Residual Risk Assessment for the Ferroalloys Source Category
in Support of the September Supplemental Proposal -
FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE
Residual Risk Assessment for the Ferroalloys Source Category
in Support of the September Supplemental Proposal
by
EPA's Office of Air Quality Planning and Standards
Office of Air and Radiation
September 2014
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Residual Risk Assessment for the Ferroalloys Source Category
in Support of the September Supplemental Proposal -
FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE
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 8
2.4 Acute Risk Screening and Refined Assessments 8
2.5 Multipathway human health and environmental risk screening 11
2.6 Dose-response assessment 15
2.6.1 Sources of chronic dose-response information 15
2.6.2 Sources of acute dose-response information 24
2.7 Risk characterization 27
2.7.1 General 27
2.7.2 Mixtures 29
2.7.3 VIACT-Allowable Risks 30
3 Risk results for the Ferroalloys source category 30
3.1 Source category description and results 30
3.2 Baseline risk characterization 33
3.3 Post-control risk characterization 41
4 General discussion of uncertainties and how they have been addressed 42
4.1 Exposure modeling uncertainties 42
4.2 Uncertainties in the dose-response relationships 43
5 References 52
Index of Tables
Table 2.2-1 AERMOD version 12345 model options for RTR modeling 6
Table 2.6-1 (A) Dose-Response Values for Chronic Inhalation Exposure to Carcinogens .... 19
Table 2.6-1 (B) Dose-Response Values for Chronic Oral Exposure to Carcinogens 21
Table 2.6-2 (A) Dose-Response Values for Chronic Inhalation Exposure to Noncarcinogens
22
Table 2.6-1 (B) Dose-Response Values for Chronic Oral Exposure to Noncarcinogens 23
Table 2.6-3 Dose-Response Values for Acute Exposure 27
Table 3.1-1 Summary of Emissions from the Ferroalloys Source Category Used in the
Residual Risk Assessment and Availability of Dose-Response Values 31
Table 3.2-1 Summary of Source Category Level Inhalation Risks for Ferroalloys 34
Table 3.2-2 Summary of Source Category Level Multipathway Screening Assessment Risk
Results for Ferroalloys 37
Table 3.2-3 Summary of Source Category Level Site-Specific Multipathway Risk Results for
Ferroalloys Baseline Scenario 39
Table 3.2-4 Summary of Source Category Environmental Risk Screening Results for
Ferroalloys 40
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Residual Risk Assessment for the Ferroalloys Source Category
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Appendices
Appendix 1 Emissions Inventory Support Memorandum
Appendix 2 Technical Support Document for HEM3 Modeling
Appendix 3 Meteorological Data for HEM3 Modeling
Appendix 4 Technical Support Document for TRIM-Based Multipathway Screening
Scenario for RTR: Summary of Approach and Evaluation
Appendix 5 Analysis of short-term emission rates relative to long-term emission rates
Appendix 6 Draft Detailed Risk Modeling Results
Appendix 7 Acute Impacts Refined Analysis
Appendix 8 Dispersion Model Receptor Revisions and Additions for Ferroalloys
Appendix 9 Protocol for Developing a TRIM.FaTE Model Scenario
Appendix 10 Technical Support Document: Human Health Multipathway Residual Risk
Assessment for the Ferroalloys Production Source Category
Index of Acronyms
AERMOD
American Meteorological Society/EPA Regulatory Model
AEGL
Acute exposure guideline level
ASTDR
US Agency for Toxic Substances and Disease Registry
CalEPA
California Environmental Agency
ERPG
Emergency Response Planning Guideline
HAP
Hazardous Air Pollutant
HEM
Human Exposure Model
HI
Hazard index
HQ
Hazard quotient
IRIS
Integrated Risk Information System
MACT
Maximum Achievable Control Technology
MIR
Maximum Individual Risk
MOA
Mode of action
NAC
National Advisory Committee
NAAQS
National Ambient Air Quality Standards
NATA
National Air Toxics Assessment
NEI
National Emissions Inventory
NPRM
Notice of Proposed Rulemaking
PAH
Polynuclear Aromatic Hydrocarbon
PB-HAP
Persistent and Bioaccumulative - HAP
POM
Polycyclic organic matter
REL
Reference exposure level
RfC
Reference concentration
RfD
Reference dose
RTR
Risk and Technology
TOSHI
Target-organ-specific hazard index
LIRE
Unit risk estimate
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Residual Risk Assessment for the Ferroalloys Source Category
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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.) [7], 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
June 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 Science Advisory Board Review of Risk and Technology
Review 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) and the results of the post-control scenario risk
assessment performed for the ferroalloys 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
Data from several CAA section 114 information collection requests (ICR) were used for this
assessment. In 2010 and 2012 we sent ICRs to the two companies which own the two
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Residual Risk Assessment for the Ferroalloys Source Category
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facilities in the U.S. which we are aware are covered by the Ferroalloys Production MACT
standard. The ICRs requested available information regarding process equipment, control
devices, point and fugitive emissions, practices used to control fugitive emissions, and other
aspects of facility operations, including stack parameters and locations. Additionally, we
requested that the two facilities conduct emissions tests for certain HAP from specific
processes that were considered representative of the industry. Pollutants tested included metal
HAP (e.g., manganese, nickel compounds) hydrochloric acid (HC1), hydrogen fluoride (HF),1
formaldehyde, mercury compounds, polynuclear aromatic hydrocarbons (PAH),
polychlorinated biphenyls (PCB), and chlorodibenzodioxins and chlorodibenzofurans
(CDD/CDF). The results of these tests and data collection efforts are described in more detail
in the memo Revised Development of the Risk and Technology Review (RTR) Emissions
Dataset for the Ferroalloys Production Source Category, available in the docket (EPA-HQ-
OAR-2010-0895) and as Appendix 1 to this document. 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 risks from
each facility in the source category were estimated using the Human Exposure Model (HEM)
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 2 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-the-
science 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.
1 As explained in the memo Revised Development of the Risk and Technology Review (RTR) Emissions Dataset
for the Ferroalloys Production Source Category, test data were received from 4 furnaces for HF. All test results
were below the detection limit of the test method. While we initially calculated numerical estimates based on the
assumption that non-detects were equal to one-half of the detection limit and included these estimates in the
inputs to the risk assessment, we have no evidence that HF is emitted from these sources.
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Residual Risk Assessment for the Ferroalloys Source Category
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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 approximately 30 miles (50 km). Appendix 3 of this
document (Meteorological Data Processing Using AERMETfor HEMS) 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],
Beginning with version 12345, AERMET allows for a minimum wind speed of 0.5 m/s to be
utilized when processing monitoring stations that are equipped with sonic anemometers and
for an adjustment to the surface velocity (u*). The public version of AERMET available at
the time we conducted the modeling for this source category did not include the surface
friction velocity adjustment. Also at that time, an updated version of AERMET (and
AERMOD) was under development that incorporated further surface friction velocity
adjustments to AERMET version 12345. In anticipation of the update, EPA processed
meteorological data using a pre-public release version of AERMET and incorporated the
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Residual Risk Assessment for the Ferroalloys Source Category
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updated surface friction velocity adjustment in the output. It was EPA's judgment that the
pre-public release version of AERMET would generate AERMOD-ready meteorological data
very close to that generated with the final public release version.
EPA has posted the AERMET meteorological data used in the ferroalloys analysis on the
EPA's FERA (Fate, Exposure, and Risk Analysis) website2 under the HEM model page.
AERMET and AERMOD (version 13350) were released to the public in late December 2013
on the EPA's Support Center for Regulatory Atmospheric Modeling (SCRAM) website.3
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 8 to this document (Dispersion Model Receptor Revisions and Additions
for the Ferroalloys 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 resident (i.e., locations where
people may reasonably be assumed to reside rather than receptor points at the fenceline of a
facility). Chronic ambient concentrations were calculated as the annual average of all
estimated short-term (one-hour) concentrations at each block centroid. Possible future
residential use of currently uninhabited areas was not considered. Census blocks, the finest
resolution available in the census data, are typically comprised of approximately 40 people or
about ten households.
In contrast to the development of ambient concentrations for evaluating long-term exposures,
which was performed only for occupied census blocks, worst-case short-term (one-hour)
concentrations were estimated both at the census block centroids and at points nearer the
facility that represent locations where people may be present for short periods, but generally
no nearer than 100 meters from the center of the facility (note that for large facilities, this
100-meter ring could still contain locations inside the facility property). Since short-term
emission rates were needed to screen for the potential for hazard via acute exposures, and
since the ICR data contains only annual emission totals, we generally apply the assumption to
all source categories that the maximum one-hour emission rate from any source is ten times
the average annual hourly emission rate for that source. 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
2 The FERA webpage is http://www2.epa.gov/fera and the HEM3 webpage is http://www2.epa.gov/fera/risk-
assessment-and-modeling-human-exposure-model-hem
3 The SCRAM webpage is http://www.epa.gov/ttn/scram/.
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an elevation is not provided for the emission source, the model uses the average elevation of
all sectors within the innermost model ring. AERMOD adjusts the plume's flow if nearby
elevated hills are expected to influence the wind patterns. For details on how hill heights
were estimated and used in the AERMOD modeling, see Appendix 2 of this document,
Technical Support Document for HEM3 Modeling.
2.3 Estimating chronic human inhalation exposure
We used the estimated annual average ambient air concentration of each HAP at each census
block centroid as a surrogate for the lifetime inhalation exposure concentration of all the
people who reside in the census block. That is, the risk analysis did not consider either the
short-term or long-term behavior (mobility) of the exposed populations and its potential
influence on their exposure.
We did not address short-term human activity for two reasons. First, our experience with the
NAT A 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
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.
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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 began 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 for each facility as the first step. When we
identify acute impacts which exceed their relevant benchmarks, we pursue refining our acute
screening estimates to the extent possible. 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 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 emissions rate is defined as the total emissions for a year divided
by the total number of operating hours in the year.) This choice of a factor of ten for acute
screening was originally based on engineering judgment.
To develop a more robust peak-to-mean emissions factor, and in response to one of the key
messages from the SAB consultation on our RTR Assessment Plan, we performed an analysis
using a short-term emissions dataset from a number of sources located in Texas (originally
reported on by Allen et al. 2004) [7], In that report, the Texas Environmental Research
Consortium Project compared hourly and annual emissions data for volatile organic
compounds for all facilities in a heavily-industrialized 4-county area (Harris, Galveston,
Chambers, and Brazoria Counties) over an eleven-month time period in 2001. We obtained
the dataset and performed our own analysis, focusing on sources which reported emitting high
quantities of HAP over short periods of time (see Appendix 5 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
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Residual Risk Assessment for the Ferroalloys Source Category
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screening. This factor is intended to cover all possible hourly peaks associated with routinely-
variable emissions. While there have been some documented emission excursions above this
level, our analysis of the data from the Texas Environmental Research Consortium suggests
that this factor should cover more than 99 percent of the short-term peak gaseous or volatile
HAP emissions from typical industrial sources. We have no data relating specifically to peak
short-term emissions of particulate HAP. In the absence of source category-specific data, we
use this same default approach for particulate emissions as well.
For the ferroalloys source category, however, maximum hourly emissions estimates were
available, so we did not use the default emissions multiplier of 10. See Appendix 1 to this
document, Revised Development of the Risk and Technology Review (RTR) Emissions Dataset
for the Ferroalloys Production Source Category, which is also available separately in the
docket, for the detailed description of how the maximum hourly emissions were developed for
the ferroalloys source category. In general, some processes operate continuously so there are
no peak emissions and these processes received a factor of 1 (i.e., maximum hourly emissions
equal annual average hourly emissions). Other processes, for example tapping and casting,
have specific cycles, with peak emissions occurring for a part of that cycle (e.g., 30 minutes
during a 2-hour period). For these processes, we used a factor of 4 in the acute assessment.
As mentioned above, when we identify acute impacts which exceed their relevant
benchmarks, we pursue refining our acute screening estimates to the extent possible. For the
ferroalloys production source category, we conducted a review of the layout of the polar
receptors around the facilities compared to the facility boundaries to determine the maximum
off-site acute impact for the facilities that did not screen out during the initial run.
Appendix 6 to this document {Draft Detailed Risk Modeling Results) contains the initial acute
results for the source category, while Appendix 7 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 assumptions in our acute
assessment approach:
• Peak 1-hour emissions were obtained from the emissions memo and based on the
operating characteristics of ferroalloys production emission sources as described in
Appendix 1.
• For facilities with multiple emission points, peak 1-hour emissions were assumed to
occur at all emission points at the same time.
• We assumed that the peak emissions 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.
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• A person was assumed to be located downwind at the point of maximum modeled
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 human health 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)4. The PB-HAP compounds or compound classes are identified
for the screening from the EPA's Air Toxics Risk Assessment Library [5], Examples of
PB-HAP are cadmium compounds, chlordane, chlorinated dibenzodioxins and furans, DDE,
heptachlor, hexachlorobenzene, hexachlorocyclohexane, lead compounds, mercury
compounds, methoxychlor, polychlorinated biphenyls (PCB), polycyclic organic matter
(POM), toxaphene, and trifluralin. Emissions of cadmium compounds, chlorinated
dibenzodioxins and furans, lead compounds, mercury compounds, PCBs, and POM (of which
PAH are a subset) were identified in the emissions inventory for the ferroalloys source
category.
With respect to PB-HAP emissions other than lead, emissions were evaluated for potential
non-inhalation risks using a tiered screening approach which was developed for use with the
EPA's peer-reviewed Total Risk Integrated Methodology - Fate, Transport, and Ecological
Exposure (TRIM.FaTE5) model. With this approach, we first determine whether the facility-
specific emission rates of each of the emitted PB-HAP are large enough to create the potential
for significant non-inhalation human health risks under reasonable worst-case conditions. To
facilitate this step, we developed emission rate screening levels for each PB-HAP using a
hypothetical upper-end screening exposure scenario developed for use in conjunction with
TRIM.FaTE. The exposure scenario includes a generic farming/fishing exposure scenario
that simulates a subsistence environment. We conducted a sensitivity analysis on the
screening scenario to ensure that its key design parameters would represent the upper end of
the range of possible values, such that it would represent a conservative but not impossible
scenario. The PB-HAP emissions from each facility in the source category were compared to
the emission rate screening levels for each of the PB-HAP identified to assess the potential for
significant human health risks via non-inhalation pathways. For the purpose of developing
emission rates for the multipathway screen, we derived emission levels for each PB-HAP
(other than lead) at which the maximum human health risk would be 1 in a million for lifetime
cancer risk or a hazard quotient of 1.0 for noncancer impacts. We call this application of the
4 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).
5 EPA's Total Risk Integrated Methodology (General Information) http://www2.epa.gov/fera/total-risk-
integrated-methodology-trim-general
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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 multipathway impacts.
If the PB-HAP emissions for a facility exceed the Tier I screening emission rate, we conduct a
Tier II multipathway screen. In the Tier II screen, the location of each facility that exceeded
the Tier I emission rate is used to refine some of the assumptions associated with the
environmental scenario while maintaining the exposure scenario assumptions. We then adjust
the risk-based Tier I screening level for each PB-HAP for each facility based on an
understanding of how exposure concentrations estimated for the screening scenario change
with meteorology and environmental assumptions. This step creates a facility-specific
emission rate screening level for each PB-HAP (i.e., the level for cadmium could be different
for every facility), unlike the Tier I emission rate screening level which was constant for the
same PB-HAP at different facilities (i.e., the level for cadmium was the same for every
facility). Facility emissions of PB-HAP that do not exceed these new Tier II screening levels
are considered to pose no significant risks. When facilities exceed the Tier II screening
levels, it does not mean that multipathway impacts are significant, only that we cannot rule
out that possibility based on the results of the screen. See Appendix 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 Tier II screen.
If the PB-HAP emissions for a facility exceed the Tier II screening emissions rate, and data
are available, we may decide to conduct a more refined multipathway assessment. A refined
assessment replaces some of the assumptions made in the Tier II screen, with site specific
data. The refined assessment also uses the TRIM.FaTE model and facility-specific emission
rate levels that are created for each PB-HAP. There are many variables to consider in a
refined multipathway assessment and we have developed a protocol to maintain consistency
across source categories. This protocol can be found in Appendix 9 of this document
(Protocol for Developing a TRIM.FaTE Model Scenario to Support a Site-Specific Risk
Assessment in the RTR Program). For the ferroalloys production source category, we did
conduct a refined multipathway assessment for one facility in the category. A detailed
discussion of the approach for this assessment can be found in Appendix 10 of this document
(.Technical Support Document: Human Health Multipathway Residual Risk Assessment for
the Ferroalloys Production Source Category).
In evaluating the potential multipathway risks from emissions of lead compounds, rather than
developing a screening emission rate for them, we compared maximum estimated chronic
atmospheric concentrations with the current National Ambient Air Quality Standard
(NAAQS) for lead. Values below the NAAQS were considered to have a low potential for
multipathway risks.
The NAAQS value, a public health policy judgment, incorporated the Agency's most recent
health evaluation of air effects of lead exposure for the purposes of setting a national ambient
air quality standard. In setting this value, the Administrator promulgated a standard that was
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requisite to protect public health with an adequate margin of safety. We consider values
below the level of the primary NAAQS to protect against multipathway risks because as
mentioned above, the primary NAAQS is set as to protect public health with an adequate
margin of safety. However, ambient air lead concentrations above the NAAQS are
considered to pose the potential for increased risk to public health. We consider this NAAQS
assessment to be a refined analysis given: 1) the numerous health studies, detailed risk and
exposure analyses, and level of external peer and public review that went into the
development of the primary NAAQS for lead, combined with: 2) the site-specific dispersion
modeling used in this assessment to estimated ambient lead concentrations due to ferroalloys
emissions. It should be noted, however, that this comparison does not account for possible
population exposures to lead from sources other than the one being modeled; for example, via
consumption of water from untreated local sources or ingestion of locally grown food.
Nevertheless, the Administrator judged that such a standard would protect, with an adequate
margin of safety, the health of children and other at-risk populations against an array of
adverse health effects, most notably including neurological effects, particularly
neurobehavioral and neurocognitive effects, in children (73 FR 67007). The Administrator, in
setting the standard, also recognized that no evidence-or risk based bright line indicated a
single appropriate level. Instead a collection of scientific evidence and other information was
used to select the standard from a range of reasonable values (73 FR 67006).
We further note that comparing ambient lead concentrations to the NAAQS for lead,
considering the level, averaging time, form and indicator, also informs whether there is the
potential for adverse environmental effects. This is because the secondary lead NAAQS, set
to protect against adverse welfare effects (including adverse environmental effects), has the
same averaging time, form, and level as the primary standard. Thus, ambient lead
concentrations above the NAAQS for lead also indicate the potential for adverse
environmental effects.
The EPA has developed a screening approach to examine the potential for adverse
environmental effects as required under section 112(f)(2)(A) of the CAA. The environmental
screen focuses on the following seven environmental HAP:
• Five persistent bioaccumulative HAP (PB-HAP) - cadmium, dioxins/furans, POM,
mercury (both inorganic mercury and methyl mercury), and lead,
• Two acid gases - hydrogen chloride (HC1) and hydrogen fluoride (HF).
HAP that persist and bioaccumulate are of particular environmental concern because they
accumulate in the soil, sediment, and water. The acid gases - HC1 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.
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For cadmium, mercury, POM, and dioxins/furans, the environmental screening analysis
consists of two tiers. In the first tier, TRIM.FaTE modeling was conducted under worst-case
environmental conditions to determine whether the facility-specific off-site emission rates of
each of the emitted environmental HAP exceeded ecological benchmarks represented as
emissions screening levels. If off-site emissions from a facility do not exceed the Tier I
screening levels the facility "passes" the screen, and therefore, is not evaluated further under
the screening approach. If off-site emissions from a facility exceed the Tier I screening
levels, we evaluate the facility further in Tier II.
In Tier II of the environmental screening analysis, the screening emission levels are adjusted
to account for local meteorology and the actual location of lakes in the vicinity of facilities
that did not pass the Tier I screen. If off-site emissions from a facility do not exceed the Tier
II screening levels, the facility passes the screen, and is typically not evaluated further. If off-
site emissions from a facility exceed the Tier II screening levels, 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 HEM3-AERMOD air dispersion
model. To evaluate the potential for adverse environmental effects from lead, we compare the
average modeled air emission concentrations of lead from each facility in the source category
emissions to the level of the secondary National Ambient Air Quality Standard (NAAQS) for
lead. The secondary lead NAAQS is a reasonable means of evaluating environmental risk
because it is set to provide substantial protection against adverse welfare effects which can
include "effects on soils, water, crops, vegetation, man-made materials, animals, wildlife,
weather, visibility and climate, damage to and deterioration of property, and hazards to
transportation, as well as effects on economic values and on personal comfort and well-
being." 6
6 A secondary standard, as defined in Section 109(b)(2), must "specify a level of air quality the attainment and
maintenance of which, in the judgment of the Administrator, based on criteria, is requisite to protect the public
welfare from any known or anticipated adverse effects associated with the presence of [the] pollutant in the
ambient air." Welfare effects as defined in section 302(h) (42 U.S.C. 7602(h)) include, but are not limited to,
"effects on soils, water, crops, vegetation, man-made materials, animals, wildlife, weather, visibility and climate,
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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 ferroalloys
source category were based on the EPA Office of Air Quality Planning and Standards'
(OAQPS) existing recommendations for HAP [9], which were also used for NATA [70], 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 |ig/m3 in air." The
SF is "an upper bound, approximating a 95 percent confidence limit, on the increased
cancer risk from a lifetime exposure to an agent. This estimate, [is] usually expressed in
units of proportion (of a population) affected per mg/kg-day..EPA disseminates dose-
response assessment information in several forms, based on the level of review. The
Integrated Risk Information System (IRIS) [77] is an EPA database that contains scientific
health assessment information, including dose-response information. All IRIS assessments
since 1996 have also undergone independent external peer review. The current IRIS
process includes review by EPA scientists, interagency reviewers from other federal
agencies, and the public, and peer review by independent scientists external to EPA. New
IRIS values are developed and old IRIS values are updated as new health effects data
become available. Refer to the "IRIS Track" website7 for detailed information on status
and scheduling of current individual IRIS assessments and updates. EPA's science policy
approach, under the current carcinogen guidelines, is to use linear low-dose extrapolation
damage to and deterioration of property, and hazards to transportation, as well as effects on economic values and
on personal comfort and well-being."
7http://cfbub.epa.gov/ncea/iristrac/
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as a default option for carcinogens for which the mode of action (MO A) has not been
identified. We expect future EPA dose-response assessments to identify nonlinear MO As
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 assessments8: "The
guidelines for developing chronic inhalation exposure levels incorporate many
recommendations of the U.S. EPA [73] and NAS [7¥]." The non-cancer information
includes available inhalation health risk guidance values expressed as chronic inhalation
reference exposure levels (RELs) [75], 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 [7(5], defined similarly to EPA's URE.
In developing chronic risk estimates, we adjusted dose-response values for some HAP based
on professional judgment, as follows:
1) Consistent with Agency policy (as mentioned above), which was supported by the SAB,9
the EPA has chosen in this instance to rely on the ATSDR MRL for manganese in the
8 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)
9 The SAB peer review of RTR Risk Assessment Methodologies is available at:
http://vosemite.epa. gov/sab/sabproduct.nsf/4AB3966E263D943A8525771F00668381/$File/EPA-SAB-10-007-
unsigned.pdf
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current ferroalloys supplemental proposal. There is an existing IRIS RfC for manganese
(Mn) published in 199310. This value was in the RTR risk assessment supporting the 2011
Ferroalloys Notice of Proposed Rulemaking. However, since the 2011 proposal ATSDR
has published an assessment of Mn toxicity (2012) which includes a chronic inhalation
reference value (i.e., an ATSDR Minimal Risk Level or MRL)11. Both the 1993 IRIS RfC
and the 2012 ATSDR MRL were based on the same study (Roels et al., 1993). In
developing their assessment, ATSDR used updated dose-response modeling methodology
(benchmark dose approach) and considered recent pharmacokinetic findings to support
their MRL derivation. Consistent with Agency policy, which was supported by SAB, the
EPA has chosen to rely on the ATSDR MRL for Mn in the current ferroalloys
supplemental proposal.
2) In the case of nickel compounds, to provide a conservative estimate of potential cancer
risks, we used the IRIS URE value for nickel subsulfide (which is considered the most
potent carcinogen among all nickel compounds) in the assessment for the 2011 proposed
rule for ferroalloys production. In the 2011 proposal rule, the determination of the percent
of nickel subsulfide was considered a major factor for estimating the risks of cancer due to
nickel-containing emissions. Nickel speciation information for some of the largest nickel-
emitting sources (including oil combustion, coal combustion, and others) suggested that at
least 35 percent of total nickel emissions may be soluble compounds and that the cancer
risk for the mixture of inhaled nickel compounds (based on nickel subsulfide, and
representative of pure insoluble crystalline nickel) was derived to reflect the assumption
that 65 percent of the total mass of nickel may be carcinogenic.
Based on consistent views of major scientific bodies (i.e., National Toxicology Program
(NTP) in their 12th Report of the Carcinogens (ROC)12, International Agency for Research
on Cancer (IARC)13, and other international agencies)14 that consider all nickel
compounds to be carcinogenic, we currently consider all nickel compounds to have the
potential of being carcinogenic to humans. The 12th Report of the Carcinogens states that
the "combined results of epidemiological studies, mechanistic studies, and carcinogenic
studies in rodents support the concept that nickel compounds generate nickel ions in target
cells at sites critical for carcinogenesis, thus allowing consideration and evaluation of
these compounds as a single group." Although the precise nickel compound (or
compounds) responsible for carcinogenic effects in humans is not always clear, studies
indicate that nickel sulfate and the combinations of nickel sulfides and oxides encountered
10 USEPA Integrated Risk Information System Review of Manganese (1993) available at
http://www.epa.gov/iris/subst/0373.htm
11 Agency for Toxic Substances & Disease Registry Toxicological Profile for Manganese (2012) available at
http://www.atsdr.cdc. gov/toxprofiles/tp.asp?id= 102&tid=23
12 National Toxicology Program (NTP), 2011. Report on carcinogens. 12th ed. Research Triangle Park, NC:
US Department of Health and Human Services (DHHS), Public Health Service. Available online at
http://ntp.niehs.nih.gov/ntp/roc/twelfth/rocl2.pdf
13 International Agency for Research on Cancer (IARC), 1990. IARC monographs on the evaluation of
carcinogenic risks to humans. Chromium, nickel, and welding. Vol. 49. Lyons, France: International Agency
for Research on Cancer, World Health Organization Vol. 49:256.
14 World Health Organization (WHO, 1991) and the European Union's Scientific Committee on Health and
Environmental Risks (SCHER, 2006).
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in the nickel refining industries cause cancer in humans (these studies are summarized in a
review by Grimsrud et al., 201015). The major scientific bodies mentioned above have
also recognized that there are differences in toxicity and/or carcinogenic potential across
the different nickel compounds.
In the inhalation risk assessment for the 2011 proposal, to take a conservative approach,
we considered all nickel compounds to have the same carcinogenic potential as nickel
subsulfide and used the IRIS URE for nickel subsulfide to estimate risks due to all nickel
emissions from the source category. However, given that there are two additional URE
values16 derived for exposure to mixtures of nickel compounds, as a group, that are 2-3
fold lower than the IRIS URE for nickel subsulfide, the EPA also considers it reasonable
to use a value that is 50 percent of the IRIS URE for nickel subsulfide for providing an
estimate of the lower end of the plausible range of cancer potency values for different
mixtures of nickel compounds. In the public comments provided in response to the
proposal and available in the docket, one facility provided additional data in the form of a
laboratory test report that indicated it would be unlikely that 100 percent of the nickel
from the furnace would be in the form of nickel subsulfide. Given our current knowledge
of the carcinogenic potential of all nickel compounds, and the potential differences in
carcinogenic potential across nickel compounds, we consider it reasonable to use a value
that is 50 percent of the IRIS URE for nickel subsulfide for providing an estimate of the
cancer potency for different mixtures of nickel compounds in the revised dataset for the
current supplemental proposal.
3) Where possible for emissions of unspecified mixtures of HAP categories such as metal
compounds and POM, we apply category-specific chemical speciation profiles appropriate
to the source category to develop a composite dose-response value for the category.
4) Where POM emissions were not speciated into individual compounds, we applied the
same simplifying assumptions to assessments that are used in NATA [77], 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.
5) In 2004, the EPA determined that the Chemical Industry Institute of Toxicology (CUT)
cancer dose-response value for formaldehyde (5.5 x 10"9 per (j,g/m3) was based on better
science than the IRIS cancer dose-response value (1.3 x 10"5 per (J,g/m3), and we switched
from using the IRIS value to the CUT value in risk assessments supporting regulatory
actions. Subsequent research published by EPA suggested that the CUT model was not
appropriate and, in 2010 EPA returned to using the 1991 IRIS value. The EPA has been
working on revising the formaldehyde IRIS assessment and the National Academy of
15 Grimsrud TK and Andersen A. Evidence of carcinogenicity in humans of water-soluble nickel salts. J Occup
Med Toxicol 2010, 5:1-7. Available online at http://www.occup-med.com/content/5/1/7
16 Two UREs (other than the current IRIS values) have been derived for nickel compounds as a group: one
developed by the California Department of Health Services
(http://www.arb.ca.gov/toxics/id/summarv/nickel tech b.pdf) and the other by the Texas Commission on
Environmental Quality (http://www.epa.gov/ttn/atw/natal999/99pdfs/healtheIfectsinfo.pdf).
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Sciences (NAS) completed its review of the EPA's draft assessment in April of 2011.17
The EPA will follow the NAS Report recommendations and will present results obtained
by implementing the biologically-based dose-response (BBDR) model for formaldehyde.
The 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, appropriate 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; EPA may also consider other
information as the science evolves.
The emissions inventory for the ferroalloys source category includes emissions of HAP with
available chronic quantitative inhalation dose-response values. Of these, 38 are classified as
known, probably, or possible carcinogens, with quantitative cancer dose-response values
available. These 38 HAP, their quantitative inhalation chronic cancer dose-response values,
and the source of each value are listed in Table 2.6-l(A). The dioxin and POM compounds
with chronic oral cancer dose-response values available (for which multipathway screening
assessments were performed) are listed in Table 2.6-l(B). Thirty HAP have quantitative
inhalation chronic noncancer threshold values available; two of these thirty HAP (cadmium
and mercury), for which a multipathway assessment was performed, also have quantitative
oral chronic noncancer threshold values available. These 30 HAP, their threshold values, and
the source of the value are listed in Table 2.6-2(A) and Table 2.6-2(B).
Table 2.6-1 (A) Dose-Response Values for Chronic Inhalation Exposure to
Carcinogens
URE (unit risk estimate for cancer)18 = cancer risk per fjg/m3 of average lifetime exposure.
Sources: IRIS = EPA Integrated Risk Information System, CAL = California EPA Office of
Environmental Health Hazard Assessment, EPA/OAQPS = interim value recommended by the
EPA Office of Air Quality Planning and Standards
Pollutant
CAS Number19
URE5
(1/M9/m3)
Source
Arsenic compounds
7440382
4.3E-3
IRIS
Cadmium compounds
7440439
0.0018
IRIS
Chlorinated Dioxin/Furans
1,2,3,4,6,7,8,9-
Octachlorodibenzo-p-dioxin
3268879
0.0099
EPA ORD
1,2,3,4,6,7,8,9-
Octochlorodibenzofuran
39001020
0.0099
EPA ORD
17 http://www.nap.edu/catalog.php7record id=13142
18 The URE is the upper-bound excess cancer risk estimated to result from continuous lifetime exposure to an
agent at a concentration of 1 (ig/m3 in air. URE's are considered upper bound estimates meaning they represent
a plausible upper limit to the true value.
19 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.
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Table 2.6-1 (A) Dose-Response Values for Chronic Inhalation Exposure to
Carcinogens
URE (unit risk estimate for cancer)18 = cancer risk per fjg/m3 of average lifetime exposure.
Sources: IRIS = EPA Integrated Risk Information System, CAL = California EPA Office of
Environmental Health Hazard Assessment, EPA/OAQPS = interim value recommended by the
EPA Office of Air Quality Planning and Standards
Pollutant
CAS Number19
URE5
(1/M9/m3)
Source
1,2,3,4,6,7,8-
Heptachlorodibenzo-p-dioxin
35822469
0.33
EPA ORD
1,2,3,4,6,7,8-
Heptachlorodibenzofuran
67562394
0.33
EPA ORD
1,2,3,4,7,8,9-
Heptachlorodibenzofuran
55673897
0.33
EPA ORD
1,2,3,4,7,8-
Hexachlorodibenzo-p-dioxin
39227286
3.3
EPA ORD
1,2,3,4,7,8-
Hexachlorodibenzofuran
70648269
3.3
EPA ORD
1,2,3,6,7,8-
Hexachlorodibenzo-p-dioxin
57653857
3.3
EPA ORD
1,2,3,6,7,8-
Hexachlorodibenzofuran
57117449
3.3
EPA ORD
1,2,3,7,8,9-
Hexachlorodibenzo-p-dioxin
19408743
3.3
EPA ORD
1,2,3,7,8,9-
Hexachlorodibenzofuran
72918219
3.3
EPA ORD
1,2,3,7,8-
Pentachlorodibenzo-p-dioxin
40321764
33
EPA ORD
1,2,3,7,8-
Pentachlorodibenzofuran
57117416
0.99
EPA ORD
2,3,4,6,7,8-
Hexachlorodibenzofuran
60851345
3.3
EPA ORD
2,3,4,7,8-
Pentachlorodibenzofuran
57117314
9.9
EPA ORD
2,3,7,8-
T etrachlorodibenzo-p-dioxin
1746016
33
EPA ORD
2,3,7,8-
T etrachlorodibenzofuran
51207319
3.3
EPA ORD
Chromium (VI)
18540299
1.2E-2
IRIS
Formaldehyde20
50000
1.3E-5
IRIS
Naphthalene
91203
3.4E-5
CAL
20 The EPA has used the CUT value (5.5xl0~9 per mg/m3) to characterize formaldehyde cancer risks in some
instances.
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Table 2.6-1 (A) Dose-Response Values for Chronic Inhalation Exposure to
Carcinogens
URE (unit risk estimate for cancer)18 = cancer risk per fjg/m3 of average lifetime exposure.
Sources: IRIS = EPA Integrated Risk Information System, CAL = California EPA Office of
Environmental Health Hazard Assessment, EPA/OAQPS = interim value recommended by the
EPA Office of Air Quality Planning and Standards
Pollutant
CAS Number19
URE5
(1/M9/m3)
Source
Nickel compounds
7440020
2.4E-421
IRIS
Polycyclic Organic Matter
246
22
2-Methylnaphthalene
91576
8.8E-5
CAL
Acenaphthene
83329
8.8E-5
CAL
Acenaphthylene
206968
8.8E-5
CAL
B enz [a] anthracene
56553
1.76E-4
CAL
Benzo[a]pyrene
50328
1.76E-3
CAL
B enzo [b] fluoranthene
205992
1.76E-4
CAL
Benzo[e]pyrene
192972
8.8E-5
CAL
Benzo[g,h,i]perylene
191242
8.8E-5
CAL
B enzo [k] fluoranthene
207089
1.76E-4
CAL
Chrysene
218019
1.76E-5
CAL
Dibenzo [a,h] anthracene
53703
1.92E-3
CAL
Fluoranthene
206440
8.8E-5
CAL
Fluorene
86737
8.8E-5
CAL
Indeno [ 1,2,3 -cd] pyrene
193395
1.76E-4
CAL
Perylene
198550
8.8E-5
CAL
Table 2.6-1 (B) Dose-Response Values for Chronic Oral Exposure to Carcinogens
SF (oral slope factor for cancer) = cancer risk per mg/kg/d of average lifetime exposure.
Sources: IRIS = EPA Integrated Risk Information System, CAL = California EPA Office of Environmental
Health Hazard Assessment
Pollutant
CAS Number
SF
(1/mg/kg/d)
Source
Dioxin/Furans
Hexachlorodibenzo-p-dioxin
19408743
6200
IRIS
2,3,7,8-T etrachlorodibenzo-p-
dioxin
1746016
150000
EPA ORD
21 We typically use the value of 4.8E-4 (l/|ig/m3) for nickel compounds, as explained earlier in this report. For
this source category, we had information to indicate that a value of 2.4E-4 (l/|ig/m3) was appropriate.
22 Assigned the URE associated with a mixture of POM compounds having a similar potency. Details of this
method, also used in the National Air Toxics Assessments, are available at
http://www.epa.gOv/ttn/atw/nata2005/methods.html#pom
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Table 2.6-1 (B) Dose-Response Values for Chronic Oral Exposure to Carcinogens
SF (oral slope factor for cancer) = cancer risk per mg/kg/d of average lifetime exposure.
Sources: IRIS = EPA Integrated Risk Information System, CAL = California EPA Office of Environmental
Health Hazard Assessment
Pollutant
CAS Number
SF
(1/mg/kg/d)
Source
Polycyclic Organic Matter
246
B enz [a] anthracene
56553
1.2
CAL
Benzo[a]pyrene
50328
7.3
IRIS
B enzo [b] fluoranthene
205992
1.2
CAL
B enzo [k] fluoranthene
207089
1.2
CAL
Chrysene
218019
0.12
CAL
Dibenzo [a,h] anthracene
53703
4.1
CAL
Indeno [ 1,2,3 -cd] pyrene
193395
1.2
CAL
Table 2.6-2 (A) Dose-Response Values for Chronic Inhalation Exposure to
Noncarcinogens
RfC (reference inhalation concentration) = an estimate (with uncertainty spanning perhaps an order
of magnitude) of a continuous inhalation exposure to the human population (including sensitive
subgroups) that is likely to be without an appreciable risk of deleterious effects during a lifetime.
Sources: IRIS = EPA Integrated Risk Information System, CAL = California EPA Office of
Environmental Human Health Assessment, ATSDR = US Agency for Toxic Substances Disease
Registry, EPA/OAQPS = interim value recommended by the EPA Office of Air Quality Planning and
Standards
Pollutant
CAS Number6
RfC
(mg/m3)
Source23
Arsenic compounds
7440382
0.000015
CAL
Cadmium compounds
7440439
0.00001
ATSDR
Chlorinated dioxins and furans
1,2,3,4,6,7,8,9-Octochlorodibenzofuran
39001020
0.00013
CAL
1,2,3,4,6,7,8,9-Octochlorodibenzo-p-
dioxin
3268879
0.00013
CAL
1,2,3,4,6,7,8-Heptachlorodibenzofuran
67562394
0.000004
CAL
1,2,3,4,6,7,8-Heptachlorodibenzo-p-
dioxin
35822469
0.000004
CAL
1,2,3,4,7,8,9-Heptachlorodibenzofuran
55673897
0.000004
CAL
1,2,3,4,7,8-Hexachlorodibenzofuran
70648269
0.0000004
CAL
1,2,3,4,7,8-Hexachlorodibenzo-p-dioxin
39227286
0.0000004
CAL
1,2,3,6,7,8-Hexachlorodibenzofuran
57117449
0.0000004
CAL
1,2,3,6,7,8-Hexachlorodibenzo-p-dioxin
57653857
0.0000004
CAL
1,2,3,7,8,9-Hexachlorodibenzofuran
72918219
0.0000004
CAL
23 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 the IRIS file.
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Table 2.6-2 (A) Dose-Response Values for Chronic Inhalation Exposure to
Noncarcinogens
RfC (reference inhalation concentration) = an estimate (with uncertainty spanning perhaps an order
of magnitude) of a continuous inhalation exposure to the human population (including sensitive
subgroups) that is likely to be without an appreciable risk of deleterious effects during a lifetime.
Sources: IRIS = EPA Integrated Risk Information System, CAL = California EPA Office of
Environmental Human Health Assessment, ATSDR = US Agency for Toxic Substances Disease
Registry, EPA/OAQPS = interim value recommended by the EPA Office of Air Quality Planning and
Standards
Pollutant
CAS Number6
RfC
(mg/m3)
Source23
1,2,3,7,8,9-Hexachlorodibenzo-p-dioxin
19408743
0.0000004
CAL
1,2,3,7,8-Pentachlorodibenzo-p-dioxin
40321764
0.00000004
CAL
1,2,3,7,8-Pentachlorodibenzofuran
57117416
0.0000013
CAL
2,3,4,6,7,8-Hexachlorodibenzofuran
60851345
0.0000004
CAL
2,3,4,7,8-Pentachlorodibenzofuran
57117314
0.00000013
CAL
2,3,7,8-T etrachlorodibenzo-p-dioxin
1746016
0.00000004
CAL
2,3,7,8-T etrachlorodibenzofuran
51207319
0.0000004
CAL
Chromium (VI) compounds
18540299
0.0001
IRIS -M
Formaldehyde
50000
0.0098
ATSDR
Hydrochloric acid
7647010
0.02
IRIS -L
Hydrofluoric acid
7664393
0.014
CAL
Lead compounds
7439921
0.00015
EPA OAQPS
Manganese compounds
7439965
0.0003
ATSDR
Mercury (elemental)
7439976
0.0003
IRIS -M
Gaseous divalent mercury
201
0.0003
IRIS -M
Particulate divalent mercury
184
0.0003
IRIS -M
Naphthalene
91203
0.003
IRIS -M
Nickel compounds
7440020
0.00009
ATSDR
Table 2.6-1 (B) Dose-Response Values for Chronic Oral Exposure to Noncarcinogens
RfD (reference dose) = an estimate (with uncertainty spanning perhaps an order of magnitude) of a
continuous oral exposure to the human population (including sensitive subgroups) that is likely to be
without an appreciable risk of deleterious effects during a lifetime.
Sources: IRIS = EPA Integrated Risk Information System
Pollutant
CAS Number
RfD
(mg/kg/d)
Source
Cadmium compounds
7440439
0.0005
IRIS -H
Mercuric chloride25
7439976
0.0003
IRIS -H
24 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 the IRIS file.
25 The multipathway exposure assessment for mercury included fate and transport analysis, that included
separate oral exposure estimates for divalent mercury and methylmercury.
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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 [75], Depending on availability, the results from screening acute assessments are
compared to both "no effects" reference levels for the general public, such as the California
Reference Exposure Levels (RELs), as well as emergency response levels, such as Acute
Exposure Guideline Levels (AEGLs) and Emergency Response Planning Guidelines
(ERPGs), with the recognition that the ultimate interpretation of any potential risks associated
with an estimated exceedance of a particular reference level depends on the definition of that
level and any limitations expressed therein. Comparisons among different available
inhalation health effect reference values (both acute and chronic) for selected HAP can be
found in an EPA document of graphical arrays [79],
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
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prevention plans, as well as real-time emergency response actions, for accidental chemical
releases at fixed facilities and from transport carriers."
The NAC/AEGL defines AEGL-1 and AEGL-2 as:
"AEGL-1 is the airborne concentration (expressed as ppm or mg/m3) of a substance above
which it is predicted that the general population, including susceptible individuals, could
experience notable discomfort, irritation, or certain asymptomatic nonsensory effects.
However, the effects are not disabling and are transient and reversible upon cessation of
exposure."
"AEGL-2 is the airborne concentration (expressed as ppm or mg/m3) of a substance above
which it is predicted that the general population, including susceptible individuals, could
experience irreversible or other serious, long-lasting adverse health effects or an impaired
ability to escape."
"Airborne concentrations below AEGL-1 represent exposure levels that can produce mild
and progressively increasing but transient and nondisabling odor, taste, and sensory
irritation or certain asymptomatic, nonsensory effects. With increasing airborne
concentrations above each AEGL, there is a progressive increase in the likelihood of
occurrence and the severity of effects described for each corresponding AEGL. Although
the AEGL values represent threshold levels for the general public, including susceptible
subpopulations, such as infants, children, the elderly, persons with asthma, and those with
other illnesses, it is recognized that individuals, subject to unique or idiosyncratic
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) [27]
for acute exposures at three different levels of severity. These guidelines represent
concentrations for exposure of the general population (but not particularly sensitive persons)
for up to 1 hour associated with effects expected to be mild or transient (ERPG-1),
irreversible or serious (ERPG-2), and potentially life-threatening (ERPG-3).
ERPG values (https ://www. aiha. org/get-
involved/aihaguidelinefoundation/emergencvresponseplanningguidelines/Pages/default.aspx')
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
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transient adverse health effects or without perceiving a clearly defined, objectionable
odor."
"ERPG-2 is the maximum airborne concentration below which it is believed that nearly all
individuals could be exposed for up to 1 hour without experiencing or developing
irreversible or other serious health effects or symptoms which could impair an individual's
ability to take protective action."
In the RTR program, EPA assesses acute risk using toxicity values derived from one hour
exposures. Based on an in-depth examination of the available acute value for nickel
[California EPA's acute (1-hour) REL], we have concluded that this value is not appropriate
to use to support EPA's risk and technology reviews rules. This conclusion takes into
account: the effect on which the acute REL is based; aspects of the methodology used in its
derivation; and how this assessment stands in comparison to the ATSDR toxicological
assessment, which considered the broader nickel health effects database.
The broad nickel noncancer health effects database strongly suggests that the respiratory tract
is the primary target of nickel toxicity following inhalation exposure. The available database
on acute noncancer respiratory effects is limited and was considered unsuitable for
quantitative analysis of nickel toxicity by both California EPA26 and ATSDR27. The
California EPA's acute (1-hour) REL is based on an alternative endpoint, immunotoxicity in
mice, specifically depressed antibody response measured in an antibody plaque assay.
In addition, the current California acute (1-hour) REL for Ni includes the application of
methods that are different from those described in EPA guidelines. Specifically, the (1-hour)
REL applies uncertainty factors that depart from the defaults in EPA guidelines and does not
apply an inhalation dosimetric adjustment factor.
Further, the ATSDR's intermediate MRL (relevant to Ni exposures for a time frame between
14 and 364 days), was established at the same concentration as the California EPA (1 - hour)
REL, indicating that exposure to this concentration "is likely to be without appreciable risk of
adverse noncancer effects" (MRL definition)28 for up to 364 days.
We have high confidence in the nickel ATSDR intermediate MRL. Our analysis of the broad
toxicity database for nickel indicates that this value is based on the most biologically-relevant
endpoint. That is, the intermediate MRL is based on a scientifically sound study of acute
respiratory toxicity. Furthermore, this value is supported by a robust subchronic nickel
toxicity database and was derived following guidelines that are consistent with EPA
guidelines.29 Finally, there are no AEGL-l/ERPG-1 or AEGL-2/ERPG-2 values available for
nickel. Thus, for all the above mentioned reasons, we will not include Ni in our acute analysis
26 http://oehha.ca.gov/air/allrels.html
27 http://www.atsdr.cdc. gov/substances/toxsubstance. asp?toxid=44
90
Agency for Toxic Substances and Disease Registry (ATSDR), Toxic Substances Portal. Minimal Risk Levels
(MRLs) http://www.atsdr.cdc. gov/mrls/index.asp
29 US EPA 2002. Review of the reference dose and reference concentration processes (EPA/630/P-02/002F
December 2002, http://www.epa.gov/raf/publications/pdfs/rfd-final.pdf
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for this source category or in future assessments unless and until an appropriate value
becomes available.
The emissions inventory for the ferroalloys source category includes emissions of six 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)
ERPG-1
(mg/m3)
ERPG-2
(mg/m3)
REL
(mg/m3)
Arsenic compounds
7440382
0.0002
Formaldehyde
50000
1.1
17
1.2
12
0.055
Hydrochloric acid
7647010
2.7
33
4.5
30
2.1
Hydrofluoric acid
7664393
0.82
20
1.6
16
0.24
Mercury (elemental)
7439976
1.7
2
0.0006
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 Analysis30, 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
30Memorandum for the Heads of Executive Departments and Agencies - Updated Principles for Risk Analysis
(September 19, 2007), From Susan E. Dudley, Administrator, Office of Information and Regulatory Affairs,
Office of Management and Budget; and Sharon L. Hays, Associate Director and Deputy Director for Science,
Office of Science and Technology Policy
http://www.whitehouse.gov/sites/default/files/omb/memoranda/fV2007/m07-24.pdf
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(including maximum individual cancer risks and noncancer hazards, as well as cancer
incidence estimates) along with a discussion of the major uncertainties associated with their
derivation to provide decision makers with the fullest picture of the assessment and its
limitations.
For each carcinogenic HAP included in an assessment that has a potency estimate available,
individual and population cancer risks were calculated by multiplying the corresponding
lifetime average exposure estimate by the appropriate URE. This calculated cancer risk is
defined as the upper-bound probability of developing cancer over a 70-yr period (i.e., the
assumed human lifespan) at that exposure. Because UREs for most 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
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.
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,
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RfCs incorporate generally conservative uncertainty factors in the face of uncertain
extrapolations, such that an HQ greater than one does not necessarily suggest the onset of
adverse effects. The HQ cannot be translated to a probability that adverse effects will occur,
and is unlikely to be proportional to adverse health effect outcomes in a population.
Screening for potentially significant acute inhalation exposures also followed the HQ
approach. We divided the maximum estimated acute exposure by each available short-term
threshold value to develop an array of HQ values relative to the various acute endpoints and
thresholds. In general, when none of these HQ values are greater than one, there is no
potential for acute risk. In those cases where HQ values above one are seen, additional
information is used to determine if there is a potential for significant acute risks.
2.7.2 Mixtures
Since most or all receptors in these assessments receive exposures to multiple pollutants
rather than a single pollutant, we estimated the aggregate health risks associated with all the
exposures from a particular source category combined.
To combine risks across multiple carcinogens, our assessments use the mixtures guidelines'
[28,29] default assumption of additivity of effects, and combine risks by summing them using
the independence formula in the mixtures guidelines.
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.
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2.7.3 MACT-Allowable Risks
The emissions data in the dataset for the ferroalloys 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 ferroalloys production 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 emissions memo in the
docket, which is also Appendix 1 to this document, Revised Development of the Risk and
Technology Review (RTR) Emissions Dataset for the Ferroalloys Production Source
Category.
3 Risk results for the Ferroalloys source category
3.1 Source category description and results
The ferroalloys production source category consists of major source facilities that produce
ferroalloys containing manganese, such as ferromanganese and silicomanganese. A ferroalloy
is an alloy of iron and one or more other elements, such as nickel, chromium, manganese,
and/or silicon. Silicon metal is also typically considered a ferroalloy product. Ferroalloy
products are consumed primarily in iron and steel making, where they are used to produce
steel and cast iron products with enhanced or special properties. Ferroalloys production
normally occurs when an electric arc furnace (EAF) is charged with raw materials to begin
smelting the ores. The molten product is "tapped" or poured from the furnace. Raw material
and product handling (e.g., crushing and screening operations) also occur as part of the
ferroalloy production process. The HAP emission sources at ferroalloys production facilities
include EAFs, tapping operations, metal oxygen refining (MOR) processes, crushing and
screening operations, ladle treatment, casting, and fugitive dust sources. The specific HAP
that are emitted, and the quantity of these emissions, are related to the amount of HAP
compounds present in the raw materials used. Metallic HAP expected to be emitted by the
furnaces include arsenic, chromium, manganese, and nickel. Emissions of polycyclic organic
matter (POM), such as PAH, are also expected but in relatively small quantities.
Additionally, chlorine is present in coal, which is used as a raw material in the furnace, and
thus hydrochloric acid emissions can be expected.
There are 2 ferroalloys production facilities operating in the U.S. Both facilities are identified
as major sources of HAP. The emissions for the ferroalloys source category dataset are
summarized in Table 3.1-1. The total HAP emissions for the source category are
approximately 200 tons per year. Based on these data, the HAP emitted in the largest
quantities are manganese compounds, hydrofluoric acid, hydrochloric acid, and
formaldehyde.31 Emissions of these four HAP make up more than 90 percent of the total
emissions by mass. Persistent and bioaccumulative HAP (PB-HAP)32 reported as emissions
31 As described in the emissions memo Revised Development of the Risk and Technology Review (RTR)
Emissions Dataset for the Ferroalloys Production Source Category, numerical estimates for HF were provided
for input to the risk assessment. Upon closer investigation, we found that all test results were below the
detection limit of the test method and therefore we believe HF is not emitted from these sources.
32 Persistent and bioaccumulative HAP are defined in the EPA's Air Toxics Risk Assessment Library, Volume I,
EPA-453K-04-001A, as referenced in the ANPRM and provided on the EPA's Technology Transfer Network
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from these facilities include lead compounds, mercury compounds, PCBs, dioxins/furans,
cadmium compounds, and polycyclic organic matter. The following environmental HAP are
emitted from the ferroalloys production source category and are included in the environmental
risk screen: cadmium, dioxins/furans, hydrogen chloride, hydrofluoric acid, lead, mercury,
and PAHs.
Table 3.1-1 Summary of Emissions from the Ferroalloys Source Category Used in the Residual Risk
Assessment and Availability of Dose-Response Values
Number of
Prioritized Inhalation Dose-Response Value
Facilities
Identified by OAQPSb
Emission
where HAP
Health
Benchmark
Values for Acute
Noncancer?
PB-
HAP?
IIAI"
Estimates
Reported or
Unit Risk
Reference
(tpy)
Estimated
Estimate for
Concentration
(2 facilities
in dataset)
Cancer?
for Noncancer?
Manganese compounds
109
2
Y
Hydrofluoric acid
41
2
Y
Y
Hydrochloric acid
25
2
Y
Y
Formaldehyde
7
2
Y
Y
Y
Polycyclic Organic Matter
2 -Methylnaphthalene
1
2
Y
Y
Acenaphthene
0.4
2
Y
Y
Acenaphthylene
1
2
Y
Y
Anthracene
0.5
2
Y
Benz[a]anthracene
0.2
2
Y
Y
Benzo[a]pyrene
0.03
2
Y
Y
Benzo [b] fluoranthene
0.2
2
Y
Y
Benzo[g,h,i]perylene
0.02
2
Y
Y
Benzo[e]pyrene
0.2
2
Y
Y
Benzo [k] fluoranthene
0.05
2
Y
Y
Chrysene
0.5
2
Y
Y
Dibenzo[a,h]anthracene
0.003
2
Y
Y
Fluorene
0.5
2
Y
Y
Fluoranthene
2
2
Y
Y
Indeno[l,2,3-cd]pyrene
0.01
2
Y
Y
Perylene
0.003
2
Y
Y
Phenanthrene
3
2
Y
Pyrene
1
2
Y
Naphthalene
2
2
Y
Y
Lead compounds
1
2
c
Y
Nickel compounds
0.4
2
Y
Y
Mercury compounds
Mercury (elemental)
0.2
2
Y
Y
Y
Gaseous divalent mercury
0.02
2
Y
Y
website for Fate, Exposure, and Risk Assessment at http://www2.epa.gov/sites/production/files/2013-
08/documents/volume_l_reflibrary.pdf
31
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Residual Risk Assessment for the Ferroalloys Source Category
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Table 3.1-1 Summary of Emissions from the Ferroalloys Source Category Used in the Residual Risk
Assessment and Availability of Dose-Response Values
IIAI"
Emission
Estimates
(tpy)
Number of
Facilities
where HAP
Reported or
Estimated
(2 facilities
in dataset)
Prioritized Inhalation Dose-Response Value
Identified by OAQPSb
PB-
HAP?
Unit Risk
Estimate for
Cancer?
Reference
Concentration
for Noncancer?
Health
Benchmark
Values for Acute
Noncancer?
Particulate divalent mercury
0.02
2
Y
Y
Chromium compounds
Chromium III
0.1
2
Chromium (VI)
0.02
2
Y
Y
Cadmium compounds
0.1
2
Y
Y
Y
Arsenic compounds
0.04
2
Y
Y
Y
Polychlorinated Biphenyls
0.0003
2
Y
Y
Chlorinated Dibenzodioxins
and furans
1,2,3,4,6,7,8,9-
Octochlorodibenzofuran
2.69E-07
2
Y
Y
Y
1,2,3,4,6,7,8,9-
Octochlorodibenzo-p-dioxin
8.76E-07
2
Y
Y
Y
1,2,3,4,6,7,8-
Heptachlorodibenzofuran
2.19E-07
2
Y
Y
Y
1,2,3,4,6,7,8-
Heptachlorodibenzo-p-
dioxin
1.59E-07
2
Y
Y
Y
1,2,3,4,7,8,9-
Heptachlorodibenzofuran
6.38E-08
2
Y
Y
Y
1,2,3,4,7,8-
Hexachlorodibenzofuran
2.54E-07
2
Y
Y
Y
1,2,3,4,7,8-
Hexachlorodibenzo-p-dioxin
6.72E-08
2
Y
Y
Y
1,2,3,6,7,8-
Hexachlorodibenzofuran
1.33E-07
2
Y
Y
Y
1,2,3,6,7,8-
Hexachlorodibenzo-p-dioxin
6.78E-08
2
Y
Y
Y
1,2,3,7,8,9-
Hexachlorodibenzofuran
6.68E-08
2
Y
Y
Y
1,2,3,7,8,9-
Hexachlorodibenzo-p-dioxin
7.67E-08
2
Y
Y
Y
1,2,3,7,8-
Pentachlorodibenzofuran
3.3E-07
2
Y
Y
Y
1,2,3,7,8-
Pentachlorodibenzo-p-
dioxin
1.06E-07
2
Y
Y
Y
2,3,4,6,7,8-
Hexachlorodibenzofuran
1.12E-07
2
Y
Y
Y
2,3,4,7,8-
Pentachlorodibenzofuran
3.29E-07
2
Y
Y
Y
2,3,7,8-
T etrachlorodibenzofuran
1.58E-06
2
Y
Y
Y
32
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Residual Risk Assessment for the Ferroalloys Source Category
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Table 3.1-1 Summary of Emissions from the Ferroalloys Source Category Used in the Residual Risk
Assessment and Availability of Dose-Response Values
IIAI"
Emission
Estimates
(tpy)
Number of
Facilities
where HAP
Reported or
Estimated
(2 facilities
in dataset)
Prioritized Inhalation Dose-Response Value
Identified by OAQPSb
PB-
HAP?
Unit Risk
Estimate for
Cancer?
Reference
Concentration
for Noncancer?
Health
Benchmark
Values for Acute
Noncancer?
2,3,7,8-T etrachlorodibenzo-
p-dioxin
1.15E-07
2
Y
Y
Y
a Notes for how HAP were speciated for risk assessment:
• For most metals, emissions reported as the elemental metal are combined with metal compound emissions (e.g.,
"cadmium" emissions modeled as "cadmium & compounds"). In the absence of speciation information, we assume the
reported mass is 100 percent metal.
• Chromium emissions were speciated based on test data. See Appendix 1 of this document (Revised Development of the
Risk and Technology Review (RTR) Emissions Datasetfor the Ferroalloys Production Source Category) for more
information.
• For emissions reported generically as "mercury" or "mercury & compounds," emissions are speciated for this category
as 80 percent "mercury (elemental)," 10 percent "gaseous divalent mercury," and 10 percent "particulate divalent
mercury." Mercury speciation profiles can be found on the EPA's Technology Transfer Network website for emissions
inventories at http://www.epa.gOv/ttn/chief/net/2005inventorv.html#inventorvdata
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-air-
pollutants
c There is no reference concentration for lead. In considering noncancer hazards for lead in this assessment, we compared the
average exposure estimates to the National Ambient Air Quality Standards (NAAQS) for lead (0.15 (ig/m3). These NAAQS
for lead were adopted in October 2008 rhttp://www.epa.gov/air/lead/actions.html'l. The primary (health-based) standard is a
maximum or not-to-be-exceeded, rolling three-month average, measured as total suspended particles (TSP). The secondary
(welfare-based) standard is identical to the primary standard.
3.2 Baseline risk characterization
This section presents the results of the baseline risk assessment for the ferroalloys production
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
assessments 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 at the highest exposure location, as well as 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 6 of
this document, Draft Detailed Risk Modeling Results.
Baseline Inhalation Risk Assessment Results Based on Actual Emissions
33
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Residual Risk Assessment for the Ferroalloys Source Category
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Tables 3.2-1 and 3.2-2 summarize the chronic and acute inhalation risk results for this source
category. The results of the chronic inhalation cancer risk assessment estimate that the
maximum lifetime individual cancer risk posed by these two facilities could be as high as
20 in a million, with PAHs, chromium compounds, and nickel compounds from process
fugitives as the major contributors to the risk. The total estimated cancer incidence from this
source category is 0.002 excess cancer cases per year, or one excess case in every 500 years.
Approximately 400 people are estimated to have cancer risks above 10 in a million and
approximately 31,000 people are estimated to have cancer risks above 1 in a million
considering the two facilities in this source category. The maximum chronic noncancer
TOSHI value for the source category could be up to 4 from emissions of manganese from
process fugitives as the major contributors to the HI. An estimated 1,500 people are exposed
to TOSHI levels above 1 due to manganese emissions.
Worst-case acute hazard quotients (HQs) were calculated for every emitted HAP that has an
acute benchmark, as shown in Table 3.1-1. For cases where the screening HQ was greater
than 1, we further refined the estimates by determining the highest HQ value that might occur
outside facility boundaries. Based on actual, baseline emissions, the highest acute screening
HQ value is 1 (based on the acute RELs for arsenic compounds, formaldehyde, and
hydrofluoric acid) and is shown in Table 3.2-1. This value includes a refinement of
determining the highest HQ value that is outside facility boundaries.
Table 3.2-1 Summary of Source Category Level Inhalation Risks for Ferroalloys
Result
HAP "Drivers"
Facilities in Source Category
Number of Facilities Estimated to be in
Source Category
2
n/a
Number of Facilities Identified in NEI and
Modeled in Risk Assessment
2
n/a
Cancer Risks
Maximum Individual Lifetime Cancer
Risk (in 1 million)
20
chromium compounds, PAH,
nickel compounds
Number of Facilities with Maximum Individual Lifetime Cancer Risk:
Greater than or equal to 100 in 1
million
0
n/a
Greater than or equal to 10 in 1 million
2
chromium compounds, PAH,
nickel compounds, arsenic
compounds, formaldehyde,
dioxins, cadmium compounds
Greater than or equal to 1 in 1 million
2
chromium compounds, PAH,
nickel compounds, arsenic
compounds, formaldehyde,
dioxins, cadmium compounds
Chronic Noncancer Risks
Maximum Neurological Hazard Index
4
manganese compounds
Number of Facilities with Maximum Neurological Hazard Index:
34
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Table 3.2-1 Summary of Source Category Level Inhalation Risks for Ferroalloys
Result
HAP "Drivers"
Greater than 1
2
manganese compounds
Acute Noncancer Refined Screening Resu
ts
Maximum Acute Hazard Quotient33
1
1
1
<1
<1
arsenic compounds (REL)
formaldehyde (REL)
hydrofluoric acid (REL)
hydrochloric acid (REL)
mercury (REL)
Number of Facilities With Potential for
0
n/a
Acute Effects
Population Exposure
Number of People Living Within 50
Kilometers of Facilities Modeled
380,000
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
400
n/a
Greater than or equal to 1 in 1 million
31,000
n/a
Number of People Exposed to Noncancer Neurological Hazard Index:
Greater than 1
1,500
n/a
Estimated Cancer Incidence (excess cancer
cases per year)
0.002
n/a
Contribution of HAP to Cancer Incidence
PAH
42%
n/a
Chromium compounds
18%
n/a
Cadmium compounds
15%
n/a
Arsenic compounds
12%
n/a
Baseline Inhalation Assessment Results Based on Allowable Emissions
Analysis of potential differences between actual emissions levels and the maximum emissions
allowable under the MACT standards (i.e., MACT-allowable emissions) were also calculated
for stack emissions for the two facilities.34 Risk estimates based on the actual emissions were
then scaled up using these factors. (See Revised Development of the Risk and Technology
Review (RTR) Emissions Dataset for the Ferroalloys Production Source Category in
33 There are no reference values available (including a lack of short-term occupational values) to assess any
potential risks from acute exposure to manganese. In addition, as described earlier, we do not believe that HF is
emitted from these sources.
34 MACT-allowable emissions were not calculated for process fugitive or fugitive emissions because the current
MACT standard only has emission limits for stack emissions. In addition, MACT -allowable emissions were not
calculated for those HAP (e.g., PAH) that do not currently have a MACT standard. Finally, for Eramet, the
allowable estimates include emissions for an idle furnace that was not included in the calculation of actual
emissions because it is not currently operating. MACT -allowable emissions, and associated risks, are likely
underestimated.
35
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Residual Risk Assessment for the Ferroalloys Source Category
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Appendix 1 of this document, or separately in the docket, for a discussion of the development
of the allowable factors.) In addition, an idled furnace at Eramet was included in the
calculation of allowable risks. Risk results from the inhalation risk assessment using the
MACT-allowable stack emissions indicate that the maximum lifetime individual cancer risk
could be as high as 100 in a million with arsenic and cadmium emissions driving the risks,
and that the maximum chronic noncancer TOSHI value could be as high as 40 at the MACT-
allowable emissions level with manganese emissions driving the TOSHI value. Risks are
likely higher than these presented here because process fugitive emissions increase with
increased stack emissions (for which there are current MACT standards), and emissions of
pollutants for which there are currently no MACT standards would also increase with
increased stack emissions.
Baseline Multipathway Assessment Results Based on Actual Emissions
To identify potential multipathway health risks from PB-HAP other than lead, we first
performed a screening analysis (Tier I) that compared emissions of PB-HAP emitted from the
ferroalloys source category (based on actual emissions) to screening emission rates (see
section 2.5). The PB-HAP emitted by facilities in this category include cadmium compounds,
chlorinated dibenzodioxins and furans (as 2,3,7,8-TCDD toxicity equivalents, or TEQ),
mercury compounds, polychlorinated biphenyls (PCBs), and POM (as benzo(a)pyrene TEQ).
PCBs are PB-HAP but do not currently have multipathway screening values and so were not
evaluated for potential non-inhalation risks. As shown in Table 3.1-1, this PB-HAP is not
emitted in appreciable quantities.
The two facilities in the source category both reported emissions of these PB-HAP, and with
the exception of cadmium emissions from one facility, both facilities had emission rates
greater than the screening emission levels for all four PB-HAP indicating that the initial
multipathway screening model does not rule out the potential for multipathway impacts of
concern. One facility's emission rates of cadmium compounds exceeded the screening level
by about 10 times, while the other was below the level. One facility's emission rates of
mercury exceeded the screening level by 100 times, while the other facility's mercury
emission rates exceeded the level by 10 times. For dioxins, one facility's emission rates
exceeded the level by 100 times, while the other facility's emission rates exceeded the level
by 80 times. For POM, one facility's emission rates exceeded the screening level by 200
times, while the other facility's emission rates exceeded the level by 30 times. Due to the
theoretical construct of the screening model, these factors are not directly translatable into
estimates of risk or hazard quotients for these facilities. Table 3.2-2 summarizes the results of
this Tier I screen.
For the PB-HAP and facilities that did not screen out during our initial Tier I multipathway
screening analysis, we improved our analysis with some additional site-specific information
to develop a Tier II screen. (See Appendix 4 of this document, Technical Support Document
for TRIM-Based Multipathway Screening Scenario for RTR: Summary of Approach and
Evaluation, for more information about the Tier II screen.) The additional site-specific
information included the land use around the facilities, the location of fishable lakes, and local
wind direction and speed. The result of this analysis was the development of site-specific
36
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Residual Risk Assessment for the Ferroalloys Source Category
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emission screening levels for each of the four PB-HAP. Based on this Tier II screening
analysis, the cadmium emission rates for both facilities were below site-specific levels for that
PB-HAP. For mercury, one facility's emissions equaled the screening level, while the other
facility had emissions of mercury that exceeded its site-specific level by nine times. The PAH
and dioxin emissions at both facilities exceeded their respective site-specific levels by a range
of 3 to 20. Table 3.2-2 presents these results.
Similar to the results from the Tier I screen, an exceedance of a screening level in Tier II
cannot be equated with a risk value or a hazard quotient (or hazard index). Rather, it
represents a high-end estimate of what the risk or hazard may be. For example, an
exceedance of 2 for a non-carcinogen can be interpreted to mean that we have high
confidence that the hazard would be lower than 2. Similarly, an exceedance of 30 for a
carcinogen means that we have high confidence that the risk is lower than 30 in a million.
Our confidence comes from the conservative, or health-protective, assumptions that are in the
screens: we choose inputs from the upper end of the range of possible values for the
influential parameters used in the screens; and we assume that the exposed individual exhibits
ingestion behavior that would lead to a high total exposure.
Table 3.2-2 Summary of Source Category Level Multipathway Screening Assessment
Risk Results for Ferroalloys
Tier I
Tier II
Facilities
Max
Emissions
of this
PBHAP
(TPY)"
Max
Emissions
Divided by
Screening
Level
Max
Emissions
Divided by
Screening
Level
PBHAP
Emitting
PBHAP
(2 in
source
category)
Facilities
Exceeding
Screening
Level
Facilities
Exceeding
Screening
Level
Carcinogens
Chlorinated Dibenzodioxins
and Furans as 2,3,7,8-
3.89E-7
100
20
T etrachlorodibenzo-p-
L
(4.48E-7)
L
L
dioxin TEQ
Polycyclic Organic Matter
as Benzo(a)Pyrene TEQ
2
5.67E-1
(1.48E-1)
200
2
20
2
Non-carcinogens
Cadmium Compounds
2
1.20E-1
10
1
0.9
0
Divalent Mercury
2
3.43E-2
100
2
9
1
Notes:
a - PAH and dioxin emissions in this column were normalized to BaP and 2,3,7,8-TCDD, respectively, for oral toxicity and Tier I modeled
environmental fate and transport (Tier II modeled environmental fate and transport in parentheses).
We conducted a more refined multipathway assessment for one of the facilities in the
ferroalloys production source category. This facility, the Eramet facility in Marietta, Ohio
was selected (1) because of its Tier II screening results and (2) based on the feasibility, with
respect to the modeling framework, of obtaining parameter values for the region surrounding
the facility. We expect that the exposure scenarios we assessed are among the highest that
might be encountered for this source category, although not the absolute highest. The
37
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Residual Risk Assessment for the Ferroalloys Source Category
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approach, data, assumptions, and results of the site-specific assessment are presented in
Appendix 10 of this document (Technical Support Document: Human Health Multipathway
Residual Risk Assessment for the Ferroalloys Production Source Category).
The site-specific assessment, as in the screening assessments, includes some hypothetical
elements, namely the hypothetical farmer and angler scenarios. We also included children in
different age ranges and adults. It is important to note that even though the multipathway
assessment has been conducted, no data exist to verify the existence of either the farmer or
angler scenario outlined below. The farmer scenario involves an individual living for a 70-
year lifetime on a farm homestead in the vicinity of the source and consuming produce grown
on, and meat and animal products raised on, the farm. The individual also incidentally ingests
surface soil at the location of the farm homestead. The angler scenario involves an individual
who regularly consumes fish caught in freshwater lakes in the vicinity of the source of interest
over the course of a 70-year lifetime. In addition, exposure estimates and risks for infants
consuming contaminated breast milk were evaluated in the case of dioxins, with the
assumption that the nursing mother was exposed to chemicals via one of the two scenarios
described above. We evaluated variations of these two scenarios using different assumptions
regarding food source (i.e., location of the farm homestead or the water body from which fish
are obtained), the age of the individual exposed (for noncancer hazards), the assumed
ingestion rate of each food type, and other factors. In particular, a range of fish ingestion
rates was evaluated to determine the possible health risks associated with that pathway.
Results of the Site-Specific Multipathway Assessment:
• Cancer risk estimates from the site-specific assessment are presented in Table 3.2-3
(Maximum Site-Specific Results column).
o Incremental lifetime cancer risk from exposure to PAHs is 10 in a million,
o Incremental lifetime cancer risk from exposure to dioxins is 4 in a million,
o For both the PAHs and dioxins, farm ingestion at the 90th percentile ingestion
rate drives the risk.
• Noncancer hazards from the site-specific assessment are also presented in Table 3.2-3
(Maximum Site-Specific Results column).
o Noncancer hazard quotients did not exceed one for any scenario for exposure
to cadmium, and was driven by fish ingestion by the adult female angler at the
99th percentile ingestion rate,
o The noncancer hazard quotient for mercury was estimated to be 1, with methyl
mercury exposure to the adult female angler at the 99th percentile ingestion rate
driving the risk. These risk estimates are the maximum results from the
refined assessment.
Because this more refined assessment uses location-specific information where appropriate
and available (e.g., location of actual lakes rather than a generic lake as in Tier I), the water
body producing the maximum risk results can change between the screen and the refined
assessments. In addition to showing the maximum results of the refined assessment, we can
also show the comparison of results from the Tier I screen, the Tier II screen, and the site-
specific assessment in order to observe the trend in screening level exceedances. The trend
also shows how obtaining more site-specific data can decrease the emission rate screening
38
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Residual Risk Assessment for the Ferroalloys Source Category
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level. For example, for mercury, we can see that the Tier I exceedance is 100 times the Tier I
emission rate level for mercury, the Tier II exceedance is 9 times the facility-specific Tier II
emission rate level for mercury, and at that same lake, the site-specific assessment produces
an HQ of 0.2. (As stated above, Tier I and Tier II results are not risk estimates.)
Table 3.2-3 Summary of Source Category Level Site-Specific
Multipathway Risk Results for Ferroalloys Baseline Scenario
PB-HAP
Emissions
Divided by
Screening Level
Site-Specific Assessment Cancer
Risk or Hazard Quotient
Tier I
Tier II
Using Same
Lake as Screen
Maximum Site-
Specific Results
Dioxins
(cancer)
80
7
4
4
PAH
(cancer)
200
20
9
10
Cadmium
(noncancer)
10
0.9
0.1
0.1
Mercury
(noncancer)
100
9
0.2
1
In evaluating the potential for multipathway effects from emissions of lead, modeled
maximum annual lead concentrations were compared to the NAAQS for lead (0.15 |ig/m3).
Results of this analysis estimate that the NAAQS for lead would not be exceeded at either of
the two facilities. This analysis estimates that the annual lead concentrations could be about
20 to 50 percent of the value of the NAAQS for lead (0.029 |ig/m3 to 0.083 |ig/m3). The
maximum post-control concentration of lead at either facility is 0.005 |ig/m3, or about three
percent of the NAAQS value.
Baseline Environmental Risk Screening Based on Actual Emissions
We conducted a screening-level evaluation of the potential adverse environmental risks
associated with emissions of the following environmental HAP from the ferroalloys
production source category: cadmium, dioxins/furans, hydrogen chloride, hydrofluoric acid,35
lead, mercury, and PAHs. The results of the environmental screening analysis are
summarized in Table 3.2-4.
In the Tier I screening analysis for PB-HAP (other than lead, which was evaluated
differently), the individual modeled Tier I concentrations for one facility in the source
category exceeded some sediment, fish - avian piscivorus, and surface soil benchmarks for
PAHs, methyl mercury, and mercuric chloride. Therefore, we conducted a Tier II screen for
PAHs, methyl mercury, and mercuric chloride.
35 As described earlier, we do not believe that HF is emitted from these sources.
39
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Residual Risk Assessment for the Ferroalloys Source Category
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In the Tier II screen for PAHs and methyl mercury, none of the individual modeled
concentrations for any facility in the source category exceeded any of the ecological
benchmarks (either the LOAEL or NOAEL). For mercuric chloride, soil benchmarks were
exceeded for one facility for some individual modeled points that collectively accounted for
five percent of the modeled area (2.5 percent of the area modeled in the category). However,
the weighted average modeled concentration for all soil parcels was well below the soil
benchmarks. For lead, we did not estimate any exceedances of the secondary lead NAAQS.
For HC1, each individual concentration (i.e., each off-site data point in the modeling domain)
was below the ecological benchmarks for all facilities. The average modeled HC1
concentration around each facility (i.e., the average concentration of all off-site data points in
the modeling domain) did not exceed any ecological benchmark.
For HF, some individual modeled points exceeded the ecological benchmarks for one facility
but accounted for less than 0.1 percent of the modeled area for that facility (0.05 percent of
the modeled area for the category). The average modeled HF concentration around each
facility (i.e., the average concentration of all off-site data points in the modeling domain) did
not exceed any ecological benchmark.36
Table 3.2-4 Summary of Source Category Environmental Risk Screening Results for
Ferroalloys
Percent of
Number of Facilities In Category
Modeled Area in
Environmental
Exceeding
Category
HAP
Exceeding2
Tier I Screen
Tier II Screen1
NOAEL
LOAEL
NOAEL
LOAEL
NOAEL
LOAEL
Pb
None
None
None
None
0%
0%
PB-
HAP
HgCk
NA
1
NA
None
NA
2.5%
Methyl Hg
1
None
None
None
0%
0%
PAH
1
None
None
None
0%
0%
Dioxins
None
None
None
None
0%
0%
Acid
HF3
NA
None
-
-
NA
0.05%
Gases
HC14
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 n analysis is performed. Otherwise, the percent area is based
on the Tier I results.
36 As described earlier, we do not believe that HF is emitted from these sources.
40
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Residual Risk Assessment for the Ferroalloys Source Category
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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 HC1, we evaluated one benchmark at the LOAEL level.
3.3 Post-control risk characterization
Process fugitive emissions of metal HAP are primarily driving the baseline risks. Given this,
using the same risk assessment methods described above, we estimated what the risks would
be if the two manganese facilities adopted control measures to limit process fugitive
emissions by 95 percent (e.g., enhanced local capture).37 Based on this scenario, we estimated
that the maximum chronic noncancer inhalation TOSHI value would be reduced to 1, from the
baseline estimate of 4, with manganese emissions from the MOR process baghouse outlet
driving the risk. There would be no people estimated to have a TOSHI greater than 1. With
respect to cancer risk, given the control scenario described above, the cancer MIR would be
reduced from 20 in a million (i.e., pre-controls) to approximately 10 in a million (i.e., post-
controls), with arsenic and chromium compounds from the MOR process baghouse outlet
driving the risk. There is an estimated reduction in cancer incidence to 0.001 excess cancer
cases per year, from 0.002 excess cancer cases per year. In addition, the number of people
estimated to have a cancer risk greater than or equal to 1 in a million would be reduced from
31,000 to 6,600. We also note that post-control, the maximum worst-case acute refined HQ
value would be reduced from a potential value of 1 to less than 1 (using the REL values for
arsenic compounds, formaldehyde, and hydrofluoric acid and the refinements described for
the baseline acute assessment).
For the baseline emissions scenario, we conducted both multipathway screening assessments
and a more refined assessment (results shown above). For the post-control scenario, however,
we only conducted a screening assessment, with the following results:
• Both facilities' cadmium emissions are below the Tier II facility-specific emission rate
levels for that PB-HAP.
• For mercury, Eramet's emissions were two times higher than its Tier II screening
emission rate level. However, as shown above, for the baseline emissions scenario,
which had higher mercury emissions, the refined multipathway assessment produced
an HQ of 1 for mercury for Eramet. The other facility's emissions were
approximately equal to its screening emission rate level.
• For dioxins, one facility's emissions were 10 times its facility-specific emission rate
level for that PB-HAP, while the other facility's emissions were 4 times its screening
emission rate level.
• For PAH, one facility's emissions were 20 times its facility-specific emission rate
level for that PB-HAP, while the other facility's emissions were 2 times its screening
rate level.
37 This post control scenario also includes the assumption that one facility will install controls for mercury.
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Appendix 4 of this document (Technical Support Document for TRIM-Based Multipathway
Screening Scenario for RTR: Summary of Approach and Evaluation) contains a full
description of the tiered multipathway screening approach, while Appendix 10 of this
document (Technical Support Document: Human Health Multipathway Residual Risk
Assessment for the Ferroalloys Production Source Category) contains the results of the tiered
screening assessment.
As mentioned above, the highest lead concentration after controls, 0.005 |ig/m3, is below the
NAAQS for lead, indicating a low potential for multipathway impacts of concern due to lead.
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 the exposure
analysis. The chronic ambient modeling uncertainties are considered relatively small in
comparison, since we are using EPA's refined local dispersion model with site-specific
parameters and reasonably representative meteorology. If anything, the population exposure
estimates are biased high by not accounting for short- or long-term population mobility, and
by neglecting processes like deposition, plume depletion, and atmospheric degradation.
Additionally, estimates of the maximum individual risk (MIR) contain uncertainty, because
they are derived at census block centroid locations rather than actual residences. This
uncertainty is known to create potential underestimates and overestimates of the actual MIR
values for individual facilities, but, overall, it is not thought to have a significant impact on
the estimated MIR for a source category. Finally, we did not factor in the possibility of a
source closure occurring during the 70-year chronic exposure period, leading to a potential
upward bias in both the MIR and population risk estimates; nor did we factor in the possibility
of population growth during the 70-year chronic exposure period, leading to a potential
downward bias in both the MIR and population risk estimates.
A sensitivity analysis performed for the 1999 NATA found that the selection of the
meteorology dataset location could result in a range of chronic ambient concentrations which
varied from as much as 17 percent below the predicted value to as much as 84 percent 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 percent lower to 84
percent 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,
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we 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 (<24 hours), short-term
(>24 hours up to 30 days), and subchronic (>30 days up to 10 percent 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
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
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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).38 In some circumstances, the true risk
could be as low as zero; however, in other circumstances the risk could also be greater.39
When developing an upper bound estimate of risk and to provide risk values that do not
underestimate risk, EPA generally relies on conservative default approaches.40 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 [37], 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
38 IRIS glossary
http://ofmpub.epa.gov/sor internet/registrv/termreg/searchandretrieve/glossariesandkevwordlists/search.do?detai
ls=&glossarvName=IRIS%20Glossarv
39 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.
40 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
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tumors would occur in humans.41 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
pharmacokinetic models are available for a compound). Uncertainty in dose estimates may
lead to either over or underestimation of risk.
(3) For the quantitative extrapolation of cancer risk estimates from experimental animals to
humans, EPA uses scaling methodologies (relating expected response to differences in
physical size of the species), which introduce another source of uncertainty. These
methodologies are based on both biological data on differences in rates of process according
to species size and empirical comparisons of toxicity between experimental animals and
humans. For a particular pollutant, the quantitative difference in cancer potency between
experimental animals and humans may be either greater than or less than that estimated by
baseline scientific scaling predictions due to uncertainties associated with limitations in the
test data and the correctness of scaled estimates.
(4) EPA cancer risk estimates, whether based on epidemiological or experimental animal data,
are generally developed using a benchmark dose (BMD) analysis to estimate a dose at which
there is a specified excess risk of cancer, which is used as the point of departure (or POD) for
the remainder of the calculation. Statistical uncertainty in developing a POD using a
benchmark dose (BMD) approach is generally addressed though use of the 95 percent lower
confidence limit on the dose at which the specified excess risk occurs (the BMDL),
decreasing the likelihood of understating risk. EPA has generally utilized the multistage
model for estimation of the BMDL using cancer bioassay data (see further discussion below).
(5) Extrapolation from high to low doses is an important, and potentially large, source of
uncertainty in cancer risk assessment. EPA uses different approaches to low dose risk
assessment (i.e., developing estimates of risk for exposures to environmental doses of an
agent from observations in experimental or epidemiological studies at higher dose) depending
on the available data and understanding of a pollutant's mode of action (i.e., the manner in
which a pollutant causes cancer). EPA's Cancer Guidelines express a preference for the use
of reliable, compound-specific, biologically-based risk models when feasible; however, such
models are rarely available. The mode of action for a pollutant (i.e., the manner in which a
41 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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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 science-
policy 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
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
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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.
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 values42 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.
42
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
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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.43 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 non-
cancer 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).
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
43 See IRIS glossary
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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
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.
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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 this facility, 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
0.15 (J,g/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 air-
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Residual Risk Assessment for the Ferroalloys Source Category
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FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE
related 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 chemical-
specific or health effect-specific information (e.g., simple irritation effects do not vary
appreciably between human individuals, hence a value of 3 is typically used), or based on the
purpose for the reference value (see the following paragraph). The UFs applied in acute
reference value derivation include: 1) heterogeneity among humans; 2) uncertainty in
extrapolating from animals to humans; 3) uncertainty in LOAEL to NOAEL adjustments; and
4) uncertainty in accounting for an incomplete database on toxic effects of potential concern.
Additional adjustments are often applied to account for uncertainty in extrapolation from
observations at one exposure duration (e.g., 4 hours) to arrive at a POD for derivation of an
acute reference value at another exposure duration (e.g., 1 hour).
Not all acute reference values are developed for the same purpose and care must be taken
when interpreting the results of an acute assessment of human health effects relative to the
reference value or values being exceeded. Where relevant to the estimated exposures, the
lack of threshold values at different levels of severity should be factored into the risk
characterization as potential uncertainties.
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Residual Risk Assessment for the Ferroalloys Source Category
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5 References
7. 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 - MACTI Petroleum
Refining Sources and Portland Cement Manufacturing. EPA-452/R-09-006.
http J/www, epa. gov/ttn/ atw/rrisk/rtrpg. html
3. US EPA, 2010. SAB's Response to EPA's RTR Risk Assessment Methodologies.
http://vosemite.epa. gov/sab/sabproduct.nsf/4AB3966E263D943A8525771F00668381/$File/E
PA- SAB-10-007-unsigned, pdf
4. US EPA, 2010. Memorandum from Dave Guinnup to RTR Wood Furniture Docket, entitled,
"EPA's Actions in Response to Key Recommendations of the SAB Review of RTR Risk
Assessment Methodologies.
5. US EPA, 2005. Revision to the Guideline on Air Quality Models: Adoption of a Preferred
General Purpose (Flat and Complex Terrain) Dispersion Model and Other Revisions; Final
Rule. 40CFRPart51. http://www.epa.gov/EPA-AIR/2005/November/Dav-09/a21627.htm
6. US EPA, 2004. Users' guide for the AMS/EPA regulatory model - AERMOD. EPA-454/B-
03-001. httpJ/www.epa.gov/scramOO 1 /7thconf7aermod/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/Proiects/AirOualitv/Proiects/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/sites/production/files/2013-
08/documents/volumel reflibrarv. pdf
9. US EPA, 2014. Table 1. Prioritized Chronic Dose-Response Values 5/9/14). Office of
Air Quality Planning and Standards, http://www2.epa.gov/fera/dose-response-assessment-
assessing-health-risks-associated-exposure-hazardous-air-pollutants
10. US EPA, 2005. 1999 National Air Toxics Risk Assessment.
http://www.epa. gov/ttn/atw/natal 999
77. US EPA, 2006. Integrated Risk Information System, http j/www.epa. gov/iris/index.html.
12. US Agency for Toxic Substances and Disease Registry. 2006. Minimum Risk Levels (MRLs)
for Hazardous Substances, http j/www. atsdr. cdc.gov/mrls/index. html.
52
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Residual Risk Assessment for the Ferroalloys Source Category
in Support of the September Supplemental Proposal -
FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE
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, 2014. Chronic Reference
Exposure Levels Adopted by OEHHA as of January 2014.
http://oehha.ca.gov/air/allrels.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/mav2005Hotspots.pdf
7 7. US EPA, 2006. Approach for modeling POM. Technical support information for the 1999
National Air Toxics Assessment.
http://www.epa.gov/ttn/atw/natal999/99pdfs/pomapproachian.pdf
18. US EPA, 2005. Table 2. Acute Dose-Response Values for Screening Risk Assessments
(6/02/2005). Office of Air Quality Planning and Standards, http://www2.epa.gov/fera/dose-
response-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://clpub.epa. gov/ncea/cfm/recordisplav. cfm?deid=211003
20. CA Office of Environmental Health Hazard Assessment, 2014. All Acute Reference
Exposure Levels developed by OEHHA as of January 2014.
http://oehha.ca.gov/air/allrels.html
21. American Industrial Hygiene Association, 2013. Current AIHA ERPG Values.
https://www. aiha. org/ get-
involved/aihaguidelinefoundation/emergencvresponseplanningguidelines/Pages/default.aspx
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/qualitv/informationguidelines/documents/EPA_InfoOualitvGuidelines.pdf
25. US EPA, 2006. Performing risk assessments that include carcinogens described in the
Supplemental Guidance as having a mutagenic mode of action. Science Policy Council Cancer
Guidelines Implementation Workgroup Communication II: Memo from W.H. Farland dated
14 June 2006. http://www.epa.gov/osa/spc/pdfs/CGIWGCommunication_II.pdf
53
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Residual Risk Assessment for the Ferroalloys Source Category
in Support of the September Supplemental Proposal -
FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE
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/canguidl.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/recordisplav.cfm?deid=22567
29. US EPA, 2000. Supplementary Guidance for Conducting Health Risk Assessment of
Chemical Mixtures. EPA-630/R-00-002.
http://www.epa.gov/raf/publications/pdfs/CHEM_MIX_08_2001.PDF
30. US EPA, 2005. Guidelines for Carcinogen Risk Assessment (2005). U.S. Environmental
Protection Agency, Washington, DC, EPA/630/P-03/001F, 2005.
http://www.epa.gov/raf/publications/pdfs/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 (RfC): 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/recordisplav. cfm? deid=71993
36. US EPA. 2002. A Review of the Reference Dose and Reference Concentration Processes.
http://www.epa.gov/raf/publications/pdfs/rfd-final.pdf
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Appendix 1
Emissions Inventory Support Memorandum
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MEMORANDUM
DATE: August 26, 2014
SUBJECT: Revised Development of the Risk and Technology Review (RTR) Emissions
Dataset for the Ferroalloys Production Source Category
FROM: Bradley Nelson, EC/R, Inc.
TO: Phil Mulrine, EPA OAQPS/SPPD/MMG
1.0 PURPOSE
The purpose of this memorandum is to document the methodologies used to estimate pollutant
emissions from processes at two ferroalloy facilities; Eramet Marietta Inc. (Eramet) and Felman
Production (Felman). The emission estimates developed in this task were used to create a
database that will be used as input to the risk assessment modeling to estimate the risks due to
emissions from these facilities as part of the "risk and technology" (RTR) review for the
Ferroalloy Production source category. This analysis supplements the original RTR analysis1
that supported proposed amendments to the national emission standards for hazardous air
pollutants (NESHAP) for the ferroalloys production source category published in the Federal
Register on November 23, 2011 (76 FR 72508).
2.0 BACKGROUND
Under the "technology review" provision of CAA Section 112, EPA is required to review
maximum achievable control technology (MACT) standards and to revise them "as necessary
(taking into account developments in practices, processes and control technologies)" no less
frequently than every 8 years. Under the "residual risk" provision of the CAA section 112,
within 8 years after promulgation of the MACT standards, EPA must evaluate the remaining
risks due to emissions of air toxics from the source category and promulgate amendments to the
standards if required to provide an ample margin of safety to protect public health or prevent an
adverse environmental effect. EPA has combined the two review activities into the RTR review
for the Ferroalloys Production source category.
1 Memorandum from Bradley Nelson, EC/R, Inc. to Conrad Chin, EPA OAQPS/SPPD/MMG, Draft Development of
the RTR Emissions Dataset for the Ferroalloys Production Source Category, October 14, 2011. EPA-HQ-OAR-
2010-0895-0040
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DRAFT FOR INTERNAL REVIEW
The MACT standards, for Ferromanganese and Silicomanganese Production apply to new and
existing ferroalloy production facilities that manufacture ferromanganese and/or silicomanganese
and are major sources of hazardous air pollutant (HAP) emissions or are co-located at major
sources of HAP emissions.
These ferroalloy products are produced using submerged electric arc furnaces, which are
furnaces in which the electrodes are submerged into the charge. The submerged arc process is a
reduction smelting operation. The reactants consist of metallic ores (ferrous oxides, silicon
oxides, manganese oxides, etc.) and a carbon-source reducing agent, usually in the form of coke,
charcoal, high- and low-volatility coal, or wood chips. Raw materials are crushed and sized, and
then conveyed to a mix house for weighing and blending. Conveyors, buckets, skip hoists, or
cars transport the processed material to hoppers above the furnace. The mix is gravity-fed
through a feed chute either continuously or intermittently, as needed. At high temperatures in
the reaction zone, the carbon source reacts with metal oxides to form carbon monoxide and to
reduce the ores to base metal.2 The molten material (product and slag) is tapped from the
furnace, sometimes subject to post-furnace refining, and poured into casting beds on the furnace
room floor. Once the material hardens, it is transported to product crushing and sizing systems
and packaged for transport to the customer.
The NESHAP established emissions standards for the following HAP emission sources at a
ferroalloy production facility:
• Submerged arc furnaces
• Metal oxygen refining (MOR) process
• Crushing and screening operations
• Fugitive dust sources.
The current rule contains emission standards that limit particulate matter (PM) emissions from
existing and new or reconstructed emission sources. The limits for the submerged arc furnaces
depend on the product produced and furnace design. The rule also includes limits for the air
pollution control devices associated with the MOR process and crushing and screening
operations. The current rule sets emission standards for fugitive dust sources by limiting the
amount of visible emissions that can be observed from the furnace buildings.
To estimate the facility HAP emissions, an Information Collection Request (ICR) under Section
114 of the Clean Air Act (CAA) was sent to both ferroalloy production facilities on April 28,
2010 and December 21, 2012 to gather source emission testing data from the furnaces (which
2 EPA, AP 42, Fifth Edition Compilation of Air Pollutant Emission Factors, Volume 1: Stationary Point and Area
Sources, Chapter 12.4. Ferroalloy Production. 10/86.
2
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DRAFT FOR INTERNAL REVIEW
include emissions that occur during charging, smelting, and tapping), MOR process and the
product crushing process. The HAP source test data that were collected from the control device
outlet for each furnace include: metal HAP (arsenic, cadmium, chromium (total and Cr+6), lead,
manganese, mercury, and nickel)3, hydrochloric acid, hydrofluoric acid, hexavalent chromium,
formaldehyde, polycyclic aromatic hydrocarbons (PAH), poly chlorinated biphenyls (PCB), and
chlorodibenzodioxins and chlorodibenzofurans (CDD/CDF). In addition, non-HAP emissions
were measured from the furnace control device outlet for: particulate matter and carbon
monoxide. The source test data collected for the crushing and sizing operations include:
particulate matter, metal HAP (arsenic, total chromium, lead, manganese, mercury, and nickel).4
The source test data collected from the MOR baghouse outlet include: particulate matter, metal
HAP (arsenic, total chromium, lead, manganese, mercury, and nickel).5
3.0 FERROALLOY FACILITY SUMMARY
The following section describes the HAP emission sources for the Eramet and Felman ferroalloy
production facilities, which are the two existing facilities in the source category. Figure 3-1
provides a schematic of the smelting, tapping, and casting emission points for a typical
ferroalloys production operation.
Eramet Marietta Inc.
The Eramet facility is located in Marietta, Ohio, and ferromanganese (FeMn) and
silicomanganese (SiMn) are produced using two furnaces identified as Furnace 1 and Furnace
12.
Furnace 1 is a submerged arc furnace rated at 30 megawatts (MW) and is equipped with a
negative pressure fabric filter to control particulate emissions and metal HAP. The fabric filter
controls emissions from the furnace smelting operation and was installed in 2011 to replace a
wet scrubber system. A separate fabric filter system identified as Furnace 1 Tapping Baghouse
is used to control captured fugitive emissions from the tapping process from this furnace. The
casting process associated with this furnace is uncontrolled.
3Total phosphorus was measured for the ICR using EPA Method 29; however this method does not distinguish
between white phosphorus (which is a non-HAP) and red phosphorus (which is a HAP). Due to the uncertainty of
the percentage of red phosphorus in the total phosphorus test results, it was concluded that phosphorus would not be
incorporated in the emissions used for modeling.
4 Total phosphorus was measured using Method 29 and therefore not included in the emissions used for modeling.
5 Again, total phosphorus was measured using Method 29 and therefore not included in the emissions used for
modeling.
3
-------
Exhaust
to Atmosphere
Furnace Fugitive Tapping Fugitive
Emissions to Atmosphere Emissions to Atmosphere
Casting Fugitive
Emissions to Atmosphere
Tapping Primary
Capture Hood
Submerged Electric
Arc Furnace
Fabric Filter
Casting Primary
Capture Hood
Tapping
Casting
Casting Bed
Furnace Smelting and
Tapping Process
Casting Process
Figure 3-1. Diagram of the Ferroalloys Process and Emission Points
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Furnace 12 is a submerged arc furnace rated at 22 MW and is equipped with two wet scrubbers
to control emissions from the furnace smelting operation and captured fugitive emissions from
the tapping process. The scrubbers operate simultaneously and exhaust through a single stack.
The casting process associated with this furnace is uncontrolled.
The facility has a third furnace, Furnace 18, which is permitted, but is considered to be
inoperable until extensive repair is made to the furnace. Therefore, Furnace 18 was not
considered an emission source and was not included in the emission inventory for Eramet for
actual emissions. However, an estimate of Furnace 18 emissions was included for the calculation
of MACT "allowable" emissions. Other HAP emission sources include the MOR process
controlled by a positive-pressure baghouse which also controls fugitive emissions captured from
casting of the product from MOR and a crushing and sizing system (C2P) that is controlled by
three negative pressure baghouses. There are also fugitive emissions that are emitted from each
of these emission sources, in addition to the fugitive emissions from the casting operations for
Furnace 1, Furnace 12, and the MOR process.
The emission sources used for modeling emissions from the Eramet facility were developed
based on the plant configuration discussed above and data and information collected through the
ICRs. For the furnaces, emission points were developed for the control device outlet emissions
and for fugitive emissions from the furnace smelting and furnace tapping. For the MOR process,
emission points were developed for the MOR baghouse outlet and for fugitive emissions from
the MOR process. For the crushing system, emission points were developed for each of the three
baghouse outlets and one emission point for fugitive emissions from the crushing operation.
Emission points were also developed for fugitive emissions from casting operations associated
with each of the two furnaces and for the MOR process.
Felman Production Inc.
The Felman facility is located in New Haven, West Virginia and produces SiMn using three
furnaces identified as Furnace 2, Furnace 5, and Furnace 7. Furnaces 2, 5, and 7 are open
submerged arc furnaces rated at 32, 20, and 20 MW respectively. Each of these furnaces is
equipped with a positive-pressure baghouse to control emissions of PM and metal HAP from the
furnace. The baghouse from each furnace also controls the captured fugitive emissions from the
associated furnace tapping and casting processes.
The facility has a crushing and sizing system controlled using a negative pressure baghouse.
There are also fugitive emissions that are emitted from each of these emissions sources, in
addition to the fugitive emissions from the casting operations for Furnace 2, Furnace 5, and
Furnace 7.
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DRAFT FOR INTERNAL REVIEW
The Felman facility emission points used for modeling emissions were developed from the plant
configuration discussed above and data and information collected through the ICR. Emission
points were developed for the control device outlets for each of the three furnaces, as well as
fugitive emission points for smelting and tapping at each of the three furnaces. For the crushing
systems, emission points were developed for the crusher baghouse outlet and fugitive emissions
from the crushing units. Emission points were also developed for casting fugitive emissions
from each of the three furnaces.
4.0 POINT SOURCE POLLUTANT EMISSION DATA
The following section describes the test data that were collected from emission point sources at
each of the ferroalloy facilities. This section will describe the test methods that were used to
measure emissions for each pollutant from the processes that were tested. A summary of the
type of pollutant data collected in the ICRs is presented in Table 4-1. More information on the
test data collected in the ICR can be found in the test data review summaries6'7 located in the
docket.
These test report reviews provide a description of how the test data were analyzed and compiled,
and provides a comparison of the reported test results with the calculated results used to estimate
the modeling emissions. The individual test reports submitted by the facilities can also be found
in the docket.
Some of the analytical results for the HAP pollutants were reported below the detection limit.
For these test results, one half of the detection limit was used to estimate the emission rate of the
HAP pollutant. The use of one half of the detection limit for calculating emission rates from
analytical data results reported below the detection limit is based on EPA's revised procedures8
for developing emission factors.
6 Memorandum from Bradley Nelson, EC/R to Conrad Chin, EPA/OAQPS/SPPD/MMG, Ferroalloys 2012 Test
Report Review, May 28, 2013.
7 Memorandum from Bradley Nelson, EC/R to Conrad Chin, EPA/OAQPS/SPPD/MMG, Ferroalloys 2013 Test
Report Review, July 29, 2013.
8 Recommended Procedures for Development of Emissions Factors and Use of the WebFIRE Emissions Factor
Database, Revised Draft Report, December 17, 2010.
http://www.epa.gov/ttn/chief/efpac/procedures/procedures draftl22010.pdf
6
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DRAFT FOR INTERNAL REVIEW
Table 4-1. Summary of HAP Emissions Data Collected in the ICRs
Facility
Process
Particulate Matter
Metal HAP
Mercury
Cr+6
HCl/HF
Form-
aldehyde
PAH, PCB, CDD/CDF
Method
5D"
Method 5
Method 29
Method
29/30B
SW Method
0061
Method 26A
Method 316
Method
23/0010
CARB
428/429
Felman
Furnace
V
V
V
V
V
V
V
Crusher
V
V
V
Eramet
Furnace
V
V
V
V
V
V
V
V
MOR
V
V
V
Crusher
V
V
V
a The furnaces at Felman are each equipped with positive pressure baghouse and therefore Method 5D was used to measure PM
emissions from these emission sources. All other emission sources are equipped with either a negative pressure baghouse or venturi
scrubber and were measured using Method 5.
7
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DRAFT FOR INTERNAL REVIEW
Particulate Matter
Test data for PM were received from both ferroalloy facilities for each furnace and for one product
crushing operation. Eramet provided PM results from the Furnace 12 scrubber outlet, the Furnace 1
baghouse outlet, the Furnace 1 tapping baghouse outlet, the MOR process baghouse outlet, and
from the crushing and sizing operation baghouses (C2P Crushing/Sizing Baghouse #1) using EPA
Method 5. Felman provided PM test results from the Furnace 2 baghouse outlet, the Furnace 5
baghouse outlet, the Furnace 7 baghouse outlet using EPA Method 5D, and the baghouse outlet
from the crushing and sizing operation using EPA Method 5.
Metal HAP
Test data for metal HAP (arsenic, cadmium, chromium, lead, manganese, mercury, and nickel) were
collected from the Furnace 12 scrubber outlet, the Furnace 1 baghouse outlet, the Furnace 1 tapping
baghouse outlet, and from Baghouse #1 from the product crushing operation at Eramet. The metal
HAP data for Felman were obtained from the Furnace 2 baghouse outlet, the Furnace 5 baghouse
outlet, and the baghouse controlling emissions from the crushing and sizing operation. The furnace
and product crushing test data from both facilities were collected using EPA Method 29 for arsenic
(As), cadmium (Cd), total chromium (Cr), lead (Pb), manganese (Mn), total mercury (Hg), and
nickel (Ni). Additional mercury test data were collected for both facilities using EPA Method 30B.
The EPA Method 29 analytical results were corrected using the reported field blank9 results from
the analytical test report.
Hexavalent Chromium
Hexavalent chromium (Cr+6) test data were collected from Furnace 2 and Furnace 12 for the Felman
and Eramet facilities respectively, using EPA SW Method 0061. No hexavalent chromium data
were requested for the product crushing operations. Hexavalent chromium emissions were
estimated for the furnaces that were not tested and the product crushing operations by using the
hexavalent chromium/PM ratio from the tested sources. A discussion of this calculation is provided
in Section 5.0.
Hydrochloric Acid/Hydrofluoric Acid
Test data for hydrochloric acid (HC1) and hydrofluoric acid (HF) were collected from the Furnace
12 scrubber outlet, and the Furnace 1 baghouse outlet at Eramet. Test data for HC1 was also
collected for the Furnace 1 tapping baghouse at Eramet. Test data for HC1 and HF were collected
9 The primary purpose of field blanks is to trace sources of artificially introduced contamination. The field blank results
include total ambient conditions during sampling and laboratory sources of contamination.
