-10 for poultry and eggs.

Equation 2-9. Chemical Concentration in Beef, Pork, or Total Dairy

where:

















   

 

n

i

Cmammal m Ba m MF SoilCh Intake m PlantCh Intake i m

1

( ) ( ) ( ) ( , )
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-27 6-29-2012

Description of MIRC

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 

2-11 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 2-12 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 2-10. 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 2-11

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 2-12

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 

2-11. 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 addendum). 

Equation 2-11. Incidental Ingestion of Chemical in Soil by Livestock

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)   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 A, Addendum 2 2-28 6-29-2012

Description of MIRC

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 

2-12. 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 2-12. 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 2-13. The equation is the same as that for aboveground 

produce in Equation 2-1, 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 2-13. 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

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 2-14 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 

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 A, Addendum 2 2-29 6-29-2012

Description of MIRC

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 2-14. The equation is the same as Equation 2-2, 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 addendum, 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 2-14. 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 2-3 and Equation 2-4, 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 2-15, are expressed in milligrams of chemical 

per kilogram of receptor body weight per day (mg/kg-day).

Equation 2-15. Average Daily Dose for Specified Age Group and Food Type

where: 

ADD(y,i) =

Average daily dose for age group y from food type or ingestion medium i (mg 

chemical/kg body weight-day)

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 A, Addendum 2 2-30 6-29-2012

Description of MIRC

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 2-15 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 2011, 

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 addendum. 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 addendum. 

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 addendum. 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 2-15. 

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,

– Beef products,

– Pork products, and
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-31 6-29-2012

Description of MIRC

– 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 addendum. The algorithms for the breast-milk ingestion pathway are 

described in Section 3.4.

3.2.1. Chemical Intake from Soil Ingestion

Equation 2-16 shows the equation used to estimate chemical intake through incidental ingestion 

of soil. 

Equation 2-16. 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 

2-17). Two types of fish are included in the exposure algorithm: trophic level 3 (TL3) fish, 

equivalent to small “pan” fish such as bluegill, and trophic level 4 (TL4) fish, equivalent to game 

fish such as trout and walleye. The chemical concentration in fish in Equation 2-17 is estimated 

as the consumption-weighted chemical concentration using Equation 2-18.

Equation 2-17. Chemical Intake from Fish Ingestion

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



  



365 days

EF

BW

μg

mg C IR FC 0.001 

ADD

(y)

Soil Soil(y) Soil

Soil(y)    

365 days

g

kg 0 001

1 1 1 2 



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



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

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





  

    

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 A, Addendum 2 2-32 6-29-2012

Description of MIRC

Equation 2-18. Consumption-weighted Chemical Concentration in Fish

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 (TL3) fish on a wet-weight 

(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 2-18) (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 addendum), the tables of fish ingestion rate options included in MIRC are from CSFII data (see Section 6.3.4 of 

this addendum) and, therefore, 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 addendum.

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 2011) and 

included those losses in its HHRAP algorithms for chemical intake from fish (L1Fish and L2Fish in 

Equation 2-17). 

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 2-19 and Equation 2-20, respectively).

    CFish  CFishT3

FT3  CFishT 4FT 4
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-33 6-29-2012

Description of MIRC

Equation 2-19. Chemical Intake from Consumption of Exposed Fruits

Equation 2-20. 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 

such as corn, cabbage, soybeans, and peas; and one for root vegetables such as carrots, 

beets, and potatoes (see Equation 2-21, Equation 2-22, and Equation 2-23, respectively). 

    

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       

g 365 days

kg 1 1 1 2 0 001 EF ADDExpFruit(y) L ExpFruit L ExpFruit CExpFruit IRExpFruit(y)

. FCExpFruit  

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     

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 A, Addendum 2 2-34 6-29-2012

Description of MIRC

Equation 2-21. Chemical Intake from Exposed Vegetables

Equation 2-22. Chemical Intake from Protected Vegetables

Equation 2-23. 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)

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 2-24 through Equation 2-28 for homegrown beef, dairy (milk), 

pork, poultry, and eggs, respectively.

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     

g 365 days

kg 1 1 0 001 EF ADDExpVeg(y) L ExpVeg CExpVeg IRExpVeg(y) . FCExpVeg   

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     

g 365 days

kg 1 1 0 001 EF ADDProVeg(y) L ProVeg CProVeg IRProVeg(y) . FCProVeg     



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       

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 A, Addendum 2 2-35 6-29-2012

Description of MIRC

Equation 2-24. 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 2-25. 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 2-26. Chemical Intake from Pork Ingestion

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

   

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

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       

g 365 days

kg 1 1 1 2 0 001 EF ADDBeef(y) L Beef L Beef CBeef IRBeef(y) . FCBeef 



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   

g 365 days

kg 0 001 EF ADDDairy(y) CDairy IRDairy(y) . FCDairy    



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

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       

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 A, Addendum 2 2-36 6-29-2012

Description of MIRC

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 2-27. 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 2-28. Chemical Intake from Egg Ingestion

where:

ADDEgg(y) =

Average daily chemical intake from ingestion of eggs for age group y (mg/kgday)

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)

    

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

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       

g 365 days

kg 1 1 1 2 0 001 EF ADDPoultry(y) L Poultry L Poultry CPoultry IRPoultry(y) . FCPoultry 



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

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

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   

g 365 days

kg 0 001 EF ADDEgg(y) CEgg IREgg(y) . FCEgg
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-37 6-29-2012

Description of MIRC

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 2-29). 

Equation 2-29. 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 Equations 2-30 through Equation 2-35 below.

Equations 2-30 to 2-35. Total Average Daily Dose of a Chemical for Different Age Groups

Equation 2-30.

Equation 2-31.

Equation 2-32.

Equation 2-33.

Equation 2-34.

Equation 2-35.

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















  



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, )



  

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

( ) ( , )
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-38 6-29-2012

Description of MIRC

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 2-36).

Equation 2-36. 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.

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 2-37. 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 2-37 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 

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 

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 

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

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

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 

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

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

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 















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 A, Addendum 2 2-39 6-29-2012

Description of MIRC

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 2-37 (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 addendum. The user 

also can overwrite those parameter values with a different value from the literature as 

appropriate.

Equation 2-37. 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 2-38)

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 2-42)

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 2-37 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 addendum) and Caqueous (described in 

Section 3.4.3 of this addendum).

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) (U.S. EPA 1998), EPA reviewed three 

first-order kinetics models for estimating chemical concentration in breast milk fat. The model 

selected for use in MIRC is the model selected in MPE. The other two models were not 

considered in MPE because one used a biotransfer factor (BTF) 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     

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 A, Addendum 2 2-40 6-29-2012

Description of MIRC

MIRC is a changing-concentration model that EPA adapted from a model by Sullivan et al. 

(1991). The model, shown in Equation 2-38, 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 2-38 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 2-38. 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 2-39)

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 addendum)

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 2-40)

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 2-41)

tbf = Duration of breast feeding (days) 

tpn =

Duration of mother’s exposure prior to parturition and initiation of breast feeding 

(days) 

Equation 2-38 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 

 



































 



 







 elim p n  fat elac b f 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 A, Addendum 2 2-41 6-29-2012

Description of MIRC

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 2-38. 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 2-38, 

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 addendum, Equation 2-35), is multiplied by an 

absorption efficiency (AEmat) or fraction of the chemical absorbed by the oral route of exposure, 

as shown in Equation 2-39. 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 2-39.

Equation 2-39. 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 addendum, Equation 2-35)

AEmat =

Absorption efficiency of the chemical by the oral route of exposure (i.e., 

chemical-specific fraction of ingested chemical that is absorbed) by the mother 

(unitless) (value from literature or EPA default – see Section 6.4 of this 

addendum) 

Equation 2-35, 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 addendum), 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 

DAImat  ADD(adult)  AEmat
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-42 6-29-2012

Description of MIRC

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 addendum.

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 2-40 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 2-40. 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 2-41 to estimate the total

chemical elimination rate for lactating women, kfat_elac (EPA 1998). 

Equation 2-41. Biological Elimination Constant for Lipophilic Chemicals

for Lactating Women

where:

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 2-40)

IRmilk = Infant milk ingestion rate over the duration of nursing (kg/d)

h

kelim

ln 2



fm mat

milk f mbm

fat elac elim f BW

IR f f

k k



 

_  
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-43 6-29-2012

Description of MIRC

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 2-41 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 2-41, 

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 2-39. Body weight 

values for the mother are described in Section 6.5 of this addendum. 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 2-42 (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 2-42. Chemical Concentration in Aqueous Phase of Breast Milk

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 2-39)

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 

2-43)

aq elac pm

mat pl bm

aqueous k f

DAI f Pc

C



 



_
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-44 6-29-2012

Description of MIRC

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 2-44)

fpm = Fraction of maternal weight that is blood plasma (unitless)

Equation 2-42 is a steady-state concentration model that, like the Equation 2-38 for Cmilkfat, is 

dependent on the maternal absorbed daily intake (DAImat). In Equation 2-42, 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 2-42 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 2-43, 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 2-43. 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

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

  bp RBC bp

bl bp

pl f Pc f

f f

f

 





1
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-45 6-29-2012

Description of MIRC

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 2-42 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 

2-40. 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 2-44. 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 2-40)

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_Add A2-28). 

This parameter could be estimated using Equation 2-43 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 

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 

kaq _ elac  kelim
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-46 6-29-2012

Description of MIRC

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 2-42).

Equation 2-45. 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_Add A2-6

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 doseresponse values identified by OAQPS for RTR, alternative methods for deriving values were 

used (see Sections 4.2 and 4.4 of this addendum).

DAIinf_MeHg  DAImat_MeHg
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-47 6-29-2012

Description of MIRC

Exhibit_Add A2-6. 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 foodb7440439 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 1.0E-09 ATSDR

2,3,7,8-

Tetrachlorodibenzofuran 51207319 1.50E+04 Derived from WHO 2005 TEFsc not available
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-48 6-29-2012

Description of MIRC

Exhibit_Add A2-6. 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 not available

2-Methylnaphthalene 91576 5.0E-01 EPA 1999, POM 

Group 72002d 4.0E-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 0 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+03 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 0 IRIS 0 IRIS

Polycyclic Organic Matter 246 5.0E-01 EPA 1999, POM 

Group 71002d not available
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-49 6-29-2012

Description of MIRC

Exhibit_Add A2-6. 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 0 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

aValues as of June 2012; these values may be updated as newer ones become available.

b

There are two chronic RfDs for cadmium—one for food and one for water.

cDose-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 addendum.

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 addendum.

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_Add A2-7 were used to derive the CSFs (shown in Exhibit_Add 

A2-6) 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 A, Addendum 2 2-50 6-29-2012

Description of MIRC

Exhibit_Add A2-7. 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-dioxinb19408743 0.1

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

aSource: 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 analyses, 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 analysis 

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 A, Addendum 2 2-51 6-29-2012

Description of MIRC

POM species reported in NEI, not just the species currently evaluated in this analysis) and the 

corresponding CSFs using this methodology are presented in Exhibit_Add A2-8. These group 

CSFs are used only when OAQPS has not, for the purposes of RTR, identified a CSF specific to 

the individual chemical. 

Exhibit_Add A2-8. 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 A, Addendum 2 2-52 6-29-2012

Description of MIRC

Exhibit_Add A2-8. 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 addendum. 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 A, Addendum 2 2-53 6-29-2012

Description of MIRC

Equation 2-46. 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 addendum, 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 A, Addendum 2 2-54 6-29-2012

Description of MIRC

Equations 2-47 to 2-53. Lifetime Cancer Risk: Chemicals with a

Mutagenic MOA for Cancer

Equation 2-47 Risk(<1) = ADD(0–<1) × 10 × CSF × (1 yr/70 yr)

Equation 2-48. Risk(1–2) = ADD(1–2) × 6.5 × CSF × (2 yr/70 yr)

Equation 2-49. Risk(3–5) = ADD(3–5) × 3 × CSF × (3 yr/70 yr)

Equation 2-50. Risk(6–11) = ADD(6–11) × 3 × CSF × (6 yr/70 yr)

Equation 2-51. Risk(12–19) = ADD(12–19) × 2 × CSF × (8 yr/70 yr)

Equation 2-52. Risk(adult) = ADD(adult) × 1 × CSF × (50 yr/70 yr)

Equation 2-53. ELCR 

In other words, Equation 2-53 indicates that the total excess lifetime cancer risk (ELCR) equals 

the sum of the age-group-specific risks estimated by Equations 2-47 through Equation 2-52, 

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 A, Addendum 2 2-55 6-29-2012

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 2-54 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 2-54. 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 2000a). The HI is the 

sum of HQs across chemicals (not age groups) as shown in Equation 2-55. 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 A, Addendum 2 2-56 6-29-2012

Description of MIRC

Equation 2-55. 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 2000a; 

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

2000a). 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 2000a). 

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 addendum. 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 2.1 and A.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 A, Addendum 2 2-57 6-29-2012

Description of MIRC

6.1. Environmental Concentrations 

As noted in Section 2 of this addendum, 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 A.2.3, the drinking water exposure pathway is 

not modeled for the scenario developed for the Tier 1 analysis 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 Excel 

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 addendum. Parameter values required 

for these HHRAP algorithms, including chemical-specific media transfer factors (e.g., soil-to-
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-58 6-29-2012

Description of MIRC

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 addendum, 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_Add A2-9. 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 A, Addendum 2 2-59 6-29-2012

Description of MIRC

Exhibit_Add A2-9. 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_Add A2-10 and Exhibit_Add A2-11 provide the chemical-specific input values that are 

the current defaults for produce FFC food types in MIRC. Exhibit_Add A2-12 presents 

additional non-chemical-specific input values for parameters used in the algorithms that 

calculate chemical concentrations in produce. Unless otherwise noted, the default parameter 

values were obtained from HHRAP. 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_Add A2-10. 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 NA e

Mercury (elemental) 0.6 NA 1.0E+03 0 f

Mercuric chloride 0.6 NA 5.8E+04 1.8E+03

Methyl mercury 0.6 NA 7.0E+03 0 f

PAHs

2-Methylnaphthalene 0.6 2.2E+02 5.0E+01 1.4E+00

7,12-

Dimethylbenz(a)anthrace

ne

0.6 6.8E+03 4.0E+03 4.2E+04

Acenaphthene 0.6 2.4E+02 3.9E+01 4.6E+00

Acenaphthylene 0.6 2.8E+02 6.8E+01 8.1E+00

Benz(a)anthracene 0.6 6.7E+03 2.9E+03 6.8E+03

Benzo(a)pyrene 0.6 9.2E+03 7.8E+03 1.7E+05
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-60 6-29-2012

Description of MIRC

Exhibit_Add A2-10. 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 A, Addendum 2 2-61 6-29-2012

Description of MIRC

Exhibit_Add A2-10. 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 2-6. 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.

cAs 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 U.S. 

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.

dAs 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_Add A2-11. 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 A, Addendum 2 2-62 6-29-2012

Description of MIRC

Exhibit_Add A2-11. 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 A, Addendum 2 2-63 6-29-2012

Description of MIRC

Exhibit_Add A2-11. 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 A, Addendum 2 2-64 6-29-2012

Description of MIRC

Exhibit_Add A2-11. 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 A, Addendum 2 2-65 6-29-2012

Description of MIRC

Exhibit_Add A2-11. 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 A, Addendum 2 2-66 6-29-2012

Description of MIRC

Exhibit_Add A2-11. 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 A, Addendum 2 2-67 6-29-2012

Description of MIRC

Exhibit_Add A2-11. 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 A, Addendum 2 2-68 6-29-2012

Description of MIRC

Exhibit_Add A2-11. 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 A, Addendum 2 2-69 6-29-2012

Description of MIRC

Exhibit_Add A2-11. 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 A, Addendum 2 2-70 6-29-2012

Description of MIRC

Exhibit_Add A2-11. 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

aAs 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.

bAs 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).

cAs 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 A, Addendum 2 2-71 6-29-2012

Description of MIRC

Exhibit_Add A2-12. 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. 

aBaes 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.

cHHRAP (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.

dYp 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.

eMAF 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 A, Addendum 2 2-72 6-29-2012

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_Add A2-13 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_Add A2-14.

Exhibit_Add A2-13. 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 A, Addendum 2 2-73 6-29-2012

Description of MIRC

Exhibit_Add A2-13. 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.

aAs 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%.

bAs 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 A, Addendum 2 2-74 6-29-2012

Description of MIRC

Exhibit_Add A2-14. 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 cattleb0.5

Silage 2.5

Forage 8.8

Grain 0.47

Dairy cattlec0.4

Silage 4.1

Forage 13.2

Grain 3.0

Swined0.37

Silage 1.4

Grain 3.3

Chicken (eggs)e0.022 Grain 0.2

Source: EPA 2005a HHRAP (Chapter 5).

aBeef 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.

dSwine 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. 

eChickens 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 2011) 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 A, Addendum 2 2-75 6-29-2012

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 2011). The body weight parameter values presented in Exhibit_Add A2-15, 

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_Add A2-15, 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

percentiles 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_Add A2-15. For 

adults, BW represents the weighted average of male and female mean body weights for all 

races, ages 18-74 years, from EPA’s 1997 EFH (EPA 1997a; Tables 7-4 and 7-5). We 

considered updating the body weight for use in the RTR assessment to the revised body 

weights in the 2011 Exposure Factors Handbook (EPA 2011). However, the 2011 EFH warns 

that assessors should consider if the dose estimate (for which the body weight is used to 

calculate) will be used to estimate risk by combining it with a dose-response that was derived 

assuming a body weight of 70 kg, and that if such an inconsistency exists, an adjustment may 

be necessary. Because a 70-kg body weight commonly is used to develop IRIS dose-response 

values, using an 80 kg body weight could result in this inconsistency. Given these issues, we 

have retained the use of an adult body weight of 71.4 kg.

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_Add A2-15, 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.
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-76 6-29-2012

Description of MIRC

Exhibit_Add A2-15. Mean and Percentile Body Weight Estimates

for Adults and Children

Lifestage

(years)

Duration 

(years)

Body Weight (kg)

Mean 5

th 10th 50th 90th 95th

Adulta (20-70) 50 71.4 52.9 56 69.3 89.7 97.6

Child < 1b1 7.83 6.03 6.38 7.76 9.24 9.66

Child 1-2

c

2 12.6 9.9 10.4 12.5 14.9 15.6

Child 3-5

d

3 18.6 13.5 14.4 17.8 23.6 26.2

Child 6-11e6 36.0 22.1 24.0 33.5 51.2 58.6

Child 12-19f8 64.2 39.5 45 64.2 83.5 89

[Child 12-19g8 64.3 41.1 44.6 60.9 88.5 98.4]

aBW represents the sample-size weighted average of male and female mean body weights (all races, 18-74 years) from EPA’s

1997 EFH (Tables 7-4 for males and 7-5 for females). Note that these weights include the weight of clothing, estimated to range 

from 0.09 to 0.28 kg. Although the 18 to 74 year age category in EPA’s EFH does not match exactly the age 20 to 70 year 

categorization of adults in MIRC, the magnitude of error in the mean and percentile body weights is likely to be very small (i.e.,

less than 1%). We considered updating the body weight for use in the RTR assessment to the revised body weights in the 2011 

Exposure Factors Handbook. However, the 2011 EFH warns that assessors should consider if the dose estimate (for which the 

body weight is used to calculate) will be used to estimate risk by combining it with a dose-response that was derived assuming a 

body weight of 70 kg, and that if such an inconsistency exists, an adjustment may be necessary. Because a 70 kg body weight 

commonly is used to develop IRIS dose-response values, using an 80 kg body weight could result in this inconsistency. Given 

these issues, we have retained the use of an adult body weight of 71.4 kg.

bEach 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.

cEach 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.

dBWs obtained directly from Table 8-3 of the 2008 CSEFH (age group 3 to <6 years).

eEach 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.

fMean 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. 

gEach 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. 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. Those values are provided for comparison purposes only and are not included in 

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_Add A2-16 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, 
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-77 6-29-2012

Description of MIRC

presented in Exhibit_Add A2-16, represent community water ingestion, both direct and indirect 

as defined above, for males and females combined, ages 20 years and older. 

Exhibit_Add A2-16. 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 <1b504 482 969 1113 1440

Child 1-2

c

332 255 687 903 1318

Child 3-5

d

382 316 778 999 1592

Child 6-11e532 417 1149 1499 2274

Child 12-19f698 473 1641 2163 3467

Adultg1219 981 2534 3087 4567

Sources: EPA 2004b, 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. 

aSource is Kahn and Stralka 2008, also presented in the CSEFH (EPA 2008a).

bEach 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. 

cEach 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.

dEach IR represents the ingestion rate for age group 3 to <6 years from Table 3-4 of the 2008 CSEFH.

eEach 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.

fEach 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.

gAdult 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 

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

2011). 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.21 

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 2011, Chapter 13): 

 

21Note 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 A, Addendum 2 2-78 6-29-2012

Description of MIRC

(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_Add A2-17. 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_Add A2-17. 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

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
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-79 6-29-2012

Description of MIRC

Exhibit_Add A2-17. 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 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

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
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-80 6-29-2012

Description of MIRC

Exhibit_Add A2-17. 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

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

aPrimary source for values was the 1987–1988 NFCS survey; compiled results are presented in Chapter 13 of 2011 Exposure 

Factors Handbook (EPA, 2011). 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.

bPrimary source for values was 1987–1988 NFCS survey, compiled results presented in Chapter 13 of 2011 Exposure Factors 

Handbook (EPA, 2011). 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.

cPrimary source for children less than 3 years of age was a Khan et al.(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.

dDefault 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 2011): 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_Add A2-17). 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” age-specific 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.