8
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DRAFT FOR INTERNAL REVIEW
for the Furnace 2 baghouse outlet at Felman. In addition, HC1 data was collected for the Furnace 5
and Furnace 7 baghouse outlets at Felman. The HC1 and HF data for both facilities were collected
using EPA Method 26A. The product crushing processes at both facilities were not tested for HC1
or HF, because it believed that these are non-combustion processes and thus do not emit these HAP
pollutants.
The test data for HC1 is a mixture of detected and non-detected analytical results. There were a
total of 29 runs that reported non-detect analytical results out of the total of 45 test runs for HC1.
For the analytical results that were reported as non-detect, one half of the detection limit was used
to estimate the HC1 concentration for the test runs. For HF, all of the test results were reported as
non-detect, and again, one half of the detection limit was used to estimate the HF concentration for
the test runs. While we calculated numerical estimates for HF based on the assumption that non-
detects were equal to V2 detection limit and included these estimates in the inputs to our risk model,
we have no evidence that HF is emitted from these sources. Therefore, for purposes of our estimates
of emissions and associated risks, we are assuming that HF is not emitted from these sources.
Formaldehyde
Testing for formaldehyde was performed on the Furnace 12 scrubber outlet, the Furnace 1 baghouse
outlet, and the Furnace 1 tapping baghouse outlet at Eramet, and the Furnace 2 baghouse outlet,
Furnace 5 baghouse outlet, and Furnace 7 baghouse outlet at Felman. All of the formaldehyde test
data was collected using EPA Method 316. Formaldehyde testing was not performed on any of the
product crushing processes, because it believed that these are non-combustion processes and thus
do not emit these HAP pollutants.
The test data for formaldehyde reported 6 runs below the detection limit out of a total of 18 test
runs. The emissions rates for the detected formaldehyde runs ranged from 0.01 to 0.26 pounds per
hour with an average and median of 0.07 and 0.05 pounds per hour, respectively. For the analytical
results that were reported as non-detect, one half of the detection limit was used to estimate the
formaldehyde concentration for the test runs.
PAH/PCB/CDD/CDF
Test data for polychlorinated biphenyls (PCB), chlorinated dibenzo-p-dioxins (CDD), and
chlorinated dibenzo-p-furans (CDF) were collected at the Furnace 12 scrubber outlet, Furnace 1
baghouse outlet, and the Furnace 1 tapping baghouse outlet at Eramet. The test methods used to
collect the samples were EPA Method 23, Method 0010, California Air Resources Board (CARB)
Method 428 and CARB 429. Test data were collected for PAH, PCB, and CDD/CDF data for the
Furnace 2 baghouse outlet and the Furnace 5 baghouse outlet at Felman using CARB methods 428
and 429. Test data for PAH were collected at the Furnace 7 baghouse outlet at Felman using
CARB method 429. No PAH, PCB, or CDD/CDF emissions data were collected for the product
9
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DRAFT FOR INTERNAL REVIEW
crushing units, because these are non-combustion processes and thus do not emit these HAP
pollutants.
The analytical results for CDD/CDF reported 230 of the individual compounds were below the
detection limit out of a total of 306 reported individual compounds. For the individual CDD/CDF
compounds that were detected, the emissions ranged from 1.8 x 10"10 to 7.3 x 10"8 pounds per hour
with an average and median of 6.5 x 10"8 and 3.5 x 10"9 pounds per hour, respectively. For PCBs,
107 out of 207 reported individual compounds were reported below the detection limit. The
emissions from these detected individual PCB compounds ranged from 7.0 x 10"10 to 7.5 x 10"6
pounds per hour with an average and median of 5.8 x 10"7 and 4.6 x 10"8 pounds per hour,
respectively. For PAHs, 18 out of 513 individual compounds analytical results were reported below
the detection limit. For the individual PAH compounds that were detected, the emission values
ranged from 1.9 x 10"6 to 1.9 x 10"1 pounds per hour with an average and median of 7.6 x 10"3 and
1.5 x 10"3 pounds per hour, respectively. For the test results that were reported below the detection
limit, one half of the detection limit was used to calculate the concentration and emission rate for
the purposes of estimating emissions for the risk assessment modeling.
Other Data
In addition to the pollutant data described above, analyses of the coal/coke and furnace exhaust
concentrations of oxygen (O2), carbon dioxide (CO2), and carbon monoxide (CO) were measured.
The O2 and CO2 concentrations were measured using EPA Method 3 A, and the CO concentration
was measured using EPA Method 10. For coal/coke, the samples were analyzed using ASTM
D3177-02 for sulfur, D3172-07 for proximate analysis, and D3176-09 for ultimate analysis. Visible
emission data from the furnace building using EPA Method 9 were also provided in the test reports.
These visible emission reports provide opacity readings from the furnace buildings for furnace
smelting and tapping operations during the furnace control device outlet tests.
5.0 METHODOLOGY FOR DEVELOPING EMISSION ESTIMATES
The test data provided in the ICR responses were used to develop a modeling emissions database
for estimating the risks associated with the HAP emissions from the ferroalloy production facilities.
The analytical results provided in the ICR responses were used to calculate emissions for each of
the pollutants. This section describes the methodology used to estimate annual emissions from all
of the sources at each of the ferroalloy facilities. A summary of the estimated annual HAP
emissions is provided at the end of this section.
Some of the pollutant emissions were based on one or more analytical results that were reported
below the detection limit. For these analytical results, one half of the detection limit was used to
estimate the pollutant concentration. For some HAP (e.g., dioxins, furans, PCBs), the majority of
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the test results were below detection limit. For HF, all the results were below detection.
Nevertheless, to be conservative (i.e., overestimating rather than underestimating emissions and
risks), we developed estimated emissions for these HAP using the method described above.
5.1 Furnace Smelting Emissions
The furnace annual emissions for all pollutants were estimated using the information provided in
the facility's ICR test reports. The annual emissions for the tested furnace units were calculated
using stack gas parameters and analytical results provided in the submitted test reports and
converted to annual emissions assuming the furnaces operate 24 hours per day for 365 days per year
(i.e., 8,760 hours of operation per year). We have PM test results for all stack emissions points. For
some of the other pollutants, we only have data for a subset of emissions points. For those
pollutants that were not tested for a specific emissions point, the annual emissions were estimated
by multiplying the PM emissions rate by the pollutant-to-PM ratio from a comparable emission
source. A more detailed description for each facility is provided in the paragraphs below.
Eramet
The Eramet facility provided ICR test data for all pollutants for the Furnace 12 scrubber outlet and
the Furnace 1 baghouse outlet with the exception of Cr+6, which was not tested at the Furnace 1
baghouse outlet. Eramet also provided test data from the Furnace 1 and 12 smelting baghouse
outlets and the Furnace 1 tapping baghouse outlet during production of FeMn and SiMn.
The main source of manganese (Mn) and mercury (Hg) emissions from the furnace smelting
process is the manganese ore used by Eramet to produce the ferroalloys product, which may contain
Hg as an impurity. The amount of Mn ore used to produce a ton of FeMn is greater than the
amount of Mn ore used to produce a ton of SiMn. Therefore, Mn and Hg emissions are higher
during the production of FeMn than during the production of SiMn. The other HAP pollutant
emissions were assumed to be equivalent during the production of either FeMn or SiMn.
To estimate the annual Mn and Hg emissions for these emission sources, it was assumed that FeMn
and SiMn were produced equally during the year by each furnace (e.g., 4,380 hours per year
producing SiMn and 4,380 hours per year producing FeMn). This assumption was based on
conversations with Eramet on the historical production of the FeMn and SiMn products at their
facility. The annual emissions were calculated by averaging the emissions during the production of
each product and multiplying that average value by the number of production hours. The FeMn and
SiMn totals were then added to calculate the annual emissions. The equation for estimating the
furnace smelting baghouse outlet and the furnace tapping baghouse Mn emissions is provided
below.
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MW-Annual MflsiMn * 4,380 + MTlpeMn * 4,380
where;
MnAnnual = Annual Mn emissions from furnace smelting baghouse outlet or the tapping baghouse
outlet when producing FeMn and SiMn,
MnsiMn = Average Mn test results when producing SiMn in pounds per hour,
MnFeMn = Average Mn test results when producing FeMn in pounds per hour,
4,380 hours per year.
The same equation is used to estimate the annual Hg emissions using the average Hg test results for
FeMn and SiMn production. The formula is as follows;
HgAnnuai = Annual Hg emissions from furnace smelting baghouse outlet or the tapping baghouse
outlet when producing FeMn and SiMn,
HgsiMn = Average Hg test results when producing SiMn in pounds per hour,
HgFeMn = Average Hg test results when producing FeMn in pounds per hour,
4,380 hours per year.
To estimate the Cr+6 emissions from the Furnace 1 baghouse outlet, the Furnace 1 baghouse outlet
PM emission rate was multiplied by the Furnace 12 scrubber outlet Cr+6-to-PM ratio. The equation
for the Cr+6 emissions estimation for the Furnace 1 baghouse outlet are shown below:
HgAnnuai HdsiMn * 4,380 + HQp^Mn * 4,380
where;
where;
Cr+6i
PMFi
Cr+6i
PMFi
Furnace 12
Furnace 1
urnace
urnace
: i = Estimated Cr+6 emissions from EAF 1 in tons per year,
i = PM test results for the Furnace 1 baghouse outlet in tons per year,
; 12 = Cr+6 test results from the Furnace 12 scrubber outlet in tons per year, and
12 = PM test results from the Furnace 12 scrubber outlet in tons per year.
Felman
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The Felman facility submitted several test reports for Furnaces 2, 5, and 7 that provided pollutant
test data. The test reports for Furnace 2 provided test data for PM, metal HAP, Cr+6, HC1, HF,
formaldehyde, PAH, CDD/CDF, and PCB. The test reports for Furnace 5 provided test data for
PM, metal HAP, HC1, formaldehyde, PAH, CDD/CDF, and PCB. The test reports for Furnace 7
provided test data for PM, metal HAP, HC1, formaldehyde, and PAH. The Cr+6 and HF emissions
for Furnaces 5 and 7 were estimated using the pollutant-to-PM ratio from Furnace 2 using the same
estimation approach used for Eramet, as described above. The CDD/CDF and PCB emissions from
Furnace 7 were assumed to be the same as the reported emissions from Furnace 5. This assumption
is based on the fact that both Furnace 5 and Furnace 7 are the same size and produce the same
product.
5.2 Furnace Smelting Fugitive Emissions
At the top of the furnaces there is hooding that captures the vast majority of the emissions coming
out of the top of the furnace smelting operation and these emissions are vented to a control device
(either a baghouse or scrubber). Therefore, we have developed estimates of the fugitive emissions
from the top of these furnaces. As shown in table 5-2, we estimate that about 98% of these
emissions are captured and directed to a control device. This estimate is based on observations of
the furnace smelting process and hood system. However, it is not a closed system and we believe
there are some fugitive emissions coming from this source.
We have not identified any emissions factors based on the direct measurement of fugitive emissions
from ferroalloys production furnaces to estimate these emissions. Therefore, we used a mass
balance approach to estimate these emissions. The specific method is described in the following
paragraphs.
The furnace smelting fugitive emissions were estimated by calculating the uncontrolled PM
emissions from the furnace and estimating the percentage of the uncontrolled PM emissions that are
captured from the furnace. The difference between the uncontrolled pollutant emissions and the
estimated captured pollutant emissions were assumed to be the furnace fugitive emissions. The
uncontrolled PM pollutant emissions were calculated using the furnace annual emissions for PM
and the estimated control efficiency. A summary of the assumed fabric filter and scrubber control
efficiencies for the pollutants is presented in Table 5-1. These control efficiencies are based on
expected control efficiencies found on the EPA Clean Air Technology Center website.10 A
summary of the assumed process fugitive capture efficiencies is provided in Table 5-2. These
capture efficiencies are based on visual observations of the fugitive capture during process
operations. It was also assumed that 92 percent of the furnace control device emissions are
generated from the furnace and 8 percent of the furnace control device emissions are generated
10 EPA Technology Transfer Network, Clean Air Technology Center, General Information on CATC Products,
http://www.epa. gov/ttncatc 1/products.html
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Table 5-1. Assumed Control Device Efficiencies used to Estimate Furnace Fugitive Emissions
Pollutant(s)
Scrubber Control
Efficiency (%)
Fabric Filter
Control
Efficiency (%)
Particulate Matter
90
98
Metal HAP
90
98
Hexavalent Chromium
90
98
Mercury
15
15
Hydrochloric Acid/Hydrofluoric Acid
90
0
Formaldehyde
90
0
PAH, PCB, CDD/CDF
50
50
Table 5-2. Assumed Baseline Capture Efficiencies used to
Estimate Process Fugitive Emissions
Pollutant(s)
Assumed Fugitive
Capture
Efficiency (%)
Eramet
Furnace 1 and 12 Smelting
98
Furnace 1 Tapping
20
Furnace 12 Tapping
30
MOR Process
40
Furnace 1 and 12 Casting
0
MOR Casting
40
Crushing and Sizing
95
Felman
Furnace 2, 5, & 7 Smelting
98
Furnace 2, 5, & 7 Tapping
70
Furnace 2, 5, & 7 Casting
40
Crushing and Sizing
85
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during tapping of the furnace. These assumptions are based on engineering judgment of the furnace
operations and the tapping fugitive capture systems. The equation used to estimate fugitive PM
emissions from the furnace smelting process is shown below;
PMsmeit = Estimated fugitive PM emissions from the furnace in tons per year,
PMout = Control device outlet PM emissions in tons per year,
CE = Control efficiency of the furnace control device,
0.92 = Percentage of uncontrolled emissions generated from the furnace, and
Captsmeit = Estimated capture efficiency of the furnace smelting operations.
Other furnace smelting fugitive HAP pollutants from the furnace smelting operation were
calculated by multiplying the calculated PM furnace smelting fugitive emissions value by the HAP
pollutant-to-PM ratio for the associated furnace. The HAP pollutants that were estimated from the
furnace smelting process include; metal HAP (e.g., Mn, As, Cd, Cr, Ni, Pb), Cr+6, HC1, HF,
formaldehyde, PAH, CDD/CDF, and PCB. The equation for estimating these fugitive HAP
pollutants from the furnace smelting process is shown below;
HAPSmeit = Estimated fugitive HAP emissions from furnace smelting in tons per year,
PMsmeit = Estimated fugitive PM emissions from furnace smelting in tons per year,
HAP out = Reported outlet HAP emissions from the associated furnace in tons per year, and
PMout = Reported outlet PM emissions from the associated furnace in tons per year.
This approach was used to calculate metal HAP (e.g., Mn, As, Cd, Cr, Ni, Pb), Cr+6, HC1, HF,
formaldehyde, PAH, CDD/CDF, and PCB fugitive emissions from the furnace smelting operation
for each of the furnaces at Eramet and Felman.
The fugitive emissions for mercury were calculated using a different methodology because of the
large difference in control efficiency between PM and mercury. As shown in Table 5-1, the control
efficiency for PM using a fabric filter is 98 percent (90 percent with a scrubber), whereas the
control efficiency for mercury using either the fabric filter or scrubber is 15 percent. The lower
control efficiency for mercury is based on the assumption that 80 percent of the total mercury
emissions from the furnace smelting process are gaseous elemental mercury and 20 percent are
PMSmelt
CPMm) . (^) . 0.92 , " [(PMM) . (j^) . 0.92
where;
where;
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gaseous mercuric chloride.11 The furnace control devices can effectively control particulate
mercury (e.g., mercuric chloride) from the exhaust stream, but little or no control of gaseous
elemental mercury emissions or gaseous mercuric chloride.
Therefore, to account for the difference in control efficiency, an equation using the different control
efficiencies for PM and mercury were substituted into the equation for calculating HAP fugitive
emissions from smelting process above. The equation used to estimate fugitive mercury emissions
from the furnace smelting process is provided below;
CEpm = Control efficiency of PM for the control device (98%),
CEHg = Control efficiency of mercury for the control device (15%),
Hgsmeit = Estimated fugitive mercury emissions from furnace smelting in tons per year,
PMsmeit = Estimated fugitive PM emissions from furnace smelting in tons per year,
Hgout = Reported outlet HAP emissions from the associated furnace in tons per year, and
PMout = Reported outlet PM emissions from the associated furnace in tons per year.
5.3 Furnace Tapping Fugitive Emissions
The furnaces at these facilities are typically tapped several times per day from the lower section of
the furnace. This process results in significant fugitive emissions. The percent capture of tapping
fugitive emissions varies significantly across furnaces. As shown in table 5-2, we estimate that the
range is about 20% to 70% capture. This variability relates to the design of the tapping system
(e.g., use of cascading tapping ladles vs. single ladles), tapping hood design and capacity of the air
handling system.
We evaluated available emissions factors (EFs) that could be used to estimate these emissions. We
did not identify reliable factors that were specific to this industry. However, we found EFs from the
steel industry, from a similar process, that we believe are appropriate to use to estimate emissions
for tapping from Ferroalloys furnaces. After searching for, and reviewing, available emissions
factors, we determined that AP-42 EF for Basic Oxygen Furnace (BOF) tapping were the best
available EFs to use. Therefore, the furnace tapping fugitive PM emissions for the Eramet and
Felman facilities were estimated using an AP-42 emission factor (EF) for Basic Oxygen Furnace
(BOF) tapping12. The AP-42 documentation for BOF tapping fugitives lists two types of EFs
11 EPA, Technology Transfer Network Clearinghouse for Inventories & Emissions Factors Speciate Version 4.3,
September 2011.
12 AP-42, Compilation of Air Pollutant Emission Factors, Chapter 12.5 Iron and Steel Production, October 1986.
http://www.epa.gov/ttn/chief/ap42/chl2/final/cl2s05.pdf (see Table 12.5-1)
where;
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available to estimate these emissions; one measured at the source (which is 0.92 pound PM per ton
of product) and one measured at the building monitor (which is 0.29 pound PM per ton of product).
After reviewing these EFs, we determined that the BOF tapping building monitor EF was not based
on measurements at the building monitor, but on measurements taken in the primary hood using an
in-stack filter. The EF incorporates a capture efficiency of 93 percent which was assumed to
estimate the building monitor EF. The BOF tapping at source EF was conducted in the hood duct
leading to the wet scrubber. Based on this information, the BOF tapping at source EF was
determined to be more appropriate and was chosen to estimate the fugitive emissions from tapping
from the ferroalloy furnaces.
As mentioned above, the EF for BOF tapping at source is listed as 0.92 pound of PM per ton of
product produced (lb/Ton). However, after reviewing the source of the EF, it was determined that
the test data used to determine the EF was based on an estimated capture of 70% of the total
emissions (with 30 percent escaping to the building). Therefore, to estimate the total PM emissions
from BOF tapping, the EF of 0.92 lb/Ton was divided by 70 percent to obtain the total PM EF of
1.3 lb/Ton. This EF and the following equation were used to estimate fugitive PM emissions from
tapping of the furnaces at Eramet and Felman.
PMtap = Estimated fugitive PM emissions from tapping in tons per year,
1.3 = PM fugitive EF for BOF tapping in pounds per ton of steel produced,
P = Reported production of product for the associated furnace in tons per hour, and
Capttap = Estimated capture efficiency of the furnace tapping operations (see Table 5-2),
8760 = Hours of operation per year,
2000 = Pounds per ton.
The emissions of metal HAP (e.g., Mn, As, Cd, Cr, Ni, and Pb), Cr+6, HC1, and HF from the furnace
tapping process were estimated using the HAP-to-PM ratio from the respective furnace outlet
emissions. The equation for estimating these fugitive HAP emissions is provided below;
HAPtap = Estimated fugitive HAP (e.g., Mn, Ni, Pb, As) emissions from tapping in tons per year,
PMtap = Estimated fugitive PM emissions from tapping in tons per year,
PMtap = (1.3) * P * (1 - Capttap) * 8760/2000
where;
where;
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HAP out = Reported outlet HAP (e.g., Mn, Ni, Pb, As) emissions from the associated furnace in tons
per year, and
PMout = Reported outlet PM emissions from the associated furnace in tons per year.
The majority of Hg, PCB, PAH, CDD/CDF and formaldehyde emissions are assumed to come from
the furnace smelting operation, due to the volatility of these HAP and the heat generated in the
smelting process. The emissions test results from the Furnace 1 tapping baghouse compared to the
test results from the primary furnace 1 stack confirms that this is the case. For example, the
measured mercury emissions were about 15,000 times higher from the furnace 1 primary baghouse
compared to the tapping baghouse. This shows that the vast majority of mercury emissions are
emitted through the stacks and not as fugitives. However, emissions of these HAP were measured
in the Furnace 1 tapping baghouse at Eramet (emissions were not zero), and therefore are assumed
to be emitted to some degree as fugitives during the furnace tapping process. To estimate these
fugitive emissions, an EF was developed using the available test data from the Furnace 1 tapping
baghouse for each of these pollutants. For mercury, the EF was developed using the PM and Hg
emission result from the Furnace 1 tapping baghouse at Eramet and the tapping PM EF used to
calculate the fugitive PM emissions from tapping. The equation for calculating the Hg EF is as
follows;
FeMn EFHgtap = Hg fugitive EF from furnace tapping in pounds per ton of FeMn produced,
1.3 = PM fugitive EF for BOF tapping in pounds per ton of steel produced,
0.0000526 = Reported outlet Hg emissions from the Furnace 1 tapping baghouse in tons per year,
and
2.34 = Reported outlet PM emissions from the Furnace 1 tapping baghouse in tons per year.
This calculation provides an Hg tapping fugitive EF of 2.91 x 10"5 pounds per ton of FeMn
produced. The Hg emission rate was calculated using 2013 test data from the Furnace 1 tapping
baghouse and the PM emissions rate was calculated using 2011, 201213 and 201314 test data from
the Furnace 1 tapping baghouse. The reported test results are based on the production of FeMn,
which produces higher Hg emissions rates in comparison to SiMn production. Because there was
no Hg test data from the Furnace 1 tapping baghouse during SiMn production, an Hg EF was
developed for SiMn production using the calculated FeMn Hg emissions factor and applying a ratio
13 Memorandum from Bradley Nelson, EC/R to Conrad Chin, EPA/OAQPS/SPPD/MMG, Ferroalloys 2012 Test Report
Review, May 28, 2013.
14 Memorandum from Bradley Nelson, EC/R to Conrad Chin, EPA/OAQPS/SPPD/MMG, Ferroalloys 2013 Test Report
Review, July 29, 2013.
0.0000526
where;
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of the Furnace 1 baghouse outlet Hg emissions for SiMn and FeMn production. The equation for
calculating the Hg EF for SiMn production is as follows;
c /0.00678\
SiMn EFHa tav = (2.91 xlO"5)* (———
Hg tap V J ^ 0.0288 /
where;
SiMn EFfjgtap = Hg fugitive EF from furnace tapping in pounds per ton of SiMn produced,
2.91 x 10"5 = Hg fugitive EF from furnace tapping in pounds per ton of FeMn produced,
0.00678 = Reported outlet Hg emissions from the Furnace 1 baghouse outlet during SiMn
production in pounds per year, and
0.0288 = Reported outlet Hg emissions from the Furnace 1 baghouse outlet during FeMn
production in pounds per year.
This calculation provides an Hg tapping fugitive EF of 6.85 x 10"6 pounds per ton of SiMn
produced. The SiMn Hg EF was used to estimate Hg fugitive emissions from the tapping process
for Furnaces 2, 5, and 7 at Felman, which produce only SiMn. This factor may overestimate the
mercury emissions from Felman, because Felman uses a lower mercury content manganese ore in
comparison to the manganese ore used by Eramet. However, since there is no specific test data
from the tapping process at Felman, it is believed that this SiMn Hg emissions factor will provide a
conservative estimate of fugitive Hg emissions from tapping. For Eramet, an average of the FeMn
and the SiMn Hg EFs (1.80 x 10"5 pounds per ton of FeMn/SiMn produced) was used to estimate
Hg fugitive emissions from the tapping process. An average was used because of the assumption
(as described in Section 5.1) of 50 percent annual SiMn production and 50 percent annual FeMn
production for both Furnaces 1 and 12 at Eramet. These EFs and the following equation were used
to estimate fugitive Hg emissions from tapping of the furnaces at Eramet and Felman.
Hgtap = (EFHg tap) * P * (1 - Capttap) * 8760/2000
where;
Hgtap = Estimated fugitive Hg emissions from tapping in tons per year,
EFHg tap = Hg fugitive EF for tapping in pounds per ton of product (SiMn or SiMn/FeMn),
P = Reported production of product for the associated furnace in tons per hour, and
Capttap = Estimated capture efficiency of the furnace tapping operations,
8760 = Hours of operation per year,
2000 = Pounds per ton.
The estimated capture efficiency of the tapping operations for each of the furnaces are provided in
Table 5-2.
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The PCB, PAH, CDD/CDF and formaldehyde fugitive emissions from the tapping process were
calculated using the same methodology used to estimate fugitive Hg emissions. An EF was
developed for each of the organic HAP compounds using the test data from the Furnace 1 tapping
baghouse. It was assumed for this analysis that there is no variation of fugitive organic HAP
fugitive emissions based on product (e.g., SiMn or FeMn production). As an example, the equation
for calculating the naphthalene EF is as follows;
EFNaphthalene tap Naphthalene fugitive EF from furnace tapping in pounds per ton of FeMn or SiMn
produced,
1.3 = PM fugitive EF for BOF tapping in pounds per ton of steel produced,
1.34 x 10"2 = Reported outlet naphthalene emissions from the Furnace 1 tapping baghouse in tons
per year, and
2.34 = Reported outlet PM emissions from the Furnace 1 tapping baghouse in tons per year.
This calculation provides a naphthalene tapping fugitive EF of 7.42 x 10"3 pounds per ton of FeMn
or SiMn produced. This methodology was used to estimate the fugitive organic HAP emissions
factor for each of the organic HAP compounds. A table of the organic HAP fugitive EFs for the
tapping process is provided in Table 5-3. These EFs and the following equation were used to
estimate fugitive organic HAP emissions from tapping of the furnaces at Eramet and Felman. An
example of the naphthalene fugitive emission calculation is provided below;
Naphthalenetap (^^vap/it/iaJene tap) * P * (l * 8760/2000
where;
Naphthalenetap = Estimated fugitive naphthalene emissions from tapping in tons per year,
EFNaphthalene tap Naphthalene fugitive EF for tapping in pounds per ton of product (SiMn or
SiMn/FeMn),
P = Reported production of product for the associated furnace in tons per hour, and
Capttap = Estimated capture efficiency of the furnace tapping operations,
8760 = Hours of operation per year,
2000 = Pounds per ton.
The estimated capture efficiencies of the tapping operations for each furnace are provided in Table
where;
5-2.
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5.4 Casting/MOR Fugitive Emissions
We also evaluated available emissions factors that could be used to estimate casting and MOR
fugitive emissions. We identified one EF for casting specific to this industry from Ohio EPA of 2.4
lb/ton. However, we could not find any documentation to support this emissions factor, so we
determined it was not appropriate to use it. We did not find any other EFs specific to this industry.
However, we did identify EFs from the Steel industry from AP-42 for a similar source known as hot
metal transfer.15 Similar to the tapping EFs, we identified two EFs (one "at the monitor" and one
"at the source"). The "hot metal transfer at monitor" EF is 0.056 lb/ton and the EF for the "hot
metal transfer at source" is 0.19 lb/ton. However, we evaluated these EFs further and concluded
there was no actual test at the "monitor". Furthermore the 0.056 lb/ton test assumed 100% capture
(the test was conducted using an in-stack filter). Therefore, we determined it was not appropriate to
use the "at monitor" EF. The only remaining available EF that seemed valid was the 0.19 lb/ton
EF. Thus, after searching for, and reviewing available emissions factors, we concluded the best
available EF to use to estimate these emissions was the 0.19 lb/ton EF.
The MOR process involves the top lance injection of oxygen to reduce the carbon content and
increase the manganese content of the molten FeMn product. The majority of the fugitive
emissions from this process occur during the transfer of the molten metal from the tapping vessel to
the MOR vessel, and during the waiting period for the MOR vessel to become free. Other fugitive
emissions from this source occur during the injection of oxygen. However this process is well
controlled and is not expected to be a significant source of fugitive emissions. Since the majority of
fugitive emissions from MOR occur during pouring and waiting, it was assumed that the 'hot metal
transfer at source" EF of 0.19 lb/ton would be appropriate for estimating fugitive emission from the
MOR.
Therefore, particulate matter emissions from casting and the MOR process were estimated using an
AP-42 PM EF for hot metal transfer (at source) of 0.19 pounds of fugitive PM emissions per ton of
hot metal. This EF was used to estimate the fugitive PM emissions from casting and MOR
operations. This EF and the following equation were used to estimate fugitive PM emissions from
product casting at Eramet and Felman and the MOR process at Eramet.
PMcast = (0.19) * P * (1 - CaVtcast) * 8760/2000
where;
PMcast = Estimated fugitive PM emissions from casting in tons per year,
0.19 = PM EF for hot metal transfer in pounds per ton of steel produced,
15 AP-42, Compilation of Air Pollutant Emission Factors, Chapter 12.5 Iron and Steel Production, October 1986.
http://www.epa.gov/ttn/chief/ap42/chl2/final/cl2s05.pdf (see Table 12.5-1)
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P = Reported production of product for the associated furnace in tons per hour, and
Captcast = Estimated capture efficiency of the casting/MOR operations,
8760 = Hours of operation per year,
2000 = Pounds per ton.
The casting/MOR fugitive emissions for metal HAP (e.g., Mn, As, Cd, Cr, Ni, Pb), Cr+6, HC1, and
HF were estimated using the HAP-to-PM profile based on furnace emissions of each of the
pollutants and multiplying that profile by the estimated casting fugitive PM emission rate. The
equation used to estimate these HAP emissions is shown below;
HAP cast = Estimated HAP emissions from casting/MOR in tons per year,
PMcast = Estimated PM emissions from casting/MOR in tons per year,
HAP out = HAP emissions from the associated EAF in tons per year, and
PMout = PM emissions from the associated EAF in tons per year.
Fugitive emissions of mercury from the casting process were calculated using the Hg tapping EFs
and a ratio of the tapping and casting fugitive PM EFs. The equation for calculating the Hg EF for
SiMn production and FeMn/SiMn production is as follows;
where;
where;
SiMn EFHg cast = Hg fugitive EF from casting in pounds per ton of SiMn produced,
6.85 x 10"6 = Hg fugitive EF for tapping in pounds per ton of SiMn produced,
0.19 = PM EF for hot metal transfer in pounds per ton of steel produced,
1.3 = PM fugitive EF for BOF tapping in pounds per ton of steel produced.
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Table 5-3. Summary of Organic HAP Fugitive Emission Factors for Tapping and Casting at
Ferroalloys Production Facilities
Organic HAP Pollutant
Furnace 1 Tapping
Baghouse Emissions
(Tons/yr)
Tapping EF (lb/Ton)
Casting EF (lb/Ton)
Naphthalene
1.34E-02
7.42E-03
1.08E-03
2-Methyl na phthal ene
8.11E-03
4.50E-03
6.57E-04
Acenaphthylene
6.55E-03
3.63E-03
5.31E-04
Acenaphthene
3.18E-03
1.76E-03
2.58E-04
Fluorene
3.66E-03
2.03E-03
2.96E-04
Phenanthrene
1.24E-02
6.88E-03
1.01E-03
Anthracene
2.44E-03
1.36E-03
1.98E-04
Fl uoranthene
2.42E-03
1.34E-03
1.96E-04
Pyrene
1.87E-03
1.03E-03
1.51E-04
Benzo(a)anthracene
2.01E-04
1.12E-04
1.63E-05
Chrysene
3.42E-04
1.89E-04
2.77E-05
Benzo(b)fl uoranthene
1.66E-04
9.23E-05
1.35E-05
Benzo(k)fl uoranthene
8.32E-05
4.61E-05
6.74E-06
Benzo(e)pyrene
2.19E-04
1.21E-04
1.77E-05
Benzo(a)pyrene
6.13E-05
3.40E-05
4.97E-06
Perylene
8.76E-06
4.86E-06
7.10E-07
1 ndeno(l,2,3-cd)pyrene
3.94E-05
2.19E-05
3.19E-06
Dibenz(a,h)anthracene
1.31E-05
7.29E-06
1.06E-06
Benzo(g,h,i)peryl ene
9.20E-05
5.10E-05
7.45E-06
Formaldehyde
3.81E-02
2.11E-02
3.09E-03
2,3,7,8-Tetrachlorodibenzo-p-dioxin
6.18E-10
3.42E-10
Not measured
1,2,3,7,8-Pentachlorodibenzo-dioxin
8.45E-10
4.69E-10
Not measured
1,2,3,4,7,8-Hexachlorodibenzo-p-dioxin
5.74E-10
3.18E-10
Not measured
1,2,3,6,7,8-Hexachlorodibenzo-p-dioxin
5.56E-10
3.08E-10
Not measured
1,2,3,7,8,9-Hexachlorodibenzo-p-dioxin
5.65E-10
3.13E-10
Not measured
1,2,3,4,6,7,8-Heptachlorodibenzo-p-dioxin
1.05E-09
5.83E-10
Not measured
1,2,3,4,6,7,8,9-Octochl orodi benzo-p-di oxi n
8.72E-09
4.83E-09
Not measured
2,3,7,8-Tetrachlorodibenzofuran
9.02E-10
5.00E-10
Not measured
1,2,3,7,8-Pentachlorodibenzofuran
6.79E-10
3.76E-10
Not measured
2,3,4,7,8-Pentachlorodibenzofuran
1.07E-09
5.95E-10
Not measured
1,2,3,4,7,8-Hexachlorodibenzofuran
6.75E-10
3.74E-10
Not measured
1,2,3,6,7,8-Hexachlorodibenzofuran
5.21E-10
2.89E-10
Not measured
2,3,4,6,7,8-Hexachlorodibenzofuran
7.84E-10
4.35E-10
Not measured
1,2,3,7,8,9-Hexachlorodibenzofuran
6.18E-10
3.42E-10
Not measured
1,2,3,4,6,7,8-Heptachlorodibenzofuran
1.53E-09
8.50E-10
Not measured
1,2,3,4,7,8,9-Heptachlorodibenzofuran
5.74E-10
3.18E-10
Not measured
1,2,3,4,6,7,8,9-Octochlorodibenzofuran
1.54E-09
8.52E-10
Not measured
Polychlorinated biphenyls
2.14E-07
1.19E-07
Not measured
Particulate Matter
2.345
—
23
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DRAFT FOR INTERNAL REVIEW
The result of this calculation gives an Hg casting fugitive EF of 1.00 x 10"6 pounds per ton of SiMn
produced. The same methodology and equation were used with the average FeMn/SiMn Hg EF
derived in Section 5.3 to calculate the FeMn/SiMn Hg EF which results in an Hg casting fugitive
EF of 2.63 x 10"6 pounds per ton of FeMn/SiMn produced. These EFs were then used to estimate
fugitive Hg emissions from casting from the furnaces at Eramet and Felman using the equation
below;
Hgcast = Estimated fugitive Hg emissions from casting in tons per year,
EFHg cast = Hg fugitive EF for casting in pounds per ton of product (SiMn or SiMn/FeMn),
P = Reported production of product for the associated furnace in tons per hour, and
Captcast = Estimated capture efficiency of the furnace casting operations (see table 5-2),
8760 = Hours of operation per year,
2000 = Pounds per ton.
The estimated capture efficiency of the casting operations for each of the furnaces are provided in
For casting, it was assumed that the fugitive emissions of PCB and CDD/CDF occur during the
tapping process, but do not occur during the casting process because these HAPs are products of
incomplete combustion and no combustion occurs outside of the furnace. Therefore, only fugitive
emissions of PAH and formaldehyde were estimated for casting. The PAH and formaldehyde
fugitive emissions from casting were calculated using the same methodology used to estimate
fugitive Hg emissions. An EF was developed for each of the organic HAP compounds using the
tapping EF and the ratio of the tapping and casting fugitive PM EFs. Again, no distinction was
made between SiMn or FeMn production. As an example, the equation for calculating the
naphthalene EF is as follows;
EFnaphthaiene cast = Naphthalene fugitive EF from furnace casting in pounds per ton produced,
7.42 x 10"3 = Naphthalene fugitive EF for BOF tapping in pounds per ton of steel produced,
0.19 = PM EF for hot metal transfer in pounds per ton of steel produced,
1.3 = PM fugitive EF for BOF tapping in pounds per ton of steel produced.
HgCast = (EFHgcast) * P * (1 - Captcast) * 8760/2000
where;
Table 5-2.
where;
24
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DRAFT FOR INTERNAL REVIEW
This calculation provides a naphthalene casting fugitive EF of 1.08 x 10"3 pounds per ton of FeMn
or SiMn produced. This methodology was used to estimate the fugitive PAH and formaldehyde
emissions factor for each of the compounds. Table 5-3 lists the organic HAP fugitive EFs for the
tapping process. These EFs and the following equation were used to estimate fugitive PAH and
formaldehyde emissions from tapping of the furnaces at Eramet and Felman. An example of the
naphthalene fugitive emission calculation is provided below;
Naphthalenecast (^^vap/it/iaJene cast) * P * (1 CcLptcaSf) * 8760/2000
where;
NaphthaleneCast = Estimated fugitive naphthalene emissions from casting in tons per year,
EFnaphthaiene cast = Naphthalene fugitive EF for casting in pounds per ton of product (SiMn or
SiMn/FeMn),
P = Reported production of product for the associated furnace in tons per hour, and
Captcast = Estimated capture efficiency of the furnace casting operations (see table 5-2),
8760 = Hours of operation per year,
2000 = Pounds per ton.
The estimated capture efficiencies of the casting operations for each of the furnaces are provided in
Table 5-2.
5.5 Product Crushing Operations Emissions
The product crushing emissions were calculated using the test data provided in the test reports
submitted by the facilities. The product crushing and sizing operation at Eramet is controlled by
three baghouses. The facility provided test data for one of the baghouses, and the other two
baghouses were assumed to have the same emissions. Felman listed two product crushing and
sizing units in their ICR, but noted that only one of the systems is operated.
The hourly emissions reported in the test report were multiplied by the annual hours of operation to
estimate the annual emission from the product crusher. In the case of Eramet, the product crushing
and sizing system is limited to 5840 hours per year by permit. Therefore, 5840 hours per year was
used to calculate annual emissions from the Eramet crushing and sizing system. For Felman, the
crushing and sizing system was estimated to operate 6240 hours per year. These hours of operation
were provided by the facilities in their estimates of baseline emissions.16'17
16 QSEM Solutions, Inc., Eramet Marietta Inc. Technical Comments to Alternative Approach to NESHAP Subpart
XXX Compliance - November 23, 2011 Proposed Rule, June 29, 2012.
17 Email from Amy M Lincoln, Beveridge & Diamond, P.C. to Conrad Chin, EPA, RE: Felman Fugitive Emissions
Control and Test Protocols, 8/06/2012.
25
-------
DRAFT FOR INTERNAL REVIEW
5.6 Product Crushing Operations Fugitive Emissions
The annual fugitive emissions from the product crushing operations were estimated by using the
AP-42 EFs for Metallic Minerals Processing.18 The facilities provided information on the number
of emission sources of fugitive emissions from the product and sizing process. These emission
sources were classified into the following EF groups; primary crushing, secondary crushing and
material handling and transfer. The filterable PM-10 EFs were used from each of the classifications
because these are the respirable portion of PM emissions from the crushing and sizing operation.
The EFs for primary crushing, secondary crushing, and material handling and transfer are 0.05,
0.12, and 0.06 pounds per ton processed respectively. The PM-10 EF for secondary crushing was
listed as non-detect, however the PM-10 EF was estimated by reducing the PM EF for secondary
crushing by a factor of 10. This is the same factor of reduction that is shown for primary crushing.
The fugitive emissions were calculated by totaling the number of each crushing and sizing
operation and multiplying that by the emissions factor, capture efficiency, and the crushing and
sizing production rate as shown in the equation below:
Efcspm = E CS * (EFCS) * Pcs * (1 - CAPcs)] * H/2000
where;
Efcspm = Estimated fugitive emissions from crushing and sizing in tons per year,
ECS = Number of crushing, screening, conveyor, or material handling emission points,
EFcs = Crushing, screening, conveyor, or material handling EF in pounds per ton,
Peaf = Crushing and sizing production rate in tons per hour,
CAPcs = Assumed capture efficiency for the crushing and sizing operations (95%),
H = number of hours of operation during the year, and
2000 pounds per ton.
The sum of the emissions from the PM fugitive crushing, screening, conveyor, and material
handling emission points provides the crushing and sizing fugitive emissions. Metal HAP fugitive
emissions were calculated using the metal HAP-to-PM ratios from the crushing baghouse emissions
data and multiplying that ratio by the calculated fugitive PM emissions for the crushing and sizing
fugitives as shown in the equation below. The number of operating hours per year is based on the
normal annual operating hours or is limited to the number of hours by permit.
HAPCS =
i.PMcs)
(HAP,
CSout
\ PM,
CSout
where;
18 AP-42, Compilation of Air Pollutant Emission Factors, Chapter 11.24 Metallic Mineral Processing, August 1982.
http://www.epa.gov/ttn/chief/ap42/chll/final/clls24.pdf (see Table 11.24-2)
26
-------
DRAFT FOR INTERNAL REVIEW
HAPcs = Estimated fugitive HAP emissions from crushing and sizing in tons per year,
PMcs = Estimated fugitive PM emissions from crushing and sizing in tons per year,
HAPcsout = Calculated HAP emissions from crushing and sizing baghouse outlet, and
PMcsout = Calculated PM emissions from crushing and sizing baghouse outlet.
5.7 Baseline Annual HAP Emissions for the Ferroalloy Production Facilities
The baseline annual HAP emissions were calculated for each of the previously described emission
points and are summarized in Table 5-4. The test data from Eramet Furnace 1 and 12 smelting
baghouse outlets and the Furnace 1 tapping baghouse outlet provided HAP emission results for both
production of FeMn and SiMn. To estimate the annual HAP emissions for these emission sources,
it was assumed that both Furnaces 1 and 12 produce FeMn 4,380 hours of the year and SiMn is
produced 4,380 hours of the year (see section 5.1). The furnaces at Felman Production produce only
SiMn, and thus no such adjustments to the annual emissions were necessary. As shown in Table 5-
4, fugitive emissions were estimated to be 82 percent of the total HAP emissions from Eramet and
73 percent of the total HAP emissions from Felman.
5.8 Enhanced Capture Annual HAP Emissions for the Ferroalloy Production Facilities
For purposes of the supplemental analysis, we developed an enhanced capture control option to
evaluate residual risk. The enhanced capture annual HAP emissions were calculated assuming 95
percent of fugitive HAP emissions would be captured by the enhanced capture system. Some of the
fugitive emission sources were assumed to only use primary capture to achieve 95 percent capture.
Other fugitive emission sources were assumed to use both primary and secondary capture to
achieve a total of 95 percent capture of fugitive emissions. Captured fugitive HAP emissions from
the enhanced primary capture system were routed to the assumed source control device and reduced
using the control efficiencies listed in Table 5-1. Secondary fugitive HAP emissions were assumed
to be routed to a new control device and reduced by the control efficiency values listed in Table 5-1.
A summary of the assumptions and calculations are provided for each of the facilities below. A
schematic of the enhanced capture system is provided in Figure 5-1.
27
-------
DRAFT FOR INTERNAL REVIEW
Table 5-4. Summary of the Baseline Facility Process Annual HAP Emissions (Tons/year) for the
Ferroalloy Production Industry
Unit Description
Arsenic
Cadmium
Chromium
(III)
Chromium
(VI)
Lead
Manganese
Mercury
Nickel
Hydrogen
Chloride
Total PAH
Form-
aldehyde
Total
CDD/CDF
Total PCBs
Total HAP
Eramet Marietta
Furnace #1 Baghouse Outlet
0.000399
0.00231
0.000714
0.000206
0.0222
1.33
0.0780
0.00316
0.661
2.75
0.338
3.35E-08
5.20E-07
5.18
Furnace #1 Fugitives
0.000374
0.00217
0.000670
0.000194
0.0208
1.25
0.0017
0.00296
0.620
2.58
0.317
3.14E-08
4.88E-07
4.79
Furnace #1 Tapping Baghouse
0.000105
0.0000482
0.000866
0.0000668
0.0108
0.325
0.0000526
0.00125
0.180
0.0553
0.0381
2.18E-08
2.14E-07
0.611
Furnace #1 Tapping Fugitives
0.00326
0.0189
0.00584
0.00169
0.181
24.3
0.0008
0.0259
5.41
1.4
0.96
5.51E-07
5.40E-06
32.3
Furnace #12 Scrubber Outlet
0.00353
0.0283
0.00158
0.00120
0.106
6.88
0.085
0.00234
0.545
0.560
0.145
8.48E-08
3.63E-07
8.35
Furnace #12 Fugitives
0.000662
0.00531
0.000297
0.000225
0.0198
1.29
0.00189
0.000439
0.102
0.105
0.0272
1.59E-08
6.81E-08
1.55
Furnace #12 Tapping Fugitives
0.00417
0.0335
0.00187
0.00142
0.125
20.4
0.0007
0.00277
0.645
1.221
0.842
4.82E-07
4.73E-06
23.3
MOR Process Baghouse Outlet
0.00110
0.00193
0.00426
0.0000821
0.000933
0.982
0.00212
0.00147
0.0373
0.0383
0.00990
NM
NM
1.08
MOR Process Fugitives
0.00486
0.00856
0.0188
0.000363
0.00413
5.22
0.000176
0.00649
0.165
0.300
0.2070
NM
NM
5.94
Furnace #1 Casting
0.000596
0.00346
0.00107
0.000308
0.0331
4.44
0.00015
0.00472
0.988
0.25
0.176
NM
NM
5.90
Furnace #12 Casting
0.000871
0.00699
0.000391
0.000297
0.0261
4.27
0.00014
0.000577
0.135
0.245
0.1691
NM
NM
4.85
MOR casting
0.00486
0.00856
0.0188
0.000363
0.00413
5.22
0.000176
0.00649
0.165
0.300
0.2070
NM
NM
5.94
C2P Crushing/Sizing Baghouse#l
0.000133
NM
0.00130
0.000103
0.000141
1.49
NM
0.00151
NM
NM
NM
NM
NM
1.49
C2P Crushing/Sizing Baghouse#2
0.000133
NM
0.00130
0.000103
0.000141
1.49
NM
0.00151
NM
NM
NM
NM
NM
1.49
C2P Crushing/Sizing Baghouse#3
0.000133
NM
0.00130
0.000103
0.000141
1.49
NM
0.00151
NM
NM
NM
NM
NM
1.49
C2P crushing and sizing fugitives
0.000567
NM
0.00555
0.000442
0.000603
6.35
NM
0.00646
NM
NM
NM
NM
NM
6.37
Fugitive Emissions Total
0.0202
0.0875
0.0534
0.00530
0.415
72.7
0.00576
0.0568
8.23
6.40
2.91
1.08E-06
1.07E-05
90.9
Eramet Marietta Total
0.0258
0.120
0.0647
0.00717
0.555
86.7
0.171
0.0695
9.65
9.81
3.44
1.22E-06
1.18E-05
111
Felman Production
Furnace No. 2 Baghouse Outlet
0.00277
0.000311
0.00564
0.00155
0.180
1.25
0.01035
0.0793
3.48
0.342
0.331
4.12E-07
3.53E-05
5.69
Furnace No. 2 Fugitives
0.00260
0.000292
0.00530
0.00146
0.169
1.17
0.000228
0.0745
3.27
0.321
0.311
3.87E-07
3.31E-05
5.33
Furnace No. 2 Tapping Fugitives
0.00329
0.000369
0.00670
0.00184
0.214
3.97
0.000091
0.0942
4.14
0.407
0.281
1.61E-07
1.57E-06
9.11
Furnace No. 5 Baghouse Outlet
0.000710
0.000175
0.0089
0.00141
0.0173
0.761
0.00219
0.00461
0.351
0.321
0.309
6.41E-07
4.85E-05
1.78
Furnace No. 5 Fugitives
0.000666
0.000164
0.00833
0.00132
0.0163
0.714
0.0000484
0.00433
0.330
0.302
0.290
6.02E-07
4.55E-05
1.67
Furnace No. 5 Tapping Fugitives
0.000459
0.000113
0.00574
0.000913
0.0112
1.96
0.0000450
0.00298
0.227
0.201
0.139
7.95E-08
7.79E-07
2.55
Furnace No. 7 Baghouse Outlet
0.00117
0.000288
0.0146
0.00232
0.0285
2.44
0.00446
0.00759
0.857
0.269
0.611
6.41E-07
4.85E-05
4.24
Furnace No. 7 Fugitives
0.00110
0.000271
0.0137
0.00218
0.0268
2.29
0.000098
0.00713
0.804
0.253
0.573
6.02E-07
4.55E-05
3.97
Furnace No. 7 Tapping Fugitives
0.000459
0.000113
0.00574
0.000913
0.0112
1.96
0.0000450
0.00298
0.337
0.201
0.139
7.95E-08
7.79E-07
2.66
Furnace No. 2 Casting
0.000961
0.0001080
0.00196
0.000539
0.0625
1.160
0.0000266
0.0275
1.21
0.1188
0.0820
NM
NM
2.66
Furnace No. 5 Casting
0.000134
0.0000331
0.00168
0.000267
0.00328
0.574
0.0000132
0.000872
0.0664
0.0588
0.0406
NM
NM
0.746
Furnace No. 7 Casting
0.000134
0.0000331
0.00168
0.000267
0.00328
0.574
0.0000132
0.000872
0.0985
0.0588
0.0406
NM
NM
0.778
Crushing/Screening System #1
0
NM
0
0
0
0
NM
0
NM
NM
NM
NM
NM
0.00
Crushing/Screening System #2
0
NM
0.000161
0.0000444
0.000463
0.210
NM
0
NM
NM
NM
NM
NM
0.211
Crushing/Screening #1 Fugitives
0
NM
0
0
0
0
NM
0
NM
NM
NM
NM
NM
0.00
Crushing/Screening#2 Fugitives
0
NM
0.00359
0.000988
0.0103
3.25
NM
0
NM
NM
NM
NM
NM
3.27
Fugitive Emissions Total
0.00980
0.00150
0.0544
0.0107
0.528
17.6
0.000609
0.215
10.5
1.92
1.90
1.91E-06
1.27E-04
32.8
Felman Production Total
0.0144
0.00227
0.0837
0.0160
0.754
22.3
0.0176
0.307
15.2
2.85
3.15
3.61E-06
2.60E-04
44.7
28
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DRAFT FOR INTERNAL REVIEW
Eramet Marietta
The methodology for calculating the enhanced capture emissions from Eramet were based on the
proposed fugitive emissions capture plan19 submitted by the facility. The submitted capture plan
proposed increasing the primary capture of fugitive tapping emissions from the furnaces to 95
percent, adding 95 percent primary capture to the casting beds for Furnaces 1 and 12, and adding
primary and secondary capture to the MOR building to achieve a total capture of 95 percent from
the MOR process and MOR casting. A mass balance approach was taken to calculate the enhanced
capture emissions from many of the emission sources that are equipped with control devices. Even
though, the baghouse or scrubber is a constant outlet device (e.g., the particulate matter
concentration in the stack gas is constant), the volumetric flow rate to the control device will
increase due to the addition of the enhanced capture fugitive streams, hence increasing the emission
rate from the control device outlet. A description of the assumptions used to calculate the enhanced
capture emissions from each of the emission sources is provided in the following sections.
Furnace 1 and Associated Emission Sources
The enhanced capture emissions from the Furnace 1 tapping process were calculated assuming an
increase of the primary capture of the fugitive emissions from the estimated 20 percent at baseline
to 95 percent at the enhanced capture level. The captured emissions from tapping were assumed to
be routed to the Furnace 1 baghouse, instead of the dedicated Furnace 1 tapping baghouse. The
Furnace 1 baghouse was noted in the control plan to have extra capacity to control fugitive emission
streams from both Furnace 1 tapping and casting. An example of the enhanced capture PM
emissions calculation for Furnace 1 tapping fugitives is shown below:
59 2
3-70=u^%r(i-95%)
where;
3.70 = Enhanced capture fugitive PM emissions from Furnace 1 tapping in tons per year,
59.2 = Baseline fugitive PM emissions from Furnace 1 tapping in tons per year,
20% = Baseline capture percent of Furnace 1 tapping emissions, and
95% = Enhance capture percent of Furnace 1 tapping emissions.
19 QSEM Solutions, Inc., Eramet Marietta Inc. Technical Comments to Alternative Approach to NESHAP Subpart
XXX Compliance - November 23, 2011 Proposed Rule, June 29, 2012.
29
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DRAFT FOR INTERNAL REVIEW
Exhaust
to Atmosphere
Furnace Fugitive Tapping Fugitive
Emissions to Atmosphere Emissions to Atmosphere
Casting Fugitive
Emissions to Atmosphere
Tapping Primary
Capture Hood
Submerged Electric
Arc Furnace
Fabric Filter
Casting Primary
Capture Hood
Tapping
Casting
Casting Bed
Furnace Smelting and
Tapping Process
Casting Process
30
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DRAFT FOR INTERNAL REVIEW
The enhanced capture casting fugitive emissions from Furnace 1 were also calculated using the
same methodology above. The only exception is that there is currently no capture of fugitive
emissions from Furnace 1 casting. The control plan submitted by Eramet proposed adding primary
capture of 95 percent to the Furnace 1 casting area and routing the captured emissions to the
Furnace 1 tapping baghouse, which in the proposed plan will only be used to control captured
fugitive emissions for Furnace 1 casting. An example of the enhanced capture PM emissions
calculation for Furnace 1 casting fugitive is shown below:
0.541 = 10.8 * (1 - 95%)
where;
0.541 = Enhanced capture fugitive PM emissions from Furnace 1 casting in tons per year,
10.8 = Baseline fugitive PM emissions from Furnace 1 casting in tons per year, and
95% = Enhanced capture percent of Furnace 1 casting emissions.
No changes to the capture system for the Furnace 1 smelting operation were proposed in Eramet's
capture plan. The current system was assumed to achieve 98 percent capture of fugitive emission
from the furnace operations, therefore the calculated fugitive emissions at the enhanced capture
level are the same as the calculated fugitive emissions from baseline. There is an increase in the
Furnace 1 baghouse outlet emissions in the enhanced capture scenario because the emissions from
Furnace 1 tapping, and the Furnace 1 tapping baghouse are now routed to the Furnace 1 baghouse.
The Furnace 1 tapping baghouse was assumed to control captured fugitive emissions from Furnace
12 tapping and Furnace 12 casting in the enhanced capture scenario. The enhanced capture
emission rate from the Furnace 1 baghouse outlet was calculated by adding the baseline emissions
from the Furnace 1 baghouse and the Furnace 1 tapping baghouse with the difference of fugitive
emissions from Furnace 1 tapping. An example of the PM emissions calculation is shown below:
10.7 = 7.23 + 2.34 + (59.2-3.70)*(l-98%)
where;
10.7 = Enhanced capture PM emissions from the Furnace 1 baghouse outlet in tons per year,
7.23 = Baseline PM emissions from the Furnace 1 baghouse outlet in tons per year,
2.34 = Baseline PM emissions from the Furnace 1 tapping baghouse in tons per year,
59.2 = Baseline fugitive PM emissions from Furnace 1 tapping in tons per year,
3.7 = Enhanced capture fugitive PM emissions from Furnace 1 tapping in tons per year, and
98% = Assumed PM control efficiency of the Furnace 1 baghouse.
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Note that the control efficiency is only applied to the fugitive casting emissions. The PM emissions
from the Furnace 1 baghouse and Furnace 1 tapping baghouse are already provided as controlled
emissions.
As noted in the calculation above, the outlet emissions from the Furnace 1 tapping baghouse were
moved to the Furnace 1 baghouse in the enhanced capture scenario. This baghouse now controls
captured fugitive emissions from Furnace 1 casting in the enhanced capture scenario. The enhanced
capture emission rate from this baghouse was calculated by adding the difference of fugitive
emissions from Furnace 1 casting. Again, a mass balance approach was taken by subtracting the
difference in Furnace 1 casting emissions from baseline to enhanced capture. An example of the
PM emissions calculation for the Furnace 1 tapping baghouse is shown below:
0.206 = (10.8-0.541)(1 -98%)
where;
0.206 = Enhanced capture PM emissions from the Furnace 1 tapping baghouse outlet in tons per
year,
10.8 = Baseline fugitive PM emissions from Furnace 1 casting in tons per year,
3.7 = Enhanced capture fugitive PM emissions from Furnace 1 casting in tons per year, and
98% = Assumed PM control efficiency of the Furnace 1 tapping baghouse.
Furnace 12 and Associated Emission Sources
The enhanced capture emissions from the Furnace 12 tapping process were calculated assuming an
increase of the primary capture of the fugitive emissions from the estimated 30 percent at baseline
to 95 percent at the enhanced capture level. The captured emissions from tapping were assumed to
be routed to a new Furnace 12 tapping/casting baghouse, instead of the Furnace 12 scrubber. The
control noted that the Furnace 12 scrubber did not have the capacity to control the enhanced capture
fugitive emission stream from both Furnace 12 tapping. An example of the enhanced capture PM
emissions calculation for Furnace 12 tapping fugitives is shown below:
49.8
3-56 = (I=309r<1-95%>
where:
3.56 = Enhanced capture fugitive PM emissions from Furnace 12 tapping in tons per year,
59.2 = Baseline fugitive PM emissions from Furnace 12 tapping in tons per year,
30% = Baseline capture percent of Furnace 12 tapping emissions, and
95% = Enhance capture percent of Furnace 12 tapping emissions.
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The enhanced capture casting fugitive emissions from Furnace 12 were also calculated using the
same methodology above. The only exception is that there is currently no capture of fugitive
emissions from Furnace 12 casting. The control plan submitted by Eramet proposed adding
primary capture of 95 percent to the Furnace 12 casting area and routing the captured emissions to a
new Furnace 12 tapping/casting baghouse. An example of the enhanced capture PM emissions
calculation for Furnace 12 casting fugitive is shown below:
0.520 = 10.4 * (1 — 95%)
where;
0.520 = Enhanced capture fugitive PM emissions from Furnace 12 casting in tons per year,
10.4 = Baseline fugitive PM emissions from Furnace 12 casting in tons per year, and
95% = Enhanced capture percent of Furnace 12 casting emissions.
No changes to the capture system for the Furnace 12 smelting operation were proposed in Eramet's
capture plan. The current system was assumed to achieve 98 percent capture of fugitive emission
from the furnace operations, therefore the calculated fugitive emissions at the enhanced capture
level are the same as the calculated fugitive emissions from baseline. There is a decrease in the
Furnace 12 scrubber outlet emissions in the enhanced capture scenario because the captured
emissions from Furnace 12 tapping are now being routed to a dedicated Furnace 12 tapping/casting
baghouse. The enhanced capture emission rate from the Furnace 12 scrubber was calculated by
assuming that captured tapping emissions are 8 percent of the total emissions from the furnace
control device outlet. Therefore, the enhanced capture Furnace 12 scrubber outlet emissions were
assumed to be 92 percent of the baseline Furnace 12 scrubber outlet emissions. An example of the
PM emissions calculation is shown below:
38.7 = 42.1 * 0.92
where;
38.7 = Enhanced capture PM emissions from the Furnace 12 scrubber outlet in tons per year,
42.1 = Baseline PM emissions from the Furnace 12 scrubber outlet in tons per year, and
0.92 = Assumed fraction of emissions from the furnace smelting process.
As noted above, the captured emissions from Furnace 12 tapping and casting are routed to a new
Furnace 12 tapping/casting baghouse in the enhanced capture scenario. The enhanced capture
emission rate from this baghouse was calculated by adding the difference of fugitive emissions
from both tapping and casting from Furnace 12. An example of the PM emissions calculation for
the Furnace 12 tapping/casting baghouse is shown below:
33
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DRAFT FOR INTERNAL REVIEW
1.19 = [(10.4 - 0.520) + (49.8 - 3.56) + (42.1 - 38.7)] * (1 - 98%)
where;
1.19 = Enhanced capture PM emissions from the Furnace 12 tapping/casting baghouse outlet in tons
per year,
10.4 = Baseline fugitive PM emissions from Furnace 12 casting in tons per year,
0.520 = Enhanced capture fugitive PM emissions from Furnace 12 casting in tons per year,
49.8 = Baseline fugitive PM emissions from Furnace 12 tapping in tons per year,
3.56 = Enhanced capture fugitive PM emissions from Furnace 12 tapping in tons per year,
42.1 = Baseline PM emissions from the Furnace 12 scrubber outlet in tons per year,
3.56 = Enhanced capture PM emissions from the Furnace 12 scrubber outlet in tons per year, and
98% = Assumed PM control efficiency of the Furnace 12 tapping/casting baghouse.
MOR and Associated Emission Sources
The enhanced capture fugitive emissions for the MOR process were calculated based on the control
plan submitted by Eramet. The control plan proposed increasing the primary capture of the MOR
process and adding secondary capture to the building for an overall capture of 95 percent of fugitive
emissions generated by the MOR process. To calculate this scenario, it was assumed that the
primary capture would be increased from 40 percent at baseline to 80 percent at the enhanced
capture level. An additional capture of 75 percent was used to estimate the secondary capture effect
to the MOR process fugitive emissions. An example of the calculation is shown below:
1,06 = (1 -40%) * (1 " 80%) * (1 " 75%)
where;
1.06 = Enhanced capture PM emissions from the MOR process in tons per year,
12.7 = Baseline fugitive PM emissions from MOR process in tons per year,
40% = Baseline capture percentage of fugitive emissions from the MOR process,
80%) = Primary capture percentage of MOR process fugitives for enhanced capture, and
75%o = Secondary capture percentage of MOR process fugitives for enhanced capture.
The enhanced capture MOR casting fugitive emissions were calculated using the same assumptions
as above for primary and secondary capture at the enhanced capture level. An example of the
enhanced capture PM fugitive emissions from MOR casting is shown below.
1,06 = (1 -40%) * (1 " 80%) * (1 " 75%)
where;
34
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DRAFT FOR INTERNAL REVIEW
1.06 = Enhanced capture PM emissions from the MOR casting in tons per year,
12.7 = Baseline fugitive PM emissions from MOR casting in tons per year,
40% = Baseline capture percentage of fugitive emissions from the MOR casting,
80% = Primary capture percentage of MOR casting fugitives for enhanced capture, and
75%) = Secondary capture percentage of MOR casting fugitives for enhanced capture.
The enhanced capture emissions from the MOR baghouse were calculated to include the primary
captured fugitive emissions from the MOR process and MOR casting. An example of the enhanced
capture PM emissions from the MOR baghouse is shown below. The equation calculates the MOR
fugitive emissions that are only captured by the primary capture system.
3.22 = 2.88 + [12.7 - , 12,7 , * (1 - 80%) + 12.7 - , 12,7 , * (1 - 80%)]*(1-98%)
L (1-40%) V J (1-40%) V Ji V '
where;
3.22 = Enhanced capture PM emissions from the MOR baghouse outlet in tons per year,
12.7 = Baseline fugitive PM emissions from the MOR process in tons per year,
40%) = Baseline capture percentage of fugitive emissions from the MOR process,
80%o = Primary capture percentage of MOR process fugitives for enhanced capture,
12.7 = Baseline fugitive PM emissions from the MOR casting in tons per year,
40%o = Baseline capture percentage of fugitive emissions from MOR casting,
80%o = Primary capture percentage of MOR casting fugitives for enhanced capture, and
98%o = Assumed PM control efficiency of the MOR process baghouse.
The fugitive emissions captured by the secondary capture system in the MOR building were
assumed to be sent to a new MOR secondary baghouse. An estimate of the PM emissions from this
baghouse is provided in the calculation below. The equation calculates the fugitive MOR emissions
that are only captured by the secondary capture system.
0.127 = [, 12 7 , * (1 - 80%) - 1.06 + , 12,7 , * (1 - 80%) - 1.06]*(l-98%)
(1—40%) V J (1-40%) V J
where;
0.127 = Enhanced capture PM emissions from the MOR secondary baghouse outlet in tons per year,
12.7 = Baseline fugitive PM emissions from the MOR process in tons per year,
40%o = Baseline capture percentage of fugitive emissions from the MOR process,
80%o = Primary capture percentage of MOR process fugitives for enhanced capture,
1.06 = Enhanced capture PM emissions from the MOR process in tons per year,
12.7 = Baseline fugitive PM emissions from the MOR casting in tons per year,
40%o = Baseline capture percentage of fugitive emissions from MOR casting,
80%o = Primary capture percentage of MOR casting fugitives for enhanced capture,
35
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DRAFT FOR INTERNAL REVIEW
1.06 = Enhanced capture PM emissions from the MOR casting in tons per year, and
98% = Assumed PM control efficiency of the MOR secondary baghouse.
Crushing & Screening Operations
The control plan submitted by Eramet did not include any capture enhancements to the crushing
and sizing system. Therefore, the enhanced capture crushing and screening baghouse and fugitive
emissions are the same as the calculated baseline emissions for these sources.
Felman Production
The methodology for calculating the enhanced capture emissions from Felman were based on the
proposed fugitive emissions capture plan20 submitted by the facility. The submitted capture plan
proposed increasing the primary capture of fugitive and adding secondary capture to the furnace
building to achieve an overall capture efficiency of 95 percent. To achieve the overall 95 percent
capture, it was assumed that the primary capture would be increased to 80 percent and the
secondary capture would capture 75 percent of the remaining fugitive emissions in the building. A
mass balance approach was taken to calculate the enhanced capture emissions from many of the
emission sources that are equipped with control devices. A description of the assumptions used to
calculate the enhanced capture emissions from each of the emission sources is provided in the
following sections.
Furnace and Associated Emission Sources
Because the furnace smelting, tapping, and casting operation for Furnace 2, 5, and 7 occur in the
same building, the same calculation methodology was used for each of these operations. The
emissions estimate for fugitive tapping emissions for the enhanced capture scenario were calculated
by applying both the primary capture efficiency and the secondary capture efficiency to the tapping
operation for Furnaces 2, 5, and 7. An example of the enhanced capture PM tapping fugitive
emissions estimate for Furnace 2 is shown in the equation below.
2,88 = (1 - 70%) * (1 " 80%) * (1 " 75%)
Where;
2.88 = Enhanced capture PM fugitive emissions from Furnace 2 tapping in tons per year,
17.3 = Baseline fugitive PM emissions from Furnace 2 tapping in tons per year,
70% = Baseline capture percentage of fugitive emissions from the Furnace 2 tapping,
80%) = Primary capture percentage of furnace tapping fugitives for enhanced capture, and
20 Chu and Gassman, Design Narrative, July 9, 2012.
36
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DRAFT FOR INTERNAL REVIEW
75% = Secondary capture percentage of furnace tapping fugitives for enhanced capture.
The casting fugitive emissions are calculated using the same methodology above for tapping
fugitive emissions. The emissions estimate for fugitive casting emissions for the enhanced capture
scenario were calculated by applying both the primary capture efficiency and the secondary capture
efficiency to the casting operation for Furnaces 2, 5, and 7. An example of the enhanced capture
PM casting fugitive emissions estimate for Furnace 2 is shown in the equation below.
0.420 = Enhanced capture PM fugitive emissions from Furnace 2 casting in tons per year,
5.04 = Baseline fugitive PM emissions from Furnace 2 casting in tons per year,
40% = Baseline capture percentage of fugitive emissions from the Furnace 2 casting,
80%) = Primary capture percentage of furnace casting fugitives for enhanced capture, and
75%o = Secondary capture percentage of furnace casting fugitives for enhanced capture.
The proposed control plan submitted by Felman also included changes to the capture of the furnace
smelting emissions for Furnace 2, 5, and 7. These changes are expected to increase the fugitive
capture from the furnace smelting operations from 98 to 99 percent. Therefore, there is a reduction
in the enhanced capture furnace fugitive emissions from Furnaces 2, 5, and 7. An example of the
furnace smelting fugitive emission calculation for Furnace 2 is shown below:
1.70 = Enhanced capture PM fugitive emissions from Furnace 2 smelting in tons per year,
13.6 = Baseline fugitive PM emissions from Furnace 2 smelting in tons per year,
98%o = Baseline capture percentage of fugitive emissions from the Furnace 2 smelting,
99% = Primary capture percentage of furnace smelting fugitives for enhanced capture, and
75%o = Secondary capture percentage of furnace smelting fugitives for enhanced capture.
The furnace baghouse outlet emissions from each of the furnaces increase due to the addition of
primary enhanced capture emissions from smelting, tapping, and casting. An example of the PM
baghouse outlet emissions from Furnace 2 is shown in the equation below:
0,42° = (1 - 40%) * (1 " 80%) * (1" 75%)
Where;
170 = (1 - 98%) * (1 " "%) * (1 " 75%)
where;
* (1 - 99%) + 17.3 - ^%) * (1 - 80%)
5.04
+5.04 — 7- —
(13^ *(1-80%)] *(1-98%)
37
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DRAFT FOR INTERNAL REVIEW
where;
14.8 = Enhanced capture PM emissions from the Furnace 2 baghouse outlet in tons per year,
14.5 = Baseline fugitive PM emissions from the Furnace 2 baghouse outlet in tons per year,
13.6 = Baseline fugitive PM emissions from Furnace 2 smelting in tons per year,
98% = Baseline capture percentage of fugitive emissions from the Furnace 2 smelting,
99% = Primary capture percentage of furnace smelting fugitives for enhanced capture,
17.3 = Baseline fugitive PM emissions from Furnace 2 tapping in tons per year,
70%) = Baseline capture percentage of fugitive emissions from the Furnace 2 tapping,
80%o = Primary capture percentage of furnace tapping fugitives for enhanced capture,
5.04 = Baseline fugitive PM emissions from Furnace 2 casting in tons per year,
40%o = Baseline capture percentage of fugitive emissions from the Furnace 2 casting,
80%o = Primary capture percentage of furnace casting fugitives for enhanced capture, and
98%o = Assumed PM control efficiency of the Furnace 2 baghouse.
The Felman control plan proposed that captured secondary fugitive emissions from the furnace
smelting, tapping, and casting processes would be controlled by a new secondary furnace baghouse.
An example of the calculated PM emissions from this baghouse is shown below:
0,742 = (i -275o/o) * (75%) * t1 - 98%)
where;
0.742 = Enhanced capture PM emissions from the secondary furnace baghouse in tons per year,
12.4 = Sum of the enhanced capture fugitive PM emissions from smelting, tapping, and casting for
Furnaces 2, 5, and 7 in tons per year,
75%o = Secondary capture percentage of the furnace building fugitives for enhanced capture, and
98%o = Assumed PM control efficiency of the secondary furnace baghouse.
38
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DRAFT FOR INTERNAL REVIEW
Crushing & Screening Operations
The control plan submitted by Felman did not include any primary capture enhancements to the
crushing and sizing system, but does include secondary capture of fugitive emissions from this
process. The secondary fugitive capture system for the crushing and screening operation is
expected to achieve 75 percent capture. An example of the PM fugitive emissions calculation for
the crushing and screening operation is shown below:
2.19 = 8.74 * (1 - 75%)
where;
2.19 = Enhanced capture fugitive PM emissions from crushing and screening in tons per year,
8.74 = Baseline fugitive PM emissions from crushing and screening in tons per year, and
75% = Secondary capture percentage for crushing and screening.
The Felman control plan proposed that captured secondary fugitive emissions from the crushing
and screening process would be controlled by a new secondary crushing baghouse. An example of
the calculated PM emissions from this baghouse is shown below:
°-0656 = (1 ^'750/0) * (75%) * t1 - 98%)
where;
0.0656 = Enhanced capture PM emissions from the secondary crushing baghouse in tons per year,
2.19 = Enhanced capture fugitive PM emissions from crushing and screening in tons per year,
75% = Secondary capture percentage of crushing fugitives for enhanced capture, and
98%) = Assumed PM control efficiency of the secondary crushing baghouse.
A summary of the primary and secondary capture assumptions for fugitive emissions from Eramet
and Felman is provided in Table 5-5. A summary of the enhanced capture emissions for Eramet
and Felman isprovided in Table 5-6.
39
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DRAFT FOR INTERNAL REVIEW
Table 5-5. Summary of Fugitive Capture Assumptions Used to Estimate the
Enhanced Capture Emissions
Fugitive Emission Source
Enhanced Capture HAP Emission Calculation Assumption
Eramet Marietta
Furnace 1 and 12 Furnace
Fugitives
No change from baseline. Source is assumed to achieve 98% capture.
Furnace 1 Tapping Fugitives
The tapping capture is assumed to increase from 20% at baseline to 95%.
Captured emissions routed to Furnace 1 baghouse.
Furnace 12 Tapping Fugitives
The tapping capture is assumed to increase from 30% at baseline to 95%.
Captured emissions routed to Furnace 12 tapping/casting baghouse
(currently Furnace 1 tapping baghouse).
Furnace 1 Casting Fugitives
The casting capture is assumed to increase from 0% at baseline to 95%.
Captured emissions routed to Furnace 1 baghouse.
Furnace 12 Casting Fugitives
The casting capture is assumed to increase from 0% at baseline to 95%.
Captured emissions routed to Furnace 12 tapping/casting baghouse
(currently Furnace 1 tapping baghouse).
MOR Process and Casting
Fugitives
The MOR process and MOR casting primary capture is assumed to
increase from 40% at baseline to 80%, and installation of secondary
capture of 75% for a total overall capture of 95%. Captured primary
emissions routed to MOR baghouse and captured secondary emissions
routed to a new MOR secondary baghouse.
Crushing and Sizing Fugitives
No change from baseline. Source is assumed to achieve 95% capture.
Felman Production
Furnace 2, 5, 7 Furnace
Fugitives
The furnace primary capture is assumed to increase from 98% at baseline
to 99%, and installation of secondary capture of 75% for a total capture
of 99.75%. Captured primary emissions routed to their respective
furnace baghouse and captured secondary emissions routed to a new
secondary baghouse.
Furnace 2, 5, 7 Tapping
Fugitives
The tapping primary capture is assumed to increase from 70% at baseline
to 80%, and installation of secondary capture of 75% for a total capture
of 95%. Captured primary emissions routed to their respective furnace
baghouse and captured secondary emissions routed to a new secondary
baghouse.
Furnace 2, 5, 7 Casting
Fugitives
The casting primary capture is assumed to increase from 40% at baseline
to 80%, and installation of secondary capture of 75% for a total capture
of 95%. Captured primary emissions routed to their respective furnace
baghouse and captured secondary emissions routed to a new secondary
baghouse.
Crushing and Sizing Fugitives
The crushing and sizing primary capture is to be 85%, and installation of
secondary capture of 75% for a total capture of 96%. Captured primary
emissions routed to the crushing and sizing baghouse and captured
secondary emissions routed to a new secondary baghouse.
40
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DRAFT FOR INTERNAL REVIEW
Table 5-6. Summary of the Enhanced Capture Ferroalloys Production Facility Annual HAP Emissions (Tons/year)
Unit Description
Arsenic
Cadmium
Chromium
(III)
Chromium
(VI)
Lead
Manganese
Mercury
Nickel
Hydrogen
Chloride
Total PAH
Form-
aldehyde
Total
CDD/CDF
Total PCBs
Total HAP
Eramet Marietta
Furnace #1 Baghouse Outlet
0.000565
0.00272
0.00169
0.000305
0.0364
2.11
0.00926
0.00490
5.91
3.46
1.28
3.14E-07
3.27E-06
12.8
Furnace #1 Fugitives
0.000374
0.00217
0.000670
0.000194
0.0208
1.25
0.00172
0.00296
0.620
2.58
0.317
3.14E-08
4.88E-07
4.79
Furnace #1 Casting Baghouse
0.0000113
0.0000657
0.0000203
0.00000586
0.000630
0.0843
0.000121
0.0000897
0.939
0.121
0.167
0.00E+00
0.00E+00
1.31
Furnace #1 Tapping Fugitives
0.000204
0.00118
0.000365
0.000105
0.0113
1.52
0.0000512
0.00162
0.338
0.0872
0.0602
3.44E-08
3.38E-07
2.02
Furnace #12 Scrubber Outlet
0.00324
0.0260
0.00145
0.00110
0.0973
6.33
0.0278
0.00215
0.501
0.515
0.133
7.80E-08
3.34E-07
7.63
Furnace #12 Fugitives
0.000662
0.00531
0.000297
0.000225
0.0198
1.29
0.00189
0.000439
0.102
0.105
0.0272
1.59E-08
6.81E-08
1.55
Furnace #12 Tapping Fugitives
0.000298
0.00239
0.000134
0.000101
0.00894
1.46
0.0000493
0.000198
0.0461
0.0872
0.0602
3.44E-08
3.38E-07
1.66
MOR Process Baghouse Outlet
0.00123
0.00216
0.00476
0.0000917
0.00104
1.12
0.00232
0.00164
0.257
0.238
0.286
NM
NM
1.92
MOR Process Fugitives
0.000405
0.000713
0.00157
0.0000302
0.000344
0.435
0.0000147
0.000541
0.0137
0.0250
0.0173
NM
NM
0.495
Furnace #1 Casting
0.0000298
0.000173
0.0000534
0.0000154
0.00166
0.222
0.00000749
0.000236
0.0494
0.0127
0.00879
NM
NM
0.295
Furnace #12 Casting
0.0000436
0.000349
0.0000195
0.0000148
0.00131
0.213
0.00000720
0.0000289
0.00673
0.0123
0.00846
NM
NM
0.242
MOR casting
0.000405
0.000713
0.00157
0.0000302
0.000344
0.435
0.0000147
0.000541
0.0137
0.0250
0.0173
NM
NM
0.495
C2P Crushing/Sizing Baghouse#l
0.000133
NM
0.00130
0.000103
0.000141
1.49
NM
0.00151
NM
NM
NM
NM
NM
1.49
C2P Crushing/Sizing Baghouse#2
0.000133
NM
0.00130
0.000103
0.000141
1.49
NM
0.00151
NM
NM
NM
NM
NM
1.49
C2P Crushing/Sizing Baghouse#3
0.000133
NM
0.00130
0.000103
0.000141
1.49
NM
0.00151
NM
NM
NM
NM
NM
1.49
C2P crushing and sizing fugitives
0.000567
NM
0.00555
0.000442
0.000603
6.35
NM
0.00646
NM
NM
NM
NM
NM
6.37
Furnace #12 Tap/Cast Baghouse
0.0000940
0.000755
0.0000422
0.0000320
0.00282
0.460
0.049729
0.0000623
0.727
0.683
0.943
2.24E-07
2.19E-06
2.87
MOR Secondary Baghouse
0.0000486
0.0000856
0.000188
0.00000363
0.0000413
0.0522
0.0000749
0.0000649
0.0824
0.0750
0.104
NM
NM
0.314
Fugitive Emissions Total
0.00299
0.0130
0.0102
0.00116
0.0652
13.2
0.00375
0.0130
1.19
2.94
0.516
1.16E-07
1.23E-06
17.9
Eramet Marietta Total
0.00858
0.0448
0.0223
0.00301
0.204
27.8
0.0930
0.0265
9.61
8.03
3.43
7.32E-07
7.02E-06
49.2
Felman Production
Furnace No. 2 Baghouse Outlet
0.00283
0.000318
0.00576
0.00159
0.184
1.30
0.0105
0.0811
7.31
0.529
0.634
5.35E-07
4.38E-05
10.1
Furnace No. 2 Fugitives
0.000325
0.0000365
0.000662
0.000182
0.0211
0.146
0.0000285
0.00931
0.409
0.0401
0.0388
4.83E-08
4.14E-06
0.666
Furnace No. 2 Tapping Fugitives
0.000548
0.0000616
0.00112
0.000307
0.0357
0.661
0.0000152
0.0157
0.690
0.0678
0.0468
2.68E-08
2.62E-07
1.52
Furnace No. 5 Baghouse Outlet
0.000721
0.000178
0.00902
0.00143
0.0176
0.789
0.00224
0.00469
0.636
0.450
0.528
8.05E-07
6.00E-05
2.44
Furnace No. 5 Fugitives
0.0000833
0.0000206
0.00104
0.000166
0.00203
0.0893
0.00000605
0.000541
0.0412
0.0377
0.0363
7.53E-08
5.69E-06
0.208
Furnace No. 5 Tapping Fugitives
0.0000765
0.0000189
0.000957
0.000152
0.00187
0.327
0.00000750
0.000497
0.0379
0.0335
0.0231
1.32E-08
1.30E-07
0.426
Furnace No. 7 Baghouse Outlet
0.00118
0.000292
0.0148
0.00235
0.0289
2.48
0.00453
0.00769
1.44
0.385
0.971
8.05E-07
6.00E-05
5.34
Furnace No. 7 Fugitives
0.000137
0.0000338
0.00171
0.000273
0.00335
0.286
0.0000123
0.000891
0.101
0.0316
0.0717
7.53E-08
5.69E-06
0.497
Furnace No. 7 Tapping Fugitives
0.0000765
0.0000189
0.000957
0.000152
0.00187
0.327
0.00000750
0.000497
0.0561
0.0335
0.0231
1.32E-08
1.30E-07
0.444
Furnace No. 2 Casting
0.0000801
0.00000900
0.000163
0.0000449
0.00521
0.0967
0.00000221
0.00229
0.101
0.00990
0.00683
NM
NM
0.222
Furnace No. 5 Casting
0.0000112
0.00000276
0.000140
0.0000222
0.000273
0.0479
0.00000110
0.0000727
0.00554
0.00490
0.00338
NM
NM
0.0622
Furnace No. 7 Casting
0.0000112
0.00000276
0.000140
0.0000222
0.000273
0.0479
0.00000110
0.0000727
0.00821
0.00490
0.00338
NM
NM
0.0649
Crushing/Screening System #1
0
NM
0
0
0
0
NM
0
NM
NM
NM
NM
NM
0.00
Crushing/Screening System #2
0
NM
0.000161
0.0000444
0.000463
0.210
NM
0
NM
NM
NM
NM
NM
0.211
Crushing/Screening #1 Fugitives
0
NM
0
0
0
0
NM
0
NM
NM
NM
NM
NM
0.00
Crushing/Screening#2 Fugitives
0
NM
0.000897
0.000247
0.00258
0.813
NM
0
NM
NM
NM
NM
NM
0.817
Secondary Crushing Baghouse
0
NM
0.0000538
0.0000148
0.000155
0.0488
NM
0
NM
NM
NM
NM
NM
0.0490
Secondary Furnace Baghouse
0.0000809
0.0000123
0.000414
0.0000793
0.00430
0.122
0.000208
0.00179
4.35
0.396
0.760
3.78E-07
2.41E-05
5.63
Fugitive Emissions Total
0.00135
0.000205
0.00779
0.00157
0.0742
2.84
0.0000815
0.0299
1.45
0.264
0.253
2.52E-07
1.60E-05
4.93
Felman Production Total
0.00616
0.00101
0.0380
0.00708
0.310
7.80
0.0175
0.125
15.2
2.02
3.15
2.78E-06
2.04E-04
28.7
41
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DRAFT FOR INTERNAL REVIEW
In addition to the changes to the fugitive capture efficiencies for enhanced capture, Eramet was also
assumed to control mercury emissions from both Furnaces 1 and 12. The mercury emissions are
generated during the smelting process from the manganese ore used to produce FeMn and SiMn in
both of the furnaces, although there may be trace amounts in the coke or coal used in the smelting
process. The production of FeMn generates considerably more mercury emissions than SiMn
production, and is considered to be a significant source of mercury emissions. Since FeMn is
produced in each of the furnaces at Eramet, activated carbon injection (ACI) was assumed to be
retrofit prior to the control device. The ACI control technology was assumed to achieve 70 percent
mercury reduction on Furnace 12 (currently equipped with a venturi scrubber) and 90 percent
reduction on Furnace 1 (currently equipped with a fabric filter). Felman produces only SiMn in
each of their three furnaces and was not found to be a significant source of mercury emissions based
on the test data results. Therefore, no mercury controls were assumed to be retrofit on the furnace
outlets.
6.0 MODELING DATABASE
Section 112(f)(2) of the Clean Air Act (CAA) directs EPA to assess the risk remaining (residual
risk) after the application of maximum achievable control technology (MACT) standards under
section 112(d). EPA is to promulgate more stringent standards for a category or subcategory of
sources subject to MACT standards under section 112(d) if promulgation of such standards is
necessary to protect public health with an ample margin of safety or to prevent (taking into
consideration various factors) adverse environmental effects.
In an effort to streamline the process of making residual risk decisions, EPA plans to address
residual risk and perform a technology review simultaneously for multiple source categories. For
this source category, the first part of this approach is to compile and review facility-specific data
collected by EPA, and to conduct preliminary risk assessments. The risk assessment will include
both chronic and acute inhalation risks. The chronic risks are based on long-term, annual average
emissions, while the acute risks are based on short-term hourly emissions and account for the
maximum potential short term emission rates for the industry.
6.1 Chronic Modeling Database
The chronic modeling database was assembled using the annual pollutant emissions calculated for
processes that were tested (e.g., furnace control device outlet, MOR baghouse outlet, crushing and
sizing baghouse outlet), and from AP-42 EFs for fugitive sources that were not tested (e.g., furnace
tapping, MOR pouring, crushing and sizing) at each of the ferroalloy facilities. The HAP emissions
and the source IDs were then assembled into a database that included other facility descriptors and
emission location parameters (i.e., latitude, longitude, emission release height, emission velocity,
emission temperature, release length and width). Negative pressure baghouse and scrubber
42
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DRAFT FOR INTERNAL REVIEW
emission outlets were set up as point sources in the database. Fugitive and positive pressure
baghouse outlets were set up as area sources.
6.2 Acute Modeling Factors
Rather than developing a separate acute modeling database, acute multipliers were developed for
the process emission points. Processes that operate continually, like the furnace control device
outlets, furnace fugitive emissions, and secondary fugitive capture devices, are considered as
steady-state operations. Estimated yearly emissions from steady-state emissions points are divided
by 8760 to calculate estimated hourly emissions. Therefore these processes were given an acute
multiplier of 1.
Other emission sources such as, tapping fugitives, MOR fugitives, and casting fugitives are the
operations that might conceivably result in an emissions spike. These emission points are assumed
to operate 25 percent of the time (30 minutes out of each two hour period.) This is a conservative
estimate, because it is very unlikely that all furnaces would ever be tapped simultaneously, and it is
impossible for tapping and casting from a given furnace to occur simultaneously since casting of
the molten metal must occur after it is tapped. This methodology is also likely to overestimate the
operating frequency of product sizing operations. Following this reasoning, estimated yearly
emissions from intermittent emissions points are divided by 2190 to calculate estimated hourly
emissions. Therefore these processes were given an acute multiplier of 4. A table of the acute
multiplier for each of the emissions sources is provided in Table 6-1.
6.3 MACT Allowables
The modeling emissions calculated from the ICR test data represent an estimate of the mass
emissions actually emitted during the specified annual time period. These "actual" emission levels
are often lower than the emission levels that a facility might be allowed to emit and still comply
with the MACT standards. The emissions level allowed to be emitted by the MACT standards is
referred to as the "MACT-allowable" emissions level. This represents the highest emissions level
that could be emitted by the facility without violating the MACT standards. The assessment of
these risks at the MACT-allowable level reflects the maximum level sources could emit and still
comply with national emission standards. For process sources, the NESHAP specifies numerical
emissions limits for particulate matter (as a surrogate for non-mercury (or particulate) metal HAP)
from the electric (submerged) arc furnaces (including smelting and tapping emissions), with the
specific limits depending on furnace type, size, and product being made. The NESHAP also
specifies a numerical concentration limit for PM (as a surrogate for non-mercury (or particulate)
metal HAP) for the crushing and screening process.
43
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DRAFT FOR INTERNAL REVIEW
Table 6-1. Listing of Acute Multipliers for Ferroalloy Emission Points
Facility
Process Description
FacilitylD
Source ID
Acute Multiplier
Era met
Furnace #1 casting
39167NEI11660
M CA0001
4
Era met
Furnace #12 casting
39167NEI11660
M CA0002
4
Era met
MOR casting
39167NEI11660
M CA0003
4
Era met
Electric Arc Furnace#l Fugitives
39167NEI11660
M OE0004
1
Era met
Electric Arc Furnace#12 Fugitives
39167NEI11660
M OE0005
1
Era met
Electric Arc Furnace#l Tapping Fugitives
39167NEI11660
M OE0006
4
Era met
Electric Arc Furnace#12 Tapping Fugitives
39167NEI11660
M OE0007
4
Era met
C2P crushing and sizing fugitives
39167NEI11660
M PC0007
4
Era met
MOR Process Fugitives
39167NEI11660
M SE0006
4
Era met
MOR Process Secondary Baghouse
39167NEI11660
M BV0001
1
Era met
Electric Arc Furnace#l Building Baghouse
39167NEI11660
M BV0101
1
Era met
Electric Arc Furnace#12 Building Baghouse
39167NEI11660
M BV1201
1
Era met
Electric Arc Furnace#l Baghouse Outlet
39167NEI11660
M OE0008
1
Era met
Electric Arc Furnace#12 Scrubber Outlet
39167NEI11660
M OE0009
1
Era met
Electric Arc Furnace#l Tapping Baghouse
39167NEI11660
M OE0017
1
Era met
Casting#12 Baghouse
39167NEI11660
M OE0019
1
Era met
C2P Crushing/Sizing Baghouse #1
39167NEI11660
M PC0014
4
Era met
C2P Crushing/Sizing Baghouse #2
39167NEI11660
M PC0015
4
Era met
C2P Crushing/Sizing Baghouse #3
39167NEI11660
M PC0016
4
Era met
MOR Process Baghouse Outlet
39167NEI11660
M SE0013
1
Felman
ELECTRIC ARC FURNACE NO. 2 CASTING FUGITIVES
54053 NEIWV053 FELMAN
M CA0001
4
Felman
ELECTRIC ARC FURNACE NO. 5 CASTING FUGITIVES
54053 NEIWV053 FELMAN
M CA0002
4
Felman
ELECTRIC ARC FURNACE NO. 7 CASTING FUGITIVES
54053 NEIWV053 FELMAN
M CA0003
4
Felman
ELECTRIC ARC FURNACE NO. 2
54053 NEIWV053 FELMAN
M OE0008
1
Felman
ELECTRIC ARC FURNACE NO. 2 FUGITIVES
54053 NEIWV053 FELMAN
M OEOOll
1
Felman
ELECTRIC ARC FURNACE NO. 5
54053 NEIWV053 FELMAN
M OE0012
1
Felman
ELECTRIC ARC FURNACE NO. 5 FUGITIVES
54053 NEIWV053 FELMAN
M OE0013
1
Felman
ELECTRIC ARC FURNACE NO. 7
54053 NEIWV053 FELMAN
M OE0014
1
Felman
ELECTRIC ARC FURNACE NO. 7 FUGITIVES
54053 NEIWV053 FELMAN
M OE0015
1
Felman
ELECTRIC ARC FURNACE NO. 2 TAPPING FUGITIVES
54053 NEIWV053 FELMAN
M OE0016
4
Felman
ELECTRIC ARC FURNACE NO. 5 TAPPING FUGITIVES
54053 NEIWV053 FELMAN
M OE0017
4
Felman
ELECTRIC ARC FURNACE NO. 7 TAPPING FUGITIVES
54053 NEIWV053 FELMAN
M OE0018
4
Felman
CRUSHING AND SCREENING SYSTEM #1 FUGITIVES
54053 NEIWV053 FELMAN
M PC0007
4
Felman
CRUSHING AND SCREENING SYSTEM #2 FUGITIVES
54053 NEIWV053 FELMAN
M PC0009
4
Felman
BUILDING EVACUATION BAGHOUSE
54053 NEIWV053 FELMAN
M BV0101
1
Felman
SECONDARY CRUSHING 1 BAGHOUSE
54053 NEIWV053 FELMAN
M BV0202
1
Felman
SECONDARY CRUSHING 2 BAGHOUSE
54053 NEIWV053 FELMAN
M BV0303
1
Felman
SECONDARY FURNACE BAGHOUSE
54053 NEIWV053 FELMAN
M BV0404
1
Felman
CRUSHING AND SCREENING SYSTEM #1
54053 NEIWV053 FELMAN
M PC0006
4
Felman
CRUSHING AND SCREENING SYSTEM #2
54053 NEIWV053 FELMAN
M PC0008
4
44
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DRAFT FOR INTERNAL REVIEW
To estimate emissions at the MACT-allowable level, a ratio of MACT-allowable to actual
emissions for each of the process source types that are regulated under the existing NESHAP (i.e.,
furnace control device outlet, MORbaghouse outlet, crushing and screening outlet). This ratio is
based on the maximum emission limits allowed by the MACT standards compared to the reported
actual emissions. These ratios for each emission point type at the facilities in this source category
were used to estimate the maximum potential risk estimates that could occur assuming emissions
are continuously emitted at the maximum allowed emissions level. Mercury is not included with
metal HAP because the emissions are not currently regulated by the MACT rule and the emissions
are primarily in gaseous elemental form. A table of the MACT-allowable ratios for metal HAP is
presented in Table 6-2. An example of the MACT-allowable calculation for the Furnace 1 baghouse
is shown below:
in o _ (35"9 * 0-50) + (29"8 * 0-50)
19-8 ~ (1.79 * 0.50) + (1.52 * 0.50)
where;
0.50 = the assumed fraction of time the facility is producing FeMn or SiMn,
35.9 = the MACT allowable PM emissions rate in pounds per hour when producing SiMn,
29.8 = the MACT allowable PM emission rate in pounds per hour when producing FeMn,
1.79 = the actual average PM emissions rate in pounds per hour based on test data collected during
the production of SiMn, and
1.52 = the actual average PM emissions rate in pounds per hour based on test data collected during
the production of FeMn.
As noted in Section 3.0, Eramet has a third furnace (Furnace 18) that is idle because of damage to
the furnace and is unlikely to be repaired and operated by the facility in the near future. However,
the furnace is still included in their operating permit; therefore, the emissions will be included in the
allowable emissions file calculations. This is consistent with RTR policy/procedures, which states
that if a process unit and/or facility has not been operating for an extended period of time (years),
and is not currently operating, but the unit or facility could theoretically reopen and start again in
the future (e.g., still in permit), the emissions should not be included in the calculation of "actual"
emissions, but should be included in the estimated potential emissions in the "allowables" file.
45
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DRAFT FOR INTERNAL REVIEW
Table 6-2. Summary of MACT-Allowable Ratios for Eramet and Felman
Emission Source
Source ID
Actual PM
Emissons (Ib/hr)
MACT-Allowable
PM Emissions
(Ib/hr)
Actual PM Cone,
(gr/dscf)
MACT-Allowable
PM Cone,
(gr/dscf)
MACT-Allowable/
Actual Ratio
Comment
Eramet (Facility ID: 39167NEl11660)
Furnace 1 Baghouse Outlet1
MOE0008
1.79/1.52
35.9/29.8
19.8
63.1652(b)(3)/
63.1652(b)(2)
Furnace 12 Scrubber Outlet1
MOE0009
12.06/7.16
35.9/29.8
3.57
63.1652(b)(3)/
63.1652(b)(2)
Furnace 18 Scrubber Outlet2
MOE0010
7.61
6.9
0.9
63.1652(a)(1)
MOR Baghouse Outlet
MSE0006
0.0004
0.03
81.1
63.1652(d)
C2P BH1 Outlet
MPC0014
0.0033
0.03
9.1
63.1652(e)(2)
C2P BH2 Outlet
MPC0015
0.0033
0.03
9.1
63.1652(e)(2)
C2P BH3 Outlet
MPC0016
0.0033
0.03
9.1
63.1652(e)(2)
Felman (Facility ID: 54053NEIWV053FELMAN)
Furnace 2 BH Outlet
MOE0008
3.32
27.2
8.2
63.1652(b)(4)
Furnace 5 BH Outlet
MOE0012
3.01
27.2
9.0
63.1652(b)(4)
Furnace 7 BH Outlet
MOE0014
4.96
27.2
5.5
63.1652(b)(4)
Sizing #2 BH
MPC0006
0.0011
0.03
27.7
63.1652(e)(2)
1 The actual and MACTallowable emissions for Furnace 1 and Furnace 12 are shown as SiMn and FeMn respectively. The MACT/Allowable ratio
assumes that the furnace produces SiMn, 50% of the time and FeMn, 50% of the time.
2 Furnace 18 is a 13.5 MW semi-sealed furnace that is assumed to have 50/50 production of SiMn and FeMn. The MACT allowable limit assumes that Furnace 18
would be a new source and would have to meet the new source limit of 0.51 Ib/hr/MW (13.5 MW * 0.51 Ib/hr/MW = 6.9 Ib/hr).
46
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DRAFT FOR INTERNAL REVIEW
Table 6-3. Summary of Assigned Emission Process Groups for the Ferroalloys SCC
SCC
SCC1 DESCRIPTION
SCC3 DESCRIPTION
SCC6 DESCRIPTION
SCC8 DESCRIPTION
Emission Process Group
30300601
Industrial Processes
Primary Metal Production
Ferroalloy, Open Furnace
50% FeSi: Electric Smelting Furnace
Open FAF
30300602
Industrial Processes
Primary Metal Production
Ferroalloy, Open Furnace
75% FeSi: Electric Smelting Furnace
Open EAF
30300603
Industrial Processes
Primary Metal Production
Ferroalloy, Open Furnace
90% FeSi: Electric Smelting Furnace
Open EAF
30300604
Industrial Processes
Primary Metal Production
Ferroalloy, Open Furnace
Silicon Metal: Electric Smelting Furnace
Open EAF
30300605
Industrial Processes
Primary Metal Production
Ferroalloy, Open Furnace
Silicomanaganese: Electric Smelting Furnace
Open EAF
30300606
Industrial Processes
Primary Metal Production
Ferroalloy, Open Furnace
80% Ferromanganese
Open EAF
30300607
Industrial Processes
Primary Metal Production
Ferroalloy, Open Furnace
80% Ferrochromium
Open EAF
30300608
Industrial Processes
Primary Metal Production
Ferroalloy, Open Furnace
Raw Material Unloading
Fugitive Dust Sources
30300609
Industrial Processes
Primary Metal Production
Ferroalloy, Open Furnace
Raw Material Crushing
Raw Material Crushing & Screening Operations
30300610
Industrial Processes
Primary Metal Production
Ferroalloy, Open Furnace
Ore Screening
Raw Material Crushing & Screening Operations
30300611
Industrial Processes
Primary Metal Production
Ferroalloy, Open Furnace
Ore Dryer
Raw Material Crushing & Screening Operations
30300613
Industrial Processes
Primary Metal Production
Ferroalloy, Open Furnace
Raw Material Storage
Fugitive Dust Sources
30300614
Industrial Processes
Primary Metal Production
Ferroalloy, Open Furnace
Ra w Materia 1 Tra nsfer
Fugitive Dust Sources
30300615
Industrial Processes
Primary Metal Production
Ferroalloy, Open Furnace
Ferromanganese: Blast Furnace
Other
30300616
Industrial Processes
Primary Metal Production
Ferroalloy, Open Furnace
Ferrosilicon: Blast Furnace
Other
30300617
Industrial Processes
Primary Metal Production
Ferroalloy, Open Furnace
Cast House
Casting
30300618
Industrial Processes
Primary Metal Production
Ferroalloy, Open Furnace
Mix House/Weighing
Fugitive Dust Sources
30300619
Industrial Processes
Primary Metal Production
Ferroalloy, Open Furnace
Raw Material Charging
Fugitive Dust Sources
30300620
Industrial Processes
Primary Metal Production
Ferroalloy, Open Furnace
Tapping
Tapping Operation
30300621
Industrial Processes
Primary Metal Production
Ferroalloy, Open Furnace
Casting
Casting
30300622
Industrial Processes
Primary Metal Production
Ferroalloy, Open Furnace
Cooling
Fugitive Dust Sources
30300623
Industrial Processes
Primary Metal Production
Ferroalloy, Open Furnace
Product Crushing
Product Crushing & Screening Operations
30300624
Industrial Processes
Primary Metal Production
Ferroalloy, Open Furnace
Product Storage
Fugitive Dust Sources
30300625
Industrial Processes
Primary Metal Production
Ferroalloy, Open Furnace
Product Loading
Fugitive Dust Sources
30300651
Industrial Processes
Primary Metal Production
Ferroalloy, Open Furnace
Sealed Furnace: Ferromanganese: Electric Arc Furnace
Other
30300652
Industrial Processes
Primary Metal Production
Ferroalloy, Open Furnace
Sealed Furnace: Ferrochromium: Electric Arc Furnace
Other
30300653
Industrial Processes
Primary Metal Production
Ferroalloy, Open Furnace
Sealed Furnace: Ferrochromium Silica: Electric Arc Furnace
Other
30300654
Industrial Processes
Primary Metal Production
Ferroalloy, Open Furnace
Sealed Furnace: FAF - Other Alloys: Specify in Comment
Other
30300699
Industrial Processes
Primary Metal Production
Ferroalloy, Open Furnace
Other Not Classified
Other
30300701
Industrial Processes
Primary Metal Production
Ferroalloy, Semi-covered Furnace
Ferromanganese: Electric Arc Furnace
Semi-closed EAF
30300702
Industrial Processes
Primary Metal Production
Ferroalloy, Semi-covered Furnace
Electric Arc Furnace: Other Alloys/Specify
Open EAF
30300703
Industrial Processes
Primary Metal Production
Ferroalloy, Semi-covered Furnace
Ferrochromium: Electric Arc Furnace
Semi-closed EAF
30300704
Industrial Processes
Primary Metal Production
Ferroalloy, Semi-covered Furnace
Ferrochromium Silicon: Electric Arc Furnace
Semi-closed EAF
Note: Highlighted SCCs are included in the Ferroalloys modeling database.
47
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DRAFT FOR INTERNAL REVIEW
6.4 Emission Process Groups
The emission sources from the test data were divided into emission process groups using the
Standard Classification Codes (SCC) supplied by the facility in the ICR. The emission process
groups provided in the SCC list for the Ferroalloys category were; open EAF, fugitive dust sources,
raw material crushing and screening operations, casting, and other. T able 6-3 presents a summary
of the SCC and the assigned emission process group. As shown in Table 6-3, the emission process
groups assigned in the modeling database for the emission sources were: Open EAF, Raw Material
Crushing and Screening Operations, Fugitive Dust Sources, and Casting.
6.5 Latitude/Longitude QA/QC
The latitude and longitude coordinates provided by the facilities were reviewed to ensure that the
coordinates matched the location of the emission points. This was done by plotting each emission
point on a map of the facility using Google Earth. The plotted emission points were then evaluated
in comparison to building layout diagrams provided by the facilities to determine if the emission
point matched the expected location of the emission point. If it was determined that the emission
point did not match the expected location on the diagrams, the point was moved to the expected
location and new latitude and longitude coordinates were determined. The map and the emission
point coordinates were sent to the facilities for confirmation of the locations in the December 21,
2013 ICR submittal.
6.6 Stack and Fugitive Parameters QA/QC
The stack and fugitive emission parameters provided by the facility in the ICR were verified to
ensure they were correct. The volumetric flow rates for the point sources were checked by
calculating the volumetric flow rate using the velocity and duct dimensions. The calculated flow
rates were compared with the volumetric flow rates provided in the test reports. If the flow rate
difference exceeded 30 percent, engineering judgments were made to revise the velocity or duct
dimension data. Fugitive parameters were assigned using the Google Earth map for each of the
facilities and estimating the length, width, and angle of the fugitive emissions. These parameters
were sent to the facilities for confirmation of the values that were determined in the December 21,
2013 ICR submittal.
48
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Appendix 2
Technical Support Document for HEM-AERMOD Modeling
-------
-------
Modeling for the Residual Risk and Technology Review
Using the Human Exposure Model 3 - AERMOD Version
Updated 01/08/2014
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
-------
Disclaimer
The research described in this document has been funded by the United States
Environmental Protection Agency contracts 68-D-01-076, EP-D-06-119, 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.
111
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1. Introduction
Contents
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 9
2.3.3 Calculation of Population Exposures and Incidence 10
2.3.4 Model Outputs 11
2.4 Data Libraries Used in HEM-3 12
2.4.1 Chemical Health Effects Information 12
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 19
3.4.1 Urban or Rural Dispersion Characteristics 19
iv
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3.4.2 Deposition and Plume Depletion 19
3.4.3 Cutoff Distance for Modeling of Individual Blocks 20
3.4.4 Facility Boundary Assumptions 20
3.5 Modeling of Multiple Facilities 22
4. Quality Assurance 23
4.1 Engineering Review 23
4.2 Geographic Pre-Modeling Checks 23
4.3 Geographic Post-Modeling Checks 24
5. Uncertainties 26
6. References 32
Figures
Figure 3-1. AERMOD Meteorological Stations 18
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 21
Table 5-1. Summary of General Uncertainties Associated with Risk and Technology Review
Risk Assessments 28
v
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1. Introduction
This document describes the general 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). It is important to note
that risk characterizations of individual source categories under the RTR program may not
follow every item/approach noted in this document. The reader is referred to the main body of
the risk assessment document for more details on source category specific approaches that may
have been included in the analysis.
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.
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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)1'2 is 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 Excel™ 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
indices (TOSHI) for HAPs and other toxic air pollutants, and estimates the numbers of people
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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 Excel™ 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.
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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 Excel™ 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.
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
Table 2-1. Parameters Used to Delineate the
Modeling Domain in HEM-3
Typical
Parameter
\ a 1 nc
Modeling domain size - maximum
radial distance to be modeled from
50 km
facility center
Cutoff distance for modeling of
individual blocksa
3,000 m
Overlap distance - where receptors
are considered on facility property21
30 m
Polar receptor network:
Distance to the innermost ringb
>100 m
Number of concentric rings
13
Number of radial directions
16
a Measured from each stack at the facility, and from the
edges of each area or volume source
b Generally model-calculated to encompass all emission
sources but not less than 100 meters from the facility
center
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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
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.
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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 HEMS
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;
• 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.
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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.
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).
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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:
For TOSHI:
where:
CRX =
Sj =
CRm =
DFy =
CF =
Zk =
Ei,k =
UREk =
TOSHIx =
TOSHIy =
RfCk =
CRX = ly CR, ,
CRy = DFy x CF x Xk [E, |x x UREk]
TOSHIx = ly TOSHIy
TOSHIy = DFy x CF x Xk [Ei.k / RfCJ
total cancer risk at a given receptor (probability for one person)
the sum over all sources i and pollutant types j (particulate or gas)
cancer risk at the given receptor for source i and pollutant type j
dilution factor [(|ig/m3) / (g/sec)] at the given receptor for source i and pollutant
typej
conversion factor, 0.02877 [(g/sec) / (ton/year)]
sum over all pollutants k within pollutant type j (particulate or gas)
emissions of pollutant k from source i and in pollutant type j
cancer unit risk factor for pollutant k
total target-organ-specific hazard index at a given receptor
target-organ-specific hazard index at the given receptor for source i and pollutant
typej
non-cancer health effect reference concentration for pollutant k
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The above equations are equivalent to the following simpler equations:
CRX = I,.k AC,.k x UREk
HIX = Xi,k ACi,k / RCk
where:
AC,.k = ambient concentration (|ig/m3) for pollutant k at the given receptor. This is the same
as [E, |x x DFi.j x 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:
ACx,k = Zi ACi,k
AC1,k = EljkxDF1Jx CF
Short term concentrations:
ACT = ZiACUt
AC* = E.kxDFy x CF x M
where:
ACx,k = total estimated ambient concentration for pollutant k at a given receptor
Xi = the sum over all sources i (|ig/m3)
AC,.k = estimated ambient concentration of pollutant k at the given receptor as a result of
emissions from source i (|ig/m3)
M = ratio between the estimated maximum short term emission rate and the long term
average emission rate (dimensionless)
2.3.2 Interpolation of Impacts at Outer Census Blocks
For Census blocks outside of the 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
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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= lAl,r+ (I.A2,r ~~ lAl,r) x (a — Al) / (A2 — Al)
IAi,r= exp{(ln (Iai,ri) + [(In (Iai,r2) - In (Iai,ri)] x [(In r) - ln(Rl)] / [ln(R2) - ln(Rl)]}
1a2,v= exp{(ln (Ia2,ri) + [(In (Ia2,r2) - In (Ia2,ri)] x [(In r) - ln(Rl)] / [ln(R2) - ln(Rl)]}
where:
Ia,r= the impact (cancer risk, hazard index, or concentration) at an angle, a, from north,
and radius, r, from the center of the modeling domain
a = the angle of the target receptor, from north
r = the radius of the target receptor, from the center of the modeling domain
Al = 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
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:
TCR = £m [CRm x Pm ] / LT
where:
TCR = the estimated total annual cancer risk, or incidence, (cancers/year) to the
population living within the modeling domain
Xm = 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
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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:
TCRy = Im [(Zk E,.k x UREk) x DF,.,m x CF / LT]
TCRi k = TCRij x E,.k x UREk / (Ik E,.k x UREk)
where:
TCRij = 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
Xn, = the sum over all Census blocks m within distance the modeling domain
Xk = the sum over all pollutant k, within pollutant type j
Eisk = emissions of pollutant k from source i (tons/year)
UREk = unit risk factor for pollutant k
DFj j ni = 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)]
TCRj.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
2.3.4Model Outputs
The following is a summary of the outputs produced by HEM-3. These are written to a
collection of files in Excel™ and dBase™ format (dbf).
• Maximum long term impacts at populated locations
o maximum lifetime individual cancer risk (MIR)
o maximum TO SHI for the following health effects
- respiratory system effects
- liver effects
- neurological system effects
- developmental effects
- reproductive system effects
- kidney effects
- ocular system effects
- endocrine system effects
- hematological system effects
- immunological system effects
- skeletal system effects
- spleen effects
- thyroid effects
- whole body effects
o locations of the maximum cancer risk and TOSHI
o Census block identification codes for the maximum cancer risk and TOSHI, and
number of people in the Census block
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o contributions of different chemicals and emission sources to the maximum risk and
TOSHI
• Maximum acute impacts
o maximum short term ambient concentration for each chemical
o threshold levels for acute health effects of each chemical (compared with the
maximum short term concentrations)
o locations of the maximum impacts for different chemicals (often polar receptors)
o Census block identification codes at the locations of maximum concentration, and
number of people in the block
o contribution of each emission source at the facility to the maximum short term
concentration of each chemical
• Outputs for all receptors
o maximum individual cancer risk and TOSHI (all target organs) for each Census
block and each user-specified discrete receptor (monitoring sites, etc.)
o maximum individual cancer risk and TOSHI (all target organs) for each polar grid
receptor
o estimated deposition (optional)
o 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
o estimated numbers of people exposed to different levels of lifetime individual
cancer risk (1 in a million, 1 in 100,000, etc.)
o estimated numbers of people exposed to different levels of TOSHI (1, 2, 10, etc.)
o total cancer risk, or incidence, in estimated cancer deaths per year, over the entire
modeling domain, and for each pollutant and source combination
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.4 The 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 (|ig/m3) in air (e.g., if the
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URE = 1.5 x 10"6 per (J,g/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 l.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
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 Land View® database For the
2000 Census7 and 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.9 The 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.
13
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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 rCi.).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.
14
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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 each 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.
Emissions data for the RTR assessments are compiled form a variety of data sources (i.e.,
the 2005 National Emissions Inventory (NEI)15 information data requests). Each source
category under the RTR program, in most cases, utilizes the latest best available data. 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
15
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the chemical name in order to link emissions to the appropriate dose-response factors. The model
will not process any pollutant that is not specifically listed in the chemical library. 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
NAT A. 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
16
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the preprocessing performed on the meteorological data used by AERMOD and includes a
detailed listing of the 824 meteorological station pairs.
17
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Figure 3-1. AERMQD Meteorological Stations
18
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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
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
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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).
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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
multiple source type-
specific factors are
also 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 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).
0 HEM-3 sets the innermost ring distance to be just outside the emission sources but not < 100 m.
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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.3 Thus, 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.
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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 Q A 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.
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
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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
o maximum individual cancer risk of over 1 in 10,000
o any maximum TO SHI above 10
• cases where no Census blocks are identified by the model within 3 km of the facility
HEM-3 produces a detailed Google Earth™ map of the modeled point and area emission
sources and surrounding receptors (including Census block centroids, polar receptors and user-
specified receptors) overlaying Google Earth™'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 pre-
modeling scrutiny and QA checks (as discussed in Section 4.2).
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
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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.
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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 long-
term exposures will be overstated for high-end estimates (as most individuals will not spend all
their time at their highly affected residences), but may understate the total population exposed
(as some individuals living outside the modeled area may regularly commute into the area for
work or school).
When considering long-term or lifetime exposures, it should be noted that relatively few
people in the United States reside in one place for their entire lives. For the purposes of this
assessment, cancer risk estimates are based on a lifetime exposure at the 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.
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
26
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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,
facility-specific 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 NAT A 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 individual-
specific 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.
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Table 5-1. Summary of General Uncertainties Associated with Risk and Technology Review Risk Assessments
Paramclcr
Assumption
I iH'c'rl;iin(\/V;iri;il)ili(\ Discussion
Polcnlial Direct ion ol° liias on
Risk I'lslimalcs
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 short-
term 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
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.
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Table 5-1. Summary of General Uncertainties Associated with Risk and Technology Review Risk Assessments
(continued)
Piii'iiiiKMor
Assumption
1 iH'c'rl;iin(\/V;iri;il)ili(\ Discussion
Polcnlhil Direct ion of lihis on
Risk I'lsliniiilcs
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.
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 short-term
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.
29
-------
Table 5-1. Summary of General Uncertainties Associated with Risk and Technology Review Risk Assessments
(continued)
Piii'iiiiKMor
Assumption
1 nccrl;iin(\/Y;iri;il)ili(\ Discussion
Polcnlhil Direct ion of lihis on
Risk llsliniiilcs
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.
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 organ-specific 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.
30
-------
Table 5-1. Summary of General Uncertainties Associated with Risk and Technology Review Risk Assessments
(continued)
Piii'iiiiKMor
Assumption
1 iH'c'rl;iin(\/V;iri;il)ili(\ Discussion
Polcnlhil Direct ion of lihis on
Risk I'lsliniiilcs
Surrogate dose- response
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 dose-response 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 category-
specific profiles applied.
There is the potential to
underestimate risks for
pollutants which are not
included in the assessment.
31
-------
6. References
1. EPA. 2004. AERMOD: Description of Model Formulation. EPA-454/R-03-004, U.S.
Environmental Protection Agency, Research Triangle Park, NC.
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/B-
03-001, U.S. Environmental Protection Agency, Research Triangle Park, NC.
www.epa. gov/scramOO 1 /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 J/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/ gloss 1 .html.
6. EPA. 2004. Air Toxics Risk Assessment Reference Library, EPA-453-K-04-001 A, U.S.
Environmental Protection Agency, Research Triangle Park, NC. April 2004.
www, epa. gov/ttn/ fera/risk_atra_vol 1. 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-Summarv_File_l/ 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/fsQ4000.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
32
-------
11. EPA. 2004. User's Guide for the AERMOD Terrain Preprocessor (AERMAP). EPA-
454/B-03-003, U.S. Environmental Protection Agency, Research Triangle Park, NC.
http://www.epa. gov/scramOO 1 /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/scramOO 1 /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/scramOO 1 /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/2005inventorv.html#inventorvdata
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
33
-------
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
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/fdsvs/pkg/FR-2012-03-27/pdf/2012-69Q3.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/20Q2nei 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/ scramOO 1 /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
34
-------
Appendix 3
Meteorological Data for HEM3 Modeling
-------
DRAFT
METEOROLOGICAL DATA PROCESSING
USING AERMET
FOR
HEM3
2
-------
Meteorological Data Processing using AERMET
For HEM3
October 18, 2013
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.
1
-------
Meteorological Data Processing using AERMET
For HEMS
October 18, 2013
Figure 1. Surface Stations
1
-------
Meteorological Data Processing using AERMET
For HEM3
October 18, 2013
Table 1. AERMET Processing Options
AERMET Options
Version
12345
ASOS Site
Yes
Surface Data Format
NCDC TD-3505 (ISHD)
Upper Air Data Format
FSL, all levels, tenths m/s
Wind Speed Threshold
0.5 m/s
Beta Option (U*)
Yes
AERMINUTE Options
Version
11325
Include 1 minute ASOS
Data
Yes, where available TD-6405
format
AERSURFACE Options
Version
13016
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 station pairs are presented in Appendix 3
1
-------
Appendix 1
ASOS Surface Stations used for AERMET (2011)
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
1 of 18
-------
Base
Elevation
Anemometer Ht
State
City
WBAN #
Latitude
Longitude
(M)
(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
111
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
2 of 18
-------
Base
Elevation
Anemometer Ht
State
City
WBAN #
Latitude
Longitude
(M)
(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
WATSON VILLE
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
3 of 18
-------
Base
Elevation
Anemometer Ht
State
City
WBAN #
Latitude
Longitude
(M)
(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
MAR IAN N A
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
4 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
5 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
6 of 18
-------
Base
Elevation
Anemometer Ht
State
City
WBAN #
Latitude
Longitude
(M)
(M)
IL
CHICAGO
94846
41.98611
-87.91417
202
10.05
IL
CHAMPAIGN/UR BAN A
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
7 of 18
-------
Base
Elevation
Anemometer Ht
State
City
WBAN #
Latitude
Longitude
(M)
(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
Ml
HOLLAND
04839
42.74611
-86.09667
207
10.05
Ml
ADRIAN
04847
41.86778
-84.07944
243
7.92
Ml
GAYLORD
04854
45.01333
-84.70139
406
10.05
Ml
BATTLE CREEK
14815
42.30750
-85.25111
283
10.05
Ml
DETROIT
14822
42.40917
-83.01000
190
10.05
Ml
FLINT
14826
42.96667
-83.74944
235
10.05
Ml
JACKSON
14833
42.25972
-84.45944
302
10.05
Ml
LANSING
14836
42.78028
-84.57889
262
10.05
Ml
MUSKEGON
14840
43.17111
-86.23667
191
10.05
8 of 18
-------
State
City
WBAN #
Latitude
Longitude
Base
Elevation
(M)
Anemometer Ht
(M)
Ml
PELLSTON
14841
45.57083
-84.79611
215
10.05
Ml
SAGINAW
14845
43.53306
-84.07972
201
10.05
Ml
SAULT STE MARIE
14847
46.46667
-84.36667
220
10.05
Ml
TRAVERSE CITY
14850
44.74083
-85.58250
186
10.05
Ml
DETROIT
14853
42.23667
-85.52611
216
10.05
Ml
HANCOCK
14858
47.16861
-88.50556
323
7.92
Ml
HOUGHTON LAKE
94814
44.36778
-84.69083
351
10.05
Ml
KALAMAZOO
94815
42.23472
-85.55194
262
10.05
Ml
PONTIAC
94817
42.66500
-83.41806
296
10.05
Ml
DETROIT
94847
42.21528
-83.34861
192
10.05
Ml
GRAND RAPTIDS
94860
42.88222
-85.52306
241
10.05
Ml
BENTON HARBOR
94871
42.12917
-86.42222
191
10.05
Ml
ANN ARBOR
94889
42.22300
-83.74400
253
10.05
Ml
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
9 of 18
-------
Base
Elevation
Anemometer Ht
State
City
WBAN #
Latitude
Longitude
(M)
(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
GASTON IA
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
10 of 18
-------
Base
Elevation
Anemometer Ht
State
City
WBAN #
Latitude
Longitude
(M)
(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
11 of 18
-------
Base
Elevation
Anemometer Ht
State
City
WBAN #
Latitude
Longitude
(M)
(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
12 of 18
-------
Base
Elevation
Anemometer Ht
State
City
WBAN #
Latitude
Longitude
(M)
(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
13 of 18
-------
Base
Elevation
Anemometer Ht
State
City
WBAN #
Latitude
Longitude
(M)
(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
WILLI AMSPORT
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
Rl
PROVIDENCE
14765
41.72194
-71.43250
16
10.05
Rl
NEWPORT
14787
41.53000
-71.28361
44
7.92
Rl
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
14 of 18
-------
Base
Elevation
Anemometer Ht
State
City
WBAN #
Latitude
Longitude
(M)
(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
BRISTOL/JOHNSON
TN
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
15 of 18
-------
Base
Elevation
Anemometer Ht
State
City
WBAN #
Latitude
Longitude
(M)
(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
16 of 18
-------
Base
Elevation
Anemometer Ht
State
City
WBAN #
Latitude
Longitude
(M)
(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
Wl
RHINELANDER
04803
45.63083
-89.46528
493
7.92
Wl
WISCONSIN RAPTIDS
04826
44.35917
-89.83694
309
7.92
Wl
FOND DU LAC
04840
43.77000
-88.48639
240
7.92
Wl
SHEBOYGAN
04841
43.76944
-87.85139
226
7.92
Wl
KENOSHA
04845
42.59500
-87.93806
223
7.92
Wl
MADISON
14837
43.14056
-89.34528
262
7.92
Wl
MILWAUKEE
14839
42.94667
-87.89694
206
7.92
Wl
WAUSAU
14897
44.92861
-89.62667
366
7.92
Wl
GREEN BAY
14898
44.51278
-88.12000
210
7.92
Wl
LA CROSSE
14920
43.87600
-91.25890
199
7.92
Wl
LONE ROCK
14921
43.21194
-90.18139
218
7.92
Wl
EAU CLAIRE
14991
44.86528
-91.48500
270
7.92
Wl
RACINE
94818
42.76111
-87.81361
203
5.79
Wl
OSHKOSH
94855
43.98444
-88.55694
246
10.05
Wl
ASHLAND
94929
46.54861
-90.91889
251
7.92
Wl
HAYWARD
94973
46.02611
-91.44417
367
10.05
Wl
MARSHFIELD
94985
44.63694
-90.18917
383
7.92
Wl
BOSCOBEL
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
17 of 18
-------
Base
Elevation
Anemometer Ht
State
City
WBAN #
Latitude
Longitude
(M)
(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
18 of 18
-------
Appendix 2
Upper Air Stations used for AERMET (2011)
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
NOMEAP
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 INTAP
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 INTAP
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
OMAHAA/ALLEY
94980
41.32000
-96.37000
MN
MINNEAPOLIS
94983
44.83000
-93.55000
ID
BOISE
24131
43.57000
-116.22000
1 of 2
-------
State
City
WBAN #
Latitude
Longitude
NV
ELKO
04105
40.87000
-115.73000
WY
RIVERTON
24061
43.06000
-108.47000
WA
SPOKANE INTNLAPT
04106
47.68000
-117.63000
UT
SALT LAKE CITY
24127
40.77000
-111.97000
IL
LINCOLN-LOGAN COUNTY AP
04833
40.15000
-89.33000
Wl
GREEN BAY
14898
44.48000
-88.13000
OH
WILMINGTON
13841
39.42000
-83.82000
Ml
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
Ml
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
2 of 2
-------
Appendix 3
Station Pairs used for AERMET (2011)
Surface
Upper Air
State
City
WBAN #
State
City
WBAN#
AK
ANNETTE
25308
AK
ANNETTE ISLAND
25308
AK
JUNEAU
25309
AK
YAKUTAT
25339
AK
HAINES
25323
AK
YAKUTAT
25339
AK
KETCHIKAN
25325
AK
ANNETTE ISLAND
25308
AK
PALMER
25331
AK
ANCHORAGE IAP/PT.
CAMPBE
26409
AK
SITKA
25333
AK
ANNETTE ISLAND
25308
AK
SKAGWAY
25335
AK
YAKUTAT
25339
AK
YAKUTAT
25339
AK
YAKUTAT
25339
AK
KALWOCK
25367
AK
ANNETTE ISLAND
25308
AK
KODIAK
25501
AK
KODIAK
25501
AK
KING SALMON
25503
AK
KING SALMON
25503
AK
ILIAMNA
25506
AK
KING SALMON
25503
AK
HOMER
25507
AK
ANCHORAGE IAP/PT.
CAMPBE
26409
AK
SELDOVIA
25516
AK
KODIAK
25501
AK
COLD BAY
25624
AK
COLD BAY
25624
AK
ST PAUL ISLAND
25713
AK
ST PAUL ISLAND
25713
AK
ANCHORAGE
26409
AK
ANCHORAGE IAP/PT.
CAMPBE
26409
AK
CORDOVA
26410
AK
ANCHORAGE IAP/PT.
CAMPBE
26409
AK
FAIRBANKS
26411
AK
FAIRBANKS
26411
AK
NORTHWAY
26412
AK
FAIRBANKS
26411
AK
DELTA JUNCTION/FT
GREELY
26415
AK
FAIRBANKS
26411
AK
EAGLE
26422
AK
FAIRBANKS
26411
AK
GULKANA
26425
AK
ANCHORAGE IAP/PT.
CAMPBE
26409
AK
NENANA
26435
AK
FAIRBANKS
26411
AK
SEWARD
26438
AK
ANCHORAGE IAP/PT.
CAMPBE
26409
AK
ANCHORAGE
26451
AK
ANCHORAGE IAP/PT.
CAMPBE
26409
AK
ANCHORAGE
26491
AK
ANCHORAGE IAP/PT.
CAMPBE
26409
AK
PORTAGE GLACIER
26492
AK
ANCHORAGE IAP/PT.
CAMPBE
26409
AK
KALTAG
26502
AK
MCGRATH
26510
AK
MC GRATH
26510
AK
MCGRATH
26510
AK
KENAI
26523
AK
ANCHORAGE IAP/PT.
CAMPBE
26409
AK
TALKEETNA
26528
AK
ANCHORAGE IAP/PT.
CAMPBE
26409
1 of 21
-------
Surface
Upper Air
State
City
WBAN #
State
City
WBAN#
AK
TANANA
26529
AK
FAIRBANKS
26411
AK
BETTLES
26533
AK
FAIRBANKS
26411
AK
KOTZEBUE
26616
AK
KOTZEBUE
26616
AK
NOME
26617
AK
NOME AP
26617
AK
KIVALINA
26642
AK
KOTZEBUE
26616
AK
DEADHORSE
27406
AK
POINT BARROW
27502
AK
BARROW
27502
AK
POINT BARROW
27502
AK
WAINWRIGHT
27503
AK
POINT BARROW
27502
AK
NUIQSUT
27515
AK
POINT BARROW
27502
AL
HUNTSVILLE
03856
AL
BIRMINGHAM (SHELBY
APT)
53823
AL
TROY
03878
AL
BIRMINGHAM (SHELBY
APT)
53823
AL
MOBILE
13838
LA
SLIDELL
53813
AL
DOTHAN
13839
FL
TALLAHASEE
93805
AL
ANNISTON
13871
AL
BIRMINGHAM (SHELBY
APT)
53823
AL
BIRMINGHAM
13876
AL
BIRMINGHAM (SHELBY
APT)
53823
AL
MOBILE
13894
LA
SLIDELL
53813
AL
MONTGOMERY
13895
AL
BIRMINGHAM (SHELBY
APT)
53823
AL
MUSCLE SHOALS
13896
AL
BIRMINGHAM (SHELBY
APT)
53823
AL
EVERGREEN
53820
AL
BIRMINGHAM (SHELBY
APT)
53823
AL
ALABASTER
53864
AL
BIRMINGHAM (SHELBY
APT)
53823
AL
TUSCALOOSA
93806
AL
BIRMINGHAM (SHELBY
APT)
53823
AR
JONESBORO
03953
AR
N LITTLE ROCK
03952
AR
HOT SPRINGS
03962
AR
N LITTLE ROCK
03952
AR
LITTLE ROCK
13963
AR
N LITTLE ROCK
03952
AR
FORT SMITH
13964
AR
N LITTLE ROCK
03952
AR
HARRISON
13971
MO
SPRINGFIELD REGIONAL
AP
13995
AR
TEXARKANA
13977
LA
SHREVEPORT REGIONAL
AP
13957
AR
BLYTHEVILLE
53869
AR
N LITTLE ROCK
03952
AR
MOUNTAIN HOME
53918
MO
SPRINGFIELD REGIONAL
AP
13995
AR
MONTICELLO
53919
AR
N LITTLE ROCK
03952
AR
RUSSELLVILLE
53920
AR
N LITTLE ROCK
03952
AR
FAYETTEVILLE/SPRINC
DALE
53922
MO
SPRINGFIELD REGIONAL
AP
13995
AR
De QUEEN
53925
LA
SHREVEPORT REGIONAL
AP
13957
AR
WEST MEMPHIS
53959
AR
N LITTLE ROCK
03952
AR
PINE BLUFF
93988
AR
N LITTLE ROCK
03952
2 of 21
-------
Surface
Upper Air
State
City
WBAN #
State
City
WBAN#
AR
EL DORADO
93992
LA
SHREVEPORT REGIONAL
AP
13957
AR
FAYETTEVILLE
93993
MO
SPRINGFIELD REGIONAL
AP
13995
AZ
WINDOW ROCK
03029
NM
ALBUQUERQUE
23050
AZ
FLAGSTAFF
03103
AZ
FLAGSTAFF/BELLEMT
(ARMY)
53103
AZ
PAGE
03162
AZ
FLAGSTAFF/BELLEMT
(ARMY)
53103
AZ
PHOENIX
03184
AZ
TUCSON
23160
AZ
SCOTTSDALE
03192
AZ
TUCSON
23160
AZ
GRAND CANYON
03195
AZ
FLAGSTAFF/BELLEMT
(ARMY)
53103
AZ
NOGALES
03196
AZ
TUCSON
23160
AZ
TUCSON
23160
AZ
TUCSON
23160
AZ
PHOENIX
23183
AZ
TUCSON
23160
AZ
PRESCOTT
23184
AZ
FLAGSTAFF/BELLEMT
(ARMY)
53103
AZ
WINSLOW
23194
AZ
FLAGSTAFF/BELLEMT
(ARMY)
53103
AZ
DOUGLAS BISBEE
93026
AZ
TUCSON
23160
AZ
ST. JOHNS
93027
AZ
FLAGSTAFF/BELLEMT
(ARMY)
53103
AZ
SAFFORD
93084
AZ
TUCSON
23160
AZ
KINGMAN
93167
NV
LAS VEGAS
03120
CA
ONTARIO
03102
CA
MIRAMAR NAS
03190
CA
PALM SPRINGS
03104
CA
MIRAMAR NAS
03190
CA
SAN DIEGO
03131
CA
MIRAMAR NAS
03190
CA
IMPERIAL
03144
CA
MIRAMAR NAS
03190
CA
LANCASTER
03159
CA
VANDENBERG
93214
CA
FULLERTON
03166
CA
MIRAMAR NAS
03190
CA
HAWTHORNE
03167
CA
MIRAMAR NAS
03190
CA
RIVERSIDE
03171
CA
MIRAMAR NAS
03190
CA
CARLSBAD
03177
CA
MIRAMAR NAS
03190
CA
SAN DIEGO
03178
CA
MIRAMAR NAS
03190
CA
CHINO
03179
CA
MIRAMAR NAS
03190
CA
LONG BEACH
23129
CA
MIRAMAR NAS
03190
CA
VAN NUYS
23130
CA
MIRAMAR NAS
03190
CA
CAMARILLO
23136
CA
VANDENBERG
93214
CA
BURBANK
23152
CA
MIRAMAR NAS
03190
CA
BAKERSFIELD
23155
CA
VANDENBERG
93214
CA
BISHOP
23157
NV
RENO
03198
CA
BLYTHE
23158
CA
MIRAMAR NAS
03190
CA
DAGGETT
23161
NV
LAS VEGAS
03120
CA
LOS ANGELES
23174
CA
MIRAMAR NAS
03190
CA
NEEDLES
23179
NV
LAS VEGAS
03120
CA
PALMDALE
23182
CA
MIRAMAR NAS
03190
CA
SANDBERG
23187
CA
VANDENBERG
93214
CA
SAN DIEGO
23188
CA
MIRAMAR NAS
03190
3 of 21
-------
Surface
Upper Air
State
City
WBAN #
State
City
WBAN#
CA
SANTA BARBARA
23190
CA
VANDENBERG
93214
CA
AVALON
23191
CA
MIRAMAR NAS
03190
CA
SANTA ROSA
23213
CA
OAKLAND INTAP
23230
CA
EMIGRANT GAP
23225
NV
RENO
03198
CA
OAKLAND
23230
CA
OAKLAND INTAP
23230
CA
SACRAMENTO
23232
CA
OAKLAND INTAP
23230
CA
SALINAS
23233
CA
OAKLAND INTAP
23230
CA
SAN FRANCISCO
23234
CA
OAKLAND INTAP
23230
CA
STOCKTON
23237
CA
OAKLAND INTAP
23230
CA
MOUNTAIN VIEW
23244
CA
OAKLAND INTAP
23230
CA
CONCORD
23254
CA
OAKLAND INTAP
23230
CA
MERCED
23257
CA
OAKLAND INTAP
23230
CA
MODESTO
23258
CA
OAKLAND INTAP
23230
CA
MONTEREY
23259
CA
OAKLAND INTAP
23230
CA
UKIAH
23275
CA
OAKLAND INTAP
23230
CA
WATSON VILLE
23277
CA
OAKLAND INTAP
23230
CA
LIVERMORE
23285
CA
OAKLAND INTAP
23230
CA
SAN JOSE
23293
CA
OAKLAND INTAP
23230
CA
MOUNT SHASTA
24215
OR
MEDFORD
24225
CA
RED BLUFF
24216
NV
RENO
03198
CA
REDDING
24257
OR
MEDFORD
24225
CA
MONTAGUE
24259
OR
MEDFORD
24225
CA
ARCATA/EUREKA
24283
OR
MEDFORD
24225
CA
CRESCENT CITY
24286
OR
MEDFORD
24225
CA
HANFORD
53119
CA
VANDENBERG
93214
CA
RAMONA
53120
CA
MIRAMAR NAS
03190
CA
OCEANSIDE
53121
CA
MIRAMAR NAS
03190
CA
OXNARD
93110
CA
VANDENBERG
93214
CA
LOS ANGELES
93134
CA
MIRAMAR NAS
03190
CA
PALM SPRINGS
93138
CA
MIRAMAR NAS
03190
CA
SANTA ANA
93184
CA
MIRAMAR NAS
03190
CA
FRESNO
93193
CA
VANDENBERG
93214
CA
SANTA MONICA
93197
CA
MIRAMAR NAS
03190
CA
MARYSVILLE
93205
CA
OAKLAND INTAP
23230
CA
SAN LUIS OBISPO
93206
CA
VANDENBERG
93214
CA
PASO ROBLES
93209
CA
VANDENBERG
93214
CA
OROVILLE
93210
NV
RENO
03198
CA
SACRAMENTO
93225
CA
OAKLAND INTAP
23230
CA
NAPA
93227
CA
OAKLAND INTAP
23230
CA
HAYWARD
93228
CA
OAKLAND INTAP
23230
CA
SOUTH LAKE TAHOE
93230
NV
RENO
03198
CA
VACAVILLE
93241
CA
OAKLAND INTAP
23230
CA
MADERA
93242
CA
OAKLAND INTAP
23230
CA
ALTURAS
94299
OR
MEDFORD
24225
CO
LAMAR
03013
KS
DODGE CITY
13985
CO
DENVER
03017
CO
DENVER/STAPLETON ARP
r 23062
4 of 21
-------
Surface
Upper Air
State
City
WBAN #
State
City
WBAN#
CO
BURLINGTON
03026
CO
DENVER/STAPLETON ARP
r 23062
CO
ALAMOSA
23061
CO
DENVER/STAPLETON ARP
r 23062
CO
GRAND JUNCTION
23066
CO
GRAND JUNCTION
23066
CO
LA JUNTA
23067
CO
DENVER/STAPLETON ARP
r 23062
CO
TRINIDAD
23070
CO
DENVER/STAPLETON ARP
r 23062
CO
CRAIG
24046
CO
GRAND JUNCTION
23066
CO
DURANGO
93005
CO
GRAND JUNCTION
23066
CO
LEADVILLE
93009
CO
DENVER/STAPLETON ARP
r 23062
CO
LIMON
93010
CO
DENVER/STAPLETON ARP
r 23062
CO
MONTROSE
93013
CO
GRAND JUNCTION
23066
CO
COLORADO SPRINGS
93037
CO
DENVER/STAPLETON ARP
r 23062
CO
PUEBLO
93058
CO
DENVER/STAPLETON ARP
r 23062
CO
DENVER
93067
CO
DENVER/STAPLETON ARP
r 23062
CO
CORTEZ
93069
CO
GRAND JUNCTION
23066
CO
ASPEN
93073
CO
GRAND JUNCTION
23066
CO
MEEKER
94050
CO
GRAND JUNCTION
23066
CT
GROTON NEW
LONDON
14707
NY
BROOKHAVEN
94703
CT
WINDSOR LOCKS
14740
NY
BROOKHAVEN
94703
CT
HARTFORD
14752
NY
BROOKHAVEN
94703
CT
NEW HAVEN
14758
NY
BROOKHAVEN
94703
CT
DANBURY
54734
NY
BROOKHAVEN
94703
CT
WILLIMANTIC
54767
NY
BROOKHAVEN
94703
CT
MERIDEN
54788
NY
BROOKHAVEN
94703
CT
BRIDGEPORT
94702
NY
BROOKHAVEN
94703
DC
WASHINGTON
13743
VA
STERLING(WASH DULLES)
93734
DC
WASHINGTON
93738
VA
STERLING(WASH DULLES)
93734
DE
GEORGETOWN
13764
VA
WALLOPS ISLAND
93739
DE
WILMINGTON
13781
VA
STERLING(WASH DULLES)
93734
FL
MAR IAN N A
03818
FL
TALLAHASEE
93805
FL
PUNTA GORDA
12812
FL
TAMPA BAY/RUSKIN
12842
FL
ORLANDO
12815
FL
TAMPA BAY/RUSKIN
12842
FL
GAINESVILLE
12816
FL
JACKSONVILLE
13889
FL
BROOKSVILLE
12818
FL
TAMPA BAY/RUSKIN
12842
FL
LEESBURG
12819
FL
TAMPA BAY/RUSKIN
12842
FL
APALACHICOLA
12832
FL
TALLAHASEE
93805
FL
DAYTONA BEACH
12834
FL
JACKSONVILLE
13889
5 of 21
-------
Surface
Upper Air
State
City
WBAN #
State
City
WBAN#
FL
FORT MYERS
12835
FL
TAMPA BAY/RUSKIN
12842
FL
KEY WEST
12836
FL
KEY WEST INT AP
12836
FL
MELBOURNE
12838
FL
TAMPA BAY/RUSKIN
12842
FL
MIAMI
12839
FL
MIAMI/FL INTL UNIV
92803
FL
ORLANDO
12841
FL
TAMPA BAY/RUSKIN
12842
FL
TAMPA
12842
FL
TAMPA BAY/RUSKIN
12842
FL
VERO BEACH
12843
FL
TAMPA BAY/RUSKIN
12842
FL
WEST PALM BEACH
12844
FL
MIAMI/FL INTL UNIV
92803
FL
FORT LAUDERDALE
12849
FL
MIAMI/FL INTL UNIV
92803
FL
ORLANDO
12854
FL
TAMPA BAY/RUSKIN
12842
FL
SARASOTA/BRADENTC
N
12871
FL
TAMPA BAY/RUSKIN
12842
FL
ST PETERSBURG/
CLEARWATER
12873
FL
TAMPA BAY/RUSKIN
12842
FL
WINTER HAVEN
12876
FL
TAMPA BAY/RUSKIN
12842
FL
MIAMI
12882
FL
MIAMI/FL INTL UNIV
92803
FL
FORT LAUDERDALE
12885
FL
MIAMI/FL INTL UNIV
92803
FL
MIAMI
12888
FL
MIAMI/FL INTL UNIV
92803
FL
FORT MYERS
12894
FL
TAMPA BAY/RUSKIN
12842
FL
FORT PIERCE
12895
FL
TAMPA BAY/RUSKIN
12842
FL
MARATHON
12896
FL
KEY WEST INT AP
12836
FL
NAPLES
12897
FL
MIAMI/FL INTL UNIV
92803
FL
CRESTVIEW
13884
FL
TALLAHASEE
93805
FL
JACKSONVILLE
13889
FL
JACKSONVILLE
13889
FL
PENSACOLA
13899
LA
SLIDELL
53813
FL
DESTIN
53853
FL
TALLAHASEE
93805
FL
JACKSONVILLE
53860
FL
JACKSONVILLE
13889
FL
POMPANO BEACH
92805
FL
MIAMI/FL INTL UNIV
92803
FL
ST PETERSBURG
92806
FL
TAMPA BAY/RUSKIN
12842
FL
HOLLYWOOD
92809
FL
MIAMI/FL INTL UNIV
92803
FL
TALLAHASSEE
93805
FL
TALLAHASEE
93805
GA
MACON
03813
GA
PEACHTREE CITY
53819
GA
AUGUSTA
03820
SC
CHARLESTON
13880
GA
SAVANNAH
03822
SC
CHARLESTON
13880
GA
ATLANTA
03888
GA
PEACHTREE CITY
53819
GA
AUGUSTA
13837
SC
CHARLESTON
13880
GA
ALBANY
13869
FL
TALLAHASEE
93805
GA
ALMA
13870
FL
JACKSONVILLE
13889
GA
ATHENS
13873
GA
PEACHTREE CITY
53819
GA
ATLANTA
13874
GA
PEACHTREE CITY
53819
GA
BRUNSWICK
13878
FL
JACKSONVILLE
13889
GA
ATLANTA
53819
GA
PEACHTREE CITY
53819
GA
GAINESVILLE
53838
GA
PEACHTREE CITY
53819
GA
ATLANTA
53863
GA
PEACHTREE CITY
53819
GA
CARTERSVILLE
53873
GA
PEACHTREE CITY
53819
GA
ROME
93801
GA
PEACHTREE CITY
53819
6 of 21
-------
Surface
Upper Air
State
City
WBAN #
State
City
WBAN#
GA
COLUMBUS
93842
GA
PEACHTREE CITY
53819
GA
VALDOSTA
93845
FL
TALLAHASEE
93805
HI
HILO
21504
HI
HILO
21504
HI
KAILUA/KONA
21510
HI
HILO
21504
HI
KAHULUI
22516
HI
HILO
21504
HI
HONOLULU
22521
HI
LIHUE/KAUAI
22536
HI
KAUNAKAKAI
22534
HI
LIHUE/KAUAI
22536
HI
KAPOLEI
22551
HI
LIHUE/KAUAI
22536
IA
BURLINGTON
14931
IA
DAVENPORT MUNICIPAL
AP
94982
IA
DES MOINES
14933
NE
OMAHAA/ALLEY
94980
IA
IOWA CITY
14937
IA
DAVENPORT MUNICIPAL
AP
94982
IA
MASON CITY
14940
MN
MINNEAPOLIS
94983
IA
SIOUX CITY
14943
NE
OMAHAA/ALLEY
94980
IA
OTTUMWA
14950
IA
DAVENPORT MUNICIPAL
AP
94982
IA
SPENCER
14972
NE
OMAHAA/ALLEY
94980
IA
WATERLOO
94910
IA
DAVENPORT MUNICIPAL
AP
94982
IA
ESTHERVILLE
94971
MN
MINNEAPOLIS
94983
IA
DAVENPORT
94982
IA
DAVENPORT MUNICIPAL
AP
94982
IA
MARSHALLTOWN
94988
IA
DAVENPORT MUNICIPAL
AP
94982
IA
LAMONI
94991
NE
OMAHAA/ALLEY
94980
ID
JEROME
04110
ID
BOISE
24131
ID
CHALLIS
04114
NV
ELKO
04105
ID
BOISE
24131
ID
BOISE
24131
ID
BURLEY
24133
ID
BOISE
24131
ID
IDAHO FALLS
24145
WY
RIVERTON
24061
ID
LEWISTON
24149
WA
SPOKANE INTNLAPT
04106
ID
MULLAN PASS
24154
WA
SPOKANE INTNLAPT
04106
ID
POCATELLO
24156
UT
SALT LAKE CITY
24127
ID
TWIN FALLS
94178
ID
BOISE
24131
ID
McCALL
94182
ID
BOISE
24131
ID
REXBURG
94194
WY
RIVERTON
24061
IL
DECATUR
03887
IL
LINCOLN-LOGAN COUNTY
AP
04833
IL
CAHOKIA/ST. LOUIS
03960
IL
LINCOLN-LOGAN COUNTY
AP
04833
IL
CHICAGO/AURORA
04808
IA
DAVENPORT MUNICIPAL
AP
94982
IL
CHICAGO/PROSPECT
HEIGHTS/WHEELING
04838
IA
DAVENPORT MUNICIPAL
AP
94982
IL
LAWRENCEVILLE
13809
IL
LINCOLN-LOGAN COUNTY
AP
04833
7 of 21
-------
Surface
Upper Air
State
City
WBAN #
State
City
WBAN#
IL
CHICAGO
14819
IL
LINCOLN-LOGAN COUNTY
AP
04833
IL
PEORIA
14842
IL
LINCOLN-LOGAN COUNTY
AP
04833
IL
CHICAGO/WAUKEGAN
14880
Wl
GREEN BAY
14898
IL
MOLINE
14923
IA
DAVENPORT MUNICIPAL
AP
94982
IL
MATTOON/CHARLEST
ON
53802
IL
LINCOLN-LOGAN COUNTY
AP
04833
IL
BLOOMINGTON/NORM
AL
54831
IL
LINCOLN-LOGAN COUNTY
AP
04833
IL
CARBONDALE/MURPH
YBORO
93810
IL
LINCOLN-LOGAN COUNTY
AP
04833
IL
SPRINGFIELD
93822
IL
LINCOLN-LOGAN COUNTY
AP
04833
IL
QUINCY
93989
IL
LINCOLN-LOGAN COUNTY
AP
04833
IL
ROCKFORD
94822
IA
DAVENPORT MUNICIPAL
AP
94982
IL
CHICAGO
94846
IA
DAVENPORT MUNICIPAL
AP
94982
IL
CHAMPAIGN/URBANA
94870
IL
LINCOLN-LOGAN COUNTY
AP
04833
IL
CHICAGO/WEST
CHICAGO
94892
IA
DAVENPORT MUNICIPAL
AP
94982
IN
TERRE HAUTE
03868
IL
LINCOLN-LOGAN COUNTY
AP
04833
IN
BLOOMINGTON
03893
OH
WILMINGTON
13841
IN
VALPARAISO
04846
IL
LINCOLN-LOGAN COUNTY
AP
04833
IN
FORT WAYNE
14827
OH
WILMINGTON
13841
IN
GOSHEN
14829
Ml
DETROIT/PONTIAC
04830
IN
LAFAYETTE
14835
IL
LINCOLN-LOGAN COUNTY
AP
04833
IN
SOUTH BEND
14848
Ml
DETROIT/PONTIAC
04830
IN
INDIANAPOLIS
53842
OH
WILMINGTON
13841
IN
SHELBYVILLE
53866
OH
WILMINGTON
13841
IN
EVANSVILLE
93817
TN
NASHVILLE
13897
IN
INDIANAPOLIS
93819
OH
WILMINGTON
13841
IN
MUNCIE
94895
OH
WILMINGTON
13841
KS
SALINA
03919
KS
TOPEKA
13996
KS
WICHITA
03928
KS
TOPEKA
13996
KS
MANHATTAN
03936
KS
TOPEKA
13996
KS
OLATHE
03967
KS
TOPEKA
13996
KS
WICHITA
03974
KS
TOPEKA
13996
KS
LAWRENCE
03997
KS
TOPEKA
13996
KS
PARSONS
03998
MO
SPRINGFIELD REGIONAL
AP
13995
8 of 21
-------
Surface
Upper Air
State
City
WBAN #
State
City
WBAN#
KS
TOPEKA
13920
KS
TOPEKA
13996
KS
CHANUTE
13981
KS
TOPEKA
13996
KS
CONCORDIA
13984
KS
TOPEKA
13996
KS
DODGE CITY
13985
KS
DODGE CITY
13985
KS
HUTCHINSON
13986
KS
DODGE CITY
13985
KS
EMPORIA
13989
KS
TOPEKA
13996
KS
TOPEKA
13996
KS
TOPEKA
13996
KS
GARDEN CITY
23064
KS
DODGE CITY
13985
KS
GOODLAND
23065
NE
NORTH PLATTE
24023
KS
OLATHE
93909
KS
TOPEKA
13996
KS
COFFEYVILLE
93967
MO
SPRINGFIELD REGIONAL
AP
13995
KS
HILL CITY
93990
KS
DODGE CITY
13985
KS
RUSSELL
93997
KS
DODGE CITY
13985
KY
PADUCAH
03816
TN
NASHVILLE
13897
KY
LONDON
03849
TN
NASHVILLE
13897
KY
JACKSON
03889
OH
WILMINGTON
13841
KY
LOUISVILLE
13810
OH
WILMINGTON
13841
KY
FRANKFORT
53841
OH
WILMINGTON
13841
KY
BOWLING GREEN
93808
TN
NASHVILLE
13897
KY
LEXINGTON
93820
OH
WILMINGTON
13841
KY
LOUISVILLE
93821
OH
WILMINGTON
13841
LA
LAKE CHARLES
03937
LA
LAKE CHARLES
03937
LA
BOOTH VILLE
12884
LA
SLIDELL
53813
LA
NEW ORLEANS
12916
LA
SLIDELL
53813
LA
ALEXANDRIA
13935
LA
LAKE CHARLES
03937
LA
MONROE
13942
LA
SHREVEPORT REGIONAL
AP
13957
LA
SHREVEPORT
13957
LA
SHREVEPORT REGIONAL
AP
13957
LA
BATON ROUGE
13970
LA
SLIDELL
53813
LA
LAFAYETTE
13976
LA
LAKE CHARLES
03937
LA
SLIDELL
53865
LA
SLIDELL
53813
LA
SHREVEPORT
53905
LA
SHREVEPORT REGIONAL
AP
13957
LA
NEW IBERIA
53915
LA
LAKE CHARLES
03937
LA
NEW ORLEANS
53917
LA
SLIDELL
53813
LA
ALEXANDRIA
93915
LA
LAKE CHARLES
03937
MA
FITCHBURG
04780
NY
ALBANY
54775
MA
BEDFORD
14702
MA
CHATHAM
14684
MA
BOSTON
14739
MA
CHATHAM
14684
MA
NANTUCKET
14756
MA
CHATHAM
14684
MA
PITTSFIELD
14763
NY
ALBANY
54775
MA
WESTFIELD/SPRINGFI
LD
14775
NY
ALBANY
54775
MA
NORWOOD
54704
MA
CHATHAM
14684
MA
BEVERLY
54733
MA
CHATHAM
14684
MA
ORANGE
54756
NY
ALBANY
54775
MA
NORTH ADAMS
54768
NY
ALBANY
54775
9 of 21
-------
Surface
Upper Air
State
City
WBAN #
State
City
WBAN#
MA
PLYMOUTH
54769
MA
CHATHAM
14684
MA
TAUNTON
54777
MA
CHATHAM
14684
MA
CHATHAM
94624
MA
CHATHAM
14684
MA
HYANNIS
94720
MA
CHATHAM
14684
MA
LAWRENCE
94723
ME
GRAY
54762
MA
VINEYARD HAVEN
94724
MA
CHATHAM
14684
MA
NEW BEDFORD
94726
MA
CHATHAM
14684
MA
WORCESTER
94746
NY
ALBANY
54775
MD
HAGERSTOWN
93706
VA
STERLING(WASH DULLES)
93734
MD
SALISBURY
93720
VA
WALLOPS ISLAND
93739
MD
BALTIMORE
93721
VA
STERLING(WASH DULLES)
93734
MD
OCEAN CITY
93786
VA
WALLOPS ISLAND
93739
ME
FRENCHVILLE
04836
ME
CARIBOU
14607
ME
AUGUSTA
14605
ME
GRAY
54762
ME
BANGOR
14606
ME
GRAY
54762
ME
CARIBOU
14607
ME
CARIBOU
14607
ME
HOULTON
14609
ME
CARIBOU
14607
ME
MILLINOCKET
14610
ME
CARIBOU
14607
ME
PORTLAND
14764
ME
GRAY
54762
ME
FRYEBURG
54772
ME
GRAY
54762
ME
WISCASSET
94623
ME
GRAY
54762
Ml
HOLLAND
04839
Ml
DETROIT/PONTIAC
04830
Ml
ADRIAN
04847
Ml
DETROIT/PONTIAC
04830
Ml
GAYLORD
04854
Ml
GAYLORD/ALPENA
04837
Ml
BATTLE CREEK
14815
Ml
DETROIT/PONTIAC
04830
Ml
DETROIT
14822
Ml
DETROIT/PONTIAC
04830
Ml
FLINT
14826
Ml
DETROIT/PONTIAC
04830
Ml
JACKSON
14833
Ml
DETROIT/PONTIAC
04830
Ml
LANSING
14836
Ml
DETROIT/PONTIAC
04830
Ml
MUSKEGON
14840
Wl
GREEN BAY
14898
Ml
PELLSTON
14841
Ml
GAYLORD/ALPENA
04837
Ml
SAGINAW
14845
Ml
DETROIT/PONTIAC
04830
Ml
SAULT STE MARIE
14847
Ml
GAYLORD/ALPENA
04837
Ml
TRAVERSE CITY
14850
Ml
GAYLORD/ALPENA
04837
Ml
DETROIT
14853
Ml
DETROIT/PONTIAC
04830
Ml
HANCOCK
14858
Wl
GREEN BAY
14898
Ml
HOUGHTON LAKE
94814
Ml
GAYLORD/ALPENA
04837
Ml
KALAMAZOO
94815
Ml
DETROIT/PONTIAC
04830
Ml
PONTIAC
94817
Ml
DETROIT/PONTIAC
04830
Ml
DETROIT
94847
Ml
DETROIT/PONTIAC
04830
Ml
GRAND RAPTIDS
94860
Ml
DETROIT/PONTIAC
04830
Ml
BENTON HARBOR
94871
Ml
DETROIT/PONTIAC
04830
Ml
ANN ARBOR
94889
Ml
DETROIT/PONTIAC
04830
Ml
IRON
MOUNTAIN/KINGSFOR
D
94893
Wl
GREEN BAY
14898
MN
ALEXANDRIA
14910
MN
MINNEAPOLIS
94983
10 of 21
-------
Surface
Upper Air
State
City
WBAN #
State
City
WBAN#
MN
DULUTH
14913
MN
INTERNATIONAL FALLS
14918
MN
INTERNATIONAL
FALLS
14918
MN
INTERNATIONAL FALLS
14918
MN
MINNEAPOLIS
14922
MN
MINNEAPOLIS
94983
MN
ROCHESTER
14925
MN
MINNEAPOLIS
94983
MN
ST CLOUD
14926
MN
MINNEAPOLIS
94983
MN
ST PAUL
14927
MN
MINNEAPOLIS
94983
MN
REDWOOD FALLS
14992
MN
MINNEAPOLIS
94983
MN
HIBBING
94931
MN
INTERNATIONAL FALLS
14918
MN
BRAINERD
94938
MN
MINNEAPOLIS
94983
MN
MINNEAPOLIS
94960
MN
MINNEAPOLIS
94983
MN
BAUDETTE
94961
MN
INTERNATIONAL FALLS
14918
MN
MINNEAPOLIS
94963
MN
MINNEAPOLIS
94983
MN
PARK RAPIDS
94967
MN
INTERNATIONAL FALLS
14918
MO
CAPE GIRARDEAU
03935
TN
NASHVILLE
13897
MO
COLUMBIA
03945
MO
SPRINGFIELD REGIONAL
AP
13995
MO
KANSAS CITY
03947
KS
TOPEKA
13996
MO
JEFFERSON CITY
03963
MO
SPRINGFIELD REGIONAL
AP
13995
MO
ST LOUIS
03966
IL
LINCOLN-LOGAN COUNTY
AP
04833
MO
POPLAR BLUFF
03975
MO
SPRINGFIELD REGIONAL
AP
13995
MO
SEDALIA
03994
MO
SPRINGFIELD REGIONAL
AP
13995
MO
JOPLIN
13987
MO
SPRINGFIELD REGIONAL
AP
13995
MO
KANSAS CITY
13988
KS
TOPEKA
13996
MO
ST LOUIS
13994
IL
LINCOLN-LOGAN COUNTY
AP
04833
MO
SPRINGFIELD
13995
MO
SPRINGFIELD REGIONAL
AP
13995
MO
ROLLAA/ICHY
13997
MO
SPRINGFIELD REGIONAL
AP
13995
MO
KIRKSVILLE
14938
IA
DAVENPORT MUNICIPAL
AP
94982
MO
LEE'S SUMMIT
53879
KS
TOPEKA
13996
MO
WEST PLAINS
53901
MO
SPRINGFIELD REGIONAL
AP
13995
MO
ST CHARLES
53904
IL
LINCOLN-LOGAN COUNTY
AP
04833
MS
JACKSON
03940
MS
JACKSON/THOMPSON FLU
03940
11 of 21
-------
Surface
Upper Air
State
City
WBAN #
State
City
WBAN#
MS
TALLULAH/VICKSBURC
i 03996
MS
JACKSON/THOMPSON FLU
03940
MS
HATTIESBURG
13833
LA
SLIDELL
53813
MS
MERIDIAN
13865
MS
JACKSON/THOMPSON FLU
03940
MS
JACKSON
13927
MS
JACKSON/THOMPSON FLU
03940
MS
GREENVILLE
13939
MS
JACKSON/THOMPSON FLU
03940
MS
GREENWOOD
13978
MS
JACKSON/THOMPSON FLU
03940
MS
PASCAGOULA
53858
LA
SLIDELL
53813
MS
TUPELO
93862
AL
BIRMINGHAM (SHELBY
APT)
53823
MS
GULFPORT
93874
LA
SLIDELL
53813
MS
MCCOMB
93919
LA
SLIDELL
53813
MT
BILLINGS
24033
MT
GREAT FALLS
04102
MT
LEWISTOWN
24036
MT
GREAT FALLS
04102
MT
MILES
24037
MT
GLASGOW
94008
MT
BOZEMAN
24132
MT
GREAT FALLS
04102
MT
BUTTE
24135
MT
GREAT FALLS
04102
MT
CUT BANK
24137
MT
GREAT FALLS
04102
MT
DILLON
24138
MT
GREAT FALLS
04102
MT
GREAT FALLS
24143
MT
GREAT FALLS
04102
MT
HELENA
24144
MT
GREAT FALLS
04102
MT
KALISPELL
24146
MT
GREAT FALLS
04102
MT
LIVINGSTON
24150
MT
GREAT FALLS
04102
MT
MISSOULA
24153
MT
GREAT FALLS
04102
MT
GLASGOW
94008
MT
GLASGOW
94008
MT
HAVRE
94012
MT
GREAT FALLS
04102
MT
WOLF POINT
94017
MT
GLASGOW
94008
MT
BAKER
94055
SD
RAPID CITY
94043
NC
HICKORY
03810
NC
GREENSBORO
13723
NC
ASHEVILLE
03812
NC
GREENSBORO
13723
NC
RALEIGH/DURHAM
13722
NC
GREENSBORO
13723
NC
GREENSBORO
13723
NC
GREENSBORO
13723
NC
WILMINGTON
13748
NC
MOREHEAD
CITY/NEWPORT
93768
NC
LUMBERTON
13776
NC
GREENSBORO
13723
NC
ELIZABETH CITY
13786
NC
MOREHEAD
CITY/NEWPORT
93768
NC
CHARLOTTE
13881
NC
GREENSBORO
13723
NC
GASTON IA
53870
NC
GREENSBORO
13723
NC
MONROE
53872
NC
GREENSBORO
13723
NC
NEW BERN
93719
NC
MOREHEAD
CITY/NEWPORT
93768
NC
CAPE HATTERAS
93729
NC
MOREHEAD
CITY/NEWPORT
93768
NC
FAYETTEVILLE
93740
NC
GREENSBORO
13723
12 of 21
-------
Surface
Upper Air
State
City
WBAN #
State
City
WBAN#
NC
ROCKY MOUNT
93759
NC
MOREHEAD
CITY/NEWPORT
93768
NC
BEAUFORT
93765
NC
MOREHEAD
CITY/NEWPORT
93768
NC
MAXTON
93782
NC
GREENSBORO
13723
NC
BURLINGTON
93783
NC
GREENSBORO
13723
NC
CHAPEL HILL
93785
NC
GREENSBORO
13723
NC
WINSTON SALEM
93807
NC
GREENSBORO
13723
ND
FARGO
14914
SD
ABERDEEN
14929
ND
GRAND FORKS
14916
MN
INTERNATIONAL FALLS
14918
ND
JAMESTOWN
14919
ND
BISMARCK
24011
ND
BISMARCK
24011
ND
BISMARCK
24011
ND
DICKINSON
24012
ND
BISMARCK
24011
ND
MINOT
24013
ND
BISMARCK
24011
ND
WILLISTON
94014
MT
GLASGOW
94008
ND
HETTINGER
94038
ND
BISMARCK
24011
NE
GRAND ISLAND
14935
NE
OMAHAA/ALLEY
94980
NE
LINCOLN
14939
NE
OMAHAA/ALLEY
94980
NE
NORFOLK
14941
NE
OMAHAA/ALLEY
94980
NE
OMAHA
14942
NE
OMAHAA/ALLEY
94980
NE
CHADRON
24017
SD
RAPID CITY
94043
NE
NORTH PLATTE
24023
NE
NORTH PLATTE
24023
NE
SCOTTSBLUFF
24028
SD
RAPID CITY
94043
NE
SIDNEY
24030
NE
NORTH PLATTE
24023
NE
VALENTINE
24032
NE
NORTH PLATTE
24023
NE
ALLIANCE
24044
NE
NORTH PLATTE
24023
NE
IMPERIAL
24091
NE
NORTH PLATTE
24023
NE
MC COOK
94040
NE
NORTH PLATTE
24023
NE
BROKEN BOW
94946
NE
NORTH PLATTE
24023
NE
HASTINGS
94949
NE
OMAHAA/ALLEY
94980
NE
FALLS CITY
94957
KS
TOPEKA
13996
NE
ORD
94958
NE
NORTH PLATTE
24023
NE
TEKAMAH
94978
NE
OMAHAA/ALLEY
94980
NH
MANCHESTER
14710
ME
GRAY
54762
NH
CONCORD
14745
ME
GRAY
54762
NH
WHITEFIELD
54728
ME
GRAY
54762
NH
JAFFREY
54770
NY
ALBANY
54775
NH
ROCHESTER
54791
ME
GRAY
54762
NH
BERLIN
94700
ME
GRAY
54762
NH
LEBANON
94765
NY
ALBANY
54775
NJ
MILLVILLE
13735
VA
WALLOPS ISLAND
93739
NJ
NEWARK
14734
NY
BROOKHAVEN
94703
NJ
TRENTON
14792
NY
BROOKHAVEN
94703
NJ
CALDWELL
54743
NY
BROOKHAVEN
94703
NJ
SOMERVILLE
54785
NY
BROOKHAVEN
94703
NJ
ATLANTIC CITY
93730
VA
WALLOPS ISLAND
93739
NJ
MOUNT HOLLY
93780
NY
BROOKHAVEN
94703
NJ
TETERBORO
94741
NY
BROOKHAVEN
94703
13 of 21
-------
Surface
Upper Air
State
City
WBAN #
State
City
WBAN#
NM
CLINES CORNERS
03027
NM
ALBUQUERQUE
23050
NM
ROSWELL
23009
NM
SANTA TERESA
03020
NM
TUCUMCARI
23048
TX
AMARILLO
23047
NM
SANTA FE
23049
NM
ALBUQUERQUE
23050
NM
ALBUQUERQUE
23050
NM
ALBUQUERQUE
23050
NM
CLAYTON
23051
TX
AMARILLO
23047
NM
RATON
23052
NM
ALBUQUERQUE
23050
NM
LAS VEGAS
23054
NM
ALBUQUERQUE
23050
NM
DEMING
23078
NM
SANTA TERESA
03020
NM
GALLUP
23081
NM
ALBUQUERQUE
23050
NM
FARMINGTON
23090
NM
ALBUQUERQUE
23050
NM
CARLSBAD
93033
TX
MIDLAND
23023
NM
TRUTH OR
CONSEQUENCES
93045
NM
SANTA TERESA
03020
NV
MERCURY
03160
NV
LAS VEGAS
03120
NV
TONOPAH
23153
NV
LAS VEGAS
03120
NV
ELY
23154
NV
ELKO
04105
NV
LAS VEGAS
23169
NV
LAS VEGAS
03120
NV
RENO
23185
NV
RENO
03198
NV
ELKO
24121
NV
ELKO
04105
NV
WINNEMUCCA
24128
NV
ELKO
04105
NV
LOVELOCK
24172
NV
RENO
03198
NV
LAS VAGAS
53123
NV
LAS VEGAS
03120
NY
NIAGARA FALLS
04724
NY
BUFFALO/GRTR ARPT
14733
NY
BINGHAMTON
04725
NY
ALBANY
54775
NY
ISLIP
04781
NY
BROOKHAVEN
94703
NY
MONTGOMERY
04789
NY
ALBANY
54775
NY
WESTHAMPTON
BEACH
14719
NY
BROOKHAVEN
94703
NY
NEW YORK
14732
NY
BROOKHAVEN
94703
NY
BUFFALO
14733
NY
BUFFALO/GRTR ARPT
14733
NY
ALBANY
14735
NY
ALBANY
54775
NY
DUNKIRK
14747
NY
BUFFALO/GRTR ARPT
14733
NY
ELMIRA/CORNING
14748
NY
BUFFALO/GRTR ARPT
14733
NY
GLEN FALLS
14750
NY
ALBANY
54775
NY
POUGHKEEPSIE
14757
NY
ALBANY
54775
NY
ROCHESTER
14768
NY
BUFFALO/GRTR ARPT
14733
NY
SYRACUSE
14771
NY
ALBANY
54775
NY
WELLSVILLE
54757
NY
BUFFALO/GRTR ARPT
14733
NY
FULTON
54773
NY
BUFFALO/GRTR ARPT
14733
NY
PENN YAN
54778
NY
BUFFALO/GRTR ARPT
14733
NY
FARMINGDALE
54787
NY
BROOKHAVEN
94703
NY
SHIRLEY
54790
NY
BROOKHAVEN
94703
NY
DANSVILLE
94704
NY
BUFFALO/GRTR ARPT
14733
NY
MASSENA
94725
NY
ALBANY
54775
NY
NEW YORK
94728
NY
BROOKHAVEN
94703
NY
SARANAC LAKE
94740
NY
ALBANY
54775
NY
WHITE PLAINS
94745
NY
BROOKHAVEN
94703
NY
NEW YORK
94789
NY
BROOKHAVEN
94703
14 of 21
-------
Surface
Upper Air
State
City
WBAN #
State
City
WBAN#
NY
WATERTOWN
94790
NY
ALBANY
54775
OH
WOOSTER
04842
PA
PITTSBURGH/MOON
TOWNSHIP
94823
OH
TOLEDO
04848
Ml
DETROIT/PONTIAC
04830
OH
LORAIN/ELYRIA
04849
PA
PITTSBURGH/MOON
TOWNSHIP
94823
OH
LIMA
04850
OH
WILMINGTON
13841
OH
DEFIANCE
04851
Ml
DETROIT/PONTIAC
04830
OH
NEW PHILADELPHIA
04852
PA
PITTSBURGH/MOON
TOWNSHIP
94823
OH
CLEVELAND
04853
PA
PITTSBURGH/MOON
TOWNSHIP
94823
OH
MARION
04855
OH
WILMINGTON
13841
OH
ASHTABULA
04857
PA
PITTSBURGH/MOON
TOWNSHIP
94823
OH
NEWARK
04858
OH
WILMINGTON
13841
OH
WILMINGTON
13841
OH
WILMINGTON
13841
OH
AKRON
14813
PA
PITTSBURGH/MOON
TOWNSHIP
94823
OH
CLEVELAND
14820
PA
PITTSBURGH/MOON
TOWNSHIP
94823
OH
COLUMBUS
14821
OH
WILMINGTON
13841
OH
FINDLAY
14825
OH
WILMINGTON
13841
OH
YOUNGSTOWN/WARR
EN
14852
PA
PITTSBURGH/MOON
TOWNSHIP
94823
OH
MANSFIELD
14891
OH
WILMINGTON
13841
OH
AKRON
14895
PA
PITTSBURGH/MOON
TOWNSHIP
94823
OH
LANCASTER
53844
OH
WILMINGTON
13841
OH
HAMILTON
53855
OH
WILMINGTON
13841
OH
DAYTON
53859
OH
WILMINGTON
13841
OH
CINCINNATI
93812
OH
WILMINGTON
13841
OH
COVINGTON/CINCINN/
Tl
93814
OH
WILMINGTON
13841
OH
DAYTON
93815
OH
WILMINGTON
13841
OH
ZANESVILLE
93824
PA
PITTSBURGH/MOON
TOWNSHIP
94823
OH
TOLEDO
94830
Ml
DETROIT/PONTIAC
04830
OK
GUYMON
03030
TX
AMARILLO
23047
OK
CLINTON
03932
OK
NORMAN
03948
OK
LAWTON
03950
OK
NORMAN
03948
OK
OKLAHOMA CITY
03954
OK
NORMAN
03948
OK
STILLWATER
03965
OK
NORMAN
03948
OK
FREDERICK
03981
OK
NORMAN
03948
OK
OKLAHOMA CITY
13967
OK
NORMAN
03948
OK
TULSA
13968
OK
NORMAN
03948
OK
PONCA CITY
13969
OK
NORMAN
03948
OK
GAGE
13975
KS
DODGE CITY
13985
OK
TULSA
53908
OK
NORMAN
03948
15 of 21
-------
Surface
Upper Air
State
City
WBAN #
State
City
WBAN#
OK
GUTHRIE
53913
OK
NORMAN
03948
OK
MC ALESTER
93950
OK
NORMAN
03948
OK
MUSKOGEE
93953
OK
NORMAN
03948
OK
HOBART
93986
OK
NORMAN
03948
OR
HERMISTON
04113
WA
SPOKANE INTNLAPT
04106
OR
SCAPPOOSE
04201
OR
SALEM
24232
OR
BAKER CITY
24130
ID
BOISE
24131
OR
PENDLETON
24155
WA
SPOKANE INTNLAPT
04106
OR
ONTARIO
24162
ID
BOISE
24131
OR
THE DALLES
24219
OR
SALEM
24232
OR
EUGENE
24221
OR
SALEM
24232
OR
MEDFORD
24225
OR
MEDFORD
24225
OR
PORTLAND
24229
OR
SALEM
24232
OR
REDMOND
24230
OR
SALEM
24232
OR
ROSEBURG
24231
OR
MEDFORD
24225
OR
SALEM
24232
OR
SALEM
24232
OR
SEXTON SUMMIT
24235
OR
MEDFORD
24225
OR
PORTLAND
24242
OR
SALEM
24232
OR
BURNS
94185
ID
BOISE
24131
OR
ASTORIA
94224
OR
SALEM
24232
OR
KLAMATH FALLS
94236
OR
MEDFORD
24225
OR
PORTLAND
94261
OR
SALEM
24232
OR
MC MINNVILLE
94273
OR
SALEM
24232
OR
AURORA
94281
OR
SALEM
24232
PA
JOHNSTOWN
04726
PA
PITTSBURGH/MOON
TOWNSHIP
94823
PA
BRADFORD
04751
NY
BUFFALO/GRTR ARPT
14733
PA
DU BOIS
04787
PA
PITTSBURGH/MOON
TOWNSHIP
94823
PA
MEADVILLE
04843
PA
PITTSBURGH/MOON
TOWNSHIP
94823
PA
PHILADELPHIA
13739
VA
STERLING(WASH DULLES)
93734
PA
HARRISBURG
14711
VA
STERLING(WASH DULLES)
93734
PA
READING
14712
VA
STERLING(WASH DULLES)
93734
PA
ALTOONA
14736
VA
STERLING(WASH DULLES)
93734
PA
ALLENTOWN
14737
NY
BROOKHAVEN
94703
PA
HARRISBURG
14751
VA
STERLING(WASH DULLES)
93734
PA
PITTSBURGH
14762
PA
PITTSBURGH/MOON
TOWNSHIP
94823
PA
SELINSGROVE
14770
VA
STERLING(WASH DULLES)
93734
PA
WILKES-
BARRE/SCRANTON
14777
NY
ALBANY
54775
16 of 21
-------
Surface
Upper Air
State
City
WBAN #
State
City
WBAN#
PA
WILLIAMSPORT
14778
NY
BUFFALO/GRTR ARPT
14733
PA
ERIE
14860
NY
BUFFALO/GRTR ARPT
14733
PA
LANCASTER
54737
VA
STERLING(WASH DULLES)
93734
PA
POTTSTOWN
54782
VA
STERLING(WASH DULLES)
93734
PA
DOYLESTOWN
54786
NY
BROOKHAVEN
94703
PA
MOUNT POCONO
54789
NY
BROOKHAVEN
94703
PA
CLEARFIELD
54792
PA
PITTSBURGH/MOON
TOWNSHIP
94823
PA
YORK
93778
VA
STERLING(WASH DULLES)
93734
PA
PHILADELPHIA
94732
NY
BROOKHAVEN
94703
PA
PITTSBURGH
94823
PA
PITTSBURGH/MOON
TOWNSHIP
94823
PR
SAN JUAN
11641
PR
SAN JUAN
11641
Rl
PROVIDENCE
14765
MA
CHATHAM
14684
Rl
NEWPORT
14787
MA
CHATHAM
14684
Rl
WESTERLY
14794
NY
BROOKHAVEN
94703
SC
GREER
03870
NC
GREENSBORO
13723
SC
FLORENCE
13744
SC
CHARLESTON
13880
SC
CHARLESTON
13880
SC
CHARLESTON
13880
SC
COLUMBIA
13883
SC
CHARLESTON
13880
SC
GREENVILLE
13886
NC
GREENSBORO
13723
SC
CLEMSON
53850
GA
PEACHTREE CITY
53819
SC
ORANGEBURG
53854
SC
CHARLESTON
13880
SC
COLUMBIA
53867
SC
CHARLESTON
13880
SC
ROCK HILL
53871
NC
GREENSBORO
13723
SC
GREENWOOD
53874
GA
PEACHTREE CITY
53819
SC
NORTH MYRTLE
BEACH
93718
SC
CHARLESTON
13880
SC
ANDERSON
93846
GA
PEACHTREE CITY
53819
SD
ABERDEEN
14929
SD
ABERDEEN
14929
SD
HURON
14936
SD
ABERDEEN
14929
SD
SIOUX FALLS
14944
SD
ABERDEEN
14929
SD
WATERTOWN
14946
SD
ABERDEEN
14929
SD
PHILIP
24024
SD
RAPID CITY
94043
SD
PIERRE
24025
SD
ABERDEEN
14929
SD
RAPID CITY
24090
SD
RAPID CITY
94043
SD
CUSTER
94032
SD
RAPID CITY
94043
SD
PINE RIDGE
94039
SD
RAPID CITY
94043
SD
MOBRIDGE
94052
ND
BISMARCK
24011
SD
MITCHELL
94950
SD
ABERDEEN
14929
SD
WINNER
94990
SD
ABERDEEN
14929
TN
JACKSON
03811
TN
NASHVILLE
13897
TN
CROSSVILLE
03847
TN
NASHVILLE
13897
TN
BRISTOL/JOHNSON
CITY/KINGSPORT
13877
VA
ROANOKE/BLACKSBURG
53829
17 of 21
-------
Surface
Upper Air
State
City
WBAN #
State
City
WBAN#
TN
CHATTANOOGA
13882
TN
NASHVILLE
13897
TN
KNOXVILLE
13891
TN
NASHVILLE
13897
TN
MEMPHIS
13893
AR
N LITTLE ROCK
03952
TN
NASHVILLE
13897
TN
NASHVILLE
13897
TX
BORGER
03024
TX
AMARILLO
23047
TX
LONGVIEW
03901
LA
SHREVEPORT REGIONAL
AP
13957
TX
COLLEGE STATION
03904
TX
FT WORTH
03990
TX
DALLAS-FT WORTH
03927
TX
FT WORTH
03990
TX
DALLAS
03971
TX
FT WORTH
03990
TX
DENTON
03991
TX
FT WORTH
03990
TX
BURNET
03999
TX
FT WORTH
03990
TX
HARLINGEN
12904
TX
BROWNSVILLE
12919
TX
VICTORIA
12912
TX
CORPUS CHRISTI
12924
TX
BEAUMONT/PORT
ARTHUR
12917
LA
LAKE CHARLES
03937
TX
HOUSTON
12918
LA
LAKE CHARLES
03937
TX
BROWNSVILLE
12919
TX
BROWNSVILLE
12919
TX
SAN ANTONIO
12921
TX
CORPUS CHRISTI
12924
TX
GALVESTON
12923
LA
LAKE CHARLES
03937
TX
CORPUS CHRISTI
12924
TX
CORPUS CHRISTI
12924
TX
ALICE
12932
TX
CORPUS CHRISTI
12924
TX
PALACIOS
12935
TX
CORPUS CHRISTI
12924
TX
COTULLA
12947
TX
CORPUS CHRISTI
12924
TX
PORT ISABEL
12957
TX
BROWNSVILLE
12919
TX
MC ALLEN
12959
TX
BROWNSVILLE
12919
TX
HOUSTON
12960
LA
LAKE CHARLES
03937
TX
HONDO
12962
TX
DEL RIO
22010
TX
SAN ANTONIO
12970
TX
CORPUS CHRISTI
12924
TX
NEW BRAUNFELS
12971
TX
CORPUS CHRISTI
12924
TX
ROCKPORT
12972
TX
CORPUS CHRISTI
12924
TX
HOUSTON
12975
LA
LAKE CHARLES
03937
TX
ANGLETON/LAKE
JACKSON
12976
LA
LAKE CHARLES
03937
TX
HOUSTON
12977
LA
LAKE CHARLES
03937
TX
AUSTIN/BERGSTROM
13904
TX
CORPUS CHRISTI
12924
TX
AUSTIN/CITY
13958
TX
FT WORTH
03990
TX
WACO
13959
TX
FT WORTH
03990
TX
DALLAS
13960
TX
FT WORTH
03990
TX
FORT WORTH
13961
TX
FT WORTH
03990
TX
ABILENE
13962
TX
FT WORTH
03990
TX
WICHITA FALLS
13966
OK
NORMAN
03948
TX
TYLER
13972
LA
SHREVEPORT REGIONAL
AP
13957
TX
JUNCTION
13973
TX
DEL RIO
22010
TX
DEL RIO
22010
TX
DEL RIO
22010
TX
CHILDRESS
23007
TX
AMARILLO
23047
18 of 21
-------
Surface
Upper Air
State
City
WBAN #
State
City
WBAN#
TX
MIDLAND
23023
TX
MIDLAND
23023
TX
SAN ANGELO
23034
TX
MIDLAND
23023
TX
WINK
23040
TX
MIDLAND
23023
TX
LUBBOCK
23042
TX
AMARILLO
23047
TX
EL PASO
23044
NM
SANTA TERESA
03020
TX
AMARILLO
23047
TX
AMARILLO
23047
TX
GUADALUPE PASS
23055
NM
SANTA TERESA
03020
TX
FORT STOCKTON
23091
TX
MIDLAND
23023
TX
CONROE
53902
LA
LAKE CHARLES
03937
TX
HUNTSVILLE
53903
LA
LAKE CHARLES
03937
TX
ARLINGTON
53907
TX
FT WORTH
03990
TX
FORT WORTH
53909
TX
FT WORTH
03990
TX
HOUSTON
53910
LA
LAKE CHARLES
03937
TX
TERRELL
53911
TX
FT WORTH
03990
TX
CORSICANA
53912
TX
FT WORTH
03990
TX
MC KINNEY
53914
TX
FT WORTH
03990
TX
DALHART
93042
TX
AMARILLO
23047
TX
MINERAL WELLS
93985
TX
FT WORTH
03990
TX
LUFKIN
93987
LA
SHREVEPORT REGIONAL
AP
13957
UT
BRYCE CANYON
23159
AZ
FLAGSTAFF/BELLEMT
(ARMY)
53103
UT
MILFORD
23176
UT
SALT LAKE CITY
24127
UT
OGDEN
24126
UT
SALT LAKE CITY
24127
UT
SALT LAKE CITY
24127
UT
SALT LAKE CITY
24127
UT
MOAB
93075
CO
GRAND JUNCTION
23066
UT
CEDAR CITY
93129
NV
LAS VEGAS
03120
UT
PRICE
93141
UT
SALT LAKE CITY
24127
UT
VERNAL
94030
CO
GRAND JUNCTION
23066
UT
LOGAN
94128
UT
SALT LAKE CITY
24127
VA
DANVILLE
13728
NC
GREENSBORO
13723
VA
LYNCHBURG
13733
VA
ROANOKE/BLACKSBURG
53829
VA
NORFOLK
13737
VA
WALLOPS ISLAND
93739
VA
RICHMOND
13740
VA
STERLING(WASH DULLES)
93734
VA
ROANOKE
13741
VA
ROANOKE/BLACKSBURG
53829
VA
CHARLOTTESVILLE
93736
VA
STERLING(WASH DULLES)
93734
VA
WALLOPS ISLAND
93739
VA
WALLOPS ISLAND
93739
VA
NEWPORT NEWS
93741
VA
WALLOPS ISLAND
93739
VA
WAKEFIELD
93773
VA
WALLOPS ISLAND
93739
VA
RICHMOND/ASHLAND
93775
VA
STERLING(WASH DULLES)
93734
VT
BURLINGTON
14742
NY
ALBANY
54775
VT
SPRINGFIELD
54740
NY
ALBANY
54775
VT
MORRISVILLE
54771
ME
GRAY
54762
VT
BENNINGTON
54781
NY
ALBANY
54775
19 of 21
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Surface
Upper Air
State
City
WBAN #
State
City
WBAN#
VT
BARRE/MONTPELIER
94705
ME
GRAY
54762
WA
MOSES LAKE
24110
WA
SPOKANE INTNLAPT
04106
WA
EPHRATA
24141
WA
SPOKANE INTNLAPT
04106
WA
SPOKANE
24157
WA
SPOKANE INTNLAPT
04106
WA
WALLA WALLA
24160
WA
SPOKANE INTNLAPT
04106
WA
PASCO
24163
WA
SPOKANE INTNLAPT
04106
WA
BELLI NGHAM
24217
WA
QUILLAYUTE
94240
WA
ELLENSBURG
24220
WA
SPOKANE INTNLAPT
04106
WA
EVERETT
24222
WA
QUILLAYUTE
94240
WA
OLYMPIA
24227
WA
QUILLAYUTE
94240
WA
SEATTLE
24233
WA
QUILLAYUTE
94240
WA
SEATTLE
24234
WA
QUILLAYUTE
94240
WA
YAKIMA
24243
WA
SPOKANE INTNLAPT
04106
WA
DEER PARK
94119
WA
SPOKANE INTNLAPT
04106
WA
PULLMAN/MOSCOW
94129
WA
SPOKANE INTNLAPT
04106
WA
SPOKANE
94176
WA
SPOKANE INTNLAPT
04106
WA
OMAK
94197
WA
SPOKANE INTNLAPT
04106
WA
HOQUIAM
94225
WA
QUILLAYUTE
94240
WA
SHELTON
94227
WA
QUILLAYUTE
94240
WA
WENATCHEE
94239
WA
SPOKANE INTNLAPT
04106
WA
QUILLAYUTE
94240
WA
QUILLAYUTE
94240
WA
RENTON
94248
WA
QUILLAYUTE
94240
WA
PORT ANGELES
94266
WA
QUILLAYUTE
94240
WA
TACOMA
94274
WA
QUILLAYUTE
94240
WA
FRIDAY HARBOR
94276
WA
QUILLAYUTE
94240
WA
VANCOUVER
94298
OR
SALEM
24232
Wl
RHINELANDER
04803
Wl
GREEN BAY
14898
Wl
WISCONSIN RAPTIDS
04826
Wl
GREEN BAY
14898
Wl
FOND DU LAC
04840
Wl
GREEN BAY
14898
Wl
SHEBOYGAN
04841
Wl
GREEN BAY
14898
Wl
KENOSHA
04845
Wl
GREEN BAY
14898
Wl
MADISON
14837
Wl
GREEN BAY
14898
Wl
MILWAUKEE
14839
Wl
GREEN BAY
14898
Wl
WAUSAU
14897
Wl
GREEN BAY
14898
Wl
GREEN BAY
14898
Wl
GREEN BAY
14898
Wl
LA CROSSE
14920
MN
MINNEAPOLIS
94983
Wl
LONE ROCK
14921
IA
DAVENPORT MUNICIPAL
AP
94982
Wl
EAU CLAIRE
14991
MN
MINNEAPOLIS
94983
Wl
RACINE
94818
Wl
GREEN BAY
14898
Wl
OSHKOSH
94855
Wl
GREEN BAY
14898
Wl
ASHLAND
94929
MN
MINNEAPOLIS
94983
Wl
HAYWARD
94973
MN
MINNEAPOLIS
94983
Wl
MARSHFIELD
94985
Wl
GREEN BAY
14898
Wl
BOSCOBEL
94994
IA
DAVENPORT MUNICIPAL
AP
94982
20 of 21
-------
Surface
Upper Air
State
City
WBAN #
State
City
WBAN#
WV
CLARKSBURG
03802
PA
PITTSBURGH/MOON
TOWNSHIP
94823
wv
PARKERSBURG
03804
PA
PITTSBURGH/MOON
TOWNSHIP
94823
WV
BLUEFIELD
03859
VA
ROANOKE/BLACKSBURG
53829
wv
HUNTINGTON
03860
OH
WILMINGTON
13841
wv
BECKLEY
03872
VA
ROANOKE/BLACKSBURG
53829
wv
ELKINS
13729
PA
PITTSBURGH/MOON
TOWNSHIP
94823
wv
MARTINSBURG
13734
VA
STERLING(WASH DULLES)
93734
wv
MORGANTOWN
13736
PA
PITTSBURGH/MOON
TOWNSHIP
94823
wv
CHARLESTON
13866
VA
ROANOKE/BLACKSBURG
53829
wv
WHEELING
14894
PA
PITTSBURGH/MOON
TOWNSHIP
94823
WY
EVANSTON
04111
UT
SALT LAKE CITY
24127
WY
CHEYENNE
24018
CO
DENVER/STAPLETON ARP
r 23062
WY
LANDER
24021
WY
RIVERTON
24061
WY
LARAMIE
24022
CO
DENVER/STAPLETON ARP
r 23062
WY
ROCK SPRINGS
24027
WY
RIVERTON
24061
WY
SHERIDAN
24029
WY
RIVERTON
24061
WY
GREYBULL
24048
WY
RIVERTON
24061
WY
RAWLINS
24057
WY
RIVERTON
24061
WY
RIVERTON
24061
WY
RIVERTON
24061
WY
WORLAND
24062
WY
RIVERTON
24061
WY
CASPER
24089
WY
RIVERTON
24061
WY
BIG PINEY
24164
WY
RIVERTON
24061
WY
GILLETTE
94023
SD
RAPID CITY
94043
WY
TORRINGTON
94053
SD
RAPID CITY
94043
WY
BUFFALO
94054
WY
RIVERTON
24061
WY
DOUGLAS
94057
SD
RAPID CITY
94043
21 of 21
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APPENDIX 4: Technical Support Document for the TRIM-Based
Multipathway Tiered Screening Methodology for RTR
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Technical Support Document
for the TRIM-Based Multipathway
Tiered Screening Methodology for RTR
December 2013
Prepared For:
U.S. Environmental Protection Agency
Office of Air Quality Planning and Standards
Research Triangle Park, NC 27711
Prepared By:
ICF International
2635 Meridian Parkway
Suite 200
Durham, NC 27713
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TRIM-Based Tiered Screening Methodology for RTR
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-7
A.2 Summary of Approach A-7
A.2.1 Overview A-7
A.2.2 Chemicals of Potential Concern A-10
A.2.3 Conceptual Exposure Scenario A-11
A.2.4 Approach to Development of the Tier 1 Scenario A-13
A.2.5 Fate and Transport Modeling (TRIM.FaTE) A-15
A.2.6 Exposure Modeling and Risk Characterization (MIRC) A-16
A.2.7 Implementation of Risk-based Emission Scaling Factors for
POM and Dioxin Emissions A-17
A.3 Description of Environmental Modeling Scenario A-21
A.3.1 Chemical Properties A-21
A.3.2 Spatial Layout A-21
A.3.3 Watershed and Water Body Parameterization A-23
A.3.4 Meteorology A-25
A.3.5 Aquatic Food Web A-27
A.3.6 Using TRIM.FaTE Media Concentrations A-28
A.4 Description of Exposure and Risk Modeling Scenario A-29
A.4.1 Calculating Concentrations in Farm Food Chain Media A-29
A.4.2 Ingestion Exposure A-30
A.4.3 Calculating Risk A-33
A.4.4 Summary of Tier 1 Assumptions A-34
A.5 Evaluation of Screening Scenario A-38
A.5.1 Introduction A-38
A.5.2 Cadmium Compounds A-38
A.5.3 Mercury Compounds A-40
A.5.4 DioxinsA-42
A.5.5 Polycyclic Aromatic Hydrocarbons A-43
A.5.6 Summary A-45
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TRIM-Based Tiered Screening Methodology for RTR
A.6 References A-46
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 Estimation of Adjustment Factors for Selected Site-Specific
Parameters B-8
B.2.1 Selection Values for Variables of Interest B-9
B.2.2 Estimation of Adjustment Factors B-12
B.3 Preparing National Databases of Lake and Meteorological Data B-16
B.3.1 Processing Lake Data for Tier 2 Analysis B-16
B.3.2 Processing Meteorological Data for Tier 2 Analysis B-20
B.4 Implementation of Tier 2 Analysis B-23
B.4.1 Facility List for Tier 2 Screen (Step 3) B-25
B.4.2 Facility/Lake Distance Table (Step 4) B-26
B.4.3 Matching Facilities to Meteorology Data (Step 5) B-27
B.4.4 Assembling Threshold Adjustment Factors (Step 6) B-28
B.4.5 Assembling Results (Step 7) B-30
B.5 References B-34
Addendum 1. Summary of TRIM.FaTE Parameters Considered for
Inclusion in Tier 2 Analysis 1
Addendum 2. Analysis of Lake Size and Sustainable Fish Population 2-1
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TRIM-Based Tiered Screening Methodology for RTR
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
Attachment A, Section 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 to
Attachment A). 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
Overview
1
December 2013
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TRIM-Based Tiered Screening Methodology for RTR
then adjusted for that facility based on an understanding of how exposure concentrations
estimated for the screening scenario change with meteorology and lake location. PB-
HAP emissions that do not exceed the adjusted threshold are assumed to pose risks
below levels of concern and no additional multipathway assessment 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 site-specific
assessment, resulting in a range of risk estimates.
The key processes and decisions that make up this approach are summarized in Exhibit 1. In
the remainder of this 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).
Overview
2
December 2013
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TRIM-Based Tiered Screening Methodology for RTR
Exhibit 1. Conceptual Decision Tree for Evaluating Non-Inhalation Exposures
for PB-HAPs
Overview
3
December 2013
-------
TRIM-Based Tiered Screening Methodology for RTR
Based on current emissions, bioaccumulation potential, and toxicity considerations, emissions
of these four PB-HAPs are expected to pose the majority of the non-inhalation risks to humans
from air emissions at sources subject to residual risk provisions of the CAA1 Thus, although
EPA has identified nine other PB-HAPs that should be evaluated as part of residual risk
assessments, the methods for multipathway assessment described here encompass only these
four at this time.
If emissions of any of the four PB-HAPs listed above are reported for a facility, the emission rate
for that PB-HAP is compared to the threshold emissions rate derived for that chemical using the
hypothetical TRIM-based screening scenario. This threshold is the emission rate that, when
input to the models used in evaluating multipathway risk for RTR, results in a specified cancer
risk or non-cancer HQ threshold level of concern. For the screening scenario, threshold
emission rates were calculated for a cancer risk of 1 in 1 million or an HQ of 1.0, depending on
the more sensitive health effect of the PB-HAP. However, due to the hypothetical nature of the
screening scenario, exceeding the threshold emissions rate by 60 times, for dioxins for
example, does not imply a resulting cancer risk of 60 in 1 million. Rather, exceeding the
threshold emissions rate by 60 times for dioxin implies that it is highly unlikely that the actual
risk would exceed 60 in 1 million.
Important to note for dioxins and POM is that the screening methodology assesses individual
congeners, taking into account differences in both the fate and transport and the toxicity among
the various congeners. The details of the methods for assessing dioxins and POM are provided
in Attachment A 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 for use. For the RTR screening scenario, the Multimedia Ingestion Risk
Calculator (MIRC) was constructed to complete the calculations required for estimating PB-HAP concentrations in
farm food chain media, average daily ingestion doses, and cancer risks and chronic non-cancer HQs. The framework
is conceptually identical to the ingestion exposure and risk analyses that TRIM intended to cover. Information about
the current status of TRIM modules and documentation of modules developed thus far can be accessed on EPA's
Technology Transfer Network (TTN) on the Fate, Exposure, and Risk Analysis website (http://www.epa.gov/ttn/fera/).
Overview
4
December 2013
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TRIM-Based Tiered Screening Methodology for RTR
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
Chemical Emissionsto Air
Cancer Risk
Hazard Quotient
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 central-
tendency values. The mix of health protective and central-tendency assumptions and
parameterization is expected to result in a scenario configuration that, on average, is likely to
overpredict environmental concentrations of PB-HAPs in media of interest for this evaluation.
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. e pa. g ov/ttn/fe ra/tri m_fate .html
Overview 5 December 2013
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TRIM-Based Tiered Screening Methodology for RTR
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 poultry/eggs
Ingestion of homegrown produce • Ingestion of homegrown pork
Ingestion of homegrown beef • Ingestion of fish
Ingestion of milk from homegrown • Ingestion of breast milk (children <1 year
cows 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 value (99th
percentile for fish and 90th percentile for all other food types) based on EPA's Exposure Factors
Handbook (EPA 2011a) or other sources as appropriate. All media were assumed to be
obtained from a location impacted by the modeled source. This approach results in an
overestimate of total chemical exposure for a hypothetical exposure scenario. For example, the
resulting total food ingestion rate is extremely high for a hypothetical consumer, with ingestion
rates at the 99th percentile for fish and the 90th percentile for every other farm food type. These
health protective exposure assumptions can be replaced in a site-specific assessment as
appropriate (e.g., with distributions of the data for key exposure factors).
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.
Overview
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TRIM-Based Tiered Screening Methodology for RTR
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 PB-
HAP 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 lake distance allowing for a constant ratio between watershed and erosion
area compared with lake area within the TRIM modeling structure.
Overview
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TRIM-Based Tiered Screening Methodology for RTR
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 is based
on the same hourly meteorology data used for RTR inhalation assessments. The meteorology
database includes annual summary statistics on wind direction, wind speed, precipitation, and
mixing heights for more than 800 surface stations located throughout the United States and is
paired with their closest upper-air station with available data (data available from the National
Oceanic and Atmospheric Administration). The lake database, based on U.S. Geological
Survey (USGS) data and including location and size information, 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 100 acres were included. Very large lakes and bays (i.e.,
those larger than 100,000 acres) are not included because their watersheds are too large and
their lake dynamics are too complex to realistically model in the TRIM.FaTE system. Lakes and
bays larger than 100,000 acres include the Great Lakes, the Great Salt Lake, Lake
Okeechobee, Lake Pontchartrain, Lake Champlain, Green Bay, and Galveston Bay. 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 within 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
Overview
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December 2013
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TRIM-Based Tiered Screening Methodology for RTR
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.
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 same meteorology station
used in RTR inhalation assessments, and the values for the four relevant meteorological
parameters at that station are recorded. The distances from the facility to 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
multipathway analyses include residual risk assessments of a petroleum refinery facility ( EPA
2013), two secondary lead smelting facilities (EPA 2011b), assessments of two coal-fired
electric utility units conducted in support of EPA's utility rule (EPA 2011c), and a case study
evaluation of a Portland cement facility included with other RTR materials presented to the
Science Advisory Board for review (Appendix I of EPA 2009).
Whereas 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
EPA (U.S. Environmental Protection Agency) (2005) Human Health Risk Assessment Protocol
for Hazardous Waste Combustion Facilities. EPA Office of Solid Waste. EPA-530/R-05-006.
09/2005.
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
Overview
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December 2013
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TRIM-Based Tiered Screening Methodology for RTR
Sources and Portland Cement Manufacturing. EPA Office of Air Quality Planning and
Standards. EPA-452/R-09-006. 06/2009.
EPA. (2011a) Exposure Factors Handbook: 2011 edition. National Center for Environmental
Assessment, Washington, DC; EPA/600/R-09/052F. Available from the National Technical
Information Service, Springfield, VA, and online at http://www.epa.gov/ncea/efh.
EPA. (2011b) Human Health Multipathway Residual Risk Assessment for the Secondary Lead
Smelting Source Category. Draft Report. Prepared by ICF International for EPA Risk and
Exposure Assessment Group. 02/15/2011.
EPA. (2011c) Technical Support Document: Case Study Analyses of Potential Local-scale
Human Health Risks Associated with Mercury Emissions from Electric Utility Steam-
generating Units. Draft. Prepared by ICF International for EPA Office of Air Quality Planning
and Standards. 02/17/2011.
EPA. (2013) Technical Support Document: Human Health Multipathway Residual Risk
Assessment for the Petroleum Refineries Sector. Draft. Prepared by ICF International for
EPA Office of Air Quality Planning and Standards. 04/10/2013.
Overview
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TRIM-Based Tiered Screening Methodology for RTR
Attachment A. Tier 1 Screening Methodology
Attachment A - Tier 1
A-1
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TRIM-Based Tiered Screening Methodology for RTR
CONTENTS, ATTACHMENT A
Attachment A. Tier 1 Screening Methodology A-1
A.1 Introduction A-7
A.2 Summary of Approach A-7
A.2.1 Overview A-7
A.2.2 Chemicals of Potential Concern A-10
A.2.3 Conceptual Exposure Scenario A-11
A.2.4 Approach to Development of the Tier 1 Scenario A-13
A.2.4.1 Modeling Framework A-13
A.2.4.2 Model Configuration and Parameterization A-15
A.2.5 Fate and Transport Modeling (TRIM.FaTE) A-15
A.2.6 Exposure Modeling and Risk Characterization (MIRC) A-16
A.2.7 Implementation of Risk-based Emission Scaling Factors for POM and
Dioxin Emissions A-17
A.2.7.1 Calculation of Scaling Factors for POM Congeners A-18
A.2.7.2 Calculation of Scaling Factors for Dioxin Congeners A-20
A.3 Description of Environmental Modeling Scenario A-21
A.3.1 Chemical Properties A-21
A.3.2 Spatial Layout A-21
A.3.3 Watershed and Water Body Parameterization A-23
A.3.3.1 Water Balance A-23
A.3.3.2 Sediment Balance A-24
A.3.4 Meteorology A-25
A.3.5 Aquatic Food Web A-27
A.3.6 Using TRIM.FaTE Media Concentrations A-28
A.4 Description of Exposure and Risk Modeling Scenario A-29
A.4.1 Calculating Concentrations in Farm Food Chain Media A-29
A.4.2 Ingestion Exposure A-30
A.4.2.1 Exposure Scenarios and Corresponding Inputs A-30
A.4.2.2 Calculating Average Daily Doses A-32
A.4.2.3 Infant Ingestion of Breast Milk A-33
A.4.3 Calculating Risk A-33
A.4.4 Summary of Tier 1 Assumptions A-34
A.5 Evaluation of Screening Scenario A-38
A.5.1 Introduction A-38
A.5.2 Cadmium Compounds A-38
A.5.2.1 Behavior in the Environment A-38
Attachment A - Tier 1 A-3 December 2013
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TRIM-Based Tiered Screening Methodology for RTR
A.5.2.2 Concentrations in Ingestible Products A-38
A.5.2.3 Average Daily Dose (ADD) A-39
A.5.3 Mercury Compounds A-40
A.5.3.1 Behavior in the Environment A-40
A.5.3.2 Concentrations in Ingestible Products A-40
A.5.3.3 Average Daily Dose A-41
A.5.4 Dioxins A-42
A.5.4.1 Behavior in the Environment A-42
A.5.4.2 Concentrations in Ingestible Products A-42
A.5.4.3 Lifetime Average Daily Dose (LADD) A-43
A.5.5 Polycyclic Aromatic Hydrocarbons A-43
A.5.5.1 Behavior in the Environment A-44
A.5.5.2 Concentrations in Ingestible Products A-45
A.5.5.3 Lifetime Average Daily Dose A-45
A.5.6 Summary A-45
A.6 References A-46
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 A - Tier 1 A-4 December 2013
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TRIM-Based Tiered Screening Methodology for RTR
EXHIBITS, ATTACHMENT A
Exhibit_Att A-1. Conceptual Decision Tree for Evaluation of Non-Inhalation
Exposures of PB-HAPs A-8
Exhibit_Att A-2. Emission Thresholds for Screening of Multipathway Exposures A-10
Exhibit_Att A-3. OAQPS PB-HAP Compounds A-11
Exhibit_Att A-4. Overview of Ingestion Exposure and Risk Screening Evaluation
Method A-14
Exhibit_Att A-5. Overview of Process Carried Out in the Multimedia Ingestion
Risk Calculator A-17
Exhibit_Att A-6. Exposure, Toxicity, and Risk Equivalency Factors Relative to
BaP for POM Congeners Currently Evaluated in Tiers 1 and 2
Analyses A-19
Exhibit_Att A-7. Relationship between Exposure and KoW for POM Congeners A-20
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-22
Exhibit_Att A-10. Summary of Key Meteorological Inputs A-26
Exhibit_Att A-11. Aquatic Biota Parameters for the TRIM.FaTE Screening
Scenario A-28
Exhibit_Att A-12. Spatial Considerations - TRIM.FaTE Results Selected for
Calculating Farm Food Chain Media Concentrations and Receptor
Exposures A-29
Exhibit_Att A-13. Summary of Ingestion Exposure Pathways and Routes of
Uptake A-31
Exhibit_Att A-14. Overview of Exposure Factors Used for RTR Multipathway
Screening3,15 A-32
Exhibit_Att A-15. Dose-response Values for PB-HAPs Addressed by the
Screening Scenario A-34
Exhibit_Att A-16. Summary of RTR Tier 1 Screening Scenario Assumptions A-34
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-44
Exhibit_Att A-20. Estimated Contributions of Modeled Food Types to PAH
Ingestion Exposures A-46
Attachment A - Tier 1 A-5 December 2013
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TRIM-Based Tiered Screening Methodology for RTR
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Attachment A -
Tier 1
A-6
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TRIM-Based Tiered Screening Methodology for RTR
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 Attachment 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.
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 TRIM-
based Tier 1 scenario described in this document (see Exhibit_Att A-2 for threshold screening
emission rates).
Attachment A -
Tier 1
A-7
December 2013
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TRIM-Based Tiered Screening Methodology for RTR
Exhibit_Att A-1. Conceptual Decision Tree for Evaluation of Non-Inhalation
Exposures of PB-HAPs
Evaluate HAP emissions by
facility
Diagram Key
Process
^ Outcome ^
Are any PB-HAPs
emitted?
Facility screens out
YES
<
z>
_l
£
HI
a:
LU
Are PB-HAPs
of primary concern
for RTR emitted?
(Cd, dioxins, Hg,
POM)?
-NO-
YES
Non-inhalation risks not likely,
but evaluate on a case-by-case
basis
Does the emission
rate for any PB-
HAP exceed Tier
1 threshold?
Facility screens out; no concern for
multipathway risk
-NO- I (check emissions of PB-HAPs other than
Cd, dioxins, Hg, POM, Pb on a case-by-
case basis)
YES
Proceed to Tier 2
Attachment A - Tier 1
A-8
December 2013
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TRIM-Based Tiered Screening Methodology for RTR
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
6For 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.
Attachment A - Tier 1
A-9
December 2013
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TRIM-Based Tiered Screening Methodology for RTR
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)3
2.58E-03
Cancer
Dioxins (as 2,3,7,8-TCDD equivalents)3
2.81 E-09
Cancer
Cadmium
1.18E-02
Non-cancer
Mercury (as divalent mercury emissions)
3.16E-04
Non-cancer
TPY = U.S. short tons per year
aSee 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 many of
the most risk influential parameters in the model, parameter values that result in high-end risk
estimates. In the real world, the probability of such risk-maximizing conditions prevailing across
multiple parameters is very low. For example, the Tier 1 scenario assumes a fishable lake
approximately 2 km from any given facility, when in reality, a lake may be more than 50 km
away. Additional conservative assumptions used in the Tier 1 screen are described in Section
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-HAPs8 that the
Office of Air Quality Planning and Standards (OAQPS) has identified as candidates for
multipathway risk assessments. OAQPS developed a list of 14 chemicals and chemical groups
that are PB-HAPs based on a two-step process taking into account the following (EPA 2004a):
7Note 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.
8Although 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.
Attachment A - Tier 1
A-10
December 2013
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TRIM-Based Tiered Screening Methodology for RTR
their presence on three existing EPA lists of persistent, bioaccumulative, and toxic
substances, and
a semiquantitative ranking of toxicity and bioaccumulation potential of the entire list of
HAPs.
The list's development and utility in hazard identification for multipathway risk assessment are
explained further in Chapter 14 and Appendix D of Volume I of EPA's Air Toxics Risk
Assessment (ATRA) Reference Library (EPA 2004a). Exhibit_Att A-3 presents the 14
chemicals and groups that are PB-HAPs.
Exhibit_Att A-3. OAQPS PB-HAP Compounds
PB-HAP Compound3
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
Hexachloro benzene
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: EPA (2004a).
The screening scenario described in this document is not configured for evaluating the risk
potential for all 14 PB-HAPs on the list. Currently, the scenario can only be used to estimate
exposures and risks quantitatively for 4 of the 14 PB-HAPs (as indicated in Exhibit_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.
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);
9Potential 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.
Attachment A - Tier 1
A-11
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TRIM-Based Tiered Screening Methodology for RTR
• A retention medium, or a transport mechanism and subsequent retention medium in
cases involving media transfer of chemicals;
. A point of potential human contact with the contaminated medium; and
. An exposure route.
An exposure route is the particular means of entry into the body. Receptors are exposed to
chemicals emitted to the atmosphere via two primary routes: either directly via inhalation, or
indirectly via ingestion or dermal contact with various media that have been contaminated with
the emitted PB-HAPs. (Inhalation pathways are assessed separately and are not considered in
the Tier 1 assessment presented here.)
PB-HAPs can persist in the environment for long periods of time and also build up in soil and in
the food chain, including fish, fruits and vegetables, and animal products (e.g., meat, dairy,
eggs). For this reason, ingestion of foods grown within an area impacted by RTR sources can
be an important source of exposure to PB-HAPs.