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 2-56

was used to calculate the “missing” age-specific consumer-only IRs:

Equation 2-56. Calculation of Age-Group-Specific and Food-Specific Ingestion Rates

IR

IR IR

IR

PC_total

CO_total PC, age_group_x

CO, age_group_x




TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-81 6-29-2012

Description of MIRC

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

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 

Various Home Produced Food Items) (EPA 2011), 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 IRs for 

children less than 6 years of age. Second, the NFCS data were collected approximately two 

decades ago. Third, the reported fish IRs are for ages 6 to 11 and 12 to 19 and are based on 

29 and 21 individuals in each age category, respectively (EPA 2011, Table 13-20). Finally, the 

IRs 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 IR data are 

very uncertain and are based on a wide variety of freshwater, estuarine, and marine fish, and 

squid (EPA 2011, Table 13-69). Therefore, a more recent survey was sought that included 

larger sample sizes, data for children younger than six years, and IRs for the parts of fish 

actually consumed. 

EPA’s (2002) analysis 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 per 
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-82 6-29-2012

Description of MIRC

capita fish IR 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 IRs for many rural populations in the United States; however, it also may 

underestimate locally caught fish IRs 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 ages 3 to 17 years and for adults, MIRC includes values for the mean and the 90th, 

95th, and 99th percentile fish ingestion rates (freshwater and estuarine fish only) based on 

EPA’s analysis of 1994-96 and 1998 CSFII data (EPA 2002, 2008a). As shown in EPA’s 

2008(a) 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 IRs that are zero result from the short duration of the CSFII survey (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 IRs, 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. 

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.22

 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 these fractions are available in the 2008 CSEFH and EPA 2002.

Equation 2-57 was used to calculate the alternative, per capita fish ingestion rates by age group 

(IRPC,y): 

 

22Most 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.
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-83 6-29-2012

Description of MIRC

Equation 2-57. 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_Add A2-18 and Exhibit_Add 

A2-19. The mean and percentile per capita fish ingestion rates estimated using this 

methodology are summarized in Exhibit_Add A2-20 and are available in MIRC. The fish 

ingestion rates provided in Exhibit_Add A2-20 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_Att A-14 and Exhibit_Att A-16, 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 angler were used. 

As noted in Section 6.4.3 of this addendum, if the user overwrites the fish IRs shown in 

Exhibit_Add A2-20 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 2-15. Suggested values are presented in Section 6.4.3.

PC,y CO,y FPC,y

IR  IR 
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-84 6-29-2012

Description of MIRC

Exhibit_Add A2-19. 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.

bAs 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_Add A2-18. 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 1-2

b

27.31 15.61 64.46 87.60 138.76*

Child 3-5

c

40.31 23.04 95.16 129.31 204.84*

Child 6-11d61.49 28.46 156.86* 247.69* 385.64*

Child 12-19e79.07 43.18 181.40* 211.15* 423.38*

Adultf81.08 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.

aPer 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 

2-57 to provide reasonable, non-zero values for all age groups and percentiles.

bA 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.
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Description of MIRC

Exhibit_Add A2-20. 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 1-2

a

1.37 0.79 3.24 4.41 6.98

Child 3-5

b

2.03 1.16 4.79 6.51 10.3

Child 6-11c2.71 1.25 6.90 10.9 17.0

Child 12-19d3.90 2.13 8.95 10.4 20.9

Adulte6.90 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.

aValues were calculated as (consumer-only IR for Child 1-2) x (fraction of population consuming fish for Child 3-5).

bValues were calculated as (consumer-only IR for Child 3-5) x (fraction of population consuming fish for Child 3-5).

cValues were calculated as (consumer-only IR for Child 6-11) x (fraction of population consuming fish for Child 6-11).

dValues were calculated as (consumer-only IR estimated for Child 12-19) x (fraction of population estimated to consume fish for 

Child 12-19).

eValues were calculated as (consumer-only IR for Adults) x (fraction of population consuming fish for Adults).

MIRC also includes values for the mean and the 90th percentile fish ingestion rates for 

recreational anglers, black and female recreational anglers, and anglers of Hispanic, Laotian, 

and Vietnamese descent which are shown in Exhibit_Add A2-21. These latter three populations 

are culturally or economically disposed to higher rates of fish ingestion than the general 

population. Recreational angler values are from the EFH (EPA, 2011). Black and female 

recreational anglers ingestion rates are presented in Burger et al. (2010). 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_Add 

A2-21 for Hispanic, Laotian, and Vietnamese fishers were derived by EPA using information 

from Shilling et al. (2010; EPA, 2010).

Exhibit_Add A2-21. 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, 1997)

bBurger et al., 2010, weights reported are post-cooking for locally caught fish.

cShilling, F., A. White, L. Lippert, and M. Lubell. 2010. Contaminated fish consumption in California’s Central Valley Delta. 

Environmental Research 110: 334-344., weights reported are post-cooking for locally caught fish Burger et al., 2010.

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 
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-86 6-29-2012

Description of MIRC

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 

addendum).

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.

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_Add A2-22 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_Add A2-22. Daily Mean and Percentile Soil Ingestion Rates 

for Children and Adults

Age Group

(years)

Soil Ingestion Rate (mg/day)

Meana50tha 90th 95th 99th

Child < 1 NA

Child 1-2 50 50 200b200b200b

Child 3-5 50 50 200b200b200b

Child 6-11 50 50 201c331d331d

Child 12-19 50 50 201c331d331d

Adult 20-70 20 20 201c331d331d

Sources: EPA 2008, EPA 2011

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.

bValues are the recommended “upper percentile” value for children from EPA’s 2011 EFH, Chapter 4, Table 4-23. The 2008 CSEFH 

and 2011 ESH included a high-end value associated with pica only, but this value has not been used.

cValues are 90th percentile adult ingestion rates calculated in Stanek et al. 1997; used to represent older children and adults.

dValues are 95th percentile adult ingestion rates calculated in Stanek et al. 1997; used to represent older children and adults.
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-87 6-29-2012

Description of MIRC

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_Add A2-23 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.

Exhibit_Add A2-23. 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 < 1a67.0% - 99.7% h322 270 599 779 1152

Child 1-2

b

100% 1,032 996 1537 1703 2143

Child 3-5

c

100% 1,066 1,020 1,548 1,746 2,168

Child 6-11d100% 1,118 1,052 1,642 1,825 2,218

Child 12-19e100% 1,197 1,093 1,872 2,231 2,975

Adultf100% 1,100 1,034 1,738 2,002 2,736

Total Food Intake (g/kg-day, as consumed)

Child < 1a67.0% - 99.7% h39 34 72 95 147

Child 1-2

b

100% 82 79 125 144 177

Child 3-5

c

100% 61 57 91 102 132

Child 6-11d100% 40 38 61 70 88

Child 12-19e100% 21 19 34 40 51

Adultg100% 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. 

hPercents 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), 
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-88 6-29-2012

Description of MIRC

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. 

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_Add A2-24. All preparation 
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Attachment A, Addendum 2 2-89 6-29-2012

Description of MIRC

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 2011).

Exhibit_Add A2-24. 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 Fruitc0.244 0.305

Exposed Vegetable 0.162d NA

Protected Fruit 0.29e NA

Protected Vegetable 0.088f NA

Root Vegetableg0.075 0.22

Beef 0.27 0.24

Pork 0.28 0.36

Poultry 0.32 0.295h

Fishi0.0 0.0 or 1.5j

Source: EPA 1997a and 2011

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 (EPA 2011, Table 13-5). For volatile or water soluble 

chemicals, a non-zero value for L2 also may be appropriate. Although EPA (2011) recommended 0.11 for L2, it varies 

substantially by chemical.

jCooking loss accounts for the difference between ingestion rates for cooked fish and calculated Hg concentrations in whole fish.

Cooking fish tends to reduce the overall weight of fish, and volatilization of mercury is unlikely to occur during cooking, increasing

the concentration of mercury by a factor of roughly 1.5 (Morgan et al. 1997). However, the default value of 0 is assumed for all 

chemicals in the screening assessment. The value of 1.5 for methyl mercury might be more appropriate, but was not used due to 

uncertainties associated with the value (also see text).

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 fruit

(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. 
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Attachment A, Addendum 2 2-90 6-29-2012

Description of MIRC

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 from a more recent and 

larger survey, EPA’s (2002) analysis of freshwater and estuarine fish consumption from the 

USDA’s 1994-96 and 1998 CSFII. That survey reports ingestion rates of fish parts actually 

consumed, and so no loss processes for preparation are needed. The zero value for L2 

assumes that no chemical is lost by volatilization and that pan juices are consumed. The user 

may reset that value where chemical-specific data are available. For example, in the case of 

methyl mercury, the chemical is not lost by volatilization during cooking, but liquid from the fish 

is lost. This lowers the mass of fish consumed, thereby increasing the methyl mercury 

concentration. As such, an L2 value of 1.5 based on the Morgan et al. (1997) study might be 

more appropriate. (Note that due to uncertainties associated with the cooking conversion factor 

of 1.5 for methyl mercury, it was not used in the RTR multipathway screening assessment. 

However, this is an area that should be investigated further for potential use in the future.)

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. An appropriate value for L1 

would be 0.3 (EPA 2011, Table 13-69). For volatile or water soluble chemicals, a non-zero 

value for L2 also may be appropriate. Although EPA recommended 0.11 for L2 (EPA 2011), it 

can vary substantially by chemical. 

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 addendum) 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_Add A2-25 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 addendum.
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Attachment A, Addendum 2 2-91 6-29-2012

Description of MIRC

Exhibit_Add A2-25. 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 2-36

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_Add A2-26 presents 

additional values for the infant body weight parameter that the user can select instead of the 

MIRC default.

Exhibit_Add A2-26. 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 7.8a9.0 9.6

5

th 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
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Attachment A, Addendum 2 2-92 6-29-2012

Description of MIRC

Exhibit_Add A2-26. 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 7.8a9.0 9.6

85th percentile 7.4 9.0 10.3 11.0

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.

aMIRC 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 2004b). Exhibit_Add A2-27 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_Add A2-27. Time-weighted Average

Body Weight for Mothers

Statistic Weight (kg)

Mean 66.0a

5

th 47.1

10th 50.2

25th 54.3

50th 62.0

75th 72.0

90th 85.7

95th 97.0

Source: EPA 2004b

aMIRC 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 
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-93 6-29-2012

Description of MIRC

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) 

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 2-38, 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 2-38) 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 2011, 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 2011, 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 2011). 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 2011). The resulting values are shown in 

the first two rows of Exhibit_Add A2-28. The MIRC screening-level default of 980 mL/day is an 

upper-bound estimate based on a one-year nursing period. 

Exhibit_Add A2-28 also includes the recommended values for four non-overlapping age 

categories from the CSEFH (U.S. EPA 2008a, Table 15-1). The values demonstrate that 
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-94 6-29-2012

Description of MIRC

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.

Exhibit_Add A2-28. 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 2011†

0 to < 12 

months 688 0.709 980a

1.01aEPA 2011†

0 to < 1 month 510 0.525 950 0.979 EPA 2008††

1 to < 3 months 690 0.711 980 1.01 EPA 2008†

3 to < 6 months 770 0.793 1,000 1.03 EPA 2008†

6 to < 12 

months 620 0.639 1,000 1.03 EPA 2008†

aMIRC 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 2-38 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 2-38 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 2-38 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_Add 

A2-29. Note that the parameters for which values are needed are different for the lipophilic 
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-95 6-29-2012

Description of MIRC

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.

Exhibit_Add A2-29. 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-04b1.4E-02 c

kfat_elac Rate constant for total elimination of lipophilic 

chemicals by lactating women (per day)

Est. using 

Equation 2-41

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.

bAn empirical value for this variable is currently missing for application of model. 

c

This value was calculated from biological half-life (h) using Equation 2-40. 

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 
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-96 6-29-2012

Description of MIRC

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). 

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 2-43. 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.
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-97 6-29-2012

Description of MIRC

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 2-43. 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. 

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 2-40. 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 2-41. 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 2-42, 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 addendum. 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.
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-98 6-29-2012

Description of MIRC

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_Add A2-30). Fish ingestion 

rates are also available from these sources; however, as described in Section 6.3.4, these data 

were not used. 

7.1.1. Fish Ingestion Rates

The fish ingestion rates for the Tier 1 screening correspond to “as prepared” total 

freshwater/estuarine fish ingestion rates at the 99th percentile of the distribution for the general 

population, inclusive of both consumers and non-consumers of fish, as estimated in EPA’s 

Estimated Per Capita Fish Consumption in the United States (EPA 2002), Section 4. The 

original data were collected as part of the 1994–96 and 1998 USDA Continuing Survey of 

Intakes by Individuals (CSFII; USDA 2000). These data reflect per capita consumption (i.e., 

consumption rates for the general population) rather than consumption only for individuals who 

consume fish. Values reflect “as prepared” ingestion rates for which cooking and preparation 

losses (L1 and L2) did not need to be considered. “Total” fish is meant to represent 

consumption of finfish plus shellfish. Some adjustments to EPA (2002) fish ingestion rates 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 the footnotes to Exhibit_Add A2-30, these adjustments 

convert the available age-specific data on fish ingestion rates to the age-specific values needed 

for MIRC using body weight to scale the ingestion rates.

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 

“high-end” recreational angler whose diet comprises a substantial proportion of fish. This 

amount of fish is close to that consumed by a “subsistence fisher,” with total exposures from fish 

consumption similar to those observed for specific populations that consume large amounts of 

fish, such as certain ethnic groups. The scenario is not, however, intended to represent the 

maximum exposure an individual subsistence fisher might experience. As shown in 

Exhibit_Add A2-30, for adults, the rate of fish ingestion assumed in the screening scenario is 

105.1 g/day. This consumption rate is similar to the “default” value of 142.4 g/day that EPA’s 

Office of Water recommends for use in estimating exposures to subsistence fishers (EPA 

2000b).

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. 

The 99th percentile values below are believed to represent a reasonable approximation of a 

high-end consumption rate along the lines of the 90th percentile values used for homegrown 

foods and soil.

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). 
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-99 6-29-2012

Description of MIRC

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. 

Exhibit_Add A2-30 also includes a sum of the 90th percentile ingestion rates for homegrown 

food categories and 99th percentile fish ingestion (preceding rows) 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_Add A2-30 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_Add A2-30. 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 milkc1.01 NA NA NA NA NA kg/day

Soil (dry) NA 200d200d201e201e201e mg/day

Fish (per individual)f NA 26.23g38.72g62.29h81.39h105.1hg/day

Fish (per kg BW)i NA 2.08 2.08 1.73 1.27 1.47 g/kg-day

Total Food Ingestion Rates for Comparison Only (not in MIRC; excludes soil and water)

Total Food: Homegrown 

onlyj NA 259 142 99 51 32 g/kg-day
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-100 6-29-2012

Description of MIRC

Exhibit_Add A2-30. 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: All Sourcesk NA 125 91 61 34 23.7 g/kg-day

Overestimate (ratio of 

Homegrown/Total) NA 2.1 1.6 1.6 1.5 1.3 (unitless)

Sources: EPA 2011, EPA 2008, unless otherwise noted.

NA = not applicable

aPrimary source for values was the 1987–1988 NFCS survey; compiled results are presented in Chapter 13 of the 2011 Exposure 

Factors Handbook (EPA, 2011). 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.

bPrimary source for values was 1987–1988 NFCS survey, compiled results are presented in Chapter 13 of the 2011 Exposure Factors 

Handbook (EPA, 2011). 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.

fValues were derived based on EPA’s Estimated Per Capita Fish Consumption in the United States (2002)—Section 4.1.1.1 Table 4 (for 

adults) and Section 4.1.1.1 Table 5 (for child age categories) and are representative of 99th percentile "as prepared" values. 

g

The fish ingestion rate for children aged 3–5 years was obtained directly from Table 5 in the EPA (2002) report; 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 (12.6 kg) to the mean body weight of children 

aged 3–5-years (18.6 kg). 

h

The EPA (2002) fish ingestion estimates and Exposure Factor Handbook (EPA 2011) body weight data were used to derive time- and 

body weight-weighted average ingestion rates that adjust for the differences between the age group ranges used for the RTR screening 

and those presented in the 2002 EPA report.

i

Fish ingestion rates, original data in g/day, have been normalized to body weight in this table to allow addition into total food estimate 

using the mean body weight for each age category.

jSum 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_Add A2-24. The rows are then summed to get the total 

post-cooking ingestion rate.

k

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_Add 

A2-31. As stated in Section 6 of this addendum, 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_Add A2-31. Mean Body Weight Estimates for Adults and Children

a

Lifestage (years) Duration (years) Mean Body Weight (kg)

Adultb(20-70) 50 71.4

Child < 1c1 7.83

Child 1-2

c

2 12.6

Child 3-5

d

3 18.6
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-101 6-29-2012

Description of MIRC

Exhibit_Add A2-31. Mean Body Weight Estimates for Adults and Children

a

Lifestage (years) Duration (years) Mean Body Weight (kg)

Child 6-11e6 36.0

Child 12-19f8 64.2

aSources: EPA 1997, 2008a

bBW represents the sample-size weighted average of male and female mean body weights (all races, 18-74 years) from EPA’s

1997 EFH (Tables 7-4 for males and 7-5 for females). Note that these weights include the weight of clothing, estimated to range 

from 0.09 to 0.28 kg. Although the 18 to 74 year age category in EPA’s EFH does not match exactly the age 20 to 70 year 

categorization of adults in MIRC, the magnitude of error in the mean and percentile body weights is likely to be very small (i.e., 

less than 1%). We considered updating the body weight for use in the RTR assessment to the revised body weights in the 2011 

Exposure Factors Handbook. However, the 2011 EFH warns that assessors should consider if the dose estimate (for which the 

body weight is used to calculate) will be used to estimate risk by combining it with a dose-response that was derived assuming a 

body weight of 70 kg, and that if such an inconsistency exists, an adjustment may be necessary. Because a 70 kg body weight 

commonly is used to develop IRIS dose-response values, using an 80 kg body weight could result in this inconsistency. Given 

these issues, we have retained the use of an adult body weight of 71.4 kg.

c

These values were calculated as time-weighted average body weight (BW) from data presented in Table 8-3 of EPA’s (2008a) 

Child-Specific Exposure Factors Handbook (CSEFH).

d

These values were obtained directly from Table 8-3 of the 2008 CSEFH. 

eEach 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_Add A2-32 presents chemical-specific parameter values for input to MIRC for the 

screening-level analysis. Values for bioavailability when ingested in soil (Bs), mammalian 

metabolism factors (MF), correction factors for belowground produce (VGrootveg), wet deposition 

fractions (Fw), air to plant transfer factors (BvAG), root concentration factors (RCF), and soilwater partition coefficient (Kds) are presented in Exhibit_Add A2-32.

Exhibit_Add A2-32. Chemical-Specific Parameter Values for Input to MIRCa

Parameter Description Benzo(a)-

pyrene

Cadmium Mercuric 

chloride

Methyl 

mercur

y

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
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-102 6-29-2012

Description of MIRC

Exhibit_Add A2-32. Chemical-Specific Parameter Values for Input to MIRCa

Parameter Description Benzo(a)-

pyrene

Cadmium Mercuric 

chloride

Methyl 

mercur

y

2,3,7,8-

TCDD Units

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

BvAG Air-to-plant 

biotransfer factor for 

aboveground 

produce for vaporphase chemical in 

air

124,742 0 1,800 0 65,500 [mg/g 

produce 

DW]/[mg/g 

air]

RCF Chemical-specific 

root concentration 

factor for tubers and 

root produce

9,684 0 0 0 39,999 L soil pore 

water/kg root 

WW

Kds Chemical-specific 

soil/water partition 

coefficient

160,000 75 58,000 7,000 38,904.

51

L soil pore 

water/kg soil 

DW

aValues presented in this exhibit are also presented in previous exhibits; however exact values used in the analysis are presented 

here, rather than values restricted by significant figures. In addition, only values for those chemicals that are specifically used in 

the screening analysis 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 analysis. The inputs that are both chemical-specific and plant-type-specific, 

as presented in Exhibit_Add A2-11, 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_Add A2-33 presents biotransfer 

factors for each of the chemicals and animal types for which screening threshold emissions 

were calculated.

Exhibit_Add A2-33. 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
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-103 6-29-2012

Description of MIRC

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 addendum. 

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 

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_Add A2-30); this assumption 

might overestimate total ingestion of homegrown foods by a factor of more than 2 (see 

Exhibit_Add A2-30).