To assess risks from hazardous waste combustion facilities, EPA identified several hypothetical
receptor scenarios, noting that these scenarios are considered appropriate for a broad range of
situations, rather than to represent any actual scenario. These scenarios are described in
EPA's Human Health Risk Assessment Protocol for Hazardous Waste Combustion Facilities, or
HHRAP (EPA 2005a). In this document, EPA recommends assessment of the following
hypothetical receptors: a Farmer, Farmer Child, Resident, Resident Child, Fisher, Fisher Child,
Acute Receptor, and Nursing Infant. These receptors are distinguished by their pathways of
exposures. EPA further notes in HHRAP that some exposure settings might warrant including
additional exposure pathways; such as including exposure through fish ingestion for the farmer
receptor. For the RTR screening scenario, risks are assessed for a single hypothetical receptor.
Based on the guidance provided in HHRAP, a health protective exposure scenario was
developed whereby the hypothetical receptor receives ingestion exposure via both the farm food
chain and the fish ingestion pathways. The exposure scenario for the RTR Tier 1 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
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).
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.
Attachment A - Tier 1
A-12
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TRIM-Based Tiered Screening Methodology for RTR
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 (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 PB-
HAPs emissions from facilities in the context of residual risk assessments conducted as part of
RTR. The Tier 1 scenario used to derive the threshold emission rates is not intended to be
representative of any particular situation. Rather, it was developed for the purpose of RTR to
portray a hypothetical exposure scenario that will generate emissions screening levels that are
health protective for any potential exposure situation that might plausibly be encountered in the
United States. A range of conditions was assessed when conceptualizing and developing the
screening scenario. The final configuration was chosen so that for a given individual, any
potential long-term exposure levels for a given geographic region would be reasonably unlikely
to exceed those of the Tier 1 configuration. These criteria were met by constructing a
hypothetical scenario that would be health-protective in key aspects, including spatial
orientation, meteorology, types of exposures, and ingestion rates. The overall result is a
scenario that is unlikely to occur at any one location but has a high likelihood of representing the
upper end of all potential exposures. This latter aspect accomplishes the goal of striking a
balance between health protectiveness and the level of accuracy called for in the ideal
screening approach previously discussed.
The development and application of the Tier 1 scenario for residual risk evaluations considered
EPA's technical and policy guidelines presented in the Residual Risk Report to Congress (EPA
1999); Volumes I and II of the Air Toxics Risk Assessment Reference Library (EPA 2004a,
2005a); and other EPA publications. The scenario described 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 analyses, as described in Attachment B. 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:
11 An exception to this generality would be reservoirs used for drinking water supplies. This situation might be worthy
of additional analysis, if warranted by the characteristics of a given assessment (e.g., to estimate PB-HAP
concentrations in treated drinking water derived from reservoirs).
Attachment A - Tier 1
A-13
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TRIM-Based Tiered Screening Methodology for RTR
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.
Exhibit_Att A-4. Overview of Ingestion Exposure and
Risk Screening Evaluation Method
Chemical Emissions to Air
/¦
Chemical fate and
transport:
Physical environment
and aquatic ecosystem
TRIM.FaTE
Uptake & transfer
into produce and
livestock
Human
ingestion
exposure
Risk & hazard
estimation
Multimedia Ingestion Risk Calculator (MIRC)
Cancer Risk
Hazard Quotient
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-lngestion 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
12
As discussed below, concentrations in fish calculated by the TRIM.FaTE model were used to estimate ingestion
exposures for humans consuming fish. Modeling offish 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.
13
Information about the current status of TRIM modules and comprehensive documentation of modules developed
thus far can be accessed on EPA's Technology Transfer Network (TTN) on the Fate, Exposure, and Risk Analysis
website (http://www.epa.gov/ttn/fera/).
Attachment A - Tier 1
A-14
December 2013
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TRIM-Based Tiered Screening Methodology for RTR
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 (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
14
TRIM.FaTE is a spatially explicit, compartmental mass balance model that describes movement and transformation
of pollutants overtime, through a user-defined, bounded system that includes both biotic and abiotic compartments.
Outputs include pollutant concentrations in multiple environmental media and biota.
Attachment A - Tier 1
A-15
December 2013
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TRIM-Based Tiered Screening Methodology for RTR
relevant media. Additional information about TRIM.FaTE, including support documentation,
software, and the TRIM.FaTE public reference library, is available at
http://www.epa.gov/ttn/fera/.
The algorithms used to model mercury species and polyaromatic hydrocarbons (PAHs) are
described in Volume II of the TRIM.FaTE Technical Support Document (EPA 2002a). A
comprehensive evaluation of the performance of TRIM.FaTE for modeling mercury was
documented in Volumes I and II of the TRIM.FaTE Evaluation Report (EPA 2002b, 2005b).
Algorithms specific to the fate and transport of 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 (EPA 2004). More recently, the TRIM.FaTE public reference
library was updated to include information on modeling for cadmium. In general, many of the
algorithms and properties included in the public reference library that are used to model mercury
(except for the mercury transformation algorithms) are also applicable to cadmium.
Comprehensive technical documentation of TRIM.FaTE algorithms specific to cadmium has not
yet been compiled; however, all chemical-specific properties used by TRIM.FaTE to model
cadmium (as well as PAHs, mercury, and dioxins) are documented in 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 (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.
15
The 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.
Attachment A - Tier 1
A-16
December 2013
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TRIM-Based Tiered Screening Methodology for RTR
Exhibit_Att A-5. Overview of Process Carried Out in the
Multimedia Ingestion Risk Calculator
Key to symbols:
Access db
process
I / 'nPut
Output I / data
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 well-
studied 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) x (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
Attachment A - Tier 1 A-17 December 2013
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TRIM-Based Tiered Screening Methodology for RTR
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 x EEFpAHi:BaP x TEFpfiHi:BaP
where:
Risk-weighted emissions of PAH, (weighted according to cancer risk
relative to BaP for oral exposures)
Emission rate of PAHj
Exposure equivalency (weighting) factor accounting for difference in
relative oral exposure between PAHj and BaP
Toxicity equivalency (weighting) factor accounting for difference in relative toxicity via
oral route between PAHj and BaP
In combination, the product of the EEF and TEF for a given substance is considered to be a
"risk equivalency factor" for the purposes of RTR evaluations that enables scaling of emissions
of a given substance for a given exposure scenario.
The TEF for each PAH and dioxin species can be calculated on the basis of relative toxicities.
Toxicities were not evaluated separately for RTR but are based on analyses conducted by EPA
elsewhere. For PAHs, oral toxicity values for individual species have been derived following the
same approach used to develop inhalation toxicity values. For dioxins, TEFs are based on the
relative toxicities developed by EPA recently and are ultimately based on the values developed
by the World Health Organization (van den Berg et al. 2006). Refer to 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 property. Exposure
EmiSSpAHiiBaP
EmisspAHj
EEFPAHiBaP
TEF,
PAHiBaP =
Attachment A - Tier 1
A-18
December 2013
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TRIM-Based Tiered Screening Methodology for RTR
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
27.5
16.4
452
7,12-Dimethylbenz(a)anthracene
Y
5.4
34.2
186
3-Methylcholanthrene
n
4.3
3.0
12.9
Dibenz(a,h)anthracene
Y
8.0
0.6
4.5
Benzo(k)fluoranthene
Y
18.0
0.2
3.0
Benzo(a)pyrene
Y
1.0
1.00
1.00
Benzo(b)flouranthene
Y
11.4
0.2
1.9
lndeno(1,2,3-cd)pyrene
Y
4.5
0.2
0.7
PAH, total
n
5.1
0.07
0.3
Polycyclic Organic Matter
n
5.1
0.07
0.3
Benzo(g,h,i)perylene
Y
4.3
0.07
0.3
Benzo(e)pyrene
n
4.5
0.07
0.3
Retene
n
3.7
0.07
0.3
Dibenzo(a,j)acridine
n
0.78
0.2
0.1
Perylene
n
1.2
0.07
0.08
Benzo(a)anthracene
Y
0.09
0.16
0.01
Chrysene
Y
0.25
0.02
0.004
2-Acetylaminofluorene
n
0.005
1.4
0.01
Fluoranthene
Y
0.04
0.07
0.003
Acenaphthylene
Y
0.04
0.07
0.003
2-Chloronaphthalene
n
0.03
0.07
0.002
Fluorene
Y
0.03
0.07
0.002
Acenaphthene
Y
0.02
0.07
0.002
1 -Methylnaphthalene
n
0.02
0.07
0.001
2-Methylnaphthalene
Y
0.02
0.07
0.001
Carbazole
n
0.01
0.003
0.00003
Anthracene
n
0.06
0.000
0.00
Phenanthrene
n
0.06
0.00
0.00
Pyrene
n
0.15
0.00
0.00
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.
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.
Attachment A - Tier 1
A-19
December 2013
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TRIM-Based Tiered Screening Methodology for RTR
Exhibit_Att A-7. Relationship between Exposure and Kow
for POM Congeners
5.E+02 5.E+03 5.E+04 5.E+05 5.E+06 5E+07
1.E-11 -
1/
rxow
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.
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-
3.8
1
3.8
TetraCDD, 2,3,7,8-
1
1
1
Dioxins, Total, w/o Indiv. Isomers Rptd.
1
1
1
Dioxins
1
1
1
2,3,7,8-TCDD TEQ
1
1
1
Attachment A - Tier 1 A-20 December 2013
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TRIM-Based Tiered Screening Methodology for RTR
Chemical
Exposure
Equivalency
Factor (EEF)
Toxicity
Equivalency
Factor (TEF)a
Risk
Equivalency
Factor (REF)
HexaCDD, 1,2,3,4,7,8-
1.6
0.1
0.2
PentaCDF, 2,3,4,7,8-
0.4
0.3
0.1
HexaCDD, 1,2,3,6,7,8-
1.0
0.1
0.1
HexaCDF, 1,2,3,7,8,9-
0.5
0.1
0.05
HexaCDF, 1,2,3,6,7,8-
0.5
0.1
0.05
HexaCDD, 1,2,3,7,8,9 -
1.0
0.04
0.04
HexaCDF, 1,2,3,4,7,8-
0.3
0.1
0.03
HexaCDF, 2,3,4,6,7,8-
0.2
0.1
0.02
PentaCDF, 1,2,3,7,8-
0.4
0.03
0.01
TetraCDF, 2,3,7,8-
0.1
0.1
0.01
HeptaCDD, 1,2,3,4,6,7,8-
1.0
0.01
0.01
HeptaCDF, 1,2,3,4,6,7,8-
0.2
0.01
0.002
HeptaCDF, 1,2,3,4,7,8,9-
0.2
0.01
0.002
OctaCDD, 1,2,3,4,6,7,8,9-
1.1
0.0003
0.0003
OctaCDF, 1,2,3,4,6,7,8,9-
0.2
0.0003
0.0001
a Values from Van den Berg et al. (2006), except for 1,2,3,7,8,9-hexaCDD, which is calculated based on the ratio of the IRIS-
based CSF for 1,2,3,7,8,9-hexaCDD to the IRIS-based CSF for 2,3,7,8-TCDD. Dose response values, including CSFs, that are
used in the screening assessment are discussed in Section 4 of 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
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
Attachment A - Tier 1
A-21
December 2013
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TRIM-Based Tiered Screening Methodology for RTR
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.
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 (oy, 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).1' For a relatively neutral atmosphere (stability class D), a 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 3o of the median line. Therefore, the plume o was set at
3 times 550 m, or approximately 1.75 km from the centerline at a distance of 10 km. The plume
Mass 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.)
17
http://ww\/v.epa.gov/scram001/userg/regrriod/isc3v2.pdf
Exhibit_Att A-9. TRIM.FaTE Surface Parcel Layout
~
Tilled Soil
3.5 km
10 km
Attachment A - Tier 1
A-22
December 2013
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TRIM-Based Tiered Screening Methodology for RTR
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) unfilled to represent pasture.
The depth of the surface soil compartments was set to 1 cm, except for Parcel N6, for which the
depth was set to 20 cm to simulate the effect of tillage. Characteristics of the soil layers (e.g.,
organic carbon content, air and water content, and subsoil depth) generally were set to
represent typical or national averages as summarized by McKone et al. (2001).
The overall shape and boundaries of the air parcel layout mirror those of the surface parcel
layout. A single air parcel (N2) overlies surface Parcels N6 and N7, and the air over the lake is
divided into air Parcels S2 and S3 (mirroring the analogous parcels on the north side of the
lake).
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:
[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]
Attachment A - Tier 1
A-23
December 2013
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TRIM-Based Tiered Screening Methodology for RTR
Note that TRIM.FaTE actually uses only some of these properties (e.g., precipitation rate and
surface runoff, but not evapotranspiration). The water characteristics assumed for the Tier 1
scenario are meant to represent a relatively wet and moderately warm location in the United
States (USGS 1987). Following are the assumptions for this scenario:
35 percent of the total precipitation leaves the scenario through evapotranspiration.
25 percent of total precipitation infiltrates into the groundwater and eventually flows into
the lake.
40 percent of total precipitation contributes to overland runoff.
For these calculations, the source parcel was considered to be outside the watershed and
therefore was not included in the water balance. The evaporation rate from the lake was
assumed to be 700 mm/yr based on data reported by Morton (1986) for various lakes. The
runoff rate was defined to be spatially constant and temporally constant (i.e., it is not linked to
precipitation events) throughout the modeled domain. Based on these assumptions, the outflow
of water from the lake is about 18 million m3/yr, which translates to a volumetric turnover rate of
about 12.2 lake volumes per year.
Other quantitative water body and watershed characteristics TRIM.FaTE uses are listed in
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.
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
Attachment A - Tier 1 A-24 December 2013
-------
TRIM-Based Tiered Screening Methodology for RTR
(back into the water column from the benthic sediments). These formula properties assume a
constant volume of particles in the sediment layer (because the densities for benthic and
suspended sediment particles were defined to the same value, the mass of particles in the
sediment is also constant).
For the Tier 1 scenario described here, the average sediment delivery rate (i.e., transfer of
sediment mass from watershed surface soil to the lake due to erosion) for the entire watershed
was estimated to be about 0.0036 kg/m2-day, based on calculations using the USLE. The
HHRAP documentation notes that using the USLE to calculate sediment load to a lake from the
surrounding watershed sometimes leads to overestimates (EPA 2005a). For the Tier 1
scenario, however, this approach was considered to be appropriate in that health protective
assumptions are a goal of the screening scenario.18 Surface soil compartments adjacent to the
lake are linked directly to the lake for the purposes of estimating erosion and runoff transfers
(see layout in Exhibit_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)
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
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.
Attachment A - Tier 1
A-25
December 2013
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TRIM-Based Tiered Screening Methodology for RTR
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
Meteorological
Property
Selected Value
Justification
Air temperature
Constant at 298 Kelvin
Recommended default value listed in HHRAP (EPA 2005a).
Value is similar to the mean daily June temperature in much of the
U.S. Southern Plains and Southeast.3
Mixing height
Constant at 710 m
Value is 5th percentile of annual average mixing heights for 75
U.S. locations, using data obtained from EPA's SCRAM Web
site.b Value is the approximate U.S. median for periods without
precipitation, based on data compiled by Holzworth (1972). Value
is conservative compared to the 1- to 2-km typical mid-latitude
daytime value (Stull 1988).
Wind direction
Blows from source
parcel into scenario
domain (west to east) 3
days per week; during
other times does not
blow into domain
A wind direction that favors the location of interest (for example, a
watershed downwind of a source of concern) will tend to result in
more emitted mass accumulating in the location of interest. For
much of the U.S. mid-Atlantic and western regions, the wind tends
to favor the eastward direction.3 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.0 For
the hypothetical RTR scenario, a more extreme example of this
pattern is represented by conditions in Yakima, Washington,
where the wind blows eastward approximately 40 percent of the
time based on a review of wind direction data compiled by the
National Weather Service (NCDC 1995). This pattern was
approximated in the RTR scenario with a configuration in which
the modeled domain is downwind of the source 3 out of 7 days.
Horizontal wind
speed
Constant at 2.8 m/sec
Set to 5th percentile of annual average speed for 239 stations
across the contiguous United States (about 50 years of data per
station). Value is similar to the annual average wind speeds of
many areas of the U.S. east coast and west coast.3
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.3
Attachment A - Tier 1
A-26
December 2013
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TRIM-Based Tiered Screening Methodology for RTR
Meteorological
Property
Selected Value
Justification
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.3 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.3
""National Climatic Data Center CliMaps (NCDC-CliMaps) (2007). http://cdo.ncdc.noaa.aov/cai-bin/climaps/climaps.pl.
bSupport Center for Regulatory Atmospheric Modeling; http://www.epa.aov/ttn/scram/.
""National 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). Zooplankton
feed on algae in the water column, while benthic invertebrates, represented as a single
compartment, consume detritus that settles to the sediment compartment. In the water column,
small young-of-the-year fish and minnows feed on zooplankton and phytopolankton. The small
fish are in turn consumed by larger or "pan" fish (e.g., bluegills, white perch), which are in turn
consumed by the top consumers (e.g., gar, pickerel). The invertebrates in the sediments of the
benthic environment support small bottom-feeding fish (young-of-the-year fish for many
species), which in turn are consumed by larger bottom-feeding fish (e.g., catfish). For
TRIM.FaTE to provide reasonable predictions of the distribution of a chemical across biotic and
abiotic compartments in aquatic systems, the biomass of the aquatic biotic compartments must
represent all biota in the system and the distribution of biomass among the trophic levels and
groups must be as realistic as possible.
To support the development of a relatively generic freshwater aquatic ecosystem in which to
model bioaccumulation in fish, a literature search, review, and analysis was conducted
(ICF 2005). This research demonstrated that the diversity of species and food webs across
U.S. aquatic ecosystems is substantial, reflecting the wide range of sizes, locations, and
physical/chemical attributes of both flowing (rivers, streams) and low-flow water bodies (ponds,
lakes, reservoirs). In general, lentic bodies of water (lakes and ponds) can accumulate higher
levels of contaminants in both sediments and biota than lotic systems (rivers, streams). Also,
that initial research (ICF 2005) suggests that a lake of at least 60 hectares (ha) or 150 acres
could support higher trophic level predatory fish, with some fraction of their diet comprising
smaller fish.
The RTR Tier 1 scenario includes a generic aquatic ecosystem with a 47-hectare (116-acre)
lake. Although slightly smaller than the size suggested by the previous review (ICF 2005), a 47-
ha lake is large enough to support higher trophic level fish given appropriate conditions (e.g.,
high productivity given a sufficient nutrient base and average temperature and growing season).
Also, this size was compatible with the overall size of the defined watershed in the screening
scenario. The fish types, biomass, diet fractions, and body weights recommended for fish
compartments for the Tier 1 scenario are listed in Exhibit_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). That assumption yields health protective (i.e., higher)
estimates of chemical concentrations in fish than would the assumption of higher standing
biomass and fish productivity.
Attachment A - Tier 1 A-27 December 2013
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TRIM-Based Tiered Screening Methodology for RTR
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, blue-
green algae
7.95
-
Autotrophic
-
Zooplankton
crustaceans,
rotifers,
protozoans
6.36
-
100% algae3
5.7 E-8
Macrophyte
hydrilla, milfoil
500
-
-
-
Water column
planktivore
young-of-the-
year, minnows
2.0
35.1%
100% zooplankton
0.025
Water column
omnivore
bluegill, white
perch
0.5
8.8%
100% water column
planktivore
0.25
Water column
carnivore
largemouth bass,
walleye
0.2
3.5%
100% water column
omnivore
2.0
Benthic
invertebrate
aquatic insect
larvae,
crustaceans,
mollusksb
20
-
detritus in sediments
0.000255
Benthic
omnivore
small catfish,
rock bass
2.0
35.1%
100% benthic invert.
0.25
Benthic
carnivore
large catfish,
sculpins
1.0
17.5%
50% benthic invert.
50% benthic omniv.
2.0
Total Fish Biomassc
5.7
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).
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).
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
Attachment A - Tier 1 A-28 December 2013
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TRIM-Based Tiered Screening Methodology for RTR
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%
offish consumed) and benthiccarnivore in lake
(50% offish 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.
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,
Attachment A - Tier 1 A-29
December 2013
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TRIM-Based Tiered Screening Methodology for RTR
root vegetables,
beef,
dairy products,
pork, and
poultry and eggs.
The algorithms used in MIRC were obtained from EPA's Human Health Risk Assessment
Protocol for Hazardous Waste Combustion Facilities (HHRAP; EPA 2005a). These algorithms
model the transfer of concentrations of PB-HAPs in FFC media using biotransfer factors.
Environmental media concentrations (i.e., the chemical source terms in these algorithms) are
obtained from TRIM.FaTE. As noted in Section 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 (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 (EPA 2011).
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Exhibit_Att A-13. Summary of Ingestion Exposure Pathways and Routes of Uptake
Ingestion
Exposure
Pathway
Intermediate
Environmental Uptake Route
Medium Ingested
Exposure
Pathway - Farm
Animals3
Medium
Process15
Incidental
ingestion of soil
Unfilled surface soil
N/A
Surface soil
Deposition; transfer via
erosion and runoff0
Direct uptake from water and
Consumption of
fish
Fish from local water
body
N/A
Fish tissue
consumption of food
compartments modeled in
TRIM.FaTE0
Consumption of
breast milkd
Breast milk
N/A
Breast milk
Contaminant ingested by
mother partitions to breast
milk
Aboveg round
produce, exposed
fruits and vegetables
N/A
Air
Air
Soil
Deposition to leaves/plants
Vapor transfer
Root uptake
Consumption of
produce
Aboveg round
produce, protected
fruits and vegetables
N/A
Soil
Root uptake
Belowground
produce
N/A
Soil
Root uptake
Ingestion of forage
Air
Direct deposition to plant
Beef
Ingestion of silage
Air
Soil
Vapor transfer to plant
Root uptake
Ingestion of grain
Soil
Root uptake
Ingestion of soil
Soil
Ingestion from surface
Ingestion of forage
Air
Direct deposition to plant
Dairy (milk)
Ingestion of silage
Air
Soil
Vapor transfer to plant
Root uptake
Consumption of
Ingestion of grain
Soil
Root uptake
farm animals
and related
food products
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
Calculation of intermediate exposure concentrations were required only for the farm animal/animal product ingestion pathways.
bProcess by which HAP enters medium ingested by humans.
'Modeled in TRIM.FaTE.
dThe 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 values for all food types except for fish,
which was set at 99th percentile values). All media were assumed to be obtained from locations
impacted by the modeled source. Although this approach could result in an overestimate of
Attachment A - Tier 1
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TRIM-Based Tiered Screening Methodology for RTR
total chemical exposure for a hypothetical exposure scenario (for example, note that the total
food ingestion rate that results is extremely high for a hypothetical consumer with ingestion
rates in the upper percentile for every food type), it was selected to avoid underestimating
exposure for any single farm food type. The exposure characteristics selected for the Tier 1
scenario are summarized in Exhibit Att A-14.
Exhibit_Att A-14. Overview of Exposure Factors Used for RTR Multipathway Screening3'13
Exposure Factor
Selection for Screening Assessment
Age group evaluated
Infants under 1 year (breast milk only)
Children 1-2 years of age
Children 3-5 years of age
Children 6-11 years of age
Children 12-19 years of age
Adult (20-70 years)
Body weight (BW; varies by age)
Weighted mean of national distribution or
recommended value
Ingestion rate (IR) for farm produce and animal
products other than fish (varies by age and medium)
90th percentile of distribution of consumers who
produce own food
Ingestion rate for fish
For adults, 99th percentile as-prepared ingestion
rate representative of subsistence fisher woman.
For children, based on 99th percentile, as-
prepared, consumer-only, national ingestion rates
- adjusted.
Exposure frequency (EF)
365 days/year
Exposure duration
Lifetime, for estimating cancer risk; varies by
chemical for chronic non-cancer evaluation
Fraction contaminated (FC) (varies by media
consumed)0
1
Cooking lossd
Assumed 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.
Food preparation/cooking adjustment factor for fish8
Mercury = 1.5
Cadmium = 1.5
Dioxin = 0.7
PAH = 1.0
aData for exposure characteristics are presented in Addendum 2 to this attachment. Exposure parameter values were based on
data obtained primarily from the Exposure Factors Handbook (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.
°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.
dCooking 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.
eBecause "as consumed," fish consumption rates are used with whole-fish concentrations, adjustments might be appropriate to
adjust the fish tissue concentrations to reflect concentrations after food preparation. See Addendum 2, Section 6.4.4 for additional
discussion.
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 (EPA 2003), which derived much of its input data
Attachment A - Tier 1
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TRIM-Based Tiered Screening Methodology for RTR
from the Exposure Factors Handbook (EPA 2011). The ingestion exposure modeling approach
embodied by 3MRA is conceptually similar to that presented in HHRAP. A discussion of
exposure dose estimation and the equations to calculate ADDs for each ingestion pathway are
provided in 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 2005a).
Exposure via the breast milk consumption pathway is estimated in MIRC for dioxins only. This
pathway is included in computing total exposure for developing the screening threshold for
dioxins. In the absence of congener-specific data, dioxin congeners were assumed to manifest
the same tendency to accumulate in breast milk as 2,3,7,8-TCDD.
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.
Attachment A -
Tier 1
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TRIM-Based Tiered Screening Methodology for RTR
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
7.3
IRIS
not available
2,3,7,8-TCDD
1.5E+5
EPA ORD
7E-10
IRIS
Source: EPA (2007).
CSF = cancer slope factor; RfD = reference dose; IRIS = EPA's Integrated Risk Information System; Cal/EPA = California
Environmental Protection Agency; EPA ORD = EPA's Office of Research and Development
aFor 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 (EPA 2005c).
Specifically, cancer potency for PAHs is assumed to be tenfold greater for the first 2 years of life
and threefold greater for the next 14 years. These factors were incorporated into a time-
weighted 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 Tier 1 Screening Scenario Assumptions
Characteristic
Value
Neutral or
Health
Protective?
Comments on Assumptions
General Spatial Attributes
Farm location
375 m from
source; generally
downwind
Health
Protective
Location dictates soil and air concentrations
and deposition rates used to calculate
chemical levels in farm produce.
Lake location
375 m from
source; generally
downwind
Health
Protective
Location dictates where impacted fish
population is located.
Attachment A - Tier 1
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TRIM-Based Tiered Screening Methodology for RTR
Characteristic
Value
Neutral or
Health
Protective?
Comments ori 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 (5h 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.
Attachment A - Tier 1
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TRIM-Based Tiered Screening Methodology for RTR
Characteristic
Value
Neutral or
Health
Protective?
Comments ori Assumptions
Soil erosion from
surface soil into lake
Varies by parcel;
ranges from 0.002
to 0.01 kg/m2-day
Neutral
Erosion rates calculated using the universal
soil loss equation (USLE); inputs to USLE
were selected to be generally conservative
with regard to concentration in the pond
(i.e., higher erosion rates were favored).
Might underestimate erosion for locations
susceptible to high erosion rates. Note that
higher erosion increases concentration in
lake (and fish) but decreases levels in
surface soil (and farm products).
Aquatic food web
structure and
components
Multilevel; includes
large, upper
trophic-level fish
Health
Protective
Inclusion of upper trophic-level fish and
absence of large-bodied
herbivore/detritivore fish favor higher
concentrations of bioaccumulative
chemicals and result in higher
concentrations in consumed fish. Linear
food-chain maximizes concentration of
bioaccumulative chemicals in higher
trophic-level fish.
Parameters for Estimating Concentrations in Farm Food Chain Media
Fraction of plants
and soil ingested by
farm animals that is
contaminated
1.0 (all food and
soil from
contaminated
areas)
Health
Protective
Assumes livestock feed sources (including
grains and silage) are derived from most
highly impacted locations.
Soil- and air-to-plant
transfer factors for
produce and related
parameters
Typical (see
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.
Attachment A -
Tier 1
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TRIM-Based Tiered Screening Methodology for RTR
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 home-
produced food
items
Health Protective
All food derived from impacted farm; total
food ingestion rate would exceed expected
body weight-normalized ingestion rates
(prevents underestimating any individual
food type).
Fish ingestion rate
Adult: 373 g/day
Child age groups:
1 to 2: 108 g/day
3 to 5: 159 g/day
6 to 11: 268 g/day
12 to 19: 331 g/day
Health Protective
The adult rate is the 99th percentile value
for adult females from Burger (2002) and is
considered representative of subsistence
anglers.
Rates for children are based on the 99th
percentile, consumer-only fish ingestion
rates from EPA 2002. Rates were adjusted
to be representative of the age groups used
in the screening scenario. 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 body-
weight-normalized rates for produce and
animal products.
Other Chemical-Specific Characteristics
General chemical
properties used in
fate and transport
modeling (Henry's
law, 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.
Attachment A - Tier 1
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TRIM-Based Tiered Screening Methodology for RTR
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 2005). Cadmium also binds strongly to organic matter, rendering the metal
relatively immobile in some soils. Nonetheless, some plants still can take up cadmium
efficiently, thus providing an entry point for cadmium into the food chain (ATSDR 2008).
Cadmium also enters surface waters, which can occur via atmospheric deposition, runoff and
erosion, or wastewater streams. Most cadmium compounds entering the water column are
quickly removed through adsorption to organic matter in sediment or to other suspended
compounds. Cadmium that remains in the water column is expected to exist primarily in the
dissolved state where it is available for uptake by aquatic organisms.
Freshwater fish accumulate cadmium primarily through direct uptake of the dissolved form
through the gills and secondarily through the diet, which plays a variable role in total cadmium
uptake (Reinfelder et al. 1998; Chen et al. 2000; Saiki et al. 1995). Although some
biomagnification of cadmium has been reported for aquatic food chains in saltwater systems,
biomagnification in freshwater systems appears to be present only at lower trophic levels (Chen
et al. 2000) and in narrowly defined niches (e.g., plankton/macroinvertebrate food chains;
Croteau et al. 2005). Biomagnification factors (BMFs) of less than 1 generally have been
reported for fish at higher trophic levels, indicating that cadmium concentrations generally
biodiminish from lower to higher trophic levels (Chen et al. 2000; Mason et al. 2000).
For the RTR screening scenario, the partitioning behavior modeled in TRIM.FaTE was
consistent with the behavior of cadmium expected in the natural environment.
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
Attachment A - Tier 1
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TRIM-Based Tiered Screening Methodology for RTR
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 nearly 100 percent of the
ADD for all groups. The combined contribution from all other exposure pathways accounts for
less than 0.7 percent of the total ADD for all age groups. Most of the additional exposure was
from ingestion of fruits and vegetables. The highest ADD corresponds to children aged 1-2
years; thus, the exposure corresponding to this group was used to determine the emission
threshold for cadmium. In other words, the threshold emissions rate for cadmium is set at the
level where the HQ for this age category is equal to 1.0.
Exhibit_Att A-17. Estimated Contributions of Modeled Food Types to
Cadmium Ingestion Exposures and Hazard Quotients
¦1.OS-OS 1
¦ Fish Meat, Dairy, & Eggs
Child 1-2 Child 3-5 Child 6-11 Child 12-19 Adul!
Attachment A - Tier 1
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TRIM-Based Tiered Screening Methodology for RTR
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 (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 (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.
Mercury could also enter the water column through atmospheric fallout. Once in the water
body, divalent mercury can be methylated through microbial activity. In addition, divalent and
methyl mercury can be further reduced to elemental mercury, which can volatilize and reenter
the atmosphere. Solid forms of inorganic mercury compounds could adsorb to particulates in
the water column or partition to the sediment bed (EPA 1997).
The solubility of mercury in water depends on the species and form of mercury present as well
as properties of the water such as water pH and chloride ion concentration (ATSDR 1999). Low
pH favors the methylation of mercury in the water column, typically performed by sulfur-reducing
bacteria in anaerobic conditions. Methyl mercury is typically of greatest concern because it
readily bioaccumulates and efficiently biomagnifies in aquatic organisms. A considerable
amount (25-60 percent) of both divalent mercury compounds and methyl mercury is strongly
bound to particulates in the water column (EPA 1997). The remaining mercury is dissolved.
Most of the elemental mercury produced as a result of reduction of divalent mercury volatilizes
back into the atmosphere.
For the screening scenario, the partitioning behavior modeled in TRIM.FaTE generally was
consistent with trends noted in the literature. Divalent mercury was the most prevalent species
in modeled surface soil, surface water, and sediment compartments, while methyl mercury was
the dominant species in fish.
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 (EPA 1997; ATSDR 1999). Low levels
of mercury are found in plants, with leafy vegetables containing higher concentrations than
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TRIM-Based Tiered Screening Methodology for RTR
potatoes, grains, legumes, and other vegetables and fruits (ATSDR 1999; EPA 1997). Cattle
demethylate mercury in the rumen and, therefore, store very little of the mercury they ingest by
foraging or consuming silage or grain. Thus, mercury content in meat and cow's milk is low
(ATSDR 1999). Concentrations of methyl mercury in fish are generally highest in larger, older
specimens at the higher trophic levels (EPA 1997).
Although data on mercury in foods other than fish are not abundant in the literature, total
mercury concentrations output by MIRC were generally consistent with the reported values that
were available. The exposure pathways that most influenced the mercury HQs in the model are
presented in Exhibit_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).
Exhibit_Att A-18. Estimated Contributions of Modeled Food Types
to Methyl Mercury Ingestion Exposures
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;
Attachment A - Tier 1
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December 2013
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TRIM-Based Tiered Screening Methodology for RTR
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
sediment, which appears to be the primary environmental sink for this chemical group (EPA
2004c). Although dioxins bound to aquatic sediment primarily become buried in the sediment
compartment, some resuspension and remobilization of congeners can occur if sediments are
disturbed by benthic organisms (ATSDR 1998).
Bioaccumulation factors (BAFs) in fish are high as a result of the lipophilic nature of chlorinated
dioxins. Although the processes by which freshwater fish accumulate dioxins are not well
understood, both fish and invertebrates bioaccumulate congeners that have partitioned to
sediment or have become suspended in water (EPA 2004c). Because most dioxins in the
aquatic environment are adsorbed to suspended particles, however, direct uptake from the
water is unlikely to be the primary route of exposure for most aquatic organisms at higher
trophic levels (ATSDR 1998). At lower trophic levels, the primary route of exposure appears to
be through uptake of water in contaminated sediment pores, and the primary route of exposure
in the higher trophic levels appears to be through food chain transfer. Following ingestion,
some fish can slowly metabolize certain congeners, such as 2,3,7,8-TCDD, and release the
polar metabolites in bile. This process ultimately might limit bioaccumulation at higher trophic
levels (ATSDR 1998).
For the RTR screening scenario, the partitioning behavior modeled in TRIM.FaTE was
consistent with the behavior of 2,3,7,8-TCDD expected in the natural environment. Also of note
is that dioxins readily partition into breast milk due to the lipophilic nature of these compounds.
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
Attachment A - Tier 1
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December 2013
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TRIM-Based Tiered Screening Methodology for RTR
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 over 97 percent of lifetime
dioxin exposure for 2,3,7,8-TCDD in the screening scenario. Daily intakes of 2,3,7,8-TCDD
from cow's milk, produce, and fish have been reported in the literature to comprise 27 percent,
11 percent, and 10 percent, respectively, of the total daily intake in the general population.
Some studies note that specific subpopulations, such as subsistence farmers and 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 (l_ADD)
(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).
Attachment A - Tier 1
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December 2013
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TRIM-Based Tiered Screening Methodology for RTR
Exhibit_Att A-19. Estimated Contributions of Modeled Food
Types to Dioxin Ingestion Exposures
Q
~
<
_l
2 50%
10%
0%
Breastmilk
Soil
— iPork
¦ Fish
¦ Fruits & Vegetables
¦ Poultry & Eggs
¦ Total Dairy
Beef
* V? j v? **
A< A" J?' J?' £>• J" J' ^ J' V>' fcV (*>"
1? 0V J? Kv a vV .>• S' v* n?- T?" «,»"
l0' r9 ^ ^ J / ^ ^Va'
O*" ^ ^ ^ jF J>- ^
xer . ^-v x
-------
TRIM-Based Tiered Screening Methodology for RTR
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 l_ADD (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 90 and 99 percent of exposure for different PAHs.
This variability can be accounted for in part by differences in the physiochemical properties that
drive the environmental fate and transport processes of these PAHs (e.g., Kow, molecular
weight, chemical structure), differences in the PAH-specific half-life in abiotic media, and the
degree to which the PAHs are metabolized in biotic media. The variability in exposure
pathways is consistent with information provided in the literature.
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 RTR 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 l_ADD.
Attachment A - Tier 1
A-45
December 2013
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TRIM-Based Tiered Screening Methodology for RTR
Exhibit_Att A-20. Estimated Contributions of Modeled Food
Types to PAH Ingestion Exposures
100% T—,
90%
80%
70%
§ 60%
<
2, 50%
c
It
o
5 40%
Q_
30%
20%
10%
0%
>f ^
^ JF
*. # ^ j?'
A1
#
? -C'
# <#
P NJ
/ ,-f ,/ ,f ^ J
/ .# J# & ,/ ^
J? 4?
A.6 References
ATSDR (Agency for Toxic Substances and Disease Registry). 1995. Toxicological profile for
polycyclic aromatic hydrocarbons. Atlanta, GA: U.S. Department of Health and Human
Services, Public Health Service.
ATSDR. 1998. Toxicological profile for chlorinated dibenzo-p-dioxins. Atlanta, GA: U.S.
Department of Health and Human Services, Public Health Service.
ATSDR. 1999. Toxicological profile for Mercury. Atlanta, GA: U.S. Department of Health and
Human Services, Public Health Service.
ATSDR. 2008. Toxicological profile for Cadmium. Atlanta, GA: U.S. Department of Health and
Human Services, Public Health Service.
Burger, J. 2002. Daily consumption of wild fish and game: Exposures of high end recreationists.
International Journal of Environmental Health Research 12:343-354.
Cal/EPA (California Environmental Protection Agency) Office of Environmental Health Hazard
Assessment (OEHHA). 2000. Air Toxics Hot Spots Program Risk 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.
Attachment A - Tier 1
A-46
December 2013
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TRIM-Based Tiered Screening Methodology for RTR
Chen, C.Y., R.S. Stemberger, B. Klaue, J.D. Blum, P.C. Pickhardt, and C.L. Folt. 2000.
Accumulation of heavy metals in food web components across a gradient of lakes.
Limnology and Oceanography 45(7): 1525-1536.
Croteau, M., S.N. Luoma, and A.R. Stewart. 2005. Trophic transfer of metals along freshwater
food webs: Evidence of cadmium biomagnification in nature. Limnology and Oceanography
50(5): 1511-1519.
Hazardous Substances Data Bank (HSDB). 2005. Bethesda, MD: National Library of Medicine,
U.S. [Last Revision Date 06/23/2005], Cadmium Compounds; Hazardous Substances
Databank Number: 6922. Available at: http://toxnet.nlm.nih.gov/cgi-bin/sis/htmlgen7HSDB
Holzworth, G.C. 1972. Mixing Heights, Wind Speeds, and Potential for Urban Air Pollution
Throughout the Contiguous United States," AP-101, January 1972, U.S. Environmental
Protection Agency, Office of Air Programs, Research Triangle Park, North Carolina.
ICF (ICF International). 2005. Memorandum: TRIM.FaTE Screening Scenario: Aquatic Food
Web Analysis; submitted to Deirdre Murphy and Terri Hollingsworth, U.S. EPA, from
Margaret McVey and Rebecca Kauffman, ICF Consulting. October 18.
Mason R.P., J. Laporte, and S. Andres. 2000. Factors controlling the bioaccumulation of
mercury, methylmercury, arsenic, selenium, and cadmium by freshwater invertebrates and
fish. Archives for Environmental Contamination and Toxicology 38(3):283-97.
McKone, T.E., A. Bodnar, and E. Hertwich. 2001. Development and evaluation of state-specific
landscape data sets for multimedia source-to-dose models. University of California at
Berkeley. Supported by U.S. Environmental Protection Agency (Sustainable Technology
Division, National Risk Management Research Laboratory) and Environmental Defense
Fund. July. LBNL-43722.
Morton, F.I. 1986. Practical estimates of lake evaporation. Journal of Climate and Applied
Meteorology 25:371-387.
NCDC (National Climatic Data Center). 1995. Hourly United States Weather Observations
(HUSWO) 1990-1995.
NCDC. 2007. National Climatic Data Center CliMaps. Available online at:
http://cdo.ncdc.noaa.gov/cgi-bin/climaps/climaps.pl.
Reinfelder, J.R., N.S. Fisher, S.N. Luoma, J.W. Nichols, and W.-X. Wang. 1998. Trace element
trophic transfer in aquatic organisms: A critique of the kinetic model approach. The Science
of the Total Environment 219: 117-135.
Saiki, M.K., D.T. Castleberry, T.W. May, B.A. Martin, and F.N. Bullard. 1995. Copper, cadmium,
and zinc concentrations in aquatic food chains from the upper Sacramento River (California)
and selected tributaries. Arch. Environ. Contam. Toxicol. 29:484-491.
Stull, R.B. 1988. An Introduction to Boundary Layer Meteorology. Kluwer Academic Publishers,
The Netherlands. 452 pp.
Turner, D.B. 1970. Workbook of Atmospheric Dispersion Estimates. PHS Publication No. 999-
AP-26. U.S. Department of Health, Education, and Welfare, National Air Pollution Control
Administration, Cincinnati, Ohio.
Attachment A - Tier 1
A-47
December 2013
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TRIM-Based Tiered Screening Methodology for RTR
EPA. 1997. Mercury Study Report to Congress. Volume III: Fate and Transport of Mercury in
the Environment. EPA-452/R-97-005. Office of Air Quality Planning and Standards and
Office of Research and Development. December.
EPA. 1998. Methodology for Assessing Health Risks Associated with Multiple Pathways of
Exposure to Combustor Emissions. National Center for Environmental Assessment.
Cincinnati, OH. EPA-600/6-90/003. December.
EPA. 1999. Short Sheet: IEUBK Model Soil/Dust Ingestion Rates. EPA-540-F-00-007; OSWER-
9285.7-33. Washington, D.C.: Office of Solid Waste and Emergency Response; December.
Available at: http://www.epa.gov/superfund/lead/products/ssircolo.pdf.
EPA. 2002a. Total Risk Integrated Methodology: TRIM.FaTE Technical Support Document.
Volume II: Description of Chemical Transport and Transformation Algorithms. EPA-453/R-
02-011b. Office of Air Quality Planning and Standards: Research Triangle Park, NC.
September.
EPA. 2002b. Evaluation of TRIM.FaTE, Volume I: Approach and Initial Findings. EPA-453/R-02-
0012. Office of Air Quality and Planning Standards: Research Triangle Park, NC.
September.
EPA. 2003. Multimedia, Multipathway, and Multireceptor Risk Assessment (3MRA) Modeling
System, Volume II: Site-based, Regional, and National Data. SAB Review Draft. EP-530/D-
03-001 b. Office of Research and Development, Athens, GA, and Research Triangle Park,
NC, and Office of Solid Waste, Washington, DC. July. Available at:
http://www.epa.gov/epaoswer/hazwaste/id/hwirwste/risk03.htm.
EPA. 2004a. Air Toxics Risk Assessment Reference Library; Volume 1 - Technical Resource
Document, Part III, Human Health Risk Assessment: Multipathway Chapter 14, Overview
and Getting Started: Planning and Scoping the Multipathway Risk Assessment. Office of Air
Quality Planning and Standards, Research Triangle Park, NC. April. Available at:
http://www.epa.gov/ttn/fera/data/risk/vol_1/chapter_14.pdf.
EPA. 2004b. Evaluation of TRIM.FaTE Volume III: Model Comparison Focusing on Dioxin Test
Case. EPA-453/R-04-002. Available at:
http://www.epa.gov/ttn/fera/data/trim/eval_rept_vol3_2005.pdf
EPA. 2004c. Exposure and Human Health Reassessment of 2,3,7,8-Tetrachlorodibenzo-p-
Dioxin (TCDD) and Related Compounds. Volume 2: Properties, Environmental Levels, and
Background Exposures. Dioxin Reassessment, NAS Review Draft. U.S. Environmental
Protection Agency, Washington, D.C., EPA/600/P-00/001Cb. Available at:
http://www.epa.gov/ncea/pdfs/dioxin/nas-review/
EPA. 2005a. Human Health Risk Assessment Protocol for Hazardous Waste Combustion
Facilities (including the Hazardous Waste Companion Database of chemical-specific
parameter values). U.S. Environmental Protection Agency, Office of Solid Waste and
Emergency Response, Washington, DC. EPA-530-R-05-006. September.
EPA. 2005b. Evaluation of TRIM.FaTE, Volume II: Model Comparison Focusing on Mercury
Test Case. EPA-453/R-05-002. Office of Air Quality and Planning Standards: Research
Triangle Park, NC. July.
EPA. 2005c. Supplemental Guidance for Assessing Susceptibility from Early-Life Exposure to
Carcinogens. EPA-630/R-03-003F. Risk Assessment Forum: Washington, DC. March.
Attachment A - Tier 1
A-48
December 2013
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TRIM-Based Tiered Screening Methodology for RTR
EPA. 2006. Risk and Technology Review (RTR) Assessment Plan. Office of Air and Radiation.
November 20, 2006. Available at:
http://www.epa.gov/sab/panels/consul_risk_and_tech_assessment_plan.htm.
EPA. 2007. Prioritized Chronic Dose-Response Values for Screening Risk Assessments (Table
1). Office of Air Quality Planning and Standards; June 12, 2007. Available at:
http://www.epa.gov/ttn/atw/toxsource/summary.html.
EPA. 2011. Exposure Factors Handbook: 2011 Edition. Office of Research and Development,
Washington, D.C. EPA/600/R-090/052F. September. Available at:
http://cfpub.epa. qov/ncea/risk/recordisplav.cfm?deid=236252.
USGS (U.S. Geological Survey). 1987. National Water Summary 1987 - Hydrologic Events and
Water Supply and Use. USGS Water-Supply Paper 2350. J.E. Carr, E.B. Chase, R.W.
Paulson, and D.W. Moody, Compilers.
Van den Berg, M., L.S. Birnbaum, M. Denison, M. DeVito, W. Farlans, M. Feeley, H. Fiedler, H.
Hakansson, A. Hanberg, L.. Haws, M. Rose, S. Safe, D. Schrenk, C. Tohyama, A. Tritscher,
J. Tuomisto, M. Tysklind, N. Walker, and R.E. Peterson. 2006. The 2005 World Health
Organization reevaluation of human and mammalian toxic equivalency factors for dioxins
and dioxin-like compounds. Toxicol Sci. 93(2): 223-41.
Wschmeier, W.H., and D. Smith. 1978. Predicting Rainfall Erosion Losses: A Guide to
Conservation Planning. USDA-ARS Agriculture Handbook No. 537, Washington, D.C. 58
pp.
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Addendum 1. TRIM.FaTE Inputs
Attachment A, Addendum 1 1-1 December 2013
TRIM.FaTE Inputs
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TRIM-Based Tiered Screening Methodology for RTR
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
Attachment A, Addendum 1
TRIM.FaTE Inputs
1-3
December 2013
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TRIM-Based Tiered Screening Methodology for RTR
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
Attachment A, Addendum 1
TRIM.FaTE Inputs
1-4
December 2013
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TRIM-Based Tiered Screening Methodology for RTR
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."
Attachment A, Addendum 1
TRIM.FaTE Inputs
1-5
December 2013
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TRIM-Based Tiered Screening Methodology for RTR
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
Assumption; vertical wind speed not used by any of the algorithms in the version
of the TRIM.FaTE library used for screening.
Wind direction
degrees
clockwise from
N (blowing
from)
3-days-on
4-days-off
On is defined as time during which wind is blowing into the model domain. A
conservative estimate of time during which wind should blow into the modeling
domain was determined by evaluating HUSWO; it was concluded that a
conservative estimate would be approximately 42% of the time.
Rainfall rate
mJ[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 steady-
state runs are needed).
isDay_SteadyState_forOther
unitless
-
Attachment A, Addendum 1
TRIM.FaTE Inputs
1-6
December 2013
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TRIM-Based Tiered Screening Methodology for RTR
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/cm3
0.0012
USEPA 1997b
Dust density
kg[dust]/m3[dust]
1,400
Bidleman 1988
Fraction organic matter
on particulates
unitless
0.2
Harnerand Bidleman 1998
Attachment A, Addendum 1
TRIM.FaTE Inputs
1-7
December 2013
-------
TRIM-Based Tiered Screening Methodology for RTR
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 - unfilled3
m
0.01
McKone et al. 2001 (p. 30).
Thickness - tilled3
m
0.20
USEPA 2005.
Erosion fraction
unitless
varies6
See Exhibit 5.
Fraction of area available for
erosion
m2[area available]/m2[total]
1
Assumption ; area assumed rural.
Fraction of area available for runoff
m2[area available]/m2[total]
1
Assumption ; area assumed rural.
Fraction of area available for vertical
diffusion
m2[area available]/m2[total]
1
Assumption ; area assumed rural.
Fraction sand
unitless
0.25
Assumption.
Organic carbon fraction
unitless
0.008
U.S. average in McKone et al. 2001 (Table 16
and A-3).
PH
unitless
6.8
Assumption.
Runoff fraction
unitless
varies6
See Exhibit_Add A1-5.
Total erosion rate
kg [soil]/m2/day
varies6
See Exhibit_Add A1-6.
Total runoff rate
mJ[water]/m2/day
1.64E-03
Calculated using scenario-specific
precipitation rate and assumptions associated
with water balance.
Water content
vo I u me [wate r]/vo lume[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]/mJ[soil]
2,600
McKone et al. 2001 (Table 3).
Fraction sand
unitless
0.25
Assumption.
Attachment A, Addendum 1
TRIM.FaTE Inputs
1-8
December 2013
-------
TRIM-Based Tiered Screening Methodology for RTR
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 - unfilled3
m
0.79
McKone et al. 2001 (Table 16 - U.S. average).
Thickness - tilled3
m
0.6
Adjusted from McKone et al. 2001 (Table 16).
Organic carbon fraction
unitless
0.008
McKone et al. 2001 (Tables 16 and A-3, U.S.
average).
PH
unitless
6.8
Assumption.
Water content
vo I u me [wate r]/vo lume[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]/mJ[soil]
2,600
Default in McKone et al. 2001 (Table 3).
Fraction sand
unitless
0.35
Assumption.
Thickness3
m
1.4
McKone et al. 2001 (Table 17).
Organic carbon fraction
unitless
0.003
McKone et al. 2001 (Tables 16 and A-3, U.S.
average).
PH
unitless
6.8
Assumption.
Water content
volume[water]/volume[compartment]
0.21
McKone et al. 2001 (Table 17 - national
average).
Groundwater Compartment Type
Thickness3
m
3
McKone et al. 2001 (Table 3).
Fraction sand
unitless
0.4
Assumption.
Organic carbon fraction
unitless
0.004
Assumption.
PH
unitless
6.8
Assumption.
Porosity
volume [total pore
space]/volume[compartment]
0.2
Default in McKone et al. 2001 (Table 3).
Density of solid material in aquifer
kg[soil]/mJ[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.
Attachment A, Addendum 1
TRIM.FaTE Inputs
1-9
December 2013
-------
TRIM-Based Tiered Screening Methodology for RTR
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 N5a
SW Pond
0.0
SurfSoil N4
0.5
SurfSoil S5
0.5
sink
0.0
SurfSoil S5a
SW Pond
0.0
SurfSoil N5
0.0
SurfSoil S4
1.0
sink
0.0
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.
Attachment A, Addendum 1
TRIM.FaTE Inputs
1-10
December 2013
-------
TRIM-Based Tiered Screening Methodology for RTR
Exhibit_Add A1-6. USLE Erosion Parameters for the TRIM.FaTE Screening Scenario
Soil
Parcel
Area
Rainfall/Erosivity
Index
Soil
Erodibility
Index
Length-
Slope
Factor
Land
Use
Cover
Mgmt
Factor
Supporting
Practices
Factor
Unit Soil Loss
Sediment
Delivery
Ratio3
Calculated
(Adjusted)
Erosion
Rate
Code
m2
R (100 ft-ton/ac)
K
(ton/ac/(100
ft-ton/acre))
LS
(USCS)
type
C
(USCS)
P
A
(ton/ac/yr)
A
(kg/m2/d)
SDRa
kg/m2/d
N1
5.8E+04
300
0.39
1.5
grass
0.1
1
17.55
0.010779
0.533
0.005740
N6
4.1E+04
300
0.39
1.5
crops
0.2
1
35.1
0.021557
0.557
0.012014
N7
7.3E+04
300
0.39
1.5
grass
0.1
1
17.55
0.010779
0.518
0.005580
N3
3.5E+05
300
0.39
1.5
grass
0.1
1
17.55
0.010779
0.385
0.004151
N4
2.0E+06
300
0.39
1.5
forest
0.1
1
17.55
0.010779
0.309
0.003331
N5
6.7E+06
300
0.39
1.5
forest
0.1
1
17.55
0.010779
0.196
0.002116
S1
5.8E+04
300
0.39
1.5
grass
0.1
1
17.55
0.010779
0.533
0.005740
S4
2.0E+06
300
0.39
1.5
forest
0.1
1
17.55
0.010779
0.309
0.003331
S5
6.7E+06
300
0.39
1.5
forest
0.1
1
17.55
0.010779
0.196
0.002116
Calculated using SDR = a * (AL)"b; where a is the empirical intercept coefficient (based on the size of the watershed), AL is the total watershed area receiving deposition (m2), and b is the
empirical slope coefficient (always 0.125).
Attachment A, Addendum 1
TRIM.FaTE Inputs
1-11
December 2013
-------
TRIM-Based Tiered Screening Methodology for RTR
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
Attachment A, Addendum 1
TRIM.FaTE Inputs
1-12
December 2013
-------
TRIM-Based Tiered Screening Methodology for RTR
Exhibit_Add A1-8. Terrestrial Plant Parameters for the TRIM.FaTE Screening Scenario
Parameter Name
Units
Coniferous3
Grass/Herb3
Value
Used
Reference
Value
Used
Reference
Leaf Compartment Type
Allow exchange
1=yes, 0=no
1
-
Seasonal13
-
Average leaf area index
m2[leaf]/
m2[area]
5.0
Harvard Forest, dom. red oak and
red maple, CDIAC website
5.0
Mid-range of 4-6 for old fields, R.J.
Luxmoore, ORNL.
Calculate wet deposition
interception fraction (Boolean)
1=yes, 0=no
0
Assumption.
0
Assumption.
Correction exponent, octanol to
lipid
unitless
0.76
From roots, Trapp 1995.
0.76
From roots, Trapp 1995.
Degree stomatal opening
unitless
1
Assumed value of 1 for daytime
(stomatal diffusion is turned off at
night using a different property,
IsDay).
1
Assumed value of 1 for daytime
(stomatal diffusion is turned off at night
using a different property, IsDay).
Density of wet leaf
kg/mJ
820
Paterson et al. 1991.
820
Paterson et al. 1991.
Leaf wetting factor
m
3.00E-04
1E-04 to 6E-04 for different crops
and elements, Mullerand Prohl
1993.
3.00E-04
1 E-04 to 6E-04 for different crops and
elements, Mullerand Prohl 1993.
Length of leaf
m
0.01
Assumption.
0.05
Assumption.
Lipid content
kg/kg wet weight
0.00224
European beech, Riederer 1995.
0.00224
European beech, Riederer 1995.
Litter fall rate
1/day
0.0021
value assumes 1st-order
relationship and that 99% of leaves
fall over 6 years
Seasonal0
Stomatal area normalized
effective diffusion path length
1/m
200
Wilmer and Fricker 1996.
200
Wilmer and Fricker 1996.
Vegetation attenuation factor
m2/kg
2.9
Grass/hay, Baes et al. 1984.
2.9
Grass/hay, Baes et al. 1984.
Water content
unitless
0.8
Paterson et al. 1991.
0.8
Paterson et al. 1991.
Wet deposition interception
fraction
unitless
0.2
Calculated based on 5 years of
local met data, 1987-1991.
0.2
Calculated based on 5 years of local met
data, 1987-1991.
Wet mass of leaf per soil area
kg [fresh
leaf]/m2[area]
2.0
Calculated from leaf area index,
leaf thickness (Simonich and Hites,
1994), density of wet foliage.
0.6
Calculated from leaf area index and Leith
1975.
Particle on Leaf Compartment Type
Allow exchange
1=yes, 0=no
1
-
Seasonal6
-
Attachment A, Addendum 1
TRIM.FaTE Inputs
1-13
December 2013
-------
TRIM-Based Tiered Screening Methodology for RTR
Exhibit_Add A1-8. Terrestrial Plant Parameters for the TRIM.FaTE Screening Scenario
Parameter Name
Units
Coniferous3
Grass/Herb3
Value
Used
Reference
Value
Used
Reference
Particle on Leaf Compartment Type, continued
Volume particle per area leaf
m3[leaf
particles]/m2[leaf]
1.00E-09
Based on particle density and size
distribution for atmospheric
particles measured on an adhesive
surface, Coe and Lindberg 1987.
1.00E-09
Based on particle density and size
distribution for atmospheric particles
measured on an adhesive surface, Coe
and Lindberg 1987.
Root Compartment Type - Nonwoody Only
Allow exchange
1=yes, 0=no
Seasonal6
-
Correction exponent, octanol to
lipid
unitless
0.76
Trapp 1995.
Lipid content of root
kg/kg wet weight
0.011
Calculated.
Water content of root
kg/kg wet weight
0.8
Assumption.
Wet density of root
kg/mJ
820
Soybean, Paterson et al. 1991.
Wet mass per soil area
kg/m2
1.4
Temperate grassland, Jackson et al.
1996.
Stem Compartment Type - Nonwoody Only
Allow exchange
1=yes, 0=no
Seasonal6
-
Correction exponent, octanol to
lipid
unitless
0.76
Trapp 1995.
Density of phloem fluid
kg/mJ
1,000
Assumption.
Density of xylem fluid
kg/cmJ
900
Assumption.
Flow rate of transpired water per
leaf area
mJ[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/mJ
830
Assumption.
Wet mass per soil area
kg/m2
0.24
Calculated from leaf and root biomass
density.
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.
°Begins November 7, ends December 6; rate = 0.15/day during this time (value assumes 99 percent of leaves fall in 30 days).
Attachment A, Addendum 1
TRIM.FaTE Inputs
1-14
December 2013
-------
TRIM-Based Tiered Screening Methodology for RTR
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.
Depth3
m
3.18
Wl DNR 2007 - calculation based
on relationship between drainage
basin and lake area size.
Dimensionless viscous
sublayer thickness
unitless
4
Ambrose et al. 1995.
Drag coefficient for water
body
unitless
0.0011
Ambrose et al. 1995.
Flush rate
1/year
12.17
Calculated based on pond
dimensions and flow calculations.
Fraction Sand
unitless
0.25
Assumption.
Organic carbon fraction in
suspended sediments
unitless
0.02
Assumption.
PH
unitless
7.3
Assumption.
Suspended sediment
deposition velocity
m/day
2
USEPA 1997b.
Total suspended sediment
concentration
kg[sediment]/mJ[water
column]
0.05
USEPA 2005.
Water temperature
degrees K
298
USEPA 2005.
aSet using the volume element properties named "top" and "bottom."
Attachment A, Addendum 1
TRIM.FaTE Inputs
1-15
December 2013
-------
TRIM-Based Tiered Screening Methodology for RTR
Exhibit_Add A1-10. Sediment Parameters for the TRIM.FaTE Screening Scenario
Parameter Name
Units
Value Used
Reference
Depth3
m
0.05
McKone et al. 2001 (Table
3).
Fraction sand
unitless
0.25
Assumption.
Organic carbon fraction
unitless
0.02
McKone et al. 2001 (Table
3).
Porosity of the sediment
zone
volume [total pore
space ]/volume[sediment
compartment]
0.6
USEPA 2005.
Solid material density in
sediment
kg[sediment]/mJ[sediment]
2,600
McKone et al. 2001 (Table
3).
PH
unitless
7.3
Assumption.
Sediment resuspension
velocity
m/day
6.69E-05
Calculated from water
balance model.
aSet using the volume element properties named "top" and "bottom."
Attachment A, Addendum 1
TRIM.FaTE Inputs
1-16
December 2013
-------
TRIM-Based Tiered Screening Methodology for RTR
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
Assumption.
Water Column
Herbivore
0%
100%
0%
0%
0%
0%
0%
0%
0.002
0.025
Assumption.
Benthic Omnivore
0%
0%
100%
0%
0%
0%
0%
0%
0.002
2.50E-01
Assumption.
Water Column
Omnivore
0%
0%
0%
100%
0%
0%
0%
0%
0.001
0.25
Assumption.
Benthic Carnivore
0%
0%
50%
0%
50%
0%
0%
0%
0.001
2.0
Assumption.
Water Column
Carnivore
0%
0%
0%
0%
0%
100%
0%
0%
0.0002
2.0
Assumption.
Zooplankton
100%
0%
0%
0%
0%
0%
0%
0%
0.0064
5.70E-08
Assumption.
Attachment A, Addendum 1
TRIM.FaTE Inputs
1-17
December 2013
-------
TRIM-Based Tiered Screening Methodology for RTR
Exhibit_Add A1-12. Cadmium Chemical-Specific Parameters
for the TRIM.FaTE Screening Scenario
Parameter Name3
Units
Value
Reference
CAS numberb
unitless
7440-43-9
-
Diffusion coefficient in pure
air
m2[air]/day
0.71
USEPA 1999 (Table A-2-
35).
Diffusion coefficient in pure
water
m2[water]/day
8.16E-05
USEPA 1999 (Table A-2-
35).
Henry's Law constant
Pa-mJ/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)
mJ[air]/mJ[octanol]
-
-
Octanol-carbon partition
coefficient (Koc)
-
-
Octanol-water partition
coefficient (Kow)
L[water]/kg[octanol]
-
-
aAII parameters in this table are TRIM.FaTE chemical properties.
bThis CAS numbers applies to elemental Cd; however, the cations of cadmium are being modeled.
Attachment A, Addendum 1
TRIM.FaTE Inputs
1-18
December 2013
-------
TRIM-Based Tiered Screening Methodology for RTR
Exhibit_Add A1-13. Mercury Chemical-Specific Parameters
for the TRIM.FaTE Screening Scenario
Parameter Name
Units
Value
Reference
Hg(0)B
Hg(2)"
MHg"
CAS number
unitless
7439-97-6
14302-87-5
22967-92-6
-
Diffusion coefficient
in pure air
m2[air]/day
0.478
0.478
0.456
USEPA 1997b.
Diffusion coefficient
in pure water
m2[water]/day
5.54E-05
5.54E-05
5.28E-05
USEPA 1997b.
Henry's Law
constant
Pa-mJ/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
mJ[air]/mJ[rain]
1,200
1.6E+06
0
USEPA 1997b,
based on Petersen
et al. 1995.
aAII 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.
Attachment A, Addendum 1
TRIM.FaTE Inputs
1-19
December 2013
-------
TRIM-Based Tiered Screening Methodology for RTR
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
m2/day
0.451
0.691
0.009
0.388
0.441
0.372
0.009
0.190
Diffusion coefficient
in pure water
m2/day
6.70E-05
6.91 E-05
8.64E-05
6.03E-05
7.78E-05
7.78E-05
8.64E-05
4.54E-05
Henry's Law
constant
Pa-m3/mol
50.56
0.20
18.50
12.70
1.22
0.07
0.05
0.03
Melting point
degrees K
307.75
396.65
366.15
365.65
433
452
441
550.15
Molecular weight
g/mol
142.20
256.35
154.21
152.20
228.29
252.32
252.32
276.34
Octanol-water
partition coefficient
(Kow)
L[water]/L[octanol]
7.24E+03
6.31 E+05
8.32E+03
1 00E+04
6.17E+05
9.33E+05
6.03E+05
4.27E+06
Parameter Name
Units
Value
BkF
Chr
DahA
Fluoranthene
Fluorene
IcdP
CAS number
unitless
207-08-9
218-01-9
53-70-3
206-44-0
86-73-7
193-39-5
Diffusion coefficient
in pure air
m2/day
0.009
0.009
0.009
0.009
0.009
0.009
Diffusion coefficient
in pure water
m2/day
8.64E-05
8.64E-05
8.64E-05
8.64E-05
8.64E-05
8.64E-05
Henry's Law
constant
Pa-mJ/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]
8.71 E+05
5.37E+05
3.16E+06
1.45E+05
1.51E+04
5.25E+0
6
Attachment A, Addendum 1
TRIM.FaTE Inputs
1-20
December 2013
-------
TRIM-Based Tiered Screening Methodology for RTR
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
m2/day
USEPA2005. Exceptions include USEPA 1997a (7,12-Dimethylbenz(a)anthracene), and
USEPA2007 (2-Methylnaphthalene, Acenaphthylene, and Benzo(g,h,i)perylene)
Diffusion coefficient in pure
water
m2/day
USEPA 2005. Exceptions include USEPA 1997a (7,12-Dimethylbenz(a)anthracene), and
USEPA 2007 (2-Methylnaphthalene, Acenaphthylene, and Benzo(g,h,i)perylene)
Henry's Law constant
Pa-mJ/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 2001 a
(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 lndeno(1,2,3-cd)pyrene), and Sangster 1993
(Benzo(b)fluoranthene)
Attachment A, Addendum 1
TRIM.FaTE Inputs
1-21
December 2013
-------
TRIM-Based Tiered Screening Methodology for RTR
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
l
CO
CAS number
unitless
3268-87-9
39001-02-0
35822-46-9
67562-39-4
55673-89-7
39227-28-6
70648-26-9
Diffusion coefficient in
pure air
m2/day
0.751
0.168
0.782
0.176
0.176
0.816
0.183
Diffusion coefficient in
pure water
m2/day
6.91 E-05
6.91 E-05
6.91 E-05
6.91 E-05
6.91 E-05
6.91 E-05
6.91 E-05
Henry's Law constant
Pa-mJ/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
2.51 E+07
7.94E+06
6.31 E+07
1.00E+07
Parameter Name
Units
Value
1,2,3,6,7,8-
HxCDD
1,2,3,6,7,8-
HxCDF
1,2,3,7,8,9-
HxCDD
1,2,3,7,8,9-
HxCDF
1,2,3,7,8-
PeCDD
1,2,3,7,8-
PeCDF
2,3,4,6,7,8-
HxCDF
CAS number
unitless
57653-85-7
57117-44-9
19408-74-3
72918-21-9
40321-76-4
57117-41-6
60851-34-5
Diffusion coefficient in
pure air
m2/day
0.816
0.183
0.816
0.183
0.854
0.192
0.183
Diffusion coefficient in
pure water
m2/day
6.91 E-05
6.91 E-05
6.91 E-05
6.91 E-05
6.91 E-05
6.91 E-05
6.91 E-05
Henry's Law constant
Pa-mJ/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
8.31 E+07
Attachment A, Addendum 1
TRIM.FaTE Inputs
1-22
December 2013
-------
TRIM-Based Tiered Screening Methodology for RTR
Exhibit_Add A1-15. Dioxin Chemical-Specific Parameters for the TRIM.FaTE Screening Scenario
Value
Parameter Name
Units
2,3,4,7,8-
PeCDF
2,3,7,8-
TCDD
2,3,7,8-
TCDF
Reference
CAS number
unitless
57117-31-4
1746-01-6
51207-31-9
-
Diffusion coefficient in
pure air
m2/day
0.192
0.899
0.203
US EPA 2005
Diffusion coefficient in
pure water
m2/day
6.91 E-05
4.84E-05
5.19E-05
US EPA 2005
Henry's Law constant
Pa-mJ/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
6.31 E+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)
Attachment A, Addendum 1
TRIM.FaTE Inputs
1-23
December 2013
-------
TRIM-Based Tiered Screening Methodology for RTR
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
m3[air]/m3[rain]
200,000
MacKay et al. 1986.
Surface Soil Compartment Type
Use input characteristic depth
(Boolean)
0 = no, Else = yes
0
Assumption.
Root Zone Soil Compartment Type
Use input characteristic depth
(Boolean)
0 = no, Else = yes
0
Assumption.
Vadose Zone Soil Compartment Type
Use input characteristic depth
(Boolean)
0 = no, Else = yes
0
Assumption.
Surface Water Compartment Type
Ratio of concentration in water to
concentration in algae to
concentration dissolved in water
L[water]/g[algae
wet wt]
1.87
McGeer et al. 2003.
Attachment A, Addendum 1
TRIM.FaTE Inputs
1-24
December 2013
-------
TRIM-Based Tiered Screening Methodology for RTR
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
Assumption.
Methylation rate
1/day
0
0
0
Assumption.
Oxidation rate
1/day
0.00385
0
0
Low end of half-life range (6 months to 2 years) in
USEPA 1997b.
Reduction rate
1/day
0
0
0
Assumption.
Washout ratio
mJ[air]/mJ[rain]
200,000
200,000
200,000
Assumption.
Surface Soil Compartment Type
Use input characteristic depth
(Boolean)
0 = no, Else = yes
0
0
0
Assumption.
Soil-water partition coefficient
L[water]/kg[soil
wet wt]
1,000
58,000
7,000
USEPA 1997b.
Vapor dry deposition velocity
m/day
50
2500
0
Hg(0) - from Lindberg et al. 1992; Hg(2) - estimate
by USEPA using the Industrial Source Complex
(ISC) Model - [See Vol. Ill, 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 unfilled 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).
Attachment A, Addendum 1
TRIM.FaTE Inputs
1-25
December 2013
-------
TRIM-Based Tiered Screening Methodology for RTR
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
Assumption.
Soil-water partition coefficient
L[water]/kg[soil
wet wt]
1,000
58,000
7,000
USEPA 1997b.
Demethylation rate
1/day
N/A
N/A
0.06
Range reported in Porvari and Verta 1995 is 3E-2
to 6E-2 /day; value is average maximum potential
demethylation rate constant under anaerobic
conditions.
Methylation rate
1/day
0
0.001
0
Range reported in Porvari and Verta 1995 is 2E-4
to 1E-3 /day; value is average maximum potential
methylation rate constant under anaerobic
conditions.
Oxidation rate
1/day
0
0
0
Value assumed in USEPA 1997b.
Reduction rate
1/day
0
3.25E-06
0
Value used for tilled surface soil (20cm), 10%
moisture content, in USEPA 1997b (Lindberg
1996; Carpi and Lindberg 1997).
Vadose Zone Soil Compartment Type
Use input characteristic depth (Boolean)
0 = no, Else = yes
0
0
0
Assumption.
Soil-water partition coefficient
L[water]/kg[soil
wet wt]
1,000
58,000
7,000
USEPA 1997b.
Demethylation rate
1/day
N/A
N/A
0.06
Range reported in Porvari and Verta 1995 is 3E-2
to 6E-2 /day; value is average maximum potential
demethylation rate constant under anaerobic
conditions.
Methylation rate
1/day
0
0.001
0
Range reported in Porvari and Verta 1995 is 2E-4
to 1E-3 /day; value is average maximum potential
methylation rate constant under anaerobic
conditions.
Oxidation rate
1/day
0
0
0
Value assumed in USEPA 1997b.
Reduction rate
1/day
0
3.25E-06
0
Value used for tilled surface soil (20cm), 10%
moisture content, in USEPA 1997b (Lindberg
1996; Carpi and Lindberg 1997).
Attachment A, Addendum 1
TRIM.FaTE Inputs
1-26
December 2013
-------
TRIM-Based Tiered Screening Methodology for RTR
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/[|jm2-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. Ill, App. A).
Demethylation rate
1/day
N/A
N/A
0.013
Average range of 1 E-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 3E-
4/day (Gilmour and Henry 1991).
Oxidation rate
1/day
0
0
0
Assumption.
Reduction rate
1/day
0
0.0075
0
Value used in USEPA 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).
Attachment A, Addendum 1 1-27 December 2013
TRIM.FaTE Inputs
-------
TRIM-Based Tiered Screening Methodology for RTR
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 1 E-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
1 E-3/day, Gilmour and Henry 1991.
Oxidation rate
1/day
0
0
0
Assumption.
Reduction rate
1/day
0
1.00E-06
0
Inferred value based on presence of Hg(0) in
sediment porewater (USEPA 1997b; Vandal et al.
1995).
aTRIM.FaTE Formula Property, which varies from 0.025 to 1.625 depending on pH and chloride concentration.
bTRIM.FaTE Formula Property, which varies from 0.075 to 1.7 depending on pH and chloride concentration.
Attachment A, Addendum 1
TRIM.FaTE Inputs
1-28
December 2013
-------
TRIM-Based Tiered Screening Methodology for RTR
Exhibit_Add A1-18. PAH Chemical-Specific Parameters for Abiotic Compartments in the TRIM.FaTE Screening Scenario
Parameter Name
Units
Value
2Methyl
712DMB
Acenaph-
thene
Acenaph-
thylene
BaA
BaP
BbF
BghiP
BkF
Air Compartment Type
Particle dry deposition velocity
m/day
500
500
500
500
500
500
500
500
500
Half-life
day
0.154
0.092
0.3
0.208
0.125
0.046
0.596
0.215
0.458
Washout 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 Cone In Algae To
Cone 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
Attachment A, Addendum 1
TRIM.FaTE Inputs
1-29
December 2013
-------
TRIM-Based Tiered Screening Methodology for RTR
Exhibit_Add A1-18. PAH Chemical-Specific Parameters for Abiotic Compartments in the TRIM.FaTE Screening Scenario
Parameter Name
Units
Value
Reference
Chr
DahA
Fluoran-
thene
Fluor-
ene
IcdP
Air Compartment Type
Particle dry deposition velocity
m/day
500
500
500
500
500
McKone et al. 2001.
Half-life
day
0.334
0.178
0.46
0.46
0.262
Howard et al. 1991 / upper bound measured or
estimated value. Exceptions include ATSDR 2005 (2-
Methylnaphthalene), USEPA 1998 (7,12-
Dimethylbenz(a)anthracene, Benzo(g,h,i)perylene,
and Fluoranthene) / average of range, HSDB 2001 d
(Acenaphthene), HSDB 2001b (Acenaphthylene), and
Spero et al. 2000 (Fluorene).
Washout ratio
200000
200000
200000
200000
200000
Mackay et al. 1986.
Surface Soil Compartment Type
User input characteristic depth
(Boolean)
0 = No,
Else = Yes
0
0
0
0
0
Assumption.
Half-life
day
1000
940
275
33
730
MacKay et al. 2000 / average of range. Exceptions
include ATSDR 2005 (2-Methylnaphthalene), USEPA
1998 (7,12-Dimethylbenz(a)anthracene,
Benzo(g,h,i)perylene, and Fluoranthene) / average of
range, HSDB 2001 d (Acenaphthene), HSDB 2001b
(Acenaphthylene), and HSDB 2001 e (Fluorene).
Root Zone Soil Compartment Type
User input characteristic depth
(Boolean)
0 = No,
Else = Yes
0
0
0
0
0
Assumption.
Half-life
day
1000
940
275
33
730
Howard et al. 1991 / upper bound measured or
estimated value. Exceptions include ATSDR 2005 (2-
Methylnaphthalene), USEPA 1998 (7,12-
Dimethylbenz(a)anthracene, Benzo(g,h,i)perylene,
and Fluoranthene) / average of range, HSDB 2001 d
(Acenaphthene), HSDB 2001b (Acenaphthylene), and
HSDB 2001 e (Fluorene).
Vadose Zone Soil Compartment Type
User input characteristic depth
(Boolean)
0 = No, Else =
Yes
0
0
0
0
0
Assumption.
Attachment A, Addendum 1
TRIM.FaTE Inputs
1-30
December 2013
-------
TRIM-Based Tiered Screening Methodology for RTR
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
Fluor-
ene
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 2001 d
(Acenaphthene) / multiplied by 2, HSDB 2001b
(Acenaphthylene) / multiplied by 2, and HSDB 2001 e
(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 2001 d
(Acenaphthene) / multiplied by 2, HSDB 2001b
(Acenaphthylene) / multiplied by 2, and HSDB 2001 e
(Fluorene) / multiplied by 2.
Surface Water Compartment Type
RatioOfConclnAlgaeToConcD
issolvedlnWater
(g[chem]/kg[algae])/
(g[chem]/L[water])
280
1388
67.4
5.8
1653
Kowfrom 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 2001 d
(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.
Attachment A, Addendum 1
TRIM.FaTE Inputs
1-31
December 2013
-------
TRIM-Based Tiered Screening Methodology for RTR
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
m3[air]/m3[rain]
91000
22000
64000
32000
32000
9000
Surface Soil Compartment Type
Input characteristic depth
m
0.08
0.08
0.08
0.08
0.08
0.08
Use input characteristic
depth (Boolean)
0 = No, Else = Yes
0
0
0
0
0
0
Halflife
day
3650
3650
3650
3650
3650
3650
Root Zone Soil Compartment Type
Input characteristic depth
m
0.08
0.08
0.08
0.08
0.08
0.08
Use input characteristic
depth
0 = No, Else = Yes
0
0
0
0
0
0
Half-life
day
3650
3650
3650
3650
3650
3650
Vadose Zone Soil Compartment Type
Input characteristic depth
m
0.08
0.08
0.08
0.08
0.08
0.08
Use input characteristic
depth (Boolean)
0 = No, Else = Yes
0
0
0
0
0
0
Half-life
day
1008
1008
1008
1008
1008
1008
Groundwater Compartment Type
Half-life
day
1008
1008
1008
1008
1008
1008
Surface Water Compartment Type
Ratio Of Cone In Algae To
Cone 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
Attachment A, Addendum 1
TRIM.FaTE Inputs
1-32
December 2013
-------
TRIM-Based Tiered Screening Methodology for RTR
Exhibit_Add A1-19. Dioxin Chemical-Specific Parameters for Abiotic
Compartments in the TRIM.FaTE Screening Scenario
Value
Parameter Name
Units
,2,3,4,7,8-
HxCDF
®o _
<®o
M y
,2,3,6,7,8-
HxCDF
,2,3,7,8,9-
HxCDD
,2,3,7,8,9-
HxCDF
1,2,3,7,8-
PeCDD
cm" I
T~
r~
T"
T~
T~
Air Compartment Type
Deposition velocity
m/day
500
500
500
500
500
500
Half-life
day
78
28
55
28
51
18
Washout ratio
mJ[air]/mJ[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
0 = No, Else = Yes
0
0
0
0
0
0
depth (Boolean)
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
0 = No, Else = Yes
0
0
0
0
0
0
depth
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
0 = No, Else = Yes
0
0
0
0
0
0
depth (Boolean)
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 Cone In Algae To
Cone 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
Attachment A, Addendum 1
TRIM.FaTE Inputs
1-33
December 2013
-------
TRIM-Based Tiered Screening Methodology for RTR
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
mJ[air]/mJ[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 Cone In Algae To
Cone Dissolved In Water
(g[chem]/g[algae])/
(g [eh em]/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
Attachment A, Addendum 1
TRIM.FaTE Inputs
1-34
December 2013
-------
TRIM-Based Tiered Screening Methodology for RTR
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)
Assumption.
Half-life
Mackay et al. 2000; the degradation rate was cited by
multiple authors, value is for 2,3,7,8-TCDD.
Root Zone Soil Compartment Type
Input characteristic depth
Not used (model set to calculate value).
Use input characteristic depth
Assumption.
Half-life
Mackay et al. 2000; the degradation rate was cited by
multiple authors, value is for 2,3,7,8-TCDD.
Vadose Zone Soil Compartment Type
Input characteristic depth
Not used (model set to calculate value).
Use input characteristic depth (Boolean)
Assumption.
Half-life
Average value of the range presented in Mackay et al. 2000;
based on estimated unacclimated aerobic biodegradation
half-life, value is for 2,3,7,8-TCDD.
Groundwater Compartment Type
Half-life
Average value of the range presented in Mackay et al. 2000;
based on estimated unacclimated aerobic biodegradation
half-life, value is for 2,3,7,8-TCDD.
Surface Water Compartment Type
Ratio Of Cone In Algae To Cone 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.
Attachment A, Addendum 1
TRIM.FaTE Inputs
1-35
December 2013
-------
TRIM-Based Tiered Screening Methodology for RTR
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
Assumption.
Particle on Leaf Compartment Type
Transfer factor to leaf
1/day
0.200
Assumption.
Root Compartment Type - Grasses and Herbsa
Root to Root Soil Partition-
Alpha of Steady State
unitless
0.95
Henning et al. 2001.
Root to Root Soil Partition-
Partitioning Coefficient
mJ[bulk root
soil]/m3[root]
0.23
Nriagu 1980; based on average
value calculated from various
agricultural plant species.
Root to Root Soil Partition-
Time to Reach Alpha
day
28
Henning et al. 2001.
Stem Compartment Type - Grasses and Herbsa
Transpiration stream
concentration factor (TSCF)
mJ[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.
Attachment A, Addendum 1
TRIM.FaTE Inputs
1-36
December 2013
-------
TRIM-Based Tiered Screening Methodology for RTR
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
Assumed based on 1% of transfer factor
from leaf particle to leaf.
Demethylation rate
1/day
N/A
N/A
0.03
Calculated from Bache et al. 1973.
Methylation rate
1/day
0
0
0
Assumed from Gay 1975, Bache et al.
1973.
Oxidation rate
1/day
1.0E+06
0
0
Assumed to be nearly instantaneous
Reduction rate
1/day
0
0
0
Assumption.
Particle on Leaf Compartment Type
Transfer factor to leaf
1/day
0.2
0.2
0.2
Assumption.
Demethylation rate
1/day
N/A
N/A
0
Assumption.
Methylation rate
1/day
0
0
0
Assumption.
Oxidation rate
1/day
0
0
0
Assumption.
Reduction rate
1/day
0
0
0
Assumption.
Root Compartment Type - Grasses and Herbsa
Alpha for root-root zone bulk soil
unitless
0.95
0.95
0.95
Selected value.
Root/root-zone-soil-water partition
coefficient
mJ[bulk root soil]/ mJ[root]
0
0.18
1.2
Hg2" geometric mean Leonard et al. 1998,
John 1972, Hogg et al. 1978; MHg-
assumed, based on Hogg et al. 1978.
t-alpha for root-root zone bulk soil
day
21
21
21
Assumption.
Demethylation rate
1/day
N/A
N/A
0
Assumption.
Methylation rate
1/day
0
0
0
Assumption.
Oxidation rate
1/day
0
0
0
Assumption.
Reduction rate
1/day
0
0
0
Assumption.
Stem Compartment Type - Grasses and Herbsa
Transpiration stream concentration
factor (TSCF)
mJ[soil pore
water]/m3[xylem fluid]
0
0.5
0.2
Calculation from Norway spruce, Scots
pine, Bishop et al. 1998.
Demethylation rate
1/day
N/A
N/A
0.03
Calculated from Bache et al. 1973.
Methylation rate
1/day
0
0
0
Assumption.
Oxidation rate
1/day
0
0
0
Assumption.
Reduction rate
1/day
0
0
0
Assumption.
aRoots and stems are not modeled for deciduous or coniferous forest in the current version of TRIM.FaTE.
Attachment A, Addendum 1
TRIM.FaTE Inputs
1-37
December 2013
-------
TRIM-Based Tiered Screening Methodology for RTR
Exhibit_Add A1-22. PAH Chemical-Specific Parameters for Plant Compartments in the TRIM.FaTE Screening Scenario
Parameter Name
Units
Value
2Methyl
712DMB
Acenaph-
thene
Acenaph-
thylene
BaA
BaP
BbF
BghiP
BkF
Leaf Compartment Type
Transfer factor to
leaf particle
1/day
1.00E-04
1.00E-04
1.00E-04
1.00E-04
1.00E-04
1.00E-04
1.00E-04
1.00E-04
1.00E-04
Half-life
day
3.50
3.50
3.50
3.50
3.50
3.50
3.50
3.50
3.50
Particle on Leaf Compartment Type
Transfer factor to
leaf
1/day
1.00E-04
1.00E-04
1.00E-04
1.00E-04
1.00E-04
1.00E-04
1.00E-04
1.00E-04
1.00E-04
Half-life
day
2.31
2.31
2.31
2.31
1.84
2.31
3.56
2.31
17.80
Root Compartment Type - Grasses and 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.
Attachment A, Addendum 1
TRIM.FaTE Inputs
1-38
December 2013
-------
TRIM-Based Tiered Screening Methodology for RTR
Exhibit_Add A1-23. PAH Chemical-Specific Parameters for Plant Compartments in the TRIM.FaTE Screening Scenario
Parameter Name
Units
Value
Reference
Chr
DahA
Fluoran-
thene
Fluorene
IcdP
Leaf Compartment Type
Transfer factor to
leaf particle
1/day
1.00E-04
1.00E-04
1.00E-04
1.00E-04
1.00E-04
Assumption.
Half-life
day
3.50
3.50
3.50
3.50
3.50
Edwards 1988 (calculated from metabolic rate
constant).
Particle on Leaf Compartment Type
Transfer factor to
leaf
1/day
1.00E-04
1.00E-04
1.00E-04
1.00E-04
1.00E-04
Assumption.
Half-life
day
4.12
17.80
2.31
2.31
17.80
Calculated as 2 times the measured photolysis
half-life from Mackay et al. 1992. Exceptions
include values that have been set equal to
Benzo(a)pyrene (2-Methylnaphthalene; 7,12-
Dimethylbenz(a)anthracene; Acenaphthene;
Acenaphthylene; Benzo(ghi)perylene;
Fluoranthene; and Fluorene).
Root Compartment Type - Grasses and Herbsa
Half-life
day
34.60
34.60
34.60
34.60
34.60
Edwards 1988 (calculated from metabolic rate
constant).
Root soil water
interaction - alpha
unitless
0.95
0.95
0.95
0.95
0.95
Assumption.
Stem Compartment Type - Grasses and Herbsa
Half-life
day
3.50
3.50
3.50
3.50
3.50
Edwards 1988 (calculated from metabolic rate
constant).
aRoots and stems are not modeled for deciduous or coniferous forest in the current version of TRIM.FaTE.
Attachment A, Addendum 1
TRIM.FaTE Inputs
1-39
December 2013
-------
TRIM-Based Tiered Screening Methodology for RTR
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
Assumption based on USEPA 2000b (an estimate for mercury) and
Trapp 1995; highly uncertain.
Half-life
day
4.4
McCrady and Maggard 1993; photodegradation sorbed to grass
foliage in sunlight; assumed 10% sunlight per day.
Root Compartment Type - Grasses and Herbsa
Half-life
day
70
Arjmand and Sandermann 1985, as cited in Komoba et al. 1995;
soybean root cell culture metabolism test data for DDE.
Root soil-water interaction - alpha
unitless
0.95
Assumption.
Stem Compartment Type - Grasses and Herbsa
Half-life
day
70
Arjmand and Sandermann 1985, as cited in Komoba et al. 1995;
soybean root cell culture metabolism test data for DDE.
aRoots and stems are not modeled for deciduous or coniferous forest in the current version of TRIM.FaTE.
Attachment A, Addendum 1
TRIM.FaTE Inputs
1-40
December 2013
-------
TRIM-Based Tiered Screening Methodology for RTR
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
Assumption.
Sediment partitioning - partition
coefficient
kg[bulk sed/kg[invertebrate wet wt]
0.27
Assumption.
Sediment partitioning - time to reach
alpha of equilibrium
day
21
Hare et al. 2001.
Benthic Omnivore Compartment Type
Assimilation efficiency from food
unitless
0.1
Assumption based on Yan and Wang 2002.
Absorption rate constant
unitless
1.23
Calculated based on body weight from regression
in Hendriks and Heikens 2001.
Elimination rate constant
unitless
1.73E-02
Assumption.
Benthic Carnivore Compartment Type
Assimilation efficiency from food
unitless
0.1
Assumption based on Yan and Wang 2002.
Absorption rate constant
unitless
0.66
Calculated based on body weight from regression
in Hendriks and Heikens 2001.
Elimination rate constant
unitless
1.68E-03
Assumption.
Water-column Herbivore Compartment Type
Assimilation efficiency from food
unitless
0.1
Assumed value based on Yan and Wang 2002.
Assimilation efficiency from plants
unitless
0.1
Assumed value based on Yan and Wang 2002.
Absorption rate constant
unitless
2.46
Calculated based on body weight from regression
in Hendriks and Heikens 2001.
Elimination rate constant
unitless
1.73E-02
Assumption.
Attachment A, Addendum 1
TRIM.FaTE Inputs
1-41
December 2013
-------
TRIM-Based Tiered Screening Methodology for RTR
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
Assumption based on Yan and Wang 2002.
Assimilation efficiency from plants
unitless
0.1
Assumption based on Yan and Wang 2002.
Absorption rate constant
unitless
1.23
Calculated based on body weight from regression
in Hendriks and Heikens 2001.
Elimination rate constant
unitless
1.73E-02
Assumption.
Water-column Carnivore Compartment Type
Assimilation efficiency from food
unitless
0.1
Assumption based on Yan and Wang 2002.
Absorption rate constant
unitless
0.66
Calculated based on body weight from regression
in Hendriks and Heikens 2001.
Elimination rate constant
unitless
1.73E-02
Assumption
Attachment A, Addendum 1
TRIM.FaTE Inputs
1-42
December 2013
-------
TRIM-Based Tiered Screening Methodology for RTR
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
Assumption.
How Much Faster Hg Elimination Is Than
For MHg
unitless
3
3
1
Assumption.
Methylation rate
1/day
0
0
0
Assumption.
Oxidation rate
1/day
0
1.0E+06
0
Assumption.
Reduction rate
1/day
0
0
0
Assumption.
Benthic Invertebrate Compartment Type
Alpha of equilibrium for sediment
partitioning
unitless
0.95
0.95
0.95
Selected value (i.e., proportion of
equilibrium achieved by time "t").
Benthic invertebrate-bulk sediment partition
coefficient
kg[bulk
sediment]/kg[invertebrate
wet wt]
0.0824
0.0824
5.04
Hg(0) - assumed based on Hg(2)
value; Hg(2) and MHg - Saouter
etal. 1991.
t-alpha for equilibrium for sediment
partitioning
day
14
14
14
Experiment duration from
Saouter et al. 1991.
All Fish Compartments Typesa
Elimination adjustment factor
unitless
3
3
1
Trudel and Rasmussen 1997.
Assimilation efficiency from food
unitless
0.06
0.06
0.5
Williams et al. 2010.
Demethylation rate
1/day
N/A
N/A
0
Assumption.
Methylation rate
1/day
0
0
0
Assumption.
Oxidation rate
1/day
1.0E+06
0
0
Assumption.
Reduction rate
1/day
0
0
0
Assumption.
Water-column Herbivore Compartment Type
Assimilation efficiency from plankton
unitless
0.06
0.06
0.5
Williams et al. 2010.
Screening scenario includes: Benthic Omnivore, Benthic Carnivore, Water-column Herbivore, Water-column Omnivore, and Water-column Carnivore.
Attachment A, Addendum 1
TRIM.FaTE Inputs
1-43
December 2013
-------
TRIM-Based Tiered Screening Methodology for RTR
Exhibit_Add A1-26. PAH Chemical-Specific Parameters for Aquatic Species in the TRIM.FaTE Screening Scenario
Parameter
Name
Value
Units
2Methyl
712DMB
Acenaph-
thene
Acenaph-
thylene
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
ml/g
7235
7235
7235
7235
7235
7235
7235
7235
7235
concentration
in water)
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
Screening scenario includes: Benthic Omnivore, Benthic Carnivore, Water-column Herbivore, Water-column Omnivore, and Water-column Carnivore.
Attachment A, Addendum 1
TRIM.FaTE Inputs
1-44
December 2013
-------
TRIM-Based Tiered Screening Methodology for RTR
Exhibit_Add A1-26. PAH Chemical-Specific Parameters for Aquatic Species in the TRIM.FaTE Screening Scenario
Parameter Name
Units
Value
Reference
Chr
DahA
Fluoran-
thene
Fluorene
IcdP
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
lndeno(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.
Attachment A, Addendum 1
TRIM.FaTE Inputs
1-45
December 2013
-------
TRIM-Based Tiered Screening Methodology for RTR
Exhibit_Add A1-26. PAH Chemical-Specific Parameters for Aquatic Species in the TRIM.FaTE Screening Scenario
Parameter Name
Units
Value
Reference
Chr
DahA
Fluoran-
thene
Fluorene
IcdP
All Fish Compartment 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
Lemairet 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.
Screening scenario includes: Benthic Omnivore, Benthic Carnivore, Water-column Herbivore, Water-column Omnivore, and Water-column Carnivore.
Attachment A, Addendum 1
TRIM.FaTE Inputs
1-46
December 2013
-------
TRIM-Based Tiered Screening Methodology for RTR
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
I
CO
to
CO x
CM" I
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
Attachment A, Addendum 1
TRIM.FaTE Inputs
1-47
December 2013
-------
TRIM-Based Tiered Screening Methodology for RTR
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
Attachment A, Addendum 1
TRIM.FaTE Inputs
1-48
December 2013
-------
TRIM-Based Tiered Screening Methodology for RTR
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
Assumption.
Sediment partitioning partition
coefficient
kg/kg
Rubinstein et al. 1990; used TCDD data forsandworm.
Sediment partitioning alpha of
equilibrium
unitless
Rubinstein et al. 1990.
Sediment Partitioning Time to Reach
Alpha of Equilibrium
days
Rubinstein et al. 1990.
Vd (ratio of concentration in benthic
invertebrates to concentration in water)
ml/g
Assumption.
Half-life
day
Rubinstein et al. 1990; used TCDD data forsandworm.
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 etal. 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.
Screening scenario includes: Benthic Omnivore, Benthic Carnivore, Water-column Herbivore, Water-column Omnivore, and Water-column Carnivore.
Attachment A, Addendum 1
TRIM.FaTE Inputs
1-49
December 2013
-------
TRIM-Based Tiered Screening Methodology for RTR
References
Adriaens, P., Q. Fu, and D. Grbic-Galic. 1995. Bioavailability and transformation of highly chlorinated
dibenzo-p-dioxins and dibenzofurans in anaerobic soils and sediments. Environmental Science and
Technology 29(9): 2252-2260.
Adriaens, P., and D. Grbic-Galic. 1993. Reductive dechlorination of PCDD/F by anaerobic cultures and
sediments. Organohalogen Compounds 12: 107-110.
Adriaens, P., and D. Grbic-Galic. 1992. Effect of cocontaminants and concentration on the anaerobic
biotransformation of PCDD/F in methanogenic river sediments. Organohalogen Compounds 8: 209-
212.
ATSDR (Agency for Toxic Substances and Disease Registry). 2005. Toxicological profile for
naphthalene, 1-methylnaphthalene, and 2-methylnaphthalene. Available
at:http://www.atsdr.cdc.gov/ToxProfiles/TP.asp?id=240&tid=43.
ATSDR. 1999. Toxicological profile for cadmium. Available at
http://www.atsdr.cdc.gov/toxprofiles/tp.asp?id=48&tid=15.
ATSDR. 1998. Toxicological profile for chlorodibenzo-p-dioxins (CDDs). Available at:
http://www.atsdr.cdc.gov/toxprofiles/tp.asp?id=938&tid=194.
Ambrose, R.A., Jr., T.A. Wool, and J.L. Martin. 1995. The water quality analysis simulation program,
WASP5, Part A: Model documentation. Athens, GA: U.S. EPA National Exposure Research
Laboratory, Ecosystems Division.
APHA (American Public Health Association). 1995. Standard methods for the examination of water and
waste water. Washington, DC.
Amyot, M., D. Lean, and G. Mierle. 1997. Photochemical formation of volatile mercury in high arctic
lakes. Environmental Toxicology and Chemistry 16(10):2054-2063.
Arjmand, M., and H. Sandermann. 1985. Metabolism of DDT and related compounds in cell suspension
cultures of soybean (Glycine max L.) and wheat (Tritucum aestivum L.) Pesticide Biochemistry and
Physiology 23:389.
Arnot, J., and F.A. Gobas. 2004. A food web bioaccumulation model for organic chemicals in aquatic
ecosystems. Environmental Toxicology and Chemistry 23(10):2343-2355.
Atkinson, R. 1996. Atmospheric chemistry of PCBs, PCDDs and PCDFs. Issues in Environmental
Science and Technology 6: 53-72.
Bache, C.A., W.J. Gutenmann, L.E. St. John, Jr., R.D. Sweet, H.H. Hatfield, and D.J. Lisk. 1973.
Mercury and methylmercury content of agricultural crops grown on soils treated with various
mercury compounds. Journal of Agricultural and Food Chemistry 21:607-613.
Baes, C.F., III, R.D. Sharp, A.L. Sjoreen, and R.W. Shor. 1984. A review and analysis of parameters for
assessing transport of environmentally released radionuclides through agriculture. ORNL-5786. Oak
Ridge National Laboratory, Oak Ridge, TN.
Barber, M.C. 2008. Dietary uptake models used for modeling the bioaccumulation of organic
contaminants in fish. Environmental Toxicology and Chemistry 27(4):755-777.
Attachment A, Addendum 1
TRIM.FaTE Inputs
1-50
December 2013
-------
TRIM-Based Tiered Screening Methodology for RTR
Berntssen, M.H.G., T.A. Giskegjerde, G. Rosenlund, B.E. Torstensen, and A.K. Lundebye. 2007.
Predicting world health organization toxic equivalency factor dioxin and dioxin-like polychlorinated
biphenyl levels in farmed Atlantic salmon (Salmo salai) based on known levels in feed.
Environmental Toxicology and Chemistry 26(1): 13-23. doi: 10.1897/06-122r.1.
Berrojalbiz, N., S. Lacorte, A. Calbet, E. Saiz, C. Barata, and J. Dachs. 2009. Accumulation and cycling
of polycyclic aromatic hydrocarbons in zooplankton. Environmental Science and Technology
43(7):2295-2301. doi: 10.1021/es8018226.
Bidleman, T.F. 1988. Atmospheric processes. Environmental Science and Technology 22:361-367.
Bishop, K.H.; Lee, Y.H.; Munthe, J.; Dambrine, E. 1998. Xylem sap as a pathway for total mercury and
methylmercury transport from soils to tree canopy in the boreal forest. Biogeochemistry 40: 101-
113.
Boyle, T. 1985. Validation and predictability of laboratory methods for assessing the fate and effects of
contaminants in aquatic ecosystems. Baltimore, MD: ASTM International.
Budavari, S. [ed.]. 1996. The Merck Index-An encyclopedia of chemicals, drugs, and biologicals.
Whitehouse Station, NJ: Merck and Co., Inc., p. 178.
CARB (California Air Resources Board). 1994. Development of intermedia transfer factors for
pollutants, Volume II: Metals and non-volatile organic compounds. PB95-260691. California: Air
Resources Board. March.
Cal EPA (California Environmental Protection Agency). 1993. CalTOX, A multimedia total-exposure
model for hazardous-waste sites, Part II: The dynamic multimedia transport and transformation.
Model Prepared for: The Office of Scientific Affairs. Department of Toxic Substances Control.
Sacramento, California. December. Draft Final.
Carpi, A., and S.E. Lindberg. 1997. Sunlight-mediated emission of elemental mercury from soil
amended with municipal sewage sludge. Environmental Science and Technology 31(7):2085-2091.
Coe, J.M., and S.E. Lindberg. 1987. The morphology and size distribution of atmospheric particles
deposited on foliage and inert surfaces. Journal of the Air Pollution Control Association 37:237-243.
Crank, J., N.R. McFarlane, J.C. Newby, G.D. Paterson, and J.B. Pedley. 1981. Diffusion processes in
environmental systems. In: Paterson etal. 1991. London: Macmillan Press, Ltd.
Del Vento, S., and J. Dachs. 2002. Prediction of uptake dynamics of persistent organic pollutants by
bacteria and phytoplankton. Environmental Toxicology and Chemistry 21(10):2099-2107.
Edwards, N.T. 1988. Assimilation and metabolism of polycyclic aromatic hydrocarbons by vegetation -
an approach to this controversial issue and suggestions for future research. In: (M. Cooke and A.J.
Dennis, eds.) Polynuclear Aromatic Hydrocarbons: A Decade of Progress. Battelle Press,
Columbus, OH; pp. 211-229.
Environment Canada. 2002. Ecosystem Health Science-Based Solutions: Canadian Tissue Residue
Guidelines for the protection of Wildlife Consumers of Aquatic Biota: Methylmercury. Report No. 1-
4.
Gay, D.D. 1975. Biotransformation and chemical form of mercury in plants. International Conference on
Heavy Metals in the Environment, pp. 87-95. Vol. II, Part 1. October.
Attachment A, Addendum 1
TRIM.FaTE Inputs
1-51
December 2013
-------
TRIM-Based Tiered Screening Methodology for RTR
Gilmour, C.C., and E.A. Henry. 1991. Mercury methylation in aquatic systems affected by acid
deposition. Environmental Pollution 71:131 -169.
Goulet, R.R., S. Krack, P.J. Doyle, L. Hare, B. Vigneault, and J.C. McGeer. 2007. Dynamic
multipathway modeling of Cd bioaccumulation in Daphnia magna using waterbourne and dietborne
exposures. Aquatic Toxicology. 81: 117-125.
Hansch, C., A. Leo, and D. Hoekman. 1995. Exploring QSAR - Hydrophobic, electronic, and steric
constants. Washington, DC: American Chemical Society 154.
Hare, L; Tessier, A; and Warren, L. 2001. Cadmium accumulation by invertebrates living at the
sediment-water interface. Environmental Toxicology and Chemistry 20: 880-889.
Harner, T., and Bidleman, T.F. 1998. Octanol-air partition coefficient for describing particle/gas
partitioning of aromatic compounds in urban air. Environmental Science and Technology 32:1494-
1502.
HSDB (Hazardous Substances Data Bank). 2005. Bethesda, MD: National Library of Medicine, U.S.
[Last Revision Date 10/27/2005], 2-Methylnaphthalene; Hazardous Substances Databank Number:
5274. Available at: http://toxnet.nlm.nih.gov/cgi-
bin/sis/search/a?dbs+hsdb:@term+@DOCNO+5274.
HSDB. 2001a. Bethesda, MD: National Library of Medicine, U.S. [Last Revision Date 08/09/2001], 7,12-
Dimethylbenz(a)anthracene; Hazardous Substances Databank Number: 2938. Available at:
http://toxnet.nlm. nih.gov/cgi-bin/sis/search/a?dbs+hsdb:@term+@DOCNO+2938.
HSDB. 2001b. Bethesda, MD: National Library of Medicine, U.S. [Last Revision Date 08/09/2001],
Acenaphthylene; Hazardous Substances Databank Number: 2661. Available at:
http://toxnet.nlm.nih.gov/cgi-bin/sis/search/a?dbs+hsdb:@term+@DOCNO+2661.
HSDB. 2001c. Bethesda, MD: National Library of Medicine, U.S. [Last Revision Date 08/09/2001],
Benzo(ghi)perylene; Hazardous Substances Databank Number: 6177. Available at:
http://toxnet.nlm. nih.gov/cgi-bin/sis/search/a?dbs+hsdb:@term+@DOCNO+6177.
HSDB. 2001 e. Bethesda, MD: National Library of Medicine, U.S. [Last Revision Date 08/09/2001],
Fluorene; Hazardous Substances Databank Number: 2165. Available at:
http://toxnet.nlm. nih.gov/cgi-bin/sis/search/a?dbs+hsdb:@term+@DOCNO+2165.
HSDB. 2001d. Bethesda, MD: National Library of Medicine, U.S. [Last Revision Date 08/09/2001],
Acenaphthene; Hazardous Substances Databank Number: 2659. Available at:
http://toxnet.nlm. nih.gov/cgi-bin/sis/search/a?dbs+hsdb:@term+@DOCNO+2659.
Hendriks, AJ; and Heikens, A. 2001. The power of size. 2. Rate constants and equilibrium ratios for
accumulation of inorganic substances related to species weight. Environmental Toxicology and
Chemistry 20: 1421-1437.
Henning, B.J., H.G. Snyman, and T.A.S. Aveling. 2001. Plant-soil interactions of sludge-borne heavy
metals and the effect on maize (Zea mays L.) seedling growth. Water SA 27(1):71-78.
Hogg, T.J., J.R. Bettany, and J.W.B. Stewart. 1978. The uptake of 203Hg-labeled mercury compounds
by bromegrass from irrigated undisturbed soil columns. Journal of Environmental Quality 7:445-450.
Holzworth, G.C. 1972. Mixing heights, wind speeds, and potential for urban air pollution throughout the
contiguous United States. Prepared for EPA Office of Air Programs. Research Triangle Park, NC.
Attachment A, Addendum 1 1-52 December 2013
TRIM.FaTE Inputs
-------
TRIM-Based Tiered Screening Methodology for RTR
Howard, P.H., R.S. Boethling, W.F. Jarvis, W.M. Meylan, and E.M. Michalenko. 1991. Handbook of
environmental degradation rates. Chelsea, Michigan: Lewis Publishers
Hudson, R., S.A. Gherini, C.J. Watras, and D. Porcella. 1994. Modeling the biogeochemical cycle of
mercury in lakes: The Mercury Cycling Model (MCM) and its application to the MTL Study Lakes. In:
C.J. Watras and J.W. Huckabee, eds. Mercury pollution integration and synthesis. Lewis Publishers,
pp. 473-523.
ICF(ICF International). 2005. Memorandum: TRIM.FaTE screening scenario: Aquatic food web analysis;
submitted to Deirdre Murphy and Terri Hollingsworth, U.S. EPA, from Margaret McVey and
Rebecca Kauffman, ICF Consulting. October 18.
Jackson, R.B., J. Canadell, J.R. Ehleringer, H.A. Mooney, O.E. Sala, and E.D. Schulze. 1996. A global
analysis of root distributions for terrestrial biomes. Oecologia 108:389-411.
John, M.K. 1972. Mercury uptake from soil by various plant species. Bulletin of Environmental
Contamination and Toxicology 8:77-80.
Kaushal, S.S., P.M. Groffman, G.E. Likens, K.T. Belt, W.P. Stack, V.R. Kelly, L.E. Band, and G.T.
Fisher, 2005. Increased Salinization of Fresh Water in the Northeastern United States. Proceedings
of the National Academy of Sciences 102:13517-13520.
Kim, M., and P. O'Keefe. 1998. The role of natural organic compounds in photosensitized degradation
of polychlorinated dibenzo-p-dioxins and dibenzofurans. Organohalogen Compounds 36: 377-380.
Komoba, D., C. Langebartels, and H. Sandermann. 1995. Metabolic processes for organic chemicals in
plants. In: Plant contamination modeling and simulation of organic chemical processes. Trapp, S.,
and Mc Farlane, J.C., eds., CRC Press, Boca Raton, FL. Pages 69-103.
Leith, H. 1975. Primary productivity in the biosphere. In: H. Leith and R.W. Whitaker. Ecological
Studies, Volume 14. Springer-Verlag.
Lemair, P., A. Mathieu, S. Carriere, J.F. Narbonne, M. Lafaurie, and J. Giudicelli. 1992. Hepatic
biotransformation enzymes in aquaculture European sea bass (Dicentrarchus labrax): kinetic
parameters and induction with benzo(a)pyrene. Comparative Biochemistry and Physiology 103(B):
847-853.
Leonard, T.L., G.E. Taylor, Jr., M.S. Gustin, and G.C.J. Fernandez. 1998. Mercury and plants in
contaminated soils: 1. Uptake, partitioning, and emission to the atmosphere. Environmental
Toxicology and Chemistry 17:2063-2071.
Lindberg, S.E., T.P. Meyers, G.E. Taylor, R.R. Turner, and W.H. Schroeder 1992. Atmosphere-Surface
Exchange of Mercury to a Forest: Results of Modelling and Gradient Approaches. Journal of
Geophysical Research 97(D2):2519-2528.
Lindberg, S.E. 1996. Forests and the global biogeochemical cycle of mercury: The importance of
understanding air/vegetation exchange processes. In: W. Baeyans et al., eds. Global and regional
mercury cycles: Sources, fluxes, and mass balances, pp. 359-380.
Mackay, D., W.Y. Shiu, and K.C. Ma. 2000. Physical-chemical properties and environmental fate
handbook. Boca Raton, FL: CRC Press LLC.
Attachment A, Addendum 1
TRIM.FaTE Inputs
1-53
December 2013
-------
TRIM-Based Tiered Screening Methodology for RTR
Mackay, D., W.Y. Shiu, and K.C. Ma. 1992. Illustrated handbook of physical-chemical properties and
environmental fate for organic chemicals: polynuclear aromatic hydrocarbons, polychlorinated
dioxins, and dibenzofurans. Chelsea, Ml: Lewis Publishers.
Mackay, D., S. Patterson, and W.H. Schroeder. 1986. Model describing the rates of transfer processes
of organic chemicals between atmosphere and water. Environmental Science and Technology
20(8): 810-816.
Mason, R.P., J.R. Reinfelder, and F.M.M. Morel. 1996. Uptake, toxicity, and trophic transfer of mercury
in a coastal diatom. Environmental Science and Technology 30(6): 1835-1845.
Mason, R.P., F.M.M. Morel, and H.F. Hemond. 1995a. The role of microorganisms in elemental mercury
formation in natural waters. Water, Air, and Soil Pollution 80:775-787.
Mason, R.P., J.R. Reinfelder, and F.M.M. Morel. 1995b. Bioaccumulation of mercury and
methylmercury. Water Air and Soil Pollution 80(1-4):915-921.
McCrady, J.K., and S.P. Maggard. 1993. Update and photodegradation of 2,3,7,8-tetrachloro-p-dioxin
sorbed to grass foliage. Environmental Science and Technology 27: 343-350.
McElroy, A. E. 1990. Polycyclic aromatic hydrocarbon metabolism in the polychaete Nereis virens.
Aquatic Toxicology 18(1):35-50.
McGeer, J.C., K.V. Brix, J.M. Skeaff, D.K. DeForest, S.I. Brigham, W.J. Adams, and A. Green. 2003.
Inverse relationship between bioconcentration factor and exposure concentration for metals:
implications for hazard assessment of metals in the aquatic environment. Environmental Toxicology
and Chemistry (22)5:1017-1037.
McKone, T.E., A. Bodnar, and E. Hertwich. 2001. Development and evaluation of state-specific
landscape data sets for multimedia source-to-dose models. University of California at Berkeley.
Supported by the U.S. Environmental Protection Agency (Sustainable Technology Division, National
Risk Management Research Laboratory) and Environmental Defense Fund. July. LBNL-43722.
Millard, E.S., D.D. Myles, O.E. Johannsson, and K.M. Ralph. 1996. Phytoplankton photosynthesis at
two index stations in Lake Ontario 1987-1992: assessment of the long-term response to
phosphorus control. Canadian Journal of Fisheries and Aquatic Sciences 53: 1092-1111.
Moermond, C. T. A., T.P. Traas, I. Roessink, K. Veltmam, A.J. Hendriks, and A.A. Koelmans. 2007.
Modeling decreased food chain accumulation of PAHs due to strong sorption to carbonaceous
materials and metabolic transformation. Environmental Science and Technology 41(17):6185-6191.
doi: 10.1021/es0702364.
Montgomery, J. 2000. Groundwater chemicals desk reference. Boca Raton, FL: CRC Press LLC, p.
1701. Morrison, H.A., D.M. Whittle, C.D. Metcalfe, and A.J. Niimi. 1999. Application of a food web
bioaccumulation model for the prediction of polychlorinated biphenyl, dioxin, and furan congener
concentrations in Lake Ontario aquatic biota. Canadian Journal of Fisheries and Aquatic Sciences
56(8):1389-1400.
Morrison, H., Whittle, D., et al. 1999. Application of a food web bioaccumulation model for the prediction
of polychlorinated biphenyl, dioxin, and furan congener concentrations in Lake Ontario aquatic
biota. Can. J. Fish. Aquat. Sci. 56: 1389-1400 (1999).
Attachment A, Addendum 1
TRIM.FaTE Inputs
1-54
December 2013
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TRIM-Based Tiered Screening Methodology for RTR
Muhlbaier, J., and G.T. Tissue. 1980. Cadmium ion the southern basin of Lake Michigan. Water, Air
and Soil Pollution (15):45-49.
Muir, D.C., W.K. Marshall, and G.R. Webster. 1985. Bioconcentration of PCDDs by fish: Effects of
molecular structure and water chemistry. Chemosphere 14(6/7):829-833.
Muller, H. and Prohl, G. 1993. ECOSYS-87: A dynamic model for assessing radiological consequences
of nuclear accidents. Health Physics 64(3):232-252.
NLM (National Library of Medicine). 2002. Hazardous Substance Data Bank (HSDB). Available at:
http://toxnet.nlm.nih.gov/cgi-bin/sis/htmlgen7HSDB.
Niimi, A., and B. Oliver. 1986. Biological half-lives of chlorinated dibenzo-p-dioxins and dibenzofurans in
rainbow trout (Salmo gairdeneri). Environmental Toxicology 5:49-53.
Niimi, A.J,, and V. Palazzo. 1986. Biological half-lives of eight polycyclic aromatic hydrocarbons (PAHs)
in rainbow trout (Salmo gairdneri). Water Research 20(4):503-507.
Nriagu, J.O. 1980. Cadmium in the environment. Part I: Ecological cycling. New York: John Wiley &
Sons. Chapter 15: Uptake and effects of cadmium in higher plants, pp. 608-609.
Opperhuizen, A., W.J. Wagenaar, F.W.M. van der Welen, M. van den Berg, K. Olie, and F.A.P.C.
Gobas.1986. Uptake and elimination of PCDD/PCDF congeners by fish after aqueous exposure to a
fly-ash extract from a municipal incinerator. Chemosphere 15(9-12):2049-2053. doi: 10.1016/0045-
6535(86)90511-4.
Passivirta, J., Sinkkonen, S., Mikkelson, P., Rantio, T., Wania, F. (1999) Estimation of vapor pressures,
solubilities and Henry's law constants of selected persistent organic pollutants as functions of
temperature. Chemosphere 39, 811-832.
Paterson, S., D. Mackay, and A. Gladman. 1991. A fugacity model of chemical uptake by plants from
soil and air. Chemosphere 23:539-565.
Petersen, G., A. Iverfeldt, and J. Munthe. 1995. Atmospheric mercury species over Central and
Northern Europe. Model calculations and comparison with observations from the Nordic Air and
Precipitation Network for 1987 and 1988. Atmospheric Environment 29:47-68.
Porvari, P., and M. Verta. 1995. Methylmercury production in flooded soils: A laboratory study. Water,
Air, and Soil Pollution 80:765-773.
Riederer, M. 1995. Partitioning and transport of organic chemicals between the atmospheric
environment and leaves. In: Trapp, S. and J. C. McFarlane, eds. Plant contamination: Modeling and
simulation of organic chemical processes. Boca Raton, FL: Lewis Publishers, pp. 153-190.
Rubinstein, N.I., R.J. Pruell, B.K. Taplin, J.A. LiVoIsi, 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.
Attachment A, Addendum 1
TRIM.FaTE Inputs
1-55
December 2013
-------
TRIM-Based Tiered Screening Methodology for RTR
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.
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.
USEPA 2003. Toxicological Review of 2-Methylnaphthalene. Available online at:
http://www.epa.gov/iris/toxreviews/1006tr.pdf.
USEPA. 2000a. Estimation Program Interface (EPI) suite. Office of Pollution Prevention and Toxics
(OPPT). Available at: http://www.epa.gov/oppt/exposure/docs/episuitedl.htm.
USEPA. 2000b. Draft exposure and human health reassessment of 2,3,7,8-tetrachlorodibenzo-p-dioxin
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/P-
00/001 Be. Available at: http://www.epa.gov/ncea/pdfs/dioxin/part1/volume3/chap-2.pdf.
USEPA. 1999. Screening level ecological risk assessment protocol for hazardous waste combustion
facilities. Peer Review Draft, November. Available at:
http://www.epa.gov/epaoswer/hazwaste/combust/ecorisk.htm
Attachment A, Addendum 1
TRIM.FaTE Inputs
1-56
December 2013
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TRIM-Based Tiered Screening Methodology for RTR
USEPA. 1998. Chemical fate half-lives for Toxics Release Inventory (TRI) chemicals. July. SRC TR 98-
008.
USEPA. 1997a. Supplemental background document; Nongroundwater pathway risk assessment;
Petroleum process waste listing determination. Appendix B. Compound specific input values.
Available at: http://www.epa.gov/osw/hazard/wastetypes/wasteid/petroref/appendb.pdf.
USEPA. 1997b. U.S. Environmental Protection Agency. Mercury study report to congress. Volume III:
Fate and transport of mercury in the environment. Office of Air Quality Planning and Standards and
Office of Research and Development.
Van den Berg, M., J. De Jongh, H. Poiger, and J.R. Olson. 1994. The toxicokinetics and metabolism of
polychlorinated dibenzo-p-dioxins (PCDDs) and dibenzofurans (PCDFs) and their relevance for
toxicity. CRC Critical Reviews in Toxicology 24(1): 1-74.
Vandal, G.M., W.F. Fitzgerald, K.R. Rolfhus, and C.H.Lamborg. 1995. Modeling the elemental mercury
cycle in Pallette Lake, Wisconsin, USA. Water, Air, and Soil Pollution 80:789-798.
Vulykh, N., and V. Shatalov. 2001. Investigation of Dioxin/Furan Composition in Emissions and in
Environmental Media: Selection of Congeners for Modeling. Technical Note 6/2001. Meteorological
Synthesizing Center - East.
Wang, X., and W.X. Wang. 2006. Bioaccumulation and transfer of benzo (a) pyrene in a simplified
marine food chain. Marine Ecology Progress Series 312:101-111.
Wlliams, J.J., J. Dutton, C.Y. Chen, and N.S. Fisher. 2010. Metal (As, Cd, Hg, and CH3Hg)
bioaccumulation from water and food by the benthic amphipod Leptocherius plumulosus.
Environmental Toxicology and Chemistry 29(8): 1755-1761.
Wlmer, C., and M. Fricker. 1996. Stomata. Second ed. New York, NY: Chapman and Hall. p. 121.
Wl DNR (Wsconsin Department of Natural Resources) 2007. A Guide to Understanding the Hydrologic
Condition of Wisconsin's Lake Superior Watersheds. Wisconsin Department of Natural Resources
(DNR).
Xiao, Z.F., D. Stromberg, and O. Lindqvist. 1995. Influence of humic substances on photolysis of
divalent mercury in aqueous solution. Water, Air, and Soil Pollution 80:789-798.
Yan, X; and Wang, WX. 2002. Exposure and potential food chain transfer factor of Cd, Se and Zn in
marine fish Lutjanus argentimaculatus. Marine Ecology Progress Series 238: 173-186.
Zhang, Q., L. Yang, and W.X. Wang. 2011. Bioaccumulation and trophic transfer of dioxins in marine
copepods and fish. Environmental Pollution 159(12):3390-3397.
Attachment A, Addendum 1
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1-57
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TRIM-Based Tiered Screening Methodology for RTR
Addendum 2. Description of Multimedia Ingestion Risk Calculator
(MIRC) Used for RTR Exposure and Risk Estimates
Attachment A, Addendum 2
Description of MIRC
2-1
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TRIM-Based Tiered Screening Methodology for RTR
CONTENTS, ADDENDUM 2
1. Introduction 2-9
1.1. Purpose and Overview 2-9
1.2. Scope of MIRC 2-9
1.3. Use in EPA's Air Toxics Program 2-10
1.4. MIRC Highlights 2-10
1.5. Organization of This Addendum 2-11
2. MIRC Overview 2-11
2.1. Software 2-12
2.2. Exposure Pathways 2-14
2.3. Receptor Groups 2-15
3. Exposure Algorithms 2-17
3.1. Farm Food Chain Algorithms 2-17
3.1.1. Estimating Chemical Concentrations in Produce 2-18
3.1.2. Estimating Chemical Concentrations in Animal Products 2-24
3.2. Chemical Intake Calculations for Adults and Non-Infant Children 2-27
3.2.1. Chemical Intake from Soil Ingestion 2-29
3.2.2. Chemical Intake from Fish Ingestion 2-29
3.2.3. Chemical Intake from Fruit Ingestion 2-31
3.2.4. Chemical Intake from Vegetable Ingestion 2-31
3.2.5. Chemical Intake from Animal Product Ingestion 2-33
3.2.6. Chemical Intake from Drinking Water Ingestion 2-35
3.3. Total Chemical Intake 2-35
3.4. Chemical Intake Calculations for Nursing Infants 2-36
3.4.1. Infant Average Daily Absorbed Dose 2-36
3.4.2. Chemical Concentration in Breast Milk Fat 2-37
3.4.3. Chemical Concentration in Aqueous Phase of Breast Milk 2-41
3.4.4. Alternative Model for Infant Intake of Methyl Mercury 2-43
4. Dose-Response Values Used for Assessment 2-44
4.1. Cadmium 2-47
4.2. Dioxins (2,3,7,8-TCDD) 2-47
4.3. Mercury 2-48
4.4. Polycyclic Organic Matter 2-48
5. Risk Estimation 2-50
5.1. Cancer Risks 2-50
5.2. Non-cancer Hazard Quotients 2-52
5.2.1. Hazard Quotients for Chemicals with a Chronic RfD 2-53
Attachment A, Addendum 2
Description of MIRC
2-3
December 2013
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TRIM-Based Tiered Screening Methodology for RTR
5.2.2. Hazard Quotients for Chemicals with RfD Based on
Developmental Effects 2-53
5.2.3. Hazard Index for Chemicals with RfDs 2-53
6. Model Input Options 2-54
6.1. Environmental Concentrations 2-55
6.2. Farm-Food-Chain Parameter Values 2-55
6.2.1. List of Farm-Food-Chain (FFC) Parameters 2-55
6.2.2. Produce Parameter Values 2-57
6.2.3. Animal Product Parameter Values 2-70
6.3. Adult and Non-Infant Exposure Parameter Values 2-72
6.3.1. Body Weights 2-73
6.3.2. Water Ingestion Rates 2-74
6.3.3. Local Food Ingestion Rates 2-75
6.3.4. Local Fish Ingestion Rates 2-79
6.3.5. Soil Ingestion Rates 2-85
6.3.6. Total Food Ingestion Rates 2-85
6.4. Other Exposure Factor Values 2-86
6.4.1. Exposure Frequency 2-86
6.4.2. Fraction Contaminated 2-87
6.4.3. Preparation and Cooking Losses 2-87
6.4.4. Food Preparation/Cooking Adjustment Factor (FPCAF) 2-89
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 I ngestion Rates 2-97
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 2-102
7.4.1. Dioxins2-102
7.4.2. Methyl Mercury 2-103
8. References 2-104
Attachment A, Addendum 2
Description of MIRC
2-4
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TRIM-Based Tiered Screening Methodology for RTR
Exhibits, Addendum 2
Exhibit_Add A2-1. Overview of MIRC Software Application for Performing Farm-
Food-Chain Ingestion Exposure and Risk Calculations 2-13
Exhibit_Add A2-2. Transfer Pathways for Modeled Farm Food Chain (FFC)
Media 2-15
Exhibit_Add A2-3. Chemical Transfer Pathways for Produce 2-18
Exhibit_Add A2-4. Estimating Chemical Concentration in Aboveground Produce 2-18
Exhibit_Add A2-5. Chemical Transfer Pathways for Animal Products 2-24
Exhibit_Add A2-6. Oral Dose-response Values Used to Calculate RTR Screening
Threshold Emission Rates for PB-HAP Chemicals3 2-45
Exhibit_Add A2-7. WHO 2005 Toxic Equivalency Factors (TEFs) for Dioxin
Congeners 2-48
Exhibit_Add A2-8. Oral Dose-response Values for Polycyclic Organic Matter
(POM) Groups3 2-49
Exhibit_Add A2-9. MIRC Parameters Used to Estimate Chemical Concentrations
in Farm Foods 2-56
Exhibit_Add A2-10. Chemical-Specific Inputs for Produce Parameters for
Chemicals Included in MIRC 2-57
Exhibit_Add A2-11. Chemical-Specific Inputs by Plant Type for Chemicals in
MIRC 2-59
Exhibit_Add A2-12. Non-Chemical-Specific Produce Inputs 2-69
Exhibit_Add A2-13. Animal Product Chemical-specific Inputs for Chemicals
Included in MIRC 2-70
Exhibit_Add A2-14. Soil and Plant Ingestion Rates for Animals 2-72
Exhibit_Add A2-15. Mean and Percentile Body Weight Estimates for Adults and
Children 2-73
Exhibit_Add A2-16. Estimated Daily Per Capita Mean and Percentile Water
Ingestion Rates for Children and Adults3 2-74
Exhibit_Add A2-17. Summary of Age-Group Specific Food Ingestion Rates for
Farm Food Items 2-75
Exhibit_Add A2-18. Fish Ingestion Rates Used in Screening Analysis 2-80
Exhibit_Add A2-19. Daily Mean and Percentile Consumer-Only Fish Ingestion
Rates for Children and Adults (IRco.yf 2-82
Exhibit_Add A2-20. Fraction of Population Consuming Freshwater/Estuarine
Fish on a Single Day (FPC,y) 2-83
Exhibit_Add A2-21. Calculated Long-term Mean and Percentile per capita Fish
Ingestion Rates for Children and Adults (IRPC,y) 2-83
Exhibit_Add A2-22. Calculated Mean and 90th Percentile Per capita Fish
Ingestion Rates for Populations of Recreational Fishers (IRpc,y) 2-84
Attachment A, Addendum 2 2-5 December 2013
Description of MIRC
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TRIM-Based Tiered Screening Methodology for RTR
Exhibit_Add A2-23. Daily Mean and Percentile Soil Ingestion Rates for Children
and Adults 2-85
Exhibit_Add A2-24. Daily Mean and Percentile Per Capita Total Food Intake for
Children and Adults 2-86
Exhibit_Add A2-25. Fraction Weight Losses from Preparation of Various Foods 2-88
Exhibit_Add A2-26. Scenario- and Receptor-Specific Input Parameter Values
Used to Estimate Infant Exposures via Breast Milk 2-91
Exhibit_Add A2-27. Average Body Weight for Infants 2-91
Exhibit_Add A2-28. Time-weighted Average Body Weight for Mothers 2-92
Exhibit_Add A2-29. Infant Breast Milk Intake Rates 2-94
Exhibit_Add A2-30. Chemical-specific Input Parameter Values for Breast Milk
Exposure Pathway 2-95
Exhibit_Add A2-31. Farm Food Category Ingestion Rates for Health Protective
Screening Scenario for Farming Households 2-99
Exhibit_Add A2-32. Mean Body Weight Estimates for Adults and Children3 2-100
Exhibit_Add A2-33. Chemical-Specific Parameter Values for Input to MIRCa 2-101
Exhibit_Add A2-34. Chemical and Animal-Type Specific Biotransfer Factor (Ba)
Values for Input to MIRC 2-102
Attachment A, Addendum 2
Description of MIRC
2-6
December 2013
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TRIM-Based Tiered Screening Methodology for RTR
Equations, Addendum 2
Equation 2-1. Chemical Concentration in Aboveground Produce 2-19
Equation 2-2. Chemical Concentration in Aboveground Produce Due to Root
Uptake 2-19
Equation 2-3. Chemical Concentration in Aboveground Produce Due to
Deposition of Particle-phase Chemical 2-20
Equation 2-4. Chemical Concentration in Aboveground Produce Due to Air-to-
Plant Transfer of Vapor-phase Chemical 2-20
Equation 2-5. Conversion of Aboveground Produce Chemical Concentration from
Dry- to Wet-Weight Basis 2-21
Equation 2-6. Chemical Concentration in Belowground Produce: Nonionic
Organic Chemicals 2-22
Equation 2-7. Chemical Concentration in Belowground Produce: Inorganic
Chemicals 2-23
Equation 2-8. Conversion of Belowground Produce Chemical Concentration from
Dry- to Wet-Weight Basis 2-23
Equation 2-9. Chemical Concentration in Beef, Pork, or Total Dairy 2-24
Equation 2-10. Chemical Concentration in Poultry or Eggs 2-25
Equation 2-11. Incidental Ingestion of Chemical in Soil by Livestock 2-25
Equation 2-12. Ingestion of Chemical in Feed by Livestock 2-26
Equation 2-13. Chemical Concentration in Livestock Feed (All Aboveground) 2-26
Equation 2-14. Chemical Concentration in Livestock Feed Due to Root Uptake 2-27
Equation 2-15. Average Daily Dose for Specified Age Group and Food Type 2-27
Equation 2-16. Chemical Intake from Soil Ingestion 2-29
Equation 2-17. Chemical Intake from Fish Ingestion 2-30
Equation 2-18. Consumption-weighted Chemical Concentration in Fish 2-30
Equation 2-19. Chemical Intake from Consumption of Exposed Fruits 2-31
Equation 2-20. Chemical Intake from Consumption of Protected Fruits 2-31
Equation 2-21. Chemical Intake from Exposed Vegetables 2-32
Equation 2-22. Chemical Intake from Protected Vegetables 2-32
Equation 2-23. Chemical Intake from Root Vegetables 2-32
Equation 2-24. Chemical Intake from Ingestion of Beef 2-33
Equation 2-25. Chemical Intake from Dairy Ingestion 2-33
Equation 2-26. Chemical Intake from Pork Ingestion 2-33
Equation 2-27. Chemical Intake from Poultry Ingestion 2-34
Equation 2-28. Chemical Intake from Egg Ingestion 2-34
Equation 2-29. Chemical Intake from Drinking Water Ingestion 2-35
Attachment A, Addendum 2
Description of MIRC
2-7
December 2013
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TRIM-Based Tiered Screening Methodology for RTR
Equations 2-30 to 2-35. Total Average Daily Dose of a Chemical for Different
Age Groups 2-35
Equation 2-31 2-35
Equation 2-32 2-35
Equation 2-33 2-35
Equation 2-34 2-36
Equation 2-35 2-36
Equation 2-36. Lifetime Average Daily Dose (LADD) 2-36
Equation 2-37. Average Daily Dose of Chemical to the Nursing Infant 2-37
Equation 2-38. Chemical Concentration in Breast Milk Fat 2-38
Equation 2-39. Daily Maternal Absorbed Intake 2-39
Equation 2-40. Biological Elimination Rate Constant for Chemicals for Non-
lactating Women 2-40
Equation 2-41. Biological Elimination Constant for Lipophilic Chemicals for
Lactating Women 2-40
Equation 2-42. Chemical Concentration in Aqueous Phase of Breast Milk 2-41
Equation 2-43. Fraction of Total Chemical in Body in the Blood Plasma
Compartment 2-42
Equation 2-44. Biological Elimination Rate Constant for Hydrophilic Chemicals 2-43
Equation 2-45. Calculation of Infant Average Daily Absorbed Dose of Methyl
Mercury 2-44
Equation 2-46. Calculation of Excess Lifetime Cancer Risk 2-51
Equations 2-47 to 2-53. Lifetime Cancer Risk: Chemicals with a Mutagenic MOA
for Cancer 2-52
Equation 2-48 2-52
Equation 2-49 2-52
Equation 2-50 2-52
Equation 2-51 2-52
Equation 2-52 2-52
Equation 2-53 2-52
Equation 2-54. Hazard Quotient for Chemicals with a Chronic RfD 2-53
Equation 2-55. Hazard Index Calculation 2-54
Equation 2-56. Calculation of Age-Group-Specific and Food-Specific Ingestion
Rates 2-78
Equation 2-57. Calculation of Alternative Age-Group-Specific Fish Ingestion
Rates 2-81
Attachment A, Addendum 2
Description of MIRC
2-8
December 2013
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TRIM-Based Tiered Screening Methodology for RTR
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
19
Fully functional versions of MIRC have been developed in both Access™-based and Excel™-based formats;
however, MIRC currently is not publicly available.
Attachment A, Addendum 2
Description of MIRC
2-9
December 2013
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TRIM-Based Tiered Screening Methodology for RTR
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.ExpO|ngestion, 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.
Attachment A, Addendum 2 2-10 December 2013
Description of MIRC
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TRIM-Based Tiered Screening Methodology for RTR
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 Child-
Specific 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
Attachment A, Addendum 2
Description of MIRC
2-11
December 2013
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TRIM-Based Tiered Screening Methodology for RTR
consumption, incidental soil ingestion, fish ingestion, and ingestion often 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.
Attachment A, Addendum 2
Description of MIRC
2-12
December 2013
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TRIM-Based Tiered Screening Methodology for RTR
Exhibit_Add A2-1. Overview of MIRC Software Application for
Performing Farm-Food-Chain Ingestion Exposure and Risk Calculations
User Selects Chemical(s) of Concern: For Each, MIRC Calculates Risks
User Specifies Environmental Concentrations for Chemical of Concern
oot-zone and Surface Soils
(jishT^ CPrTnking Wate?^
Vegetables, Fruits
Grains
Hay, Grass
Animal Products
Farm Food Chain Biotransfer Calculations
r
User Selects Receptor Characteristics
z^Home Grown \
( r- r. x \ ( Fish and Water \
Food Product J ( . D , )
Vijestion Ratg^ Motion Rates/
<^ody WeighJ)
From Options or Over-write
Average Daily Dose (ADD) for Age Group y; y =1 to 5
I
_£
Chemical Intake with Food/
Medium Type i; i = 1 to 10
Exposure Module
Lifetime Average
Daily Dose
J
Adult ADD x
absorption efficiency
User Option to Add Breast Milk Pathway
Chemical
Toxicity
Reference
Values:
SF and RfDs
User Selects BMP
Parameter Values
Duration Breast Feeding; maternal
.and infant characteristics
Risk Characterization Module
Age-specific & Lifetime Exposure Doses
Lifetime Cancer Risks
Age-Specific Hazard Quotients
Breast Milk Exposure
Module
c
ADD Maternal
[C] in milk
Infant Dose
3
Attachment A, Addendum 2
Description of MIRC
2-13
December 2013
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TRIM-Based Tiered Screening Methodology for RTR
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 unfilled),
Chemical concentration in root-zone soils (two locations; can be tilled and unfilled), 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).
Attachment A, Addendum 2
Description of MIRC
2-14
December 2013
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TRIM-Based Tiered Screening Methodology for RTR
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 grain3
• Soil ingestion
Pork
• Ingestion of silage and grain3
• Soil ingestion
Poultry and eggs
• Ingestion of grain3
• 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.
Attachment A, Addendum 2
Description of MIRC
2-15
December 2013
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TRIM-Based Tiered Screening Methodology for RTR
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. Overtime, different EPA programs have used
different child age groupings to evaluate body weights, ingestion rates, and other parameter
values needed to estimate chemical exposures and risks to children.
To improve the match between age groups used to estimate values across exposure
parameters, in 2005, EPA recommended a standard set of child age categories for exposure
and risk assessments (EPA 2005b). EPA recommended four age groups for infants: birth to < 1
month; 1 to < 3 months; 3 to < 6 months; and 6 to < 12 months. For young children, EPA
recommended an additional four age groups: 1 to < 2 years; 2 to < 3 years; 3 to < 6 years; and
6 to < 11 years. Two age groupings were recommended for teenagers and young adults: 11 to
< 16 years; and 16 to < 21 years. These age groupings correspond to different developmental
stages and reflect different food ingestion rates per unit body weight, with the highest ingestion
rates occurring for the youngest, most rapidly growing, age groups.
For purposes of RTR assessment using MIRC, the selection of age categories is limited by the
categories for which most of the FFC food ingestion rates have been calculated. In Chapter 13
of both its Exposure Factors Handbook (EFH; EPA 2011a) and its Child-Specific Exposure
Factors Handbook (CSEFH; EPA 2008a), EPA summarized home-grown/raised food ingestion
rates for four children's age groups: 1 to < 3 years; 3 to < 6 years; 6 to < 12 years; and 12 to <
20 years. Intake rates were not calculated for children younger than 1 year because infants are
unlikely to consume those foods. They are more likely to be nursing or to be fed formula and
other commercial baby-food products.
Although the age groupings used to estimate FFC ingestion rates do not match precisely the
groupings that EPA recommended in 2005 for Agency exposure assessments (EPA 2005b),
they are the only age-groupings for which such data are available. The U.S. Department of
Agriculture's (USDA's) 1987-1988 Nationwide Food Consumption Survey (USDA 1992, 1993,
1994a) remains the most recent survey of ingestion rates for home-grown foods, and EPA's
analysis of those data, published in its 2011 EFH, remains the most recently published major
analysis of those data. Because ingestion of home-grown produce and animal products are the
primary exposure pathways for which MIRC was developed, those are the age groupings used
for all child parameter values used to estimate exposure and risk in MIRC.
Thus, in MIRC, values for each exposure parameter were estimated for adults (20 to 70 years)
and five children's age groups:
infants under 1 year (i.e., 0 to < 1 year);
children ages 1 through 2 years (i.e., 1 to < 3 years);
children ages 3 through 5 years (i.e., 3 to < 6 years);
children ages 6 through 11 years (i.e., 6 to < 12 years); and
children ages 12 through 19 years (i.e., 12 to < 20 years).
See Sections 5.1 and 5.2 for descriptions of the risk characterization algorithms used to
calculate cancer and non-cancer effects, respectively, for the above age groupings. Exposure
and risks to infants under 1 year of age are estimated only for the breast-milk-ingestion
pathway.
Attachment A, Addendum 2
Description of MIRC
2-16
December 2013
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TRIM-Based Tiered Screening Methodology for RTR
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.
Attachment A, Addendum 2
Description of MIRC
2-17
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TRIM-Based Tiered Screening Methodology for RTR
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 PB-
HAPs in its RTR assessments (EPA 2008b), and as described in the Technical Support
Document. Sections 3.1.1.1 and 3.1.1.2 below describe the transfer pathways and algorithms
for aboveground and belowground produce, respectively.
Exhibit_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, Exhibit_Add A2-4. Estimating Chemical
chemical mass is assumed to be Concentration in Aboveground Produce
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.prDdUce) is
estimated using an empirical bioconcentration factor (BrAG.prDdUce) that relates the chemical
" * ¦««
si
\1 ~
Attachment A, Addendum 2
Description of MIRC
2-18
December 2013
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TRIM-Based Tiered Screening Methodology for RTR
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 (Csmot-zone_produce)¦ These equations all
assume measurements on a dry-weight (DW) basis.
Equation 2-1. Chemical Concentration in Aboveground Produce
^AG-produce-DW(i) ~ AG-produce-DW (i) ^^(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)
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
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)
Chemical concentration in edible portion of aboveground produce type i due to
air-to-plant transfer (|jg/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
^^AG-produce-DW(i) ~ ^^root-zone produce X ®^AG-produce-DW(i)
Concentration of chemical in edible portion of aboveground produce type /',
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)
Average chemical concentration in soil at root-zone depth in produce-growing
area (mg/kg soil DW)
Chemical-specific plant/soil chemical bioconcentration factor for edible portion
of aboveground produce type /', exposed or protected (g soil DW / g produce
DW)
where:
C AG-produce-DW(i)
Pdo,
Pr,
AG-produce-DW(i)
PVa
where:
Pl~AG -produce-DW(i)
CSroot-zone_produce
BrAG
-produce-DW(i) ~
Attachment A, Addendum 2
Description of MIRC
2-19
December 2013
-------
TRIM-Based Tiered Screening Methodology for RTR
Equation 2-3. Chemical Concentration in Aboveground Produce Due to Deposition of
Particle-phase Chemical
_ 1,000 X (Drdp + {Fw x Drwp)) x Rp{j) x (1 - e(-kp(iyTp(i)))
(/)~ V-p,)X/cp,)
Chemical concentration in aboveground produce type /' on a dry-weight (DW)
= basis due to particle deposition (mg/kg produce DW); set equal to zero for
protected aboveground produce
= Average annual dry deposition of particle-phase chemical (g/m2-yr)
Fraction of wet deposition that adheres to plant surfaces; 0.2 for anions, 0.6
for cations and most organics (unitless)
= Average annual wet deposition of particle-phase chemical (g/m2-yr)
= Interception fraction of the edible portion of plant type /' (unitless)
= Plant surface loss coefficient for plant type /' (yr1)
Length of exposure to deposition in the field per harvest of the edible portion of
plant type /' (yr)
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 Qx(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 dry-
and 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:
Pdo)
Drdp
Fw
Drwp
RPd)
kPo)
TPo)
YPO)
Dw _Cax Fv x Bvag()) x VGag(/)
rv{i) -
P<
where:
Attachment A, Addendum 2
Description of MIRC
2-20
December 2013
-------
TRIM-Based Tiered Screening Methodology for RTR
Concentration of chemical in edible portion of aboveground produce type /'
Pv(0 = from air-to-plant transfer of vapor-phase chemical on a dry-weight (DW) basis
(|jg/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)
VAG(i)
gv _ Air-to-plant biotransfer factor for aboveground produce type /' for vapor-phase
AGt,) chemical in air ([mg/g produce DW] / [mg/g air], i.e., g air/ g produce DW)
Empirical correction factor for aboveground exposed produce type /' to address
VGAG(i) = possible overestimate of the diffusive transfer of chemical from the outside to
the inside of bulky produce, such as fruit (unitless)
pa = 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 QxFv 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 vapor-
phase chemical in units of |jg-sec/g-m3. For MIRC, the user inputs the average annual total air
concentration of the chemical, Ca, for a specific location relative to the source in units of g/m3;
MIRC includes a chemical-specific default value for Fv for chemicals included in its database.
The air concentration might be a value measured near that location or a value estimated by a
fate and transport model such as TRIM.FaTE. Users of TRIM.FaTE should note that the
average annual concentration of the total chemical in air (i.e., total of both vapor and particulate
phases), Ca, output from TRIM.FaTE is in units |jg/m3; therefore, the user must adjust the value
to units of g/m3 (i.e., divide by 1,000 |jg/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:
^AG-produce-VWV(i) ~ CAG-produce-DW(/) x
^(100 -MAF{i))]
100
AG-produce-WW(i)
•^AG-produce-DW(i)
Chemical concentration in edible portion of aboveground produce type i on a wet-
weight (WW) basis (mg/kg produce WW)
Chemical concentration in edible portion of aboveground produce type i on a dry-
weight (DW) basis (mg/kg produce DW)
Moisture adjustment factor for aboveground produce type i to convert the chemical
MAF(0 = concentration estimated for dry-weight produce to the corresponding chemical
concentration for full-weight fresh produce (percent water)
Attachment A, Addendum 2 2-21 December 2013
Description of MIRC
-------
TRIM-Based Tiered Screening Methodology for RTR
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 root-
zone 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 (Csr0ot-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:
_ CSroot-zone_ produce x RCF X VG rootveg
BG-Produce-WW " " KdSxUCF
Concentration of chemical in belowground (BG) produce (i.e., tuber or root
vegetable) on a wet-weight (WW) basis (mg chemical/kg produce WW)*
Average chemical concentration in soil at root-zone depth in produce-growing
area, on a dry-weight (DW) basis (mg chemical/kg soil DW)
Chemical-specific root concentration factor for tubers and root produce (L soil
pore water/kg root WW)*
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) *
Chemical-specific soil/water partition coefficient (L soil pore water/kg soil DW)
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
Attachment A, Addendum 2 2-22 December 2013
Description of MIRC
C BG-produce-WW
CSroot-Zone_produce
RCF
VG,
rootveg
Kds
UCF
-------
TRIM-Based Tiered Screening Methodology for RTR
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
'B G-produce-D l/V
root-zone_produce x ^^BG-produce-DW rootveg
where:
y BG-produce-DW
Cs,
root-zone_produce
Br,
BG-produce-DW
VG,
rootveg
Concentration of chemical in edible portion of aboveground produce, due to
root uptake from soil at root-zone depth in the produce-growing area, on a dry-
weight (DW) basis (mg/kg produce DW)
Average chemical concentration in soil at root-zone depth in produce-growing
area (mg/kg soil DW)
Chemical-specific root/soil chemical bioconcentration factor for edible portion
of belowground produce (g soil DW/g produce DW)
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
CBG-produce-WW ~ CBG-produce-DW x
(100 -MAFbg)
100
where:
y BG-produce-WW
y BG-produce-DW
MAF,
¦
-------
TRIM-Based Tiered Screening Methodology for RTR
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 /' (PlantCh-mtake(i,mj) and
incidental ingestion of soil for ground-foraging animals (SoilCh-mtake(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-mtake(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 grain3
• Incidental soil ingestion
Pork
• Ingestion of silage and grain3
• Incidental soil ingestion
Poultry and eggs
• Ingestion of grain3
• 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
Ba{m)xMFx
Soil,
Ch-lntake(m)
ZP/aAlf<
Ch-lntake(i ,m)
/=1
Attachment A, Addendum 2
Description of MIRC
2-24
December 2013
-------
TRIM-Based Tiered Screening Methodology for RTR
where:
Concentration of chemical in mammalian animal product m, where m = beef,
pork, or total dairy (mg chemical/kg animal product WW)
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)
Chemical-specific mammalian metabolism factor that accounts for endogenous
degradation of the chemical (unitless)
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
For livestock (animal product) type m, ingestion of chemical from plant feed
type /' (mg chemical/kg livestock WW); see Equation 2-12 below
(If m =beef or total dairy, then n = 3 and /' = forage, silage, and grain; m = pork,
then n = 2 and /' = silage and grain; m = poultry, then n = 1 and I = grain.)
Equation 2-10. Chemical Concentration in Poultry or Eggs
^poultry(m) ~ ®®(m) x {Soilch-lntake(m) Pl&HtCh-lntake(i,m))
Concentration of chemical in food product m, where m = poultry or eggs (mg
chemical/kg animal product WW)
Chemical-specific biotransfer factor for food product m, where m = poultry or
eggs (day/kg animal product WW)
Incidental ingestion of chemical in surface soils by consumption of food on the
ground (mg chemical/day) where m = poultry; see Equation 2-11
For poultry (and eggs), animal m, ingestion of the chemical in plant feed type /'
(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-mtake(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-nVestock, 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
Soilch Intake(m) ~ x S-liVestock x
•Tnammalfm)
Bd(m)
MF =
Soil,
Ch -lntake(m)
Plant,
Ch-lntake(i,m)
where:
Cpoultry(m)
Bd(m)
Soilch -lntake(m)
Plantch -lntake(i,m)
Attachment A, Addendum 2
Description of MIRC
2-25
December 2013
-------
TRIM-Based Tiered Screening Methodology for RTR
where:
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)
Quantity of soil eaten by animal type m each day (kg soil DW/day)
Chemical concentration in surface soil in contaminated area where livestock
feed (mg chemical/kg soil DW)
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: /' = 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, /', PlantCh-mtake(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
Soilch -lntake(m)
QS(m) =
CSs-HVestock ~
Bs =
Plar>tch-lntake(i,m) ^~(i,m) x x ^feed(i)
where:
Ingestion of chemical in plant feed type /' (mg chemical/day), where /' = forage,
silage, or grain, for livestock type m
Fraction of plant feed type /' obtained from contaminated area used to grow
animal feed, where I = forage, silage, or grain (unitless) for livestock type m
Quantity of plant feed type /' consumed per animal per day (kg plant feed
DW/day), where /' = forage, silage, or grain, for livestock type m
Concentration of chemical in ingested plant feed type /' (mg chemical/kg plant
feed DW), where /' = 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)
Plant Ch-lntake(i,m)
F(i,m) =
QP(i,m) =
Cfeed(i) ~
Cfeed(i) ~ Prfeed(i) + ^ (i) + (i)
where:
£ _ Concentration of chemical in plant feed type /' on a dry-weight (DW) basis (mg
feed(i> chemical/kg plant feed DW), where /' = forage, silage, or grain
Attachment A, Addendum 2
Description of MIRC
2-26
December 2013
-------
TRIM-Based Tiered Screening Methodology for RTR
Concentration of chemical in plant feed type /' due to root uptake from soil
feed(o (mg/kg DW), where /' = forage, silage, or grain; see Equation 2-14 below
Concentration of chemical in plant feed type /' due to wet and dry deposition of
Pd(i) = particle-phase chemical (mg/kg DW), where /' = forage, silage, or grain; when /' =
grain, the Pd term equals zero
Concentration of chemical in plant feed type /' due to air-to-plant transfer of
Pv(0 = vapor-phase chemical (|jg/g [or mg/kg] DW) where /' = forage, silage, or grain;
when /' = 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 Brvalue
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 /' = 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
^^feed(i) ^^root zone_feedIj) * ^^feed(i)
where:
Concentration of chemical in plant feed type /' due to root uptake from soil on a
Prfeedw = dry-weight (DW) basis (mg chemical/kg plant feed DW), where /' = forage, silage,
or grain
Average chemical concentration in soil at root-zone depth in area used to grow
nejeedo) p|ant feec| type y ^mg chemical/kg soil DW), where /' = forage, silage, or grain
„ _ Chemical-specific plant-soil bioconcentration factor for plant feed type /' (kg soil
feed(i) - DW/kg plant feed DW), where/' = forage, silage, or grain
The algorithms used to calculate Pd(i) and Pv(i) when plant feed type /' = 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 DWfeed to WW feed, because all feed ingestion rates for livestock
are based on DWfeed.
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
(
ADD(y,0 ~
c(i) X IR,„n X FCm X ED,
(y,i)
<0
J(y)
V
BW(y) X AT(y)
EF,
(y)
365 days
Attachment A, Addendum 2
Description of MIRC
2-27
December 2013
-------
TRIM-Based Tiered Screening Methodology for RTR
where:
Average daily dose for age group y from food type or ingestion medium /' (mg
chemical/kg body weight-day)
Concentration of chemical in food type /' harvested from the contaminated area
(mg chemical/kg food or mg food/L water)
Ingestion rate for age group y of food type /' (kg/day or L/day)
Fraction of food type /'that was harvested from contaminated area (unitless)
Exposure duration for age group y (years)
Body weight for age group y (kg)
Averaging time for calculation of daily dose (years) for age group y, set equal to
ED in MIRC
Annual exposure frequency for age group y (days)
Equation 2-15 takes into account the chemical concentration in each food type /' (or in water),
the quantity of food brought into the home for consumption, the loss of some of the mass of the
foods due to preparation and cooking, how much of the food is consumed per year, the amount
of the food obtained from contaminated areas, and the consumer's body weight (EPA 2011a,
2003a). In MIRC, ADDs are calculated separately for each chemical, home-grown food type,
and consumer age group.
ADD values, expressed as intakes, not absorbed doses, are appropriate for comparison with
RfDs and for use with cancer slope factors (CSFs) to estimate risk, as discussed in Section 5 of
this 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(yJ) from the contaminated area for food group /' 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,
ADD(yj) -
C(d =
IR(yJ) =
FC(i) =
ED(y) =
BW(y) =
AT(y) =
EF(y) =
Attachment A, Addendum 2
Description of MIRC
2-28
December 2013
-------
TRIM-Based Tiered Screening Methodology for RTR
- Beef products,
- Pork products, and
- Poultry and eggs;
Ingestion of drinking water from specified source; and
Ingestion of breast milk by infants.
The algorithms for the first six exposure pathways listed above are described in Sections 3.2.1
through 3.2.6 of this 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
ADD
Soil (y)
Cs0„ x IRsoil(y) X FCqnii X 0.001
'Soil
mg
V9
BW,
(y)
EF
365 days
where:
ADDsoil(y)
Csoi!
IRsoil(y)
FCsoil
BW(y)
EF =
Average daily chemical intake from incidental ingestion of soil or ingestion by child in
age group y (mg chemical/kg body weight-day)
Concentration of chemical in soil from contaminated area on a dry-weight (DW) basis
(|jg/g soil DW)
Soil ingestion rate for age group y (g DW/day)
Fraction of soil ingested that is from contaminated area (unitless)
Body weight for age group y (kg)
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 offish are included in the exposure algorithm: trophic level 3.5 (abbreviated
as TL3) fish, equivalent to benthic carnivores such as catfish and trophic level 4 (TL4) fish in the
water column, equivalent to game fish such as lake trout and walleye. The chemical
concentration in fish in Equation 2-17 is estimated as the consumption-weighted chemical
concentration using Equation 2-18.
Attachment A, Addendum 2
Description of MIRC
2-29
December 2013
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TRIM-Based Tiered Screening Methodology for RTR
Equation 2-17. Chemical Intake from Fish Ingestion
( \,n \
( EF
CFish x ^Fish(y) x 0-001 ^ X FCpjsh
g
/
ADDFjSh(y) - (1 /-1F/Sh)x(l L2Fjsh)x- —— -X
BWfy)
v
365 days
Equation 2-18. Consumption-weighted Chemical Concentration in Fish
^Fish = (^FishTL3 x ^TLs) + (CpishTL4 x ?TL4)
where:
n nn - Average daily chemical intake from ingestion of local fish for age group y (mg/kg-
AUUFishM ~ -i_..n
Flsh(y> day)
* Weight of fish brought into home that is discarded during preparation (e.g., head,
Flsh 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)
r _ Chemical concentration in whole fish for trophic level 3.5 (TL3) fish on a wet-
FishTL3 weight (WW) basis (mg/kg WW)
'FishTL4
Chemical concentration in whole fish for trophic level 4 (TL4) fish on a wet-weight
(WW) basis (mg/kg WW)
Ftl3 = Fraction offish intake that is from TL3 (unitless)
FTl4 = Fraction offish intake that is from TL4 (unitless)
'Fish
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 offish ingestion rate options included in MIRC are on an "as consumed" basis (i.e., after
preparation and cooking losses), and L1 and L2 therefore are set equal to zero. If the user wishes to enter local fish
ingestion rates on an "as harvested" basis, the user also should enter L1 and L2 values as specified in Section 6.4.3 of
this 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 2011a) and
included those losses in its HHRAP algorithms for chemical intake from fish (L1Fish and L2Fish in
Equation 2-17).
Attachment A, Addendum 2
Description of MIRC
2-30
December 2013
-------
TRIM-Based Tiered Screening Methodology for RTR
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).
Equation 2-19. Chemical Intake from Consumption of Exposed Fruits
( \ ( \ f kg ^ f EF
ADDExpFrUjt(y) = (1 - ^ExpFrujt)x (1 - L2ExpFrujt)x CExpFrujt x IRExpFrUjt(y) x 0.001— x FCExpFrujt
/
g
Equation 2-20. Chemical Intake from Consumption of Protected Fruits
365 days
ADDproFruit(y) - (l LApr0Fruit)x
^ kg ^ f EF ^
^ProFruit x ^ProFruit(y) x 0.001— X FCProFrun X 205 days
where:
ADDExpFruit(y) _ Average daily chemical intake from ingestion of exposed fruit or protected fruit
ADDproFrujt(y) (depending on subscript) (mg chemical/kg body weight-day)
Mean reduction in fruit weight resulting from removal of skin or peel, core or pit,
L1ExPFruit = stems or caps, seeds and defects, and from draining liquids from canned or
frozen forms (unitless)
^ _ Mean reduction in fruit weight that results from paring or other preparation
proFruit techniques for protected fruits (unitless)
. _ _ Mean reduction in fruit weight that results from draining liquids from cooked
ExpFruit forms of the fruit (unitless)
_ Chemical concentration in whole exposed fruits or whole protected fruits
ExpFruit _ (depending on subscript) on a wet-weight (WW) basis (mg chemical/kg exposed
ProFruit r... -x » a a a i\
EF =
fruit WW)
Exposure frequency; number of days per year of exposure for family(ies) as
specified for scenario (< 365 days)
FCExpFrUjt _ Fraction of exposed fruits or protected fruits (depending on subscript) obtained
FCProFruit from contaminated area (unitless)
IRExPFruit(y) _ Ingestion rate of home-grown exposed fruits or protected fruits (depending on
IRproFruit(y) ~ 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 (Lt£xpF™fand L1Pr0Fruit', 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
Attachment A, Addendum 2 2-31 December 2013
Description of MIRC
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TRIM-Based Tiered Screening Methodology for RTR
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).
Equation 2-21. Chemical Intake from Exposed Vegetables
w £/= ^
^D^ExpVeg(y) 0 ^ExpVeg )x f ^ExpVeg x ^ExpVeg(y) x 0.001 x ^^ExpVeg
y
365 days
Equation 2-22. Chemical Intake from Protected Vegetables
ADDp^^^ (l ^-1pro\/eg)x [ ^ProVeg x ^ProVeg(y) x 0.001 X FCpro\/eg
( EF ^
x
/
g
Equation 2-23. Chemical Intake from Root Vegetables
v365 daysy
ADDRootVeg(y) 0 ^RootVeg )x 0 RootVeg)^ |^Roof\/eg x ^RootVeg(y) x 0.001 ^ X FCp^n/ar, X
where:
A (
EF
365 days
ADDExpVeg(y} Average chemical intake from ingestion of exposed vegetables, protected
ADDproVeg(y) = vegetables, or root vegetables (depending on subscript) for age group y (mg
ADDRootveg(y) chemical/kg body weight-day)
^ _ Mean net preparation and cooking weight loss for exposed vegetables (unitless);
ExpVeg includes removing stalks, paring skins, discarding damaged leaves
^ _ Mean net cooking weight loss for protected vegetables (unitless); includes
proveg removing husks, discarding pods of beans and peas, removal of outer leaves
^ _ Mean net cooking weight loss for root vegetables (unitless); includes losses from
Rootveg removal of tops and paring skins
^2 _ Mean net post cooking weight loss for root vegetables from draining cooking
Rootveg liquids and removal of skin after cooking (unitless)
CExPveg Chemical concentration in exposed vegetables, protected vegetables, or root
Cproveg = vegetables (depending on subscript) on a wet-weight (WW) basis (mg
CRootveg chemical/kg vegetable WW)
Exposure frequency; number of days per year of exposure for family(ies) as
specified for scenario (<365 days)
EF =
FC
^xpveg ^ Fraction of exposed vegetables, protected vegetables, or root vegetables
Proveg (depending on subscript) obtained from contaminated area (unitless)
' ^RootVeg
^ExpVeg(y)
IR —
proveg(y) (depending on subscript) for age group y (g vegetable WW/kg body weight-day)
' RootVeg (y)
Expveg(y) ^ ingestion rate of exposed vegetables, protected vegetables, or root vegetables
I KPmV&aM ~ /-I i: i ¦ _ x\ r .. / — t a n a i/i i i ¦ i_x _i \
Attachment A, Addendum 2
Description of MIRC
2-32
December 2013
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TRIM-Based Tiered Screening Methodology for RTR
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.
Equation 2-24. Chemical Intake from Ingestion of Beef
ADD Beef(y) - (1 ^Beef)x (l L2Beef)>
(
CBeef x/Hgee^y) x 0.001 X FCBeef
g
"\ f
X
EF
365 days
where:
ADDgeef(y)
I-1 Beef
l~2seef
Cseef
Average daily chemical intake from ingestion of beef for age group y (mg/kg-
day)
Mean net cooking loss for beef (unitless)
Mean net post cooking loss for beef (unitless)
Concentration of contaminant in beef (mg/kg WW))
Exposure frequency; number of days per year of exposure for family(ies) as
specified for scenario (< 365 days)
EF
IRBeefM = 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
(
ADD,
Dairy(y)
^Dairy x Dairyfy) x 0.001—— X FCqa)fy
9
"\ f
X
EF
365 days
where:
ADDoairyfy)
CDairy
EF
!R Dairyfy)
FCDaiv
Average daily chemical intake from ingestion of total dairy for age group y
(mg/kg-day)
Average concentration of contaminant in total dairy (mg/kg WW)
Exposure frequency; number of days per year of exposure for family(ies) as
specified for scenario (< 365 days)
Ingestion rate of contaminated total dairy for age group y (g WW/kg-day)
Fraction of total dairy products from contaminated area (unitless)
Equation 2-26. Chemical Intake from Pork Ingestion
ADDPotk - 0 ^Pork ) x L-2-pork)^
f
Cpork x IRpork(y) x 0.001 X FCpofk
g
"\ f
X
EF
365 days
Attachment A, Addendum 2
Description of MIRC
2-33
December 2013
-------
TRIM-Based Tiered Screening Methodology for RTR
where:
ADDPork(y)
L1 Pork
L2pork
Cpork
EF
IRpork(y)
FCpork
Average daily chemical intake from ingestion of pork for age group y (mg/kg-
day)
Mean net cooking loss for pork (unitless); includes dripping and volatile losses
during cooking; averaged over various cuts and preparation methods
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
= Concentration of contaminant in pork (mg/kg WW)
Exposure frequency; number of days per year of exposure for family(ies) as
specified for scenario (<365 days)
= Ingestion rate of contaminated pork for age y (g WW/kg-day)
= Fraction of pork obtained from contaminated area (unitless)
Equation 2-27. Chemical Intake from Poultry Ingestion
ADDpn,,itn//w\ (l /-1 poultry)x 0 Poultry)x | ^Poultry x ^Poultry(y) x 0-001 x FC,
¦^Pouttry(y)
, kg
g
A
Poultry
EF
365 days
where:
ADDPoultry(y)
L1 Poultry
L2 Poultry
C Poultry
EF
IRPoultry(y)
FCpoultry
Average daily dose (chemical intake) from ingestion of poultry (mg/kg-day)
Mean net cooking loss for poultry (unitless)
Mean net post cooking loss for poultry (unitless)
Concentration of chemical in poultry (mg/kg WW)
Exposure frequency; number of days per year of exposure for family(ies) as
specified for scenario (<365 days)
Ingestion rate of poultry for age group y (g WW/kg-day)
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
(
ADD.
Egg(y)
C£„x'R£«»x0.00lSxFc£m
y
y ef ^
, lv365daysy
where:
A nn - Average daily chemical intake from ingestion of eggs for age group y (mg/kg-
Egg(y) ~
day)
Attachment A, Addendum 2
Description of MIRC
2-34
December 2013
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TRIM-Based Tiered Screening Methodology for RTR
CEgg = Concentration of contaminant in eggs (mg/kg WW)
_ Exposure frequency; number of days per year of exposure for family(ies) as
specified for scenario (< 365 days)
IREgg(y) = Ingestion rate of contaminated eggs for age group y (g WW/kg-day)
FCEgg = Fraction of eggs obtained from contaminated area (unitless)
3.2.6. Chemical Intake from Drinking Water Ingestion
If the user chooses to evaluate chemical ingestion via drinking water, the user specifies a
chemical concentration in g/L (equivalent to mg/ml_) based on their particular scenario. The
chemical concentration could represent water from groundwater wells, community water, nearby
surface waters, or other source. For this exposure pathway, ingestion rates are in units of
milliliters of water per day (mL/day) (see Equation 2-29).
Equation 2-29. Chemical Intake from Drinking Water Ingestion
ADDDW(y)
^ CdW x DW(y) x FCDw ^ ( EF
BW(y)
V
365 days
where:
ADD = Average daily chemical intake from ingestion of drinking water from local
°w(y) residential water source for age group y (mg/kg-day)
CDW = Concentration of contaminant in drinking water (g/L)
IRdwm = 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 home-
grown produce, and five types of home-raised animals or animal products), the media-specific
chemical intakes are summed for each age group. Total average daily 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
ADD( i} = ADDhreastmjlk
Equation
2-30.
Equation
2-31.
Equation
2-32.
Equation
2-33.
ADQ.<-v =L1/DD(«.)
ADq 3_5| =2,1/Dq3_5>„
<400(6-1,, <^00<6-n/i
Attachment A, Addendum 2 2-35 December 2013
Description of MIRC
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TRIM-Based Tiered Screening Methodology for RTR
Equation 2-34. ADD{U_19) = qi2-iW)
Equation 2-35. ADD(,dm - V" ADD(.dIllu)
where /' represents the ith food type or ingestion medium and n equals the total number of food
types or ingestion media, and ADD parameters are defined below:
Total average daily dose of chemical for infants less than one year from
ingestion of breast milk (mg/kg-day)
Total average daily dose of chemical from all ingestion sources for children
ages 1 through 2 years (mg/kg-day)
Total average daily dose for children ages 3 through 5 years (mg/kg-day)
Total average daily dose for children ages 6 through 11 years (mg/kg-day)
Total average daily dose for children ages 12 through 19 years (mg/kg-day)
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)
LADD = ADD,,!^ + ADDIU2I[,|j + ADD,^ j + ADD^,„[^ ] + ADD^.,,^ + ADDIM)[ |j)
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 (DAIin,) 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 (IRmiik), the absorption efficiency of the
ADD(<1) —
ADD(i-2) =
ADD (3-5) =
ADD(6-h) =
ADD (12-19) =
ADD(adult) =
Attachment A, Addendum 2
Description of MIRC
2-36
December 2013
-------
TRIM-Based Tiered Screening Methodology for RTR
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 (Cm„Waf) and the concentration of the
chemical in the aqueous phase of breast milk (Caqueous)¦ The remainder of the DAIinrassociated
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 Cmiikfat 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
_ [(Cmilkfat x ^rabm)+ (paqueous x 0 ~ ^mbm))] x IRmilk x jnf X ED
inf~ BWjnf x AT
Average daily dose of chemical absorbed by infant (mg chemical/kg body
weight-day)
Concentration of chemical in lipid phase of maternal milk (mg chemical/kg milk
lipid; calculated using Equation 2-38)
Fraction of fat in breast milk (unitless)
Concentration of chemical in aqueous phase of maternal milk (mg chemical/kg
aqueous phase milk; calculated using Equation 2-42)
Infant milk ingestion rate over the duration of nursing (kg milk/day)
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)
Exposure duration, i.e., duration of breast feeding (days)
Body weight of infant averaged over the duration of nursing (kg)
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 non-
lipid 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 Cmi|kfat (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) (EPA 1998), EPA reviewed three first-
order kinetics models for estimating chemical concentration in breast milk fat. The model
Attachment A, Addendum 2 2-37 December 2013
Description of MIRC
L
where:
DAI inf =
Cmiikfat ~
fmbm ~
Caqueous ~
IR milk =
AEmf =
ED =
BWinf =
AT =
-------
TRIM-Based Tiered Screening Methodology for RTR
selected for use in MIRC is the model selected in MPE. The other two models were not
considered in MPE because one used a biotransfer factor (BTF) approach considered more of a
screening model than a predictive tool (Travis et al. 1988) and the other assumed that the
contaminant concentration in the maternal fat compartment is at steady state and that the
concentration in breast milk fat is the same as in maternal body fat (Smith 1987). The model in
MIRC is a changing-concentration model that EPA adapted from a model by Sullivan etal.
(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
'milkfat
DAImat X ff
kelim x ffm
*elim
fat elac
kfat elac x ^bf
—k t
"j 0 elim pn
*elim
fat_elac y
0 kfat _e!ac^bf ^
where:
Cmiikfat = Concentration of chemical in lipid phase of maternal milk (mg chemical/kg lipid)
DAI mat
Daily absorbed maternal chemical dose (mg chemical/kg maternal body weight-
day; calculated using Equation 2-39)
Fraction of total maternal body burden of chemical that is stored in maternal fat
ff = (mg chemical in body fat / mg total chemical in whole body; value from literature
or EPA default - see Section 6.5 of this addendum)
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)
kelim
ffm = Fraction of maternal body weight that is fat stores (unitless)
Chemical-specific rate constant for total elimination of chemical in the lipid
'
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TRIM-Based Tiered Screening Methodology for RTR
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 (DAImatffl keiim ffm) also assumes that the
biological half-life of the chemical in the mother's breast milk fat is small relative to the duration
of the mother's exposure. However, for chemicals with half-lives that are longer than the
exposure duration, which are the chemicals of concern in the applications of MIRC to date, an
additional term is needed to determine the average concentration in the milk fat over the
duration of her exposure.
To account for breast feeding losses and longer chemical half-lives in the mother than the
exposure duration, an additional term is included in Equation 2-38. This term includes a fraction
dependent on two rate constants, kenm and the elimination constant for a lipophilic chemical in
lactating women via the lipid phase of breast milk (kfat_eiac), the duration of the mother's
chemical exposure prior to nursing (tpn), and the duration of breast feeding (tbf). The whole body
concentration (DAImatffl kenm 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(aduit), 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
DAImat - ADD(adult) x AE
mat
where:
= Daily maternal dose of chemical absorbed from medium /' (mg/kg-day)
Average daily dose to the mother (mg/kg-day) (calculated by MIRC - see
Section 3.3 of this addendum, Equation 2-35)
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(adu,t): 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
Attachment A, Addendum 2 2-39 December 2013
Description of MIRC
DAImat
ADD(aduit)
AEmat
-------
TRIM-Based Tiered Screening Methodology for RTR
assumption in the breast-milk exposure pathway is that those ingestion rates also are applicable
to nursing mothers. The original data for ingestion rates for soil, drinking water, and fish are on
a per person basis for males and females combined. MIRC divides those chemical intakes by
an adult body weight for males and females combined as specified by the user (e.g., 71.4 kg
mean value) to estimate the ADD normalized to body weight from those sources. If the user
finds that those exposure media contribute the majority of the chemical intake for the risk
scenario under consideration, the user may use alternative ingestion rates for those media and
alternative body weights for nursing women, as described in Section 6.5 of this 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
_ln2
elim ~ ^
where:
^ _ Chemical-specific elimination rate constant for elimination of the chemical for
elim non-lactating women (per day; e.g., via urine, bile to feces, exhalation)
In2 = 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, kfaL_eiac{EPA 1998).
Equation 2-41. Biological Elimination Constant for Lipophilic Chemicals
for Lactating Women
kfat elac ^elim
l^milk xff x fmbm
ffm X BWmat
where:
Rate constant for total elimination of chemical during nursing (per day); accounts
kfat_eiac = for both elimination by adults in general and the additional chemical elimination
via the lipid phase of milk in nursing women
Attachment A, Addendum 2 2-40 December 2013
Description of MIRC
-------
TRIM-Based Tiered Screening Methodology for RTR
^ Elimination rate constant for chemical from adults, including non-lactating
elim = 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)
IRmiik = Infant milk ingestion rate over the duration of nursing (kg/d)
Fraction of total maternal body burden of chemical that is stored in maternal fat
ff = (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)
gyy _ Maternal body weight over the entire duration of the mother's exposure to the
mat ~ 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 Kfal_eiac is
estimated by adding an estimate of the first-order elimination constant for breast feeding losses
to keum, 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 (IRmnk), 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 Cmiikfatand 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
p _ DAImat X fpi X PCbm
Uaqueous ~ . r
aq _elac pm
Attachment A, Addendum 2
Description of MIRC
2-41
December 2013
-------
TRIM-Based Tiered Screening Methodology for RTR
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)
Fraction of chemical in the body (based on absorbed intake) that is in the blood
fpi = plasma compartment (unitless; value from literature or calculated by Equation
2-43)
pc _ Partition coefficient for chemical between the plasma and breast milk in the
bm aqueous phase (unitless); assumed to equal 1.0
Chemical-specific rate constant for total elimination of chemical in the aqueous
kaq_eiac = 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 Cmukfat, 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 (fpi)
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_eiac) and the fraction of the mother's weight that is plasma (fpt). 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 (fpt) is required. Ideally, an empirical value for fpt should be used. If
empirical values are not available, fpt 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 (fb{, EPA 1998).
Equation 2-43. Fraction of Total Chemical in Body in the Blood Plasma Compartment
/
where:
Fraction of chemical in body (based on absorbed intake) that is in the blood
plasma compartment (unitless); chemical-specific
Attachment A, Addendum 2
Description of MIRC
2-42
December 2013
-------
TRIM-Based Tiered Screening Methodology for RTR
f _ Fraction of chemical in body (based on absorbed intake) in the whole blood
bl compartment (unitless); chemical-specific
fbp = Fraction of whole blood that is plasma (unitless)
d dds- - Partition coefficient for chemical between red blood cells and plasma (unitless);
PCKdu - I ¦ . f.
chemical-specific
If the fraction of the total chemical in the body that is in the whole blood compartment (fbl) is
known for a given chemical, then the fraction of that chemical that is in blood plasma depends
only on the partition coefficient for the chemical between the red blood cells and the plasma
(¦Pcrbc) and the fraction of whole blood that is plasma (fbp).
Another parameter for which a value is needed to solve Equation 2-42 is the total chemical
elimination rate for lactating women for hydrophilic chemicals, kaq_eiac. As for kfat_eiac for lipophilic
chemicals, kaq_eiac for hydrophilic chemicals would be equal to kenm 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 keiim, the elimination of the chemical from a non-lactating woman, as shown in Equation
2-40. The extent to which kenm is an underestimate of kaq_eiac for a given chemical will determine
the extent of health protective bias in kaq_eiac.
Equation 2-44. Biological Elimination Rate Constant for Hydrophilic Chemicals
Is — Is
rxaq_elac elim
where:
Chemical-specific rate constant for total elimination of chemical by lactating
women for hydrophilic chemicals (per day)
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 (fp!, see Exhibit_Add A2-29).
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 (fbi) could be found. However, the value found for
fw 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
Attachment A, Addendum 2 2-43 December 2013
Description of MIRC
"aq_elac
^elim
-------
TRIM-Based Tiered Screening Methodology for RTR
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 etal.
(1999).
We did find, however, that Byczkowski and Lipscomb (2001) had added a lactational transfer
module to the Clewell et al. (1999) model. Byczkowski and Lipscomb compared their model's
predictions to epidemiological data from mother-nursing-infant pairs obtained following an
accidental high-dose poisoning in Iraq (Amin-Zaki etal. 1976) and from 34 mother-nursing-
infant pairs examined in a low-dose, chronic exposure environment (Fujita and Takabatake
1977). Using data from the Iraq incident, Byczkowski and Lipscomb (2001) found good
agreement between their model's predictions and the clinical data relating MeHg concentrations
in breast milk to MeHg concentrations in infant's blood with time following the poisoning. To
compare their model's predictions to data from chronic exposure to low doses of MeHg,
Byczkowski and Lipscomb (2001) simulated MeHg intake for 500 days prior to conception,
continued through gestation, and 6.5 months (200 days) of lactation. Their model's predictions
were consistent with Fujita and Takabatake's (1977) study, although use of hair/blood partition
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) |jg/kg-day and the average infant intake of MeHg to be 0.80 ± 0.38 |jg/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
DAIinf_MeHg ~ DAImat_MeHg
where:
DAIinf_MeHg = Average daily dose of MeHg absorbed by infant from breast milk (mg/kg-day)
n.. _ Average daily dose of methyl mercury absorbed by the mother, predominantly
mat_MeHg - from fjsh (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 dose-
response 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 dose-
response values identified by OAQPS for RTR, alternative methods for deriving values were
used (see Sections 4.2 and 4.4 of this addendum).
Attachment A, Addendum 2
Description of MIRC
2-44
December 2013
-------
TRIM-Based Tiered Screening Methodology for RTR
Exhibit_Add A2-6. Oral Dose-response Values Used to Calculate RTR Screening Threshold
Emission Rates for PB-HAP Chemicals3
Chemical
CAS No.
Cancer Slope Factor
Reference Dose
Value
(mg/kg-day)"1
Source
Value
(mg/kg-
day)
Source
Inorganics
Cadmium compounds in food6
7440439
not available
1.0E-03
IRIS
Mercury (elemental)
7439976
not available
not available
Mercuric chloride
7487947
not available
3.0E-04
IRIS
Methyl mercury (MeHg)
22967926
not available
1.0E-04
IRIS
Dioxins
1,2,3,4,6,7,8-
Heptachlorodibenzo-p-dioxin
35822469
1.50E+03
Derived from WHO
2005 TEFsc
not available
1,2,3,4,6,7,8-
Heptachlorodibenzofuran
67562394
1.50E+03
Derived from WHO
2005 TEFsc
not available
1,2,3,4,7,8,9-
Heptachlorodibenzofuran
55673897
1.50E+03
Derived from WHO
2005 TEFsc
not available
1,2,3,4,7,8-
Hexachlorodibenzo-p-dioxin
39227286
1.50E+04
Derived from WHO
2005 TEFsc
not available
1,2,3,4,7,8-
Hexachlorodibenzofuran
70648269
1.50E+04
Derived from WHO
2005 TEFsc
not available
1,2,3,6,7,8-
Hexachlorodibenzo-p-dioxin
57653857
1.50E+04
Derived from WHO
2005 TEFsc
not available
1,2,3,6,7,8-
Hexachlorodibenzofuran
57117449
1.50E+04
Derived from WHO
2005 TEFsc
not available
1,2,3,7,8,9-
Hexachlorodibenzo-p-dioxin
19408743
6.20E+03
IRIS
not available
1,2,3,7,8,9-
Hexachlorodibenzofuran
72918219
1.50E+04
Derived from WHO
2005 TEFsc
not available
2,3,4,6,7,8-
Hexachlorodibenzofuran
60851345
1.50E+04
Derived from WHO
2005 TEFsc
not available
1,2,3,4,6,7,8,9-
Octachlorodibenzo-p-dioxin
3268879
4.50E+01
Derived from WHO
2005 TEFsc
not available
1,2,3,4,6,7,8,9-
Octachlorodibenzofuran
39001020
4.50E+01
Derived from WHO
2005 TEFsc
not available
1,2,3,7,8-
Pentachlorodibenzo-p-dioxin
40321764
1.50E+05
Derived from WHO
2005 TEFsc
not available
1,2,3,7,8-
Pentachlorodibenzofuran
57117416
4.50E+03
Derived from WHO
2005 TEFsc
not available
2,3,4,7,8-
Pentachlorodibenzofuran
57117314
4.50E+04
Derived from WHO
2005 TEFsc
not available
2,3,7,8-
Tetrachlorodibenzo-p-dioxin
1746016
1.50E+05
EPA ORD
7E-10
IRIS
2,3,7,8-
Tetrachlorodibenzofuran
51207319
1.50E+04
Derived from WHO
2005 TEFsc
not available
Attachment A, Addendum 2
Description of MIRC
2-45
December 2013
-------
TRIM-Based Tiered Screening Methodology for RTR
Exhibit_Add A2-6. Oral Dose-response Values Used to Calculate RTR Screening Threshold
Emission Rates for PB-HAP Chemicals3
Chemical
CAS No.
Cancer Slope Factor
Reference Dose
Value
(mg/kg-day)"1
Source
Value
(mg/kg-
day)
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
5.0E-02
ATSDR
3-Methylcholanthrene
56495
2.2E+1
CalEPA
not available
7,12-Dimethylbenz(a)anthracene
57976
2.5E+02
CalEPA
not available
Acenaphthene
83329
5.0E-01
EPA 1999, POM
Group 72002d
6.0E-02
IRIS
Acenaphthylene
208968
5.0E-01
EPA 1999, POM
Group 72002d
not available
Anthracene
120127
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
lndeno(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
Attachment A, Addendum 2
Description of MIRC
2-46
December 2013
-------
TRIM-Based Tiered Screening Methodology for RTR
Exhibit_Add A2-6. Oral Dose-response Values Used to Calculate RTR Screening Threshold
Emission Rates for PB-HAP Chemicals3
Chemical
CAS No.
Cancer Slope Factor
Reference Dose
Value
(mg/kg-day)"1
Source
Value
(mg/kg-
day)
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.
bThere are RfDs for both water ingestion and food ingestion for cadmium - the RfD for food is used.
°Dose-response values for these dioxin congeners are not available from EPA sources. CSFs for these congeners were derived as
discussed in Section 4.2 of this addendum.
dThe 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.pdfand 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.
Attachment A, Addendum 2
Description of MIRC
2-47
December 2013
-------
TRIM-Based Tiered Screening Methodology for RTR
Exhibit_Add A2-7. WHO 2005 Toxic Equivalency Factors (TEFs) for
Dioxin Congeners
Dioxin Congener
CAS No.
WHO 2005 Toxic
Equivalency Factor3
1,2,3,4,6,7,8-Heptachlorodibenzo-p-dioxin
35822469
0.01
1,2,3,4,6,7,8-Heptachlorodibenzofuran
67562394
0.01
1,2,3,4,7,8,9-Heptachlorodibenzofuran
55673897
0.01
1,2,3,4,7,8-Hexachlorodibenzo-p-dioxin
39227286
0.1
1,2,3,4,7,8-Hexachlorodibenzofuran
70648269
0.1
1,2,3,6,7,8-Hexachlorodibenzo-p-dioxin
57653857
0.1
1,2,3,6,7,8-Hexachlorodibenzofuran
57117449
0.1
1,2,3,7,8,9-Hexachlorodibenzo-p-dioxinb
19408743
0.1
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.
bFor 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 offish and
shellfish (EPA 2001 b). 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
Attachment A, Addendum 2
Description of MIRC
2-48
December 2013
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TRIM-Based Tiered Screening Methodology for RTR
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) Groups3
Individual POM or POM Group
CAS No.
Cancer Slope Factor15
(mg/kg-day)"1
POM Group 71002
Benz(a)anthracene/chrysene (7-PAH)
103
Total PAH
234
Polycyclic organic matter
246
0.5
16-PAH
40
16-PAH-7-PAH
75040
POM Group 72002
Anthracene
120127
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
0.5
Methyl benzopyrenes
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
Attachment A, Addendum 2
Description of MIRC
2-49
December 2013
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TRIM-Based Tiered Screening Methodology for RTR
Exhibit_Add A2-8. Oral Dose-response Values for
Polycyclic Organic Matter (POM) Groups3
Individual POM or POM Group
CAS No.
Cancer Slope Factor15
(mg/kg-day)"1
POM Group 75002
3-Methylcholanthrene
56495
Dibenzo(a,e)pyrene
192654
5-Methylchrysene
3697243
10
Benzo(a)pyrene
50328
Dibenzo(a,h)anthracene
53703
POM Group 76002
Benzo(b+k)fluoranthene
102
lndeno(1,2,3-c,d)pyrene
193395
Benzo(j)fluoranthene
205823
Benzo(b)fluoranthene
205992
1
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
aThese group CSFs are used only when OAQPS has not, for the purposes of RTR, identified a CSF specific to
the individual chemical.
bThe 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):
Attachment A, Addendum 2
Description of MIRC
2-50
December 2013
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TRIM-Based Tiered Screening Methodology for RTR
Equation 2-46. Calculation of Excess Lifetime Cancer Risk
ELCR = LADD x CSF
Estimated excess lifetime cancer risk from a chemical summed across all
exposure pathways and media (unitless)
Lifetime average total daily dose from all exposure pathways and media (mg/kg-
day)
= 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 age-
dependent adjustment factors (ADAFs) of 10, 3, and 1 for the first two lifestages and for adults,
respectively.
These three lifestages do not match the age categories for the home-grown food ingestion
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:
where:
ELCR
LADD
CSF
ADAF(<1) = 10 ADAF(6_jj) = 3
= (I0*1yr)+(3x1yr) = a5 ADAr = (3x 4 yrs)+(1x 4 yrs) = 2
f1-2J - 2 ~ (12-19>
ADAF^3_5 j = 3 ADAF(adult) = 1
8
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 /' = 1 to 6 age groups.
Attachment A, Addendum 2
Description of MIRC
2-51
December 2013
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TRIM-Based Tiered Screening Methodology for RTR
Equations 2-47 to 2-53. Lifetime Cancer Risk: Chemicals with a
Mutagenic MOA for Cancer
Equation
2-47.
Risk(<1)
- ADD(0-<1)
X
10
X
CSF
X
(1 yr/70yr)
Equation
2-48.
Risk(u2)
= ADD(U2)
X
6.5
X
CSF
X
(2 yr/70 yr)
Equation
2-49.
Risk(3_ 5)
= ADD(3-5)
X
3
X
CSF
X
(3 yr/70 yr)
Equation
2-50.
Risk(6-n)
= ADD(6-h)
X
3
X
CSF
X
(6 yr/70 yr)
Equation
2-51.
Risk(12-19)
= ADD(12-19)
X
2
X
CSF
X
(8 yr/70 yr)
Equation
2-52.
Risk(aduit)
— ADD (aduit}
X
1
X
CSF
X
(50 yr/70 yr)
Equation
2-53.
ELCR =
L1,R/s^o
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(e-u) = Risk from chemical ingestion from age 6 through 11 years of age
Risk(i2-19) = Risk from chemical ingestion from age 12 through 19 years of age
Risk(aduit) = 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(e-u) = 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(aduit) = 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)
Risk0) = Risk from chemical ingestion for the fh 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.
Attachment A, Addendum 2
Description of MIRC
2-52
December 2013
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TRIM-Based Tiered Screening Methodology for RTR
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:
Hazard quotient for chemical (unitless)
Average daily ingested dose of chemical (mg/kg-day) from all food types and
ingested media for the age group
Chronic oral reference dose for chemical (mg/kg-day)
5.2.2. Hazard Quotients for Chemicals with RfD Based on Developmental
Effects
For chemicals for which the toxicity reference value is an RfD based on developmental effects,
a shorter exposure duration (ED) and averaging time (AT) may be required. For this type of
chemical (e.g., methylmercury, 2,3,7,8-TCDD), the appropriate ED/AT and sensitive lifestage for
exposure may need to be estimated from the information provided in the critical developmental
study(ies) from which the RfD was derived (e.g., in consultation with the RfD documentation in
EPA's IRIS or in a toxicological profile developed for the chemical). For screening-level risk
assessments, however, a health protective approach is to compare the highest ADD from
among the child age categories provided in MIRC to the RfD, as is done for all PB-HAPs. This
approach ensures that the highest exposure from among the various age groups evaluated is
taken into consideration, regardless of which age group might be most relevant to the health
effect of interest (i.e., the age group on which the RfD is based).
5.2.3. Hazard Index for Chemicals with RfDs
When conducting screening-level assessments for multiple chemicals, it can be informative to
calculate a hazard index (HI) for toxicologically similar chemicals (EPA 2000). The HI is the
sum of HQs across chemicals (not age groups) as shown in Equation 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.
HQ =
ADD =
RfD =
Attachment A, Addendum 2
Description of MIRC
2-53
December 2013
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TRIM-Based Tiered Screening Methodology for RTR
Equation 2-55. Hazard Index Calculation
HI = HQ1+HQ2... HQn
where:
HI = Hazard index (unitless)
HQi = Hazard Quotient for chemical 1 (unitless)
HQ2 = Hazard Quotient for chemical 2 (unitless)
HQn = Hazard Quotient for chemical n (unitless)
The HI approach can be appropriate for chemicals with the same MOA and same target organ;
however, MOA often is difficult to determine. An HI usually is "developed for each exposure
route of interest, and for a single toxic effect or for toxicity to a single target organ" (EPA 2000; p
79). If a receptor is exposed to multiple chemicals that affect different target organs or that
operate by different MOAs, and if more than one HQ is close to 1, the risk assessor in some
circumstances, may consider whether chemical interactions play a role in chemical toxicity (EPA
2000). Exposures to more than one chemical can result in a greater or lesser toxic response
than might be predicted on the basis of one or the other chemical acting alone (toxicologically
independent) or acting in concert (toxicologically similar chemicals). Users are referred to
EPA's Supplementary Guidance for Conducting Health Risk Assessment of Chemical Mixtures
for approaches to assessing the potential for adverse health effects from exposure to multiple
chemicals (EPA 2000).
Note that users of MIRC are responsible for determining how to interpret HQs for multiple
chemicals.
6. Model Input Options
This section describes the input options currently included in MIRC. Required user inputs for
environmental media concentrations and air deposition rates, such as those predicted by
(output of) TRIM.FaTE, are described in Section 6.1 of this addendum. Values for farm-food-
chain (FFC) parameters for specific types of produce and animal products are discussed in
Section 6.2. Options for parameterizing receptor characteristics are described in Section 6.3,
including age-group-specific values for body weight, water ingestion, and food ingestion by food
type. Options for other exposure parameter 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.
Attachment A, Addendum 2
Description of MIRC
2-54
December 2013
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TRIM-Based Tiered Screening Methodology for RTR
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 |jg/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-
Attachment A, Addendum 2 2-55 December 2013
Description of MIRC
-------
TRIM-Based Tiered Screening Methodology for RTR
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-produce-
DW(i)
Chemical-specific plant/soil chemical bioconcentration factor for
edible portion of aboveground produce type i, exposed or
protected
Unitless (g soil DW/ g
produce DW)
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
kPa)
Plant-specific surface loss coefficient for aboveground exposed
produce and animal forage and silage
yr1
MAF(i)
Moisture adjustment factor for aboveground produce type /' to
convert the chemical concentration estimated for dry-weight
produce to the corresponding chemical concentration for full-
weight fresh produce
Percent water
RCF
Chemical-specific 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
Attachment A, Addendum 2
Description of MIRC
2-56
December 2013
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TRIM-Based Tiered Screening Methodology for RTR
Exhibit_Add A2-9. MIRC Parameters Used to Estimate Chemical Concentrations
in Farm Foods
Parameter
Description
Units
Bel (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
(dairy)
Biotransfer factor in dairy
day/kg FW tissue
B3(pork)
Biotransfer factor in pork
day/kg FW tissue
(poultry)
Biotransfer factor in poultry
day/kg FW tissue
Ba
-------
TRIM-Based Tiered Screening Methodology for RTR
Exhibit_Add A2-10. Chemical-Specific Inputs for Produce Parameters
for Chemicals Included in MIRC
Chemical
Fraction of
Wet
Deposition
(Fw)
(unitless)3
Root
Concentration
Factor (RCF)
(belowground)
(L/kg)b
Soil-Water
Partition
Coefficient
(Kds)
(L/kg)c
Chemical Air-to-
Plant
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
lndeno(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
Attachment A, Addendum 2
Description of MIRC
2-58
December 2013
-------
TRIM-Based Tiered Screening Methodology for RTR
Exhibit_Add A2-10. Chemical-Specific Inputs for Produce Parameters
for Chemicals Included in MIRC
Chemical
Fraction of
Wet
Deposition
(Fw)
(unitless)3
Root
Concentration
Factor (RCF)
(belowground)
(L/kg)b
Soil-Water
Partition
Coefficient
(Kds)
(L/kg)c
Chemical Air-to-
Plant
Biotransfer
Factor (BvAGm)
(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.
a6E-01 is the value for cations and most organic chemicals. As described in HHRAP (EPA 2005a), Appendix B (available at
http://www.epa.gov/osw/hazard/tsd/td/combust/finalmact/ssra/05hhrapapb.pdf), EPA estimated this value (EPA 1994a, 1995a)
from a study by Hoffman et al. (1992) in which soluble gamma-emitting radionuclides and insoluble particles tagged with gamma-
emitting 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.
bFor 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 chemical-
specific values for RCF from empirical regression equations developed by Briggs et al. (1982) based on their experiments
measuring uptake of compounds into barley roots from growth solution. Briggs' regression equations allow calculation of RCF
values from log Kow. For metals and mercuric compounds, empirical values for soil to root vegetable transfer on a dry-weight
basis are available in the literature, thus the RCF was not needed.
°As discussed in HHRAP (EPA 2005a), Appendix A, Kds describes the partitioning of a compound between soil pore-water and
soil particles and strongly influences the release and movement of a compound into the subsurface soils and underlying aquifer.
Kds values for mercuric compounds were obtained from EPA (1997b). Kds for cadmium compounds were obtained from EPA
1996. For all PAHs and dioxins, Kds was calculated by multiplying Koc times the screening scenario's fraction organic carbon
content (0.008). Empirical information for Koc was available for acenaphthene, benz(a)anthracene, benzo(a)pyrene,
dibenz(a,h)anthracene, fluoranthene, and fluorene in USEAP 1996. For all other organic compounds, the Koc was calculated
using the correlation equations presented in USEAP 2005a.
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).
elt 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 Bio-
Concentration
Factor
(Bfa G-produce-DWfiil
(unitless)3
Empirical Correction
Factor- Belowground
Produce
(VGrootveg) (unitless)b
Empirical
Correction Factor-
Aboveground
Produce
(VGAgo)) (unitless)0
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
Attachment A, Addendum 2
Description of MIRC
2-59
December 2013
-------
TRIM-Based Tiered Screening Methodology for RTR
Exhibit_Add A2-11. Chemical-Specific Inputs by Plant Type for Chemicals in MIRC
Compound Name
Plant Part
Plant-Soil Bio-
Concentration
Factor
[Bfa G-produce-DWfiil
(unitless)3
Empirical Correction
Factor- Belowground
Produce
(VGrootveg) (unitless)b
Empirical
Correction Factor-
Aboveground
Produce
(VGAgo>) (unitless)0
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
Attachment A, Addendum 2
Description of MIRC
2-60
December 2013
-------
TRIM-Based Tiered Screening Methodology for RTR
Exhibit_Add A2-11. Chemical-Specific Inputs by Plant Type for Chemicals in MIRC
Compound Name
Plant Part
Plant-Soil Bio-
Concentration
Factor
[Bfa G-produce-DWfiil
(unitless)3
Empirical Correction
Factor- Belowground
Produce
(VGrootveg) (unitless)b
Empirical
Correction Factor-
Aboveground
Produce
(VGAgo>) (unitless)0
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
Attachment A, Addendum 2
Description of MIRC
2-61
December 2013
-------
TRIM-Based Tiered Screening Methodology for RTR
Exhibit_Add A2-11. Chemical-Specific Inputs by Plant Type for Chemicals in MIRC
Compound Name
Plant Part
Plant-Soil Bio-
Concentration
Factor
[Bfa G-produce-DWfiil
(unitless)3
Empirical Correction
Factor- Belowground
Produce
(VGrootveg) (unitless)b
Empirical
Correction Factor-
Aboveground
Produce
(VGAgo>) (unitless)0
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
Attachment A, Addendum 2
Description of MIRC
2-62
December 2013
-------
TRIM-Based Tiered Screening Methodology for RTR
Exhibit_Add A2-11. Chemical-Specific Inputs by Plant Type for Chemicals in MIRC
Compound Name
Plant Part
Plant-Soil Bio-
Concentration
Factor
[Bfa G-produce-DWfiil
(unitless)3
Empirical Correction
Factor- Belowground
Produce
(VGrootveg) (unitless)b
Empirical
Correction Factor-
Aboveground
Produce
(VGAgo>) (unitless)0
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
lndeno(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
Attachment A, Addendum 2
Description of MIRC
2-63
December 2013
-------
TRIM-Based Tiered Screening Methodology for RTR
Exhibit_Add A2-11. Chemical-Specific Inputs by Plant Type for Chemicals in MIRC
Compound Name
Plant Part
Plant-Soil Bio-
Concentration
Factor
[Bfa G-produce-DWfiil
(unitless)3
Empirical Correction
Factor- Belowground
Produce
(VGrootveg) (unitless)b
Empirical
Correction Factor-
Aboveground
Produce
(VGAgo>) (unitless)0
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
Attachment A, Addendum 2
Description of MIRC
2-64
December 2013
-------
TRIM-Based Tiered Screening Methodology for RTR
Exhibit_Add A2-11. Chemical-Specific Inputs by Plant Type for Chemicals in MIRC
Compound Name
Plant Part
Plant-Soil Bio-
Concentration
Factor
[Bfa G-produce-DWfiil
(unitless)3
Empirical Correction
Factor- Belowground
Produce
(VGrootveg) (unitless)b
Empirical
Correction Factor-
Aboveground
Produce
(VGAgo>) (unitless)0
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
Attachment A, Addendum 2
Description of MIRC
2-65
December 2013
-------
TRIM-Based Tiered Screening Methodology for RTR
Exhibit_Add A2-11. Chemical-Specific Inputs by Plant Type for Chemicals in MIRC
Compound Name
Plant Part
Plant-Soil Bio-
Concentration
Factor
[Bfa G-produce-DWfiil
(unitless)3
Empirical Correction
Factor- Belowground
Produce
(VGrootveg) (unitless)b
Empirical
Correction Factor-
Aboveground
Produce
(VGAgo>) (unitless)0
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
Attachment A, Addendum 2
Description of MIRC
2-66
December 2013
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TRIM-Based Tiered Screening Methodology for RTR
Exhibit_Add A2-11. Chemical-Specific Inputs by Plant Type for Chemicals in MIRC
Compound Name
Plant Part
Plant-Soil Bio-
Concentration
Factor
[Bfa G-produce-DWfiil
(unitless)3
Empirical Correction
Factor- Belowground
Produce
(VGrootveg) (unitless)b
Empirical
Correction Factor-
Aboveground
Produce
(VGAgo>) (unitless)0
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
Attachment A, Addendum 2
Description of MIRC
2-67
December 2013
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TRIM-Based Tiered Screening Methodology for RTR
Exhibit_Add A2-11. Chemical-Specific Inputs by Plant Type for Chemicals in MIRC
Compound Name
Plant Part
Plant-Soil Bio-
Concentration
Factor
[Bfa G-produce-DWfiil
(unitless)3
Empirical Correction
Factor- Belowground
Produce
(VGrootveg) (unitless)"
Empirical
Correction Factor-
Aboveground
Produce
(VGAgo>) (unitless)0
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-Dwo¦> 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. Braboveground 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 K™,
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 K™, 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).
°As discussed in HHRAP (EPA 2005a), Appendix B, VGag represents an empirical correction factor that reduces aboveground
produce concentration and was developed to estimate the transfer of PB-HAP into leafy vegetation versus bulkier aboveground
produce (e.g., apples). Because of the protective outer skin, size, and shape of bulky produce, transfer of lipophilic PB-HAP (log
Kow greater than 4) to the center of the produce is not likely. In addition, typical preparation techniques, such as washing, peeling,
and cooking, further reduces residues. For aboveground produce, HHRAP (EPA 2005a) recommends using a VGag value of 0.01
for PB-HAP with a log Kow greater than 4 and a value of 1.0 for PB-HAP with a log K™, 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 site-
specific silage, this value may under- or overestimate the actual value.
Attachment A, Addendum 2
Description of MIRC
2-68
December 2013
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TRIM-Based Tiered Screening Methodology for RTR
Exhibit_Add A2-12. Non-Chemical-Specific Produce Inputs
Plant Part
Interception
Fraction
(RPr/))
(unitless)3
Plant
Surface
Loss
Coefficient
(kPoi) „
(1/year)
Length of
Plant
Exposure to
Deposition
{TPo))
(year)0
Yield or
Standing
Crop
Biomass
(YPoiJ
(kg/m )
Plant Tissue-
Specific
Moisture
Adjustment
Factor (MAF(i))
(percent)6
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 [Vp]) 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.
bThe term kp is a measure of the amount of chemical that is lost to natural physical processes (e.g., wind, water) overtime. 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.
°HHRAP (EPA 2005a) recommends using a Tp value of 0.16 years for aboveground produce and cattle silage. This is consistent
with earlier reports by EPA (1994a, 1998) and NC DEHNR (1997), which recommended treating Tp as a constant based on the
average period between successive hay harvests. Belcher and Travis (1989) estimated this period at 60 days. Tp is calculated
as 60 days 365 days/year = 0.16 years. For forage, the average of the average period between successive hay harvests (60
days) and the average period between successive grazing (30 days) is used (that is, 45 days), and Tp is calculated as (60 days +
30 days)/ 2 365 days/yr = 0.12 yr. Two key uncertainties are associated with use of these values for Tp: (1) The average period
between successive hay harvests (60 days) may not reflect the length of the growing season or the length between successive
harvests for site-specific aboveground produce crops. The concentration of chemical in aboveground produce due to direct (wet
and dry) deposition (Pd) will be underestimated if the site-specific value of Tp is less than 60 days, or overestimated if the site-
specific 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 = Yhil Ahi, where Yhi= Harvest yield of Ith crop (kg DW) and Ahi = Area planted to fh crop (m2), and using values for Yh
and Ah from USDA (1994b and 1994c). A production-weighted U.S. average Vp of 0.8 kg DW/m2 for silage was obtained from
Shor etal. 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.
Attachment A, Addendum 2
Description of MIRC
2-69
December 2013
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TRIM-Based Tiered Screening Methodology for RTR
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 Bio-
Availability
Factor (Bs)
(unitless)
Biotransfer Factors (Bam) (day/kg FW tissue)3
and Metabolism Factors (MF) (unitless)b
Mammal
Non-mammal
Beef
(B^teef)
Dairy
{Ba daily)
Pork
(Bapork)
MF
Eggs
[Ba caps)
Poultry
(B3p0Uitrv)
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
lndeno(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
Attachment A, Addendum 2
Description of MIRC
2-70
December 2013
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TRIM-Based Tiered Screening Methodology for RTR
Exhibit_Add A2-13. Animal Product Chemical-specific Inputs
for Chemicals Included in MIRC
Compound Name
Soil Bio-
Availability
Factor (Bs)
(unitless)
Biotransfer Factors (Bam) (day/kg FW tissue)3
and Metabolism Factors (MF) (unitless)b
Mammal
Non-mammal
Beef
[BSbeef)
Dairy
(Bd dairy)
Pork
(Bapork)
MF
Eggs
(BS CQQs)
Poultry
(Ba poultry)
MF
HeptaCDF,
1,2,3,4,6,7,8-
1
1.6E-02
3.5E-03
2.0E-02
1
6.9E-03
1.2E-02
NA
HeptaCDF,
1,2,3,4,7,8,9-
1
2.4E-02
5.1E-03
3.0E-02
1
1.0E-02
1.8E-02
NA
HexaCDD, 1,2,3,4,7,8-
1
1.1E-02
2.3E-03
1.3E-02
1
4.6E-03
8.1E-03
NA
HexaCDF, 1,2,3,4,7,8-
1
2.3E-02
4.8E-03
2.8E-02
1
9.6E-03
1.7E-02
NA
HexaCDD, 1,2,3,6,7,8-
1
6.8E-03
1.4E-03
8.2E-03
1
2.9E-03
5.0E-03
NA
HexaCDF, 1,2,3,6,7,8-
1
9.7E-03
2.0E-03
1.2E-02
1
4.1E-03
7.1E-03
NA
HexaCDD, 1,2,3,7,8,9
1
6.8E-03
1.4E-03
8.2E-03
1
2.9E-03
5.0E-03
NA
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.
Attachment A, Addendum 2
Description of MIRC
2-71
December 2013
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TRIM-Based Tiered Screening Methodology for RTR
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(im (kg/day)
Beef cattleb
0.5
Silage
2.5
Forage
8.8
Grain
0.47
Dairy cattle0
0.4
Silage
4.1
Forage
13.2
Grain
3.0
Swined
0.37
Silage
1.4
Grain
3.3
Chicken (eggs)8
0.022
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 ofthe 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 ofthe 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 ofthe 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.
bThe 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.
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 ofthe 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 2011a) and Chi Id-Specific
Exposure Factors Handbook (CSEFH; EPA 2008a). Where values were reported for age
groupings other than those used in MIRC (see Section 2.3 above for MIRC age groups), time-
weighted average values were estimated for the MIRC age groups from the available data.
Attachment A, Addendum 2
Description of MIRC
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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
weighAg-day) (EPA 2011a). 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
percentile values for adults and the five children's age groups: <1 year; 1-2 years; 3-5 years;
6-11 years; and 12-19 years. For its default screening assessment, EPA uses the mean BW
for each age group. The BWs currently in the MIRC database are listed in Exhibit_Add A2-15.
In general, BW values for the five children's age groups were calculated from the summary data
provided in Table 8-3 of EPA's 2008 CSEFH. For purposes of comparison, alternative BW
values for children ages 12 through 19 years also were estimated using data from Portier et al.
(2007). These values are listed in the last row of Exhibit_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.
Exhibit_Add A2-15. Mean and Percentile Body Weight Estimates
for Adults and Children
Lifestage
(years)
Duration
(years)
Body Weight (kg)
Mean
5tn
10tn
50tn
90tn
95tn
Adult3 (20-70)
50
80.0a
52.9
56
69.3
89.7
97.6
Child < 1b
1
7.83
6.03
6.38
7.76
9.24
9.66
Child 1-2C
2
12.6
9.9
10.4
12.5
14.9
15.6
Child 3-5d
3
18.6
13.5
14.4
17.8
23.6
26.2
Child 6-11e
6
36.0
22.1
24.0
33.5
51.2
58.6
Child 12-19f
8
64.2
39.5
45
64.2
83.5
89
[Child 12-199
8
64.3
41.1
44.6
60.9
88.5
98.4]
aBW represents the recommended body weight from EPAs 2011 EFH. Although the 18 to 74 year age category in EPA's EFH
does not match exactly the age 20 to 70 year categorization of adults in MIRC, the magnitude of error in the mean and percentile
body weights is likely to be very small (i.e., less than 1 %).
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.
°Each BW represents a time-weighted average of body weights for age groups 1 to <2 years and 2 to <3 years from Table 8-3 of
the 2008 CSEFH. Original sample sizes for each of these age groups can also be found in Table 8-3.
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.
'Mean BW estimated using Table 8-22 of the 2008 CSEFH, which is based on NHANES IV data as presented in Portier et al.
(2007). This estimate was calculated as the average of the 8 single-year age groups from 12 to 13 years through 19 to 20 years.
Values for the other percentiles were estimated using Portier et al., 2007.
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. 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.
Attachment A, Addendum 2
Description of MIRC
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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,
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 Adults3
Lifestage (years)
Ingestion Rates, Community Water (mL/day)
Mean
50tn
90tn
95tn
99tn
Child <1b
504
482
969
1113
1440
Child 1-2C
332
255
687
903
1318
Child 3-5d
382
316
778
999
1592
Child 6-11e
532
417
1149
1499
2274
Child 12-19f
698
473
1641
2163
3467
Adult9
1219
981
2534
3087
4567
Sources: EPA 2004, 2008a
"The sample size does not meet minimum reporting requirements as described in EPA 2008a. For some of these MIRC age
groupings, the values are based on the time-weighted average value for 2 or more age ranges from CSEFH Table 3-4. One or
more age ranges within the group may not meet the minimum reporting requirements, but not necessarily all of them fall within this
category.
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.
°Each IR represents a time-weighted average of ingestion rates for age groups 1 to <2 years and 2 to <3 years from Table 3-4 of
the 2008 CSEFH.
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.
'Each IR represents a time-weighted average of ingestion rates for age groups 11 to <16 years, 16 to <18, and 18 to <21 years
from Table 3-4 of the 2008 CSEFH. Note that estimated values include 11-year-olds and individuals through age 20, which
contributes to uncertainty in the estimates for 12 to 19 years.
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.
Attachment A, Addendum 2
Description of MIRC
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TRIM-Based Tiered Screening Methodology for RTR
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
2011a). Consumers-only means that individuals who did not report eating a specified type of
food during the three-day period covered by the food ingestion part of the survey were not
included in the analysis of ingestion rates for that food type. The questionnaire included the
options for a household to self-identify in one or more of five categories: as a household that
gardens, raises animals, hunts, fishes, or farms. As of September 2008, that survey was the
most recent NFCS available (EPA 2008a, CSEFH), and we are not aware of any that might be
more recent.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 2011a, Chapter 13):
(1) households that farm (F) and (2) households that garden or raise animals (HG for
homegrown). This division reflects EPA's data analysis. EPA tabulated IRs for fruits and
vegetables separately for households that farm and households that garden. Similarly, EPA
tabulated IRs for animals and animal products for households that farm and for households that
raise animals. Thus, the first type of household, F, represents farmers who may both grow
crops and raise animals and who are likely to consume more homegrown/raised foods than the
second type of household. The second type of household, HG, represents the non-farming
households that may consume lower amounts of home-grown or raised foods (i.e., HG
encompasses both households that garden and households that raise animals).
The food-specific ingestion rates are based on the amount of each food type that households
that farm (F) or households that garden and raise animals (HG) produced and brought into their
homes for consumption and the number of persons consuming the food. EPA averaged the
actual consumption rate for home-grown foods over the 1-week survey period.