 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 
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-104 6-29-2012

Description of MIRC

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. 

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TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-109 6-29-2012

Description of MIRC

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Exposure to Combustor Emissions. National Center for Environmental Assessment, 

Cincinnati, OH. EPA-600-R-98-137. Available at:

http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=55525.

EPA. 1999. 1999 National-Scale Air Toxics Assessment Results; Approach for Modeling POM. 

Available at: http://www.epa.gov/ttn/atw/nata1999/nsata99.html.

EPA. 2000a. Supplementary Guidance for Conducting Health Risk Assessment of Chemical 

Mixtures. Risk Assessment Forum, Washington D.C. EPA/630/R-00/002. August. Available 

at: http://www.epa.gov/ncea/raf/pdfs/chem_mix/chem_mix_08_2001.pdf.

EPA. 2000b. Methodology for Deriving Ambient Water Quality Criteria for the Protection of 

Human Health. EPA-822-B-00-004. October.

EPA. 2001a. Peer Review of EPA’s Hazardous Waste Identification Rule Risk Assessment 

Model: Breast milk exposure model for the HWIR 3MRA Model. Prepared by Eastern 

Research Group for EPA Office of Solid Wastes. 68-W5-0057.

EPA. 2001b. Water Quality Criterion for the Protection of Human Health: Methylmercury. Office 

of Water, Office of Science and Technology. Washington D.C. EPA-823-R-01-001. January.

Available at: http://www.epa.gov/ncea/raf/pdfs/chem_mix/chem_mix_08_2001.pdf.

EPA. 2002. Estimated Per capita Fish Consumption in the United States. Office of Water, Office 

of Science and Technology, Washington, D.C. EPA-821- C- 02-003. August. Available at: 

http://www.epa.gov/waterscience/fish/files/consumption_report.pdf. 

EPA. 2003a. Chapter 10 In: Multimedia, Multipathway, and Multireceptor Risk Assessment 

(3MRA) Modeling System, Volume II: Site-based, Regional, and National Data. SAB Review 

Draft. EP-530/D-03-001b. Office of Research and Development, Athens, GA, and Research 
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-110 6-29-2012

Description of MIRC

Triangle Park, NC, and Office of Solid Waste, Washington, D.C. July. Available at:

http://www.epa.gov/osw/hazard/wastetypes/wasteid/hwirwste/risk03.htm.

EPA. 2003b. Methodology for Deriving Ambient Water Quality Criteria for the Protection of 

Human Health (2000): Technical Support Document. Volume 2: Development of National 

Bioaccumulation Factors. Office of Water, Office of Science and Technology, Washington, 

D.C. EPA-822-R-03-030. December. Available at:

http://www.epa.gov/waterscience/criteria/humanhealth/method/tsdvol2.pdf.

EPA. 2003c. CSFII Analysis of Food Intake Distributions. Office of Research and Development, 

National Center for Environmental Assessment, Washington, D.C. EPA-600-R-03-29.

Available at: http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=56610.

EPA. 2004a. Superfund Chemical Data Matrix. OERR. Washington, D.C. January.

EPA. 2004b. Estimated Per capita Water Ingestion and Body Weight in the United States – An 

Update. Office of Water, Office of Science and Technology, Washington, D.C. EPA-822-R00-001. October. Available at: 

http://www.epa.gov/waterscience/criteria/drinking/percapita/2004.pdf

EPA. 2004c. Risk Assessment Guidance for Superfund. Volume I: Human Health Evaluation 

Manual (Part E, Supplemental Guidance for Dermal Risk Assessment), Final. Office of 

Superfund Remediation and Technology Innovation, Washington, D.C. EPA/540/R/99/005; 

OSWER 9285.7-02EP; NTIS PB99-963312. July.

EPA. 2005a. Human Health Risk Assessment Protocol for 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 .
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-111 6-29-2012

Description of MIRC

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. Personal communication from Zachary Pekar, Ambient Standards Group, Office of 

Air and Radiation, U.S. Environmental Protection Agency, to Heidi Hubbard, ICF 

International, [December 15, 2010]

EPA. 2011. Exposure Factors Handbook: 2011 Edition. Office of Research and Development, 

Washington, D.C. EPA/600/R-090/052F September 2011 Available at:

http://cfpub.epa.gov/ncea/risk/recordisplay.cfm?deid=236252.

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.
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TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 3 3-1 6-29-2012

Dermal Risk Screening

Addendum 3. Dermal Risk Screening
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TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 3 3-3 6-29-2012

Dermal Risk Screening

CONTENTS, ADDENDUM 3

1. Hazard Identification and Dose Response Assessment .................................................. 3-5

2. Dermal Exposure Estimation........................................................................................... 3-6

2.1. Equations for Estimating Dermal Exposure.............................................................3-6

2.2. Exposure Factors and Assumptions .......................................................................3-7

2.3. Receptor-Specific Parameters ................................................................................3-8

2.4. Scenario-Specific Parameters ................................................................................3-8

2.5. Chemical-Specific Parameters................................................................................3-9

3. Screening-Level Cancer Risks and Non-Cancer Hazard Quotients............................... 3-10

3.1. Dermal Cancer Risk..............................................................................................3-10

3.2. Dermal Hazard Quotient .......................................................................................3-11

4. Dermal Screening Results ............................................................................................ 3-11

5. References ................................................................................................................... 3-14
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 3 3-4 6-29-2012

Dermal Risk Screening

Exhibits, Addendum 3

Exhibit_Add A3-1. Cancer Slope Factors and Reference Doses Based on 

Absorbed Dose................................................................................................. 3-6

Exhibit_Add A3-2. Receptor-Specific Body Surface Area Assumed to be 

Exposed to Chemicals ...................................................................................... 3-8

Exhibit_Add A3-3. Scenario-Specific Exposure Values for Water and Soil 

Contact ............................................................................................................. 3-9

Exhibit_Add A3-4. Chemical-Specific Dermal Exposure Values for Water and 

Soil Contact .................................................................................................... 3-10

Exhibit_Add A3-5. Summary of Dermal Non-Cancer Hazards.............................................. 3-12

Exhibit_Add A3-6. Summary of Dermal Cancer Risks .......................................................... 3-13
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 3 3-5 6-29-2012

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 (U.S. EPA 2006, Cal/EPA 2000). This section demonstrates that for the 

conservative tierd 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 (U.S. EPA 2004c). 

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 (U.S. EPA 2004c). 

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 A, Addendum 3 3-6 6-29-2012

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_Add A3-1, 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_Add A3-1.

Exhibit_Add A3-1. 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

aOral dose response values are presented in Appendix 2. Only the resulting adjusted dose response values are presented in 

this table.

bAccording 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 A, Addendum 3 3-7 6-29-2012

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 2004c) 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.



 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 A, Addendum 3 3-8 6-29-2012

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_Add A3-2.

Exhibit_Add A3-2. 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 18,150g6,878h

Child <1b3,992 1,772

Child 1-2

c

5,700 2,405

Child 3-5

d

7,600 3,354

Child 6-11e10,800 4,501

Child 12-19f17,150 6,906

aSources 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.

bRepresents a time-weighted average for age groups birth to <1 month, 1 to <3 months, 3 to <6 months, and 6 

to <12 months. 

cRepresents a time-weighted average for age groups 1 to <2 years and 2 to <3 years. 

dValues for age group 3 to <6 years in the 2008 CSEFH.

eValues 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.

fRepresents 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.

gRepresents the average total surface area of adults from Table C-1 of RAGS Part E.

hRepresents 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_Add A3-3 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 A, Addendum 3 3-9 6-29-2012

Dermal Risk Screening

Exhibit_Add A3-3. 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_Add A3-4. 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 A, Addendum 3 3-10 6-29-2012

Dermal Risk Screening

Exhibit_Add A3-4. 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 0.045a0.03 0.13 Values from RAGS Part E, 

Exhibit 3-4, unless otherwise 

noted

aValue 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 A, Addendum 3 3-11 6-29-2012

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_Add A3-5 presents a summary of estimated dermal non-cancer hazards by age group. 

A summary of estimated lifetime dermal cancer risks is provided in Exhibit_Add A3-6. 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 A, Addendum 3 3-12 6-29-2012

Dermal Risk Screening

Exhibit_Add A3-5. 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 <1Child

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 <1Child

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 A, Addendum 3 3-13 6-29-2012

Dermal Risk Screening

Exhibit_Add A3-6. 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

aRepresents 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 A, Addendum 3 3-14 6-29-2012

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.

U.S. EPA (Environmental Protection Agency). 2004c. 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

U.S. 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.

U.S. 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 .
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TRIM-Based Tiered Screening Methodology for RTR

Attachment B – Tier 2 B-1 6-29-2012

Attachment B. Tier 2 Screening Methodology
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TRIM-Based Tiered Screening Methodology for RTR

Attachment B – Tier 2 B-3 6-29-2012

CONTENTS, ATTACHMENT B

Attachment B. Tier 2 Screening Methodology.................................................................B-1

B.1 Overview of Approach.....................................................................................................B-5

B.2 Selection of Site-Specific Characteristics to Include in the Tier 2 Analysis ......................B-7

B.3 Estimation of Adjustment Factors for Selected Site-Specific Parameters ........................B-8

B.3.1 Selection Values for Variables of Interest .............................................................. B-9

B.3.2 Estimation of Adjustment Factors ........................................................................ B-12

B.3.2.1 General Approach .................................................................................B-12

B.3.2.2 Incorporation of the Risk Equivalency Approach....................................B-14

B.4 Preparing National Databases of Lake and Meteorological Data...................................B-16

B.4.1 Processing Lake Data for Tier 2 Analysis ............................................................ B-16

B.4.2 Processing Meteorological Data for Tier 2 Analysis ............................................. B-19

B.4.2.1 Sources of Meteorological Data.............................................................B-19

B.4.2.2 Coverage of Meteorological Stations Compared with Facility 

Locations...............................................................................................B-20

B.4.2.3 Data Processing ....................................................................................B-20

B.5 Implementation of Tier 2 Analysis .................................................................................B-23

B.5.1 Facility List for Tier 2 Screen (Step 3).................................................................. B-26

B.5.2 Facility/Lake Distance Table (Step 4)................................................................... B-26

B.5.3 Finding the Closest Meteorological Station (Step 5)............................................. B-27

B.5.4 Assembling Threshold Adjustment Factors (Step 6) ............................................ B-29

B.5.5 Finalizing Threshold Adjustment Factors and Assembling Screening 

Results (Step 7)................................................................................................... B-30

B.5.6 Assembling Results (Step 8)................................................................................ B-30

B.6 References ...................................................................................................................B-33
TRIM-Based Tiered Screening Methodology for RTR

Attachment B – Tier 2 B-4 6-29-2012

EXHIBITS, ATTACHMENT B

Exhibit_Att B-1. Basic Implementation Process for Implementing Tier 2.................................B-6

Exhibit_Att B-2. Lake Distance and Meteorological Parameter Values for which 

Adjustment Factors were Developed in Tier 2a,b.............................................B-10

Exhibit_Att B-3. Layouts for Tier 2 TRIM.FaTE Simulations Using Alternate 

Distances Between the Facility and the Fishable Lakea.................................B-13

Exhibit_Att B-4. Maximum Fish Ingestion Rate (g/day) Associated with 

Sustainable Fishinga.......................................................................................B-18

Exhibit_Att B-6. An Early Analysis of the Distance Between the Point Source 

Facilities and the Closest Meteorological Stationa,b,c.......................................B-22

Exhibit_Att B-7. Example of the Dashboard To Conduct the Tier 2 Analysis ........................B-25

Exhibit_Att B-8. Example of Global Inputs Used in the Tier 2 Analysis.................................B-25

Exhibit_Att B-9. Example of the Facility Input Data Required To Conduct the 

Tier 2 Analysis ................................................................................................B-26

Exhibit_Att B-10. Example of the Lake Distance Data Required To Conduct the 

Tier 2 Analysis ................................................................................................B-27

Exhibit_Att B-11. Example Results of the Meteorological Station Matching 

Required To Conduct the Tier 2 Analysis........................................................B-28

Exhibit_Att B-12. Example Results of the Octant Analysis Required To Conduct 

the Tier 2 Analysis ..........................................................................................B-29

Exhibit_Att B-13. Example of the TRIM.FaTE Matrix Results Required To 

Conduct the Tier 2 Analysis ............................................................................B-30

Exhibit_Att B-15. Example of Detailed Output Table Created by the Tier 2 Toola.................B-32

Exhibit_Att B-16. Example of Individual Output Table Created by the Tier 2 Tool ................B-32
TRIM-Based Tiered Screening Methodology for RTR

Attachment B – Tier 2 B-5 6-29-2012

B.1 Overview of Approach

The Tier 1 screening scenario 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. Based on screening analyses conducted for RTR to date, 

many facilities can “fail” the Tier 1 screen. Because the Tier 1 screen uses “worst case” 

assumptions, the analysis must be refined to determine whether the failing facility is actually 

expected to pose a risk. 

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 wherever possible. However, this 

approach would not be feasible given the resource intensive nature of a site-specific 

assessment and the number of facilities that tend to fail the Tier 1 screen in the different 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 worst-case assumptions in a Tier 1 

screen with more site-appropriate values. Specifically, for Tier 2, the following variable values 

are varied from their Tier 1 values:

 Meteorological characteristics, including the fraction of time the wind blows in the 

direction of the farm and lake (using wind direction), the wind speed, the precipitation 

rate, and the mixing height; and 

 Location of the nearest fishable lake(s) relative to the facility.23

In selecting the fate and transport variables listed above to include in Tier 2, a balance was 

struck between the: 1) degree of impact on the potential risk estimate; 2) ease of 

implementation in TRIM.FaTE; and 3) ease of obtaining site-specific values on a facility-byfacility basis. Because of the expected variability in exposure parameters (such as fish or food 

ingestion rates) amongst any population living around a given facility, only the fate and transport 

parameters were candidates to be varied in the Tier 2 analysis, and the exposure parameters 

remain at their health-protective Tier 1 values. 

The overall implementation of Tier 2 is shown in Exhibit_Att B-1. First (shown in green), the 

starting point is the ratio of the facility emission to the Tier 1 threshold. Next, two different sets of 

information are needed. 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 estimates of risk must be estimated (shown in orange). 

Because so many facilities need to be evaluated in Tier 2, the implementation focuses on 

estimating refined risk using pre-calculated databases rather than trying to gather the input 

parameters and running them through TRIM.FaTE and MIRC for each facility.

 

23The lake size was also changed for each new facility lake distance. This change allowed the simulations to maintain 

a constant ratio between watershed and erosion area compared with the lake area.
TRIM-Based Tiered Screening Methodology for RTR

Attachment B – Tier 2 B-6 6-29-2012

Exhibit_Att B-1. Basic Implementation Process for Implementing Tier 2

First, databases of the relevant U.S. meteorological and lake data were created that could be 

accessed readily during a Tier 2 evaluation (shown in red in the figure). These meteorological 

and lake data are discussed in more detail in Section B.4. The meteorological database 

includes annual-average summary statistics on wind direction, wind speed, and precipitation for 

more than 1,500 surface stations located throughout the United States. These surface data 

cover years 2006 through 2010 and were obtained from the National Climatic Data Center’s 

Quality Controlled Local Climatological Data. The mixing heights for each surface station were 

estimated using data from the upper-air station closest to the surface station. These mixing 

height values cover years 1987 through 1990 (where available) for over 50 stations, and they 

are available from EPA’s Support Center for Regulatory Atmospheric Modeling. 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.

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 

(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. 
TRIM-Based Tiered Screening Methodology for RTR

Attachment B – Tier 2 B-7 6-29-2012

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 baseline 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:

 

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® Excel 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 closest surface meteorological station, and the values for the four 

relevant parameters at that station are recorded (wind speed, wind direction, precipitation rate, 

and mixing height). The distance from the facility to the nearest qualifying lake in each octant 

around the facility are estimated using GIS and are also imported. 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 baseline Tier 1 exposure). The Tier 1 screening emission threshold for a PBHAP is then multiplied by the appropriate adjustment factors to obtain an updated Tier 2 

emission threshold for that PB-HAP at that facility. A facility then passes the Tier 2 screen if the 

emission is below the Tier 2 threshold. Selection of Site-Specific Characteristics to Include in 

the Tier 2 Analysis

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.24 To determine which scenario characteristics 

 

24Only 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 
TRIM-Based Tiered Screening Methodology for RTR

Attachment B – Tier 2 B-8 6-29-2012

should be incorporated into the Tier 2 analysis, 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 Tier 1 screening scenario set-up?

Addendum 1 to this attachment provides an exhibit showing all the TRIM.FaTE variables 

considered for the Tier 2 analysis. 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 

analysis:

 Wind direction (the percent of time the wind blows toward the lake and farm),

 Wind speed,

 Precipitation,

 Mixing height, and

 Location of the nearest fishable lake 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.

B.2 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 

analysis 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. 

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. The appropriate 

adjustment factors are then applied to the Tier 1 threshold for that PB-HAP when evaluating a 

facility in Tier 2. 

 

exposure characteristics used in MIRC are considered to be generally consistent across different locations and 

facilities.
TRIM-Based Tiered Screening Methodology for RTR

Attachment B – Tier 2 B-9 6-29-2012

The analyses conducted to select the parameters to derive the threshold adjustment factors are 

described in this section. Section B.2.2 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.

B.2.1 Selection Values for Variables of Interest

For each site-specific parameter identified in Section 0 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 a 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).

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 analysis 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
TRIM-Based Tiered Screening Methodology for RTR

Attachment B – Tier 2 B-10 6-29-2012

stations nationwide).25 This value is similar to the annual average wind speeds of the U.S. Deep 

South.26 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_Att B-2; 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.

Exhibit_Att B-2. 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

aBold indicates the value is equal to the value used in Tier 1.

bWind direction is not shown here because its effect on modeled exposure and risk in TRIM.FaTE is linear.

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 analysis 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 Coast27. Though 

the rate was an estimate, it does correspond to approximately the 95th percentile (i.e., higher 

 

25http://ols.nndc.noaa.gov/plolstore/plsql/olstore.prodspecific?prodnum=C00095-PUB-A0001#TABLES – this website 

is updated every year, so the data it currently shows is not the exact data used to develop the wind speeds for 

screening analyses.

26National Climatic Data Center CliMaps (NCDC-CliMaps) (2007). http://cdo.ncdc.noaa.gov/cgibin/climaps/climaps.pl

27National Climatic Data Center Historical Climate Series (NCDC-HCS) (2007).
TRIM-Based Tiered Screening Methodology for RTR

Attachment B – Tier 2 B-11 6-29-2012

rate) precipitation in the National Climatic Data Center’s (NCDC) 30-year (1981–2010) data

from U.S. stations.

28

 To estimate adjustment factors in the Tier 2 analysis, 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_Att B-2), 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.

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 analysis assumed a mixing height of 710 meters. 

This value is the 5

th percentile (i.e., lower) of annual average mixing heights for 463 U.S. 

locations, based on data obtained from EPA’s SCRAM Web site.29 To estimate adjustment 

factors in the Tier 2 analysis, 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_Att B-2). 

These values correspond to North Little Rock, Arkansas, Boise, Idaho, and Tucson, Arizona and 

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 

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_Att B-2). 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_Att B-3 provides a diagram of the TRIM.FaTE layout in each of the alternate lake 

 

28http://www.ncdc.noaa.gov/oa/climate/normals/usnormals.html

29Support Center for Regulatory Atmospheric Modeling; http://www.epa.gov/scram001/tt24.htm
TRIM-Based Tiered Screening Methodology for RTR

Attachment B – Tier 2 B-12 6-29-2012

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.

B.2.2 Estimation of Adjustment Factors 

Adjustment factors were estimated for each variable described above and applied as multipliers 

to the Tier 1 emissions thresholds. The resulting Tier 2 emissions thresholds are used to 

assess whether facilities with corresponding configurations pose multipathway risks. Notably, 

facilities substantially exceeding a Tier 2 emission threshold carry some potential for significant 

multipathway health risk.

B.2.2.1 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 hazard quotient of 1, depending on the toxic effect of the chemical in 

question).
TRIM-Based Tiered Screening Methodology for RTR

Attachment B – Tier 2 B-13 6-29-2012

Exhibit_Att B-3. 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

Attachment B – Tier 2 B-14 6-29-2012

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 analysis 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 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.

B.2.2.2 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:

 H H H

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 

evaluated (most based on direct TRIM.FaTE modeling, and a small number assumed to behave 

like TCDD). 
TRIM-Based Tiered Screening Methodology for RTR

Attachment B – Tier 2 B-15 6-29-2012

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 species emitted at a rate EPAH at a facility, the risk-equivalent BaP emission 

level can be expressed as: 

 Q H H H 

Then, the Tier 1 risk ratio is estimated by comparing the risk-equivalent BaP emissions to the 

BaP emission threshold:

 H 

 Q 

 

If the ratio is less than 1, the facility “screens out” of the Tier 1 analysis. Similarly, for Tier 2, the 

ratio of risk-equivalent BaP emissions to the Tier 2 BaP threshold may be expressed as:

 H 

 Q 

 

Using the definition of the risk-equivalent BaP emissions, this can be re-expressed for a given 

PAH species as:

 H 

 H H H 

 

This expression may be further reconfigured, after some algebraic rearrangement, in terms of 

the Tier 1 ratio as:

 H H 

 

 

 

 H 

 H 

 

 

 

 

 H 

 H 

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.

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: 
TRIM-Based Tiered Screening Methodology for RTR

Attachment B – Tier 2 B-16 6-29-2012

 H H 

 

 

 

 H 

 H 

 H 

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 be deemed to have failed the Tier 2 screen for the PAH group.

B.3 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.

B.3.1 Processing Lake Data for Tier 2 Analysis

The lake database was built using a geospatial file (U.S. Water Bodies) provided by ESRI for 

their ArcGIS products.30 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 analysis, 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 analysis 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 run through the Tier 2 methods, 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 analysis must focus 

on lakes large enough to support relatively intensive angling pressure to be compatible with the 

characteristics inherent to the assumed exposure scenario. To estimate the relationship 

between fish consumption at a high level, harvest rates, and lake size, the following key 

assumptions were made. Information and citations to peer-reviewed literature that support 

these assumptions are provided in Addendum 2 to this attachment. Note that in the TRIM.FaTE 

model screening scenario, trophic level 3 fish (TL3) assumed to be consumed by anglers are 

represented by benthic carnivores (BC) and trophic level 4 fish (TL4) assumed to be consumed 

by anglers are represented by water column carnivores (WCC). Together, these two trophic 

levels are referred to as piscivorous fish.

1. Piscivorous fish, when present, comprise approximately 21 percent of the standing biomass 

of fish in an annual average. TL3 fish represent 17.5 percent of the standing fish biomass; 

 

30 Specifically, the geospatial file in the ESRI Data & Maps 2009 Data Update for ArcGIS 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

Attachment B – Tier 2 B-17 6-29-2012

TL4 fish account for 3.5 percent of this total fish biomass. Thus, TL4 fish, when present, 

represent the limiting compartment for angler fish harvesting and consumption.

2. Humans can harvest 10 percent of the TL4 biomass without threatening the population due 

to overharvesting.

3. The minimum viable effective population size for a single TL4 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 a TL4 fish is edible fillet muscle. A 0.33 edible fraction is used to 

estimate total fish biomass associated with human consumption.

5. A productivity level of 40 g wet weight/m2represents an approximate upper bound for 

natural ponds and lakes in the United States.

Using the above assumptions and a series of equations (see Addendum 2 to this attachment), 

the maximum fish ingestion rates as a function of standing biomass and lake size were 

estimated. Exhibit_Att B-4 presents these findings, where the shading in the exhibit provides an 

indication of where short- and longer term sustainable fishing is expected to occur (see exhibit 

notes). 

The Tier 2 analysis 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_Att B-4 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). Thus, a lake size 

threshold of 25 acres was used. The final database included over 100,000 lakes with surface 

areas of at least 25 acres but smaller than 100,000 acres. Larger lakes were not considered 

since they cannot be readily modeled in TRIM.FaTE. For the purposes of proximity matching 

lakes to emitting facilities (as described in Section B.4.2), the location of each lake is identified 

as the geographic centroid inside the lake.

The 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 analyses for subsistence anglers (i.e., 26 g ww fillet per day in a 25-acre 

lake compared with 78.13 g ww fillet per day in the Tier 1 analysis). However, the decision was 

made to use the smallest lake size that supported an upper trophic level population without 

explicitly linking the lake size with the subsistence angler ingestion rate in order to maintain an 

overall health-protective approach. 

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 was 0.
TRIM-Based Tiered Screening Methodology for RTR

Attachment B – Tier 2 B-18 6-29-2012

Exhibit_Att B-4. 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

aDark 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. 

bRepresents the standing biomass of TL4 fish. At the upper-limit standing biomass of 40 g ww/m2

, a lake that is 25 acres in size would sustain a maximum fish ingestion rate of 26 g/day for a 

single angler.
TRIM-Based Tiered Screening Methodology for RTR

Attachment B – Tier 2 B-19 6-29-2012

B.3.2 Processing Meteorological Data for Tier 2 Analysis

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 2010a). The current effort 

builds on this practice but increases the number of available meteorological stations as 

described below.

B.3.2.1 Sources of Meteorological Data

To construct a database of meteorological statistics for all available U.S. surface stations, EPA 

obtained surface meteorological data from the Quality Controlled Local Climatological Data 

(QCLCD), a dataset available from the National Climatic Data Center (NCDC 2012). The 

processing began with over 2,000 stations located across the 50 states plus Puerto Rico and 

the U.S. Virgin Islands with at least some data in the 5-year period of 2006 through 2010. We

narrowed the stations to consider over 1,500 stations with data at both the beginning and end of 

the period. 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).

In the QCLCD data, many stations had unreliable or missing precipitation data. The numeric or 

text flag used to indicate missing data was not consistent, making automated processing of the 

whole dataset with respect to this one variable difficult. To reduce the uncertainty in the 

precipitation estimates, 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 hundred stations in the QCLCD 

dataset, we used the QCLCD precipitation data where available. If 30-year normal precipitation 

data and QCLCD precipitation data were not available for a station, as was the case for over 50 

stations in the QCLCD dataset, we flagged the station. 

As described later in Section B.3.2.3, over 50 additional stations were flagged due to missing 

wind data. Stations with precipitation or wind flags, totaling over 100, were not removed from the 

Tier 2 meteorology database. Flagged stations were kept in the database for informational 

purposes (i.e., so the user knows that the station was in the original 5-year dataset but its data 

cannot be used for Tier 2 screening methods). 

The QCLCD surface meteorological data cannot provide information on mixing heights, which 

must be estimated from upper air measurements. EPA’s Support Center for Regulatory 

Atmospheric Modeling archived estimated morning and afternoon mixing heights at more than 

50 locations across the 50 states and Puerto Rico corresponding to years in the late 1980s and 

early 1990s (EPA 2010b). More recent years are not available in that archived dataset. We 

selected a 4-year period (1987 through 1990; an arbitrary selection) of mixing height data for the 

Tier 2 analysis. Each surface meteorological station was paired with the closest mixing height 
TRIM-Based Tiered Screening Methodology for RTR

Attachment B – Tier 2 B-20 6-29-2012

location. At this time, these pairings of surface meteorological stations with mixing height 

locations were not further scrutinized.

B.3.2.2 Coverage of Meteorological Stations Compared with Facility Locations

Early in the process of examining the meteorology data, we compared the locations of the U.S. 

point source facilities (from the 2005 NATA) to locations of surface and upper-air stations. 

These locations are shown in Exhibit_Att B-5 so that the spatial coverage of the surface 

meteorological stations can be compared to the spatial coverage of most of the point source 

facilities that might undergo an RTR multipathway analysis in the future.31. It should be noted 

that there are a couple hundred meteorology stations that were included in the final dataset 

discussed in the previous section but not shown in the exhibit. This is because additional 

scrutiny and processing of the meteorology data took place after the exhibit was created, 

leading to the inclusion of a couple hundred additional stations. These differences are not 

expected to significantly affect the analysis presented in the next paragraph. 

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. Exhibit_Att B-6 again shows 

each of the U.S. point source facilities from the 2005 NATA, and the location markers are 

shaded and sized according to how far the facility is from the closest of the surface 

meteorological stations. See again the cautionary note in the previous paragraph regarding the 

completeness of the meteorology dataset. The average and median distances are 21 km and 

15 km, respectively, between a facility and its closest surface meteorological station with 

complete data. This average distance is far smaller than the average distance between the 

petroleum refining facilities and meteorology stations used in the 2009 RTR report (72 km; EPA 

2009) and is likely much smaller than the average distance between all facilities and 

meteorological stations used in the 1996 NATA (less than 50 km; EPA 2001b). Approximately 

32 percent of facilities are less than 10 km from a surface meteorological station, while 29 

percent are between 10 and 20 km, 7 percent are farther than 50 km, and less than 1 percent

are farther than 100 km. If this analysis were performed again using the final meteorology 

dataset used in Tier 2, these facility-to-station distances would likely decrease by small amounts 

due to a greater number of stations in the final dataset.

B.3.2.3 Data Processing

To facilitate application of the meteorological data to the Tier 2 analysis, EPA gathered wind 

information in directional octants that could be linked to the direction of the closest lake (see 

Introduction and Section B.4). 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

 

31The 2005 NATA was the most recent, comprehensive, finalized dataset of nationwide point source emitters of 

hazardous air pollutants, and 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

Attachment B – Tier 2 B-21 6-29-2012

Exhibit_Att B-5. An Early Analysis of the Locations of Meteorological Stations and Point Source Facilitiesa,b

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.

bNote that this analysis was performed early in the Tier 2 development process. The final meteorology dataset used in Tier 2 contains a couple hundred more surface stations than are 

shown here.
TRIM-Based Tiered Screening Methodology for RTR

Attachment B – Tier 2 B-22 6-29-2012

Exhibit_Att B-6. An Early Analysis of the Distance Between the Point Source Facilities and the Closest Meteorological 

Stationa,b,c

a

The high spatial density of facilities in some areas (e.g., the Great Lakes area) causes some facility dots to be plotted on top of other facility dots. In these areas, most or all stations 

might appear to have distances of a certain range (e.g., less than 50 km, denoted by blue shades), when in fact some other distance values (e.g., at least 50 km but less than 100 km, 

denoted by green shades) might also be present but are not visible.

b

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.

cNote that this analysis was performed early in the Tier 2 development process. The final meteorology dataset used in Tier 2 contains a couple hundred more surface stations than are 

shown here.
TRIM-Based Tiered Screening Methodology for RTR

Attachment B – Tier 2 B-23 6-29-2012

Using the data for each of the surface meteorological stations, EPA developed a software 

program to calculate the following statistics for each station-month combination in the 5-year 

period of surface meteorological data:

 Number of hourly observations,

 Number of hours with calm winds or missing winds,

 Number of hours the wind was blowing into each directional octant,

 Average wind speed blowing into each octant, and

 Total precipitation (irrespective of wind octant and preferring 30-year normal data if 

available).

This program then calculated the following statistics for each station’s period of record, using 

the above monthly totals and averages:

 Number of months with more than 10 percent of hours missing wind data,

 Percentage of time the wind blows into each octant (after excluding missing wind hours),

 Median wind speed when wind was blowing into each octant, and

 Average annual precipitation at the station.

The program flags a station if more than 10 percent of the months in the data period are missing 

wind data for more than 10 percent of the hours in those months. The program also flags a 

station if no precipitation data are available (as described in Section B.3.2.1). These flagged 

stations, totaling over 100, remained in the Tier 2 meteorology database for informational 

purposes, but their data were not used.

Mixing height averages were calculated outside of the software program used for surface 

meteorological data. The average annual mixing height was calculated at each mixing height 

location (averaged between the morning and afternoon mixing height values provided in the 

EPA upper-air dataset). These mixing height locations, with their average mixing height values, 

were proximity matched to surface meteorological stations and their wind and precipitation 

statistics.

B.4 Implementation of Tier 2 Analysis

The Tier 2 screening analysis is conducted using a Microsoft ® Excel tool. The tool has two 

main functions: 1) finding the closest surface meteorological station to a facility, and 2) 

calculating Tier 2 threshold values. The tool is created so that all facilities in multiple source 

categories can be screened at once, if desired. The tool is controlled by a dashboard control 

panel (see Exhibit_Att B-7), where each of 8 sequential steps are controlled by a button on the 

panel. From the dashboard, the user is prompted to enter data on four worksheets. Steps 1 and 

2 are user input steps to prepare the tool for the analysis. In Step 1, the user verifies that the 

Tier 2 matrix results developed using the TRIM.FaTE model are current. In Step 2, the user 

enters basic ‘global inputs’ that include the PB-HAPs32 and source categories included in the 

analysis (Exhibit_Att B-8). Steps 1 and 2 on the dashboard should be performed prior to the 

start of any analysis to ensure that the user is using the most current information. Two 

additional input tables are created using the Tier 1 Microsoft Access screening tool and the 

ArcGIS ® lake database (see Section B.2.1), as described in Sections B.4.1 and B.4.2 below. 

 

32 At this time, the user cannot “turn on” or “turn off” processing of individual species of a larger PB-HAP “group”. For 

example, the user can select to process all PAH species, or none, but not some.
TRIM-Based Tiered Screening Methodology for RTR

Attachment B – Tier 2 B-24 6-29-2012

Once these tables are created, the user enters the associated data in Steps 3 and 4, 

respectively. After all these input data have been supplied, Steps 5 and 6 conduct the Tier 2 

analysis and Steps 7 and 8 produce output tables that summarize the results, as described in 

Sections B.4.3 and B.4.4.

To facilitate explanation of the Tier 2 analysis implementation, example source categories with 

hypothetical PB-HAP emissions were run through the tool and screen shots including these 

results are provided to illustrate the overall 8-step process. These screen shots and 

accompanying descriptions represent the tool at one point in time (approximately three months 

into its development). While the tool is enhanced and improved periodically (e.g, to fix “bugs” 

and add/change features), these modifications generally do not significantly alter the look, feel, 

and purpose of the tool. 
TRIM-Based Tiered Screening Methodology for RTR

Attachment B – Tier 2 B-25 6-29-2012

Exhibit_Att B-7. Example of the Dashboard To Conduct the Tier 2 Analysis

Exhibit_Att B-8. Example of Global Inputs Used in the Tier 2 Analysis
TRIM-Based Tiered Screening Methodology for RTR

Attachment B – Tier 2 B-26 6-29-2012

B.4.1 Facility List for Tier 2 Screen (Step 3)

After clicking Button 3 on the dashboard (see Exhibit_Att B-7), the user is brought to the facility 

input sheet to enter a list of all facilities in a source category (or multiple source categories) with 

emissions for any PB-HAP. Included in this list are the average latitude and longitude of the 

emission sources at the facility, the list of chemicals emitted (and their respective PB-HAP 

groups), and the ratio of the REF-adjusted emission rate to the Tier 1 screening threshold for 

each evaluated chemical. This table is generated using the Tier 1 Microsoft® Access screening 

tool, and it is pasted into the “Facility Inputs” sheet of the Excel tool, as shown in Exhibit_Att 

B-9. 

Exhibit_Att B-9. Example of the Facility Input Data Required 

To Conduct the Tier 2 Analysis

B.4.2 Facility/Lake Distance Table (Step 4)

After clicking Button 4 on the dashboard (see Exhibit_Att B-7), the user is brought to the lake 

distance sheet to enter details on the closest lake to each facility in each of the directional 

octants (N, NE, E, SE, S, SW, W, and NW). These lake data are assembled outside of this 

Excel tool using the following steps. First, the location of each facility is imported into ArcGIS ® 

along with the final database of lake centroids (Section B.2.1). Then, from within the ArcGIS ® 

software, all lakes with centroids within 50 km of each facility are identified. A table is created 

that shows the lake name, location relative to the facility (octant and distance), and size (note: 

the lake size is informational only, it is not used as part of Tier 2 except to be sure it is not 

smaller than 25 acres or larger than 100,000 acres). If no lake is present within 50 km in a 

given octant, the table includes dashes to indicate no lake is present within the radius selected. 

The lake names (where available) are scrutinized to subjectively remove industrial and 
TRIM-Based Tiered Screening Methodology for RTR

Attachment B – Tier 2 B-27 6-29-2012

treatment water bodies, as discussed in Section B.2.1. This table is pasted in the 

“LakeDistance” sheet as shown in Exhibit_Att B-10. 

Exhibit_Att B-10. Example of the Lake Distance Data Required 

To Conduct the Tier 2 Analysis

B.4.3 Finding the Closest Meteorological Station (Step 5)

Now that the user has entered all required input data, clicking Button 5 initiates an analysis that 

finds the closest surface meteorological station (having complete summary statistics) to each 

facility in the Facility List using straight-line distance. If the facility and the meteorological 

station are more than 50 km apart, the tool returns the closest station but flags the match with 

the text “Review” to alert the user that further scrutiny of the facility/station pair might be 

necessary. In addition, the tool also alerts the user if the closest station was flagged for 

incomplete data, in which case the next closest station was selected instead. The results of the 

meteorological analysis are shown in the “MetAnalysis” sheet (an example is shown in 

Exhibit_Att B-11). In the example in Exhibit_Att B-11, the closest surface meteorological station 

to Facility 005 was approximately 34 km away, but the methodology rejected that station 

because it had too much missing data, choosing instead a different station approximately 37 km 

away with fewer missing data. For Facility 008, the closest station had complete data but was 

located 57 km from the facility, causing the analysis to display a warning about being beyond 

50 km away.
TRIM-Based Tiered Screening Methodology for RTR

Attachment B – Tier 2 B-28 6-29-2012

Exhibit_Att B-11. Example Results of the Meteorological Station Matching 

Required To Conduct the Tier 2 Analysis
TRIM-Based Tiered Screening Methodology for RTR

Attachment B – Tier 2 B-29 6-29-2012

B.4.4 Assembling Threshold Adjustment Factors (Step 6)

Next, the tool calculates the facility-specific, octant-specific threshold adjustment factor for each 

chemical. When Button 6 is clicked (see Exhibit_Att B-7), the tool uses the meteorological 

parameters and lake locations to find the correct adjustment factor for each of the Tier 2 

characteristics, as discussed in Section B.2. See example screen shot in Exhibit_Att B-12. The 

completed analysis is shown in the “OctantAnalysis” sheet. For each octant associated with a 

facility, the tool pairs its lake and meteorology parameters (lake location, wind speed, mixing 

height, and precipitation) with the closest matches in the Tier 2 matrix of EEF and screening 

threshold adjustment factors (Section B.2 describes this matrix, and a sample of the matrix is 

shown in Exhibit_Att B-13). The selection of adjustment factors is done in a health-protective 

manner, taking note of the direction of the correlation of the variable with risk. For example, 

smaller mixing heights are associated with larger risks, so a facility’s mixing height is matched 

with the closest evaluated Tier 2 value that is smaller than the facility’s value. Each octant also 

is associated with a wind direction adjustment factor, where the frequency of winds blowing into 

the Tier 1 screening scenario (43 percent) is ratioed to the observed frequency of winds blowing 

into the octant. The octant’s total adjustment factor is the product of the EEF, screening 

scearnio, and wind direction adjustment factors. 

The adjustment factors indicate the ratio of the Tier 2 threshold to the Tier 1 threshold. Thus, 

values above 1 (which nearly all are) indicate a risk reduction in Tier 2 relative to Tier 1, and 

larger and larger values (above 1) indicate larger and larger risk reductions. The Tier 1 result 

(the ratio of emissions to the Tier 1 emission screening threshold) is divided by the Tier 2 

adjustment factor, providing the Tier 2 result (the ratio of emissions to the Tier 2 emission 

screening threshold). This evaluation occurs for each chemical in each octant of each facility, 

and for PAHs and dioxins the emissions are converted to BaP- and TCDD-equivalent using the 

REFs. For PAHs and dioxins, the species-specific Tier 2 ratios (equivalent emissions/threshold) 

at a facility are summed to create the total PAH and total dioxin Tier 2 ratios. To maintain a 

health- protective focus in the analysis, the octant analysis then identifies the octant with the 

largest Tier 2 ratio for each PB-HAP at each facility. 

Exhibit_Att B-12. Example Results of the Octant Analysis Required 

To Conduct the Tier 2 Analysis
TRIM-Based Tiered Screening Methodology for RTR

Attachment B – Tier 2 B-30 6-29-2012

Exhibit_Att B-13. Example of the TRIM.FaTE Matrix Results Required 

To Conduct the Tier 2 Analysis

B.4.5 Finalizing Threshold Adjustment Factors and Assembling Screening 

Results (Step 7)

This step was removed from the tool because its information was largely redundant relative to 

other sheets. 

B.4.6 Assembling Results (Step 8)

By clicking Button 8, the Excel tool creates three separate results tables, shown in the sheets 

“SummaryOutput,” “DetailedOutput,” and “IndividualOutput.” 