The default food-specific ingestion rates in MIRC for adults are those for farming households (F)
in Exhibit_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)
Beef
N/A
4.14
4.00
3.77
1.72
1.93
Dairy6
N/A
91.6
50.9
27.4
13.6
2.96
Eggs3
N/A
2.46
1.42
0.86
0.588
0.606
Exposed Fruit3
N/A
6.14
2.60
2.52
1.33
1.19
21 Note 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.
Attachment A, Addendum 2
Description of MIRC
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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
Exposed Vegetable3
N/A
3.48
1.74
1.39
1.07
1.38
Pork3
N/A
2.23
2.15
1.50
1.28
1.10
Poultry3
N/A
3.57
3.35
2.14
1.50
1.37
Protected Fruit3
N/A
16.6
12.4
8.50
2.96
5.19
Protected Vegetable3
N/A
2.46
1.30
1.10
0.78
0.862
Root Vegetable3
N/A
2.52
1.28
1.32
0.94
1.03
Water (mL/day)c
N/A
332
382
532
698
1218
Median ingestion rates (g/kg-day)
Beef
N/A
2.51
2.49
2.11
1.51
1.55
Dairy6
N/A
125
66.0
34.4
15.5
2.58
Eggs3
N/A
1.51
0.83
0.561
0.435
0.474
Exposed Fruit3
N/A
1.82
1.11
0.61
0.62
0.593
Exposed Vegetable3
N/A
1.89
1.16
0.64
0.66
0.812
Pork3
N/A
1.80
1.49
1.04
0.89
0.802
Poultry3
N/A
3.01
2.90
1.48
1.30
0.922
Protected Fruit3
N/A
7.59
5.94
3.63
1.23
2.08
Protected Vegetable3
N/A
1.94
1.04
0.79
0.58
0.564
Root Vegetable3
N/A
0.46
0.52
0.57
0.56
0.59
Water (mL/day)c
N/A
255
316
417
473
981
90th percentile ingestion rates (g/kg-dayf
Beef
N/A
9.49
8.83
11.4
3.53
4.41
Dairy6
N/A
185
92.5
57.4
30.9
6.16
Eggs3
N/A
4.90
3.06
1.90
1.30
1.31
Exposed Fruit3
N/A
12.7
5.41
6.98
3.41
2.37
Exposed Vegetable3
N/A
10.7
3.47
3.22
2.35
3.09
Pork3
N/A
4.90
4.83
3.72
3.69
2.23
Poultry3
N/A
7.17
6.52
4.51
3.13
2.69
Protected Fruit3
N/A
44.8
32.0
23.3
7.44
15.1
Protected Vegetable3
N/A
3.88
2.51
2.14
1.85
1.81
Root Vegetable3
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)
Beef
N/A
12.9
12.5
12.5
3.57
5.83
Dairyb
N/A
167
89.9
56.0
32.3
7.80
Eggs3
N/A
5.38
3.62
2.37
1.43
1.59
Exposed Fruit3
N/A
14.6
6.07
11.7
4.78
3.38
Exposed Vegetable3
N/A
11.9
6.29
5.47
3.78
4.46
Pork3
N/A
6.52
6.12
4.73
6.39
2.60
Poultry3
N/A
8.10
7.06
5.07
3.51
3.93
Attachment A, Addendum 2
Description of MIRC
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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
Protected Fruit3
N/A
48.3
35.1
26.9
11.4
19.2
Protected Vegetable3
N/A
9.42
5.10
3.12
2.20
2.83
Root Vegetable3
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)
Beef
N/A
20.9
19.8
13.3
4.28
6.84
Dairyb
N/A
180
87.2
54.8
34.7
9.20
Eggs3
N/A
16.2
11.2
8.19
4.77
1.83
Exposed Fruit3
N/A
25.2
32.5
15.7
5.9
13.0
Exposed Vegetable3
N/A
12.1
7.36
13.3
5.67
8.42
Pork3
N/A
8.71
9.74
6.61
4.29
3.87
Poultry3
N/A
9.63
10.24
6.12
4.60
4.93
Protected Fruit3
N/A
109
71.2
58.2
19.1
34.4
Protected Vegetable3
N/A
9.42
5.31
5.40
2.69
5.56
Root Vegetable3
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 a). When data were unavailable for a particular age group, intake rate for all age groups was used
multiplied by the age-specific ratio of intake based on national population intake rates from CSFII.
bPrimary source for values was 1987-1988 NFCS survey, compiled results presented in Chapter 13 of 2011 Exposure Factors
Handbook (EPA, 2011 a). When data were unavailable for a particular age group, intake rate for all age groups was used multiplied
by the age-specific ratio of intake based on national population intake rates from an NHANES 2003-2006 analysis in Chapter 11 of
the Exposure Factors Handbook.
°Primary source for children less than 3 years of age was a Kahn and Stralka (2008) analysis of CSFII data, and from EPA's
analysis of NHANES 2003-2006 data for children and adults greater than three. All data tables that were used and justifications for
data sources are presented in Chapter 3 of the 2011 Exposure Factors' Handbook.
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 2011a): 1-2 years,
3-5 years, 6-11 years, and 12-19 years. Sample sizes were insufficient to distinguish IRs for
children in different types of households; hence, for children, a single IR value represents both F
and HG households for a given food type and age category (Exhibit_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
Attachment A, Addendum 2
Description of MIRC
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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 totai 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 IRcojotai, 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
ip _ ^COJtota! x IRPC, age_group_x
CO, age_group_x ~ 77^
IKPC_total
where:
Mean or percentile-specific consumer-only intake of the food type from all
sources, as consumed, for the specific child age group X
Mean or percentile-specific consumer-only intake of the farm food item, as
brought into the home, for the total NFCS survey population
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
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.
IRCO, age_group_x
IRcojotai =
IRpC, age_group_x ~
I RpCJotal =
Attachment A, Addendum 2
Description of MIRC
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TRIM-Based Tiered Screening Methodology for RTR
6.3.4. Local Fish Ingestion Rates
6.3.4.1. Screening Scenario
The USDA's 1987 to 1988 Nationwide Food Consumption Survey (NFCS) (USDA 1993, 1994a),
as presented in Chapter 13 of the Agency's Exposure Factors Handbook (i.e., Intake Rates for
Various Home Produced Food Items) (EPA 2011a), includes family-caught fish ingestion rates
by age category. There are several disadvantages, however, to using that data source to
estimate fish ingestion rates. First, due to inadequate sample sizes, EPA did not report fish
ingestion rates for children less than 6 years of age. Second, the NFCS data were collected
more than two decades ago. Third, the reported fish ingestion rates are for ages 6 to 11 and 12
to 19 and are based on 29 and 21 individuals in each age category, respectively (EPA 2011a,
Table 13-20). Finally, the ingestion rates from NFCS data are based on total weight of fish as
brought into the home, and do not include losses from preparation of the fish (i.e., removal of
inedible parts and, possibly, the skin). Estimates of preparation losses for fish intended to apply
to the NFCS fish ingestion rate data are very uncertain and are based on a wide variety of
freshwater, estuarine, and marine fish, and squid (EPA 2011a, Table 13-69). Additionally, when
considering the multipathway screening methodology, it is important that potential health effects
to those individuals who are most likely to have the greatest PB-HAP exposure are not
underestimated and, therefore, ingestion rates that are reflective of subsistence fisher ingestion
rates are desired. Therefore, a more recent survey was sought that included larger sample
sizes, data for children younger than six years, ingestion rates for the parts of fish actually
consumed, and ingestion rates reflective of subsistence fisher ingestion rates.
Taking all of these issues into consideration, the default adult fish ingestion rate selected for use
in MIRC is 373 g/day, which is the the estimated 99th percentile of fish ingestion rates for
woman anglers as reported by Burger (2002). This rate is based upon ingestion of "wild-caught"
fish, which includes freshwater, estuarine, and marine species, while our screening scenarios
focus only on freshwater fish from lakes. This is notable because a number of studies indicate
that fish ingestion rates are limited by species and habitat (i.e., lake, river, estuary, and ocean)
and that the majority of the fish consumed in the United States are from river, marine and
estuarine habitats versus lakes. Thus, although the fish ingestion rate for this group of
subsistence fishers is not the highest fish ingestion rate available for use by EPA, we do believe
it strikes the appropriate balance between being health protective and having screening
scenarios so conservative that they are of limited use in the decision making process. This
high-end fish ingestion rate is appropriate in the context of the conservative screening scenario
used in the RTR process. This methodology is particularly applicable for national rulemakings
given that it is very likely that subsistence woman fishers of child bearing age are located
throughout the United States. Finally, we note that using a high-end subsistence fish ingestion
rate is consistent with section 112 of the CAA, which focuses on risks associated with maximally
exposed individuals.
Because Burger (2002) did not estimate fish ingestion rates for children, another data source
was needed to develop ingestion rates for the child age categories that are used in MIRC. The
child ingestion rates need to be consistent with the Burger adult ingestion rate, reflective of
subsistence fisher ingestion rates, and based on adequate sample sizes. To satisfy these
requirements, data on child ingestion rates from EPA's Estimated Per Capita Fish Consumption
in the United States (EPA 2002) were selected for use. Specifically, the estimated 99th
percentile of as-prepared, consumer-only ingestion rates for finfish and shellfish were selected
(see Section 4.2.1.1 Table 5 of EPA 2002).The original data were collected as part of the 1994-
96 and 1998 USDA Continuing Survey of Intakes by Individuals (CSFII; USDA 2000). Values
reflect "as prepared" ingestion rates for which cooking and preparation losses (L1 and L2) did
Attachment A, Addendum 2
Description of MIRC
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TRIM-Based Tiered Screening Methodology for RTR
not need to be considered. "Total" fish as reported in this source represents consumption of
finfish plus shellfish.
Because the child age categories used in MIRC differ from the CSFII age categories presented
in EPA 2002, the CSFII data were adjusted for use in MIRC. The CSFII data did not provide
ingestion rates for the 1-2 year age category. To estimate ingestion rates for this age group,
EPA used the ingestion rate for the 3-5 year age category scaled downward by the ratio of the
mean body weight of the 1-2 year age group to the mean body weight of the 3-5 year age
group. Because MIRC uses a 3-5 year age category, no adjustment was needed for CSFII data
from that age category. For the 6-11 and 12-19 age categories, time-weighted average
ingestion rates were calculated based on the CSFII ingestion rates. Exhibit_Add A2-18 provides
the fish ingestion rates used in the screening analysis.
Exhibit_Add A2-18. Fish Ingestion Rates Used in Screening Analysis
Ingestion Rates (g/day)
Infants
Child
Child
Child
Child
Adult
<1 yr
1-2 yrs
3-5 yrs
6-11 yrs
12-19 yrs
20-70 yrs
NA
107.7a
159.0b
268.2C
331.0C
373d
aA fish ingestion rate for ages 1-2 years was not available. The value represents the consumer-only fish ingestion rate for
ages 3 to 5 from EPA (2002) (Section 4.2.1.1 Table 5 [freshwater/estuarine habitat]), scaled down by the ratio of the mean
Child 1 -2 body weight to the mean Child 3-5 body weight.
bThis value represents the consumer-only fish ingestion rate for ages 3 to 5 from EPA (2002), Section 4.2.1.1 Table 5
(freshwater/estuarine habitat) rounded to the nearest full number.
°These values represent time-weighted average consumer-only fish ingestion rates based on ingestion rates from EPA
(2002), Section 4.2.1.1 Table 5 (freshwater/estuarine habitat).
dThis value represents the 99th percentile ingestion rate of wild caught fish for women as reported by Burger (2002).
6.3.4.2. Other values
EPA's (2002) analysis of freshwater and estuarine fish consumption from the USDA's
Continuing Survey of Food Intake by Individuals (CSFII) for 1994-96 and 1998 was chosen to
provide fish ingestion rate options by age category in MIRC. Although the fish consumption
rates reported in the CSFII include all sources, commercial and self-caught, for purposes of
screening level risk assessments, it was assumed that all freshwater and estuarine fish
consumed are self-caught. The inclusion of commercially obtained and estuarine fish will
overestimate locally caught freshwater fish ingestion rates for most populations in the United
States; however, it also might underestimate locally caught fish ingestion rates for some
populations (e.g., Native Americans, Asian and Pacific Island communities, rural African
American communities). Because consumption of locally caught fish varies substantially from
region to region in the United States and from one population or ethnic group to the next, users
of MIRC are encouraged to use more locally relevant data whenever available.
For children, MIRC also includes values for the mean and the 90th, 95th, and 99th percentile
fish per-capita ingestion rates (freshwater and estuarine fish only) based on EPA's analysis of
1994-96 and 1998 CSFII data (EPA 2002, 2008a). Those rates include individuals who eat fish
and those who do not eat fish. As shown in EPA's 2008 CSEFH, Table 10-7, the 90th percentile
per capita ingestion rates estimated from the two-day CSFII recall period are zero for some child
age groups. Although not presented in CSEFH Table 10-7, median ingestion rates for all child
age groups would be zero (considering the "consumer only" sample sizes [CSEFH Table 10-9]
relative to the "per-capita" sample sizes in Table 10-7).
The high percentile fish ingestion rates that are zero result from the short duration of the CSFII
recall period (two days) compared with the averaging time of interest (a year) and the relatively
Attachment A, Addendum 2
Description of MIRC
2-80
December 2013
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TRIM-Based Tiered Screening Methodology for RTR
infrequent consumption of fish (e.g., on the order of once a week to once a month or less)
compared with the near daily ingestion of other types of food products (e.g., dairy, produce,
meat). Use of zero for fish ingestion rates, however, is not useful in MIRC. As a result, an
alternative method was used to estimate fish ingestion rates for children and adults that could
provide reasonable, non-zero values for all age groups and percentiles.
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 those 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):
Equation 2-57. Calculation of Alternative Age-Group-Specific Fish Ingestion Rates
'RpC,y = '^CO,y X FpC,y
where:
Per capita fish ingestion rate for age group y (g/day)
Consumer-only fish ingestion rates for age group y (g/day) (EPA 2002, Section
5.2.1.1, Table 5, for freshwater/estuarine habitat)
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 A2-19 and Exhibit_Add
A2-20. The mean and percentile per capita fish ingestion rates estimated using this
methodology are summarized in Exhibit_Add A2-21 and are available in MIRC. The fish
ingestion rates provided in Exhibit_Add A2-21 and included in MIRC are intended to represent
the harvest and consumption of fish in surface waters in a hypothetical depositional area. For
site-specific application of this tool, users should consider using more localized survey data to
estimate more appropriate fish ingestion rates. The fishing season varies substantially across
22
Most of these data also are provided in Table 10-9 of the CSEFH; the median values, however, are not presented in
the CSEFH, and values for the mean and all other percentiles are slightly different due to rounding.
Attachment A, Addendum 2 2-81 December 2013
Description of MIRC
IRpC.y ~
IRcO.y =
Fpc.y =
-------
TRIM-Based Tiered Screening Methodology for RTR
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 fisher were used.
As noted in Section 6.4.3 of this addendum, if the user overwrites the fish IRs shown in
Exhibit_Add A2-21 with fresh-weight as caught values (e.g., values obtained from a local creel
survey), the user is advised to set non-zero values for the preparation and cooking loss factors
L1 and L2 in Equation 2-15. Suggested values are presented in Section 6.4.3.
Exhibit_Add A2-19. Daily Mean and Percentile Consumer-Only Fish Ingestion Rates
for Children and Adults (IRCo,y)a
Lifestage (years)
Ingestion Rates, All Fish (g/day)
Mean
50th
90tn
95tn
99tn
Child <1
NA
NA
NA
NA
NA
Child 1-2b
27.31
15.61
64.46
87.60
138.76*
Child 3-5c
40.31
23.04
95.16
129.31
204.84*
Child 6-11d
61.49
28.46
156.86*
247.69*
385.64*
Child 12-19e
79.07
43.18
181.40*
211.15*
423.38*
>
Q.
C
I-H
81.08
47.39
199.62*
278.91
505.65*
Sources: EPA 2002, 2008a
NA = not applicable; it is assumed that children < 1 year of age do not consume fish.
'Indicates that the sample size does not meet minimum reporting requirements as described in EPA 2002. Owing to the small
sample sizes, these upper percentiles values are highly uncertain.
aPer capita fish ingestion (Fl) 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 Fl 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.
°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.
dThese 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.
6These 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.
'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.
Attachment A, Addendum 2
Description of MIRC
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December 2013
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TRIM-Based Tiered Screening Methodology for RTR
Exhibit_Add A2-20. Fraction of Population Consuming Freshwater/Estuarine Fish on a
Single Day (FPC,y)
Lifestage (years)
Fraction Consuming Fish
Child 3-5
0.0503a
Child 6-11
0.0440b
Child 12-19
0.0493°
Adult
0.08509d
Sources: EPA 2002, 2008a
aThis 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.
°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.
dThe 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 A2-21. Calculated Long-term Mean and Percentile per capita Fish Ingestion
Rates for Children and Adults (IRPC,y)
Lifestage (years)
Ingestion Rates, All Fish (g/day)
Mean
50th
90tn
95tn
99tn
Child <1
NA
NA
NA
NA
NA
Child 1-2a
1.37
0.79
3.24
4.41
6.98
Child 3-5b
2.03
1.16
4.79
6.51
10.3
Child 6-11c
2.71
1.25
6.90
10.9
17.0
Child 12-19d
3.90
2.13
8.95
10.4
20.9
Adult6
6.90
4.03
16.99
23.73
43.02
Sources: EPA 2002, 2008a
NA = not applicable; it is assumed that children < 1 year of age do not consume fish.
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).
"Values 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).
Attachment A, Addendum 2
Description of MIRC
2-83
December 2013
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TRIM-Based Tiered Screening Methodology for RTR
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 A2-22. 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, 2011a). Black and female
recreational anglers ingestion rates are presented in Burger (2002). The fish ingestion rates for
Hispanic, Laotian, and Vietnamese populations were derived from a study by Shilling etal.
(2010) of contaminated fish consumption in California's Central Valley Delta. Shilling etal.
(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
A2-22 for Hispanic, Laotian, and Vietnamese fishers were derived by EPA using information
from Shilling etal. (2010; EPA, 2010).
Exhibit_Add A2-22. Calculated Mean and 90th Percentile Per capita Fish Ingestion Rates
for Populations of Recreational Fishers (IRpc,y)
Subpopulation
Percentile
Units
Recrea-
tional
Fisher3
Female
Recrea-
tional
Fisherb
Black
Recrea-
tional
Fisherb
Hispanic
Recrea-
tional
Fisher0
Laotian
Recrea-
tional
Fisher0
Vietnamese
Recrea-
tional
Fisher0
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
a1997 Exposure Factors Handbook (USEPA, 1997a)
bBurger (2002) weights are "as consumed" for locally caught fish.
°Shilling, F., A. White, L. Lippert, and M. Lubell. 2010. Contaminated fish consumption in California's Central Valley Delta.
Environmental Research 110:334-344.
Applications to date of MIRC have used whole fish concentrations estimated by TRIM.FaTE.
The proportion of lipid in TL3 and TL4 fish in TRIM.FaTE is assumed to be 5.7 percent (by
weight) for the whole fish, based on information provided by Thomann (1989). The lipid content
of the part(s) of the fish normally consumed is likely to be less than 5.7 percent. For example,
EPA estimated a consumption-weighted mean lipid value for fillets of fish from TL3 to be 2.6
percent and from TL4 to be 3.0 percent (Table 6-9 in EPA 2003b). If a user of MIRC wishes to
account for reduced chemical concentration in fillet compared with whole fish for lipophilic
chemicals, the user can specify a "preparation" loss of chemical (see Section 6.4 of this
addendum).
For lipophilic chemicals (e.g., log KqW greater than 4), which partition primarily into the fatty
tissues offish, 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.
Attachment A, Addendum 2
Description of MIRC
2-84
December 2013
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TRIM-Based Tiered Screening Methodology for RTR
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 A2-23 and
included in MIRC are based on very limited data, as is evident from the values listed. The
studies evaluated by EPA for children generally focused on children between the ages of 1 and
3 to 6 years and were not specific to families that garden or farm. The default ingestion rates in
MIRC are the 90th percentile values, as for other ingestion rate parameters.
Exhibit_Add A2-23. Daily Mean and Percentile Soil Ingestion Rates
for Children and Adults
Age Group
(years)
Soil Ingestion Rate (mg/day)
Mean3
50th a
90th
95th
99th
Child < 1
NA
Child 1-2
50
50
200b
200b
200b
Child 3-5
50
50
200b
200b
200b
Child 6-11
50
50
201c
331d
331d
Child 12-19
50
50
201c
331d
331d
Adult 20-70
20
20
201c
"O
CO
CO
"O
CO
CO
Sources: EPA 2008a, EPA 2011 a
aFor 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.
"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.
"Values are 90th percentile adult ingestion rates calculated in Stanek et al. 1997; used to represent older children and adults.
Values are 95th percentile adult ingestion rates calculated in Stanek et al. 1997; used to represent older children and adults.
6.3.6. Total Food Ingestion Rates
Although not included in MIRC for deterministic screening-level exposure and risk assessments,
total food ingestion rates would be included in any probabilistic module developed for MIRC.
The total food ingestion rates presented in Exhibit_Add A2-24 will be used to normalize or to
truncate the sum of food-specific ingestion rates to reasonable values. This procedure is
particularly important when chemical intake from multiple upper-percentile food ingestion rates
for different types of food are added together. Individuals representing the upper percentile
ingestion rate for one food category might not be the same individuals who reported high
percentile ingestion rates for one or any of the other food categories.
Attachment A, Addendum 2
Description of MIRC
2-85
December 2013
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TRIM-Based Tiered Screening Methodology for RTR
Exhibit_Add A2-24. Daily Mean and Percentile Per Capita Total Food Intake for
Children and Adults
Lifestage (years)
Percent of Group
Consuming Food
Mean
50th
90th
95th
99th
Total Food Intake (g/day, as consumed)
Child < 1a
67.0% - 99.7% h
322
270
599
779
1152
Child 1-2b
100%
1,032
996
1537
1703
2143
Child 3-5c
100%
1,066
1,020
1,548
1,746
2,168
Child 6-11d
100%
1,118
1,052
1,642
1,825
2,218
Child 12-19e
100%
1,197
1,093
1,872
2,231
2,975
>
Q.
C
I-H
100%
1,100
1,034
1,738
2,002
2,736
Total Food Intake (g/kg-day, as consumed)
Child < 1a
67.0% - 99.7% h
39
34
72
95
147
Child 1-2b
100%
82
79
125
144
177
Child 3-5c
100%
61
57
91
102
132
Child 6-11d
100%
40
38
61
70
88
Child 12-19e
100%
21
19
34
40
51
Adult9
100%
14.8
13.9
23.7
27.6
35.5
Sources: EPA 2005e, 2008a
aThese 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.
These values were obtained from Table 14-3 of the 2008 CSEFH (age group 3 to <6 years, N=4,112).
dThese 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.
6These 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.
'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.
gThese 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 5Aofthe 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 "non-
consumers" of food.
6.4. Other Exposure Factor Values
The other exposure parameters included in the MIRC algorithms are exposure frequency
(Section 6.4.1), fraction of the food type obtained from the contaminated area (Section 6.4.2),
and reduction in the weight of the food types during preparation and cooking (Section 6.4.3).
For the breast milk ingestion pathway, additional exposure parameters are included in the FFC
algorithms (Section 6.5).
6.4.1. Exposure Frequency
The exposure frequency (EF) represents the number of days per year that an individual
consumes home-produced food items that are contaminated with the chemical being evaluated.
In MIRC, the default value for EF is 365 days/year for all exposure sources and all potential
receptors. This assumption is consistent with the food ingestion rates used in MIRC (i.e., daily
intake rates equivalent to annual totals divided by 365 days) and does not imply that residents
necessarily consume home-produced food products every day of the year.
Attachment A, Addendum 2 2-86 December 2013
Description of MIRC
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TRIM-Based Tiered Screening Methodology for RTR
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 A2-25. All preparation
and cooking loss parameter values were estimated as specified in the exhibit's endnotes from
data presented in Chapter 13 of the EPA's 1997 and 2011 EFH (EPA 1997a and 2011a).
Attachment A, Addendum 2
Description of MIRC
2-87
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TRIM-Based Tiered Screening Methodology for RTR
Exhibit_Add A2-25. Fraction Weight Losses from Preparation of Various Foods
Product
Mean Cooking, Paring, or
Preparation Loss
(Cooking Loss Type 1 [L1 ])
(unitless)a
Mean Net Post Cooking
(Cooking Loss Type 2 [L2])
(unitless)b
Exposed Fruif
0.244
0.305
Exposed Vegetable
0.162d
NA
Protected Fruit
0.298
NA
Protected Vegetable
0.088f
NA
Root Vegetable9
0.075
0.22
Beef
0.27
0.24
Pork
0.28
0.36
Poultry
0.32
0.295h
Fish'
0.0
0.0
Source: EPA 1997a and 2011a
NA = Not Available
aFor 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.
bFor 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.
These values represent averages of means for all fruits with available data (except oranges) (Table 13-6).
dThis 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.
eThis value was set equal to the value for oranges (Table 13-6).
'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.
gThese values represent averages of means for all root vegetables with available data (Table 13-7). Root vegetables include
beets, carrots, onions, and potatoes.
hThis value represents an average of means for chicken and turkey (Table 13-5).
'If the user changes fish ingestion rates to match a survey of the whole weight offish brought into the home from the field (divided
by the consumers of the fish), an appropriate value for L1 would be 0.31 and an appropriate L2 would be 0.11 (EPA 2011a).
There are substantial uncertainties associated with the L1 and L2 parameters, including the
wide variation in values across produce types that were averaged together to recommend a
central tendency value for each. For example, the L2 factor does not distinguish between
weight loss during cooking by water evaporation, which might leave the chemical in the food
(chemical not lost), and pouring the cooking liquid down the drain (chemical lost) or using the
liquid to create a sauce (chemical not lost). In addition, the concentration of chemical might be
highest in the skin, which often is discarded, and lower in the consumed portion of many bulky
fruits and vegetables. Finally, the data EPA used to evaluate L1 included negative losses (i.e.,
weight gains) due to hydration of dried vegetables (e.g., peas and lima beans), which increases
the range of L1 values across different vegetables.
Note that the default L1 and L2 values for fish are set to zero. That is because the data source
for the fish ingestion rates is not the USDA's 1987 to 1988 NFCS (USDA 1993, 1994a) as
reported in EPA's EFH, which reported food as brought into the home, as is the case for the
other food categories. Instead, the fish IR data included in MIRC are based on parts actually
consumed, and so no loss processes for preparation are needed.
If the user manually changes fish ingestion rates to match a local survey of the whole weight of
fish brought into the home from the field (divided by number of persons consuming the fish), the
user should also set the L1 and L2 parameter to non-zero values.
Attachment A, Addendum 2 2-88 December 2013
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TRIM-Based Tiered Screening Methodology for RTR
6.4.4. Food Preparation/Cooking Adjustment Factor (FPCAF) for Fish
In addition to estimating the weight of the food that is lost to preparation and cooking, there also
can be changes in the chemical concentrations due to cooking. Because the fish consumption
rates are "as consumed" and the fish concentration is based on uncooked fish, adjustments
should be made to reflect the chemical concentrations in fish after cooking. In order to account
for this phenomenon, an FPCAF can be applied to the uncooked fish concentration to estimate
a concentration in cooked fish. The following sections discuss FPCAFs for each of the 4 PB-
HAPs.
6.4.4.1. Mercury
In the U.S. EPA Revised Technical Support Document: National-Scale Assessment of Mercury
Risk to Populations with High Consumption of Self-caught Freshwater Fish (EPA 2011b), a food
preparation/cooking adjustment factor (FPCAF) of 1.5 was used to adjust methyl-mercury
(MeHg) concentrations in consumed fish (i.e., a 50% increase in MeHg concentration due to
cooking). Cooking fish typically increases MeHg levels per unit fish (as consumed) because
MeHg concentrates in the muscle, while preparation involves removal primarily of non-muscle
elements of the fish. The value is based on a study by Morgan et al. (1997).
6.4.4.2. Cadmium
Similar to mercury, cadmium will bind to muscle and will be retained during the cooking process.
As such, the same FPCAF of 1.5 that is used for mercury is assumed for cadmium.
6.4.4.3. Dioxin
Dioxins are lipophilic and have been demonstrated to be lost during cooking. Based on a
literature review, an FPCAF of 0.7 to is applied to account for these losses during the cooking
process. A brief summary of supporting literature follows.
Schecter et al. (1998) found that the mass of PCDD and PCDF in fresh catfish fillet (skin
on) decreased by about 50 percent per serving portion during cooking. Given the
simultaneous losses of moisture/fats during broiling of the catfish, the PCDDs and
PCDFs concentrations decreased by 33 percent (i.e., multiply uncooked concentration in
fresh fish by a factor of 0.66 = 0.70 to one significant digit).
Reinert et al. (1972) reported higher losses of another highly lipophilic chemical, DDT,
from cooking fish fillets of bloaters, yellow perch, lake trout, and coho salmon.
Concentrations of DDT in fish fillet portions for lake trout and coho salmon, top
predators, were reduced by 64 to 72 percent by frying or broiling, primarily through
preferential loss of fat (and lipophilic DDT) during cooking. The investigators did not
report skin on or off; however, they used steak cuts instead of flat fillets, which provide a
smaller ratio of skin to muscle than is the case for fillets that constitute one side of the
fish.
Zabik and Zabik (1995) quantified the reduction in TCDD concentration of cooked, with
the skin off, fillets compared with uncooked fillet with skin for fish harvested from the
Great Lakes. Concentrations in the cooked fish with the skin off were reduced relative to
the raw fillet with the skin on by approximately 44 percent for walleye, 80 percent for
white bass, and 61 percent for lake trout. Comparing losses of TCDD for fillets cooked
with the skin on versus fillets that were both skinned and cooked, Zabik and Zabik
(1995) found reductions in TCDD concentrations of approximately 43 percent for
Chinook Salmon cooked with the skin on and 57 percent for Chinook salmon cooked with
Attachment A, Addendum 2
Description of MIRC
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TRIM-Based Tiered Screening Methodology for RTR
the skin off. They found a 37 percent reduction of TCDD concentration for carp fillets
cooked with the skin on and 54 percent reduction if the skin was removed.
The three studies listed above indicate that the 0.7 factor is not likely to overestimate loss of
PCDD/PCDFs from fish during cooking (pan frying, broiling, grilling). Reductions in TCDD
concentrations could be much higher with skin removal and trimming of fat.
6.4.4.4. PAHs
While it is reasonable to assume that there might be losses of lipophilic PAHs during the
cooking process, there is insufficient information to distinguish what the net loss (or gain) during
cooking might be because cooking can create PAHs from proteins in the tissue. The literature
acknowledges these competing forces, but does not provide information to disentangle the gain
and loss mechanisms. As such, a neutral approach was taken, which is to assume an
adjustment factor of 1.0 (i.e., no adjustment) for PAHs.
6.5. Breast-Milk Infant Exposure Pathway Parameter Values
Values used for parameters in the breast-milk exposure pathway algorithms (see Section 3.4 of
this 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 scenario-
specific or for which empirical values are required, the default values provided in MIRC are
listed. For parameters for which MIRC calculates values, the appropriate equation is listed.
Scenario- and receptor-specific parameters are discussed in Section 6.5.1 and chemical-
specific parameters are discussed in Section 6.5.2.
6.5.1. Receptor-specific Parameters
Receptor-specific values are needed for parameters that describe the characteristics or
activities of the exposed individual. In this context, there are two relevant receptors: the mother
and the infant. Exhibit_Add A2-26 lists the parameters and their default values. The text that
follows describes the input value or value options for each exposure parameter required by
MIRC to calculate the infant absorbed chemical intake rate, or DAIinf. For parameter values that
can be estimated when empirical values are not available, see the equation description in
Section 3.4 of this addendum.
Attachment A, Addendum 2
Description of MIRC
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TRIM-Based Tiered Screening Methodology for RTR
Exhibit_Add A2-26. Scenario- and Receptor-Specific Input Parameter Values Used to
Estimate Infant Exposures via Breast Milk
Parameter
Description
Default Value
AT
Averaging time for infant's exposure via breast milk, i.e., duration of
nursing (days)
=ED
BWinf
Body weight of infant (kg) averaged over duration of nursing exposure
7.8
BWmat
Body weight of mother (kg) averaged over duration of mother's
exposure
66
DAI mat
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 BWmf, 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 A2-27 presents additional
values for the infant body weight parameter that the user can select instead of the MIRC default.
Exhibit_Add A2-27. Average Body Weight for Infants
Statistic
0 to < 6 months
(kg)
0 to < 12 months
(kg)
0 to < 18 months
(kg)
0 to < 24 months
(kg)
Mean
6.5
7.8a
9.0
9.6
5th percentile
5.0
6.0
7.0
7.5
10th percentile
5.3
6.4
7.4
7.8
15th percentile
5.5
6.7
7.7
8.2
25th percentile
5.8
7.0
8.1
8.7
50th percentile
6.4
7.8
8.9
9.5
75th percentile
7.1
8.6
9.9
10.5
85th percentile
7.4
9.0
10.3
11.0
Attachment A, Addendum 2 2-91 December 2013
Description of MIRC
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TRIM-Based Tiered Screening Methodology for RTR
Exhibit_Add A2-27. Average Body Weight for Infants
Statistic
0 to < 6 months
(kg)
0 to < 12 months
(kg)
0 to < 18 months
(kg)
0 to < 24 months
(kg)
Mean
6.5
7.8a
9.0
9.6
90th percentile
7.7
9.2
10.6
11.3
95th percentile
8.0
9.7
11.1
11.8
Source: EPA (2008a); each value is the time-weighted average from the data summaries presented in the CSEFH, Table 8-3.
aMIRC default
Maternal body weight (BWma1). 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_eiac)¦ 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 non-
pregnant women between the ages of 15 and 44 (i.e., women of child-bearing age), lactating
women, and pregnant women (EPA 2004). Exhibit_Add A2-28 presents additional values for
the maternal body weight parameter which the user may choose to use instead of the MIRC
default. The BWmat value is not the value that MIRC uses to estimate the mother's absorbed
daily intake (DAImat). The daily ingestion rates for home-grown/raised food products in MIRC
are for men and women combined, with the rates normalized to body weight. The ingestion
rates for soil, water, and fish included in MIRC are not normalized to body weight but are based
on both men and women. For those ingestion rates, MIRC uses an average body weight value
for males and females to estimate the average daily dose (intake) of the chemical in mg/kg-day.
These values are subject to the assumption that the body-weight normalized ingestion rates and
resulting ADD values are applicable to nursing mothers.
Exhibit_Add A2-28. Time-weighted Average
Body Weight for Mothers
Statistic
Weight (kg)
Mean
66.0a
5th
47.1
10th
50.2
25th
54.3
50th
62.0
75th
72.0
90th
85.7
95th
97.0
Source: EPA 2004
aMIRC default value
Exposure duration (ED). See discussion of AT and ED above.
Fraction of mother's whole blood that is plasma (fhn). Steinbeck (1954) reported that plasma
volume accounts for approximately 60 percent of the total blood volume in non-lactating human
females (EPA 1998). Harrison (1967) and Ueland (1976) reported plasma volumes between 63
to 70 percent in postpartum women (EPA 1998). The default value in MIRC of 65 percent (0.65)
Attachment A, Addendum 2 2-92 December 2013
Description of MIRC
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TRIM-Based Tiered Screening Methodology for RTR
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-p-
dioxins show a correlation between percent body fat and the elimination rate of dioxins, with
longer half-lives for dioxins in individuals with a higher proportion of fat in their bodies (ATSDR
1998). In the context of a screening model, however, EPA recommends a default value for the
fraction of a mother's body comprised of fat of 0.3 based on data and discussions presented by
Smith (1987) and Sullivan etal. (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 (fmhm). The Cmwat 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 etal. 1994, Hong etal. 1994, McLachlan 1993, Bates etal. 1994, NAS 1991, Butte et
al. 1984, Maxwell and Burmaster 1993, EPA 2011a, Smith 1987, Sullivan etal. 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 (IRmiik). Milk ingestion rates vary with several factors, including
the age and size of the infant and use of other foods such as formula. Based on its review of a
several studies, EPA recommended time-weighted average and upper percentile milk ingestion
rates for infants that nurse for six and for twelve months (EPA 2011a, Table 15-3). To estimate
an "average" value, EPA first estimated study-sample-size weighted average values for 1
through 12 months of age and then developed time-weighted average milk ingestion rates from
those (EPA 2011a). EPA estimated an upper percentile (upper bound) value as the mean plus
two standard deviations. MIRC converts the ingestion rates measured volumetrically (mL/day)
to mass-based estimates (kg/day) assuming the density of human milk to be 1.03 g/ml_
(reported by NAS 1991 and recommended by EPA 2011a). The resulting values are shown in
the first two rows of Exhibit_Add A2-29. The MIRC screening-level default of 980 mL/day is an
upper-bound estimate based on a one-year nursing period.
Exhibit_Add A2-29 also includes the recommended values for four non-overlapping age
categories from the CSEFH (EPA 2008a, Table 15-1). The values demonstrate that although
infants grow substantially from birth to one year of age, the "upper bound" estimates of their milk
ingestion rates are very close to 1 liter per day at all stages of development in the first year.
Attachment A, Addendum 2
Description of MIRC
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December 2013
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TRIM-Based Tiered Screening Methodology for RTR
Exhibit Add A2-29. Infant Breast Milk Intake Rates
Age Category
Average
(mL/d)
Average
(kg/d)
"Upper Bound"
(mL/d)
"Upper Bound"
(kg/d)
Reference
1 to 6 months
742
0.764
1,033
1.064
EPA 2011aT
Oto < 12
months
688
0.709
980a
1.01a
EPA 2011a*
0 to < 1 month
510
0.525
950
0.979
EPA 2008an
1 to < 3 months
690
0.711
980
1.01
EPA 2008aT
3 to < 6 months
770
0.793
1,000
1.03
EPA 2008aT
6 to < 12
months
620
0.639
1,000
1.03
EPA 2008at
aMIRC default;f Based on review of multiple studies;ft Based on a single study
Duration of breast feeding (thf). 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
A2-30. Note that the parameters for which values are needed are different for the lipophilic
chemicals (i.e., dioxins), for which lactational transfer is assumed to occur via milk fat, and
inorganic chemicals, for which the transfer is assumed to occur via the aqueous phase of breast
milk (i.e., mercury). All dioxin congeners were assumed to manifest identical values as TCDD in
regard to breast milk-related parameters.
Attachment A, Addendum 2 2-94 December 2013
Description of MIRC
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TRIM-Based Tiered Screening Methodology for RTR
Exhibit_Add A2-30. Chemical-specific Input Parameter Values for
Breast Milk Exposure Pathway
Parameter and Description
2,3,7,8-
TCDD
MeHg
AEjnf
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 identified6
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
^elim
Rate constant for elimination of chemical for non-
lactating women (per day; related to chemical half-
life)
1.9E-04b
1.4E-02 c
kfat_elac
Rate constant for total elimination of lipophilic
chemicals by lactating women (per day)
Est. using
Equation 2-41
NA
E
.a
O
o_
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.
aThis 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.
°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 (AE-,„f). The
models included in MIRC assume that the AEmf from the lipid phase of breast milk is equal to the
AEjnf 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 AEmf from
the breast milk fat and the AEmf from the aqueous phase of breast milk (EPA 2001a). However,
since the MIRC assumption is that chemicals will partition to either the lipid or aqueous phase of
milk, it is not necessary at this time to have multiple AEinf values for a given chemical. If data on
the AE from the mother or an adult but not for the infant are available, data for the adult may be
used for AEmf. 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).
Attachment A, Addendum 2 2-95 December 2013
Description of MIRC
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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 (fhj). 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 |jg 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 (ft). 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 (fn,). 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 fpt 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 fbi, and the value found for mercury may not be appropriate for a
chronic exposure analysis (see above).
Chemical half-life in non-lactatinq women (/?). 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 etal. (1978)
measured blood clearance rates for MeHg in lactating Iraqi women exposed accidentally to
MeHg via bread prepared from wheat treated with a fungicide that contained MeHg. The data
indicated a mean half-life for MeHg of approximately 42 days. Sherlock et al. (1984) reported
an average measured half-life for MeHg of 50 days with a range of 42-70 days. The MIRC
default for MeHg is set to the longer average half-life of 50 days.
Chemical elimination rate constant for lactating women - aqueous (kaq Piar). The parameter
kaq_eiac is equal to keiim 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_e,ac is equal to keiim as discussed
for Equation 2-43. The extent to which kenm is an underestimate of kaq_eiac for a given chemical
will determine the extent of health protective bias in kaq_eiac.
Attachment A, Addendum 2 2-96 December 2013
Description of MIRC
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TRIM-Based Tiered Screening Methodology for RTR
Chemical elimination rate constant for non-lactating women (kPnm). Although values for this
parameter often are reported directly in the literature, MIRC estimates kenm from chemical half-
life assuming first-order kinetics as shown in Equation 2-40. For example, for a biological half-
life of 3,650 days for dioxins, keiim is estimated to be 1.9E-04 per day. Assuming a biological
half-life of 50 days for MeHg, the value for keiim is estimated to be 0.014 per day.
Rate constant for total elimination of lipophilic chemicals by lactatinq women (kfat Piar). Although
values for this parameter might be found in the scientific literature for some chemicals, in MIRC,
kfat_eiac 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 (PChm). 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 (PcpBr).
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.
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 A2-31). Fish ingestion
rates are also available from these sources; however, as described in Section 6.3.4, these
sources were not used to obtain fish ingestion rates.
Attachment A, Addendum 2
Description of MIRC
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TRIM-Based Tiered Screening Methodology for RTR
7.1.1. Fish Ingestion Rates
The adult fish ingestion rate was obtained from Burger (2002), a study that examined daily
consumption of wild-caught fish for high-end recreationalists (white, black and female) in South
Carolina. For female high-end consumers of wild-caught fish, Burger identified average and
higher-percentile consumption rates as follows: 39.1 g/day (mean), 123 g/day (90th percentile),
172 g/day (95th percentile), and 373 g/day (99th percentile). As shown in Exhibit_Add A2-31 and
discussed in Section 6.3.4, for adults, the rate offish ingestion assumed in the screening
scenario is 373 g/day, which corresponds to the 99th percentile value estimated by Burger for
adult females. This value was selected to be representative of subsistence fishers.
For the child age groups, as discussed in Section 6.3.4, the baseline fish ingestion rates for the
screening scenario are based on "as prepared" total freshwater/estuarine fish ingestion rates at
the 99th percentile of the distribution for the consumer-only population (i.e., inclusive only of
people who consume fish, rather than per-captita rates, which include both consumers and non-
consumers), as estimated in EPA (2002), Section 4.2.1.1. Some adjustments were necessary
because the age groups evaluated for RTR (which correspond to the age groups for which farm
food ingestion rates are available) do not all directly correspond to the age groups in the EPA
(2002) report. As described in Section 6.3.4, these adjustments convert the available age-
specific data on fish ingestion rates to the age-specific values needed for MIRC.
For the screening-level fish ingestion exposure scenario, the consumer evaluated is an
individual who regularly consumes a large amount of fish that he or she has caught locally over
the course of a 70-year lifetime. Modeled exposures are intended to encompass those of a
subsistance fisher whose diet comprises a substantial proportion offish. The scenario is not,
however, intended to represent the maximum possible exposure an individual subsistence fisher
might experience.
Although the fish ingestion rates presented here are representative of the 99th percentile of the
evaluated data set, the use of these inputs (compared with 90th percentile values used for other
food types) is not considered to be inconsistent. This is due to the idiosyncrasies of the survey
data on fish consumption, the fact that the data sets for homegrown foods and fish are not
parallel, and the consideration of rates appropriate for subsistence fishers, as described above.
As discussed above, EPA believes that use of these fish ingestion rates strikes the appropriate
balance between being health protective and having screening scenarios so conservative that
they are of limited use in the decision making process. This high-end fish ingestion rate is
appropriate in the context of the conservative screening scenario used in the RTR process and
is applicable for national rulemakings given that it is very likely that subsistence woman anglers
of child bearing age are located throughout the United States. Using a high-end subsistence fish
ingestion rate also is consistent with section 112 of the CAA, which focuses on risks associated
with maximally exposed individuals.
7.1.2. Farm Food Chain Ingestion
The default settings assume that all food types are obtained from the area of chemical
deposition specified by the user (i.e., fraction of food from contaminated area = 1.0).
For estimates of screening threshold emission rates for PB-HAPS, environmental
concentrations and air deposition rates were estimated using TRIM.FaTE for the area of
maximal deposition in the vicinity of a hypothetical facility, and thus represent risks estimated for
a maximally exposed individual/farm/family.
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Description of MIRC
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Exhibit_Add A2-31 also includes a sum of the 90th percentile ingestion rates for homegrown
food categories and 99th percentile fish ingestion to show the implied total food ingestion rate
associated with setting multiple food-type-specific ingestion rates at upper percentiles. Because
these upper percentile values for each farm food category are likely to reflect different
individuals, it is likely that addition of multiple upper percentile intake values will exceed the total
food ingestion rates expected for the general population. This sum is shown on the third row
from the bottom (Total Food: Homegrown Only).
The second row from the bottom presents the 90th percentile of the distribution of individual total
food ingestion rates from the USDA's 1994-96 and 1998 Continuing Survey of Food Intakes by
Individuals (CSFII) (USDA 2000) data sets, as analyzed by EPA (EPA 2005e). The total
ingestion rate for the farming households (third row from bottom) takes into account the cooking
losses typical of each food category to provide a better comparison with the 90th percentile
individual total food ingestion rates from CSFII (which are based on consumption of prepared
foods). The final row of Exhibit_Add A2-31 shows the likely magnitude of the overestimates by
age category by presenting the ratio of the two preceding rows. The values in this row
demonstrate the potential for overestimating intake by using upper percentile values for all food
groups. This bias may be considered when evaluating the results estimated by MIRC.
Exhibit_Add A2-31. Farm Food Category Ingestion Rates for Health Protective Screening
Scenario for Farming Households
Product
Screening-Level Consumer Ingestion Rate
Units
Infants
<1 yr
Child
1-2 yrs
Child
3-5 yrs
Child
6-11 yrs
Child
12-19 yrs
Adult
20-70 yrs
Farm Food Item
Beef
NA
9.49
8.83
11.4
3.53
4.41
g/kg-day
Dairy6
NA
185
92.5
57.4
30.9
6.16
g/kg-day
Eggs3
NA
4.90
3.06
1.90
1.30
1.31
g/kg-day
Exposed Fruit3
NA
12.7
5.41
6.98
3.41
2.37
g/kg-day
Exposed Vegetable3
NA
10.7
3.47
3.22
2.35
3.09
g/kg-day
Pork3
NA
4.90
4.83
3.72
3.69
2.23
g/kg-day
Poultry3
NA
7.17
6.52
4.51
3.13
2.69
g/kg-day
Protected Fruit3
NA
44.8
32.0
23.3
7.44
15.1
g/kg-day
Protected Vegetable3
NA
3.88
2.51
2.14
1.85
1.81
g/kg-day
Root Vegetable3
NA
7.25
4.26
3.83
2.26
2.49
g/kg-day
Other
Breast milkc
1.01
NA
NA
NA
NA
NA
kg/day
Soil (dry)
NA
200°
200°
201e
201e
201e
mg/day
Fish (per individual)'
NA
107.7s
159.0s
268.2h
331,0h
373
g/day
Attachment A, Addendum 2
Description of MIRC
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TRIM-Based Tiered Screening Methodology for RTR
Exhibit_Add A2-31. Farm Food Category Ingestion Rates for Health Protective Screening
Scenario for Farming Households
Product
Screening-Level Consumer Ingestion Rate
Units
Infants
<1 yr
Child
1-2 yrs
Child
3-5 yrs
Child
6-11 yrs
Child
12-19 yrs
Adult
20-70 yrs
Total Food Ingestion Rates for Comparison Only (not in MIRC; excludes soil and water)
Total Food: Homegrown
only'
NA
259
142
99
51
35.5
g/kg-day
Total Food: All Sources'
NA
125
91
61
34
23.7
g/kg-day
Overestimate (ratio of
Homegrown/Total)
NA
2.1
1.6
1.6
1.5
1.3
(unitless)
Sources: EPA 2011a, EPA 2008a, unless otherwise noted.
NA = not applicable
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 a). When data were unavailable for a particular age group, the intake rate for all age groups was
multiplied by the age-specific ratio of intake based on national population intake rates from CSFII.
bPrimary source for values was 1987-1988 NFCS survey, compiled results are presented in Chapter 13 of the 2011 Exposure Factors
Handbook (EPA, 2011 a). When data were 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.
°lnfants are assumed to consume only breast milk for one year.
dThese 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.
eThese values are 90th percentile adult ingestion rates calculated in Stanek et al. 1997, and they are used to represent older children
and adults.
fThe ingestion rate for adults was obtained from Burger (2002) and is the 99th percentile value for adult females considered high-end
recreationists; this value is believed to be representative of subsistence fishers. The 99th percentile values for children were derived
based on EPA's Estimated Per Capita Fish Consumption in the United States (2002)—Section 4.2.1.1 Table 5 (for child age categories)
adjusted and scaled. Values reflect "as prepared" ingestion rates.
gThe fish ingestion rate for children aged 3-5 years was obtained directly from Section 4.2.1.1, Table 5 in the EPA (2002) report (value
presented is rounded); for these children, the RTR age-group range matches the EPA (2002) age category. Fish ingestion rates for
children less than 3 years old, however, were not provided. Therefore, for children aged 1-2 years, the fish ingestion rate was calculated
using the ingestion rate for children aged 3-5 years scaled downward by the ratio of the mean body weight of children aged 1-2 years
to the mean body weight of children aged 3-5-years.
hTime-weighted average ingestion rates were calculated using the EPA (2002) fish ingestion estimates in order to adjust for the
differences between the age group ranges used for the RTR screening and those presented in the 2002 EPA report.
'Sum of post-cooking food ingestion rates. This estimate is calculated by multiplying the food ingestion rates on previous rows (excluding
soil and water) by (1 -Li)x(1 -L2), where U and L2 are the loss rates from Exhibit_Add A2-25. The rows are then summed to get the total
post-cooking ingestion rate.
'go"1 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
A2-32. As stated in Section 6 of this addendum, EPA recommends using the mean BWfor
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 A2-32. Mean Body Weight Estimates for Adults and Children3
Lifestage (years)
Duration (years)
Mean Body Weight (kg)
Adult" (20-70)
50
80.0
Child < 1c
1
7.83
Child 1-2C
2
12.6
Child 3-5d
3
18.6
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Description of MIRC
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TRIM-Based Tiered Screening Methodology for RTR
Exhibit_Add A2-32. Mean Body Weight Estimates for Adults and Children3
Lifestage (years)
Duration (years)
Mean Body Weight (kg)
Child 6-11e
6
36.0
Child 12-19f
8
64.2
aSources: EPA 1997, 2008a
b EPA-recommended value (EPA 2011 a).
dThese 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.
'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 ofthe 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 A2-33 presents chemical-specific parameter values for input to MIRC for the
screening-level analysis. Values for bioavailability when ingested in soil (6s), mammalian
metabolism factors (MF), correction factors for belowground produce (VGr0otveg), wet deposition
fractions (Fw), air to plant transfer factors (BvAG), root concentration factors (RCF), and soil-
water partition coefficient (Kds) are presented in Exhibit_Add A2-33.
Exhibit_Add A2-33. Chemical-Specific Parameter Values for Input to MIRCa
Parameter
Description
Benzo(a)-
pyrene
Cadmium
Mercuric
chloride
Methyl
mercury
2,3,7,8-
TCDD
Units
Bs
Soil bioavailability
factor for livestock
1
1
1
1
1
unitless
MF
Mammalian
metabolism factor
0.01
1
1
1
1
unitless
VGrootveg
Empirical correction
factor for
belowground
produce, i.e., tuber
or root vegetable, to
account for possible
overestimate ofthe
transfer of chemicals
from the outside to
the inside of bulky
tubers or roots
(based on carrots
and potatoes)
0.01
1
1
0.01
0.01
unitless
Fw
Fraction of wet
deposition that
adheres to plant
surfaces; 0.2 for
anions, 0.6 for
cations and most
organics
0.6
0.6
0.6
0.6
0.6
unitless
Attachment A, Addendum 2
Description of MIRC
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Exhibit_Add A2-33. Chemical-Specific Parameter Values for Input to MIRCa
Parameter
Description
Benzo(a)-
pyrene
Cadmium
Mercuric
chloride
Methyl
mercury
2,3,7,8-
TCDD
Units
BvAG
Air-to-plant
biotransfer factor for
aboveground
produce for vapor-
phase 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 over-
or underestimating risks were not investigated. Finally, Exhibit_Add A2-34 presents biotransfer
factors for each of the chemicals and animal types for which screening threshold emissions
were calculated.
Exhibit_Add A2-34. Chemical and Animal-Type Specific Biotransfer Factor (Ba) Values
for Input to MIRC
([mg chemical/kg WW tissue or dairy] / [mg chemical intake/day] = day/kg WW tissue or dairy)
Chemical
Beef
Dairy
Pork
Eggs
Poultry
Benzo(a)pyrene
3.8E-02
8.0E-03
4.6E-02
1.6E-02
2.8E-02
Cadmium
1.2E-04
6.5E-06
1.9E-04
2.5E-03
1.1E-01
Mercuric chloride
1.1E-04
1.4E-06
3.4E-05
2.4E-02
2.4E-02
Methyl mercury
1.2E-03
1.7E-05
5.1E-06
3.6E-03
3.6E-03
2,3,7,8-TCDD
3.6E-02
7.7E-03
4.4E-02
1.5E-02
2.7E-02
7.4. Screening-Level Parameter Values for Nursing Infant Exposure
MIRC is configured to evaluate risk to nursing infants exposed to dioxins and to methylmercury
(MeHg) in their mother's milk for a family farming and catching fish in the area of maximal air
deposition of chemical. Input values were summarized in Section 6.5 of this 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
Attachment A, Addendum 2
Description of MIRC
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following screening-level assumptions used in that model should bias the results toward health-
protective estimates of risks.
Duration of nursing is a full year and no other foods or liquids are consumed by the
infant; a more "typical" value would be six months.
. Absorption efficiency of dioxin in food or milk by mother and infant are 100 percent; this
assumption might overestimate absorption but probably by no more than 15 percent
(see Section 6.5.2).
The fat content of human milk is assumed to be 4 percent, a value toward the high end
of the reported range of values (1 to 5 percent).
The maternal chemical intake is estimated using upper percentile ingestion rates for the
different homegrown foods (see discussion for Exhibit_Add A2-31); this assumption
might overestimate total ingestion of homegrown foods by a factor of more than 2 (see
Exhibit_Add A2-31).
If the fraction of the maternal body burden of dioxin that is in the body fat compartment is
greater than 90 percent, as suggested by ATSDR (1998), then actual exposures of the
infant may be less than estimated.
There also are parameter values and model assumptions for the lipid-phase breast-milk
pathway for which possible bias is unknown.
The accuracy of the model is unknown; it has not been verified or validated with
empirical data.
Using a half-life of 10 years for dioxins may over- or under-estimate risks.
Finally, there is one assumption that might possibly introduce some bias toward underestimating
risks. The model results are sensitive to the biological half-life of the chemical in the mother
relative to the length of her exposure prior to the lactation period. Using an exposure duration
for the mother equal to the assumed half-life for dioxins, 10 years, may underestimate the
duration of exposure of the mother.
7.4.2. Methyl Mercury
For MeHg, empirical data from a single human study (Fujita and Takabatake 1977) was used in
conjunction with a physiologically based pharmacokinetic (PBPK) model of lactational transfer of
MeHg developed and partially validated by Byczkowski and Lipscomb (2001) to support a very
simple predictive model. Both the human data and the PBPK model indicated that for relatively
low MeHg exposures, the concentration of MeHg in the nursing infant's blood is similar to its
concentration in the mother's blood. The PBPK model suggested in addition that the average
daily dose of MeHg absorbed from milk by the nursing infant (DAIinf) is indistinguishable from the
dose of MeHg absorbed by its mother from her food (DAImat). The data are limited, and the
model includes various assumptions; however, there is no known directional bias in the
estimates.
Attachment A, Addendum 2
Description of MIRC
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8. References
Altman, P.L., and D.S. Dittmer, Eds. 1964. Biology Data Book. Volume 1. Bethesda, MD. 263-
264 (As cited in EPA 1998).
Amin-Zaki, L., S. Elhassani, M.A. Majeed, T.W. Clarkson, R.A. Doherty, M.R. Greenwood, and
T. Giovanoli-Jakubczak. 1976. Perinatal methylmercury poisoning in Iraq. Am. J. Diseases
in Children 130: 1070-1076 (As cited in Byczkowski and Lipscomb 2001).
ATSDR (Agency for Toxic Substances and Disease Registry). 1992. Toxicological profile for
selected PCBs (Arochlor- 1260, 1254, 1248, 1242, 1232, 1221, and 1016). Atlanta, GA: U.S.
Department of Health and Human Services, Public Health Service (As cited in EPA 1998).
ATSDR. 1998. Toxicological profile for chlorinated dibenzo-p-dioxins. Atlanta, GA: U.S.
Department of Health and Human Services, Public Health Service.
Bacci E., M. Cerejeira, C. Gaggi, G. Chemello, D. Calamari, and M. Vighi. 1992. Chlorinated
dioxins: Volatilization from soils and bioconcentration in plant leaves. Bull. Environ. Contam.
Toxicol. 48: 401-408.
Baes, C.F., R.D. Sharp, A.L. Sjoreen, and R.W. Shor. 1984. Review and analysis of parameters
and assessing transport of environmentally released radionuclides through agriculture.
ORNL-5786. Oak Ridge National Laboratory. Oak Ridge, Tennessee. September.
Bates, M.N., D.S. Hannah, S.J. Buckland, J.A. Taucher, and T. van Mannen. 1994. Chlorinated
organic contaminants in breast milk of New Zealand women. Environmental Health
Perspectives 102(Supplement 1): 211-217.
Belcher, G.D., and C.C. Travis. 1989. Modeling support for the RURA and municipal waste
combustion projects: Final report on sensitivity and uncertainty analysis for the terrestrial
food chain model. Interagency Agreement No. 1824-A020-A1, Office of Risk Analysis,
Health and Safety Research Division, Oak Ridge National Laboratory. Oak Ridge,
Tennessee. October.
Boone, F.W., Y.C. Ng, and J.M. Palm. 1981. Terrestrial pathways of radionuclide particulates.
Health Physics 41:735-747.
Briggs, G.G., R.H. Bromilow, and A.A. Evans. 1982. Relationships between lipophilicity and root
uptake and translocation of non-ionized chemicals by barley. Pesticide Science 13: 495-504
(As cited in EPA 2005a, Appendix A-2).
Burger J. 2002. Daily consumption of wild fish and game: Exposures of high end
recreationalists. International Journal of Environmental Health Research 12:343-354.
Butte, N.F., C. Garza, E.O. Smith, and B.L. Nichols. 1984. Human milk intake and growth in
exclusively breast-fed infants. The Journal of Pediatrics 104(2): 187-195.
Byczkowski, J.Z., and J.C. Lipscomb. 2001. Physiologically based pharmacokinetic modeling of
the lactational transfer of methylmercury. Risk Analysis 21(5): 869-882.
Chamberlain, A.C. 1970. Interception and retention of radioactive aerosols by vegetation.
Atmospheric Environment 4: 57-78.
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Description of MIRC
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Clewell, H.J, J.M. Gearhart, R.P. Gentry, T.R Covington, C.B. VanLandingham, C.B. Crump,
and A.M. Shipp. 1999. Evaluation of the uncertainty in an oral reference dose for
methylmercury due to interindividual variability in pharmacokinetics. Risk Analysis 19: 547-
558 (As cited in Byczkowski and Lipscomb 2001).
Conley, C.L. 1974. The blood. Medical Physiology 13(2). V.B. Moutcasle, Ed. 1997. The C.V.
Mosby Company, St. Louis, MO. 1027-1046 (As cited in EPA 1998).
Ensminger, M.E. 1980. Poultry Science. Interstate Printers and Publishers, Inc. Danville, Illinois.
Fries, G. F. 1982. Potential polychlorinated biphenyl residues in animal products from
application of contaminated sewage sludge to land. J. Environ. Qua!. 11:14.
Fries, G.F. 1994. Personal communication between G.F. Fries, U.S. Department of Agriculture,
and Glenn Rice and Jennifer Windholtz, U.S. Environmental Protection Agency, Office of
Research and Development. Agricultural Research Service. March.
Fujita, M., and E. Takabatake. 1977. Mercury levels in human maternal and neonatal blood, hair
and milk. Bull. Environ. Contam. Toxicol. 18(2): 205-209.
Gearhart, J.M., H.J. Clewell III, K.S. Crump, A.M. Shipp, and A. Silvers. 1995. Pharmacokinetic
dose estimates of mercury in children and dose-response curves of performance tests in a
large epidemiological study. Water, Air, Soil Pollut. 80: 49-58 (As cited in Byczkowski and
Lipscomb 2001).
Gearhart, J., T. Covington, and H. Clewell III. 1996. Application of a physiologically based
pharmacokinetic model for MeHg in a dose reconstruction of the Iraqi accidental exposures.
Paper presented at the Fourth International Conference on Mercury as a Global Pollutant,
Congress Centre, Hamburg, Germany; August (As cited in Byczkowski and Lipscomb 2001).
Greenwood, M.R., T.W. Clarkson, R.A. Doherty, A.H. Gates, L. Amin-Zaki, S. Elhassani, and
M.A. Majeed. 1978. Blood clearance half-times in lactating and nonlactating members of a
population exposed to mercury. Environ. Res. 16: 48-54.
Harrison, K.A. 1967. Blood volume changes in severe anemia in pregnancy. Lancet 1(7480):20-
25.
Hofelt, C.S., M. Honeycutt, J.T. McCoy, and L.C. Haws. 2001. Development of a metabolism
factor for polycyclic aromatic hydrocarbons for use in multipathway risk assessments of
hazardous waste combustion facilities. Reg. Toxicol. Pharmacol. 33:60-65.
Hoffman, F.O., K.M. Thiessen, M.L. Frank, and B.G. Blaylock. 1992. Quantification of the
interception and initial retention of radioactive contaminants deposited on pasture grass by
simulated rain. Atmospheric Environ. 26a(18): 3313-3321.
Hollins, J.G., R.F. Willes, F.R. Bryce, S.M. Charbonneau, and I.C. Munro. 1975. The whole
body retention and tissue distribution of Hg methylmercury in adult cats. Toxicol. App.
Pharmacol. 33: 438-449.
Hong, C.S., J. Xiao, A.C. Casey, B. Bush, E.F. Fitzgerald, and S.A. Hwang. 1994. Mono-ortho
and non-ortho-substituted polychlorinated biphenyls in human milk from Mohawk and control
women: Effects of maternal factors and previous lactation. Arch. Environ. Contam. Toxicol.
27(3): 431-437.
Attachment A, Addendum 2
Description of MIRC
2-105
December 2013
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TRIM-Based Tiered Screening Methodology for RTR
Jensen, A.A. 1987. Polychlorinated biphenyls (PCBs), polychlorodibenzo-p-dioxins (PCDDs)
and polychlorodibenzofurans (PCDFs) in human milk, blood and adipose tissue. Sci. Total
Environ. 64(3): 259-293.
Kahn, H., and K. Stralka. 2008. Estimated daily average per capita water ingestion by child and
adult age categories based on USDA's 1994-96 and 1998 continuing survey of food intakes
individuals. J. Expo. Anal. Environ. Epidemiol. 1-9.
Kershaw, T.G., T.W. Clarkson , and P.H. Dhahir. 1980. The relationship between blood levels
and dose of methylmercury in man. Arch. Environ. Health 35(1): 28-36.
Lorber, M. 1995. Development of an air-to-plant vapor phase transfer for dioxins and furans.
Presented at the 15th International Symposium on Chlorinated Dioxins and Related
Compounds. August 21-25, 1995 in Edmonton, Canada. Abstract in Organohalogen
Compounds 24:179-186.
Lorber, M., and P. Pinsky. 2000. An evaluation of three empirical air-to-leaf models for
polychlorinated dibenzo-p-dioxins and dibenzofurans. Chemosphere 41(6):931-41.
Maxwell, N.I., and D.E. Burmaster. 1993. A simulation model to estimate a distribution of lipid
intake from breast milk during the first year of life. Journal of Exposure Analysis and
Environmental Epidemiology 3(4): 383-406.
McLachlan, M.S. 1993. Digestive tract absorption of polychlorinated dibenzo-p-dioxins,
dibenzofurans and biphenyls in a nursing infant. Toxicology and Applied Pharmacology
123(1): 68-72.
Miller, C.W., and F.O. Hoffman. 1983. An examination of the environmental half-time for
radionuclides deposited on vegetation. Health Physics 45(3): 731-744.
Morgan, J.N., M.R. Berry, and R.L. Graves. 1997. Effects of Commonly Used Cooking Practices
on Total Mercury Concentration in Fish and Their Impact on Exposure Assessments.
Journal of Exposure Analysis and Environmental Epidemiology 7(1): 119-133.
NAS (National Academy of Sciences). 1987. Predicting Feed intake of Food-Producing Animals.
National Research Council, Committee on Animal Nutrition, Washington, D.C.
NAS. 1991. Nutrition During Lactation. National Academies Press. Washington, DC.
NC DEHNR (North Carolina Department of Health, Environment, and Natural Resources). 1997.
North Carolina Protocol for Performing Indirect Exposure Risk Assessments for Hazardous
Waste Combustion Units. January.
Portier K., J. Tolson, and S. Roberts. 2007. Body weight distributions for risk assessment. Risk
Anal. 27(1): 11-26.
Reinert, RE; Stewart, D; Seagran, HL. 1972. Effects of dressing and cooking on DDT
concentrations in certain fish from Lake Michigan. Journal of Fisheries Research Board of
Canada. 29(5): 525-529.
RTI. 2005. Methodology for predicting cattle biotransfer factors. Prepared for U.S.
Environmental Protection Agency (EPA) Office of Solid Waste. EPA Contract No. 68-W-03-
042. September. Available at: http://www.epa.gov/waste/hazard/tsd/td/combust/
finalmact/ssra/btfreportfull05.pdf.
Attachment A, Addendum 2 2-106 December 2013
Description of MIRC
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TRIM-Based Tiered Screening Methodology for RTR
Schecter, A., P. Furst, C. Furst, O. Papke, M. Ball, J. Ryan, H. Cau, L. Dai, H. Quynh, H.Q.
Cuong, N. Phuong, P. Phiet, A. Beim, J. Constable, J. Startin, M. Samedy, and Y. Seng.
1994. Chlorinated dioxins and dibenzofurans in human tissue from general populations: A
selective review. Environmental Health Perspectives 102(Supplement 1): 159-171.
Sherlock, J., D. Hislop, G. Newton, G. Topping, and K. Whittle. 1984. Elevation of mercury in
human blood from controlled ingestion of methylmercury in fish. Human Toxicology 3: 117-
131 (As cited in USFDA 2009).
Shilling, F., White, A. Lippert, L, and M. Lubell. 2010. Contaminated fish consumption in
California's Central Valley Delta. Environmental Research 110: 334-344.
Shor, R.W., C.F. Baes, and R.D. Sharp. 1982. Agricultural production in the United States by
county: A compilation of information from the 1974 census of agriculture for use in terrestrial
food-chain transport and assessment models. Oak Ridge National Laboratory Publication.
ORNL-5786
Sim, M.R. and J.J. McNeil. 1992. Monitoring chemical exposure using breast milk: A
methodological review. American Journal of Epidemiology 136(1): 1-11.
Smith, A.H. 1987. Infant exposure assessment for breast milk dioxins and furans derived from
waste incineration emissions. Risk Analysis 7:347-353.
Stanek, E.J., E.J. Calabrese, R. Barnes, P. Pekow. 1997. Soil ingestion in adults - results of a
second pilot study. Toxicol. Environ. Safety 36:249-257.
Steinbeck, A.W. 1954. Plasma and blood volumes of normal Australian females. Australian
Journal of Experimental Biology and Medical Science 32(1): 95-9.
Stephens, R.D., M. Petreas, and G.H. Hayward. 1995. Biotransfer and bioaccumulation of
dioxins and furans from soil: Chickens as a model for foraging animals. Science Total
Environment 175: 253-273. July 20.
Sullivan, M.J., S.R. Custance, and C.F. Miller. 1991. Infant exposure to dioxin in mother's milk
resulting from maternal ingestion of contaminated fish. Chemosphere 23(8-10): 1387-1396.
Thomann, R.V. 1989. Bioaccumulation model of organic-chemical distribution in aquatic food-
chains. Environ. Sci. Technol. 23(6): 699-707.
Travis, C.C., and A.D. Arms. 1988. Bioconcentration of organics in beef, milk, and vegetation.
Environ. Sci. Technol. 22:271-274.
Travis, C.C., H.A. Hattemer-Frey and A.A. Arms. 1988. Relationship between dietary intake of
organic chemicals and their concentrations in human adipose tissue and breast milk. Arch.
Environ. Contam. Toxicol. 17:473-478.
Ueland, K. 1976. Maternal cardiovascular dynamics. VII. Maternal cardiovascular dynamics.
Intrapartum blood volume changes. Am. J. Obstetrics Gynecol. 126(6): 671-677.
USDA (U.S. Department of Agriculture). 1992. Changes in Food Consumption and Expenditures
in American Households during the 1980's. USDA, Washington, D.C. Statistical Bulletin o.
849. (As cited in EPA 1997)
Attachment A, Addendum 2
Description of MIRC
2-107
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TRIM-Based Tiered Screening Methodology for RTR
USDA. 1993. Food and Nutrient Intakes by Individuals in the United States, 1 Day, 1987-88.
Nationwide Food Consumption Survey 1987-88, NFCS Report No. 87-1-1. (As cited in EPA
1997)
USDA. 1994a. Food Consumption and Dietary Levels of Households in the United States, 1987-
88. Agricultural Research Service, Report No. 87-H-1. (As cited in EPA 1997)
USDA. 1994b. Vegetables 1993 Summary. National Agricultural Statistics Service, Agricultural
Statistics Board. Washington, D.C. Vg 1-2 (94). Jan.
USDA. 1994c. Noncitrus Fruits and Nuts 1993 Summary. National Agricultural Statistics
Service, Agricultural Statistics Board, Washington, D.C. Fr Nt 1-3 (94).
USDA. 2000. 1994-96, 1998 Continuing Survey of Food Intakes by Individuals (CSFII). CD-
ROM. Agricultural Research Service, Beltsville Human Nutrition Research Center, Beltsville,
MD. Available from the National Technical Information Service, Springfield, VA, Accession
Number PB-2000500027. (As cited in EPA 2008a, Chapter 14)
EPA (US. Environmental Protection Agency). 1990. Interim Final Methodology for Assessing
Health Risks Associated with Indirect Exposure to Combustor Emissions. Environmental
Criteria and Assessment Office. ORD.EPA-600-90-003. January.
EPA. 1992. Technical Support Document for the Land Application of Sewage Sludge: Volumes I
and II. EPA 822/R-93-001a. Office of Water. Washington, D.C.
EPA. 1994a. Revised Draft Guidance for Performing Screening Level Risk Analysis at
Combustion Facilities Burning Hazardous Wastes. Attachment C, Draft Exposure
Assessment Guidance for RCRA Hazardous Waste Combustion Facilities. Office of
Emergency and Remedial Response. Office of Solid Waste. December 14.
EPA. 1994b. Estimating Exposure to Dioxin-Like Compounds. Volume II: Properties, Sources,
Occurrence, and Background Exposures. External Review Draft. Office of Research and
Development. Washington, DC. EPA/600/6-88/005Cc. June.
EPA. 1994c. Estimating Exposure to Dioxin-Like Compounds. External Review Draft. Office of
Research and Development, Washington, D.C. EPA/600/6-88/005Cb. June. Available at:
http://oaspub.epa. gov/eims/eimscomm.getfile?p_download_id=438673.
EPA. 1995a. Review Draft Development of Human Health-Based and Ecologically-Based Exit
Criteria for the Hazardous Waste Identification Project. Volumes I and II. Office of Solid
Waste. March 3.
EPA. 1995b. Memorandum Regarding Further Studies for Modeling the Indirect Exposure
Impacts from Combustor Emissions. From Mathew Lorber, Exposure Assessment Group,
and Glenn Rice, Indirect Exposure Team, Environmental Criteria and Assessment Office.
Washington, D.C. January 20.
EPA. 1995c. Further Issues for Modeling the Indirect Exposure Impacts from Combustor
Emissions. Office of Research and Development. Washington, D.C. January 20.
EPA. 1995d. Waste Technologies Industries Screening Human Health Risk Assessment
(SHHRA): Evaluation of Potential Risk from Exposure to Routine Operating Emissions.
Volume V. External Review Draft. U.S. EPA Region 5, Chicago, Illinois.
Attachment A, Addendum 2
Description of MIRC
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TRIM-Based Tiered Screening Methodology for RTR
EPA. 1996. Soil Screening Guidance: User's Guide. Office of Solid Waste and Emergency
Response, Washington D.C. EPA/540/R-96/018, April 1996.
EPA. 1997a. Exposure Factors Handbook. Volumes I, II, and III. Office of Research and
Development, Washington, D.C. EPA-600-P-95-002Fa,b,c. August. Available at:
http://www.epa.gov/nceawww1/efh/.
EPA. 1997b. Mercury Study Report to Congress. Volume III: Fate and Transport of Mercury in
the Environment. Office of Air Quality Planning and Standards and Office of Research and
Development. EPA-452/R-97-005. December.
EPA. 1997c. Parameter Guidance Document. National Center for Environmental Assessment,
NCEA-0238.
EPA. 1998. Methodology for Assessing Health Risks Associated with Multiple Pathways of
Exposure to Combustor Emissions. National Center for Environmental Assessment,
Cincinnati, OH. EPA-600-R-98-137. Available at:
http://cfpub.epa. gov/ncea/cfm/recordisplay.cfm?deid=55525.
EPA. 1999. 1999 National-Scale Air Toxics Assessment Results; Approach for Modeling POM.
Available at: http://www.epa.gov/ttn/atw/nata1999/nsata99.html.
EPA. 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
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. 2004. 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-R-
Attachment A, Addendum 2
Description of MIRC
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00-001. October. Available at:
http://www.epa.gov/waterscience/criteria/drinking/percapita/2004.pdf
EPA. 2004c. Risk Assessment Guidance for Superfund. Volume I: Human Health Evaluation
Manual (Part E, Supplemental Guidance for Dermal Risk Assessment), Final. Office of
Superfund Remediation and Technology Innovation, Washington, D.C. EPA/540/R/99/005;
OSWER 9285.7-02EP; NTIS PB99-963312. July.
EPA. 2005a. Human Health Risk Assessment Protocol for Hazardous Waste Combustion
Facilities. Office of Solid Waste and Emergency Response, Washington, DC. EPA-530-R-
05-006. September. Available at: http://www.epa.gov/osw/hazard/tsd/td/combust/risk.htm.
EPA. 2005b. Guidance on Selecting Age Groups for Monitoring and Assessing Childhood
Exposures to Environmental Contaminants. Risk Assessment Forum. Washington, DC.
November. EPA/630/P-03/003F. Available at:
http://cfpub.epa. gov/ncea/cfm/recordisplay.cfm?deid=146583.
EPA. 2005c. Guidelines for Carcinogen Risk Assessment. Risk Assessment Forum,
Washington, DC. EPA/630/P-03/001F. March. Available from:
http://www.epa.gov/IRIS/cancer032505-final.pdf.
EPA. 2005d. Supplemental Guidance for Assessing Susceptibility from Early-Life Exposure to
Carcinogens. Risk Assessment Forum: Washington, D.C. EPA-630/R-03-003F. March.
Available at: http://www.epa.gov/ttn/atw/childrens_supplement_final.pdf.
EPA. 2005e. Analysis of Total Food Intake and Composition of Individual's Diet Based on the
U.S. Department of Agriculture's 1994-96, 1998 Continuing Survey of Food Intakes By
Individuals (CSFII) (Final). Office of Research and Development, National Center for
Environmental Assessment, Washington, D.C. EPA/600/R-05/062F. Available at:
http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=132173.
EPA. 2007a. Prioritized Chronic Dose-Response Values for Screening Risk Assessments
(Table 1). Office of Air Quality Planning and Standards; June 12, 2007. Available at:
http://www.epa.gov/ttn/atw/toxsource/summary.html.
EPA. 2007b. Toxicological Review of 1,1,1-Trichloroethane (CAS No. 71-55-6) In Support of
Summary Information on the Integrated Risk Information System (IRIS). Office of Research
and Development, Washington, DC. EPA/635/R-03/006. August. Available at:
http://www.epa.gov/iris
EPA. 2008a. Child-Specific Exposure Factors Handbook. Office of Research and Development,
Washington, D.C. EPA/600/R-06/096F. September. Available at:
http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=199243 .
EPA. 2008b. Draft Report on EPA OAQPS Risk and Technology Review Methodologies: For
Review by the EPA Science Advisory Board; Case Studies - MACT I Petroleum Refining
Sources, Portland Cement Manufacturing. Office of Air Quality Planning and Standards,
Office of Air and Radiation, Research Triangle Park, NC. July 14, 2008.
EPA. 2011a. Exposure Factors Handbook: 2011 Edition. Office of Research and Development,
Washington, D.C. EPA/600/R-090/052F. September. Available at:
http://cfpub.epa. qov/ncea/risk/recordisplav.cfm?deid=236252.
Attachment A, Addendum 2
Description of MIRC
2-110
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TRIM-Based Tiered Screening Methodology for RTR
EPA. 2011b. Revised Technical Support Document: National-Scale Assessment of Mercury
Risk to Populations with High Consumption of Self-caught Freshwater Fish; In Support of
the Appropriate and Necessary Finding for Coal- and Oil-Fired Electric Generating Units.
Office of Air Quality Planning and Standards, Research Triangle Park, NC. EPA-452/R-11-
009. December.
Van den Berg, M., L.S. Birnbaum, M. Denison, M. De vito, W. Farlans, M. Feeley, H. Fiedler, H.
Hakansson, A. Hanberg, L.. Haws, M. Rose, S. Safe, D. Schrenk, C. Tohyama, A. Tritscher,
J. tuomisto, M. Tysklind, N. Walker, and R.E. Peterson. 2006. The 2005 World Health
Organization reevaluation of human and mammalian toxic equivalency factors for dioxins
and dioxin-like compounds. Toxicol Sci. 93(2): 223-41.
Zabik, ME; Zabik, MJ. 1995. Tetra-chlorodibenzo-p-dioxin residue reduction by
cooking/processing of fish fillets harvested from the Great Lakes. Bulletin of Environmental
Contamination and Toxicology. 55:264-269.
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Addendum 3. Dermal Risk Screening
Attachment A, Addendum 3
Dermal Risk Screening
3-1
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TRIM-Based Tiered Screening Methodology for RTR
CONTENTS, ADDENDUM 3
Addendum 3. Dermal Risk Screening 3-1
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
Attachment A, Addendum 3
Dermal Risk Screening
3-3
December 2013
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TRIM-Based Tiered Screening Methodology for RTR
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
Attachment A, Addendum 3
Dermal Risk Screening
3-4
December 2013
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TRIM-Based Tiered Screening Methodology for RTR
Non-inhalation exposure to PB-HAPs can occur by way of the dermal pathway through contact
with PB-HAP-contaminated soil and water. However, dermal absorption of chemicals that are
originally airborne is generally a relatively minor pathway of exposure compared to other
exposure pathways (EPA 2006, Cal/EPA 2000). This section demonstrates that for the
conservative 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 (EPA 2004).
1. Hazard Identification and Dose Response Assessment
To assess the potential contribution of dermal exposure to non-inhalation exposure, we
evaluated the potential for cancer and chronic non-cancer effects for the four PB-HAPs currently
assessed in the multipathway screening evaluation for RTR: cadmium, divalent mercury,
2,3,7,8-TCDD, and benzo(a)pyrene. EPA has not developed carcinogenic potency slope
factors (CSFs) and non-cancer reference doses (RfDs) specifically for evaluating potential
human health concerns associated with dermal exposure to PB-HAPs. Instead, dermal toxicity
values can be derived from oral toxicity values via route-to-route extrapolation by adjusting for
gastrointestinal (Gl) absorption. EPA recommends making this adjustment only when Gl
absorption of the chemical is significantly less than 100% (i.e., less than 50 percent).
Otherwise, a default value of complete (100 percent) oral absorption is assumed, and no
adjustment is made (EPA 2004).
The absorbed cancer slope factor (CSFAbs) is based on the oral cancer slope factor (CSF0) and
the fraction of the contaminant absorbed in the gastrointestinal track (ABSGi), as follows:
CSFaDQ = CSF°
ABS absgi
where:
CSFAbs = Absorbed slope factor (mg/kg-day)1
CSF0 = 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 (RFD0) and the
fraction of the contaminant absorbed in the gastrointestinal tract (ABSGi), as shown below.
RfDABs = RfDo x abSGi
where:
RfDABs = Absorbed reference dose (mg/kg-day)
RfD0 = Oral reference dose (mg/kg-day)
ABSGi = Fraction of chemical absorbed in gastrointestinal tract (unitless)
Attachment A, Addendum 3
Dermal Risk Screening
3-5
December 2013
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TRIM-Based Tiered Screening Methodology for RTR
The Gl 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 Gl Tract (ABSGi)
(unitless)
Absorbed Cancer
Slope Factor (CSFabs)"
(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, Gl 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.
DAD _ Prevent * EV X ED X EF X SA
BWxAT
where:
_ Absorbed dose per event; chemical-specific; equation for DAevent also differs
event depending on water or soil contact (mg/cm2-event)
EV = Event frequency (events/day)
ED = Exposure duration (years)
Attachment A, Addendum 3 3-6 December 2013
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TRIM-Based Tiered Screening Methodology for RTR
EF = Exposure frequency (days/year)
S/4 = 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: DAevent =Cwx2xFAxKp ^6xre^x W
Water - Inorganic Chemicals: DAevent =CmxKpx tevent
Soil - All Chemicals: DAevent =Csx AF x ABS x CF
where:
DAevent = Absorbed dose per event (mg/cm2-event)
c ?
-w = Chemical concentration in water (mg/cm ) or soil (mg/kg)
C/c
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)
Tevent = Chemical-specific la9 time per event (hr/event)
tevent = Receptor-specific event duration (hr/event)
AF = Receptor- and activity-specific adherence factor of soil to skin (mg/cm2-event)
ABS = Chemical-specific dermal absorption fraction (unitless)
CF = Conversion factor (10"6 kg/mg)
2.2. Exposure Factors and Assumptions
The exposure parameters included in this assessment and their default and other value options
are summarized in this subsection. Default values were selected to result in a highly
conservative estimate of exposure (i.e., exposures are likely overestimated). Parameter values
were primarily obtained or estimated from RAGS Part E (EPA 2004) and the Child-Specific
Exposure Factors Handbook (CSEFH, EPA 2008). Receptor-and scenario-specific exposure
assumptions are discussed first, and a discussion of chemical-specific parameters values
follows. Estimated water and soil exposure concentrations are presented at the end of this
subsection.
Attachment A, Addendum 3
Dermal Risk Screening
3-7
December 2013
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TRIM-Based Tiered Screening Methodology for RTR
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 Group3
Surface Area for
Surface Area for
(years)
Water Exposure (cm2)
Soil Exposure (cm2)
Adult 20-70
18,150s
6,878h
Child <1b
3,992
1,772
Child 1-2C
5,700
2,405
Child 3-5d
7,600
3,354
Child 6-11e
10,800
4,501
Child 12-19r
17,150
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.
Represents 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.
'Represents a time-weighted average for age groups 11 to <16 years and 16 to <21 years. Note that
estimated values include 11 -year-olds and individuals through age 20, which contributes to uncertainty in the
estimates for 12 to 19 years.
Represents the average total surface area of adults from Table C-1 of RAGS Part E.
Represents the average surface area of adults for head, forearms, hands, lower legs, and feet from Table
C-1 of RAGS Part E.
2.4. Scenario-Specific Parameters
Exhibit_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.
Attachment A, Addendum 3
Dermal Risk Screening
3-8
December 2013
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TRIM-Based Tiered Screening Methodology for RTR
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
from RAGS Part E, Exhibit 3-2
Adult
0.58
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)
x 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 (unfilled 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 soil-
bound chemicals is based on the use of a dermal absorption fraction (ABS), which is a measure
of how much of a chemical the skin absorbs through contact with soil.
Attachment A, Addendum 3
Dermal Risk Screening
3-9
December 2013
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TRIM-Based Tiered Screening Methodology for RTR
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 B-
4 (inorganics)
Fraction absorbed water
(FA) (unitless)
NA
NA
0.5
1.00
Values from RAGS Part E,
Exhibits B-3; only used for
organic chemicals
Lag time per event (event)
(hr/event)
NA
NA
6.82
2.69
Values from RAGS Part E,
Exhibits B-3; only used for
organic chemicals
Dermal absorption fraction
(ABS) from soil (unitless)
0.001
0.045a
0.03
0.13
Values from RAGS Part E,
Exhibit 3-4, unless otherwise
noted
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 x 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).
Attachment A, Addendum 3
Dermal Risk Screening
3-10
December 2013
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TRIM-Based Tiered Screening Methodology for RTR
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.
Attachment A, Addendum 3
Dermal Risk Screening
3-11
December 2013
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TRIM-Based Tiered Screening Methodology for RTR
Exhibit_Add A3-5. Summary of Dermal Non-Cancer Hazards
2,3,7,8-TCDD
Child
6-11
Child Adult
12-19 20-70
Receptor
Cadmium
Child
6-11
Child
12-19
Adult
20-70
Receptor
Divalent Mercury
(g 0.003
Child Child Child Child Child Adult
<1 1-2 3-5 6-11 12-19 20-70
Receptor
Attachment A, Addendum 3
Dermal Risk Screening
3-12
December 2013
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TRIM-Based Tiered Screening Methodology for RTR
Exhibit_Add A3-6. Summary of Dermal Cancer Risks
PB-HAP
Dermal Lifetime
Cancer Risk
Magnitude of Difference3
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
Represents the magnitude of difference between the estimated dermal risk and
the potential ingestion risk associated with the screening scenario.
Based on these results and taking into consideration the extremely conservative nature of the
dermal exposure calculations, it was assumed that it is not necessary to incorporate dermal
exposures in calculating multipathway screening threshold levels. Specifically, the daily
exposure durations of 0.58 hour for adults and 1 hour for children used to calculate dermal
exposure from water are highly conservative and assume that the individual is bathing in
surface water taken directly from a contaminated lake or is swimming in the lake for 350 days of
the year. The exposure frequency of 350 days and corresponding skin surface area available
for contact with contaminated soils (i.e., head, hands, arms, legs, and feet) likely also grossly
overestimates dermal exposure to soil.
Attachment A, Addendum 3
Dermal Risk Screening
3-13
December 2013
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TRIM-Based Tiered Screening Methodology for RTR
5. References
Cal/EPA (California Environmental Protection Agency) Office of Environmental Health Hazard
Assessment (OEHHA). 2000. Air Toxics Hot Spots Program Risk Assessment Guidelines;
Part IV, Exposure Assessment and Stochastic Analysis Technical Support Document.
Section 6, Dermal Exposure Assessment. September. Available at:
http://www.oehha.ca.gov/air/hot_spots/pdf/chap6.pdf.
Hrudey, S.E., W. Chen, and C.G. Roussex, 1996. Bioavailability in environmental risk
assessment. CRC Press, Inc, Lewis publishers.
EPA (Environmental Protection Agency). 2004. Risk Assessment Guidance for Superfund
Volume 1: Human Health Evaluation Manual (Part E, Supplemental Guidance for Dermal
Risk Assessment). EPA/540/R99/005. Available at:
http://www.epa.gov/oswer/riskassessment/ragse/index.htm
EPA. 2006. Risk and Technology Review (RTR) Assessment Plan. Office of Air and Radiation.
November 20. Available at:
http://www.epa.gov/sab/panels/consul_risk_and_tech_assessment_plan.htm.
EPA. 2008. Child-Specific Exposure Factors Handbook. Office of Research and Development,
Washington, D.C. EPA/600/R-06/096F. September. Available at:
http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=199243 .
Attachment A, Addendum 3
Dermal Risk Screening
3-14
December 2013
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TRIM-Based Tiered Screening Methodology for RTR
Attachment B. Tier 2 Screening Methodology
Attachment B - Tier 2
B-1
December 2013
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[This page intentionally left blank.]
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TRIM-Based Tiered Screening Methodology for RTR
CONTENTS, ATTACHMENT B
Attachment B. Tier 2 Screening Methodology B-1
B.1 Overview of Approach B-5
B.2 Estimation of Adjustment Factors for Selected Site-Specific Parameters B-8
B.2.1 Selection Values for Variables of Interest B-9
B.2.2 Estimation of Adjustment Factors B-12
B.2.2.1 General Approach B-12
B.2.2.2 Incorporation of the Risk Equivalency Approach B-14
B.3 Preparing National Databases of Lake and Meteorological Data B-16
B.3.1 Processing Lake Data for Tier 2 Analysis B-16
B.3.2 Processing Meteorological Data for Tier 2 Analysis B-20
B.3.2.1 Sources of Meteorological Data B-20
B.3.2.2 Coverage of Meteorological Stations Compared with Facility
Locations B-20
B.3.2.3 Data Processing B-21
B.4 Implementation of Tier 2 Analysis B-23
B.4.1 Facility List for Tier 2 Screen (Step 3) B-25
B.4.2 Facility/Lake Distance Table (Step 4) B-26
B.4.3 Matching Facilities to Meteorology Data (Step 5) B-27
B.4.4 Assembling Threshold Adjustment Factors (Step 6) B-28
B.4.5 Assembling Results (Step 7) B-30
B.5 References B-34
Addendum 1. Summary of TRIM.FaTE Parameters Considered for
Inclusion in Tier 2 Analysis 1
Addendum 2. Analysis of Lake Size and Sustainable Fish Population 2-1
Attachment B - Tier 2 B-3 December 2013
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TRIM-Based Tiered Screening Methodology for RTR
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 Lake3 B-13
Exhibit_Att B-4. Maximum Fish Ingestion Rate (g/day) Associated with
Sustainable Fishing3 B-19
Exhibit_Att B-5. The Locations of Meteorological Stations and Point Source
Facilities3 B-22
Exhibit_Att B-6. Example of the Dashboard To Conduct the Tier 2 Analysis B-24
Exhibit_Att B-7. Example of Global Inputs Used in the Tier 2 Analysis3 B-25
Exhibit_Att B-8. Example of the Facility Input Data Required To Conduct the
Tier 2 Analysis3 B-26
Exhibit_Att B-9. Example of the Lake Distance Data Required To Conduct the
Tier 2 Analysis3 B-27
Exhibit_Att B-10. Example Results of the Meteorological Station Matching
Required To Conduct the Tier 2 Analysis3 B-28
Exhibit_Att B-11. Example Results of the Octant Analysis Required To Conduct
the Tier 2 Analysis (shown in 2 pieces due to size)3 B-29
Exhibit_Att B-12. Example of the TRIM.FaTE Matrix Results Required To
Conduct the Tier 2 Analysis B-30
Exhibit_Att B-13. Example of Summary Output Table Created by the Tier 2 Tool3 B-31
Exhibit_Att B-14. Example of Detailed Output Table Created by the Tier 2 Tool3 B-32
Exhibit_Att B-15. Example of Individual Output Table Created by the Tier 2 Tool B-33
Attachment B - Tier 2 B-4 December 2013
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TRIM-Based Tiered Screening Methodology for RTR
B.1 Overview of Approach
The Tier 1 screening scenario is, by design, generic and health-protective. It was constructed to
allow for quick application to a large number of facilities in a source category with a minimal
chance of returning false negatives for risk. Based on screening 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 adjustments, a
balance was struck between: 1) the degree of impact on the potential risk estimate; 2) the ease
of implementation in TRIM.FaTE; and 3) the ease of obtaining site-specific values on a facility-
by-facility 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.
Tier 2 screening assessments are performed for those facilities that fail the Tier 1 screening
assessment. The overall implementation of Tier 2 is shown in Exhibit_Att B-1. The starting point
(shown in green) is the ratio of the facility emission rate for the PB-HAP of concern to the Tier 1
threshold for that PB-HAP. Next, the facility-specific estimates of the Tier 2 meteorological and
lake-location parameters listed above must be gathered for each facility (shown in red). Then,
the associated TRIM.FaTE and MIRC estimates of risk must be estimated (shown in orange).
Because of the volume of facilities that need to be evaluated in Tier 2, the implementation
focuses on estimating refined risk using pre-calculated databases (discussed below) rather than
gathering the input data and performing TRIM.FaTE and MIRC modeling speartely for each
facility.
23
The 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.
Attachment B - Tier 2 B-5 December 2013
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TRIM-Based Tiered Screening Methodology for RTR
Exhibit_Att B-1. Basic Implementation Process for Implementing Tier 2
First, databases of the relevant U.S. meteorological arid lake data were created that can be
accessed during a Tier 2 evaluation (shown in red in the figure). These meteorological and lake
data are discussed in more detail in Section B.4. The meteorological database includes annual-
average summary statistics on wind direction, wind speed, and precipitation for more than 800
surface stations paired with their closest upper-air stations located throughout the United States.
These surface data cover year 2011 and are the sameAERMOD-ready data used by EPA
OAQPS for RTR inhalation modeling. As discussed below, in most cases the 2011 precipitation
data were not used, and instead, the 30-year average annual precipitation data for each station
were used. The database of lakes, available from ESRI® and based on U.S. Geological Survey
data, includes information on the location, size, use or type designation, and name (if available)
of all lakes in the United States. To focus on lakes that can support angling of upper trophic
level fish, lakes used for disposal, evaporation, or treatment were excluded, and only lakes
greater than 100 acres in area are included. Lakes larger than 100,000 acres in area are not
included because the sizes of their watersheds and the complexity of their lake dynamics are
not feasible to model with the TRIM.FaTE modeling system.
In parallel to the meterological and lake data collection, a series of TRIM.FaTE simulations was
performed that systematically varied the values for four of the five selected fate and transport
variables (shown in orange in the figure, consisting of lake location, wind speed, precipitation
rate, and mixing height). Wind direction affects only whether the chemical mass advects toward
the farm and lake, so the effect of site-specific wind directions can be evaluated outside
TRIM.FaTE simulations. These simulations do not simulate specific facilities; instead, four or
five alternative values for each of the four variables were selected using statistics on U.S.
meteorological data or professional judgment to capture the expected range in the facility data.
TRIM.FaTE simulations were performed for every possible combination of these variable values
to enable the estimation of appropriate site-specific threshold adjustment factors for scenarios
with the corresponding characteristics. Based on the TRIM.FaTE results of these simulations
(and the subsequent exposure and risk characterization, conducted using MIRC), a matrix of
Attachment B - Tier 2
B-6
December 2013
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TRIM-Based Tiered Screening Methodology for RTR
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.
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:
Tier 2 Ratio = Tier 1 Ratio + Tier 2 Adjustment Factor
Matrices of threshold adjustment factors from the TRIM.FaTE and MIRC simulations were
separately developed for the four PB-HAPs that currently have screening emission thresholds in
the Tier 1 process: benzo(a)pyrene (BaP, representative of PAHs), cadmium, divalent mercury
and 2,3,7,8-Tetrachlorodibenzo-p-Dioxin (TCDD, representative of dioxins). The wind direction
threshold adjustment factors were irrespective of chemical (specific only to the facility). In
implementing the Tier 2 process, a risk equivalency approach was used to convert speciated
emissions of PAHs and dioxins into BaP and TCDD equivalents, respectively, similar to the Tier
1 screening approach.
Finally, to implement the Tier 2 screen, a Microsoft® 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 PB-
HAP 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
Attachment B - Tier 2
B-7
December 2013
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TRIM-Based Tiered Screening Methodology for RTR
information for the facilities being evaluated.24 To determine which scenario characteristics
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),
Wnd 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 site-
specific 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
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
exposure characteristics used in MIRC are considered to be generally consistent across different locations and
facilities.
Attachment B - Tier 2
B-8
December 2013
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TRIM-Based Tiered Screening Methodology for RTR
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.
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 that is assessed in the Tier 2 analysis, other than wind
direction, adjustment factors were estimated that correspond to a set of four to six particular
values for the parameter. The adjustment factor for wind direction directly relates Tier 1 and
site-specific Tier 2 wind direction frequencies, with respect to the directions of the lake and farm.
These individual adjustment factors can the be multiplied for a particular variable combination to
get an overall adjustment factor. To facilitate this, bins were created for each parameter of
interest and a relevant range of values, with the exception of wind direction (as described below,
representative bins were not necessary for this parameter). The rationale for selecting the
range for each bin for each parameter of interest is described below.
Wind Direction: Within the context of the hypothetical exposure scenario used in Tier 1 (and
under actual conditions), when the frequency with which the wind blows towards the modeled
domain (i.e., where the hypothetical farm and lake are located) increases, greater pollutant
deposition will occur over and around the farm and lake. The percentage of time the wind blows
toward the farm and lake is therefore positively correlated with ingestion exposure and risk. In
the screening scenario used to estimate Tier 1 thresholds, the wind is assumed to blow toward
the modeled domain 3 days a week, or 43 percent of the time. This assumption is intended to
approximate an unusually consistent long-term wind pattern and is representative of wind
direction patterns in Yakima, Washington (where the wind blows eastward approximately 40
percent of the time, based on a review of wind direction data compiled by the National Weather
Service).
In the model runs to develop the Tier 2 bins, this factor was changed to 1, 2, and 4 days per
week, corresponding to 14 percent, 29 percent, and 57 percent of the time. This range of
values was chosen to obtain a good understanding of the impact of wind direction on risk for the
range of wind direction patterns likely to be present at real facilities. Review of these results
indicated that, within this modeling scenario, estimated ingestion exposure varies directly with
percentage of time the wind blows toward the farm and lake. Given the exactly linear nature of
the relationship observed in model results obtained from these runs, the adjustment of the
threshold for wind direction in Tier 2 is a direct, linear adjustment using the actual site value
rather than an incremental, bin-based approach. In other words, the Tier 1 threshold is adjusted
for wind direction in direct proportion to the difference between conditions for the actual facility
location and the wind direction parameters included in the Tier 1 screening scenario (i.e.,
blowing toward the lake/farm 43 percent of the time on average).
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
Attachment B - Tier 2
B-9
December 2013
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TRIM-Based Tiered Screening Methodology for RTR
speed of 2.8 m/s, corresponding to the 5th percentile (i.e., slower) of annual average speed for
the contiguous United States (distribution was based on data from a climate publication from the
National Oceanic and Atmospheric Administration [NOAA], which used data from over 200
stations nationwide).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
25http://ols.nndc.noaa.aov/plolstore/plsal/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/cai-
bin/climaps/climaps.pl
Attachment B - Tier 2 B-10 December 2013
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TRIM-Based Tiered Screening Methodology for RTR
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
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 5th percentile (i.e., lower) of annual average mixing heights for 463 U.S.
locations, based on data obtained from EPA's SCRAM Web site.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 health-
27National Climatic Data Center Historical Climate Series (NCDC-HCS) (2007).
28http://www.ncdc.noaa.qov/oa/climate/normals/usnormals.html
29Support Center for Regulatory Atmospheric Modeling; http://www.epa.aov/scramO01 /tt24.htm
Attachment B - Tier 2
B-11
December 2013
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TRIM-Based Tiered Screening Methodology for RTR
protective, a facility was assigned the lower end value of the bin into which it was placed. A
facility with a lake closer than 2 km was assumed to have a lake at 2 km.
In resituating the lake at these alternative locations, we maintained ratios consistent with those
included in the Tier 1 screening scenario for (1) lake area to total 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
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).
Attachment B - Tier 2
B-12
December 2013
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TRIM-Based Tiered Screening Methodology for RTR
Exhibit_Att B-3. Layouts for Tier 2 TRIM.FaTE Simulations Using Alternate Distances
Between the Facility and the Fishable Lake3
aThe no-lake scenario is not shown.
Attachment B - Tier 2
B-13
December 2013
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TRIM-Based Tiered Screening Methodology for RTR
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 DitBCtion = 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:
REFPAH = EEFPAH x TEFPAH
The EEFs for PAHs represent the ratio of the exposure to a particular species to the exposure
to BaP (and similarly for dioxins and TCDD) at equivalent emission rates. These ratios are thus
specific to the TRIM.FaTE layout and input assumptions. The TEFs for PAHs and dioxins
represent the ratio of the oral cancer slope factor (CSF) for a particular species to the CSF for
BaP and TCDD, respectively, and are the same in Tier 1 and in Tier 2.
The EEFs depend on the TRIM.FaTE configuration, including layout and meteorlogical input
values. For example, the exposure profile (i.e., how the different ingestion pathways contribute
to total exposure and risk) is different for each PAH. For a PAH where fish is a dominant driver
of risk, moving the lake will have a large effect on the overall risk; however, if produce is the
dominant driver of risk, moving the lake will have a much smaller effect on overall risk. Thus, for
Tier 2, EEFs were recomputed for each of the representative scenarios modeled in Tier 2
separately for the PAH species currently evaluated (some based on direct TRIM.FaTE
modeling, others 15 based on KoW-based regression estimates) and the dioxin species currently
evaluated (most based on direct TRIM.FaTE modeling, and a small number assumed to behave
like TCDD).
Attachment B - Tier 2
B-14
December 2013
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TRIM-Based Tiered Screening Methodology for RTR
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 EPAHat a facility, the risk-equivalent BaP emission
level can be expressed as:
EbAPEQIV = EpAH SPECIES x EEFPAH SPECIES x TEFPAH SPECIES
Then, the Tier 1 risk ratio is estimated by comparing the risk-equivalent BaP emissions to the
BaP emission threshold:
_ _ ETiER1_BAPEQUiV
KatlOriER 1 PAH SPECIES -
ThresholdTiER i_bap
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:
_ _ Etier 2_BAP EQUIV
r\ail°TiER 2 PAH SPECIES ~
ThresholdTiER 2_bap
Using the definition of the risk-equivalent BaP emissions, this can be re-expressed for a given
PAH species as:
_ _ EpahSPECIES x EEFtier 2_PAHspecies x TEFpahSPECIES
rxail°TiER 2 PAH SPECIES 77"7TT73
ThresholdTiER 2_bap
This expression may be further reconfigured, after some algebraic rearrangement, in terms of
the Tier 1 ratio as:
„ _ n ThresholdTIER f_BAP EEFTIER2_PAh species
rxail°TiER 2 PAH SPECIES ~ ^atl0TIER 1 PAH SPECIES x X "EEE
i nresnoiaTIER 2 bap tieri pah species
t ^ ^ t ThresholdTiER-i BAP EEFTiER2 PAhspecies
Tier 2 Adjustment FactorPAH cPFr,Fc = — ;—— = x -=== =
ThresholdTiER 2_bap EEFTiER i_PAh species
These equations simply state that to adjust the Tier 1 threshold to a Tier 2 threshold for a
particular PAH species, the adjustment factor must include the ratio of the BaP Tier 1 and 2
thresholds (as is true for cadmium, mercury, and TCDD, as discussed above) and the ratio of
the EEFs for the particular PAH species in Tier 2 and Tier 1. This additional EEF factor is
needed to account for the fact that the EEFs are different for each Tier 2 TRIM.FaTE
configuration.