The summary table shows the Tiers 1 and 2 human health multipathway screening results at the 

level of source category and PB-HAP group (cadmium, mercury, total PAH, and total dioxin; see 

example screen shot in Exhibit_Att B-14). Cells with red highlighting call attention to cases 

where facilities exceeded the Tier 1 or Tier 2 thresholds.33 Using Source Category A as an 

example – there were 52 facilities in the category, 51 of which emitted cadmium, 46 emitted 

dioxins, 51 emitted mercury, and 20 emitted PAHs. No facilities in this category emitted 

cadmium or mercury at levels exceeding the Tier 1 threshold, so the summary table shows 

“Below Tier 1 Threshold” as the result for these two PBHAP groups. Because they did not 

exceed the Tier 1 threshold, they by definition did not exceed the Tier 2 threshold; the Tier 2 

analysis was still conducted for these PB-HAPs from these facilities, for simplicity and 

transparency in displaying Tier 1 results alongside Tier 2 results. Five of the facilities in the 

category exceeded the Tier 1 threshold for PAHs, with the maximum-emitting facility exceeding 

the threshold by a factor of 3.8 (or 4; in practice, these numbers are often interpreted by 

rounding). The Tier 2 methodology brought 4 of these 5 facilities below the Tier 2 threshold; the 

 

33 The tool shading schemes have been revised so that only ratios of at least 1.5 indicate an exceedance of the 

threshold. The 1.1 value in the fourth data row would not be shaded red, and its value for number of facilities 

exceeding would go from 1 to 0. 
TRIM-Based Tiered Screening Methodology for RTR

Attachment B – Tier 2 B-31 6-29-2012

fifth facility was very close to the Tier 2 threshold (1.1; typically interpreted by rounding to 1). All 

46 dioxin-emitting facilities in the category exceed the Tier 1 dioxin threshold. Three of the 46 

facilities did not exceed the Tier 2 threshold, leaving 43 exceeding the Tier 2 threshold but by 

much smaller amounts compared to Tier 1 (the maximum-emitting facility exceeds the Tier 2 

threshold by a factor of 958, compared to a factor of 5,570 for for Tier 1).

Exhibit_Att B-14. Example of Summary Output Table Created by the Tier 2 Toola

a

The tool shading schemes have been revised so that only ratios of at least 1.5 indicate an exceedance of the threshold. The 1.1 

value in the fourth data row would not be shaded red, it would instead say “Below Tier 2 Threshold”, and its value for number of 

facilities exceeding would go from 1 to 0.

The detailed summary table shows much of the same information as the summary table, but the 

screening results for each facility are shown (see example screen shot in Exhibit_Att B-15). This 

detailed table is helpful because it shows which facilities exceeded the thresholds and by how 

much. As discussed in the previous paragraph, the summary table shows that 46 and 43 

facilities, respectively, exceeded the Tiers 1 and 2 thresholds for dioxins, with the maximumemitting facility exceeding Tiers 1 and 2 thresholds by respective factors of 5,570 and 958. This 

detailed summary table shows that Facility 001 is the facility emitting the most dioxins after 

adjusting the emissions for toxicity and exposure. The screen shot in Exhibit_Att B-15 shows 

exceedance factors for dioxins and PAHs for a subset of 8 other facilities in Source Category A 

(other facilities and source categories are in the table but not shown in the screen shot). 

The individual summary table is similar to the detailed summary table, but it adds the details of 

the screening results for each chemical (not just PB-HAP groups) and the Tier 2 adjustment 

factors (see example screen shot in Exhibit_Att B-16). The Tier 2 adjustment factors are 

specific to each chemical and each facility. The ratios to screening level by chemical are 

summed at the facility level to produce the ratios to screening level by PB-HAP group. 
TRIM-Based Tiered Screening Methodology for RTR

Attachment B – Tier 2 B-32 6-29-2012

Exhibit_Att B-15. Example of Detailed Output Table Created by the Tier 2 Toola

a

The tool shading schemes have been revised so that only ratios of at least 1.5 indicate an exceedance of the threshold. The 1.1 

value in the tenth data row would not be shaded red and it would instead say “Below Tier 2 Threshold”.

Exhibit_Att B-16. Example of Individual Output Table Created by the Tier 2 Tool

a

The tool shading schemes have been revised so that only ratios of at least 1.5 indicate an exceedance of the threshold. For 

example, the 1.3 value in the third data row would not be shaded red.
TRIM-Based Tiered Screening Methodology for RTR

Attachment B – Tier 2 B-33 6-29-2012

B.5 References

National Climatic Data Center (NCDC). (2012) Quality Controlled Local Climatological Data. 

Available online at http://cdo.ncdc.noaa.gov/qclcd/QCLCD?prior=N

EPA. (2001a) NATA – Evaluating the National-scale Air Toxics Assessment, 1996 DATA – An 

SAB Advisory. U.S EPA Science Advisory Board. EPA-SAB-EC-ADV-02-001. 12/2001.

EPA (U.S. Environmental Protection Agency). (2001b) National-scale Air Toxics Assessment for 

1996, Draft for EPA Science Advisory Board Review. EPA Office of Air Quality Planning and 

Standards. EPA-453/R-01-003. 01/18/2001.

EPA. (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.

EPA. (2010a) 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. (2010b) Support Center for Regulatory Atmospheric Modeling (SCRAM) Mixing Height 

Data. Available online at http://www.epa.gov/ttn/scram/mixingheightdata.htm. Accessed 

01/15/2012. Last updated 05/13/2010.

USGS (U.S. Geological Survey). (2012) National Hydrography Dataset. Available online at 

http://nhd.usgs.gov/. 
TRIM-Based Tiered Screening Methodology for RTR

Attachment B, Addendum 1 B1-1 6-29-2012

TRIM.FaTE Parameters

Addendum 1. Summary of TRIM.FaTE Parameters Considered for 

Inclusion in Tier 2 Analysis
[This page intentionally left blank.]
Attachment B, Addendum 1 B1-3 6-29-2012

TRIM.FaTE Parameters

Exhibits, Addendum 1

Exhibit Add B1-1. TRIM.FaTE Parameters Considered for Inclusion in Tier 2 

Analysis ............................................................................................................ 1-4
Attachment B, Addendum 1 B1-4 6-29-2012

TRIM.FaTE Parameters

Exhibit Add B1-1. TRIM.FaTE Parameters Considered for Inclusion in Tier 2 Analysis

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
Attachment B, Addendum 1 B1-5 6-29-2012

TRIM.FaTE Parameters

Exhibit Add B1-1. TRIM.FaTE Parameters Considered for Inclusion in Tier 2 Analysis

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
Attachment B, Addendum 1 B1-6 6-29-2012

TRIM.FaTE Parameters

Exhibit Add B1-1. TRIM.FaTE Parameters Considered for Inclusion in Tier 2 Analysis

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
Attachment B, Addendum 1 B1-7 6-29-2012

TRIM.FaTE Parameters

Exhibit Add B1-1. TRIM.FaTE Parameters Considered for Inclusion in Tier 2 Analysis

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 

ArcGIS (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
Attachment B, Addendum 1 B1-8 6-29-2012

TRIM.FaTE Parameters

Exhibit Add B1-1. TRIM.FaTE Parameters Considered for Inclusion in Tier 2 Analysis

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 watersoluble or particle-attached 

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
Attachment B, Addendum 1 B1-9 6-29-2012

TRIM.FaTE Parameters

Exhibit Add B1-1. TRIM.FaTE Parameters Considered for Inclusion in Tier 2 Analysis

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 particlebound 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
Attachment B, Addendum 1 B1-10 6-29-2012

TRIM.FaTE Parameters

Exhibit Add B1-1. TRIM.FaTE Parameters Considered for Inclusion in Tier 2 Analysis

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
Attachment B, Addendum 2 B2-1 6-29-2012

TRIM.FaTE Parameters

Addendum 2. Analysis of Lake Size and 

Sustainable Fish Population
[This page intentionally left blank.]
Attachment B, Addendum 2 B2-3 6-29-2012

Lake Size Analysis

CONTENTS, ADDENDUM 2

1. Introduction..................................................................................................................... 2-4

1.1. Angler Behavior......................................................................................................2-4

1.2. Fish Biology............................................................................................................2-5

1.2.1. Lake Productivity ..................................................................................... 2-5

1.2.2. Proportion of Fish Biomass by Trophic Level........................................... 2-7

1.2.3. Minimum Viable Population Size ............................................................. 2-8

1.3. Summary of Assumptions for the Lake Size Analysis .............................................2-9

1.4. Equations Used to Determine Lake Fish Populations .............................................2-9

1.5. References ...........................................................................................................2-11
Attachment B, Addendum 2 B2-4 6-29-2012

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 angler at a specified fish 

ingestion rate requires consideration of many factors. Some factors depend on assumptions 

about the behavior of anglers who consume fish from a lake (see Section 1.1 below). Other 

factors depend upon the general biology of fish populations in North American ecoregions (see 

Section 1.2). 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 1.3). Then, equations were developed (see Section 

1.4) that were used to create Exhibit_Att B-4, which was used to determine the threshold lake 

size of 25 acres.

1.1. Angler Behavior

Several assumptions regarding angler behavior are important for estimating a minimum lake 

size that is fishable. The first assumption is that anglers (and their family members) consume 

about 50:50 TL3 and TL4 fish by biomass. TL3 fish are benthic carnivores (BC) such as catfish 

and chub that consume benthic invertebrates and small benthic fish. TL4 fish are pelagic 

piscivores—water column carnivores (WCC) such as largemouth bass and walleye. This 

assumption of 50:50 TL3 and TL4 consumption appears reasonable without being overly 

conservative. Although anglers might prefer to catch and consume the pelagic (TL4) game fish 

species, their generally lower abundance compared with lower trophic-level fish dictates that 

anglers will more often capture the benthic (TL3) 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 anglers 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 analysis, 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 associated with human consumption.

A third assumption relates to ingestion rates of the angler or angler family. Fish ingestion rates 

used for the purpose of the Tier 2 analysis are the same as those in Tier 1 and are consistent 

with subsistence angler ingestion rates. 

Another important consideration related to angler behavior is how much fish removal in a lake is 

possible without resulting in local fish populations declining to extirpation. 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 various models to 

predict sustainable harvests of different fisheries are numerous and complex. Species-specific 

parameters of importance 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 
Attachment B, Addendum 2 B2-5 6-29-2012

Lake Size Analysis

of predator (TL4) fish. For this lake size analysis, angler harvesting of fish is assumed to be 10 

percent of the biomass of pelagic TL4 fish.

1.2. Fish Biology 

Estimation of the minimum surface area of a pond or lake that is fishable also must be based on 

assumptions concerning fish biology, and these assumptions have been drawn from several 

published studies across the United States. 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 Tier 2: 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 (i.e., 

TL3 and TL4 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).

1.2.1. 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 for three categories of lakes:

Oligo- ( 0 μg/ ) = 1.9 g ww/m2

Meso-eutro (TP = 30 μg/ ) = 3.7 g ww/m2

Eutro- yp ( 00 μg/ ) = 8.5 g ww/m2

Nürnberg (1996) also summarized total fish biomass limits from Bachmann et al. (1996) for the 

lake trophic status categories based on a sample of 60 lakes in Florida:

Oligo-meso (TP 0 μg/ ) = 7.4 g ww/m2

Meso- ( 30 μg/ ) = 10.6 g ww/m2

Eutro- yp ( 00 μg/ ) = 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 

121° E to 122° W longitude. Their linear regression relating TP to total fish standing biomass 

(B) used the following equation and had a correlation coefficient (r

2

) of 0.84:

B = 0.792 + 0.072 (TP)
Attachment B, Addendum 2 B2-6 6-29-2012

Lake Size Analysis

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/m2for 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/m2over 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/m2in 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.

Ideally, one would have data indicating TP levels in lakes in the vicinity of facilities for a Tier 2 

analysis. Such data, however, are rarely readily available. The general ranges of fish 

productivity across lakes of various surface areas, therefore, are considered to be independent 

of lake size over a wide range of lake sizes. Thus, lake size alone cannot be used to estimate 

fish productivity or maximal fish harvesting rates.

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 that related TP 

to total fish standing biomass (B) used the following equation and had an r2of 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 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.

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 

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.
Attachment B, Addendum 2 B2-7 6-29-2012

Lake Size Analysis

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 somewhat higher than a mean value for representative samples

of natural lakes in the United States. For the purpose of the Tier 2 analysis, we therefore 

rounded that value down to a single significant digit of 40 g ww/m2as the upper limit for total fish 

biomass in a lake.

1.2.2. Proportion of Fish Biomass by Trophic Level

As indicated in Section 1.1 of this addendum, for the Tier 2 analysis, the proportion of fish in an 

angler’s diet that consists of TL3 and TL4 fish 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 (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 TL3 and TL4 

relative to total standing fish biomass are examined assuming TL4 fish are present.

Examination of several studies of fish biomass by trophic habits indicated that top trophic level 

fish might comprise approximately 20 percent of the standing fish biomass in many locations. 

Ploskey and Jenkins (1982) estimated that piscivorous fish, both those that are generally freeswimming 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). Their model was designed to predict the 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—especially during early development, but also as adults, when they may switch between 

zooplankton and zoobenthos and from small fish to larger fish as they grow. A “distribution 

coefficient” was used 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, Håkanson 

and Boulion (2004) 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.

Based on the information above, the top trophic level (TL3 and TL4) 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 
Attachment B, Addendum 2 B2-8 6-29-2012

Lake Size Analysis

total fish biomass in a benthic carnivore (TL3) compartment and 3.5 percent of the total in a 

pelagic piscivore (TL4) compartment. Thus, the TL4 fish, when present, represent the limiting 

compartment for angler fish harvesting and consumption. 

1.2.3. Minimum Viable Population Size

The final step in estimating the minimum lake size that can support sustainable fishing of its TL4

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 general 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).

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 as 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 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” 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 TL3 or TL4. For this Tier 2 analysis, 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. 
Attachment B, Addendum 2 B2-9 6-29-2012

Lake Size Analysis

1.3. Summary of Assumptions for the Lake Size Analysis

The following assumptions were used in processing lake data for the Tier 2 analysis and in 

estimating the relationship between sustainable fish ingestion and harvest rates and lake size

(see Section B.3.1 of the main report).

1. Piscivorous fish (TL3 plus TL4), when present, comprise approximately 21 percent of the 

standing biomass of fish (ignoring seasonal changes). The TL3 fish represent 17.5 percent 

of the standing fish biomass; TL4 fish account for 3.5 percent of this total fish biomass. 

Thus, TL4 fish, when present, represent the limiting compartment for angler fish harvesting 

and consumption.

2. Humans can harvest 10 percent of the TL4 biomass without threatening the population due 

to overharvesting.

3. The MVP size for a single TL4 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 a TL4 fish is edible fillet muscle.

1.4. Equations Used to Determine Lake Fish Populations

The standing biomass of TL4 fish supported in Lake X (TL4 SB) 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. 

TL4 SB = Total SB × Fraction TL4 (Equation 1)

where:

TL4 SB = Standing biomass of TL4 fish (g wet weight [ww]/m2

) in Lake X

Total SB = Total standing biomass of fish (g ww/m2

) in Lake X

Fraction TL4 = Fraction of TL4 fish in Lake X (0.035)

Using TL4 SB and the size of Lake X (Lake Size), the total number of TL4 fish supported in 

Lake X can be calculated using Equation 2 below.

(Equation 2)

where:

No. TL4 = Total number of TL4 fish in Lake X

Lake Size = Size of Lake X (acres)

TL4 SB = Standing biomass of TL4 fish (g ww/m2

; from Equation 1)

CF = 4047 m2

/acre

BWa = Body weight of adult TL4 fish (2000 g; assumed based on professional judgment)

The likely annual productivity of TL4 fish (kg/year) in Lake X can be estimated using Equation 3.

BWa

LakeSize T4SB CF

No T4

× ×

. =
Attachment B, Addendum 2 B2-10 6-29-2012

Lake Size Analysis

(Equation 3)

where:

Productivity TL4 = Likely annual productivity of TL4 fish in Lake X (kg/year)

Lake Size = Size of the Lake X (acres)

TL4 SB = Standing biomass of TL4 fish (g ww/m2

; from Equation 1)

CF1 = 4047 m2

/acre

CF2 = 1000 g/kg

The maximum fish ingestion rate (g/day) for TL3 plus TL4 fish associated with sustainable 

fishing can be predicted using Equation 4. It assumes 50:50 consumption behaviors of TL3 and 

TL4 fish, represented by the factor of 2 in Equation 4. 

(Equation 4)

where:

Max Sustain IR TL3 + 

TL4 = Predicted maximum sustainable ingestion rate for TL3 plus TL4 fish (g/day)

Productivity TL4 = Likely annual productivity of TL4 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 g/kg

CF2 = 365 days/year

CF2

LakeSize T4SB CF1

Productivity T4

× ×

=

CF2

2 ProductivityT4 FF HF CF1

MaxSustainIR T3 T4

× × × ×

( + ) =
Attachment B, Addendum 2 B2-11 6-29-2012

Lake Size Analysis

1.5. 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.

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 

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.
Attachment B, Addendum 2 B2-12 6-29-2012

Lake Size Analysis

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.

Schaffer, 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 5

Detailed Risk Modeling Results 
1 of 2

Facility NEI ID Facility Name Address City State

NEI13475

Ash Grove Cement Company

4343 Highway 108 Foreman AR

NEI880 Clean Harbors El Dorado, LLC 309 American Circle El Dorado AR

NEIIL031069A

Safety-Kleen Systems, Inc. - Dolton Recycle 

Center 633 E. 138th Street Dolton IL

NEIIL03153

Clean Harbors Recycling Services of Chicago 

LLC 1445 W 42nd St Chicago IL

NEI16354 Essroc Cement Corp. 3084 W. CR 225 S. Logansport KS

NEIIN423 RECLAIMED ENERGY COMPANY, INC. 1500 WESTERN AVENUE CONNERSVILLE IN

NEIIN00345 Tradebe Treatment and Recycling, LLC 4343 Kennedy Avenue East Chicago IN

NEI32342

Evonik Degussa Corporation Tippecanoe 

Laboratories 1650 LILLY ROAD LAFAYETTE IN

NEI16356

Ash Grove Cement Company

1801 N. Santa Fe Chanute KS

NEI16355 Systech Environmental Corporation 1420 South Cement Plant Road Fredonia KS

NEIKY103236

Safety-Kleen Systems, Inc. - Smithfield Recycle 

Center 3700 La Grange Road Smithfield KY

NEI32966 ROHM & HAAS COMPANY LOUISVILLE PLANT 4300 CAMPGROUND RD. LOUISVILLE KY

NEILA0930002 GRAMERCY ALUMINA LLC 1111 AIRLINE HWY GRAMERCY LA

NEIMILF450 WAYNE DISPOSAL INC 49350 N. I-94 Service Drive Belleville MI

NEIMIN0731

PETRO-CHEM & SOLVENT DISTILLERS 

GROUPS OF NORTRU. 421 LYCASTE DETROIT MI

NEINE00009 Clean Harbors Environmental Services, Inc. 2247 S Highway 71 Kimball NE

NEI16605 Thermalkem (Norlite) 628 S. SARATOGA ST. Cohoes NY

NEI37022 EASTMAN KODAK CO KODAK PARK 1669 LAKE AVE ROCHESTER NY

NEIOHT$5915

Heritage-WTI, Inc. d/b/a Heritage Thermal 

Services 1250 St. George Street East Liverpool OH

NEIOHT$5677 Clean Harbors Recycling Services of Ohio L.L.C. 581 Milliken Drive S.E. Hebron OH

NEI40462 ROSS INCINERATION SERVICES, INC. 36790 GILES ROAD GRAFTON OH

NEI11681 Systech Environmental Corporation 11397 County Rd 176 Paulding OH

NEI12988

CONOCOPHILLIPS CO PONCA CITY REFI 

NERY 1000 S PINE ST PONCA CITY OK

NEIOK4013107 Systech Environmental Corporation 2701 N. 145th E. Ave Tulsa OK

NEI16665 Keystone Cement Company Route 329 Bath PA

NEI16783 Giant Cement Company 654 Judge Street Harleyville SC

NEI41416 ENGELHARD CORP 554 ENGELHARD DR SENECA SC

NEI11295 DOW CHEMICAL CO FREEPORT FACILITY 2301 N BRAZOSPORT BLVD BUI Freeport TX

NEI7868 Safety-Kleen Systems Denton Recycle Center 1722 Cooper Creek Road Denton TX

Table 1 - Facility Identification Information
2 of 2

Facility NEI ID Facility Name Address City State

Table 1 - Facility Identification Information

NEI7162 SAFETY KLEEN DEER PARK INCORPORATED 2027 BATTLEGROUND ROAD DEER PARK TX

NEI11200

Valero Refining Co. - Port Arthur (prev. Premcor 

Refining Group Inc.) 1801 S. GULFWAY DRIVE PORT ARTHUR TX

NEI6631 ONYX ENVIRONMENTAL SERVICES LLC HWY. 73, W. OF TAYLOR BAYOUPort Arthur TX

NEI6481 DUPONT VICTORIA 2695 OLD BLOOMINGTON RD N VICTORIA TX

NEIUT19459 Clean Harbors Aragonite, LLC 11600 North Aptus Road Aragonte UT

NEI17512 Solite LLC RTE. 1 STATE RD. 652 Arvonia VA

NEI51710SEWELL US Navy Norfolk Naval Base-Sewells Point 1510 Gilbert St Norfolk VA

NEI42539 Hydrite Chemical Co. 114 N. Main Street Cottage Grove WI

NEI18116 WRR ENVIRONMENTAL SERVICES CO., INC 5200 STATE ROAD 93 EAU CLAIRE WI
1 of 1

Cancer 

MIR

Cancer

 Incidence

Noncancer 

Max HI

Target 

Organ

Cancer 

MIR

Noncancer 

Max HI

Target 

Organ

NEI13475 3.69E-09 1.71E-07 2.75E-05 respiratory 1.42E-06 2.38E-02 respiratory 0%

NEI880 2.62E-06 6.66E-05 2.72E-03 liver 3.33E-06 1.72E-02 respiratory 79%

NEIIL031069A 2.72E-07 3.50E-05 1.95E-03 neurological 2.89E-07 6.83E-03 respiratory 94%

NEIIL03153 1.52E-10 1.17E-07 9.36E-05 developmental 2.01E-08 3.43E-03 respiratory 1%

NEI16354 1.60E-06 2.77E-05 1.24E-02 respiratory 2.05E-04 4.24E+00 respiratory 1%

NEIIN423 5.12E-07 5.46E-06 3.23E-02 reproductive 2.57E-05 3.23E-02 reproductive 2%

NEIIN00345 2.16E-08 1.59E-06 2.65E-04 neurological 2.16E-08 2.65E-04 neurological 100%

NEI32342 0.00E+00 0.00E+00 1.41E-03 whole_body 2.29E-07 2.50E-02 respiratory 0%

NEI16356 6.12E-08 6.93E-07 3.72E-04 respiratory 6.80E-08 7.16E-03 respiratory 90%

NEI16355 2.91E-07 9.50E-07 2.86E-03 neurological 3.51E-07 9.74E-03 respiratory 83%

NEIKY103236 3.97E-08 7.09E-06 2.24E-03 respiratory 3.97E-08 2.24E-03 respiratory 100%

NEI32966 4.24E-13 1.85E-10 1.13E-05 respiratory 6.09E-07 8.52E-02 respiratory 0%

NEILA0930002 0.00E+00 0.00E+00 9.34E-04 respiratory 0.00E+00 9.34E-04 respiratory 0%

NEIMILF450 2.66E-08 4.91E-06 6.09E-06 respiratory 9.89E-08 2.10E-04 kidney 27%

NEIMIN0731 1.19E-11 4.91E-09 1.26E-05 neurological 2.57E-09 5.06E-05 kidney 0%

NEINE00009 8.82E-09 3.30E-08 9.55E-05 neurological 6.04E-07 1.78E-02 respiratory 1%

NEI16605 7.94E-07 2.39E-05 6.12E-03 respiratory 2.27E-05 1.94E-01 respiratory 3%

NEI37022 0.00E+00 0.00E+00 1.69E-03 respiratory 3.51E-06 1.36E-01 respiratory 0%

NEIOHT$5915 6.28E-06 4.94E-04 2.96E-03 liver 6.83E-06 2.52E-02 respiratory 92%

NEIOHT$5677 4.64E-07 2.85E-05 3.44E-02 neurological 4.68E-07 3.45E-02 neurological 99%

NEI40462 2.02E-06 6.92E-05 8.93E-02 respiratory 2.87E-06 9.39E-02 respiratory 70%

NEI11681 2.00E-10 6.46E-08 4.54E-06 neurological 2.37E-06 4.49E-03 respiratory 0%

NEI12988 0.00E+00 0.00E+00 1.91E-04 respiratory 2.35E-05 1.39E-01 neurological 0%

NEIOK4013107 5.03E-09 3.82E-07 4.57E-05 neurological 1.28E-07 2.54E-02 neurological 4%

NEI16665 2.69E-06 5.65E-05 1.36E-02 respiratory 3.40E-06 4.01E-02 respiratory 79%

NEI16783 1.24E-06 2.36E-05 9.18E-03 respiratory 1.38E-06 4.85E-01 respiratory 90%

NEI41416 4.27E-07 2.69E-05 5.98E-01 respiratory 4.27E-07 5.98E-01 respiratory 100%

NEI11295 3.91E-09 3.77E-07 7.27E-04 respiratory 4.25E-05 4.45E+00 respiratory 0%

NEI7868 2.46E-07 1.94E-05 2.32E-03 respiratory 3.67E-07 2.87E-03 respiratory 67%

NEI7162 9.06E-06 1.55E-02 1.04E-02 respiratory 9.13E-06 1.60E-02 respiratory 99%

NEI11200 0.00E+00 0.00E+00 1.04E-07 neurological 2.69E-06 1.29E-01 neurological 0%

NEI6631 4.60E-06 1.82E-03 5.41E-03 respiratory 4.69E-06 5.93E-03 respiratory 98%

NEI6481 1.69E-09 1.39E-07 3.13E-05 neurological 7.21E-07 7.54E-03 respiratory 0%

NEIUT19459 6.70E-10 1.68E-09 5.91E-06 immunological 2.12E-08 3.54E-04 respiratory 3%

NEI17512 2.02E-07 2.22E-06 3.30E-04 respiratory 3.45E-07 5.31E-02 neurological 59%

NEI51710SEWELL 3.77E-09 3.87E-08 3.70E-05 respiratory 2.04E-06 2.41E-02 respiratory 0%

NEI42539 0.00E+00 0.00E+00 4.01E-02 reproductive 8.42E-08 3.80E-02 reproductive 0%

NEI18116 1.72E-07 1.98E-06 1.77E-02 neurological 2.54E-07 1.78E-02 neurological 68%

1 BOLD indicates a cancer risk great than 1 in a million or a noncancer risk greater than 1

Table 2 – Maximum Predicted HEM-3 Chronic Risks

Actual Emissions

Facility NEI ID

 Category Chronic Risk 1 Facility Chronic Risk 1

SC % of 

Facility-wide 

Cancer Risk
 1 of 9

REL AEGL1 AEGL2 ERPG1 ERPG2

NEI11200 Styrene 3.62E-04 8.95E-05 1.38E-05 3.62E-05 6.92E-06

NEI11295 1,2-Epoxybutane 0.00E+00 1.78E-06 9.14E-07 0.00E+00 0.00E+00

NEI11295 Acrolein 5.00E-02 1.81E-03 5.43E-04 1.14E-03 3.67E-04

NEI11295 Carbon tetrachloride 6.58E-05 4.46E-07 1.04E-07 9.61E-07 1.98E-07

NEI11295 Chloroform 2.50E-03 0.00E+00 1.21E-06 0.00E+00 1.56E-06

NEI11295 Epichlorohydrin 1.06E-02 6.25E-04 1.51E-04 7.23E-04 1.81E-04

NEI11295 Ethylene dichloride 0.00E+00 0.00E+00 0.00E+00 5.00E-06 1.23E-06

NEI11295 Hexachlorobutadiene 0.00E+00 0.00E+00 0.00E+00 3.41E-05 1.17E-05

NEI11295 Methylene chloride 6.25E-05 1.27E-06 4.60E-07 8.75E-07 3.36E-07

NEI11295 Propionaldehyde 0.00E+00 1.36E-05 2.42E-06 0.00E+00 0.00E+00

NEI11295 Propylene oxide 2.42E-05 4.41E-07 1.09E-07 6.25E-07 1.27E-07

NEI11295 Styrene 2.20E-04 5.44E-05 8.41E-06 2.20E-05 4.20E-06

NEI11295 Tetrachloroethene 2.50E-05 2.08E-06 3.12E-07 7.35E-07 3.57E-07

NEI11295 Trichloroethylene 0.00E+00 3.57E-07 1.04E-07 4.63E-07 9.26E-08

NEI11681 Acetonitrile 0.00E+00 7.28E-06 2.97E-07 0.00E+00 0.00E+00

NEI11681 Acrylonitrile 0.00E+00 7.46E-08 5.74E-09 3.39E-08 9.68E-09

NEI11681 Benzene 1.63E-06 1.24E-08 8.13E-10 1.32E-08 4.40E-09

NEI11681 Dimethyl formamide 0.00E+00 0.00E+00 4.31E-10 1.94E-08 3.88E-10

NEI11681 Ethyl acrylate 0.00E+00 2.22E-08 5.04E-09 1.84E-05 6.30E-09

NEI11681 Ethyl benzene 0.00E+00 2.24E-06 6.55E-08 0.00E+00 0.00E+00

NEI11681 Methanol 6.90E-04 2.80E-05 7.16E-06 7.43E-05 1.49E-05

NEI11681 Methyl chloride 0.00E+00 0.00E+00 1.90E-06 0.00E+00 4.34E-06

NEI11681 Methyl tert-butyl ether 0.00E+00 8.75E-06 7.50E-07 0.00E+00 0.00E+00

NEI11681 Methylene chloride 1.19E-06 2.41E-08 8.76E-09 1.67E-08 6.40E-09

NEI11681 n-Hexane 0.00E+00 0.00E+00 1.57E-07 0.00E+00 0.00E+00

NEI11681 Styrene 7.48E-06 1.85E-06 2.85E-07 7.48E-07 1.43E-07

NEI11681 Tetrachloroethene 7.84E-06 6.54E-07 9.81E-08 2.31E-07 1.12E-07

NEI11681 Toluene 1.44E-04 7.13E-06 1.19E-06 2.81E-05 4.86E-06

NEI11681 Xylenes (mixed) 4.29E-05 1.68E-06 2.36E-07 0.00E+00 0.00E+00

NEI12988 Glycol Ethers 4.43E-02 0.00E+00 0.00E+00 0.00E+00 0.00E+00

NEI12988 Hydrochloric acid 2.15E-02 1.67E-02 1.37E-03 1.00E-02 1.50E-03

NEI13475 Benzene 2.47E-04 1.89E-06 1.24E-07 2.01E-06 6.69E-07

NEI13475 Chloroform 2.14E-03 0.00E+00 1.03E-06 0.00E+00 1.34E-06

NEI13475 Ethyl benzene 0.00E+00 2.91E-06 8.49E-08 0.00E+00 0.00E+00

NEI13475 Formaldehyde 1.77E-01 8.86E-03 5.73E-04 8.12E-03 8.12E-04

NEI13475 Methanol 3.04E-03 1.23E-04 3.15E-05 3.27E-04 6.55E-05

NEI13475 Methylene chloride 1.92E-04 3.89E-06 1.41E-06 2.69E-06 1.03E-06

NEI13475 Tetrachloroethene 4.86E-06 4.05E-07 6.08E-08 1.43E-07 6.94E-08

NEI13475 Toluene 1.38E-04 6.81E-06 1.14E-06 2.69E-05 4.65E-06

NEI13475 Xylenes (mixed) 2.92E-05 1.15E-06 1.61E-07 0.00E+00 0.00E+00

NEI16354 Chlorobenzene 0.00E+00 6.50E-05 4.33E-06 0.00E+00 0.00E+00

NEI16354 Ethyl benzene 0.00E+00 1.90E-03 5.53E-05 0.00E+00 0.00E+00

NEI16354 Hexachlorobutadiene 0.00E+00 0.00E+00 0.00E+00 2.06E-04 7.09E-05

NEI16354 Phenol 2.12E-03 2.12E-04 1.38E-04 3.24E-04 6.48E-05

NEI16354 Styrene 6.13E-03 1.51E-03 2.34E-04 6.13E-04 1.17E-04

NEI16354 Toluene 2.84E-02 1.40E-03 2.34E-04 5.54E-03 9.57E-04

NEI16354 Trichloroethylene 0.00E+00 1.85E-05 5.39E-06 2.40E-05 4.79E-06

NEI16355 Acetonitrile 0.00E+00 6.84E-04 2.79E-05 0.00E+00 0.00E+00

NEI16355 Benzene 2.95E-02 2.25E-04 1.47E-05 2.39E-04 7.98E-05

NEI16355 Ethyl benzene 0.00E+00 1.33E-04 3.87E-06 0.00E+00 0.00E+00

NEI16355 Methanol 9.28E-04 3.77E-05 9.62E-06 9.99E-05 2.00E-05

NEI16355 Methyl tert-butyl ether 0.00E+00 3.23E-03 2.77E-04 0.00E+00 0.00E+00

Facility NEI ID Pollutant

Table 3 – Maximum Predicted Acute Risks

Actual Emissions

Maximum Hazard Quotient1
 2 of 9

Facility NEI ID Pollutant REL AEGL1 AEGL2 ERPG1 ERPG2

Table 3 – Maximum Predicted Acute Risks

Actual Emissions

Maximum Hazard Quotient1

NEI16355 Styrene 6.61E-04 1.63E-04 2.52E-05 6.61E-05 1.26E-05

NEI16355 Tetrachloroethene 9.29E-04 7.74E-05 1.16E-05 2.73E-05 1.33E-05

NEI16355 Toluene 1.60E-02 7.89E-04 1.32E-04 3.12E-03 5.38E-04

NEI16355 Xylenes (mixed) 2.25E-02 8.85E-04 1.24E-04 0.00E+00 0.00E+00

NEI16356 Benzene 7.81E-03 5.97E-05 3.91E-06 6.35E-05 2.12E-05

NEI16356 Chlorobenzene 0.00E+00 9.28E-10 6.18E-11 0.00E+00 0.00E+00

NEI16356 Chloroform 4.51E-02 0.00E+00 2.18E-05 0.00E+00 2.82E-05

NEI16356 Ethyl benzene 0.00E+00 2.32E-05 6.78E-07 0.00E+00 0.00E+00

NEI16356 Formaldehyde 4.74E-01 2.37E-02 1.53E-03 2.17E-02 2.17E-03

NEI16356 Methanol 4.06E-02 1.65E-03 4.21E-04 4.37E-03 8.75E-04

NEI16356 Methyl tert-butyl ether 0.00E+00 9.18E-05 7.87E-06 0.00E+00 0.00E+00

NEI16356 Tetrachloroethene 1.04E-05 8.67E-07 1.30E-07 3.06E-07 1.49E-07

NEI16356 Toluene 1.34E-03 6.63E-05 1.10E-05 2.62E-04 4.52E-05

NEI16356 Xylenes (mixed) 5.92E-04 2.32E-05 3.25E-06 0.00E+00 0.00E+00

NEI16605 1,1,1-Trichloroethane 1.97E-05 1.03E-06 4.07E-07 7.06E-07 3.53E-07

NEI16605 Acetaldehyde 2.11E-04 1.23E-06 2.03E-07 5.52E-06 2.76E-07

NEI16605 Acetonitrile 0.00E+00 4.80E-04 1.95E-05 0.00E+00 0.00E+00

NEI16605 Aniline 0.00E+00 2.98E-05 1.94E-05 0.00E+00 0.00E+00

NEI16605 Benzene 3.81E-03 2.92E-05 1.91E-06 3.10E-05 1.03E-05

NEI16605 Carbon tetrachloride 6.27E-05 4.26E-07 9.93E-08 9.17E-07 1.89E-07

NEI16605 Chlorobenzene 0.00E+00 5.70E-05 3.80E-06 0.00E+00 0.00E+00

NEI16605 Chloroform 6.62E-04 0.00E+00 3.20E-07 0.00E+00 4.14E-07

NEI16605 Cumene 0.00E+00 9.19E-06 1.53E-06 0.00E+00 0.00E+00

NEI16605 Dimethyl formamide 0.00E+00 0.00E+00 1.51E-05 6.78E-04 1.36E-05

NEI16605 Dimethyl sulfate 0.00E+00 9.56E-04 1.85E-04 0.00E+00 0.00E+00

NEI16605 Ethyl acrylate 0.00E+00 2.34E-05 5.30E-06 1.94E-02 6.62E-06

NEI16605 Ethyl benzene 0.00E+00 9.97E-05 2.91E-06 0.00E+00 0.00E+00

NEI16605 Ethylene dibromide 0.00E+00 9.17E-07 6.62E-07 0.00E+00 0.00E+00

NEI16605 Ethylene dichloride 0.00E+00 0.00E+00 0.00E+00 5.74E-07 1.42E-07

NEI16605 Ethylene glycol ethyl ether 2.15E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00

NEI16605 Ethylene glycol methyl ether 8.54E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00

NEI16605 Formaldehyde 2.31E-01 1.15E-02 7.47E-04 1.06E-02 1.06E-03

NEI16605 Glycol Ethers 1.67E-02 0.00E+00 0.00E+00 0.00E+00 0.00E+00

NEI16605 Methanol 5.69E-03 2.31E-04 5.90E-05 6.12E-04 1.22E-04

NEI16605 Methyl methacrylate 0.00E+00 3.72E-05 5.32E-06 0.00E+00 0.00E+00

NEI16605 Methyl tert-butyl ether 0.00E+00 2.06E-05 1.77E-06 0.00E+00 0.00E+00

NEI16605 Methylene chloride 1.24E-02 2.53E-04 9.17E-05 1.74E-04 6.70E-05

NEI16605 n-Hexane 0.00E+00 0.00E+00 4.28E-07 0.00E+00 0.00E+00

NEI16605 Phenol 4.17E-04 4.17E-05 2.72E-05 6.36E-05 1.27E-05

NEI16605 Styrene 1.07E-04 2.65E-05 4.10E-06 1.07E-05 2.05E-06

NEI16605 Tetrachloroethene 8.35E-05 6.96E-06 1.04E-06 2.46E-06 1.19E-06

NEI16605 Toluene 2.53E-03 1.25E-04 2.08E-05 4.93E-04 8.51E-05

NEI16605 Trichloroethylene 0.00E+00 7.82E-07 2.28E-07 1.01E-06 2.03E-07

NEI16605 Triethylamine 1.55E-04 0.00E+00 0.00E+00 0.00E+00 0.00E+00

NEI16605 Vinyl acetate 0.00E+00 3.31E-05 1.26E-06 4.41E-05 3.06E-06

NEI16605 Xylenes (mixed) 6.07E-04 2.38E-05 3.34E-06 0.00E+00 0.00E+00

NEI16665 1,1,1-Trichloroethane 5.29E-04 2.77E-05 1.09E-05 1.89E-05 9.47E-06

NEI16665 Benzene 1.22E-02 9.35E-05 6.11E-06 9.93E-05 3.31E-05

NEI16665 Benzyl chloride 2.29E-05 0.00E+00 0.00E+00 1.06E-06 1.06E-07

NEI16665 Biphenyl 0.00E+00 0.00E+00 3.42E-08 0.00E+00 0.00E+00

NEI16665 Carbon disulfide 1.58E-02 2.45E-03 1.96E-04 3.16E-02 6.12E-04

NEI16665 Carbon tetrachloride 1.87E-02 1.27E-04 2.96E-05 2.74E-04 5.64E-05
 3 of 9

Facility NEI ID Pollutant REL AEGL1 AEGL2 ERPG1 ERPG2

Table 3 – Maximum Predicted Acute Risks

Actual Emissions

Maximum Hazard Quotient1

NEI16665 Chloroform 4.56E-03 0.00E+00 2.21E-06 0.00E+00 2.85E-06

NEI16665 Cumene 0.00E+00 4.27E-08 7.12E-09 0.00E+00 0.00E+00

NEI16665 Dimethyl formamide 0.00E+00 0.00E+00 1.50E-08 6.74E-07 1.35E-08

NEI16665 Ethyl benzene 0.00E+00 1.36E-05 3.98E-07 0.00E+00 0.00E+00

NEI16665 Ethylene dichloride 0.00E+00 0.00E+00 0.00E+00 1.40E-07 3.47E-08

NEI16665 Formaldehyde 2.43E+00 1.22E-01 7.87E-03 1.12E-01 1.12E-02

NEI16665 Methanol 7.74E-04 3.14E-05 8.03E-06 8.34E-05 1.67E-05

NEI16665 Methyl tert-butyl ether 0.00E+00 6.38E-06 5.47E-07 0.00E+00 0.00E+00

NEI16665 Methylene chloride 8.88E-03 1.80E-04 6.55E-05 1.24E-04 4.78E-05

NEI16665 n-Hexane 0.00E+00 0.00E+00 7.17E-07 0.00E+00 0.00E+00

NEI16665 Phenol 2.46E-08 2.46E-09 1.60E-09 3.75E-09 7.50E-10

NEI16665 Styrene 2.27E-05 5.60E-06 8.66E-07 2.27E-06 4.33E-07

NEI16665 Tetrachloroethene 2.65E-04 2.21E-05 3.31E-06 7.80E-06 3.79E-06

NEI16665 Toluene 2.29E-04 1.13E-05 1.88E-06 4.46E-05 7.71E-06

NEI16665 Trichloroethylene 0.00E+00 2.73E-05 7.96E-06 3.54E-05 7.07E-06

NEI16665 Vinyl acetate 0.00E+00 2.88E-04 1.10E-05 3.84E-04 2.66E-05

NEI16665 Xylenes (mixed) 7.88E-05 3.10E-06 4.33E-07 0.00E+00 0.00E+00

NEI16783 1,4-Dioxane 1.64E-04 8.08E-06 4.11E-07 0.00E+00 0.00E+00

NEI16783 Acetonitrile 0.00E+00 1.07E-02 4.36E-04 0.00E+00 0.00E+00

NEI16783 Acrylonitrile 0.00E+00 2.34E-02 1.80E-03 1.06E-02 3.04E-03

NEI16783 Benzene 4.74E-03 3.63E-05 2.37E-06 3.85E-05 1.28E-05

NEI16783 Cumene 0.00E+00 9.86E-06 1.64E-06 0.00E+00 0.00E+00

NEI16783 Dimethyl formamide 0.00E+00 0.00E+00 8.22E-06 3.70E-04 7.40E-06

NEI16783 Ethyl benzene 0.00E+00 1.14E-04 3.34E-06 0.00E+00 0.00E+00

NEI16783 Formaldehyde 9.41E-02 4.71E-03 3.05E-04 4.31E-03 4.31E-04

NEI16783 Methanol 2.10E-02 8.51E-04 2.18E-04 2.26E-03 4.52E-04

NEI16783 Methyl tert-butyl ether 0.00E+00 1.55E-03 1.33E-04 0.00E+00 0.00E+00

NEI16783 Methylene chloride 6.06E-02 1.23E-03 4.46E-04 8.48E-04 3.26E-04

NEI16783 m-Xylene 9.64E-04 0.00E+00 0.00E+00 0.00E+00 0.00E+00

NEI16783 n-Hexane 0.00E+00 0.00E+00 3.44E-05 0.00E+00 0.00E+00

NEI16783 o-Xylene 4.93E-04 0.00E+00 0.00E+00 0.00E+00 0.00E+00

NEI16783 Phenol 1.28E-04 1.28E-05 8.31E-06 1.95E-05 3.89E-06

NEI16783 p-Xylene 9.86E-04 0.00E+00 0.00E+00 0.00E+00 0.00E+00

NEI16783 Styrene 1.15E-03 2.84E-04 4.39E-05 1.15E-04 2.20E-05

NEI16783 Tetrachloroethene 3.20E-04 2.67E-05 4.01E-06 9.43E-06 4.58E-06

NEI16783 Toluene 1.14E-02 5.63E-04 9.38E-05 2.22E-03 3.84E-04

NEI16783 Trichloroethylene 0.00E+00 4.23E-06 1.23E-06 5.48E-06 1.10E-06

NEI16783 Triethylamine 2.38E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00