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:
Attachment B - Tier 2
B-15
December 2013
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TRIM-Based Tiered Screening Methodology for RTR
ALL PAHs
RatiOjiER 2_ALL PAHs ~ I Ratio
ThresholdTIER f BAP EEFTiEr2 pah species
TIER 1_PAH SPECIES x X PPP
i nresnoiaTIER 2 bap tieri pah species
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 assessed using the Tier 2 methods
and tools, which can lead to the permanent removal of some lakes from the dataset.
Early in the process of compiling this database, we encountered the question: "What size water
body qualifies as a 'lake' for the purposes of this assessment?" The Tier 2 analysis must focus
on lakes large enough to support relatively intensive angling pressure to be compatible with the
assumed exposure scenario. To estimate the relationship between high fish consumption rates,
harvest rates, and lake size, the following five key assumptions were made. Information and
citations to peer-reviewed literature that support these assumptions are provided in Addendum
2 to this attachment. Note that in the TRIM.FaTE model screening scenario, water-column
carnivores (WCCs) are modeled as trophic level 4 (TL4) fish (e.g., pickerel, largemouth bass),
with all of their diet consisting of smaller "prey" or "pan" fish in the water column that are
simulated as trophic level 3 (TL3). The benthic carnivores (BCs) in TRIM.FaTE are modeled to
represent an intermediate trophic level between 3 and 4, i.e., TL3.5. The BCs (e.g., catfish)
obtain half of their diet from TL2 (benthic invertebrates that feed on detritus at the sediment
surface) and half from TL3 fish in the benthic environment. For the screening scenario, we
assume that anglers consume fish biomass in a ratio of 50:50 from the BC and WCC
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).
Attachment B - Tier 2
B-16
December 2013
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TRIM-Based Tiered Screening Methodology for RTR
compartments, respectively. Together, these two fish compartments are referred to as
piscivorous fish.
1. Piscivorous fish, when present, comprise approximately 21 percent of the standing biomass
of fish. BC fish represent 17.5 percent of the standing fish biomass in natural lakes; WCC
fish account for 3.5 percent of this total fish biomass. Thus, WCC (or TL4) fish represent the
limiting compartment for angler fish harvesting and consumption.
2. Humans can harvest 10 percent of any single fish compartment without threatening the
population due to overharvesting.
3. The minimum viable effective population size for a single fish species is at least 50 adult fish
for a local population to survive over the short term (e.g., more than a decade).
4. Only 33 percent of the fish caught for consumption is edible fillet muscle. A 0.33 edible
fraction is used to estimate total fish biomass associated with human consumption.
5. A total fish standing biomass of 40 g wet weight/m2 represents an approximate upper bound
for natural ponds and lakes in the United States.
Using the above assumptions and a series of equations (see 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 grey shading indicating when
WCC fish would probably not be present, the white (unshaded) cells represent combinations of
lake size and productivity that could sustain the listed fish ingestion rates over some time, and
the yellow cells represent likely long-term sustainability associated with more than 500 adult
WCC fish in the lake (see Exhibit notes).
The Tier 2 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).
The fish ingestion rate supported by a 25-acre lake is not as high as the adult ingestion rate
used in the Tier 1 and Tier 2 analyses (i.e., 373 g ww fillet per day). Even a 100-acre lake is
unlikely to sustain harvesting of piscivorous fish to support the 373 g ww fillet/day ingestion rate
assumed for a subsistence angler. Exhibit_Att B-4 indicates that at a total fish biomass
productivity of 40 g ww/m2, the maximum sustainable fish harvest from the WCC compartment
would correspond to an ingestion rate of only 103 g/day (with half that from the WCC and half
from BC). However, to be health protective and to ensure that small lakes that might be more
highly contaminated than estimated by the TRIM.FaTE screens were not eliminated, we
selected 100 acres as the "cutoff' for the minimum size for an actual lake near a facility to be
included in the Tier 2 analysis. In addition, larger lakes (larger than 100,000 acres) were not
considered since they cannot be readily modeled in TRIM.FaTE. For the purposes of proximity
matching lakes to emitting facilities (as described in Section B.4.2), the location of each lake is
identified as the geographic centroid inside the lake.
Lakes smaller than 100 acres could be stocked annually at a rate adequate to support the
assumed fish ingestion rate. For stocked fish, however, we would have to assume that when
introduced to the lake, the fish were uncontaminated by the chemicals of interest. Moreover,
the period over which accumulation of chemical from the lake could occur would be roughly
Attachment B - Tier 2
B-17
December 2013
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TRIM-Based Tiered Screening Methodology for RTR
three to six months (fishing season) for the majority of the fish stocked as adults (i.e., at
approximately 2 kg), instead of several years for fish hatched in the lake.
To place an upper bound on the radial distance from the source up to which lake-derived risks
need to be assessed, we took into account the limitations of TRIM.FaTE. We limited the lake
analysis to an outward radial bound of 50 km from the stack. For facilities with no lakes within
50 km, lake-derived risk is assumed 0.
Attachment B - Tier 2
B-18
December 2013
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TRIM-Based Tiered Screening Methodology for RTR
Exhibit_Att B-4. Maximum Fish Ingestion Rate (g/day) Associated with Sustainable Fishing3
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/ m2estimated for natural lakes, 25 acres could support a water-column TL4 fish population, but
would provide for no more than 26 grams of fillet per day for a single angler over a full year.
Attachment B - Tier 2
B-19
December 2013
-------
TRIM-Based Tiered Screening Methodology for RTR
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 2010). 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 for use
in multipathway screening assessments, EPA started with the same U.S. meteorology dataset
used in RTR inhalation assessments. RTR inhalation assessments use data from 824 ASOS
(Automated Surface Observing System) stations that record hourly and sub-hourly
measurements. These data represent the year 2011, and the surface stations are paired with
their closest, regularly-reporting upper-air stations. This number of stations is far greater than
the 350 and 158 stations, respectively, used in the 1996 NATA report (EPA 2001b) and the
2009 RTR report (EPA 2009).
The 2011 precipitation measurements reflect 2011 weather conditions, and, like any other year,
some areas of the country experienced rainfall that was significantly less than normal, and some
areas received much more rainfall than normal. To reduce this bias in precipitation data, we
used average annual precipitation data from the 1981-2010 National Climatic Data Center 30-
year normal dataset wherever possible. If 30-year normal precipitation data were not available
for a station, as was the case for a few stations in the dataset, we used the ASOS precipitation
data as-is.
B.3.2.2 Coverage of Meteorological Stations Compared with Facility Locations
Exhibit_Att B-5 shows the proximity of the evaluated meteorology sites to the locations of U.S.
point source facilities from the 2005 NATA. Generally, the spatial density of the surface
meteorological stations in this dataset was similar to the spatial density of the 2005 NATA
facilities. That is, the density tends to be greatest in the Great Lakes region, along the East and
West Coasts, and in the Southern Plains, and tends to be lowest in the Rockies (except
Colorado) and Northern Plains.
Attachment B - Tier 2
B-20
December 2013
-------
TRIM-Based Tiered Screening Methodology for RTR
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
Attachment B - Tier 2
B-21
December 2013
-------
TRIM-Based Tiered Screening Methodology for RTR
Exhibit_Att B-5. The Locations of Meteorological Stations and Point Source Facilities3
.. J
$
^ Mixing Height Locations
, Surface Met. Stations with Complete Data
NATA 2005 Point Source Facilities
aThe 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.
Attachment B - Tier 2
B-22
December 2013
-------
TRIM-Based Tiered Screening Methodology for RTR
A software program was developed to calculate the following statistics for each of these
stations:
Number of hourly observations,
Number of hours with calm winds or missing winds,
Percentage of time the wind blows into each octant (after excluding missing wind hours),
Median wind speed blowing into each octant,
Median mixing height used (if heat flux > 0, convective mixing height was used,
otherwise mechanical mixing height was used), and
. Average annual precipitation (irrespective of wind octant and preferring 30-year normal
data if available).
Total annual precipitation data in the 2011 meteorological data included anomalies (relative to
normal conditions) in areas that experienced extreme drought conditions or large rainfall
surplusses. To address this, the 30-year (1981-2010) average annual precipitation was used
instead.31
The choice of using median values for wind speed and mixing height was based on a
comparison of median and mean values. For nearly all stations, the median value was smaller
than the mean value; because smaller values are more health protective, we selected the
median statistic for all stations..
B.4 Implementation of Tier 2 Analysis
The Tier 2 screening analysis is conducted using a Microsoft® Excel™ tool. The tool was
created so that all facilities in multiple source categories can be screened concurrently, if
desired. The tool is controlled by a dashboard control panel (see Exhibit_Att B-6), where each
of 7 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 basic 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; this
matrix remains static unless the methods used to derive the matrix must be updated or the
chemicals currently in the matrix must be augmented. In Step 2, the user enters basic "global
inputs" that include the source categories included in the analysis (Exhibit_Att B-7). 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.
The remaining steps implementing the Tier 2 analysis are described in the below sections.
Sections B.4.1 and B.4.2 contain discussions on Steps 3 and 4, where data for two additional
input tables are entered into the Tier 2 analysis, after having been created using the Tier 1
Microsoft® Access™ screening tool and the ArcGIS™ lake database. Section B.4.3 contains a
discussion on Step 5, where facilities in the analysis are linked with meteorology statistics from
the same meteorology data used for the facilities in the RTR inhalation modeling. After all these
input data have been supplied, Sections B.4.4 and B.4.5 respectively contain discussions on
Step 6 (conducting the Tier 2 analysis) and Step 7 (producing output tables that summarize the
results).
31
30-year average annual precipitation was obtained from the National Climatic Data Center (NCDC).
http://www.ncdc.noaa.aov/oa/climate/normals/usnormals.html.
Attachment B - Tier 2
B-23
December 2013
-------
TRIM-Based Tiered Screening Methodology for RTR
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 the
results, are provided to illustrate the overall 7-step process. These screen shots and
accompanying descriptions represent the tool at one point in time (over a year 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.
Exhibit_Att B-6. Example of the Dashboard To Conduct the Tier 2 Analysis
RTR Tier 2 Analysis - Dashboard Select Assessment Type:
1) Update Tier2
Matrix Results
Tier 2 Matrix results are used to create Tier 2 Screening Results. This table should only be updated if its data have been updated (see AJ Overton).
2) Enter Global
Inputs
Enter information into the following tables:
Input Parameters - Meteorological station distance threshold, text to use for results tables
PB-HAP Group - List of PB-HAP group names used in input tables and descriptions of the groups
Source Category - List of source category names used in input tables and descriptions of the categories
3) Enter Facility
Information
Enter Tier 1 Screening Results for each source category, facility, and chemical (and, for eco analyses, each assessment endpoint and benchmark effects
level). For PAHs and dioxins, the data must be specific to individual congeners. For cadmium and mercury, the data must be rolled up to cadmium and
mercury (emissions of divalent mercury; analyzed for exposure to methyl mercury for human health, and to methyl and divalent mercury for eco). Facility-
average latitude and longitude must also be provided.
4) Enter Lake
Information
Enter lake information for the source category. For each facility, it must include the closest applicable lake in each directional octant, with only one lake
allowed per octant. Lake details must include distance from facility to lake (km), size of the lake (acres), name (if available), coordinates, and the unique
object ID from the geospatial file. An applicable lake is one that is at least 25 acres and no more than 100,000 acres in surface area, is within 50 km of the
facility, and is a reservoir or pondlake not used for treatment, evaporation, sewage, fly ash, etc. It is possible for there to be no applicable lakes in a particular
octant of a facility. This table must be updated for each new set of facility information I see Chris Holder).
5) Enter Met Station
Matching
Enter facility information linked to a met station. Required inputs include Source Category, NEIID, and WBAN of the station. You must click the Return button
to complete the processing.
6) Find WorstOctant
For each octant at each facility in a source category, this step uses meteorological and lake data to find the Tier 2 ratio of emissions to screening threshold for
each chemical. Once all octant-specific Tier 2 ratios are claculated, this step sums the ratios across all chemicals within a PB-HAP group. It then determines
the octant with the largest Tier 2 ratio for each PB-HAP group. In the eco analysis, each of these calculations is further subset by each assessment endpoint
and benchmark effects level.
7) Create Summary
Tables
Creates a summary results table and several other results tables using the information produced in Step 6. The summary table shows information at the level
of source category, the detailed table shows information at the level of the facility, and the individual output table shows information at the level of the facility
and chemical. The "Exceedances > 100" table shows details of any octants whose Tier 2 ratio of emissions to screening threshold exceeded 100 (for any
octant, not just the one with the largest result, aka the "worst octant").
Go To Summary
Output
Go To Detailed
Output
Go To Individual Go To
Output Exceedences>100
< ~ H | 0) DashBoard 1) Tier2Matrix 2) Globallnputs 3)FacilityInputs 4) LakeDistance 5;
5) MetAnatysis / 6) QctantAnalysis
Attachment B -
Tier 2
B-24
December 2013
-------
TRIM-Based Tiered Screening Methodology for RTR
Exhibit_Att B-7. Example of Global Inputs Used in the Tier 2 Analysis3
RTR Tier 2 Analysis - Global Inputs
**ClickWhen Complete** to Log
Completion and Return to Dashboard
1
Input Parameters
Basic input parameters
Distance criteria for met stations
50
(km)
Text to indicate the facility does
Not Emitted
not emit a particular PBHAP
Text to indicate no qualifying
No Lake
lakes within 50 km
Source Categories
Source Categories
Number of Facilities
Source Category A
10
Source Category B
5
01 DashBoard / 1) Tier2Matrbc 2) Globallnputs 3)FadlityInputs 4) LakeDistance 5)MetStationMatch
aThe screen shot examples shown in this section are for actual facilities; however their source categories, NEI IDs, and coordinates
have been altered or masked so the data are not linked to specific facilities. Only a portion of the table might be shown.
B.4.1 Facility List for Tier 2 Screen (Step 3)
After clicking Button 3 on the dashboard (see Exhibit_Att B-6), 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 of 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), the emissions of those chemicals, the Tier 1 exposure factors and screening
thresholds, and the ratio of the emission rate (including REF-adjusted emission rate if
applicable) 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-8.
Attachment B -
Tier 2
B-25
December 2013
-------
TRIM-Based Tiered Screening Methodology for RTR
Exhibit_Att B-8. Example of the Facility Input Data Required
To Conduct the Tier 2 Analysis3
Tier 1 Screening (Analysis Performed Outside this Workbook, in MS-
Facility Information
Oil
Source Category
NEI ID
Latitude
Longitude
PB-HAP
Grp.
Chemical
IA»D1
{F)
Sem.
Thresh„
This PB-
HAP Grp.
(TPVt
Date
Bcnchmnrk
Value
_Cre«led
(GJ
Exccedance
Ratio. This
Chtrm
IE*F]
Source Category A
NEI1C
Mercury
(methyl)
Mercury (methyl)
0 002961235
1
1
1
Hg No
FitF
O.OOZ9C.124
0.W02S6U&
20130924
10.35175892
Source Category A
NE110
Mercury
(nwthyl)
PAH
Mercury (methyl)
o oo?miw
1
3 0137
1
1
1.3.227
HE - NO
Rtr
?01309?4
0.00273015
?.Qfi6Br-06
0,00029606
0.00254452
20130924
?0130924
9.54394 /899
0.000820114
Soy icc Category A
NEI ID
3 Mclhyfeliotaislhreric
1 5777E 07
¦» 3889
Source Calegory A
NEI1D
PAH
7,12-Diri!0iriytt>eii2(a)antriracefie
1 4074E-06
34 247
5 6828
194.6?
20130924
0.0002739
0.00254452
70130924
0.107644908
Source Category A
NEUD
PAH
Acwaphlhene
1 68751E-05
00685
00251
0,0017
20130924
2.9069E 06
0.00254452
20130524
1.14241E 05
Source Calegory A
NHIO
PAH
Acfliiaphinylewi
0 00Q66Q793
0 068!i
0 0396
0.0027
2013092*1
1.791 E-Ob
0.00254452
20130924
0.000/03851
Source Category A
NEI10
HAH
Affihracotw
0 00050/241
3 3792L 06
li
01644
0 0632
0.0875
0
0.0144
20130924
20130924
0
4.862F-08
0.00254452
0.00254452
20130924
0
Source Calegory A
NEUD
PAH
LtwtfofAJanftirucufio
20130924
1.91077C-05
Source Calegory A
NEI1D
PAH
BemoCAJpyrerie
5.75113E-05
1
1
1
20130924
5.751 IE OS
0.002154457
20130924
0.022601994
Source Category A
NEI1D
PAH
Beiizo(B>flouiaifllH>w
0 000018683
01644
11.909
1-9576
20130924
3.657SF 05
0.00254452
20130924
0.014373866
Source Calegory A
HfFIID
PAH
Benzo(g.h,i)peryS0fie
0 011246562
0 0685
4 4281
0.3033
20130924
0.00341103
0.00254452
20130924
1.340537723
Source Category A
NbHD
PAH
Wetv7o(K>1lisoran8hone
1 37955E 06
0 1644
18 755
3.083
20130924
4.2532L-05
0.00254452
20130924
0.01b/14938
Sourco Calegory A
Sniittsa Calfinoi v A
JasMSaart? I) !«f2Maow 2) QbbaS
NE110
ylrputs 4) I
afceDstaner
PAH
WeStatan
IJ»iifo|<>|Pyrena
0 00000228
0 0685
4 582
0.3138
20130924
7.1554E-Q7
7 fti£l2£ifl6
0.00254452
n iws.t.is.3
•vTTBWRT
20130924
vrMinq/u
0.00028121
_2_no22f-m
wufc '' J)lacill
¦am
-rr&mSh
aThe screen shot examples shown in this section are for actual facilities; however, their source categories, NEI IDs, and coordinates
have been altered or masked so the data are not linked to specific facilities. Only a portion of the table might be shown.
B.4.2 Facility/Lake Distance Table (Step 4)
After clicking Button 4 on the dashboard (see Exhibit_Att B-6), 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 iakes 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 100 acres or larger than 100,000 acres). The table also specifies if there is no
qualifying lake. The lake names (where available) are scrutinized to subjectively remove
industrial and treatment water bodies, as discussed in Section B.2.1. This table is pasted in the
"LakeDistance" sheet as shown in Exhibit Att B-9.
Attachment B -
Tier 2
B-26
December 2013
-------
TRIM-Based Tiered Screening Methodology for RTR
Exhibit_Att B-9. Example of the Lake Distance Data Required
To Conduct the Tier 2 Analysis3
Input Filename
Date and Time
Notes
"Click When
Complete** to Log
Completion and Return
Tier2LakeAnalysis_Sour
ceCatsAB 20130927.xlsx
9/27/2013
RTR Tier 2 Analysis - Lake Distance
Lake Distance within each Octant by Facility by Source Category
Source Category
NEI ID
Octant
Lake Name
Lake Size
(Acres)
Facility-to-
Lake
Distance
(km)
Lake
Latitude
Lake
Longitude
Lake
Object ID
Source Category A
NEI1C
N
; .
326.1791
15 292858
liiiii
IPP
Source Category A
NEI1C
NE
No Lake
No Lake
wMA,
Source Category A
NEI1C
E
116.13953
15.601542
WMMMik
¦n
Source Category A
NEI1C
SE
49.421076
5.496688
wmmrn
mmm
Source Category A
NEI1C
S
46 950022
8 709438
mmm
mmm
mmm
Source Category A
NEI1C
SW
148 26323
13 590198
Source Category A
NEI1C
w
192.7422
17 808892
mmm
-mmm
Source Category A
NEI1C
NW
64 247399
22 502183
WM?m
Source Category A
NEI1D
N
153 20534
20 806486
ifillfl
mmm.
mmm
Source Category A
NEI1D
NE
44.478969
41 316683
mmm
SSflP
Source Category A
NEI1D
E
No Lake
No Lake
WMMM.
Source Category A
NEI1D
SE
163.08955
17 982657
Source Category A
NEI1D
S
34.594753
1.876989
Source Category A
NEI1D
SW
No Lake
No Lake
Source Category A
NEI1D
w
No Lake
No Lake
Source Category A
NEI1D
NW
32.1237
49 596933
f/iWi
Source Category A
NEI1E
N
126.02375
2.578494
iiifii
mmm
Source Category A
NEI1E
NE
96.371099
5.41776
Ww#
WMM
Source Category A
NE11E
E
56382.035
12.535626
WM
mmm.
WmZMM.
Source Category A
NEI1E
SE
2693 4487
4.71699
Wmrnm
mmm
W&f/W&k
Source Category A
NEI1E
S
93.900045
6.154928
mmmm
mmm
Source Category A
NEI1E
SW
32.1237
1.694421
WkmwA
Source Category A
NEI1E
w
wmmmMmimm
840.1583
2.781787
0) DashBoard 1) Tier2Matrix 2) Globaflnputs 3)FacilityInputs 4) LakeDistance 5)MetStationMatch ]
aThe screen shot examples shown in this section are for actual facilities; however, their their source categories, NEI IDs, and
coordinates have been altered or masked so the data are not linked to specific facilities. Only a portion of the table might be shown.
B.4.3 Matching Facilities to Meteorology Data (Step 5)
The last required input data step is to assign a meteorological station to each of the facilities.
By clicking Button 5, the user is brought to the "MetStationMatch" tab. On this tab, the user
provides a list of facilities and the associated meteorological station WBAN ID that should be
used in the analysis. These station WBANs must already be present in the tool with the wind,
mixing height, and precipitation statistics used in the Tier 2 methods (i.e., the meteorology
stations used in RTR inhalation modeling; in a separate, hidden sheet). Clicking the return
button will check that all input stations are acceptable. An example is provided in Exhibit_Att
B-10.
Attachment B -
Tier 2
B-27
December 2013
-------
TRIM-Based Tiered Screening Methodology for RTR
Exhibit_Att B-10. Example Results of the Meteorological Station Matching
Required To Conduct the Tier 2 Analysis3
Input Filename
Date and Time Notes
Source Cats A B Met Data xlsx
9/20/2013
"Click When Complete" to Log
Completion and Return to
Dashboard
RTR Tier 2 Analysis
- Matching Met Stations
Met Station Match to Facilities
Source Category
NEIID
WBAN
Source Category A
NEI1C
114819
Source Category A
NEI1D
03937
Source Category A
NEI1E
12916
Source Category A
NEI1F
[94847
Source Category A
NEI1G
14927
Source Category A
NEI1H
04848
Source Category A
NE111
04848
Source Category A
NEI1J
14895
Source Category A
NEI1K
13739
Source Category A
NEI1L
13739
Source Category B
NEI2M
13893
Source Category B
NEI2N
23044
Source Category B
NEI20
12917
Source Category B
NEI2P
12917
Source Category B
NEI2Q
r24217
ri) EKwriRnarii . i)T«?Mairo< JJGbbafl
nouC. TJFaelcvftiOi*'; 4)Tflfe
aThe screen shot examples shown in this section are for actual facilities; however, their source categories, NEI IDs, and coordinates
have been altered or masked so the data are not linked to specific facilities. Only a portion of the table might be shown,
B.4.4 Assembling Threshold Adjustment Factors (Step 6)
Next, the tool calculates the facility-specific, octarit-specific threshold adjustment factor for each
chemical. When Button 6 is clicked (see Exhibit_Att B-6), 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-11. 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-12). 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 math to go from the Tier 1 results to the Tier 2 results can be performed a number of
different ways. To improve transparency and understanding of the Tier 2 analysis, the current
tool normalizes the raw emissions of PAH and dioxin congeners to their surrogate congeners
(BaP and TCDD, respectively) using the Tier 2 EEF adjustment factor (EmissionsSurrogate =
Emissions x TEF x (EEFjien x EEFAdjFactjie^))- Then, the adjusted emissions (or raw
emissions in the case of mercury and cadmium) are ratioed to the Tier 2 emission screening
thresholds (ResultTier2 = EmissionsSurrogate * (ThresholdTieri * ThresholdAdjFactor-ne^)
(WindDirectionAdjFactorTier2))-
Attachment B - Tier 2
B-28
December 2013
-------
TRIM-Based Tiered Screening Methodology for RTR
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-equivalents using 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-11. Example Results of the Octant Analysis Required
To Conduct the Tier 2 Analysis (shown in 2 pieces due to size)3
RTR Tiar 2 Analysis - Octant Analysis
F*
-ffly. Interna
<*>
Sent.
ThraWi..
Thlli PB-
hWOrp.
Octant and
Lake Data for Tiar 2
M.
PB-HJU*
(BJ
TEF
TtU-t
£C>
EEF
This
(D)
SEP,
Tin*
tT3i
(El
UultrSJin.
Adj
This cn*m
(IP*)
[ A « O ]
(OJ
EiceaOane*
Raoa, Tint
ChM.
[G *-f J
EicHdinca
Rlt», That
PE-KAP Qrp.
[VumO]
Mat.
Sialic n
sua m
Lata
Duunca
to Lake
Bl
Cb
ioutcr Category A
NtUC
MertKV
f rrwthyl|
Mercury tmptttyl|
ft-ooa
J
t
0.0019612
0-00029
10-3517SS92
10.35175492
N
t4S19
5erinftton fte\erVolt
326.18
S5.292&6
Scjrcc Category A
NtUC
Meici^y
| methyl)
Mercury (methyll
c-ro?
1
1
0.0029612
0.ODO29
10.S5175S92
10-JS175892
NE
!!-•.
Ho Lai*
Mo laie
No Lake
Category A
NfllC
ImeChyO
Mercury [methyl}
0.003
,
,
0.062^612
0.00029
10-i517&893
10.J5175S92
E
14*14
Name Wot Prevised
116.14
15.60154
ic-j'ce Category A
N1UC
Mercury
(methy1|
Mercury i«nctnyl|
J
1
0.0029612
0.00029
10JS17SS92
uusintn
jl
14(819
Name Hot Provided
49,«1
5.496688
5auce Category A
NUIC
II
Mereury [methyl]
l\r.;u
1
1
0.0029612
0.00029
10J517S892
10.S5175892
SW
14819
Narcw Not Provided
148.26
13.5902
Category A
NfllC
Merely
ImeWiWI
Mercury |oi«th«4|
0-003
1
,
0.003-9612
aoocw#
10-35175492
10.35175*97
w
t4*19
Hoopw> Reservoir
192 74
17.B0&S9
«uiee Category A
NfllC
Mercury
(methyll
Mercury I methyl I
0.003
1
,
0,0024612
O-O0O29
1&3517SS42
10_1517SW2
NW
14819
Name Mot Picrv*ied
64-14?
22.50218
Sojicc Category A
NtllD
Mercury
Mercury | methyl |
0.002?
1
1
0.C02?J02
0.00029
9.SAJ947899
9.54S9A7*W
N
3931
Nam* Not Prcwded
IS J. 21
2080649
Sou«ee Category A
NfiJD
|methyl|
Mercury (methyl |
O.OQ77
-
1
0.0027302
000029
9.54394 7K99
9.54394 7S99
M
3937
Nome Not Prmvdwi
-i-1.479
41.31668
» " 0) Qnsh&iafd
i) tier**
im. ira
ana
ntAnaly«
,
If
Final S
Value
elected
to Mate
15
or "Bar 2
Main*
Tier 2 Sen
senino
Tuna
Slowing
Octam
Median Average
Sp-eeo preclpat
«n i' ion. sua
Octant wide
Average
Mixing
Hwgmt.
Site-
mam
D»ur
Frequency,
TlMl octam
(Ttar t to
TlarlJ
(!)
AQJ. Fact.
Tor EEF.
TIM*
Cham
Octam
(Tier 1 to
Tier 2)
w
Ad; Fact.
For fcrn
Tfireeti..
T*il» PB-
MWQrp.
Octant
(Tteri to
T»er 2)
.9?
992.8*6
4W
10
*,»
1187
¦r
2-559MWI
1
i UIHI
\
0.0029612
0.0029612
0.0015M
I.W54535J
No
0,tJ6
449
992.836
tm
10
4
1187
710
J.16176471
1
2.334102
t
0.0029612
0.0029612* 0.0021111
1.402699635
1-402699635
No
O.MS
4.11
992.886
4«
20
4
1187
710
J.7391KI43
:
A 730419
1
0.0029612
00029612
0-0050S975
0.58SK3377
0-585253377
No
4 56
1460.25
307
20
4
1500
710
1B0530973
,
4-3)8097
0 0027302
0.00477223
0.57S14fM3
0.573HH4S
2.84
1460.2!.
307
i.46774194
j
,
0.0027502
0.01044758.
0 261MB961
0-2611189bl
>}P«»Mtil H
aaonawr-" MPmsuwimciw—
* oj v*U4*ivw««yM>
aThe screen shot examples shown in this section are for actual facilities; however, their source categories, NEI IDs, and coordinates
have been altered or masked so the data are not linked to specific facilities. Only a portion of the table might be shown.
Attachment B -
Tier 2
B-29
December 2013
-------
TRIM-Based Tiered Screening Methodology for RTR
Exhibit_Att B-12. Example of the TRIM.FaTE Matrix Results Required
To Conduct the Tier 2 Analysis
"Click When Complete" to
Log Completion and Return
to Dashboard
TRIM.FaTE - Tier 2 Matrix Builder
HH Matrix
Chemical
TRIM.FaTE Scenario
PBHAP Group
Lake
Distance
Wind-
speed
Mixing
Height
Rainfall
Ratio
ThreshT2/
Threshj,
Ratio
EEFt,/
EEFT2
Cadmium
L02 2 8 710 512
Cadmium
2
2.8
710
512
2.344
1.000
Cadmium
L02 2 8 710 924
Cadmium
2
2 8
710
924
1.488
1.000
Cadmium
L02 2.8 710 1187
Cadmium
2
2.8
710
1187
1.214
1.000
Cadmium
L02 2.8 710 1500
Cadmium
2
2.8
710
1500
1.000
1.000
Cadmium
L02 2.8 865 512
Cadmium
2
2.8
865
512
2.844
1.000
Cadmium
L02 2.8 865 924
Cadmium
2
2.8
865
924
1.800
1.000
Cadmium
L02 2.8 865 1187
Cadmium
2
2.8
865
1187
1.467
1.000
Cadmium
L02 2.8 865 1500
Cadmium
2
2.8
865
1500
1.206
1.000
Cadmium
L02 2.8 1079 512
Cadmium
2
2.8
1079
512
3.535
1.000
Cadmium
L02 2.8 1079 924
Cadmium
2
2.8
1079
924
2.232
1.000
Cadmium
L02 2.8 1079 1187
Cadmium
2
2.8
1079
1187
1.816
1.000
Cadmium
L02 2.8 1079 1500
Cadmium
2
2.8
1079
1500
1.491
1.000
Cadmium
L02 2.8 1537 512
Cadmium
2
2.8
1537
512
5.013
1.000
Cadmium
L02 2.8 1537 924
Cadmium
2
2 8
1537
924
3.157
1.000
Cadmium
L02 2.8 1537 1187
Cadmium
2
2.8
1537
1187
2.563
1.000
Cadmium
L02 2.8 1537 1500
Cadmium
2
2.8
1537
1500
2.100
1.000
Cadmium
L02 3.5 710 512
Cadmium
2
3.5
710
512
2.912
1.000
Cadmium
L02 3.5 710 924
Cadmium
2
3.5
710
924
1.843
1.000
Cadmium
L02 3.5 710 1187
Cadmium
2
3.5
710
1187
1.501
1.000
Cadmium
L02 3.5 710 1500
Cadmium
2
3.5
710
1500
1.234
1.000
Cadmium
L02 3.5 865 512
Cadmium
2
3.5
865
512
3.536
1.000
Cadmium
L02 3.5 865 924
Cadmium
2
3.5
865
924
2.233
1.000
Cadmium
L02 3.5 865 1187
Cadmium
2
3.5
865
1187
1.817
1.000
Cadmium
L02 3.5 865 1500
Cadmium
2
3.5
865
1500
1.491
1.000
Cadmium
L02 3.5 1079 512
Cadmium
2
3.5
1079
512
4.398
1.000
Cadmium
L02 3.5 1079 924
Cadmium
2
3.5
1079
924
2.772
1.000
Cadmium
L02 3.5 1079 1187
Cadmium
2
3.5
1079
1187
2.252
1.000
Cadmium
L02 3.5 1079 1500
Cadmium
2
3.5
1079
1500
1.847
1.000
Cadmium
L02 3.5 1537 512
Cadmium
2
3.5
1537
512
6.242
1.000
¦i.mjm—
~ H I 0) DashBoard 1) Tier2Matrix 2) Globallnputs 3)Faciltylnputs 4) LakeDistance 5)MetStationMatch
B.4.5 Assembling Results (Step 7)
By clicking Button 7, 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-13). Cells with red highlighting call attention to cases
where facilities exceeded the Tier 1 or Tier 2 thresholds.32 Using Source Category A as an
example - there were 10 facilities in the category. For this source category, four, eight, and five
facilities emitted dioxins, mercury, and PAHs, respectively, at levels exceeding the Tier 1
threshold (there were no emissions of cadmium). The table indicates which facility's emissions
32 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.
Input Filer Date and Tir Notes
Attachment B - Tier 2
B-30
December 2013
-------
TRIM-Based Tiered Screening Methodology for RTR
most exceeded each threshold and by how much. The Tier 2 adjustment factors caused many
but not all of these facilities to screen out at Tier 2, leaving three, five, and three facilities
respectively exceeding the Tier 2 screening thresholds for dioxins, mercury, and PAHs,
respectively. The largest Tier 2 exceedance ratios were far smaller than the largest Tier 1
exceedance ratios.
Exhibit_Att B-13. Example of Summary Output Table Created by the Tier 2 Tool3
RTR Tier 2 Analysis - Summary
Summary Output
Facility Information
Tier 1 Screening (Analysis Performed Outside this Workbook, in
MS-Access Tool)
Tier 2 Screening
Source Category
PB-HAP
Grp.
Num.
Facilities
in Source
Category
(Emitting
Any HAP)
Num.
Pacilities
Emitting
this PB-
HAP
(A)
Num.
Pacilities
Exceeding
Scm.
Thresh.
(B)
Facility with
Largest
Exceedance
Ratio
(C)
Exceedance
Ratio at This
Facility
[E*F]
(D)
Emissions
at This
Facility
(TPY)
(E)
Multipath.
Adj. Emiss.
at This
Facility
(TPY)
[ D * EcoEF
for This PB-
HAP]
(F)
Scm.
Thresh.
(TPY)
(G)
Num.
Facilities
Exceeding
Scrn.
Thresh.
(H)
Facility with
Largest
Exceedance
Ratio
(I)
Exceedance
Ratio at This
Facility
[ K * L ]
(J)
Emissions
at This
Facility
(TPY)
(K)
Multipath.
Adj. Emiss.
at This
Facility
(TPY)
[J « EcoEF
for This PB-
HAP]
(L)
Scrn.
Thresh.
(TPY)
Source Category A
Dioxin
10
4
4
NEI1F
15.45
9.27E-09
4.20E-08
2.72 E-09
3
NEI1F
5.36
9.27E-09
4.20E-08
7.83E-09
Source Category A
Mercury
(methyl)
10
8
8
NEID
12.28
3.51E-03
3.51E-03
2.86E-04
5
NEI1D
4.84
2.73E-03
2.73E-03
5.64E-04
Source Category A
PAH
10
5
5
NEI1L
127.02
2.63E-07
3.23E-01
2.54E-03
3
NEI1L
38.07
2.63 E-07
3.15E-01
8.26E-03
Source Category B
Dioxin
5
4
4
NEI2N
59.01
1.12E-08
1.60E-07
2.72 E-09
4
NEI2Q
19.06
1.55E-08
1.01E-07
5.27E-09
Source Category B
Mercury
(methyl)
5
5
5
NEI2Q
12.44
3.56E-03
3.56E-03
2.86E-04
1
NEI2Q
6.19
3.56E-03
3.56E-03
5.75E-04
Source Category B
PAH
5
4
4
NEI2Q
123.98
6.06E-06
3.15E-01
2.54E-03
3
NEI2Q
60.84
6.06 E-06
2.00E-01
3.29E-03
0) DashBcard 1) Tier2Matrix 2) Globallnputs 3)FacilityInputs 4) LakeDistance
7) SummaryOutput
aThe screen shot examples shown in this section are for actual facilities; however, their source categories, NEI IDs, and coordinates
have been altered or masked so the data are not linked to specific facilities. Only a portion of the table might be shown.
The 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-14). This
detailed table is helpful because it shows which facilities exceeded the thresholds and by how
much. It also shows the "worst" octant per facility and PB-HAP group as well as the lake
analyzed in that octant. Green shading in Tier 2 columns call attention to cases where the Tier 1
screening threshold was exceeded but the Tier 2 threshold was not.
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-15). 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. The
shading is specific to the PB-HAP-total ratios rather than the chemical ratios.
Attachment B - Tier 2
B-31
December 2013
-------
TRIM-Based Tiered Screening Methodology for RTR
Exhibit_Att B-14. Example of Detailed Output Table Created by the Tier 2 Tool3
2.B6C-04
2.86!;-04
2.54k 03
Relum to
Dashboard I
RTR Tier 2 Analysis - Detailed Summary !
Formatting
Currently:
fum Formatting
OWOFF
Detailed Summary Output
Source Category A
Source Category A
NLI1I
NEI 16
Mercury
(methyl)
Dioxin
2.01E-03
8.90E 09
Name Not Provided
Aidrich Pond
46.95
29-6526
8.728814
30.6129
Source Category A
Source Category A
NEI1II
NfcllH
Mercury
(methyl)
PAH
3.10E-03
i.om-os
Name Not Provided
Name Not Provided
&6.71B5
66.7185
9.092128
9.092128
Source Category A
Name Not Provided
37.0658 1.8S57711
Mercui
0) QasftBoarri ,J
7) ffcbafrwJt
IJfaSyiiitMi"" "-ITMittsEaTO 5)HrtSa»nM,«d. .A1:1.'1
» 7) Dctall«iOulp
44.01
15.45
3.95E-03
1.12L-01
4.20E 08
(0)
Scrn.
Thrash
m
Source Category NEI ID
Source Category A NEHC
Source Category A NEI1D
PB-HAP
GrP
Mercury
(methyl)
Mercury
(methyl)
(A)
Emissions
tTPV)
-------
TRIM-Based Tiered Screening Methodology for RTR
Exhibit_Att B-15. Example of Individual Output Table Created by the Tier 2 Tool
RTR Tier 2 Analysis - Individual Summary
Individual Output
Source Category
NEI ID
PB-HAP
Group
Chemical
(A)
Emissions
(TPY)
(B)
TEF
(C)
EEF
(D)
REF
[BxCl
(E)
Multipath.
Adj.
Emiss.,
This
Chem.
(TPY)
[AxDl
(F)
Multipath.
Adj.
Emiss.,
This PB-
HAP Grp.
(TPY)
[ Sum El
(G)
Scrn.
Thresh.
(TPY)
(H)
Exceedance
Ratio, This
Chem.
r E -5- G 1
(i)
Exceedance
Ratio, This
PB-HAP Grp.
[ Sum H,
also F + G 1
Source Category A
NEI1C
Mercury
(methyl
Mercury (methyl)
2.96E-03
1
1.00E+00
1.00E+00
2.96E-03
2.96E-03
2.86E-04
10.35
10.35
Source Category A
NEI1D
Mercury
(methyl
Mercury (methyl)
2.73E-03
1
1.00E+00
1.00E+00
2.73E-03
2.73E-03
2.86E-04
9.54
9.54
Source Category A
NEI1D
PAH
3-Methylcholanthrene
1.58E-07
3.013699
4.39E+00
1.32E+01
2.09E-06
3.95E-03
2.54E-03
8.20E-04
1.55
Source Category A
NEI1D
PAH
7,12-Dimethylbenz(a)anthracene
1.41E-06
34.24658
5.68E+00
1.95E+02
2.74E-04
3.95E-03
2.54E-03
1.08E-01
1.55
Source Category A
NEI1D
PAH
Acenaphthene
1.69E-05
0.068493
2.51E-02
1.72E-03
2.91E-08
3.95E-03
2.54E-03
1.14E-05
1.55
Source Category A
NEI1D
PAH
Acenaphthylene
6.60E-04
0.068493
3.96E-02
2.71E-03
1.79E-06
3.95E-03
2.54E-03
7.04E-04
1.55
Source Category A
NEI1D
PAH
Anthracene
5.07E-04
0
6.32E-02
O.OOE+OO
O.OOE+OO
3.95E-03
2.54E-03
O.OOE+OO
1.55
Source Category A
NEI ID
PAH
Benzo(A)anthracene
3.38E-06
0.164384
8.75E-02
1.44E-02
4.86E-08
3.95E-03
2.54E-03
1.91E-05
1.55
Source Category A
NEI1D
PAH
Benzo(A)pyrene
5.75E-05
1
1.00E+00
1.00E+00
5.75E-05
3.95E-03
2.54E-03
2.26E-02
1.55
Source Category A
NEI1D
PAH
Benzo(B)flouranthene
1.87E-05
0.164384
1.19E+01
1.96E+00
3.66E-05
3.95E-03
2.54E-03
1.44E-02
1.55
Source Category A
NEI1D
PAH
Benzo(g,h,i)perylene
1.12E-02
0.068493
4.43E+00
3.03E-01
3.41E-03
3.95E-03
2.54E-03
1.34
1.55
Source Category A
NEI1D
PAH
Benzo(K)fluoranthene
1.38E-05
0.164384
1.88E+01
3.08E+00
4.25E-05
3.95E-03
2.54E-03
1.67E-02
1.55
Source Category A
NEI1D
PAH
Benzo[e]Pyrene
2.28E-06
0.068493
4.58E+O0
3.14E-01
7.16E-07
3.95E-03
2.54E-03
2.81E-04
1.55
Source Category A
NEI1D
PAH
Chrysene
1.69E-05
0.016438
2.54E-01
4.18E-03
7.05E-08
3.95E-03
2.54E-03
2.77E-05
1.55
Source Category A
NEI1D
PAH
Dibenz[a,h]anthracene
2.14E-05
0.561644
8.30E+00
4.66E+00
9.98E-05
3.95E-03
2.54E-03
3.92E-02
1.55
Source Category A
NEI1D
PAH
Fluoranthene
4.72E-04
0.068493
3.88E-02
2.66E-03
1.26E-06
3.95E-03
2.54E-03
4.93E-04
1.55
Source Category A
NEI1D
PAH
Fluorene
1.88E-04
0.068493
3.19E-02
2.18E-03
4.11E-07
3.95E-03
2.54E-03
1.61E-04
1.55
Source Category A
NEI1D
PAH
lndeno(l,2,3-cd)pyrene
2.45E-05
0.164384
4.63E+00
7.62E-01
1.86E-05
3.95E-03
2.54E-03
7.32E-03
1.55
Source Category A
NEI1D
PAH
Methylnaphthalene, 2-
1.34E-04
0.068493
1.68E-02
1.15E-03
1.54E-07
3.95E-03
2.54E-03
6.05E-05
1.55
Source Category A
NEI1D
PAH
Phenanthrene
1.22E-03
0
6.46E-02
O.OOE+OO
O.OOE+OO
3.95E-03
2.54E-03
O.OOE+OO
1.55
Source Category A
NEI1D
PAH
Pyrene
1.61E-05
0
1.59E-01
O.OOE+OO
O.OOE+OO
3.95E-03
2.54E-03
O.OOE+OO
1.55
Source Category A
NEI1E
PAH
Acenaphthene
6.&0E-02
0.068493
2.51E-02
1.72E-03
1.14E-04
1.12E-01
2.54E-03
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16.58
ut / 7) Detailed
-------
TRIM-Based Tiered Screening Methodology for RTR
B.5 References
National Climatic Data Center (NCDC). (2012) Quality Controlled Local Climatological Data.
Available online at http://cdo.ncdc.noaa.qov/qclcd/QCLCD?prior=N
National Climatic Data Center. (2007). Cimate Maps of the United States (CLIMAPS). Available
online at http://cdo.ncdc.noaa.gov/cqi-bin/climaps/climaps.pl
EPA (U.S. Environmental Protection Agency). (2001a) NATA - Evaluating the National-scale Air
Toxics Assessment, 1996 DATA - An SAB Advisory. U.S EPA Science Advisory Board.
EPA-SAB-EC-ADV-02-001. 12/2001.
EPA. (2001b) National-scale Air Toxics Assessment for 1996, Draft for EPA Science Advisory
Board Review. EPA Office of Air Quality Planning and Standards. EPA-453/R-01-003.
01/18/2001.
EPA. (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. (2010) Review of EPA's draft entitled, "Risk and Technology Review (RTR) Risk
Assessment Methodologies: For Review by the EPA's Science Advisory Board with Case
Studies - MACT I Petroleum Refining Sources and Portland Cement Manufacturing." EPA
Science Advisory Board. EPA-SAB-10-007. 05/07/2010.
USGS (U.S. Geological Survey). (2012) National Hydrography Dataset. Available online at
http://nhd.usqs.gov/.
Attachment B -
Tier 2
B-34
December 2013
-------
TRIM-Based Tiered Screening Methodology for RTR
Addendum 1. Summary of TRIM.FaTE Parameters Considered for
Inclusion in Tier 2 Analysis
Attachment B, Addendum 1
TRIM.FaTE Parameters
1-1
December 2013
-------
[This page intentionally left blank.]
-------
TRIM-Based Tiered Screening Methodology for RTR
Exhibits, Addendum 1
Exhibit Add B1-1. TRIM.FaTE Parameters Considered for Inclusion in Tier 2
Analysis 1-5
Attachment B, Addendum 1 1-3 December 2013
TRIM.FaTE Parameters
-------
[This page intentionally left blank.]
-------
TRIM-Based Tiered Screening Methodology for RTR
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 annually-
averaged 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
TRIM.FaTE Parameters
1-5
December 2013
-------
TRIM-Based Tiered Screening Methodology for RTR
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 PB-
HAPs (PAHs, cadmium, and
mercury).
Moderate Effort to
Implement: In implementing
changes in precipitation in
TRIM, care must be taken to
also preserve the overall water
balance in the model.
Low to
Moderate: The
annually-
averaged
precipitation rate
can be estimated
for the subset of
surface
meteorological
stations that
capture rainfall
data. Then,
facilities can be
linked to the
closest surface
meteorological
station with
available data.
High
Attachment B, Addendum 1
TRIM.FaTE Parameters
1-6
December 2013
-------
TRIM-Based Tiered Screening Methodology for RTR
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
TRIM.FaTE Parameters
1-7
December 2013
-------
TRIM-Based Tiered Screening Methodology for RTR
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
TRIM.FaTE Parameters
1-8
December 2013
-------
TRIM-Based Tiered Screening Methodology for RTR
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 water-soluble 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
TRIM.FaTE Parameters
1-9
December 2013
-------
TRIM-Based Tiered Screening Methodology for RTR
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 particle-bound chemicals.
It would also result in greater
chemical transport onto
farmlands, but this might be
counterbalanced by equally
greater erosion off farmland.
Highly Significant: Previous
analyses have shown risk to
be sensitive to this parameter
for some chemicals.
Moderate Effort to
Implement: In implementing
changes in the erosion rate
and fraction in TRIM, care
must be taken to also preserve
the overall water balance in
the model.
High: As with the
flush rate, the
erosion rate and
fraction for any
lake near a
facility cannot be
readily
determined.
Medium
Attachment B, Addendum 1
TRIM.FaTE Parameters
1-10
December 2013
-------
TRIM-Based Tiered Screening Methodology for RTR
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 offish.
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 1
TRIM.FaTE Parameters
1-11
December 2013
-------
[This page intentionally left blank.]
-------
TRIM-Based Tiered Screening Methodology for RTR
Addendum 2. Analysis of Lake Size and
Sustainable Fish Population
Attachment B, Addendum 2
TRIM.FaTE Parameters
2-1
December 2013
-------
[This page intentionally left blank.]
-------
TRIM-Based Tiered Screening Methodology for RTR
CONTENTS, ADDENDUM 2
1. Introduction 2-5
2. Angler Behavior 2-5
3. Fish Biology 2-6
3.1. Lake Productivity 2-6
3.2. Proportion of Fish Biomass by Trophic Level 2-8
3.3. Minimum Viable Population Size 2-9
4. Summary of Assumptions for the Lake Size Analysis 2-10
5. Equations Used to Determine Lake Fish Populations 2-10
6. References 2-12
Attachment B, Addendum 2
Lake Size Analysis
2-3
December 2013
-------
TRIM-Based Tiered Screening Methodology for RTR
[This page intentionally left blank.]
Attachment B, Addendum 2
Lake Size Analysis
2-4
December 2013
-------
TRIM-Based Tiered Screening Methodology for RTR
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 2 below). Other
factors depend upon the general biology of fish populations in North American ecoregions (see
Section 3). Based on evaluation of these factors, a set of assumptions was developed to
support the estimation of minimum lake sizes that are needed to sustain a particular total human
ingestion rate in grams/day (see Section 4). Then, equations were developed (see Section 5)
that were used to create Exhibit_Att B-4, which was used to determine the threshold lake size of
100 acres.
2. Angler Behavior
Several assumptions regarding angler behavior are important for estimating a minimum lake
size that is fishable. The first is a conservative assumption that anglers (and their family
members) consume about 50:50 top carnivore fish from both the benthos and from the water
column. Benthic carnivores (BC), such as catfish and chub that consume benthic invertebrates
and small benthic fish, can grow to large sizes (e.g., 2 kg). Assuming a diet of 50 percent
benthic invertebrates and 50 percent small benthic fish (e.g., young of the year that feed on
algae or detritus), the BC fish category would represent trophic level (TL) 3.5. Pelagic
piscivores—water column carnivores (WCC)—include species such as largemouth bass, lake
trout, pickerel, and walleye. The WCC are modeled as TL 4, with 100 percent of their diet
comprised of water column omnivores (WCO, e.g., pan fish such as bluegill and white perch),
and the diet of the WCO is assumed to be 100 percent minnow-sized fish that feed on
zooplankton and algae . The assumption of 50:50 WCC and BC consumption is conservative,
because smaller, pan fish, are not included. Although anglers might prefer to catch and
consume the pelagic (TL4) game fish species, their generally lower abundance compared with
fish that also consume benthic invertebrates dictates that anglers will more often capture the
benthic (TL3.5) carnivores. Some TL2 (herbivorous) fish such as carp attain "catchable" size,
but they generally are not popular fish for consumption and are not considered here. .
A second assumption is that 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 required to support specified human fish
consumption rates.
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 (see Exhibit_Att A-16. Summary of RTR Tier 1
Screening Scenario Assumptions).
The final assumption is that the lake must support a sustainable fishery despite fish harvesting
by humans; in other words, the lake shouldn't be "fished out" by the harvest rate required to
meet the subsistence angler fish ingestion rate. The productivity of any particular fishery (local
population of a species of fish) and the proportion of adult fish that can be harvested for human
consumption are difficult values to estimate, and models to predict sustainable harvests of
different fisheries are numerous and complex. Species-specific parameters of key to such
Attachment B, Addendum 2 2-5 December 2013
Lake Size Analysis
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TRIM-Based Tiered Screening Methodology for RTR
models include fecundity with age and size; survivorship of eggs, fry, and juveniles to sexual
maturity (recruitment); natural predation pressures; and temporal variation in food availability.
For the purpose of this analysis, simplifying assumptions are required. In the analysis by
Hakanson and Boulion (2004), which included a survey of 122 lakes, the authors noted that a
typical loss from fishing by birds, mammals, and humans would be approximately 10 percent of
the fish biomass in the prey fish compartment (TL3) and 10 percent of the biomass in the
predator fish compartment (TL4). The authors also found that as overall lake productivity
increased, the biomass of prey (TL3) fish increased more rapidly than the biomass of predator
(TL4) fish. For our lake size analysis, we assumed that anglers can harvest 10 percent of the
biomass of pelagic WCC fish without diminishing the fish population size.
3. Fish Biology
Fish life histories also are key to estimating the minimum surface area of a pond or lake that
could support a sustainable fish population of WCC. The productivity and trophic structure of
fish communities in ponds and lakes across the United States are varied. Thus, any set of
assumptions is unlikely to all hold true at any given location. Nonetheless, three factors are
important to any estimate of a minimum lake surface area for sustainable pelagic TL4 fishing:
the general productivity of a lake (expressed as grams of fish wet weight per meter squared, g
ww/m2); the maximum likely proportion of the total fish biomass in a lake that is comprised of the
top trophic level fish; and the minimum viable population (MVP) size required for the fish
species to be self-sustaining in the short term (for at least a few decades).
3.1. Lake Productivity
The general productivity of a lake depends on many factors, including latitude, seasonal
temperatures, and nutrients supporting the base of the food web. For lakes at approximately
the same latitude in the same climate, nutrients play a key role in the total fish biomass that a
lake might support. In a regression analysis of data on total phosphorus (TP) and fish biomass
for 31 lakes across North America, Europe, and Russia, Nurnberg (1996) summarized the
"limits" among three TP-defined lake trophic status categories with respect to total fish wet
weight biomass per unit area:
Nurnberg (1996) also summarized total fish biomass limits from Bachmann et al. (1996) for the
same lake trophic status categories based on a sample of 60 lakes in Florida:
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
Attachment B, Addendum 2 2-6 December 2013
Lake Size Analysis
Oligo-meso (TP = 10 pg/L)
Meso-eutro (TP = 30 pg/L)
Eutro-hypereutro (TP = 100 pg/L)
1.9 g ww/m'
3.7 g ww/m'
8.5 g ww/rrr
Oligo-meso (TP = 10 pg/L)
Meso-eutro (TP = 30 pg/L)
Eutro-hypereutro (TP = 100 pg/L)
7.4 g ww/m
10.6 g ww/m
15.6 g ww/m
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TRIM-Based Tiered Screening Methodology for RTR
121° E to 122° W longitude. Their linear regression relating TP to total fish standing biomass
(B) had a correlation coefficient (r2) of 0.84:
B = 0.792 + 0.072 (TP)
where:
B = total fish biomass (kg/hectare)
jp = total phosphorous (jjg/L)
The regression model of Hanson and Legget (1982) predicted total fish biomass densities in
lakes of 3.0-9.5 g ww/m2 for TP concentrations ranging from 10-50 |jg/L for oligo-mesotrophic
to mid-range eutrophic lakes. Another regression model from Hoyer and Canfield (1991)
predicted fish biomass densities in streams of 2.6-6.6 g ww/m2 over the same range of TP
concentrations.
In general, for very small lakes, relatively low fish productivity is likely. For example, Demers et
al. (2001) found fish standing biomass values of 2.73 and 3.81 g ww/m2 in two lakes of 27 and
22 acres (11 and 9 hectares), respectively, in south-central Ontario. Bronmark and Weisner
(1996) reported on aquatic communities from a sample of 44 small ponds in southern Sweden
(most were less than 5 hectares « 12 acres). They found no fish in 5 of the smaller ponds
(mean surface area of 0.20 ± 0.097 acres)—which also exhibited lower TP concentrations than
the larger ponds—and no piscivorous fish in another 11 of the 44 ponds (mean surface area of
0.46 ± 0.27 acres). For the 28 ponds with piscivorous (TL4) fish present, the mean pond
surface area was 1.4 (±1.3 SD) acres.
Scientists have also examined the relationship between TP and total fish biomass in reservoirs.
Yurk and Ney (1989) examined the relationship between TP and standing stock of fish in 22
reservoirs in southern Appalachia sampled in 1973. Their logarithmic regression relating TP to
total fish standing biomass (B) used the following equation and had an r2 of 0.75:
Log-to (B) = 1.07+ 1.14 * Log10 (TP)
Use of the equations from Hanson and Legget (1982) and Yurk and Ney (1989) yielded similar
predications of total fish biomass at low to intermediate TP concentrations. At low TP (e.g., 10
|jg/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 jjg/L), fish biomass predicted by the two
models were 15.5 and 22.4 g ww/m2, respectively.
Ideally, one would have data indicating TP levels in lakes in the vicinity of facilities for a Tier 2
analysis. Such data, however, are rarely readily available. For purposes of the screening
assessment, therefore, we assume that fish productivity per unit area is independent of lake
size over a wide range of lake sizes and that TP levels are unknown.
Leidy and Jenkins (1977) reported analyses of several large data sets to support modeling of
fish productivity and carrying capacity in reservoirs across the United States for the National
Reservoir Research Program. The analyses included studies of fish standing biomass by
species in 61 reservoirs across the midwestern and eastern United States sampled at different
times between 1952 and 1975. Only reservoirs of at least 500 acres (202 hectares) in size were
included, with some exceeding 65,000 acres (in the Missouri drainage basin). Considering all
61 reservoirs, the mean biomass density offish was 41.3 (± 30.4 SD) g ww/m2. The minimum
Attachment B, Addendum 2
Lake Size Analysis
2-7
December 2013
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TRIM-Based Tiered Screening Methodology for RTR
and maximum total fish biomass densities were 3.2 and 133.2 g ww/m2, respectively, and the
median value was 30.9 g ww/m2. Reservoirs typically have large drainage basins, which in
some areas can receive excess TP from large expanses of agricultural areas.
In summary, the fish productivity in lakes and reservoirs can vary by more than three orders of
magnitude. The reservoirs surveyed by Leidy and Jenkins (1977) in general were much larger
(and were often more shallow and nutrient rich) than the natural lakes surveyed by others
discussed above. The mean standing fish biomass of approximately 41.3 g ww/m2 from the
reservoir survey is likely to be higher than a mean value for representative samples of natural
lakes in the United States. For the purpose of estimating the minimum lake size that would
support a sustainable WCC fishery, we rounded that value down to a single significant digit of
40 g ww/m2 as the upper limit for total fish biomass in a lake. That standing biomass is higher
than predicted by the regression models of Hanson and Legget (1982), Yurk and Ney (1989),
and Nurnberg (1996) at a high total phosphorus of 100 |jg/L (where phosphorous is the limiting
nutrient). Less productive lakes would support fewer fish per unit area, and, therefore, would
have to be larger to support a specified fish ingestion rate.
3.2. Proportion of Fish Biomass by Trophic Level
As indicated previously, for the Tier 2 analysis, the proportion of fish in an angler's diet that
consists of WCC (TL4) and BC (TL3.5) is assumed to be 50:50 by biomass (not numbers) for
lakes that support the four trophic levels. In smaller lakes, TL4 fish are likely to be missing or
rare, with TL3 fish in the water column (e.g., sunfish) being the highest trophic level supported
by the primary productivity (algal/plant production) in some lakes. As a "rule of thumb" in
ecology, 10 percent or less of the energy produced at one trophic level usually can be converted
to biomass in the next trophic level (i.e., approximately 90 percent loss of energy) per trophic
step. However, with different species having different energy assimilation efficiencies and with
smaller species generally having higher turnover rates than larger species, the 10 percent
energy rule does not necessarily translate into a standing biomass pyramid of similar
proportions. In this section, the proportion of fish (based on biomass) that might be expected in
the WCC and the BC fish compartments relative to total standing fish biomass are examined
assuming that the lake is large enough to support WCC (pelagic TL4 fish).
Examination of several studies of fish biomass by trophic level indicated that top trophic level
fish might comprise up to 20 percent of the standing fish biomass in many locations. Ploskey
and Jenkins (1982) estimated that piscivorous fish, both those that are generally free-swimming
or pelagic (e.g., pike, gar, walleye) and those that rest and forage primarily in the benthos (e.g.,
various species of catfish, suckers) comprise 22 percent of the total fish biomass in DeGray
Lake, Arkansas (averaged across several years). Leidy and Jenkins (1977) estimated that 18
percent of the fish biomass across the 61 reservoirs they examined was piscivorous (minimum
of 14 percent and maximum 24 percent). Demers et al. (2001) categorized 2 percent and 15
percent of the total fish biomass in two small lakes of 27 and 22 acres in size, respectively, as
piscivorous/benthivorous fish (e.g., largemouth bass, creek chub); primary benthivores (e.g.,
catfish, suckers) dominated at >70 percent in both lakes.
One of the more recent food web models for freshwater lakes is that of Hakanson and Boulion
(2004). They designed their model to predict productivity and standing crop of prey and
predatory fishes in lakes of northern Europe. The authors acknowledged that fish feeding
patterns are complicated by the fact that fish change their feeding preferences as they
age. Some fish species consume zooplankton or benthic invertebrates in their first year, and
switch to small fish and then to larger fish as they mature and grow in size.
Attachment B, Addendum 2
Lake Size Analysis
2-8
December 2013
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TRIM-Based Tiered Screening Methodology for RTR
Hakanson and Boulion (2004) created a "distribution coefficient" to indicate what proportion of
the total fish biomass in a lake is prey versus predatory fish. Based on data from 122 lakes in
Europe and North America, they concluded that 27 percent by biomass is a "normal" portion of
predatory fish in a balanced system. They noted further, however, that for eutrophic lakes with
TP levels >100 |jg/L, the proportion offish represented by piscivores declines to less than 20
percent. The piscivores included both benthic and pelagic species. We note that most benthic
piscivores also consume benthic macroinvertebrates.
Based on the information above, the top trophic level fish are assumed to comprise 21 percent
of the total fish biomass. With the bulk of productivity in lakes originating from detritus in the
benthos, the total biomass of strictly pelagic game fish is expected to be less than that of
benthic fish. Therefore, for purpose of this lake size analysis, the piscivorous/benthivorous fish
were separated into two compartments, with 17.5 percent of the total fish biomass in a benthic
carnivore (TL3.5 or BC) compartment and 3.5 percent of the total in a pelagic piscivore (WCC)
compartment. Thus, the TL4 fish, when present, represent the limiting compartment for angler
fish harvesting and consumption.
3.3. Minimum Viable Population Size
The final step in estimating the minimum lake size that can support sustainable fishing of its
WCC fish species is to invoke the concept of minimum viable population (MVP) size. MVP is a
concept used frequently in conservation biology for animals and is defined as the smallest
population that will persist for a specified duration (e.g., 100 years) with a given probability (e.g.,
95 percent). MVP for any given species and location depends on many attributes of the species
biology (e.g., body size, reproductive rate, home range size, corridors between populations,
variability in environmental characteristics that impact fecundity and survival). At lower
numbers, the likelihood of population extinction increases due to environmental and
demographic stochasticity (Menzie et al., 2008). As for fisheries biology, entire text books have
been dedicated to applied population ecology with population simulations incorporating
demographic and life-history characteristics, spatial separation of habitat patches and
metapopulations, the probability of local catastrophes, genetic variation (e.g., drift), and other
factors with predictions of time-to-extinction or probability of extinction within specified time
periods (e.g., Soule, 1987; Ak?akaya et al., 1999). Consideration of such models in population-
level ecological risk assessment has begun, but faces many challenges (Barnthouse et al.,
2008). Moreover, that level of effort is beyond the resources available for screening-level
analyses.
Much of the initial work on MVP investigated the genetic minima required for short-term survival,
continuing adaptation to environmental change, and ultimately evolution. Inbreeding has been
considered the primary threat to short-term survival and genetic drift the principal threat to losing
the genetic variation required for adaptation (Shaffer, 1987). Several analyses (Senner, 1980;
Franklin, 1980; Soule, 1980; Frankel and Soule, 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
Attachment B, Addendum 2 2-9 December 2013
Lake Size Analysis
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TRIM-Based Tiered Screening Methodology for RTR
population viability analyses in the United States has been conducted on the spotted owl
(Boyce, 1993). Given the number and complexity of factors that influence MVP, however,
including the definitions of time horizon (e.g., 100 years) and probability of survival (e.g., 95
percent), population biologists caution against using a "rule of thumb" for MVP across
circumstances (Ewens et al., 1987).
Note that the MVP is appropriate for a single species of fish, not for generic categories of fish
such as WCC or TL4. For this Tier 2 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.
4. 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 fish ingestion and sustainable harvest rates and lake size
(see Section B.3.1 of Attachment B).
1. Piscivorous fish (WCC and BC), when present, comprise approximately 21 percent of the
standing biomass offish (ignoring seasonal changes). The BC fish represent 17.5 percent
of the standing fish biomass; WCCs account for 3.5 percent of the total fish biomass. Thus,
WCC fish, when present, represent the limiting compartment for angler fish harvesting and
consumption.
2. Humans can harvest 10 percent of the biomass of a fish compartment without threatening
the population due to overharvesting.
3. The MVP size for a single WCC species is at least 50 adult fish for a local population to
survive over the short term (more than a decade).
4. Only 33 percent of the fish is edible fillet muscle.
5. Equations Used to Determine Lake Fish Populations
The standing biomass of WCC (TL4) fish supported in Lake X can be calculated as the total
standing biomass offish (Total SB) multiplied by 0.035, based on the assumption thatTL4 fish
represent approximately 3.5 percent of the standing biomass in Lake X.
WCC SB = Total SB x Fraction WCC (Equation 1)
where:
WCC SB = Standing biomass of WCC fish (g wet weight [ww]/m2) in Lake X
Total SB = Total standing biomass offish (g ww/m2) in Lake X
Fraction WCC = Fraction of WCC fish in Lake X (i.e., 0.035)
Using WCC SB and the size of Lake X (Lake Size), the total number of WCC fish supported in
Lake X can be calculated using Equation 2 below.
No WCC Ldke Size x WCC SB x CF
BWa (Equation 2)
where:
Attachment B, Addendum 2 2-10 December 2013
Lake Size Analysis
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TRIM-Based Tiered Screening Methodology for RTR
No. WCC = Total number of WCC fish in Lake X
Lake Size = Size of Lake X (acres)
WCC SB = Standing biomass of WCC fish (g ww/m2; from Equation 1)
CF = 4047 (unit conversion factor m2/acre)
BWa = Body weight of adult TL4 fish (2000 g; assumed)
The likely annual productivity of WCC fish (kg/year) in Lake X can be estimated using .
| , , .Lake Size x WCC SB x CF1
Productivity WCC = (Equation 3)
CF2
where:
Productivity WCC= Likely annual productivity of WCC fish in Lake X (kg/year)
Lake Size = Size of the Lake X (acres)
WCC SB = Standing biomass of WCC fish (g ww/m2; from Equation 1)
CF1 = 4047 (unit conversion factor 1, m2/acre)
CF2 = 1000 (unit conversion factor 2, g/kg)
The maximum fish ingestion rate (g/day) for WCCs plus BCs associated with sustainable fishing
can be predicted using Equation 4. It assumes that the anglers consume 50 percent WCC and
50 percent BC, represented by the factor of 2 in Equation 4.
2 x Productivity WCC x FF x HF x CFl
Max Sustain IR (BC + WCC) =
CF2 (Equation 4)
where:
Max Sustain IR (BC + WCC) = Predicted maximum sustainable ingestion rate for BC and WCCfish (g/day)
Productivity WCC = Likely annual productivity of WCC fish in Lake X (kg/year; from )
FF = Fillet fraction; represents the assumed edible portion offish (0.33; unitless)
HF = Annual harvest fraction (0.10; unitless)
CF1 = 1000 (unit conversion factor 1, g/kg)
CF2 = 365 (unit conversion factor 2, days/year)
Attachment B, Addendum 2
Lake Size Analysis
2-11
December 2013
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TRIM-Based Tiered Screening Methodology for RTR
6. References
Ak?akaya, H.R., Burgman, M.A., and Ginzburg, L.R. 1999. Applied Population Ecology:
Principles and Computer Exercises using RAMAS EcoLab. Sunderland, MA: Sinauer
Associates, Inc. Publishers.
Bachmann, RW; Jones, BL; Fox, DD; Hoyer, M; Bull, l_A; Canfield, DE. 1996. Relations
between trophic state indicators and fish in Florida (USA) lakes. Canadian Journal of
Fisheries and Aquatic Sciences. 53:842-855.
Barnthouse, L.W., Munns, W.R. Jr., and Sorensen, M.T. 2008. Population-Level Ecological Risk
Assessment. Boca Raton, FL: CRC Taylor & Francis Group; Pnesacola, FL: Society of
Environmental Toxicology and Chemistry (SETAC).
Boyce, M.S. 1993. Population viability analysis: adaptive management for threatened and
endangered species. In: Wildlife Management Institute, Transactions of the 58th North
American Wildlife and Natural Resources Conferences. Proceedings WWRC-93-19, pp.
520-527.
Bronmark, C. and S.E.B. Weisner. 1996. Decoupling of cascading trophic interactions in a
freshwater benthicfood 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. Soule (ed.) Viable Populations for
Conservation. United Kingdom, Cambridge: Cambridge University Press, pp. 59-68.
Frankel, O.H., and Soule, M.E. 1981. Conservation and evolution. United Kingdom, Cambridge:
Cambridge University Press.
Franklin, I.R. 1980. Evolutionary change in small populations. In: M.E. Soule and B.A. Wlcox
(eds.) Conservation Biology: An Evolutionary-Ecological Perspective. Sunderland, MA:
Sinauer Associates; pp. 135-150. As cited by Shaffer, 1987.
Hakanson, 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 offish 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. Soule (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
Attachment B, Addendum 2 2-12 December 2013
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by the National Reservoir Research Program, U.S. Fish and Wildlife Service, for the U.S.
Army Corps of Engineers, Waterways Experiment Station, Vicksburg, Mississippi, USA.
Menzie, C., Bettinger, N., Fritz, A., Kapustka, L., Regan, H., Moller, V., and Noel, H. 2008.
Population protection goals. In: Barnthouse, L.W., Munns, W.R. Jr., and Sorensen, M.T.
2008. Population-Level Ecological Risk Assessment. Boca Raton, FL: CRC Taylor & Francis
Group; Pnesacola, FL: Society of Environmental Toxicology and Chemistry (SETAC), pp.
41-68.
NRC (National Research Council). 1986. Ecological Knowledge and Environmental Problem
Solving. Committee on the Applications of Ecological Theory to Environmental Problems.
Washington, DC: National Academies of Science Press.
Nurnberg, 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. Soule
and B.A. Wlcox (eds.) Conservation Biology: An Evolutionary-Ecological Perspective.
Sunderland, MA: Sinauer Associates; pp. 209-244. As cited by Shaffer, 1987.
Scaffer, M.L. 1981. Minimum population sizes for species conservation. Bioscience 31: 131-
134.
Shaffer, M. 1987. Minimum viable populations: coping with uncertainty. In: M.E. Soule (ed.)
Viable Populations for Conservation. United Kingdom, Cambridge: Cambridge University
Press, pp. 69-86.
Soule, M.E. 1980. Thresholds for survival: maintaining fitness and evolutionary potential. In:
M.E. Soule and B.A. Wlcox (eds.) Conservation Biology: An Evolutionary-Ecological
Perspective. Sunderland, MA: Sinauer Associates; pp. 151-170. As cited by Shaffer, 1987.
Soule, 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.
Attachment B, Addendum 2
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Appendix 5:
Analysis of data on short-term emission rates
relative to long-term emission rates
-------
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
2
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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 ozone-
producing 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
3
-------
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:
4
-------
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
I No I
3221-61-2)
90008
2-Methylpentane
] No I
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
5
-------
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
Benzofalpyrene
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
-------
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 1 T
Dimethylcyclopentane
i 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 Aery late
Yes
140-88-5
43438
Ethyl Alcohol
No
64-17-5.
43302
Ethyl Benzene
I Yes
100-41-41
45203
Ethyl Chloride
Yes
75-00-3
43812
Ethylcyclohexane
f
I No
1678-91-71
43288
ethylacetylene
No
107-00-6
43281
Ethyl Benzene
Yes
100-41-4
45203
Ethylene Oxide
Yes
75-21-81
43601
ethylmethylbenzene
No
25550-14-5!
45104
formaldehyde
Yes
50-00-0
43502
Furfural
No
98-01-11
45503
straight-run middle distillate
No
64741-44-2
Gasoline
No
86290-81-5
Gasoline
No
86290-81-5
Heavy Olefins
No
I 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
f
I No
107-83-5!
43229
hexane
Yes
110-54-3
43231
Hexene
No
25264-93-1
43289
lndeno[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
-------
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 i
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
78-93-31
43552
Methyl isobutenyl ketone
Yes
141-79-7
Methanol
Yes
67-56-11
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
lodomethane
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
592-27-81
43296
2-Methyl nonane
No
871-83-01
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
8030-30-61
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
-------
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
9C02-88-4
Poly(lsobutylene)
No
9003-27-41
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
106-42-31
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
i 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
nonei
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
nonei
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
I No
27987-06-0
-------
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
1,1-Di(t-Amylperoxy)
Cyclohexane
No
No
88-12-0
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-
d Hydroperoxide
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
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
1,2,4-Trichlorobenzene
Yes
' 120-82-1
45208
2-(2-Butoxyethoxy)ethanol
Yes
112-34-5
43312
2,3,4-trihydroxybenzophenone
Ester
No
I 1143-72-21
Methyl n-amyl ketone
No
110-43-01
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
1 1
| 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
I 1
Phenol Formaldehyde Resin,
Novolac
No
1 1
Propylene Glycol Monomethyl
Ether
No
1 107-98-21
70011
Pyrocatechol
No
120-80-91
Carbon Disulfide
Yes
75-15-0
43934
Hexene
No
592-41-6
43245
VOC
No
j 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
97-88-11
85008
dipropyl ether
No
111-43-3
n-Propanol
No
71-23-8
43303
Propyl propionate
No
106-36-5
86052
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-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-propyl benzene
No
103-65-1
-------
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
13
-------
Table 2. Event emissions in the Houston-Galveston area. Major emitters
reporting at least one release event of a representative substance.
2004 VOC Emission
Company Name Rate (Ib/h)
ATOFINA PETROCHEMICALS LA PORTE PLANT 47.88
BALL METAL BEVERAGE CONTAINER CONROE 24.18
FACILITY
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 51.30
PORTE PLANT
EQUISTAR CHEMICALS CHANNELVIEW COMPLEX 275.4
EQUISTAR CHEMICALS CHOCOLATE BAYOU 84.87
COMPLEX
EQUISTAR CHEMICALS LA PORTE COMPLEX 90.97
EXXON MOBIL CHEMICAL BAYTOWN OLEFINS PLANT 84.73
EXXONMOBIL CHEMICAL BAYTOWN CHEMICAL 313.7
PLANT
EXXONMOBIL CHEMICAL MONT BELVIEU PLASTICS 40.64
PLANT
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 111.8
REFINERY
MOBIL CHEMICAL HOUSTON OLEFINS PLANT 26.29
MORGANS POINT PLANT 31.03
PASADENA PLANT 13.40
14
-------
Table 2. Event emissions in the Houston-Galveston area. Major emitters
reporting at least one release event of a representative substance.
2004 VOC Emission
Company Name Rate (Ib/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
Cumulative
Frequency 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
-------
Table 4. Statistics for ratio of event emission
rate to long-term emission rate
Statistic for Ratio
Median
75th %ile
90th %ile
95th %ile
96th %ile
97th %ile
98th %ile
99th %ile
Max
Average
Value
0.043923
0.342655
2.204754
3.344422
3.400832
3.8126
4.790098
8.973897
74.37138
0.815352
Figure 1. Cumulative probability density for ratio of event to routine emission rates.
Cumulative probability of event ratios
0
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
Ratio of event emission rate to long-term emission rate
1.E+02
1.E+03
1.E+04
16
-------
Figure 2. Relationship between ratio of event to duration emission rate and emission
duration.
Event ratio vs. duration
2 1.E+01
§ 1.E+00
£ 1.E-01
a>
1.E-02
O
O
Z 1.E-03
2
c
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E
1.E-05
1.E-06
£ 1.E-07
1.E-08
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10
100 1,000
Event duration (min)
10,000
100,000
Figure 3. Relationship between ratio of event to duration emission rate and emission duration,
percentage of total time.
Event ratio vs. duration
i
f
~
A
K
~
+
~
~
1
t
~
ft <
i
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~
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w
w
E
« 1.E-05
1.E-06
1.E-07
3% 4% 5% 6%
Event duration (as % of total time)
-------
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+02
£ 1.E+01
TO
£
% 1.E+00
W
E
1.E-02
O
O
Z 1.E-03
5
c
.2 1.E-04
w
w
E
« 1.E-05
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® 1.E-06
O
S. 1E-07
1.E-08
1 10 100 1,000
Long-term VOC releases (Ib/hr)
~
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18
-------
Technical Memorandum
TO: EPA Docket No. EPA-HQ-OAR-2010-0682
FROM: Brenda Shine, Environmental Engineer
Refining and Chemicals Group, SPPD (E143-01)
DATE: March 15, 2012
SUBJECT: Derivation of Hourly Emission Rates for Petroleum Refinery Emission Sources
Used in the Acute Risk Analysis
I. Purpose:
This memorandum documents the assumptions used to derive the hourly emission rates used to
estimate acute risks from emissions of petroleum refinery emission sources. In past Risk and
Technology (RTR) rulemakings, when we have lacked hourly emissions data or other specific
information about the source category processes, we assumed that the 1-hour emission rate for
any emission point could be 10 times higher than its average hourly emissions, calculated by
dividing annual emissions by 8760 hours per year. The basis for this assumption was derived
from an analysis of short term release information collected from facilities in the Houston-
Galveston area and a comparison against routine emission rates for an entire facility. The
conclusions of this analysis were that the hourly emissions from any single release event to the
average annual VOC release rate for an entire facility was seldom greater than a factor of 10.
For the petroleum refinery source category, we have additional knowledge of the emission point
release characteristics that enable us to refine the default factor of 10 to something more realistic
for this source category.
II. Acute Factors for the Petroleum Refinery Source Category:
Instead of using the default factor of 10 described above, we estimated variability of hourly
emissions based on the operating characteristics of refinery emission sources in the following
manner:
a. For many sources that are associated with continuous operations and are essentially
steady state, such as Fluid Catalytic Cracking Units (FCCU), and sulfur recovery plants
(SRU), we would not expect significant variability in hourly emissions. Typical source
test variability is typically ±30 to 50%. We have applied an hourly multiplier of 2 to
estimate maximum hourly rates as an upper range of the expected variability. This
assumption was used for the following types of sources: FCCU, SRU, Chemical
Processes, miscellaneous process vents, refinery processes (not specified) and
incinerators.
b. For delayed cokers, we considered the average venting cycle time in calculating our
hourly emission rate. The process cycle for delayed cokers is 1 day (24 hours), with the
-------
depressurization vent having an average venting cycle time of, on average, 2 to 3 hours
per cycle. Since the depressurization vent opens for 730 to 1,095 hours per year, a factor
of 8 to 12 times the annual average emission rate could be used to account for this
emission process. The emissions are expected to be somewhat variable, with higher
emissions at the start of the venting cycle. We therefore applied an hourly multiplier of
20 to estimate maximum hourly rates for delayed coker emissions.
c. There are three types of catalytic reformers: Continuous, cyclic, and semi-regenerative.
Continuous reformers (SCC 30601602) are expected to have stable operations (much like
the FCCU and SRU), so a factor of 2 is appropriate for estimating the maximum hourly
emissions rate. Cyclic reformers (SCC 30601603) typically vent for 2,000 to 4,000 hours
per year, suggesting a factor of 2 to 4 for these units. However, their emissions over the
venting period are expected to be somewhat variable, so a factor of 10 is applied as a
conservative estimate. Semi-regenerative reformers (SCC 30601604) are expected to
vent approximately 10-15 days per year, suggesting a factor of 30 or 40. The venting is
not expected to be uniform over this period, so a factor of 60 is applied for these units.
d. For evaporative loss sources such as wastewater, we expect moderate variability in
maximum hourly emissions from annual average emissions. Emissions from wastewater
are dependent on organic loading to the unit and on the mass transfer (volatilization) rate
from the wastewater surface (which is dependent on the wastewater temperature and air
wind speed). The loading rates are expected to vary by a factor of 2 during normal
operations and the mass transfer rates are expected to vary by a factor of 2 due to
seasonal/meterological variations. We therefore applied an hourly multiplier of 4 to
estimate maximum hourly rates from wastewater sources.
e. For fugitives from equipment leaks, we note that the current methods of estimating
emissions make use of correlation equations in conjunction with Method 21 readings that
ultimately provide an hourly emission rate for the monitoring period in question. The
actual emission rates estimated based on the direct Method 21 readings are commonly
divided by two to estimate the average emission rate between monitoring intervals (i.e.,
assuming the leak started mid-way between monitoring intervals as described in the
emissions protocol). We expect the emission estimates from the direct Method 21
readings to provide a direct measure of the maximum hourly emissions (as leak repair
will be applied to reduce hourly emissions immediately after a monitoring cycle).
Therefore, we applied an hourly multiplier of 2 to estimate maximum hourly rates from
the annual average emission rates.
f. For storage vessels, higher hourly rates from loading operations can occur; however, the
majority of tanks are floating roof tanks; emissions from these tanks are associated with
wind-driven losses from fitting controls and clingage from exposure of external floating
roof tank shells. These emissions would not be expected to vary as significantly as fixed
roof tanks, but the emissions from floating roof tanks are expected to be dependent on
temperature (seasonal variability) and, in the case of external floating roofs, wind speed.
2
-------
There may also be variability in the crude oil processed or the intermediates and products
stored in a given tank. As with wastewater sources, each of these factors is expected to
cause a factor of 2 variability. We therefore applied an hourly multiplier of 4 to estimate
maximum hourly rates from storage tanks,
g. For other transfer and loading operations, maximum hourly rates can be approximated
from pumping rates; a factor of 2 for these sources would not adequately account for
higher maximum hourly emissions in most cases. Considering the hours of actual
product loading during the year (commonly 1,000 to 2,000 hours per year), we applied a
factor of 10 to these emission sources.
3
-------
Appendix 6 Detailed Risk Modeling Results
-------
Table 1 - Facility Identification Information
Source Category
Facility NEI ID
Facility Name
Address
City
State
Ferroalloys
39167NEI11660
Eramet
16705 State Route 7 South
Marietta
OH
Ferroalloys
54053NEIWV053FELMAN
NEW HAVEN PLANT
US ROUTE 62 NORTH
NEW HAVEN
WV
1 of 1
-------
Table 2 - Maximum Predicted HEM-3 Chronic Risks
Source Category Chronic Risk 1
Cancer
Cancer
Noncancer
Target
Facility NEI ID
MIR
Incidence
Max HI
Organ
Baseline scenario
54053NEIWV053FELMAN
1.97E-05
7.12E-04
3.05E+00
neurological
39167NEI11660
1.05E-05
1.36E-03
3.70E+00
neurological
Control Scenario
54053NEIWV053FELMAN
9.39E-06
3.81E-04
9.82E-01
neurological
39167NEI11660
6.28E-06
6.97E-04
1.03E+00
neurological
1 BOLD indicates a cancer risk greater than 1 in a million or a noncancer risk greater than 1
1 of 1
-------
Table 3 - Maximum Predicted Acute Risks (HEM3)
Ferroalloys
Baseline Modeling Scenario
Facility NEI ID
Pollutant
Maximum Hazard Quotient1
REL
AEGL1
ERPG1
AEGL2
ERPG2
39167NEI11660
Arsenic compounds
2.2E+00
0.0E+00
0.0E+00
0.0E+00
0.0E+00
39167NEI11660
Hydrofluoric acid
2.1E+00
6.0E-01
3.1E-01
2.5E-02
3.1E-02
54053NEIWV053FELMAN
Arsenic compounds
1.6E+00
0.0E+00
0.0E+00
0.0E+00
0.0E+00
54053NEIWV053FELMAN
Hydrofluoric acid
1.5E+00
4.5E-01
2.3E-01
1.8E-02
2.3E-02
39167NEI11660
Formaldehyde
1.1E+00
5.6E-02
5.2E-02
3.7E-03
5.2E-03
54053NEIWV053FELMAN
Formaldehyde
8.4E-01
4.2E-02
3.9E-02
2.7E-03
3.9E-03
54053NEIWV053FELMAN
Mercury (elemental)
2.1E-01
0.0E+00
0.0E+00
7.6E-05
6.4E-05
54053NEIWV053FELMAN
Hydrochloric acid
1.8E-01
1.4E-01
8.2E-02
1.1E-02
1.2E-02
39167NEI11660
Mercury (elemental)
1.2E-01
0.0E+00
0.0E+00
4.4E-05
3.7E-05
39167NEI11660
Hydrochloric acid
1.0E-01
8.2E-02
4.9E-02
6.7E-03
7.3E-03
Post-Control Modeling Scenario
Facility NEI ID
Pollutant
Maximum Hazard Quotient1
REL
AEGL1
ERPG1
AEGL2
ERPG2
54053NEIWV053FELMAN
Hydrofluoric acid
6.4E-01
1.9E-01
9.5E-02
7.6E-03
9.5E-03
54053NEIWV053FELMAN
Formaldehyde
4.2E-01
2.1E-02
1.9E-02
1.4E-03
1.9E-03
54053NEIWV053FELMAN
Arsenic compounds
3.7E-01
0.0E+00
0.0E+00
0.0E+00
0.0E+00
39167NEI11660
Arsenic compounds
3.5E-01
0.0E+00
0.0E+00
0.0E+00
0.0E+00
54053NEIWV053FELMAN
Mercury (elemental)
2.1E-01
0.0E+00
0.0E+00
7.4E-05
6.3E-05
39167NEI11660
Hydrofluoric acid
1.8E-01
5.3E-02
2.7E-02
2.2E-03
2.7E-03
39167NEI11660
Formaldehyde
1.7E-01
8.4E-03
7.7E-03
5.4E-04
7.7E-04
39167NEI11660
Mercury (elemental)
7.7E-02
0.0E+00
0.0E+00
2.7E-05
2.3E-05
54053NEIWV053FELMAN
Hydrochloric acid
5.8E-02
4.5E-02
2.7E-02
3.7E-03
4.0E-03
39167NEI11660
Hydrochloric acid
9.8E-03
7.6E-03
4.6E-03
6.2E-04
6.9E-04
1 Some maximum acute impacts may be at onsite locations.
Note: BOLD indicates acute risks greater than 1
1 of 1
-------
Table 4- Maximum Predicted Acute Risks Greater than 1 (Refined Approach)
Ferroalloys
Baseline Modeling Scenario
Facility NEI ID
Pollutant
Criteria
HEM-3
(Screening)
Refined
Results 1
Refined Modeling Approach 2
54053NEIWV053FELMAN
Hydrofluoric acid
REL
2
1
Examined results for maximum off-site impact
54053NEIWV053FELMAN
Arsenic compounds
REL
2
1
39167NEI11660
Arsenic compounds
REL
2
1
39167NEI11660
Hydrofluoric acid
REL
2
1
Post-Control Modeling Scenario
Facility NEI ID
Pollutant
Criteria
HEM-3
(Screening)
Ketined
Results 1
Refined Modeling Approach 2
54053NEIWV053FELMAN
Hydrofluoric acid
REL
0.6
0.6
Examined results for maximum off-site impact
54053NEIWV053FELMAN
Arsenic compounds
REL
0.4
0.4
39167NEI11660
Arsenic compounds
REL
0.4
0.4
39167NEI11660
Hydrofluoric acid
REL
0.2
0.2
1 of 1
-------
Appendix 7
Acute Impacts Refined Analysis Figures
-------
Refined Acute Modeling Approach
Initial acute screening risk calculations were performed with the HEM3 model
using the maximum hourly emissions estimates described in the memorandum Revised
Development of the Risk and Technology Review (RTR) Emissions Dataset for the
Ferroalloys Production Source Category, which can be found in Appendix 1 to this
report. HEM3 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 HEM3 to estimate maximum 1 -hour exposures
also likely overestimates exposures. To estimate maximum 1-hour concentrations at each
receptor, HEM3 sums the maximum concentrations attributed to each source, regardless
of whether those maximum concentrations occurred during the same hour. In other
words, HEM3 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 addresses refinements to determine the maximum off-site values by
plotting the HEM3 polar grid results on aerial photographs of the facilities for those
facilities and pollutants that exceeded short-term health benchmarks. These photographs
were examined to determine off-site locations that may be accessible to the public (e.g.,
roadways and public buildings.). The attached figures present the estimated property
lines of the facilities and 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. Table 1 provides the resulting change in acute risks taking
into account these off-site locations for the baseline modeling scenario.
Table 1. Refined Acute Risks Due to Off-site Receptors - Baseline Modeling
Scenario
Original
Acute
Hazard
Quotient
Off-site
Facility NEI ID
Pollutant
Criteria
Hazard
Quotient
39167NEI11660
Arsenic compounds
REL
2
1
39167NEI11660
Hydrofluoric acid
REL
2
<1
54053NEIWV053FELMAN
Arsenic compounds
REL
2
<1
54053NEIWV053FELMAN
Hydrofluoric acid
REL
2
<1
1
-------
Ferroalloys Facilities
-------
Figure 1 - Estimated Eramet Fenceline
-------
Figure 2 - Estimated Felman Fenceline
-------
Ferroalloys Facilities -
Baseline Modeling Scenario
-------
Figure 3 - 39167NE111660 Acute Arsenic HQ (REL)
-------
Figure 4-39167NE111660 Acute HF HQ (REL)
-------
Figure 5 - NEIWV053FELMAN Acute Arsenic HQ (REL)
-------
Figure 6 - NEIWV053FELMAN Acute HF HQ (REL)
-------
Appendix 8
Dispersion Model Receptor Revisions and Additions
-------
Dispersion Model Receptor Revisions and Additions for the Ferroalloys Source
Category
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 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. 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 this source category, 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 the Ferroalloys
Source Category
Centroid Revisions
Block ID
NEI ID
New
Latitude
New
Longitude
Comment
Not representive of
391670203001029
39.36404
-81.53392
population
540539548022127
38.952101
-81.922325
On plant property
Not representive of
540539548022183
38.944595
-81.921297
population
39167NEI11660
39.384305
-81.509796
Additional receptor
39167NEI11660
39.386712
-81.517476
Additional receptor
39167NEI11660
39.373718
-81.532765
Additional receptor
39167NEI11660
39.36754
-81.535303
Additional receptor
54053NEIWV053FELMAN
38.948407
-81.923071
Additional receptor
54053NEIWV053FELMAN
38.960334
-81.939735
Additional receptor
-------
Appendix 9 - Draft Protocol for Site-
Specific Multipathway Risk Assessment
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Protocol for Developing a TRIM.FaTE Model Scenario to Support a
Site-Specific Risk Assessment in the RTR Program
WORKING DRAFT
February 2014
Prepared For:
U.S. Environmental Protection Agency
Office of Air Quality Planning and Standards
Research Triangle Park, NC 27711
Contract No. EP-W-12-010
Prepared By:
ICF International
2635 Meridian Parkway
Suite 200
Durham, NC 27713
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July 2013
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CONTENTS
1. Introduction 1
1.1 Regulatory Context and Approach to Risk Assessment for PB-HAPs 1
1.2 Purpose of this Protocol 2
1.3 Scope and Limitations 2
1.4 Caveats 2
1.5 Protocol Road Map 3
2. A Brief Introduction to TRIM.FaTE Input Requirements 3
2.1 TRIM.FaTE Input Files and Contents 3
2.2 Recommended Sequence of Activities for TRIM.FaTE Set Up 4
3. Meteorological Data Development 5
3.1 The Role of Meteorological Data in TRIM.FaTE 5
3.2 Summary of Meteorological Parameterization in TRIM.FaTE 5
3.3 Required Meteorological Parameters and Data Time Steps 6
3.4 Selecting Appropriate Surface and Upper-air Data 7
3.5 Replacing Missing Data 8
3.6 Aggregate and Duplicate 10
3.7 Plume Rise 10
4. Air and Surface Parcel Design 11
4.1 The Role of Spatial Layouts in TRIM.FaTE 11
4.2 Theoretical Considerations Influencing Parcel Design 11
4.2.1 Cross-Wind Dispersion 11
4.2.2 Numerical Diffusion 11
4.2.3 Shape of the Deposition Profile with Distance from Source 12
4.3 Empirical Evidence Relating to TRIM.FaTE Parcel Design 12
4.4 Recommended Best Practices for Air and Surface Parcel Design 12
5. Surface Hydrology and Erosion Property Definitions 15
5.1 Surface Parcel Chemical Transfer Dynamics in TRIM.FaTE 15
5.2 Estimating Runoff and Erosion Fractions without Sophisticated GIS
Software 16
5.3 Estimating Runoff and Erosion Fractions with Sophisticated GIS
Software 17
6. Compartment Properties Recommended for Site-Specific Parameterization 18
6.1 The Role of Properties in TRIM.FaTE 18
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6.2 Approach to Prioritizing Properties for Site-Specific Parameterization 18
6.3 Elimination of Properties from Site-Specific Parameterization 19
6.3.1 Process-based Elimination of Parameters 19
6.3.2 Data Availability-based Elimination of Parameters 20
6.3.3 Combination of Data- and Sensitivity-based Elimination of
Parameters 20
6.3.4 Elimination of Physical and Chemical Characteristics 21
6.4 Properties Recommended for Site-Specific Parameterization 21
7. Properties Recommended for Land Use-Based Values 22
8. Properties Recommended for National Values 23
9. The FaTEmaster Scenario Builder 24
10. Potential Future Improvements 24
References 25
Appendix A. Documentation of Empirical Analyses Used to Prioritize TRIM.FaTE
Properties A-1
Appendix B. TRIM.FaTE National Property Values B-1
Appendix C. The FaTEmaster Scenario Builder Tool C-1
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TABLES
Table 3-1. Meteorological Parameters Required for Meteorology Input File for
TRIM.FaTE 6
Table 3-2. Potential Sources of Hourly Surface Meteorological Data (not all-
inclusive) 9
Table 6-1. TRIM.FaTE Properties Recommended for Site-Specific
Parameterization 22
Table 7-1. TRIM.FaTE Properties Recommended for Land use-Specific
Parameterization 23
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DRAFT Protocol for Developing a TRIM.Fa TE Model Scenario to Support a Site-Specific Risk Assessment in the RTR Program
1. Introduction
This document presents a protocol for developing TRIM.FaTE scenarios in support of site-specific risk
assessments conducted within the RTR program using the TRIM.FaTE environmental fate and transport
model.
This section describes the regulatory context, intended purpose of the protocol, the scope and limitations
of the protocol, and some caveats to its use. It also presents a road-map to the content and structure of
this document.
1.1 Regulatory Context and Approach to Risk Assessment for PB-HAPs
Section 112 of the Clean Air Act (CAA) directs the U.S. Environmental Protection Agency (EPA) to assess
the risk remaining (i.e., residual risk) from emissions of persistent and bioaccumulative hazardous air
pollutants (PB-HAPs) following the implementation of maximum achievable control technology (MACT)
standards for emission sources. Such risk assessments for various emission source categories are a
major component of EPA's Risk and Technology Review (RTR) program.
To evaluate ingestion exposures and human health risks for RTR on a source category basis, an iterative
approach is currently employed. The approach enables EPA to confidently screen out PB-HAP emissions
unlikely to pose health risks above levels of concern (i.e., a cancer risk exceeding 1-in-one million or a
non-cancer hazard quotient exceeding 11) and to focus additional resources on sources of greater
concern within the category.
Two models are used to estimate ingestion exposure and ingestion risk in the RTR program:
• The Fate, Transport, and Ecological Exposure module of EPA's Total Risk Integrated Methodology
(TRIM.FaTE) is used to model the fate and transport of pollutants released to the environment; and
• The Multimedia Ingestion Risk Calculator (MIRC) is used to estimate transfer and uptake into the food
chain and exposure to receptors consuming contaminated food products and soil. A subset of media
concentration estimates from TRIM.FaTE serve as inputs to MIRC, which also depends on other
exposure and biotransfer-related input parameters.
The RTR approach is divided into three steps of increasing refinement:
1. Tier 1 of the approach identifies facility-level emissions of PB-HAPs within a source category and
compares them to risk-based emission thresholds.
2. Tier 2 uses the actual location of the facility emitting PB-HAPs to refine a subset of the assumptions
associated with the modeled Tier 1 environmental scenario while maintaining the Tier 1 ingestion
exposure scenario assumptions; and
3. The final step, for facilities that cannot be ruled out based on the Tier 1 and Tier 2 screening process,
is to conduct a more refined assessment, up to and sometimes including site-specific multipathway
risk assessment. A site-specific risk assessment is intended to incorporate location- or facility-specific
characteristics regarding the environment to which PB-HAPs are emitted, relevant exposure
pathways, ingestion rates or other exposure factors, and other parameters. Site-specific risk
assessments undertaken in the past as part of the RTR process have involved extensive literature
searches for model parameters and required more time and resources to complete than the Tier 1
and Tier 2 screening analyses.
1
EPA considers "cancer risks exceeding 1-in-one million" to refer to risks of at least 1.5-in-one million, and "non-
cancer hazard quotients exceeding 1" to refer to hazard quotients of at least 1.5.
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1.2 Purpose of this Protocol
The site-specific protocol presented in this document is intended to serve as a guiding framework to set
up and parameterize scenarios in TRIM.FaTE that support accurate and cost-effective site-specific risk
assessments as part of the RTR framework.
The purpose of the protocol is to develop a standard set of guidelines and recommendations for
conducting site-specific assessments, providing a streamlined and replicable framework for configuring
and parameterizing the TRIM.FaTE model. The protocol aims to balance modeling accuracy with cost-
effectiveness in implementation, and to facilitate consistency and transparency across diverse
assessments. This protocol is also intended to function as part of the technical documentation for future
site-specific residual risk assessments by providing a clear and transparent description of the approach to
parameterization and some of the relevant sources. Deviations from this protocol would need to be
documented on a case-by-case basis. This document also provides guidance on ICF's FaTEmaster
Scenario Builder tool and discusses how that tool can be used to support site-specific risk assessments
using the TRIM.FaTE model.
1.3 Scope and Limitations
The site-specific protocol presented in this document focuses on the fundamental aspects of setting up a
scenario in TRIM.FaTE from an RTR perspective. While the TRIM.FaTE User's Guide (U.S. EPA 2005)
provides guidance on the mechanistic aspects of designing a simulation, the protocol focuses on
identifying best practices that optimize model set-up efficiency while maintaining a high level of model
precision in the RTR context.
These best practices have been developed with a focus on the impact of alternative model configuration
and parameterization approaches on ingestion risk in the RTR process. Thus, if two alternative model
configuration approaches are estimated to have similar impacts on risk estimates in the RTR process, the
protocol will recommend the less effort-intensive approach where appropriate. For instance, the protocol
identifies only a limited set of TRIM.FaTE model properties as requiring site-specific parameterization,
while proposing land use-specific or nationally representative or health protective values for others based
on the finding that relatively few model parameters substantially influence risk in the RTR context.
However, the protocol is not driven exclusively by considerations of cost-effectiveness. In some
instances, the protocol aims to provide superior methods of model configuration based on model
accuracy and scientific considerations that were previously not clearly articulated in available TRIM.FaTE
guidance, and that have a focus on the RTR program.
This protocol is not intended to serve as a substitute for the TRIM.FaTE User's Guide (U.S. EPA 2005) or
the TRIM.FaTE Technical Support Document (U.S. EPA 2002). It is not step-by-step guide to running the
model. It is recommended that the protocol be read in conjunction with the User's Guide and the
Technical Support Documentation to provide a holistic perspective on how the model should be used in
site-specific RTR applications.
1.4 Caveats
The findings and recommendations presented in this document are subject to several caveats:
• Some of the conclusions presented in this protocol are based on a combination of available empirical
evidence, theoretical considerations, and expert judgment. A "brute-force" empirical approach to test
an extensive range of scenarios and parameters was not feasible.
• For some model parameters, ICF relied on sensitivity analyses performed on previous configurations
of the model. It is possible that the results of previous sensitivity analyses differ slightly from the
current Tier 1 model configuration.
• ICF did not test the sensitivity of model parameters in alternative model configurations; and
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• ICF did not research and identify land use-specific parameter values for soil properties values as part
of this protocol, although it recommends their use.
Despite these limitations, the current recommendations are expected to meet the objectives of providing a
cost-effective and accurate approach to site-specific risk assessment in the RTR program. However,
users are encouraged to extend site-specific model design and parameterization beyond the levels
proposed here as circumstances permit.
1.5 Protocol Road Map
This protocol contains the following types of information:
• best practices for TRIM.FaTE model configuration for use in site-specific RTR applications;
• documentation of the rationale for best practice recommendations;
• nationally representative or health protective model parameter values for site-specific applications of
TRIM.FaTE; and
• a guide to the FaTEmaster Scenario Builder tool to create input files for TRIM.FaTE.
These distinct elements are woven together in the following structure:
• Section 2 sets the context with a summary of TRIM.FaTE input files and their content;
• Section 3 discusses the model's meteorological data requirements, potential data sources,
approaches to address missing data, data processing requirements, and the issue of plume rise;
• Section 4 presents recommendations and rationale for best practices for designing air and surface
parcels in TRIM.FaTE;
• Section 5 presents recommendations and rationale for best practices for defining surface hydrology
and erosion parameters required by TRIM.FaTE;
• Section 6 identifies parameters recommended for site-specific parameterization;
• Section 7 identifies parameters recommended for land-use specific parameterization;
• Section 8 identifies parameters recommended for national default parameterization;
• Section 9 presents a guide to using ICF's FaTEmaster Scenario Builder tool in site-specific RTR
applications; and
• Section 10 discusses potential future improvements and enhancements to the protocol.
2. A Brief Introduction to TRIM.FaTE Input Requirements
TRIM.FaTE is a spatially and temporally explicit multimedia environmental fate and transport model that
estimates the concentrations of emitted chemicals in biotic and abiotic environmental media. The model
uses a compartmental box model approach to track the movement of chemicals in environmental media.
The model is based on representing environmental media as compartments, moving chemical mass
between interacting compartments consistent with a set of governing mathematical algorithms that
describe environmental physical and chemical processes, and assuming instantaneous mixing within
each compartment.
2.1 TRIM.FaTE Input Files and Contents
TRIM.FaTE requires a variety of inputs from users to define the modeled environment and to quantify the
various environmental mass transfer processes. These inputs are provided to the model in the form of the
following files:
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• A "volume elements" file defines the spatial layout of the modeled domain in terms of three-
dimensional abiotic compartments. Each volume element provides a frame of reference for one or
more biotic compartments within it.
• A "compartments" file places biotic and abiotic compartments (modeling unit containing chemical
mass) within the volume elements.
• A library file contains all the model algorithms, properties, and emission source information. Examples
of the kinds of properties that are defined in the library file include:
- scenario characteristics (e.g., start/stop time, modeling time parameters, output options);
- source characteristics (e.g., chemicals emitted, location, emission rate);
chemical-specific properties, including physical-chemical (e.g., molecular weight, Kow) and abiotic
chemical-specific (e.g., degradation half-life);
non-chemical-specific characteristics of biota (e.g., body weight, food intake rate);
- site-specific ecological setting data and characteristics of biota (e.g., type of species present,
population and density information, food web relationships); and
abiotic environmental setting data such as abiotic media characteristics (e.g., air/water content of
soil, pH of surface water, suspended sediment density), runoff/erosion fractions for adjacent
surface soil compartments, and water flow between connected surface water compartments.
• A properties file typically contains: (i) simulation- and site-specific property values that are used to
overwrite default library values, and (ii) the location of time-varying input files for parameters such as
meteorological and vegetation parameters.
These input files must be developed using syntax that is consistent with TRIM.FaTE requirements. ICF's
FaTEmaster Scenario Builder tool, discussed further in Section 9, provides a spreadsheet-based
interface that facilitates the automated generation of syntactically accurate TRIM.FaTE input files from
user-specified inputs. Further detail on the required syntax of the input files, and the process of setting up
and running the model using these input files, is available in the TRIM.FaTE User's Guide (U.S. EPA,
2005).
Much of the challenge in a site-specific TRIM.FaTE application lies in designing a spatial layout that is
consistent with the nature of the governing algorithms and that reflects the environmental dynamics of the
modeled domain, researching and estimating numerous environmental properties that serve as inputs
into the model, finding and preparing appropriate meteorological and climate-related data, and setting up
the input files. The following sections will discuss the optimal methods of performing these tasks from the
perspective of a site-specific RTR application.