NEI16783 Xylenes (mixed) 2.61E-03 1.03E-04 1.44E-05 0.00E+00 0.00E+00

NEI17512 Methyl tert-butyl ether 0.00E+00 3.31E-02 2.83E-03 0.00E+00 0.00E+00

NEI17512 Toluene 2.92E-03 1.44E-04 2.40E-05 5.68E-04 9.81E-05

NEI17512 Xylenes (mixed) 1.67E-03 6.57E-05 9.19E-06 0.00E+00 0.00E+00

NEI18116 Acetonitrile 0.00E+00 1.94E-04 7.91E-06 0.00E+00 0.00E+00

NEI18116 Methanol 1.02E-02 4.16E-04 1.06E-04 1.10E-03 2.21E-04

NEI18116 Methylene chloride 3.06E-02 6.21E-04 2.26E-04 4.29E-04 1.65E-04

NEI18116 n-Hexane 0.00E+00 0.00E+00 4.13E-06 0.00E+00 0.00E+00

NEI18116 Tetrachloroethene 8.96E-04 7.47E-05 1.12E-05 2.64E-05 1.28E-05

NEI18116 Toluene 1.68E-02 8.26E-04 1.38E-04 3.26E-03 5.63E-04

NEI18116 Trichloroethylene 0.00E+00 8.41E-05 2.45E-05 1.09E-04 2.18E-05

NEI18116 Xylenes (mixed) 2.28E-02 8.96E-04 1.25E-04 0.00E+00 0.00E+00

NEI32342 Acetonitrile 0.00E+00 2.25E-03 9.18E-05 0.00E+00 0.00E+00

NEI32966 Ethyl acrylate 0.00E+00 7.62E-09 1.73E-09 6.32E-06 2.16E-09
 4 of 9

Facility NEI ID Pollutant REL AEGL1 AEGL2 ERPG1 ERPG2

Table 3 – Maximum Predicted Acute Risks

Actual Emissions

Maximum Hazard Quotient1

NEI32966 Ethyl benzene 0.00E+00 7.97E-10 2.33E-11 0.00E+00 0.00E+00

NEI32966 Hydrochloric acid 6.91E-05 5.37E-05 4.40E-06 3.22E-05 4.84E-06

NEI32966 Methyl methacrylate 0.00E+00 1.91E-06 2.73E-07 0.00E+00 0.00E+00

NEI32966 Toluene 1.81E-06 8.93E-08 1.49E-08 3.52E-07 6.09E-08

NEI37022 Carbon disulfide 5.14E-04 7.96E-05 6.37E-06 1.03E-03 1.99E-05

NEI37022 Methanol 3.49E-03 1.42E-04 3.62E-05 3.76E-04 7.53E-05

NEI40462 1,1,1-Trichloroethane 3.64E-04 1.91E-05 7.51E-06 1.30E-05 6.52E-06

NEI40462 2,4-Toluene diisocyanate 0.00E+00 2.47E-01 5.86E-02 4.87E-01 3.14E-02

NEI40462 Acetonitrile 0.00E+00 1.17E-03 4.76E-05 0.00E+00 0.00E+00

NEI40462 Acrylonitrile 0.00E+00 1.27E-03 9.74E-05 5.75E-04 1.64E-04

NEI40462 Aniline 0.00E+00 4.70E-05 3.07E-05 0.00E+00 0.00E+00

NEI40462 Benzene 2.48E-02 1.90E-04 1.24E-05 2.02E-04 6.72E-05

NEI40462 Benzyl chloride 4.43E-03 0.00E+00 0.00E+00 2.04E-04 2.04E-05

NEI40462 Carbon disulfide 3.79E-05 5.88E-06 4.71E-07 7.59E-05 1.47E-06

NEI40462 Carbon tetrachloride 7.54E-03 5.12E-05 1.19E-05 1.10E-04 2.27E-05

NEI40462 Chlorobenzene 0.00E+00 2.85E-04 1.90E-05 0.00E+00 0.00E+00

NEI40462 Chloroform 1.21E-01 0.00E+00 5.87E-05 0.00E+00 7.58E-05

NEI40462 Cumene 0.00E+00 5.99E-06 9.98E-07 0.00E+00 0.00E+00

NEI40462 Dimethyl formamide 0.00E+00 0.00E+00 1.02E-05 4.57E-04 9.14E-06

NEI40462 Dimethyl sulfate 0.00E+00 1.28E-03 2.47E-04 0.00E+00 0.00E+00

NEI40462 Ethyl acrylate 0.00E+00 3.66E-04 8.30E-05 3.04E-01 1.04E-04

NEI40462 Ethyl benzene 0.00E+00 7.54E-04 2.20E-05 0.00E+00 0.00E+00

NEI40462 Ethylene dichloride 0.00E+00 0.00E+00 0.00E+00 6.30E-04 1.56E-04

NEI40462 Formaldehyde 3.71E-01 1.86E-02 1.20E-03 1.70E-02 1.70E-03

NEI40462 Maleic anhydride 0.00E+00 0.00E+00 0.00E+00 2.56E-03 2.56E-04

NEI40462 Methanol 6.43E-03 2.61E-04 6.67E-05 6.93E-04 1.39E-04

NEI40462 Methyl methacrylate 0.00E+00 8.74E-04 1.25E-04 0.00E+00 0.00E+00

NEI40462 Methyl tert-butyl ether 0.00E+00 1.09E-05 9.37E-07 0.00E+00 0.00E+00

NEI40462 Methylene chloride 2.98E-03 6.04E-05 2.19E-05 4.17E-05 1.60E-05

NEI40462 m-Xylene 9.26E-05 0.00E+00 0.00E+00 0.00E+00 0.00E+00

NEI40462 n-Hexane 0.00E+00 0.00E+00 3.20E-06 0.00E+00 0.00E+00

NEI40462 o-Xylene 4.22E-05 0.00E+00 0.00E+00 0.00E+00 0.00E+00

NEI40462 Phenol 1.84E-03 1.84E-04 1.20E-04 2.80E-04 5.61E-05

NEI40462 p-Xylene 4.07E-05 0.00E+00 0.00E+00 0.00E+00 0.00E+00

NEI40462 Styrene 3.95E-03 9.76E-04 1.51E-04 3.95E-04 7.54E-05

NEI40462 Tetrachloroethene 1.42E-03 1.18E-04 1.77E-05 4.17E-05 2.02E-05

NEI40462 Titanium tetrachloride 0.00E+00 3.66E-03 2.53E-04 3.95E-04 9.87E-05

NEI40462 Toluene 7.98E-03 3.94E-04 6.56E-05 1.55E-03 2.68E-04

NEI40462 Trichloroethylene 0.00E+00 3.96E-05 1.15E-05 5.13E-05 1.03E-05

NEI40462 Triethylamine 1.69E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00

NEI40462 Vinyl acetate 0.00E+00 3.00E-04 1.14E-05 4.01E-04 2.77E-05

NEI40462 Vinyl chloride 1.10E-05 3.11E-06 6.41E-07 1.53E-06 1.53E-07

NEI40462 Xylenes (mixed) 1.06E-02 4.16E-04 5.83E-05 0.00E+00 0.00E+00

NEI41416 Chlorine 1.14E+00 1.60E-01 4.14E-02 8.27E-02 2.76E-02

NEI41416 Hydrazine 0.00E+00 2.52E-04 1.93E-06 5.04E-05 5.04E-06

NEI41416 Hydrochloric acid 3.45E-01 2.68E-01 2.20E-02 1.61E-01 2.42E-02

NEI42539 Glycol Ethers 2.20E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00

NEI42539 Methanol 2.15E-03 8.73E-05 2.23E-05 2.32E-04 4.63E-05

NEI42539 n-Hexane 0.00E+00 0.00E+00 1.78E-08 0.00E+00 0.00E+00

NEI42539 Toluene 1.49E-02 7.34E-04 1.22E-04 2.90E-03 5.01E-04

NEI42539 Xylenes (mixed) 2.25E-02 8.84E-04 1.24E-04 0.00E+00 0.00E+00

NEI6481 Benzene 2.85E-04 2.18E-06 1.43E-07 2.32E-06 7.73E-07
 5 of 9

Facility NEI ID Pollutant REL AEGL1 AEGL2 ERPG1 ERPG2

Table 3 – Maximum Predicted Acute Risks

Actual Emissions

Maximum Hazard Quotient1

NEI6481 Tetrachloroethene 1.82E-04 1.52E-05 2.28E-06 5.36E-06 2.60E-06

NEI6481 Vinyl chloride 6.87E-07 1.93E-07 3.99E-08 9.51E-08 9.51E-09

NEI6631 1,1,1-Trichloroethane 2.34E-03 1.22E-04 4.82E-05 8.37E-05 4.19E-05

NEI6631 1,3-Butadiene 0.00E+00 3.73E-05 4.66E-06 2.54E-03 1.27E-04

NEI6631 1,4-Dioxane 1.69E-03 8.29E-05 4.21E-06 0.00E+00 0.00E+00

NEI6631 Acetonitrile 0.00E+00 3.82E-03 1.56E-04 0.00E+00 0.00E+00

NEI6631 Acrylic acid 8.73E-04 1.19E-03 3.74E-05 1.75E-03 3.49E-05

NEI6631 Aniline 0.00E+00 1.99E-03 1.30E-03 0.00E+00 0.00E+00

NEI6631 Benzene 4.89E-02 3.74E-04 2.45E-05 3.97E-04 1.32E-04

NEI6631 Biphenyl 0.00E+00 0.00E+00 1.19E-04 0.00E+00 0.00E+00

NEI6631 Carbon disulfide 6.47E-04 1.00E-04 8.02E-06 1.29E-03 2.51E-05

NEI6631 Carbon tetrachloride 4.49E-02 3.04E-04 7.10E-05 6.56E-04 1.35E-04

NEI6631 Chlorobenzene 0.00E+00 1.87E-03 1.24E-04 0.00E+00 0.00E+00

NEI6631 Chloroform 3.90E-01 0.00E+00 1.89E-04 0.00E+00 2.44E-04

NEI6631 Cumene 0.00E+00 1.79E-05 2.98E-06 0.00E+00 0.00E+00

NEI6631 Dimethyl formamide 0.00E+00 0.00E+00 6.56E-05 2.95E-03 5.90E-05

NEI6631 Epichlorohydrin 6.25E-03 3.69E-04 8.93E-05 4.27E-04 1.07E-04

NEI6631 Ethyl benzene 0.00E+00 1.61E-04 4.69E-06 0.00E+00 0.00E+00

NEI6631 Ethylene dichloride 0.00E+00 0.00E+00 0.00E+00 2.62E-04 6.46E-05

NEI6631 Formaldehyde 2.06E-01 1.03E-02 6.67E-04 9.45E-03 9.45E-04

NEI6631 Glycol Ethers 4.98E-01 0.00E+00 0.00E+00 0.00E+00 0.00E+00

NEI6631 Hydrochloric acid 3.72E-02 2.90E-02 2.37E-03 1.74E-02 2.61E-03

NEI6631 Hydrofluoric acid 4.41E-02 1.29E-02 5.29E-04 6.62E-03 6.62E-04

NEI6631 Maleic anhydride 0.00E+00 0.00E+00 0.00E+00 2.45E-02 2.45E-03

NEI6631 Methanol 1.05E-02 4.28E-04 1.09E-04 1.14E-03 2.27E-04

NEI6631 Methyl methacrylate 0.00E+00 8.83E-05 1.26E-05 0.00E+00 0.00E+00

NEI6631 Methyl tert-butyl ether 0.00E+00 3.64E-05 3.12E-06 0.00E+00 0.00E+00

NEI6631 Methylene chloride 1.01E-02 2.04E-04 7.41E-05 1.41E-04 5.42E-05

NEI6631 n-Hexane 0.00E+00 0.00E+00 2.67E-05 0.00E+00 0.00E+00

NEI6631 Phenol 7.16E-03 7.16E-04 4.67E-04 1.09E-03 2.19E-04

NEI6631 Propylene oxide 1.30E-03 2.37E-05 5.85E-06 3.36E-05 6.84E-06

NEI6631 Styrene 2.52E-04 6.22E-05 9.62E-06 2.52E-05 4.81E-06

NEI6631 Tetrachloroethene 7.92E-03 6.60E-04 9.90E-05 2.33E-04 1.13E-04

NEI6631 Titanium tetrachloride 0.00E+00 1.09E-01 7.54E-03 1.18E-02 2.94E-03

NEI6631 Toluene 1.49E-02 7.33E-04 1.22E-04 2.89E-03 5.00E-04

NEI6631 Trichloroethylene 0.00E+00 5.67E-05 1.65E-05 7.35E-05 1.47E-05

NEI6631 Xylenes (mixed) 1.36E-02 5.35E-04 7.48E-05 0.00E+00 0.00E+00

NEI7162 1,1,1-Trichloroethane 2.11E-04 1.10E-05 4.34E-06 7.54E-06 3.77E-06

NEI7162 2,4-Toluene diisocyanate 0.00E+00 1.29E-01 3.06E-02 2.54E-01 1.64E-02

NEI7162 Acetonitrile 0.00E+00 1.78E-04 7.24E-06 0.00E+00 0.00E+00

NEI7162 Acrylic acid 7.92E-04 1.08E-03 3.40E-05 1.58E-03 3.17E-05

NEI7162 Acrylonitrile 0.00E+00 3.06E-04 2.35E-05 1.39E-04 3.97E-05

NEI7162 Allyl chloride 0.00E+00 2.40E-04 1.24E-05 2.25E-04 1.63E-05

NEI7162 Aniline 0.00E+00 6.49E-04 4.24E-04 0.00E+00 0.00E+00

NEI7162 Benzene 2.84E-02 2.17E-04 1.42E-05 2.31E-04 7.69E-05

NEI7162 Benzyl chloride 3.35E-03 0.00E+00 0.00E+00 1.55E-04 1.55E-05

NEI7162 Biphenyl 0.00E+00 0.00E+00 4.33E-05 0.00E+00 0.00E+00

NEI7162 Carbon disulfide 2.18E-04 3.39E-05 2.71E-06 4.37E-04 8.46E-06

NEI7162 Carbon tetrachloride 1.58E-02 1.07E-04 2.50E-05 2.31E-04 4.77E-05

NEI7162 Chlorobenzene 0.00E+00 5.91E-04 3.94E-05 0.00E+00 0.00E+00

NEI7162 Chloroform 9.54E-02 0.00E+00 4.62E-05 0.00E+00 5.96E-05

NEI7162 Cumene 0.00E+00 2.11E-06 3.52E-07 0.00E+00 0.00E+00
 6 of 9

Facility NEI ID Pollutant REL AEGL1 AEGL2 ERPG1 ERPG2

Table 3 – Maximum Predicted Acute Risks

Actual Emissions

Maximum Hazard Quotient1

NEI7162 Dimethyl formamide 0.00E+00 0.00E+00 5.87E-06 2.64E-04 5.28E-06

NEI7162 Epichlorohydrin 8.13E-04 4.80E-05 1.16E-05 5.56E-05 1.39E-05

NEI7162 Ethyl acrylate 0.00E+00 5.08E-05 1.15E-05 4.21E-02 1.44E-05

NEI7162 Ethyl benzene 0.00E+00 5.96E-04 1.74E-05 0.00E+00 0.00E+00

NEI7162 Ethylene dichloride 0.00E+00 0.00E+00 0.00E+00 2.76E-04 6.82E-05

NEI7162 Ethylene glycol ethyl ether 1.43E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00

NEI7162 Formaldehyde 2.70E-02 1.35E-03 8.74E-05 1.24E-03 1.24E-04

NEI7162 Glycol Ethers 1.14E-02 0.00E+00 0.00E+00 0.00E+00 0.00E+00

NEI7162 Hexachlorobutadiene 0.00E+00 0.00E+00 0.00E+00 3.36E-04 1.15E-04

NEI7162 Maleic anhydride 0.00E+00 0.00E+00 0.00E+00 6.60E-04 6.60E-05

NEI7162 Methanol 6.86E-03 2.78E-04 7.11E-05 7.39E-04 1.48E-04

NEI7162 Methyl bromide 9.08E-04 0.00E+00 4.32E-06 0.00E+00 1.86E-05

NEI7162 Methyl chloride 0.00E+00 0.00E+00 1.11E-06 0.00E+00 2.54E-06

NEI7162 Methyl methacrylate 0.00E+00 1.33E-05 1.90E-06 0.00E+00 0.00E+00

NEI7162 Methylene chloride 8.85E-03 1.80E-04 6.52E-05 1.24E-04 4.76E-05

NEI7162 Methylene diphenyl diisocyanate 0.00E+00 0.00E+00 0.00E+00 0.00E+00 6.07E-05

NEI7162 m-Xylene 5.60E-05 0.00E+00 0.00E+00 0.00E+00 0.00E+00

NEI7162 n-Hexane 0.00E+00 0.00E+00 5.03E-08 0.00E+00 0.00E+00

NEI7162 Phenol 1.17E-03 1.17E-04 7.63E-05 1.79E-04 3.57E-05

NEI7162 Styrene 1.10E-03 2.73E-04 4.21E-05 1.10E-04 2.11E-05

NEI7162 Tetrachloroethene 1.32E-03 1.10E-04 1.65E-05 3.87E-05 1.88E-05

NEI7162 Titanium tetrachloride 0.00E+00 1.12E-02 7.79E-04 1.21E-03 3.04E-04

NEI7162 Toluene 8.01E-03 3.95E-04 6.59E-05 1.56E-03 2.69E-04

NEI7162 Trichloroethylene 0.00E+00 2.40E-05 7.00E-06 3.11E-05 6.22E-06

NEI7162 Vinyl acetate 0.00E+00 4.51E-03 1.72E-04 6.01E-03 4.16E-04

NEI7162 Vinyl chloride 2.93E-06 8.25E-07 1.70E-07 4.06E-07 4.06E-08

NEI7162 Xylenes (mixed) 7.26E-03 2.85E-04 3.99E-05 0.00E+00 0.00E+00

NEI7868 Ethyl benzene 0.00E+00 1.07E-04 3.13E-06 0.00E+00 0.00E+00

NEI7868 Hydrochloric acid 6.48E-03 5.04E-03 4.13E-04 3.03E-03 4.54E-04

NEI7868 Hydrofluoric acid 1.16E-03 3.41E-04 1.40E-05 1.75E-04 1.75E-05

NEI7868 Methanol 5.36E-04 2.18E-05 5.56E-06 5.77E-05 1.15E-05

NEI7868 Methylene chloride 6.98E-03 1.42E-04 5.14E-05 9.77E-05 3.76E-05

NEI7868 Toluene 4.90E-03 2.42E-04 4.03E-05 9.53E-04 1.65E-04

NEI7868 Xylenes (mixed) 2.06E-03 8.10E-05 1.13E-05 0.00E+00 0.00E+00

NEI880 Hydrazine 0.00E+00 9.95E-03 7.61E-05 1.99E-03 1.99E-04

NEI880 Hydrofluoric acid 5.39E-03 1.58E-03 6.46E-05 8.08E-04 8.08E-05

NEI880 Methylene chloride 9.57E-02 1.94E-03 7.05E-04 1.34E-03 5.15E-04

NEI880 Tetrachloroethene 1.55E-02 1.29E-03 1.93E-04 4.55E-04 2.21E-04

NEI880 Titanium tetrachloride 0.00E+00 6.09E-04 4.22E-05 6.58E-05 1.65E-05

NEIIL031069A Ethyl benzene 0.00E+00 5.82E-05 1.70E-06 0.00E+00 0.00E+00

NEIIL031069A Methanol 2.71E-04 1.10E-05 2.81E-06 2.92E-05 5.84E-06

NEIIL031069A Methylene chloride 3.35E-06 6.80E-08 2.47E-08 4.69E-08 1.81E-08

NEIIL031069A Tetrachloroethene 9.72E-06 8.10E-07 1.21E-07 2.86E-07 1.39E-07

NEIIL031069A Toluene 2.18E-03 1.08E-04 1.80E-05 4.25E-04 7.35E-05

NEIIL031069A Trichloroethylene 0.00E+00 4.55E-09 1.33E-09 5.90E-09 1.18E-09

NEIIL031069A Xylenes (mixed) 1.45E-03 5.71E-05 7.99E-06 0.00E+00 0.00E+00

NEIIL03153 Acetonitrile 0.00E+00 1.54E-04 6.27E-06 0.00E+00 0.00E+00

NEIIL03153 Dimethyl formamide 0.00E+00 0.00E+00 9.59E-10 4.32E-08 8.63E-10

NEIIL03153 Ethyl benzene 0.00E+00 3.09E-07 9.01E-09 0.00E+00 0.00E+00

NEIIL03153 Methanol 1.27E-02 5.17E-04 1.32E-04 1.37E-03 2.74E-04

NEIIL03153 Methylene chloride 8.67E-06 1.76E-07 6.39E-08 1.21E-07 4.67E-08

NEIIL03153 m-Xylene 5.31E-06 0.00E+00 0.00E+00 0.00E+00 0.00E+00
 7 of 9

Facility NEI ID Pollutant REL AEGL1 AEGL2 ERPG1 ERPG2

Table 3 – Maximum Predicted Acute Risks

Actual Emissions

Maximum Hazard Quotient1

NEIIL03153 n-Hexane 0.00E+00 0.00E+00 1.01E-11 0.00E+00 0.00E+00

NEIIL03153 Phenol 3.20E-07 3.20E-08 2.08E-08 4.88E-08 9.76E-09

NEIIL03153 Tetrachloroethene 8.25E-06 6.87E-07 1.03E-07 2.43E-07 1.18E-07

NEIIL03153 Toluene 1.69E-05 8.33E-07 1.39E-07 3.29E-06 5.68E-07

NEIIN00345 Acetonitrile 0.00E+00 8.65E-05 3.52E-06 0.00E+00 0.00E+00

NEIIN00345 Ethyl benzene 0.00E+00 1.99E-05 5.82E-07 0.00E+00 0.00E+00

NEIIN00345 Formaldehyde 7.51E-03 3.76E-04 2.43E-05 3.44E-04 3.44E-05

NEIIN00345 Methanol 6.97E-04 2.83E-05 7.22E-06 7.50E-05 1.50E-05

NEIIN00345 Methylene chloride 1.64E-03 3.34E-05 1.21E-05 2.30E-05 8.85E-06

NEIIN00345 Styrene 1.10E-04 2.71E-05 4.20E-06 1.10E-05 2.10E-06

NEIIN00345 Tetrachloroethene 1.35E-04 1.12E-05 1.68E-06 3.96E-06 1.92E-06

NEIIN00345 Toluene 2.43E-03 1.20E-04 2.00E-05 4.74E-04 8.19E-05

NEIIN00345 Trichloroethylene 0.00E+00 9.49E-07 2.77E-07 1.23E-06 2.46E-07

NEIIN00345 Vinyl acetate 0.00E+00 3.85E-05 1.47E-06 5.14E-05 3.56E-06

NEIIN00345 Xylenes (mixed) 1.37E-03 5.40E-05 7.56E-06 0.00E+00 0.00E+00

NEIIN423 Ethyl benzene 0.00E+00 1.25E-05 3.65E-07 0.00E+00 0.00E+00

NEIIN423 Glycol Ethers 1.01E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00

NEIIN423 Methanol 9.07E-04 3.68E-05 9.40E-06 9.77E-05 1.95E-05

NEIIN423 Methylene chloride 2.08E-05 4.23E-07 1.54E-07 2.92E-07 1.12E-07

NEIIN423 n-Hexane 0.00E+00 0.00E+00 3.00E-06 0.00E+00 0.00E+00

NEIIN423 Styrene 1.04E-04 2.58E-05 3.98E-06 1.04E-05 1.99E-06

NEIIN423 Tetrachloroethene 6.57E-05 5.47E-06 8.21E-07 1.93E-06 9.38E-07

NEIIN423 Toluene 1.00E-03 4.94E-05 8.24E-06 1.95E-04 3.37E-05

NEIIN423 Trichloroethylene 0.00E+00 2.21E-05 6.45E-06 2.86E-05 5.73E-06

NEIIN423 Xylenes (mixed) 1.19E-03 4.66E-05 6.53E-06 0.00E+00 0.00E+00

NEIKY103236 1,2-Epoxybutane 0.00E+00 1.85E-04 9.47E-05 0.00E+00 0.00E+00

NEIKY103236 Acetaldehyde 4.52E-01 2.62E-03 4.34E-04 1.18E-02 5.90E-04

NEIKY103236 Benzene 4.50E-06 3.44E-08 2.25E-09 3.66E-08 1.22E-08

NEIKY103236 Carbon tetrachloride 4.51E-04 3.06E-06 7.15E-07 6.60E-06 1.36E-06

NEIKY103236 Chloroform 1.02E-02 0.00E+00 4.92E-06 0.00E+00 6.35E-06

NEIKY103236 Ethyl benzene 0.00E+00 2.72E-04 7.93E-06 0.00E+00 0.00E+00

NEIKY103236 Ethylene dichloride 0.00E+00 0.00E+00 0.00E+00 8.41E-07 2.08E-07

NEIKY103236 Methanol 1.09E-03 4.43E-05 1.13E-05 1.17E-04 2.35E-05

NEIKY103236 Methylene chloride 4.47E-05 9.07E-07 3.29E-07 6.26E-07 2.41E-07

NEIKY103236 n-Hexane 0.00E+00 0.00E+00 1.60E-06 0.00E+00 0.00E+00

NEIKY103236 Propionaldehyde 0.00E+00 5.52E-04 9.79E-05 0.00E+00 0.00E+00

NEIKY103236 Styrene 1.15E-03 2.83E-04 4.37E-05 1.15E-04 2.19E-05

NEIKY103236 Toluene 1.25E-02 6.17E-04 1.03E-04 2.44E-03 4.21E-04

NEIKY103236 Trichloroethylene 0.00E+00 6.69E-09 1.95E-09 8.67E-09 1.73E-09

NEIKY103236 Vinyl acetate 0.00E+00 2.80E-07 1.07E-08 3.73E-07 2.58E-08

NEIKY103236 Xylenes (mixed) 4.29E-03 1.69E-04 2.36E-05 0.00E+00 0.00E+00

NEILA0930002 Chlorine 4.01E-02 5.62E-03 1.45E-03 2.90E-03 9.68E-04

NEIMILF450 Glycol Ethers 1.41E-04 0.00E+00 0.00E+00 0.00E+00 0.00E+00

NEIMILF450 Xylenes (mixed) 1.79E-06 7.05E-08 9.87E-09 0.00E+00 0.00E+00

NEIMIN0731 Methanol 1.49E-05 6.04E-07 1.54E-07 1.60E-06 3.20E-07

NEIMIN0731 Methylene chloride 2.43E-06 4.93E-08 1.79E-08 3.40E-08 1.31E-08

NEIMIN0731 Tetrachloroethene 5.67E-07 4.72E-08 7.08E-09 1.67E-08 8.10E-09

NEIMIN0731 Xylenes (mixed) 1.89E-05 7.44E-07 1.04E-07 0.00E+00 0.00E+00

NEINE00009 Formaldehyde 1.14E+00 5.70E-02 3.69E-03 5.23E-02 5.23E-03

NEINE00009 Methanol 5.27E-02 2.14E-03 5.47E-04 5.68E-03 1.14E-03

NEINE00009 Methyl tert-butyl ether 0.00E+00 1.19E-03 1.02E-04 0.00E+00 0.00E+00

NEINE00009 Phenol 9.77E-03 9.77E-04 6.37E-04 1.49E-03 2.98E-04
 8 of 9

Facility NEI ID Pollutant REL AEGL1 AEGL2 ERPG1 ERPG2

Table 3 – Maximum Predicted Acute Risks

Actual Emissions

Maximum Hazard Quotient1

NEINE00009 Styrene 2.70E-03 6.67E-04 1.03E-04 2.70E-04 5.15E-05

NEINE00009 Xylenes (mixed) 4.51E-02 1.77E-03 2.48E-04 0.00E+00 0.00E+00

NEIOHT$5677 1,1,1-Trichloroethane 1.80E-03 9.43E-05 3.71E-05 6.45E-05 3.22E-05

NEIOHT$5677 Ethyl benzene 0.00E+00 2.39E-04 6.97E-06 0.00E+00 0.00E+00

NEIOHT$5677 Methanol 2.73E-04 1.11E-05 2.83E-06 2.94E-05 5.88E-06

NEIOHT$5677 Methylene chloride 8.33E-02 1.69E-03 6.14E-04 1.17E-03 4.49E-04

NEIOHT$5677 Tetrachloroethene 6.23E-02 5.19E-03 7.78E-04 1.83E-03 8.90E-04

NEIOHT$5677 Toluene 1.75E-03 8.63E-05 1.44E-05 3.41E-04 5.89E-05

NEIOHT$5677 Trichloroethylene 0.00E+00 9.53E-05 2.78E-05 1.24E-04 2.47E-05

NEIOHT$5677 Xylenes (mixed) 1.49E-03 5.85E-05 8.20E-06 0.00E+00 0.00E+00

NEIOHT$5915 1,1,1-Trichloroethane 2.74E-05 1.44E-06 5.66E-07 9.82E-07 4.91E-07

NEIOHT$5915 1,1-Dimethylhydrazine 0.00E+00 0.00E+00 2.97E-06 0.00E+00 0.00E+00

NEIOHT$5915 1,3-Butadiene 0.00E+00 1.69E-08 2.12E-09 1.15E-06 5.77E-08

NEIOHT$5915 1,4-Dioxane 1.17E-04 5.77E-06 2.93E-07 0.00E+00 0.00E+00

NEIOHT$5915 2,4-Toluene diisocyanate 0.00E+00 2.54E-04 6.02E-05 5.01E-04 3.23E-05

NEIOHT$5915 Acetaldehyde 3.10E-04 1.80E-06 2.97E-07 8.09E-06 4.04E-07

NEIOHT$5915 Acetonitrile 0.00E+00 9.65E-05 3.93E-06 0.00E+00 0.00E+00

NEIOHT$5915 Acrolein 6.77E-04 2.45E-05 7.36E-06 1.54E-05 4.98E-06

NEIOHT$5915 Acrylic acid 8.46E-07 1.15E-06 3.63E-08 1.69E-06 3.38E-08

NEIOHT$5915 Acrylonitrile 0.00E+00 3.38E-06 2.60E-07 1.54E-06 4.40E-07

NEIOHT$5915 Aniline 0.00E+00 3.22E-06 2.10E-06 0.00E+00 0.00E+00

NEIOHT$5915 Benzene 6.24E-04 4.77E-06 3.12E-07 5.07E-06 1.69E-06

NEIOHT$5915 Carbon disulfide 1.11E-04 1.71E-05 1.37E-06 2.21E-04 4.28E-06

NEIOHT$5915 Carbon tetrachloride 4.18E-04 2.83E-06 6.61E-07 6.11E-06 1.26E-06

NEIOHT$5915 Chlorobenzene 0.00E+00 1.30E-05 8.66E-07 0.00E+00 0.00E+00

NEIOHT$5915 Chloroform 4.43E-03 0.00E+00 2.15E-06 0.00E+00 2.77E-06

NEIOHT$5915 Chloromethyl methyl ether 0.00E+00 0.00E+00 6.35E-06 0.00E+00 3.08E-06

NEIOHT$5915 Cumene 0.00E+00 3.72E-07 6.20E-08 0.00E+00 0.00E+00

NEIOHT$5915 Dichlorvos 0.00E+00 1.37E-05 2.65E-06 0.00E+00 0.00E+00

NEIOHT$5915 Dimethyl formamide 0.00E+00 0.00E+00 8.46E-07 3.81E-05 7.62E-07

NEIOHT$5915 Dimethyl sulfate 0.00E+00 2.82E-04 5.46E-05 0.00E+00 0.00E+00

NEIOHT$5915 Epichlorohydrin 5.86E-05 3.46E-06 8.37E-07 4.01E-06 1.00E-06

NEIOHT$5915 Ethyl acrylate 0.00E+00 9.95E-07 2.26E-07 8.25E-04 2.82E-07

NEIOHT$5915 Ethyl benzene 0.00E+00 6.07E-06 1.77E-07 0.00E+00 0.00E+00

NEIOHT$5915 Ethylene dibromide 0.00E+00 5.47E-07 3.95E-07 0.00E+00 0.00E+00

NEIOHT$5915 Ethylene dichloride 0.00E+00 0.00E+00 0.00E+00 2.90E-06 7.17E-07

NEIOHT$5915 Ethylene oxide 0.00E+00 0.00E+00 2.88E-05 0.00E+00 2.59E-05

NEIOHT$5915 Formaldehyde 6.71E-03 3.35E-04 2.17E-05 3.07E-04 3.07E-05

NEIOHT$5915 Glycol Ethers 4.40E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00

NEIOHT$5915 Hexachlorobutadiene 0.00E+00 0.00E+00 0.00E+00 2.74E-05 9.41E-06

NEIOHT$5915 Hydrazine 0.00E+00 3.91E-05 2.99E-07 7.81E-06 7.81E-07

NEIOHT$5915 Hydrochloric acid 5.64E-06 4.39E-06 3.59E-07 2.63E-06 3.95E-07

NEIOHT$5915 Hydrofluoric acid 9.17E-05 2.68E-05 1.10E-06 1.37E-05 1.37E-06

NEIOHT$5915 Maleic anhydride 0.00E+00 0.00E+00 0.00E+00 1.16E-04 1.16E-05

NEIOHT$5915 Methanol 1.50E-04 6.07E-06 1.55E-06 1.61E-05 3.22E-06

NEIOHT$5915 Methyl bromide 7.81E-06 0.00E+00 3.71E-08 0.00E+00 1.60E-07

NEIOHT$5915 Methyl chloride 0.00E+00 0.00E+00 2.40E-08 0.00E+00 5.50E-08

NEIOHT$5915 Methyl hydrazine 0.00E+00 0.00E+00 1.73E-07 0.00E+00 0.00E+00

NEIOHT$5915 Methyl iodide 0.00E+00 0.00E+00 0.00E+00 2.26E-07 1.17E-07

NEIOHT$5915 Methyl methacrylate 0.00E+00 1.33E-06 1.90E-07 0.00E+00 0.00E+00

NEIOHT$5915 Methylene chloride 5.55E-05 1.13E-06 4.09E-07 7.77E-07 2.99E-07

NEIOHT$5915 n-Hexane 0.00E+00 0.00E+00 1.62E-08 0.00E+00 0.00E+00
 9 of 9

Facility NEI ID Pollutant REL AEGL1 AEGL2 ERPG1 ERPG2

Table 3 – Maximum Predicted Acute Risks

Actual Emissions

Maximum Hazard Quotient1

NEIOHT$5915 Phenol 2.05E-04 2.05E-05 1.33E-05 3.12E-05 6.24E-06

NEIOHT$5915 Styrene 3.47E-06 8.56E-07 1.32E-07 3.47E-07 6.62E-08

NEIOHT$5915 Tetrachloroethene 4.13E-05 3.44E-06 5.16E-07 1.21E-06 5.90E-07

NEIOHT$5915 Toluene 1.87E-04 9.23E-06 1.54E-06 3.64E-05 6.30E-06

NEIOHT$5915 Trichloroethylene 0.00E+00 1.23E-06 3.59E-07 1.60E-06 3.19E-07

NEIOHT$5915 Triethylamine 3.02E-06 0.00E+00 0.00E+00 0.00E+00 0.00E+00

NEIOHT$5915 Vinyl chloride 9.12E-07 2.56E-07 5.30E-08 1.26E-07 1.26E-08

NEIOK4013107 Acetonitrile 0.00E+00 9.65E-05 3.93E-06 0.00E+00 0.00E+00

NEIOK4013107 Benzene 2.45E-03 1.87E-05 1.23E-06 1.99E-05 6.64E-06

NEIOK4013107 Ethyl benzene 0.00E+00 1.52E-05 4.42E-07 0.00E+00 0.00E+00

NEIOK4013107 Methanol 1.52E-04 6.16E-06 1.57E-06 1.63E-05 3.27E-06

NEIOK4013107 Methyl tert-butyl ether 0.00E+00 3.60E-04 3.08E-05 0.00E+00 0.00E+00

NEIOK4013107 Styrene 5.06E-05 1.25E-05 1.93E-06 5.06E-06 9.65E-07

NEIOK4013107 Tetrachloroethene 1.06E-04 8.85E-06 1.33E-06 3.12E-06 1.52E-06

NEIOK4013107 Toluene 2.44E-03 1.20E-04 2.01E-05 4.75E-04 8.21E-05

NEIOK4013107 Xylenes (mixed) 3.14E-03 1.23E-04 1.73E-05 0.00E+00 0.00E+00

NEIUT19459 1,1,1-Trichloroethane 1.20E-02 6.28E-04 2.48E-04 4.30E-04 2.15E-04

NEIUT19459 Benzene 6.61E-02 5.06E-04 3.31E-05 5.37E-04 1.79E-04

NEIUT19459 Chloroform 2.29E-01 0.00E+00 1.11E-04 0.00E+00 1.43E-04

NEIUT19459 Methanol 3.72E-02 1.51E-03 3.85E-04 4.00E-03 8.00E-04

NEIUT19459 Methylene chloride 5.53E-02 1.12E-03 4.07E-04 7.74E-04 2.98E-04

NEIUT19459 n-Hexane 0.00E+00 0.00E+00 7.88E-06 0.00E+00 0.00E+00

NEIUT19459 Toluene 6.97E-04 3.44E-05 5.73E-06 1.36E-04 2.34E-05

NEIUT19459 Trichloroethylene 0.00E+00 1.23E-05 3.58E-06 1.59E-05 3.18E-06

NEIUT19459 Vinyl acetate 0.00E+00 7.16E-04 2.73E-05 9.55E-04 6.61E-05

NEIUT19459 Xylenes (mixed) 8.60E-03 3.38E-04 4.73E-05 0.00E+00 0.00E+00

 Note: BOLD indicates acute risks greater than 1

1 Some maximum acute impacts may be at onsite locations.
 1 of 1

Facility NEI ID Pollutant Tier 11 Tier 21

NEIOHT$5915 PAH Below Tier 1 Threshold Below Tier 1 Threshold

Table 4 – Multipathway Screening Analysis Results

Actual Emissions

1 Numeric value indicates number of times facility level emissions above screening threshold.
Appendix 6 Acute Impacts Refined Analysis 
Refined 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. 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. 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.). The attached figure 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. 
HEM‐3

(Screening)

Refined

Results

NEI16665 Formaldehyde REL 2.4 0.5 Examined facility for maximum off-site 

NEI42539 Glycol Ethers REL 2.2 1.0 Examined facility for maximum off-site 

Table 1 – Maximum Predicted Acute Risks Greater than 1 (Refined Approach)

Off-Site Waste and Recovery Operations 

1 Indicates modeling technique used to refined estimates; see Appendix 6 for figures depicting off‐site impacts

Facility NEI ID Pollutant Criteria Refined Modeling Approach 

Baseline ‐ Actuals
Figure 1 – NEI16665 Acute 

Formaldehyde HQ (REL)
Figure 2 – NEI42539 Acute 

Glycol Ethers HQ (REL)
Appendix 7 

Dispersion Model Receptor Revisions and Additions 
Dispersion Model Receptor Revisions and Additions for the 

Off-Site Waste and Recovery Operations 

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 Off-Site Waste and Recovery Operations 

 Recommended Corr. 

Facility ID Latitude Longitude Note 

NEIKS325 37.081590 -95.587250 Add User Defined Receptor 

NEIKY103236 38.437410 -85.236770 Add User Defined Receptor

NEIKY103236 38.43216 -85.238493 

NEI32966 38.210103 -85.841025 Add User Defined Receptor

NEIMIN0731 42.35858 -82.962159 Add User Defined Receptor

NEIOHT$5677 39.984220 -82.466750 Add User Defined Receptor

NEI40462 41.322633 -82.037665 Add User Defined Receptor

NEI40462 41.322409 -82.039941 Add User Defined Receptor

NEI42539 43.078957 -89.194301 Add User Defined Receptor

NEI18116 44.754340 -91.452468 Add User Defined Receptor

NEI18116 44.757789 -91.453563 Add User Defined Receptor