2.2 Recommended Sequence of Activities for TRIM.FaTE Set Up
The following sections of this document focus on various aspects of TRIM.FaTE set up as discrete
elements in the model configuration process. There are, however, interconnections between the research
required to guide various components of the set up process. Although there are no firm rules governing
the order in which the model's input files must be developed, this protocol recommends a sequence of
activities as a means to enhance efficiency and accuracy in the model configuration process:
1. Perform qualitative spatial analyses of topography, hydrography (boundaries of watersheds, flow
lines), and land cover around the site. This will aid in identifying meteorology data, identifying
modeled lakes and farms or potential farmland, and shaping the model domain;
2. Identify meteorological data based on RTR considerations (e.g., what meteorology did the RTR
inhalation risk assessment use for the site?), data availability, data quality, and the
representativeness of the data and instrument siting with respect to the modeled facility. Further,
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create the meteorological file and use the meteorological data and emission source parameters to
estimate plume rise where necessary;
3. Identify lakes to model based on lake size and a preliminary assessment of risk potential and data
availability;
4. Identify farms or potential farmland to model based on a preliminary assessment of risk potential and
data availability;
5. Create spatial layout;
6. Estimate and define surface hydrology and erosion dynamics within the layout;
7. Gather data on site-specific properties per the protocol;
8. Enter input property data into FaTEmaster Scenario Builder tool;
9. Generate TRIM.FaTE input files; and
10. Run TRIM.FaTE.
3. Meteorological Data Development
3.1 The Role of Meteorological Data in TRIM.FaTE
The algorithms that simulate the advection of chemicals between the air compartments and the
simultaneous deposition of chemicals to the underlying surface compartments depend on numerous
meteorological parameters such as wind speed, wind direction, mixing height, and rainfall rate, amongst
others. Sensitivity analyses have indicated that meteorological parameters are among the most risk
influential parameters in the TRIM.FaTE model (U.S. EPA 2009). Previous evaluations have also
suggested that the effects of these parameters are important in TRIM.FaTE, favoring the use of time-
varying meteorological data at the temporal resolution at which the measurements are reported, rather
than data averaged over longer time periods.
For these reasons, it is recommended that all meteorological parameters be site-specific. This accounts
for the potential interactive effects between meteorological parameters. The meteorology data should also
be hourly, where possible—averaging to coarser time steps can obscure real trends in the data.
This section discusses best practices in incorporating site-specific meteorological parameters in
TRIM.FaTE applications.
3.2 Summary of Meteorological Parameterization in TRIM.FaTE
The following steps, discussed in greater detail in subsequent sections, serve as a general guide to
generating TRIM.FaTE meteorology input data in RTR site-specific applications:
1. Determine the meteorology time step to be used (e.g., hourly, n hours per day, daily, etc.). Surface
meteorology data available from federal agencies is typically hourly. Longer time steps reduce model
run time but can obscure real and significant trends in the data. Hourly values are recommended;
2. If it is appropriate to use the same meteorology data used in RTR inhalation risk assessments, obtain
those data. Otherwise, identify the source(s) of meteorology data that meet the following criteria:
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The data contain all the meteorology fields required by TRIM.FaTE. Note that upper-air
radiosonde data do not contain mixing height values. Mixing height values must be calculated,
typically using a combination of surface and upper-air data (see Bullet 3 below);.
a. The data have no more than 10 percent missing data for any of the meteorology fields required
by TRIM.FaTE; and
b. The data are as representative as possible of the area being modeled.
3. If necessary, use a meteorological preprocessor or other method of estimating mixing heights at the
desired time step (upper-air data is typically measured twice daily with a radiosonde and does not
include mixing heights);
4. Replace all missing data with values that are as reasonable as possible;
5. If desired (although not recommended), aggregate the meteorology data to larger time steps. Then,
replicate the data as needed to create a meteorology input file that covers the entire period of
modeling; and
6. The combination of emission source characteristics (e.g., exit gas temperatures and velocities) and
meteorological characteristics (e.g., stability, ambient temperature) might lead to significant emission
plume rise during some time steps. In these cases, the user may use the emission source
characteristics and meteorology data to estimate plume rise values.
3.3 Required Meteorological Parameters and Data Time Steps
TRIM.FaTE meteorology data must include the fields in Table 3-1. Mixing heights are not part of observed
surface meteorology data. Mixing heights can be manually estimated using a vertical profile plot of upper-
air data from radiosondes, though the typical method of calculating mixing heights relies on using surface
and upper-air observed data in a meteorological processor such as AERMOD's meteorological processor
(AERMET) or the U.S. EPA mixing height program. These meteorological processors, sources of surface
and upper-air data, and sources of pre-calculated mixing heights are discussed in Section 3.4.
Surface meteorology data are typically available in hourly time steps, with some wind data available at
smaller time steps. Upper-air data from radiosondes are typically available twice per day. These data time
steps (hourly for surface data, twice-daily for upper-air data) are typically required by meteorological
processors to estimate hourly mixing height values. The TRIM.FaTE meteorology data file does not
require hourly time steps, although hourly data are typically used in site-specific assessments. Larger
time steps will shorten model run time. The aggregation of data into larger time steps (although not
recommended) should take place a/fer mixing height values are determined.
Table 3-1. Meteorological Parameters Required for Meteorology Input File for TRIM.FaTE
Parameter
Format
Units
Further Description and Notes
Date
M/D/YYYY
NA
NA
Hour
Numeric
NA
NA
Time Zone
e.g., "EST"
NA
NA
Horizontal Wind
Speed
Numeric
m/s
NA
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Parameter
Format
Units
Further Description and Notes
Wind Direction
Numeric degrees
degrees clockwise
from north; blowing
from
e.g., from north is 360 degrees; from
east is 90 degrees; from south is 180
degrees; and so on. 0 degrees is
reserved for calm winds (e.g., wind
speed = 0 m/s)
Air Temperature
Numeric
K
NA
Mixing Height
Numeric
m
Not an observed parameter. Must be
calculated, typically using a
meteorology processor with surface
and upper-air data inputs.
Rain Rate
Numeric
m/day
NA
Cumulative Rain
Numeric
m
Total precipitation in a precipitation
event. A multi-hour event will have
equal cumulative rainfall values for
each hour.
Is Day
Boolean (i.e., 1 or
0)
NA
Daytime (value of 1; after sunrise) or
nighttime (value of 0; after sunset).
Calculated using U.S. EPA's SR-
SS.exe program, available with
TRIM.FaTE.
3.4 Selecting Appropriate Surface and Upper-air Data
RTR inhalation risk assessments match a facility with the Automated Surface Observing Station that is
closest to the facility and that has relatively complete data for one recent year (that year is currently
2011). That surface station is coupled with its closest, regularly reporting upper-air station. Unless
meteorology data were measured at the facility itself, it is usually appropriate to use those same
meteorology stations for RTR ingestion risk assessments, subject to EPA discretion and the availability of
good quality data for multiple recent years.
Surface and upper-air radiosonde data can be obtained from the National Climatic Data Center (NCDC),
regional climate centers, and third party vendors. Some sources of hourly surface meteorological data
across the United States are shown in Table 3-2. These data sources are shown with generally the more
current and more spatially dense data (i.e., the large and freely available Integrated Surface Hourly data)
listed first. Some state agencies also maintain their own station networks and might be good sources of
meteorology data.
Twice-daily upper-air radiosonde data from stations operated by federal agencies can be obtained for free
from the National Oceanic and Atmospheric Administration (NOAA) Earth System Research Laboratory
(ESRL) in Forecast Systems Laboratory (FSL) format for over 100 U.S. stations and for 1994 through
present, where available (ESRL 2011). ESRL also has archived FSL-formatted upper-air data going back
to 1946, available for purchase on disc. Mixing heights are not part of observational data. Hourly mixing
heights can be manually estimated or they can be calculated using a combination of surface and upper-
air data. Typically, these hourly mixing height calculations are performed in a meteorology data
preprocessor such as AERMET (U.S. EPA 2012a). AERMET accepts some of the surface meteorology
data formats presented in Table 3-2 and FSL-formatted upper-air data. Pre-calculated, twice-daily mixing
height data also are available from U.S. EPA for over 70 U.S. stations for 1984 through 1991 (U.S. EPA
2010a) and can be used in the computer version of the meteorological processor for the Regulatory Air
Model (PCRAMMET; U.S. EPA 2012c).
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The user should select appropriate data based on proximity of the station to the modeling site and on the
station site's representativeness of the modeling site (e.g., elevation, land use, and wind flow). Evaluating
the representativeness of the site and data is subjective. The surface and upper-air stations closest to the
modeling site often will be the best choices. Because the upper-air station network is much sparser than
the surface network, proximity might be a less restrictive criterion in choosing upper-air stations compared
to choosing surface stations. Instead, it might be more important to choose an upper-air site that
experiences boundary layer characteristics that are similar to those of the modeling site. For example, if
the modeling site is well inland, it might not be appropriate to use an upper-air station on the coast.
The availability and quality of the data must also be considered. It is typically recommended that no more
than 10 percent of the data be missing, and the data values should be within expected bounds. The user
should also note that some meteorological data are archived in UTC, or Zulu time, which is functionally
the same as Greenwich Mean Time and equivalent to Eastern Standard Time plus five hours. The data
should be converted to local standard time for use in TRIM.FaTE, and standard time must be used
throughout the year (most archived meteorology data are in standard time).
One year of data might be sufficient, as long as the data on average are representative of recent
climatological averages at the site. NCDC 30-year climate normals are recommended for comparison
(NCDC 2011). When data from a single year are found to be significantly different from typical conditions,
the user can use several consecutive years that, together on average, are closer to typical conditions.
3.5 Replacing Missing Data
TRIM.FaTE requires that there be no missing data in its meteorology fields. U.S. EPA's recommended
guidance for replacing missing meteorology data (U.S. EPA 1992) has a series of objective data
replacement steps as a first pass, but those steps might not fill in all missing data. The guidance suggests
some subjective procedures for filling in remaining missing data; however, these are manual steps and do
not cover all possible cases of missing data (e.g., if more than a few contiguous hours of data are
missing). A meteorologist or experienced air quality modeler should perform these subjective data fill
procedures. The user should expect that the quality of substituted values will be worse for longer
contiguous periods of missing data versus only a few contiguous hours of missing data. However, as long
as the amount of data originally missing is no more than 10 percent, and as long as the substituted values
are not out of normal bounds, then substituted data will have only a small impact on modeling results—
especially for TRIM.FaTE assessments where the desired outputs are final accumulated media
concentrations after several decades of modeling.
ICF developed a tool (AERMET2TRIM), based in Microsoft Access, that fills in missing data in all
meteorology fields needed by TRIM.FaTE. The procedures are based on those provided in U.S. EPA
(1992). AERMET2TRIM requires first that AERMET be run on the surface and upper-air data in order to
estimate hourly mixing heights. AERMET2TRIM then reformats the data into the format required by
TRIM.FaTE.
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Table 3-2. Potential Sources of Hourly Surface Meteorological Data (not all-inclusive)
Name
Notes on Measured
Data
Years Available
Spatial
Coverage
Source
Other Notes
Integrated Surface
Hourly (ISH) Data
NA
Over 100 years to
today, where
available
Thousands of
stations
worldwide
NCDC (2008)
Available in two formats ~ abbreviated
and full. AERMET requires the full format.
The availability and structure of the
formats have changed through the years
and through the different available data
storage media.
Free via online download.
Quality Controlled
Local Climatological
Data (QCLCD)
NA
2005 to today
Approx. 1,600
U.S. stations
NCDC (2013)
Free via online download.
U.S. EPAAERMOD-
formatted data for the
Human Exposure
Model (HEM-3)
Not strictly observational
data - processed
observational data
through AERMOD's
preprocessors to produce
AERMOD-ready format.
2011 only
Over 800 U.S.
stations
U.S. EPA
(2013)
Free from EPA.
Solar and
Meteorological
Surface Observation
Network (SAMSON)
NA
1961 through 1990,
where available
Approx. 237
U.S. stations
NCDC (2012)
File formats changed after approx. 2007.
Available for purchase on disc.
U.S. EPA Support
Center for Regulatory
Atmospheric
Modeling (SCRAM)
No precipitation data
(request DSI-3240 format
precipitation data from
NCDC)
1984 through 1992,
where available
Approx. 250
U.S. stations
U.S. EPA
(2010b)
DSI-1440 format.
Free via online download.
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3.6 Aggregate and Duplicate
If desired (though not recommended), the user should aggregate the data to conform to the modeling
time step being used. Values of wind speed, air temperature, mixing height, and rain rates should be
averaged. For wind direction, the hourly values of wind speed and wind direction should be used to
calculate the vector components of the wind (i.e., u and v values), those vector components should be
averaged, and the averaged vectors should be used to calculate the average wind direction. Calculate
new cumulative rain values after averaging the rain rates. Use professional judgment to determine
appropriate values for the "Is Day" parameter.
Previous site-specific assessments conducted using TRIM.FaTE typically have used 50-year modeling
periods. The meteorology data in TRIM.FaTE format should be duplicated as necessary to produce a 50-
year data period (or other appropriate time period that matches the facility lifetime/emissions period
selected during the initial assessment planning stage). For example, if the meteorology data represent
years 2008 through 2011, the user should copy-and-paste that four-year period to create 50 years of data
(e.g., 2000 through 2049). The user should make sure that the year values are changed appropriately,
and that leap years contain data for February 29. If the user's original meteorology data do not contain a
leap day, simply duplicate data from February 28 and label as February 29.
3.7 Plume Rise
Emission plumes in nature usually have a vertical component to their dispersion pattern (i.e., plume rise).
Multiple forces can affect vertical dispersion, including the temperature of the plume compared to the
ambient air temperature, the wind speed at the release point, the ambient vertical temperature gradient,
atmospheric stability, the diameter of the release point, and the temperature and velocity of the emissions
as they exit the release point.
TRIM.FaTE does not calculate plume rise. Instead, emitted chemicals are advected horizontally through
fully mixed air compartments whose dimensions are defined by the user. This advection occurs in one of
two air volume compartments—the mixing layer compartment (where people, plants, animals, soil, and
water are exposed to the chemicals) or the upper-air compartment above the mixing layer (where the
chemicals are removed from ground-level exposure; i.e., a sink). Because an emission source's release
height is static, the mixing height for a given modeling time step determines which air layer the chemicals
are emitted into in that time step.
AERMET2TRIM has a function that estimates hourly plume rise given the physical parameters of an
emission source (i.e., latitude-longitude coordinates, release point height and diameter, and exit gas
temperature and velocity), the horizontal distance from the source at which to estimate the plume rise
(i.e., the radius of the modeled source parcel), and the meteorology data being used for the TRIM.FaTE
analysis. Because AERMET output data do not contain cloud ceiling heights, the AERMET2TRIM plume
rise calculations require a supplementary file containing those data (such ceiling data are available in ISH
and SAMSON data, for example). These plume rise estimations are based on methods summarized by
Seinfeld and Pandis (1998). The output from this function is a text file with hourly values for effective
release height, which is the actual release height plus the hourly plume rise value. If desired, the user can
then aggregate the data to the time step being modeled.
Including hourly effective release heights will increase model runtime. In previous TRIM.FaTE site-specific
assessments, a 5% rule has been used to judge whether or not to use hourly heights; that is, if the
effective release height was above the mixing height less than 5% of the time, hourly heights were not
used (i.e., the static, physical stack height was used). The user also has the option to estimate an
average effective release height for the entire modeling period, and simply use that average height as the
release height for all modeling times.
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4. Air and Surface Parcel Design
4.1 The Role of Spatial Layouts in TRIM.FaTE
One of the primary inputs required by the TRIM.FaTE model is the specification of a spatial layout using
Cartesian coordinates to define the vertices of surface and air parcels and volume elements. This
information is input into the model via the "volume elements" input file. To construct the volume elements
input file, users are required to divide the modeled domain into two-dimensional air parcels and surface
parcels. Air and surface parcels need not line up in all cases. Each parcel is also associated with a
height, which may vary in time. The parcel coordinates and height are combined to define three-
dimensional abiotic volume elements that contain biotic and abiotic compartments used to model the
movement of chemical mass in TRIM.FaTE.
In a site-specific assessment, the spatial layout should capture the features of interest (e.g., farms or
lakes) at the surface level and also specify how the overlying air domain is to be divided to produce
accurate and informative estimates. Although the TRIM.FaTE User's Guide (U.S. EPA 2005) provides
useful mechanistic guidelines and rules of thumb on the design of air and surface parcels, those
recommendations are not specific to the RTR context and are not based on an ingestion risk perspective.
The following guidelines, as noted in the introduction to this document, are intended to support site-
specific risk assessments in the RTR program and should be considered in addition to the instructions
and recommendations provided in the User's Guide and Technical Support Document (U.S. EPA 2002).
4.2 Theoretical Considerations Influencing Parcel Design
Discussed below are the theoretical considerations related to the TRIM.FaTE model that were used to
inform the air and surface parcel design recommendations presented later in this section.
4.2.1 Cross-Wind Dispersion
TRIM.FaTE models the movement of chemical mass based on first-order differential equations.
Consequently, the model does not account for dispersive processes in air that carry mass in a cross-wind
direction. Air parcel design must be conscious of, and attempt to compensate for, this limitation of the
TRIM.FaTE model. For instance, using a square grid for air parcels may result in "pipeline flow" with the
bulk of the emitted chemical concentrated in a compartment directly downwind. The concentration
estimates based on such a design would not align well with empirical evidence or with theoretical
Gaussian plume models that are based on the second-order advection dispersion equation.
Although a compartmental model with instantaneous mixing (such as TRIM.FaTE) could never produce
identical spatial results to a Gaussian plume model, air parcel design in TRIM.FaTE must attempt to
ensure that an appropriate amount of lateral dispersion is permitted to occur. The Pasquill-Gifford
estimates of cross-wind dispersion are commonly applied as parameters in air dispersion models for
alternative atmospheric conditions (U.S. EPA 1995). These cross-wind dispersion estimates are in the
form of graphs which plot cross-wind dispersion as a function of downwind distance. The Turner
equations are mathematical representations of these plots. A plot of the Turner equations, which
numerically approximate the Pasquill-Gifford estimates of cross-wind dispersion, suggests that 99% of the
emitted mass in neutral atmospheric conditions is likely to be contained within a cone subtending an
angle of 20 degrees from the source, assuming a constant wind direction through the centerline. This
provides a guideline about the ideal breadth of air parcels, especially those downwind of features of
interest.
4.2.2 Numerical Diffusion
All compartmental models potentially experience the issue of "numerical diffusion," which refers, in the
context of an air dispersion model, to the propagation of mass further downwind at a given point in time
than is physically possible given the wind speed. Maintaining a fixed relationship between the size of the
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time step used in the simulation, the length of compartments, and the wind speed helps prevent issues of
numerical diffusion. Users interested in strict time accuracy are advised to ensure that compartment
lengths are shorter than the wind speed multiplied by the model time-step. For RTR purposes, however,
average annual values in the 50th year are of primary interest, and strict restraints on parcel length for a
given time-step are considered less important but still good practice to implement.
4.2.3 Shape of the Deposition Profile with Distance from Source
The TRIM.FaTE model produces a deposition profile that has the largest values in the source
compartment and decreases with increasing distance in a pattern similar to an exponential curve.
Theoretical and empirical evidence suggests, however, that the maximum deposition rate would not be
experienced at the point of emission, but further downwind. At a wind speed of 5 m/s, the point of
maximum deposition for fine particulates and gases was estimated to be between 1 and 5 km (Overcamp
1976). Users of the TRIM.FaTE model should therefore be conscious of potential overestimation of
deposition in surface parcels adjacent to the source and a potential for bias in the deposition profile.
4.3 Empirical Evidence Relating to TRIM.FaTE Parcel Design
A limited number of TRIM.FaTE scenarios were modeled to provide empirical data about the impact on
risks (in the RTR context) from air and surface parcel layout design. Most of these scenarios were based
on simple variations of the Tier 1 screening scenario layout. The main findings are summarized here:
• Extending an air parcel beyond the outer boundary of the underlying surface parcel (i.e., having an air
parcel "overhang" the surface parcel beneath it) reduces deposition over the surface parcel compared
to air parcels that are co-terminus with surface parcels (i.e., having the air parcel directly overlay the
surface parcel);
• The number of air parcels that precede the air parcel overlying the surface feature of interest (i.e., the
number of air parcels between the source parcel and an air parcel of interest) does not materially
impact deposition over that surface parcel;
• Soil concentrations in the surface compartment adjacent to the source are considerably higher than if
the same surface compartment is set back by a short distance of approximately 400m (i.e., a 400-m
buffer parcel between the source and the parcel of interest). This is consistent with TRIM.FaTE's
exponential deposition gradient; and
• Increasing the length and/or breadth of a surface compartment might substantially reduce surface soil
concentrations
More detailed results from these scenarios are documented in Appendix A.
4.4 Recommended Best Practices for Air and Surface Parcel Design
The following recommendations for air and surface parcel design are intended to maintain a high degree
of modeling accuracy while reducing design effort and potentially optimizing computer run time. While
these guidelines are intended to facilitate optimal parcel design, every scenario is unique and might
require site-specific adjustments beyond the suggested approach provide here. Any adjustments or
improvements to the proposed approach should aim to be consistent with the governing principles and
empirical evidence discussed earlier in Section 4.
Step 1: Identify Features of Interest
Several steps are recommended for identifying features of interest:
• Use geospatial data (e.g., aerial imagery, data on watersheds and water bodies, and remotely-
sensed land cover and crop growth) to identify features of potential interest from the RTR
perspective, such as farms and lakes;
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Geospatial data can include Google Earth (Google 2013), the National Hydrography Dataset
(USGS 2013), the National Land Cover Database (MRLC 2013), and the Cropland Data Layer
(USDA 2013).
• Consider only lakes that are at least as large as 25 acres (approx. 10 hectares)2 and that research
suggests are fishable; and
• Use the following guidance to finalize the selection of lakes and farms for modeling:
Limit farms to those within roughly 5 km of the source;
Do not select multiple farms in the same direction from the source;
Do not select farms adjacent to the source parcel. Choose a buffer distance of at least 400 m;
Prefer farms and lakes closer to the emission source versus those farther away;
Prefer lakes for which preliminary research suggests good availability of modeling data (e.g.,
flush rates, depth, pH, total phosphorus levels, suspended sediment concentration);
Prefer lakes and farms that are frequently downwind from the emission source, if they exist,
based on the meteorology data selected for modeling; and
Prefer the lake(s) selected in the Tier 2 screening analysis.
Step 2: Breadth of Air Parcels (Air Parcel Radials)
Several steps are recommended for defining the breath of air parcels:
1. Emissions center point: Identify the center point of what will later become the emission source parcel.
It should roughly be the centroid of all the actual emission sources of concern at the facility;
2. Draw 20-deqree radials: Manually or using geospatial software, overlay radials that intersect at the
source center point, are 20 degrees apart. These radials roughly create triangles and represent
preliminary breadth boundaries of air parcels;
3. Adjust the radial arid: Rotate the radial grid so that the maximum number of surface features of
interest (e.g., farms and lakes) falls within single triangles;
4. Smaller features of interest: For features that fall within a single triangle, use the boundaries of the
triangle to define the breadth dimension of the air parcel overlying the surface feature;
5. Larger features of interest: For features that fall within multiple triangles, merge and adjust the
multiple triangles into a single triangle that fully contains the feature. For instance, if a lake subtends
an angle of 50 degrees with respect to the source, three 20 degree triangles should be merged and
adjusted to form a 50 degree triangle to define the breadth of the overlying air parcel. This process
will distort the shape of the surrounding triangles (i.e., they will be greater than the suggested 20
degrees), but that is acceptable; and
6. Areas not overlying features of interest: For air parcels not overlying any features of interest, less
resolution is required. Merge three 20 degree triangles to create a 60 degree triangle to define the
breadth dimension of the overlying air parcel.
2
Based on available data, for RTR multipathway emission screening analyses, EPA defines potentially fishable lakes
as those larger than 100 acres, without exceeding 100,000 acres. Even a 100-acre lake is unlikely to be large enough
to sustain harvesting the number of piscivorous fish required for the current screening ingestion rate (i.e., 373 g ww
fillet/day). This is discussed in Section B.3.1 of Attachment B in Appendix 4 to the Risk Report. However, EPA
includes smaller lakes (as small as 25 acres) in site-specific RTR multipathway analyses to be health protective and
to ensure that small lakes that might be more highly contaminated than estimated by the screening analyses are not
eliminated.
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Step 3: Define the Outer Boundaries of the Domain
Draw the outer boundaries of the domain approximately 5 km beyond the farthest feature of interest (in
relation to the emission source), using the remaining parcel radials as the design template. This 5-km
buffer is flexible depending on the characteristics of the nearby watersheds and how they might impact
the runoff and erosion of chemical into the features of interest. Truncate the outer boundary of the domain
in areas without features of interest, provided that these areas contribute much less significantly than
other areas toward chemical runoff or erosion towards the features of interest.
Step 4: Complete Air and Surface Parcels for Features of Interest
Within the triangle overlying the feature (defined in Step 2), encase the feature within a trapezoid (O) by
drawing straight lines at an angle to the sides of the triangle to define the inside boundary (the side
closest to the source) and outside boundary of the feature. The trapezoid should be perpendicular to an
imaginary radial originating at the emission source center point; put another way, these new lines should
be parallel to each other and perpendicular to an imaginary radial bisecting the triangle, thus creating a
trapezoid.
Where possible, the surface parcel representing the feature of interest should coincide with the air parcel
trapezoid described above. This might require slightly distorting the dimensions of the surface feature of
interest, but that is a permissible approach when the shape of the feature of interest is not very different
from the overlying air parcel trapezoid.
For irregular surface features (i.e., those whose shapes are very different from the overlying air parcel
trapezoid), create additional surface parcels as required to fill the space between the actual boundaries of
the feature of interest and the boundaries of the overlying air parcel trapezoid. The additional surface
parcels adjacent to the features of interest should be constructed subject to the consistency of land cover,
terrain patterns, and/or hydrography. A single adjacent parcel should never surround another parcel
entirely. Instead, two adjacent parcels should bound the irregularly shaped feature on either side.
Step 5: Draw Air and Surface Parcels for the Emission Source
The air and surface parcels for the emission source should line up. The source parcel should be centered
on the center point identified in Step 2 above and should accurately reflect where chemicals of concern
are actually being emitted at the facility (i.e., it should fully contain all of the actual emission sources of
concern). It should be a polygon where each side connects the sides of each air parcel triangle, and each
side is perpendicular to an imaginary radial bisecting the triangle. For example, if the air parcel triangles
each subtend exactly 60 degrees, and if the real emission sources span a 500-m distance, then the
source parcel would be a perfect hexagon that is centered on the emission source center point and has a
diameter of 500 m.
Step 6: Complete Air and Surface Parcels Upwind of the Features of Interest
Within the two radials that bound a feature of interest, create a single air parcel between the feature and
the source parcel (i.e., upwind of the feature of interest). Like the air parcel above the feature itself, this
upwind air parcel will be a trapezoid bounded by two lines that define the inside and outside boundaries
(parallel to the inside and outside lines of the feature, relative to the emission source) and by the radials.
There should be a corresponding surface parcel that lines up with the upwind air parcel. If the upwind
region contains several different regimes of land cover, terrain, and/or hydrography that are large relative
to the region, then divide the surface parcel into separate parcels for each of those different, major
regimes. For example, for an upwind surface parcel oriented north-south, if the northern half contains
urban land cover and the southern half is a deciduous forest, then it might be reasonable to divide the
parcel into a northern parcel (with no vegetation) and a southern parcel (with deciduous forest). On the
other hand, if the region is a scattered mix of forested and pasture/hay land cover, it would not be
reasonable or efficient to create many very small parcels for each small area of forest or pasture/hay.
Step 7: Air and Surface Parcels Downwind of the Features of Interest
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Create an air parcel downwind of the feature of interest (i.e., on the side of the feature opposite the
emission source) with a length dimension no larger than 5 km. The relative small size of this downwind
parcel increases the accuracy of chemical transfer via runoff and erosion.
There should be a corresponding surface parcel that lines up with the downwind air parcel. If the
downwind region contains several different regimes of land cover, terrain, and/or hydrography that are
large relative to the region, then divide the surface parcel into separate parcels for each of those different,
major regimes.
Step 8: Complete Air and Surface Parcels Crosswind of the Features of Interest
Several considerations are recommended for defining air parcels crosswind of features of interest (i.e.,
parcels to the "left" and "right" of the feature, relative to the emission source):
• The outside boundary of an air parcel immediately crosswind of a feature of interest should not
extend beyond the outside boundary of the feature itself (i.e., the outside boundaries of the feature
parcel and the crosswind parcel, relative to the emission source, should connect). This is to increase
the accuracy of chemical transfer via runoff and erosion;
• The breadth dimensions of a crosswind air parcel should be defined by the radial grid. That is, the
"left" and "right" sides of the crosswind parcels (relative to the emission source) should be defined by
the radial grid developed in Step 2 above;
• The inside boundary is the source parcel, unless there is already a parcel drawn between the source
and the crosswind parcel (i.e., a parcel related to a different feature of interest); and
• There should be a corresponding surface parcel that lines up with the crosswind air parcel. If the
crosswind region contains several different regimes of land cover, terrain, and/or hydrography that
are large relative to the region, divide the surface parcel into separate parcels for each of those
different, major regimes.
Step 9: All Other Air and Surface Parcels
All other air and surface parcels should be fitted within the boundaries defined by: (i) the air parcel radials
(Step 2); (ii) the boundaries of the features of interest and their upwind, downwind, and crosswind
neighbors (Steps 6-8); and (iii) the outer boundaries of the domain (Step 3). These other air and surface
parcels are subject to continuity of land cover, terrain, and/or hydrography, as discussed in the above
steps.
5. Surface Hydrology and Erosion Property Definitions
5.1 Surface Parcel Chemical Transfer Dynamics in TRIM.FaTE
The TRIM.FaTE model incorporates the ability to account for chemical transfers between adjacent
surface parcels via runoff and erosion. The algorithms that model surface runoff and erosion in
TRIM.FaTE simulate the advective chemical transfer dynamics between surface parcels without requiring
spatial elevation information or land-cover details as inputs. Instead, the algorithms depend on inputs
explicitly specifying the destination of erosion and runoff from a specific parcel. In other words, for each
surface parcel, users must specify the proportion of the erosion and runoff originating in that parcel that
reaches specific adjacent parcels. These inputs are known as link properties in TRIM.FaTE and are
typically specified in the TRIM.FaTE "properties" file discussed in Section 2. Users must also separately
specify the average runoff and erosion rate for each surface parcel. These inputs are combined internally
with estimates of the chemical concentration in surface soil and soil water to estimate mass transfers that
occur in conjunction with erosion and runoff processes.
The inter-parcel runoff and erosion parameter inputs in TRIM.FaTE are inherently site-specific because
there is no logical default value for the percentage of runoff and erosion from one parcel that reaches an
adjacent parcel. Simulations indicate that ingestion risk in the RTR process is sensitive to the choice of
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these values (refer to Appendix A). This section discusses options for parameterizing these inputs in site-
specific TRIM.FaTE applications for RTR.
For users not having access to (or expertise in using) geographical information systems (GIS) software
with features to quantitatively analyze surface hydrology and erosion, the recommended method of
estimating parcel-to-parcel runoff/erosion fractions is summarized in Section 5.2. If sophisticated GIS
software with features to analyze surface hydrology and erosion based on elevation is to be used, the
recommended method is summarized in Section 5.3.
5.2 Estimating Runoff and Erosion Fractions without Sophisticated GIS Software
Without a license for sophisticated GIS software, the user can still obtain free GIS viewing tools that allow
the user to display multiple layers of geospatial data and that have limited interaction with the data,
including querying the data and measuring distances. With such viewing software, the method for
estimating runoff/erosion fractions provided in Module 11 of the TRIM.FaTE User's Guide (U.S. EPA
2005) is appropriate. This method is summarized briefly here, with some additional tips not provided in
the User's Guide.
Step 1: Assemble Hydrological and Elevation Data
The user should obtain geospatial data indicating boundaries of hydrological units relevant to the
modeling domain. These hydrological data are available from the U.S. Geological Survey (USGS)
National Hydrography Dataset (NHD; USGS 2013). These hydrography data should already have been
obtained and used to inform the design of the modeling parcels. The NHD offers several levels of
hydrological units, typically from regions (the most spatially coarse) to subwatersheds (typically the
highest spatial resolution). Considering that the typical site-specific TRIM.FaTE modeling domain has a
radius less than 50 km and is divided into several surface parcels, subbasins or watersheds will usually
offer the most appropriate resolution for use in configuring parcels and estimating runoff/erosion fractions.
The NHD also offers directional flow lines of streams, rivers, and other hydrographic features.
The user should also obtain elevation data for the modeling domain. High resolution data are available
from the USGS National Elevation Dataset (NED; USGS 2006). These elevation data should already
have been obtained and used to help construct the modeling parcels. The data with the highest spatial
resolution are not necessary; 30-m resolution usually is appropriate.
Step 2: Relate Surface Modeling Parcels to Each Other and to Hydrological Units
The user should display the modeling surface parcels along with the appropriate hydrologic unit
boundaries from the NHD. For each parcel ("sending parcel"), do the following:
• For each hydrologic unit that occupies at least part of the sending parcel, estimate (or calculate, if
able) the ratio [surface area of the part of the hydrologic unit that is inside the sending parcel] to
[surface area of the sending parcel];
• Identify each neighboring parcel ("receiving parcel"), including sinks where appropriate for the sides
of the sending parcel that lie along the outer boundary of the modeling;
• For each hydrologic unit that occupies at least part of the sending parcel, estimate or calculate the
length of each interface between the hydrologic unit and each receiving parcel (not discussed in the
TRIM.FaTE User's Guide); and
• Estimate or calculate the fraction of the sending parcel's perimeter that interfaces with each receiving
parcel (not discussed in the TRIM.FaTE User's Guide).
Step 3: Estimate Fraction of Runoff and Erosion
For each hydrologic unit that occupies at least part of a sending parcel, one should use NED elevation
data and NHD flow lines to estimate the fraction of runoff that will flow from the hydrologic unit into each
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receiving parcel and, where appropriate, into sinks outside the modeling domain. A fraction might be 0 if
the elevation and flow lines suggest that all water in the hydrologic unit flows away from a receiving
parcel.
The User's Guide (U.S. EPA 2005) Section A.5 discusses runoff/erosion fractions. Although not
discussed there, the NHD flow lines can help estimate the relative distribution of runoff from a sending
parcel to its receiving parcels, or from a hydrologic unit in the sending parcel to a receiving parcel. One
can examine the flow lines along each sending-receiving boundary to get a sense how much of the
boundary has flows from the sending area to the receiving area. This information can be combined with
information on how much of the sending area's perimeter interfaces with the receiving area in question,
aiding the user in developing runoff/erosion fractions.
As discussed in the User's Guide Sections A.5 and A.6—separately for each hydrologic unit in a sending
parcel, multiply [the fraction of sending parcel's area covered by the hydrologic unit] by [the runoff/erosion
fraction from the hydrologic unit to the receiving parcel] for each of the sending parcel's receiving parcels.
Then, for each of these receiving parcels, sum this product across the hydrologic units. This sum provides
the final fraction of runoff/erosion from each sending parcel to each receiving parcel. For each sending
parcel, the fractions will sum to 1 when sinks are included as appropriate.
Another option is to estimate the runoff and erosion fractions based on visual inspection. This approach
does not explicitly relate the area of each hydrologic unit to each sending parcel. Therefore, it does not
explicitly assume that water cannot cross the boundaries of hydrologic units. Like the methods described
above, this option uses flow lines and the interfacial length between adjacent parcels. In this option, for
each sending parcel, the user visually examines the NHD flow lines to see where (if at all) flow lines cross
each interfacial boundary and into the receiving parcels. For each sending-receiving pair of parcels, the
user should estimate (or measure, if possible) the length of the part of the interfacial boundary that has
flow lines crossing into the receiving parcel. Then, divide that length by the total perimeter length of the
sending parcel. This ratio provides the fraction of runoff/erosion from the sending parcel into the receiving
parcel. Some professional judgment is required to subjectively adjust these fractions based on the relative
magnitude of runoff across the various interfacial boundaries. These relative magnitudes can consider the
overall terrain and flow patterns throughout the sending parcel (i.e., a flow into the receiving parcel with a
relatively small fetch will likely carry less chemical into the receiving parcel than a flow with a relatively
long fetch).
5.3 Estimating Runoff and Erosion Fractions with Sophisticated GIS Software
The method discussed in this section requires the use of ESRI® ArcGIS™ software. The software license
must enable the "Spatial Analyst" extension. ICF is currently developing an ArcGIS™ model, coupled with
a Microsoft® Excel™ post-processing tool, that largely automates the below procedures.
In ArcGIS, select the "Flow Direction" tool of the "Spatial Analyst" extension. Given a raster elevation
dataset (such as the NED), this tool will determine the flow direction of each raster cell to the steepest
downhill neighboring raster cell. The output of this tool will be a raster, where the value of each raster cell
will indicate the flow direction.
Then, select the "Flow Accumulation" tool of the "Spatial Analyst" extension. The input to the "Flow
Accumulation" tool is the output of the "Flow Direction" tool described above. Separately for each input
raster cell, the "Flow Accumulation" tool will follow the flow direction into the appropriate neighboring cell,
and continue following the flow direction of that cell into a third cell, and so on, "connecting the dots" of
the flow vectors until an endpoint is reached. This creates flow lines across the raster. Then, the tool
calculates the number of these flow lines that cross each raster cell. This is the "flow accumulation"
number produced by this tool. The flow accumulation is unitless, as it does not represent an actual
amount of water or chemical flowing from one place to another; the accumulation values should be
viewed relative to each other.
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For each sending parcel, the user would use the combination of flow direction and flow accumulation data
from the above tools to calculate the total flow (unitless) from the sending parcel to each receiving parcel.
The runoff/erosion fraction from the sending parcel into receiving parcel "A" would be the accumulated
flow from the sending parcel to receiving parcel "A" divided by the total accumulated flow from the
sending parcel to all its receiving parcels.
6. Compartment Properties Recommended for Site-Specific
Parameterization
6.1 The Role of Properties in TRIM.FaTE
The TRIM.FaTE model is dependent on hundreds of user-specified properties that describe the biotic and
abiotic environments being modeled. Properties in TRIM.FaTE can be broadly divided into the following
types:
• non-chemical specific properties that define biotic compartments (e.g., biomass of game fish in a
lake, the length of a leaf on a deciduous plant),
• non-chemical specific properties that define abiotic compartments (e.g., porosity of surface soil, the
total suspended solids concentration in a lake),
• chemical-specific properties (including system-wide chemical properties such as the Henry's Law
constant, the octanol-water partition coefficient, and compartment-specific chemical properties such
as reaction and degradation rate constants in various environmental media), and
• simulation-specific properties (e.g., model run-time, model time step).
All user-defined (e.g., non-formula) properties in a TRIM.FaTE scenario can be assigned simulation- or
site-specific values. In theory, the more properties that are assigned site-specific values, the more
accurately the simulation will represent chemical fate and transport at that location. Following this logic,
the user should try to find site-specific values for as many properties as possible. However, although each
model property is potentially important in defining a particular environmental fate and transport process, it
is apparent based on theoretical considerations and empirical evidence (i.e., analysis of model results
and model evaluations) that there is a subset of model properties that more significantly influences the
environmental concentrations that drive the risks of importance in the exposure scenarios evaluated in
RTR assessments. The fact that some parameters are more influential on results is true for complex
models in general. This is the focus of sensitivity analyses.
In previous site-specific risk assessments using TRIM.FaTE, which were conducted for RTR and in other
regulatory applications, a substantial portion of the level of effort required to perform the assessments
was directed towards site-specific property parameterization. One of the specific objectives of this
protocol is to take advantage of the results of sensitivity analyses and model evaluations conducted of
TRIM.FaTE. Based on these results, we have identified those compartment properties that are a high
priority for site-specific parameterization, those that can be adequately represented by regional or land-
use-specific default values, and those for which nationally representative or health-protective values are
adequate. This classification scheme is intended to reduce the level of effort required to adequately
parameterize site-specific assessments while maintaining a high level of accuracy in risk estimates for
RTR.
6.2 Approach to Prioritizing Properties for Site-Specific Parameterization
ICF relied on a combination of theoretical reasoning and empirical evidence to prioritize TRIM.FaTE
properties for the purposes of this protocol. In this way, ICF was able to limit the need for "brute-force"
empirical evaluations (e.g., comprehensive sensitivity analyses that systematically vary all or most of the
user-defined inputs, such as those conducted prior to the 2009 SAB review (U.S. EPA 2009)) and
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additional resource-intensive literature searches. ICF's justification for determining that properties were
not high priority was based on three lines of evidence:
1. ICF followed a "process"-based approach to rule out a large subset of TRIM.FaTE properties from
the need for site-specific parameterization. This approach was founded on the idea that the
TRIM.FaTE model produces greater than necessary resolution (in terms of the number of
concentrations that are calculated for different environmental media types) when viewed from the
RTR perspective. The individual human ingestion exposure scenarios evaluated for RTR rely most
directly on results from TRIM.FaTE for surface soil compartments at the location of a farm and fish
compartments in a lake of interest. All fate and transport processes—and the properties that
exclusively define those processes—that do not strongly influence these concentrations can
reasonably be ruled out from requiring site-specific parameterization. The implications of this
approach will be discussed in greater detail below.
2. ICF also used practical considerations regarding data availability to rule out certain properties from
site-specific parameterization. Over the course of numerous site-specific assessments and the
parameterization of the screening scenarios, ICF has conducted literature searches on numerous
TRIM.FaTE properties. ICF used the insight gained from these exercises to identify certain sets of
parameters as being too data-scarce to parameterize on a site-specific basis at this time without
expending a substantial amount of time and money (for possibly uncertain results).
3. Physical constants and physicochemical properties of the modeled PB-HAPs were also ruled out
from site-specific parameterization based on their largely unchanging nature in the environment for
the chemicals considered for RTR.
ICF evaluated the parameters not eliminated by the above considerations to determine which properties
should be the focus of data collection efforts during site-specific TRIM.FaTE modeling for RTR. ICF
conducted a limited number of evaluations and used the results of previous sensitivity analyses to decide
which of these shortlisted parameters should be prioritized for site-specific parameterization, for land-use-
based parameterization, or for regional parameterization.
Other scenario properties such as emission period and the model's numerical integration time-step are
typically not varied between site-specific assessments. These properties are not discussed further in this
protocol, but are documented in Appendix B.
6.3 Elimination of Properties from Site-Specific Parameterization
6.3.1 Process-based Elimination of Parameters
The operative principle in the process-based elimination of parameters is that fate and transport
processes that do not substantially influence concentrations of interest from an RTR perspective are less
important to parameterize. ICF used theoretical considerations based on the evaluation of the underlying
TRIM.FaTE algorithms, combined with empirical evidence from TRIM.FaTE simulations, to identify the
less important fate and transport processes and eliminate the need to parameterize those processes on a
site-specific basis. The specific processes identified as being of less importance in the RTR context and
the underlying justification for ruling them out from site-specific consideration are listed below:
• Chemical transport via water percolation through the sub-surface soil layers (not including surface
soil) does not affect farm soil or lake water concentrations. Theoretical considerations suggest that
chemical, once transported into the lower soil layers, will not substantially make its way back to the
surface compartments of interest;
• Chemical transport via sub-surface soil diffusive processes, although having the potential to transfer
mass upwards, are not sizeable in comparison to advective transfer processes. An evaluation of
relative mass flux in the Tier 1 screening scenario supports this assertion for all the chemicals
evaluated; and
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• Chemical transport to the lake via horizontal groundwater flow and recharge is negligibly small
compared to other advective chemical inputs into the lake. The relative mass flux for this process
compared to other advective transfer processes carrying chemical into the lake in the Tier 1
screening scenario supports this assertion for all chemicals evaluated.
Because the RTR user has no intrinsic interest in the concentrations prevailing in the lower soil layers, all
of the above processes have been ruled out from consideration for site-specific parameterization. As a
consequence, it is possible to rule out all sub-surface soil compartment properties from requiring site-
specific parameterization.
6.3.2 Data Availability-based Elimination of Parameters
Chemical-Specific Aquatic Biota Properties: The aquatic biota compartments in TRIM.FaTE—currently
including benthic invertebrates and five types offish—are characterized by several potentially site-specific
properties that control algorithms influencing the uptake, degradation, and elimination of chemicals in the
aquatic organisms. These chemical-specific properties include the absorption rates of chemical into each
type offish from surface water, elimination rates from fish digestive systems, degradation rates within the
fish, and other parameters. In the course of parameterizing TRIM.FaTE for the screening scenario and
conducting extensive evaluations of parameter sensitivity, it has become apparent that only a limited
number of studies are available for several of these properties for most combinations of chemicals and
organisms.
Although these properties may potentially differ in alternative climates and conditions, it appears unlikely
that additional literature searches and evaluations would yield better, more appropriate site-specific
values than the current defaults. Until such time as more studies on these properties are available,
practical considerations suggest that these chemical-specific aquatic biota properties be ruled out from
site-specific parameterization.
Chemical-Specific Abiotic Compartment Properties: TRIM.FaTE algorithms model chemical reaction
and degradation processes in several abiotic compartments (e.g., surface soil). These algorithms depend
on chemical-specific parameters such as degradation rates (or half-lives), transformation rates, and other
properties. Literature searches conducted during previous site-specific assessments in the RTR process
and other regulatory applications using TRIM.FaTE have suggested that data are limited for these
properties.
These chemical properties (with the exception of oxidation, reduction, and methylation and demethylation
rates influencing mercury) therefore are currently ruled out from site-specific consideration. The mercury
transformation properties have been shown to be highly risk-influential as well as variable across different
ecosystem types and conditions, and these properties are reserved for site- or land-use-specific
parameterization in the future, subject to greater data availability and the results of additional evaluations.
6.3.3 Combination of Data- and Sensitivity-based Elimination of Parameters
Terrestrial Vegetation: The terrestrial vegetation compartments in TRIM.FaTE—currently including
grass, coniferous forest, deciduous forest, wetland grass, and wetland forest—are not directly part of the
RTR risk assessment calculations (i.e., chemical concentrations in these compartments are not used as
inputs to the MIRC ingestion exposure model). However, these compartments act as sinks for chemicals
and also transfer chemicals from air to soil via litterfall. In this way, the choice of terrestrial vegetation
influences surface soil concentrations and, ultimately, risk.
The terrestrial vegetation compartments depend on properties such as the lipid content of leaves, wet
density of leaves, area indices of leaves, etc. Although it is possible that these properties differ on a site-
specific basis—for instance, the characteristics of coniferous trees in Oregon are different from those of
coniferous trees in North Carolina—these differences are not expected to have a substantial influence on
risk. ICF's simulations indicate that the impact on risk of alternative vegetation scenarios is limited after
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accounting for differences in erosion regimes specific to land-use type (see Appendix A). It is expected
that site-specific differences within a single vegetation type would be even lower.
Literature searches during previous site-specific assessments in the RTR process have indicated that
highly intensive literature search would be required to parameterize the full range of terrestrial vegetation
parameters required by the TRIM.FaTE algorithms. Based on the limited risk impact of terrestrial
vegetation properties, and limited data availability at the site-specific level, these properties are currently
ruled out from site-specific parameterization.
6.3.4 Elimination of Physical and Chemical Characteristics
Algorithms in the TRIM.FaTE model frequently depend on physical and chemical parameters, such as the
Henry's Law constant and the octanol-water partition coefficient, to partition chemicals between phases
within a compartment. These properties are, by their nature, relatively unchanging across most standard
environmental conditions for the non-ionic organic compounds currently evaluated (i.e., dioxins and
PAHs)3. These properties are thus ruled out from requiring site-specific parameterization for the time
being.
6.4 Properties Recommended for Site-Specific Parameterization
Following the elimination process described above, ICF identified a set of parameters for further
evaluation based theoretical considerations as well as higher sensitivity potential displayed in previous
sensitivity analyses (e.g., U.S. EPA 2009). To estimate the risk influence of these parameters, ICF
performed a limited set of additional sensitivity analyses. The evaluated parameters are listed below,
grouped by compartment type:
• Air: dust load, fraction of organic matter.
• Surface Soil: unit soil loss, inter-compartment drainage and erosion fractions, soil particle density, soil
air fraction, soil organic content, soil pH, soil water content.
• Surface Water and Sediment: suspended solids concentration, bed sediment density, suspended
solids density, bed sediment porosity.
• Aquatic Biota: biomass of various aquatic biota compartments.
• Terrestrial Vegetation: "Allow exchange" and "litterfall" file inputs.
Unlike previous analyses, these sensitivity analyses were not based on fixed perturbations from the
default values but instead used reasonable high and/or low bounds approximately corresponding to the
range found in the environment. The impacts on risk were computed with respect to the Tier 1 screening
scenario results at equivalent emission rates.
ICF extended the scope of the current analyses by also using the results of TRIM.FaTE sensitivity
analyses conducted in previous regulatory applications and pertaining to air, surface soil, and surface
water and sediment. Although these analyses were performed on a different version of the Tier 1
screening scenario set up, the results are considered informative.
The specific details of the analyses conducted as part of this protocol development are reported in
Appendix A, while other supporting evidence has been drawn from previous reports (e.g., U.S. EPA
2009). Based on the results of these analyses, Table 6-1 contains TRIM.FaTE properties recommended
for site-specific parameterization in the RTR process. These properties have been further classified as
high, medium, and low priority to facilitate an appropriate allocation of available resources in the
parameterization process.
3
For mercury, some analogous properties, such as the partition coefficient for mercury in the aqueous phase, do vary
according to pH; these relationships are incorporated into the model as formula properties.
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Table 6-1. TRIM.FaTE Properties Recommended for Site-Specific Parameterization
Compartment
Property
Priority
Remark
Surface Water
Depth
High
Having depth as well as flush rate helps serve as
Flush Rate
High
a check on surface hydrology assumptions.
Suspended Solids
Concentration
High
Attempt to find a column-averaged value.
PH
Moderate
Important for metals.
Algae Density
Moderate
May be estimated from total phosphorus
concentrations in the absence of measured
values.
Organic Carbon
Fraction
Moderate
Important for TCDD (U.S. EPA 2009). Data
availability may be limited.
Water
Temperature
Moderate
Sensitive but unlikely to manifest wide range.
Aquatic Biota
Biomass
Moderate
May be estimated from total phosphorus
concentrations in absence of measured values.
Surface Soil
PH
Moderate
Important for metals.
Terrestrial Biota
"Allow Exchange"
and "Litterfall" data
files
Low
These files govern how long leaves remain open
forstomatal exchange during different times of
the year and also when the leaves fall off the
trees onto the surface soil. Although the impact
of these properties has not been empirically
tested, theoretical considerations suggest they
will have a low impact when estimating average
annual risks.
In addition to these values, meteorology parameters, surface hydrology and erosion-related parameters,
and the spatial layout are fundamentally site-specific elements of a TRIM.FaTE simulation, as noted in
the previous sections.
7. Properties Recommended for Land Use-Based Values
In addition to the properties identified in Section 6 as desirable for site-specific parameterization, we
identified properties that also influence risk substantially but for which the impacts on risk are expected to
be largely captured by land use-specific parameters. In other words, for these properties, accounting for
variations that correspond to land use is expected to adequately account for any variation in these
parameters (to the extent that they influence risk). Additional variation in parameter values resulting from
site-specific variations within a particular land use category is not expected to be significant. For example,
differences in surface soil erosion (as expressed by the unit soil loss rate property in TRIM.FaTE) are
expected to be larger between the average deciduous forest and the average parcel of tilled soil than
between different types of deciduous forest or between different types of tilled soil. The use of land use-
specific values for such properties is expected, therefore, to adequately capture their impact on risk
estimates in the RTR process.
The rationale for identifying properties as land use-based in this protocol is a combination of risk
sensitivity analysis (Appendix A and U.S. EPA 2009), professional judgment about the range exhibited in
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the environment, and expected data availability at the site-specific level. For land use-specific properties,
users performing a site-specific assessment should identify the land use type of each surface parcel in
the FaTEmaster Scenario Builder tool. The tool would then automatically assign the appropriate land use-
specific property values. It should be noted, however, that the tool is not currently parameterized with
land-use specific property values.
Table 7-1 lists the TRIM.FaTE parameters that are recommended for land use-specific parameterization.
These parameters are all related to the surface soil parcel and assume distinct values for each of the land
use types modeled in TRIM.FaTE. These land use types currently include deciduous forest, coniferous
forest, grass, agricultural soil, unfilled soil, forested wetlands, and grassy wetlands. Other land use types
may also be defined using the FaTEmaster Scenario Builder tool. Land use type is not an explicit input in
TRIM.FaTE but is implicitly reflected in the TRIM.FaTE property values corresponding to each surface
parcel.
Table 7-1. TRIM.FaTE Properties Recommended for Land use-Specific Parameterization
Property
Remark
Organic carbon fraction
Fraction of dry soil solids that is organic in origin.
Water content
The sum of the water and air content fractions of a soil determines its
porosity.
Air content
Particle density
Refers to the dry density of the average soil particle.
Rainfall/erosivity index
Universal Soil Loss Equation (USLE) properties used in FaTEmaster
Scenario Builder Tool to compute each surface soil compartment's
average erosion rate.
Soil erodibility index
Topographical (LS) factor
Cover/management factor
Supporting practices factor
Fraction of precipitation that
evapotranspires
Water balance-related property used in FaTEmaster Scenario Builder
Tool to compute each surface soil compartment's average runoff rate.
Fraction of precipitation subject
to overland runoff
8. Properties Recommended for National Values
Nationally-representative or health-protective values are recommended for all TRIM.FaTE properties that
are not identified for site-specific or land use-based parameterization in Sections 6 and 7 above. These
properties are expected either to (1) not substantially influence risk in the RTR process, (2) not have
adequate data to support site-specific parameterization, or (3) be relatively constant in the environment,
as discussed in greater detail in the approach described in Section 6. These properties have been
previously characterized in the RTR Tier 1 and Tier 2 screening threshold derivation analyses by either
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nationally-representative values or health-protective values. The same values are recommended for
these properties in site-specific analyses and are listed with references in Appendix B.
9. The FaTEmaster Scenario Builder
All user inputs can be provided to TRIM.FaTE via text-based or delimited data input files. The files must
be defined in syntax specific to TRIM.FaTE; a full description of syntax requirements is provided in the
TRIM.FaTE User's Guide (U.S. EPA 2005)
ICF's FaTEmaster Scenario Buildertool provides a Microsoft® Excel™-based environment that facilitates
translation of user inputs into appropriately formatted TRIM.FaTE input files that can be used to set up
and run site-specific scenarios. The FaTEmaster Scenario Buildertool does not, however, create the
TRIM.FaTE Master Library file, which contains library properties and can be used to set property values
that are not expected to vary between scenarios. The recommendations for the use of site-specific
properties made earlier in this document are not envisaged to require changes to the TRIM.FaTE Master
Library file. The FaTEmaster Scenario Builder tool is further documented in Appendix C.
10. Potential Future Improvements
This protocol represents a first attempt at documenting the current state of knowledge related to
conducting site-specific environmental modeling in support of RTR multipathway risk assessments. The
protocol could be enhanced in the future by documenting best practices and developing
recommendations regarding the following issues (among others):
• Identification of land use-specific parameters for the identified soil properties based on literature
review;
• Application of enhanced technical approaches, such as the use of a sensitivity score approach, to
identify the most influential model properties;
• Potential development of regional parameters for a subset of model properties based on the results of
further sensitivity analysis and data availability assessments;
• Greater use of graphics and figures to illustrate model set-up concepts;
• Enhanced technical editing to help the protocol be more self-explanatory and independent of other
TRIM.FaTE support documents in its scope;
• Researching the potential for geographically variable biotransfer factors and other parameters in
MIRC; and
• Further research and development of GIS-based approaches to surface hydrology and erosion
property parameterization.
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References
ESRL (Earth System Research Laboratory). 2011. NOAA/ESRL Radiosonde Database Access.
Available online at: http://www.esrl.noaa.gov/raobs/. Accessed March 3, 2011.
Google. 2013. Google Earth. Available at http://www.google.com/earth/index.html. Accessed
July 24, 2013.
Multi-resolution Land Characterization (MRLC) Consortium. 2013. National Land Cover
Database. Available online at http://www.mrlc.gov/index.php. Web page last updated
February 7, 2013.
NCDC (National Climatic Data Center). 2008. Integrated Surface Database. Available at:
http://www.ncdc.noaa.gov/oa/climate/isd/index.php. Web page last updated August 20,
2008.
NCDC. 2011. NCDC 1981-2010 Climate Normal. Available at:
http://www.ncdc.noaa.gov/oa/climate/normals/usnormals.html. Web page last updated
January 5, 2011.
NCDC. 2012. NCDC CD-ROM and DVD Climate Products. Available at:
http://ols.nndc. noaa.gov/plolstore/plsg l/olstore.prodlist?category=C&subcatc=01&groupi
n=CDV.
NCDC. 2013. Quality Controlled Local Climatological Data (QCLCD). Available at:
http://www.ncdc.noaa.gov/data-access/land-based-station-data/land-based-
datasets/guality-controlled-local-climatological-data-gclcd.
Overcamp, TJ. 1976. A General Gaussian Diffusion-Deposition Model for Elevated Point
Sources. Journal of Applied Meteorology 15:1167-1171.
Seinfeld, JH; Pandis, SN. 1998. Atmospheric Chemistry and Physics: From Air Pollution to
Climate Change. Wiley-lnterscience, New York, pp. 931-933.
U.S. Department of Agriculture (USDA). 2013. CropScape - Cropland Data Layer. Available
online at http://nassgeodata.gmu.edu/CropScape/.
U.S. EPA (U.S. Environmental Protection Agency). 1992. Procedures for Substituting Values for
Missing NWS Meteorological Data for Use in Regulatory Air Quality Models (Dennis
Atkinson and Russell F. Lee). July 7, 1992. Available online at
http://www.epa.gov/ttn/scram/surface/missdata.txt. Last accessed March 07, 2011.
U.S. EPA. 1995. User's Guide for the Industrial Source Complex (ISC3) Dispersion Models
Volume II - Description of Model Algorithms. September 1995. (EPA-454/B-95-003b)
U.S. EPA. 2002. TRIM.FaTE Technical Support Document. U.S. EPA Office of Air Quality
Planning and Standards. Available at:
http://www.epa.gov/ttn/fera/trim fate.html#current user
U.S. EPA. 2005. TRIM.FaTE User's Guide. Office of Air Quality Planning and Standards.
September, 2005. Available at: http://www.epa.gov/ttn/fera/trim fate.html#current user.
Working Draft
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U.S. EPA. 2009. Risk and Technology Review (RTR) Risk Assessment Methodologies: For
Review by the EPA's Science Advisory Board. Attachment C-3 in Appendix C. (EPA-
452/R-09-006)
U.S. EPA. 2010a. SCRAM (Support Center for Regulatory Atmospheric Modeling) Mixing Height
Data. Available at: http://www.epa.gov/ttn/scram/mixingheightdata.htm. Web page last
updated May 13, 2010.
U.S. EPA. 2010b. SCRAM Surface Meteorological Archived Data: 1984-1992. Available at:
http://www.epa.gov/ttn/scram/surfacemetdata.htm. Web page last updated May 13,
2010.
U.S. EPA. 2012a. AERMET Meteorological Preprocessor. Available at:
http://www.epa.gov/ttn/scram/metobsdata procaccprogs.htm#aermet. Web page last
updated December 18, 2012.
U.S. EPA. 2012b. Mixing Height Program. Available at:
http://www.epa.gov/ttn/scram/metobsdata procaccprogs.htm#mixing. Web page last
updated December 18, 2012.
U.S. EPA. 2012c. PCRAMMET Meteorological Processor. Available at:
http://www.epa.gov/ttn/scram/metobsdata procaccprogs.htm#pcrammet. Web page last
updated December 18, 2012.
U.S. EPA. 2013. HEM Download Page. Available at:
http://www.epa.gov/ttn/fera/hem download.html. Web page last updated June 19, 2013.
USGS (U.S. Geological Survey). 2006. National Elevation Dataset. Available at:
http://ned.usgs.gov/. Web page last updated August, 2006.
USGS. 2013. National Hydrography Dataset. Available at http://nhd.usgs.gov/. Web page last
updated July 19, 2013.
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Appendix A. Documentation of Empirical Analyses Used to Prioritize
TRIM.FaTE Properties
A.1. Introduction
ICF performed a series of empirical analyses to prioritize TRIM.FaTE model properties for site-specific
parameterization. These analyses were based on changing the value of one or more model properties
relative to the Tier 1 screening scenario and measuring the relative impact on risk. Unlike in a traditional
sensitivity analysis, this analysis changed property values to approximate high and low-end values within
the environmental range of the property of interest, instead of using a fixed perturbation. The measured
impacts on risk, the expected range in the environment, and data availability were considered in
prioritizing model properties for site-specific parameterization, as discussed in Sections 6 and 7.
Table A-1 summarizes the various empirical analyses that were conducted, the risk impact of the scenario
modifications, and conclusions from the analyses.
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Table A-1. Results and Conclusions from Empirical Analyses Used to Prioritize TRIM.FaTE Properties
Normalized Risk Relative to
Scenario Description
Tier 1 Screening Scenario
Scenario
Name
(with respect to Tier
1 Screening
Scenario)
TetraCDD,
2,3,7,8-
Benzo(A)
pyrene
Cadmium
Methyl
Mercury
Risk Impact of Scenario
Modification
Conclusions
Tier 1 SS
Tier 1 Screening
Scenario.
1.00
1.00
1.00
1.00
Designed to produce
most conservative risk
estimate.
All relative risks for
modified scenarios are
measured relative to the
Tier 1 screening scenario.
WF1
Reduce watershed
flows (erosion and
runoff) to half
screening scenario
levels. Redirect
remainder to sink.
Maintain same flow
directions as
screening scenario.
0.56
0.69
0.21
0.38
Reducing the quantity of
runoff and erosion
reaching receiving
compartments reduces
chemical inputs into those
compartments, including
the lake, and reduces
risk.
Surface hydrology and
erosion flows (where and
how much of the erosion
and runoff from a
compartment reaches) are
potentially highly sensitive
properties in the model
(influencing risk by up to a
factor of 10) and are
recommended for site-
specific parameterization.
WF2
Reduce watershed
flows (erosion and
runoff) to 1/10
screening scenario
levels. Redirect
remainder to sink.
Maintain same flow
directions as
screening scenario.
0.34
0.68
0.11
0.25
ERO
Switch off erosion.
0.39
1.02
1.10
0.43
Turning off erosion
reduces chemical inputs
into the lake and reduces
chemical removal off the
farm.
Although erosion is a
relatively important
process, its maximum
impact on risk is less than
a factor of 3, even when
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Scenario Description
(with respect to Tier
1 Screening
Scenario)
Normalized Risk Relative to
Tier 1 Screening Scenario
TetraCDD, Benzo(A) _ . . Methyl
_ . _ . Cadmium ..
2,3,7,8- pyrene Mercury
Risk Impact of Scenario
Modification
Conclusions
ER1
Double erosion rates.
1.09
0.99
0.86
0.86
Increasing erosion
produces competing
effects: while it increases
chemical inputs into the
lake, it also increases the
burial rate of sediment
and increases chemical
removal from the farm.
accounting for variable
runoff rates. A land-use
specific parameterization
approach is recommended
for the average erosion
rates of surface soil
compartments.
ER-RUN1
Double erosion and
runoff rates (same
flush rate; higher lake
depth).
0.94
0.96
0.74
0.89
Increasing the runoff rate
increases the input of
soluble chemicals into the
lake and decreases the
removal of those
chemicals from the farm.
ER-RUN2
Double erosion and
runoff rates (higher
flush rate; same lake
depth).
1.09
0.99
0.74
0.85
Increased runoff rates
can be accommodated by
means of increased lake
depths or increased flush
rates.
RUN1
Switch off runoff;
maintain flush rate and
depth.
0.99
1.00
0.70
0.96
Nullifying chemical
transfer through runoff
reduces chemical input
into the lake and reduces
chemical removal from
the farm.
Runoff rates have a limited
impact on risk. A land-use
specific parameterization
approach is recommended
for average runoff rates
from surface soil
compartments.
RUN2
Implement cumulative
runoff regime.
1.02
1.00
1.14
1.10
Assumes runoff from one
compartment does not
evaporate but contributes
to runoff from the
receiving compartment.
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A-3
February 2014
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DRAFT Protocol for Developing a TRIM.Fa TE Model Scenario to Support a Site-Specific Risk Assessment in the RTR Program
Normalized Risk Relative to
Scenario Description Tier 1 Screening Scenario
Scenario (with respect to Tier
Name 1 Screening TetraCDD, Benzo(A) _ . .
Qronarir»\ n n - 0 Cddm UITI
scenario) 2,3,7,8- pyrene
FR1
Double lake depth,
half flush rate, same
rainfall, and same
runoff fraction.
0.71
0.96
1.00
1.15
Doubling depth reduces
concentrations but
halving the flush rate
reduces chemical output
from the lake.
Lake depth and flush rate
have a modest impact on
risk. However, knowledge
of both these parameters
can help guide the surface
hydrology and erosion
direction flows in the
watershed which can more
substantially influence risk.
Site-specific
parameterization is
recommended for lake
depth and flush rate.
FR2
Half lake depth,
double flush rate,
same rainfall, and
same runoff fraction.
1.28
1.07
1.00
0.92
Halving depth increases
concentrations but
doubling the flush rate
increases chemical output
from the lake.
FR3
Double depth, same
flush rate, same
rainfall, same runoff
fraction (violate water
balance in screening
scenario).
0.69
0.96
0.58
1.02
Doubling depth reduces
lake concentrations for
most chemicals.
FR4
Double flush rate,
same depth, same
rainfall, and same
runoff fraction (violate
water balance in
screening scenario).
0.95
1.00
0.58
0.89
Doubling flush rate
reduces lake
concentrations.
PERC1
Implement balanced
percolation regime.
0.99
1.00
0.62
0.99
Assumes runoff from one
compartment does not
evaporate but percolates
in the receiving
compartment.
Percolation rate (the
fraction of rainfall that is
subject to percolation into
the sub-surface) has a
modest impact on risk.
Land use-based
parameterization is
recommended for this
property.
Working Draft A-4 February 2014
Methyl
Mercury
Risk Impact of Scenario
Modification
Conclusions
-------
DRAFT Protocol for Developing a TRIM.Fa TE Model Scenario to Support a Site-Specific Risk Assessment in the RTR Program
Scenario Description
(with respect to Tier
1 Screening
Scenario)
Normalized Risk Relative to
Tier 1 Screening Scenario
TetraCDD, Benzo(A) _ . . Methyl
_ . _ . Cadmium ..
2,3,7,8- pyrene Mercury
Risk Impact of Scenario
Modification
Conclusions
R1
Reduce rainfall down
to 1 /3rd SS value;
same lake depth;
runoff rates and flush
rate down to 1 /3rd.
0.64
0.59
0.92
0.58
Reducing rainfall reduces
chemical washout from
air.
This run, when combined
with earlier runs focusing
on the impacts of flush
rate, suggests that the
chemical washout impact
of rainfall has more
influence on risk than the
impact of rainfall levels on
hydrological properties like
flush rate. This reinforces
the argument for site-
specific meteorological
parameters.
V_C
Set all surface
compartments except
farm to coniferous
forests.
0.79
0.75
0.40
0.87
The choice of vegetation
in surface soil
compartments impacts
risk by absorbing
chemicals from air and
soil and then redepositing
them onto the surface soil
via litterfall.
Land use-type has a
limited impact on risk.
Based on these results,
terrestrial vegetation
parameters are
recommended for land-use
specific parameterization.
In interpreting these
results, it is important to
note that these runs have
not been normalized for
erosion rates. Therefore,
the impacts on risk
presented here are from a
combination of impacts
from differential erosion
rates and vegetation types.
V_D
Set all surface
compartments except
farm to deciduous
forests.
0.34
0.92
0.49
0.39
V_G
Set all surface
compartments except
farm to grassland.
0.88
0.82
0.45
0.92
v_u
Set all surface
compartments except
farm to unfilled soil.
0.42
0.73
0.37
0.81
v_ww
Set all surface
compartments except
farm to forested
wetlands.
0.36
0.92
0.49
0.47
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A-5
February 2014
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DRAFT Protocol for Developing a TRIM.Fa TE Model Scenario to Support a Site-Specific Risk Assessment in the RTR Program
Scenario Description
Normalized Risk Relative to
Tier 1 Screening Scenario
Scenario
Name
(with respect to Tier
1 Screening
Scenario)
TetraCDD,
2,3,7,8-
Benzo(A)
pyrene
Cadmium
Methyl
Mercury
Risk Impact of Scenario
Modification
Conclusions
V_WG
Set all surface
compartments except
farm to grassy
wetlands.
0.86
0.83
0.46
0.94
BM1
Increase aquatic
biomass uniformly by
a factor of 10.
0.84
0.39
0.90
0.99
Increasing aquatic
biomass reduces
chemical concentration in
biomass as the same
amount of chemical is
Risk is sensitive to the
aquatic biomass levels.
These properties are
therefore recommended
for site-specific
parameterization. In
interpreting the results of
these runs, it may be noted
that all biomass levels
were uniformly raised. In
real applications, the
BM2
Increase aquatic
biomass uniformly by
a factor of 100.
0.35
0.32
0.29
0.79
distributed in a higher
amount of biomass.
biomass levels of the
upper trophic levels may
constitute a lower
percentage of the total
biomass as total biomass
increases, suggesting
slightly lower risk
sensitivity than apparent
here.
Air_DL1
Increase air dust load
by a factor of 10.
2.34
2.31
0.50
0.98
Increasing the dust load
in air increases
Although these runs
indicate that air dust load
Working Draft
A-6
February 2014
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DRAFT Protocol for Developing a TRIM.Fa TE Model Scenario to Support a Site-Specific Risk Assessment in the RTR Program
Scenario Description
(with respect to Tier
1 Screening
Scenario)
Normalized Risk Relative to
Tier 1 Screening Scenario
TetraCDD, Benzo(A) _ . . Methyl
_ . _ . Cadmium ..
2,3,7,8- pyrene Mercury
Risk Impact of Scenario
Modification
Conclusions
Air_DL2
Increase air dust load
by a factor of 100.
4.14
2.71
0.50
0.90
particulate deposition to
the surface.
moderately influences risk,
literature search indicated
that the range manifested
by this property is relatively
small and the default value
used is already in the high
end of the observed range
in the U.S. Therefore, this
property is not
recommended for site-
specific parameterization.
Air_FOM1
Halve the fraction of
organic matter in air
solids.
0.87
0.66
0.50
1.00
The organic content of air
solids can differentially
influence chemical
adherence to the solid
phase.
Although these runs
indicate that the fraction of
organic matter in air solids
moderately influences risk,
literature search indicated
that site-specific data may
be difficult to obtain. This
property is not
recommended for site-
specific parameterization.
Air_FOM2
Double the fraction of
organic matter in air
solids.
1.23
1.43
0.50
1.00
Soil_Air
Double the soil air
content.
1.21
1.29
0.50
1.14
Increasing the soil air
fraction reduces soil
solids, which distributes
the same amount of
chemical over a lower
solids content, thereby
increasing soil
concentrations.
Although these runs
indicate that air dust load
moderately influences risk,
literature search indicated
that the range manifested
by this property is relatively
small and the default value
used is already in the high
end of the observed range
in the U.S. Therefore, this
property is not
recommended for site-
Soil_FOC
Increase the soil
organic fraction
content by a factor of
10.
1.04
1.01
0.60
1.00
Increasing soil organic
content increases
chemical adherence to
soil for some chemicals.
Working Draft
A-7
February 2014
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DRAFT Protocol for Developing a TRIM.Fa TE Model Scenario to Support a Site-Specific Risk Assessment in the RTR Program
Normalized Risk Relative to
Scenario Description Tier 1 Screening Scenario
Scenario (with respect to Tier
Name 1 Screening TetraCDD, Benzo(A) _ . .
Qronarir»\ n n - 0 Cddm UITI
scenario) 2,3,7,8- pyrene
Soil_pH1
Set soil pH at 4.
1.00
1.00
0.39
1.00
Soil pH can influence
chemical adherence to
soil solids for some
chemicals.
specific parameterization.
Soil_pH2
Set soil pH at 10.
1.00
1.00
0.66
1.00
Soil_Rho
Set soil solids density
at 1000 kg/m3.
1.41
1.43
0.50
1.16
Decreasing soil particle
density increases soil
concentrations when
normalized by soil weight.
Soil_Water
Double the soil water
content.
1.00
1.00
0.50
1.00
Increasing soil water
content increases
chemical removal by
percolation for some
chemicals.
SusSed TS
S1
Increase lake
suspended solids
concentration by a
factor of 2.
0.73
0.98
1.01
0.74
Increasing suspended
solids in water causes
more chemical to be
deposited to sediment.
Suspended solids
concentration in lakes has
a moderate influence on
risk. Due to the wide range
potentially exhibited by this
property, it has been
recommended for site-
specific parameterization.
SusSed TS
S2
Increase lake
suspended solids
concentration by a
factor of 10.
0.33
0.98
0.46
0.38
Sed_Bur
Halve sediment burial
rate; same erosion
rate (violate solids
balance in screening
scenario).
1.11
1.00
1.12
1.31
Decreasing the burial rate
reduces the removal of
chemicals from the
sediment layer.
Sediment properties have
a moderate impact on risk,
given the limited range of
values assumed by them
in the environment.
Methyl
Mercury
Risk Impact of Scenario
Modification
Conclusions
Working Draft
A-8
February 2014
-------
DRAFT Protocol for Developing a TRIM.Fa TE Model Scenario to Support a Site-Specific Risk Assessment in the RTR Program
Scenario Description
Normalized Risk Relative to
Tier 1 Screening Scenario
Scenario
Name
(with respect to Tier
1 Screening
Scenario)
TetraCDD,
2,3,7,8-
Benzo(A)
pyrene
Cadmium
Methyl
Mercury
Risk Impact of Scenario
Modification
Conclusions
Sed_Rho
Decrease bed
sediment particle
density to 1000 kg/m3.
1.36
1.00
0.63
2.74
The lower the sediment
particle density, the lower
the volumetric
resuspension rate from
sediment and the higher
the volumetric burial rate.
Sed_Por
Halve sediment bed
porosity.
0.85
1.00
0.42
0.78
The lower the sediment
porosity, the lower the
volumetric resuspension
rate from sediment and
the lower the volumetric
burial rate.
Working Draft
A-9
February 2014
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Appendix B. TRIM.FaTE National Property Values
B.1. Introduction
This protocol recommends the use of nationally representative or health protective values (referred to as
national values) for TRIM.FaTE model properties that have not been identified for site-specific or land
use-based parameterization.
These national values are readily accessible in the FaTEmaster Scenario Builder tool, with references.
This tool is included with the protocol and is documented in Appendix C. The national values are also
documented in Appendix 4 of the Risk Report (see its Attachment A, Addendum 1).
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Appendix C. The FaTEmaster Scenario Builder Tool
C.1. Introduction
A TRIM.FaTE scenario requires a variety of inputs from users to define the modeled environment and to
parameterize the algorithms for physical, chemical, and biological processes that transfer and transform
chemical mass between and within environmental media. These inputs are provided to the TRIM.FaTE
model in the form of the following files:
• A "volume elements" file, which defines the spatial layout of the modeled domain in terms of three-
dimensional abiotic compartments. Each volume element provides a frame of reference for one or
more biotic compartments within it.
• A "compartments" file, which places biotic and abiotic compartments (modeling units containing
chemical mass) within the volume elements.
• A Master Library file, which contains all the model algorithms, properties, and emission source
information.
• A properties file, which typically contains (i) simulation- and scenario-specific properties that overwrite
default values specified in the Master Library and (ii) the location of time-varying input files for
parameters such as meteorological and vegetation parameters.
These input files must be constructed in syntax consistent with TRIM.FaTE requirements, as described in
Module 3 of the TRIM.FaTE User's Guide.
ICF's FaTEmaster Scenario Builder tool provides an MS-Excel spreadsheet-based interface that
facilitates the automated generation of syntactically accurate TRIM.FaTE input files derived from user-
specified inputs. The tool does not output the TRIM.FaTE Master Library file.
This appendix describes how the FaTEmaster Scenario Builder tool can be used to construct input files
for the TRIM.FaTE model for use in site-specific risk assessments in the RTR process. While this
document discusses the principal steps required to operate the tool, it does not provide line-by-line
guidance for each of the hundreds of inputs contained within it. The tool already incorporates comments
and guidance for each input cell and is largely self-explanatory. This overview of the tool is intended to
facilitate an enhanced understanding of the scope of the tool and its operational structure. This document
is not intended to serve as a guide to running the TRIM.FaTE model itself.
C.1.1. Output Files from the FaTEmaster Scenario Builder Tool
The FaTEmaster Scenario Builder Tool produces five text output files, which serve in turn as input files to
the TRIM.FaTE model. The filenames, contents, and purpose of the output files generated by the tool are
described in Table C-1.
C.1.2. Summary Worksheets in the FaTEmaster Scenario Builder Tool
The FaTEmaster Scenario Builder Tool consists of 15 worksheets. The names, contents, and purpose of
these worksheets are described in Table C-2, which has been color-coded in an identical manner to the
worksheet "tabs" in the tool.
Working Draft
C-1
February 2014
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Table C-1. Output Files Generated by the FaTEmaster Scenario Builder Tool
File Name
Contents
Purpose
Sources.txt
Coordinates specifying the
location of the emission sources
and the chemical emission rates.
This file facilitates the creation of
a unique scenario-specific
TRIM.FaTE Sources Library. This
file must be manually imported
through the TRIM.FaTE graphical
interface and saved as a library.
This Sources Library must then
be loaded to the site-specific
TRIM.FaTE scenario together
with the TRIM.FaTE Master
Library.
Volume Elements.txt
Coordinates specifying the spatial
dimensions of the volume
elements and specification of the
primary abiotic compartment
within each volume element.
This file serves as a TRIM.FaTE
input file to define the spatial
layout of the modeled domain in
terms of three-dimensional abiotic
compartments, each of which can
contain other biotic and abiotic
compartments.
Compartments, txt
The names of biotic and non-
primary abiotic compartments
located within each primary
abiotic compartment defined by
the Volume Elements file.
This file serves as a TRIM.FaTE
input file to situate user-specified
biotic and abiotic compartments
within each volume element.
These compartments are the
discrete units that contain
chemical mass in TRIM.FaTE.
Properties.txt
Values defining site-specific
properties relating to the scenario,
biotic and abiotic compartments,
and user-specified link properties.
This files serve as a TRIM.FaTE
input file to define the values of
various properties that define the
scenario. It overwrites default
values for these properties
specified in the TRIM.FaTE
Master Library file.
Other Properties.txt
Values defining properties that
are not included amongst the
standard sections of the tool for
user-specification. This is a
discretionary or optional file that
may be useful when overwriting
library properties that are not
redefined by the standard
elements of the tool.
This file serves as a TRIM.FaTE
input file to define the values of
any of the various properties that
define the scenario. It overwrites
default values for these properties
specified in the TRIM.FaTE
Master Library file.
Working Draft
C-2
February 2014
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Table C-2. Summary of Worksheets Generated by the FaTEmaster Scenario Builder Tool
Worksheet Name Contents Purpose
Tracking
• Command button
• Documentation of version changes.
Contains "Export All" button that
generates TRIM.FaTE input files
based on values specified in other
worksheets. Also serves as a
documentation sheet for tool
software developers only.
Parameters
• User-specified property values for
several TRIM.FaTE model properties.
Provides a user-friendly interface
for the definition of site-specific
model properties. Adds the
properties defined here to the
Properties file.
Layout
• Coordinates of the vertices of the
parcels that define the spatial layout.
• Coordinates of the location of the
emission sources.
• Emission rates of each chemical
species from each source.
Serves as a basis for the creation
of the Volume Elements and the
Sources output files.
Land
• User-specified parameter values for the
USLE equation for different land use
types.
Computes average erosion rates
based on the USLE equation for
surface soil compartments and
adds those estimates to the
Properties file. Also contains
elements used to construct the
Compartments file.
Soil
• User-specified property values for all
soil layers differentiated by land use
types.
Computes average runoff and
percolation rates, and adds these
estimates and other soil-related
properties to the Properties file.
Also contains elements used to
construct the Compartments file.
Plants
• User-specified vegetation types and
vegetation components for each land
use category.
Places the appropriate vegetation
composite compartments within
soil compartments based on user-
specified land use. This is used to
construct the Compartments file.
Watersheds
• User-specified inter-compartment
erosion and runoff directions and
percentages.
Defines inter-compartmental link
properties for runoff and erosion
and adds them to the Properties
file.
Lakes
• User-specified lake and sediment
properties.
• Equations to compute lake flush
rates/depth and sediment resuspension
velocity based on watershed flows.
Adds lake and sediment related
properties, and computed lake
and sediment hydrodynamic
parameters, to the Properties file.
Fish
• User-specified aquatic food web and
biomass levels.
Adds aquatic food web and
biomass properties to the
Properties file. These elements
are also used to construct the
Compartments file.
Working Draft
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February 2014
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Worksheet Name
Contents
Purpose
Soil Data
• User-specified soil properties
differentiated by land use type.
Serves as an input sheet for soil
properties that is called by the
"Soil" worksheet. May potentially
be parameterized on a regional
basis.
Sources
No user inputs are required on these worksheets. They are constructed from the
inputs specified in the previous tabs and contain the content of the output files
that will later be generated by the tool as text files.
Volume
Cmpts
Props
OtherProps
User may enter supplementary properties into this worksheet consistent with
TRIM.FaTE syntax. This worksheet can be used to overwrite properties that are
defined in the Master Library file or to define properties that are not previously
defined either in the library file or other input files.
C.1.3. Additional Salient Features of Worksheets in the FaTEmaster Scenario
Builder Tool
This section provides a limited description of the features of the various worksheets within the
FaTEmaster Scenario Builder, with a focus on the most salient operational aspects from the user
perspective.
A general rule when working with the tool, also mentioned clearly at the top of each worksheet, is that
only cells color-coded green may be modified by users. Blue and white color-coded cells are not to be
modified by users.
(i) The Tracking Worksheet
• Users should click the "Export All" command button after they have made all the necessary input
modifications to the other worksheets. This will be the final step in operating the tool.
• The remainder of the worksheet is intended for software developers only to document version
changes in the tool.
(ii) The Parameters Worksheet
• Specify a "set up" file directory in cell D9.
Model output will be directed to this directory into a sub-folder named "Output".
- This directory will also be the destination to which the tool will write its five output files.
- This directory should contain the time-varying meteorological values file, leaf "allow exchange"
file, and litterfall data file later referenced in the worksheet.
• In cell D48, enter the average annual precipitation based on the average from the time-varying site-
specific meteorological file. This averaging must be performed manually offline.
• In cell D49, update the formula reference with the name of the applicable meteorological values file if
they have changed.
• In cells D80, D92 and D103 update the formula with the names of the leaf allow exchange data files if
they have changed.
• In cells D84, D96 and D107 update the formula with the names of the litterfall data files if they have
changed.
• Note: Cells commented as "Reported Value" will be reported in the tables for documentation
purposes but will not be used in TRIM.FaTE input files.
Working Draft C-4 February 2014
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• Note: All other input cells may be updated as required. Cell comments have been provided for
guidance.
(iii) The Layout Worksheet
• Based on the spatial layout for air and surface parcels developed in GIS or through manual mapping,
enter the Cartesian coordinates of each vertex point in columns C and D. The center of the emissions
source (or facility) parcel should be defined as the origin of the system.
• Specify offset coordinates in cells E9 and F9 to situate the layout spatially, consistent with the
specified map projection system in cell D4. (This is similar to a latitude and longitude specification. It
does not affect TRIM.FaTE results, however.)
• Specify the name of each parcel in the spatial layout and the vertex points that define the parcel in a
clockwise or anticlockwise direction around the perimeter of the parcel in columns H and K.
• Specify the parcel category from the available options in column I.
• Specify the land use corresponding to each parcel in column J.
• Specify the source name, source parcel location, and source elevation in column U.
• Specify the chemical emission rates in column U.
(iv) The Land Worksheet
• In columns E through L, enter the USLE parameters that are used to compute average erosion rates
for each land use type.
• Enter the vegetation type within each land use type.
(v) The Soil Worksheet
• This worksheet requires no user inputs. It draws inputs from other worksheets and calculates average
runoff and percolation rates based on those values.
(vi) The Plants Worksheet
• Specify the vegetation components corresponding to each land use type.
• Note: If new types of vegetation components are being defined, their corresponding properties and
algorithms should be defined in the Master Library or separately within property files.
(vii) The Watersheds Worksheet
• For each parcel, specify which of its adjoining parcels receive the runoff and erosion originating from
that parcel and in what amounts (specified in percentages).
• Click on the "Refresh" button after updating the worksheet.
(viii) The Lakes Worksheet
• Specify lake and sediment properties in column K.
• Specify one of either lake depth or flush rate in cells K18 and K19.
• Note: The worksheet will compute the unspecified property (either lake depth or flush rate) using a
water balance. The water balance assumes that runoff entering the lake is the sum of runoff entering
the lake from adjacent soil compartments and cumulative runoff from the larger watershed.
• Note: The worksheet also computes sediment resuspension velocity and sediment burial rate using
principles discussed in the TRIM.FaTE Technical Support Document (Section 4.2.2).
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C-5
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(ix) The Fish Worksheet
• Define the aquatic food web by specifying the diet fraction for each aquatic organism.
• Specify the total biomass of each organism type and the single body weight of each organism.
(x) The Soil Data Worksheet
• For each land-use type, specify properties for all soil layers.
• (Note: The tool has the capacity to accommodate region-specific definitions of soil parameters too but
this functionality has not currently been parameterized.)
C.1.4. Generating Output Files from the FaTEmaster Scenario Builder Tool
After entering site-specific inputs into the various worksheets within the tool as required, the following
steps should be used to complete the generation of output files:
• Navigate to the "Layout" tab, and click "Refresh".
• Navigate to the "Watersheds" tab, and click "Refresh".
• Navigate to the "Tracking" tab, and click "Export AN".
The five output files generated by the tool can then be used as input files to set up the site-specific
TRIM.FaTE scenario. (It is reiterated that the tool does not generate the TRIM.FaTE Master Library file,
the contents of which are largely invariable between applications.
Working Draft
C-6
February 2014
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Appendix 10 - Ferroalloys Multipathway
Site-Specific Assessment
-------
Technical Support Document: Human Health Multipathway Residual
Risk Assessment for the Ferroalloys Production Source Category
DRAFT
February 21, 2014
Prepared For:
U.S. Environmental Protection Agency
Office of Air Quality Planning and Standards
Research Triangle Park, NC 27711
Prepared By:
ICF International
2635 Meridian Pkwy.
Suite 200
Durham, NC 27713
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February 2014
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Human Health Multipathway Residual Risk Assessment for the Ferroalloys Production Source Category
CONTENTS
Tables v
Figures vi
1. Introduction 1
2. Screening-level Assessments 2
2.1 Tier 1 Emission Screening Analysis 2
2.2 Tier 2 Emission Screening Analysis 3
3. Scope of Site-specific Risk Assessment 8
4. Analysis Methods 10
4.1 Fate and Transport Modeling 11
4.1.1 Overview of the Area Surrounding the Facility 11
4.1.2 Meteorological Data for Modeling 13
4.1.3 Characteristics of the Modeled Emission Source
Compartment 15
4.1.4 Modeling Domain and Parcel Design 19
4.1.5 Abiotic Environment 26
4.1.6 Biotic Environment 27
4.2 Methods for Exposure and Risk Modeling 27
4.2.1 Methods for Calculating Exposure Concentrations 28
4.2.2 Ingestion Exposure Assessment 29
4.2.3 Risk Characterization 33
5. Results 33
5.1 Media Concentrations 33
5.2 Risk Assessment Results 34
5.2.1 Risk Assessment Summary 34
5.2.2 Hazard Quotients Associated With Mercury Exposure 35
5.2.3 Hazard Quotients Associated With Cadmium Exposure 35
5.2.4 Cancer Risks Associated With PAH Exposure 42
5.2.5 Cancer Risks Associated With Dioxin Exposure 45
5.2.6 Comparison to Screening-level Assessment Results 48
6. Discussion of Uncertainties and Limitations 49
6.1 Uncertainties Related to Fate and Transport Modeling (TRIM.FaTE) 50
6.2 Uncertainties Related to Exposure Modeling and Risk
Characterization (MIRC) 54
7. References 56
Appendix A: User-Specified Values for TRIM.FaTE Properties A-1
Appendix B: Modeled Media Concentrations B-1
Draft iv February 2014
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Human Health Multipathway Residual Risk Assessment for the Ferroalloys Production Source Category
TABLES
Table 2-1. Tier 1 Screening Results for the Ferroalloys Production Source
Category 3
Table 2-2. Tier 2 Screening Results for the Ferroalloys Production Source
Category 4
Table 2-3. Tiers 1 and 2 Screening Results for NEI11660, by PB-HAP Chemical
and PB-HAP Group Total 6
Table 2-4. Meteorological and Lake Location Information Used in Tier 1 (all
facilities) and in Tier 2 (specific to NEI11660) 8
Table 4-1. Wind and Precipitation Statistics for the Meteorological Data Used in
this Site-specific Assessment 14
Table 4-2. Mixing Height Statistics for the Meteorological Data 15
Table 4-3. Modeled Emissions Amounts for Each Grouped Emission Source 16
Table 4-4. Lake Sizes 25
Table 4-5. Farm Food Chain and Soil Ingestion Rates Used for Farmer Scenario 31
Table 4-6. Fish Ingestion Rates Used for Angler Scenarios Evaluated 32
Table 5-1. Hazard Quotients from Exposure to Methyl Mercury for Angler
Scenarios 36
Table 5-2. Hazard Quotients from Exposure to Divalent Mercury for Subsistence
Farmer Scenarios 37
Table 5-3. Hazard Quotients by Ingested Medium, per Age Group, from
Exposure to Divalent Mercury at "Farm_SE" 38
Table 5-4. Hazard Quotients from Exposure to Cadmium for Angler Scenarios 39
Table 5-5. Hazard Quotients from Exposure to Cadmium for Subsistence Farmer
Scenarios 40
Table 5-6. Hazard Quotients by Ingested Medium, per Age Group, from
Exposure to Cadmium at "Farm_SE" 41
Table 5-7. Incremental Lifetime Cancer Risks from Exposure to PAHs for Angler
Scenarios 42
Table 5-8. Incremental Lifetime Cancer Risk from Exposure to PAHs for
Subsistence Farmer Scenarios 42
Table 5-9. Incremental Lifetime Cancer Risk by Ingested Medium from Exposure
to PAHs at "Farm_NNW" 44
Table 5-10. Incremental Lifetime Cancer Risks from Exposure to Dioxins for
Angler Scenarios 45
Table 5-11. Incremental Lifetime Cancer Risk from Exposure to Dioxins for
Subsistence Farmer Scenarios 45
Table 5-12. Incremental Lifetime Cancer Risk by Ingested Medium from
Exposure to Dioxins at "Farm_NNW" 47
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Human Health Multipathway Residual Risk Assessment for the Ferroalloys Production Source Category
Table 5-13. Comparison of Site-specific Hazard Quotients and Cancer Risks to
the Results of the Screening-level Assessments 49
Table 6-1. Sources of Uncertainty in the Current Site-specific Assessment 51
FIGURES
Figure 3-1. Conceptual Exposure Model for Subsistence Farmer Scenario 10
Figure 3-2. Conceptual Exposure Model for Angler Scenario 10
Figure 4-1. Overview of Ingestion Exposure and Risk Screening Evaluation
Methods 11
Figure 4-2. Location of the Assessed Facility (NEI11660) 12
Figure 4-3. Wind Rose for the Meteorological Data Used in this Site-specific
Assessment 14
Figure 4-4. Modeled Surface Parcels for NEI11660 and Their Modeled
Vegetation or Soil Types 20
Figure 4-5. Modeled Surface Parcels for NEI11660 and Land Cover Base Map 21
Figure 4-6. Modeled Surface Parcels for NEI11660 and Terrain Base Map 22
Figure 4-7. Modeled Surface Parcels for NEI11660 and County Base Map 23
Figure 4-8. Modeled Surface Parcels for NEI11660, Zoomed on Farms and
Lakes Near the Facility 24
Figure 4-9. Modeled Air Parcels for NEI11660 26
Figure 5-1. Contribution to Hazard Quotients by Ingested Medium, per Age
Group, from Exposure to Divalent Mercury at "Farm_SE" 37
Figure 5-2. Contribution to Hazard Quotients by Ingested Medium, per Age
Group, from Exposure to Cadmium at "Farm_SE" 40
Figure 5-3. Contribution to Incremental Lifetime Cancer Risk by Ingested Medium
from Exposure to PAHs at "Farm_NNW" 43
Figure 5-4. Contribution to Incremental Lifetime Cancer Risk by Ingested Medium
from Exposure to Dioxins at "Farm_NNW" 46
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Human Health Multipathway Residual Risk Assessment for the Ferroalloys Production Source Category
1. Introduction
This report provides the details and results of an assessment of human health risks, via the
ingestion pathway, from exposure to persistent and bioaccumulative hazardous air pollutants
(PB-HAPs) emitted to the air from a ferroalloys production facility. Cadmium, dioxins, mercury,
and polycyclic aromatic hydrocarbons (PAHs) were the evaluated PB-HAP groups,1 selected
from a larger list of PB-HAPs, based in part on ranking their toxicity-weighted air emissions
across all source categories (explained further in Appendix 4 of the Risk Report). As detailed in
Appendix 4 of the Risk Report, the risks evaluated were chronic cancer risks (for PAHs and
dioxins) and chronic non-cancer hazards (for cadmium and mercury). This assessment was
conducted to show an example of the differences in risk estimates between the multipathway
"tiered" screening results and those of a site-specific multipathway analysis.
As described in Section 4 of this document and Appendix 4 of the Risk Report, EPA uses the
TRIM.FaTE2 model to evaluate the environmental transport, transfer, and fate of PB-HAP
emissions. The MIRC3 program then calculates farm food chain (FFC) chemical uptake, human
exposure, and human health risk. For the assessment described in this report, these models
were used to conduct one site-specific case study for a ferroalloys production facility with
emissions that might elevate risks to human health from ingestion of food products and soil
contaminated with PB-HAPs from the facility. This facility is the Eramet facility near Marietta in
Washington County, Ohio (NEI11660 in the National Emissions Inventory [NEI]; approx.
39.37°N, 81.52°W).
Two ferroalloys production facilities make up the source category: NEI11660 and
NEIWV053FELMAN. In the iterative ("tiered") screening-level ingestion analysis discussed in
greater detail in Section 2 of this document, both facilities exceeded the Tier 1 and Tier 2
emission screening thresholds for at least one of the PB-HAP groups evaluated. NEI11660
exceeded the Tier 2 thresholds of cadmium, mercury, and PAHs by larger margins than the
other facility in the source category (the New Haven facility), making it the highest facility in the
source category for Tier 2 screening. NEI11660 was selected based on its Tier 2 screening
results and based on the feasibility, with respect to the modeling framework, of parameterizing
the region surrounding the facility. The ingestion exposure scenarios assessed for the selected
facility do not necessarily represent the highest potential ingestion exposures and risks for all
humans living in the vicinity of ferroalloys production facilities, but the exposure and risk
estimates should be among the highest possible for this source category.
The approach, data, assumptions, and results of the site-specific assessment are presented in
this report. Beyond the description and discussion of this site-specific assessment presented in
this document, the Protocol for Developing a TRIM.FaTE Model Scenario to Support a Site-
specific Risk Assessment in the RTR Program (which is Appendix 9 to the Risk Report)
provides additional considerations, suggested guidelines, and justifications for developing a site-
specific assessment (not specific to this source category). EPA generally followed this protocol
1
The phrase "PB-HAP group" is used to distinguish the individual PB-HAP chemicals and congeners from the overall
family (grouping) of those chemicals. For example, EPA's ingestion risk methods in its Risk and Technology Review
(RTR) program evaluate emissions of 17 individual congeners of chlorinated dibenzo-p-dioxins and chlorinated
dibenzofurans in the "dioxin" PB-HAP group.
2
TRIM.FaTE stands for the Fate, Transport, and Ecological Exposure module of the Total Risk Integrated
Methodology modeling system. Additional TRIM.FaTE information can be found at:
http://www.epa.aov/ttn/fera/trim gen.html.
3
MIRC stands for the Multimedia Ingestion Risk Calculator.
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Human Health Multipathway Residual Risk Assessment for the Ferroalloys Production Source Category
in developing this site-specific assessment. The Technical Support Document for the TRIM-
Based Multipathway Tiered Screening Methodology forRTR, which is Appendix 4 to the Risk
Report, provides a more comprehensive description of the iterative approach (focusing
especially on Tiers 1 and 2) that EPA uses to evaluate the potential for ingestion risks resulting
from PB-HAP emissions.
2. Screening-level Assessments
As described in more detail in Appendix 4 to the Risk Report, EPA developed an iterative
screening approach for evaluating the potential for human health risks above levels of concern
resulting from non-inhalation exposures (i.e., via the ingestion pathway) to selected PB-HAPs in
air emissions. In the screening approach, PB-HAP emissions are "screened out" if they are
unlikely to pose human health ingestion risks above levels of concern, therefore enabling EPA
to focus additional resources on emission sources of greater concern. The levels of concern are
an incremental lifetime human cancer risk exceeding 1-in-one million (for PAHs and dioxins)
and a chronic non-cancer hazard quotient (HQ) exceeding 1 (for cadmium and mercury).4 The
Tiers 1 and 2 screening assessments and results are discussed in Section 2.1 and Section 2.2.
2.1 Tier 1 Emission Screening Analysis
As described in more detail in Appendix 4 of the Risk Report (Sections 1 and 2, and
Attachment A), the objective of the Tier 1 emission screening analysis is to rank PB-HAP
emission sources within a source category (or across source categories), enabling EPA to
screen out emission sources unlikely to pose human health ingestion risks above levels of
concern. The confidence in screening out emission sources is achieved by avoiding the
underestimation of media concentrations and human exposure resulting from the emissions.
The Tier 1 emission screening thresholds are intended to apply to any U.S. facility; that is, they
are designed to be applicable to every facility regardless of the facility's characteristics and
surrounding environment and the population exposed in its vicinity (see Attachment A of
Appendix 4 of the Risk Report for a discussion of development of the screening thresholds).
Each evaluated PB-HAP group has a single Tier 1 screening threshold, as shown in Table 2-1.
For Tier 1, the emissions of all dioxin congeners are normalized to 2,3,7,8-tetrachlorodibenzo-p-
dioxin-equivalent emissions, and the emissions of all PAH congeners are normalized to
benzo(a)pyrene-equivalent emissions.5 The equivalency factors used to relate individual
compounds to the surrogates are based on oral toxicity factors and factors related to the
congeners' environmental fate and transport characteristics as analyzed in the TRIM.FaTE
model (explained further in Appendix 4 to the Risk Report: Section A.2.7 of Attachment A). For
mercury, emissions of divalent mercury are evaluated, but the Tier 1 emissions screening
threshold reflects ingestion of methyl mercury from contaminated fish, only because ingestion of
fish is the dominant exposure medium for mercury and methyl mercury (formed from the
methylation of divalent mercury) represents 95 percent or more of the total mercury
concentration in fish.
As shown in Table 2-1, the emissions from both ferroalloys production facilities exceeded the
Tier 1 emission screening threshold for mercury, dioxins, and PAHs, and from one facility
4
EPA considers "cancer risks exceeding 1-in-one million" to refer to risks of at least 1.5-in-one million, and "non-
cancer HQs exceeding 1" to refer to HQs of at least 1.5.
5
2,3,7,8-TCDD and BaP are sometimes used in this report as shorthand terms for 2,3,7,8-tetrachlorodibenzo-p-dioxin
and benzo(a)pyrene, respectively.
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Human Health Multipathway Residual Risk Assessment for the Ferroalloys Production Source Category
(NEI11660) for cadmium. Tier 1 screening quotients were calculated as the ratio of a facility's
emissions to the Tier 1 emission screening threshold. Where a facility's PB-HAP-specific
emission rate exceeds the Tier 1 screening threshold emission rate, the screening quotient
exceeds 1. The highest screening quotients estimated in the source category varied by
PB-HAP, with NEI11660 being the highest emitter for mercury, cadmium, and PAHs:
mercury - 100 at NEI11660 (meaning the highest emitting facility exceeded the Tier 1
emission screening threshold by a factor of 100),
cadmium - 10 at NEI11660,
. PAHs-200 at NEI11660, and
. dioxins - 100 at NEIWV053FELMAN (80 at NEl 11660).
When the Tier 1 screening quotients are summed across the four PB-HAP groups, NEI11660
ranked first in the source category.
Table 2-1. Tier 1 Screening Results for the Ferroalloys Production Source Category
Facility
Tier 1 Emission Screening Analysis
Facility
PB-HAP
Group
(a) Emission
Screening
Threshold (TPY)a
(b) Facility-level
Emissions
(TPY)b
(c) Screening
Quotient
[(b)/(a)]c
NEI11660
Mercury0 (non-cancer)
3.16E-04
3.43E-02
100
Cadmium (non-cancer)
1.18E-02
1.20E-01
10
PAHs as BaP (cancer)
2.58E-03
5.67E-01
200
Dioxins as 2,3,7,8-TCDD (cancer)
2.81 E-09
2.34E-07
80
NEIWV053FELMAN
Mercury0 (non-cancer)
3.16E-04
3.52E-03
10
Cadmium (non-cancer)
1.18E-02
2.27E-03
0.2
PAHs as BaP (cancer)
2.58E-03
6.50E-02
30
Dioxins as 2,3,7,8-TCDD (cancer)
2.81 E-09
3.89E-07
100
aTPY = tons per year
bPAH and dioxin emissions in this column were normalized to BaP and 2,3,7,8-TCDD, respectively, for oral toxicity and Tier 1
modeled environmental fate and transport.
°Red highlights indicate screening quotients that are greater than 1 (i.e., 2 or larger when rounded; screening quotients rounded to
one significant figure).
dThe emission screening threshold for mercury applies to emissions of divalent mercury, although it was derived based on hazards
associated with ingestion offish contaminated with methyl mercury (i.e., emissions of divalent mercury, which transforms to methyl
mercury within the environment and accumulates in fish tissue).
2.2 Tier 2 Emission Screening Analysis
The generic site characteristics used in the Tier 1 screening analysis can differ from those
actually present at many facilities. In a Tier 2 screening analysis, as described in more detail in
Appendix 4 to the Risk Report (Sections 1 and 3, and Attachment B), some site-specific
characteristics are taken into account when developing Tier 2 emission screening thresholds.
Meteorological conditions (temperature, wind speed and direction, mixing height, and
precipitation totals) and locations of potentially fishable lakes are the site-specific characteristics
used to adjust the emission screening thresholds in Tier 2. These characteristics were selected
because sensitivity analyses have shown them to influence modeled media concentrations
substantially. Moreover, their use to adjust the emission screening thresholds does not require
significant re-modeling (discussed further in Attachment B in Appendix 4 to the Risk Report).
Meteorological conditions at a specific facility can differ significantly from those used for all
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facilities in Tier 1 and can impact modeled media concentrations significantly. The locations of
lakes near a specific facility also can differ from the location of the lake used for all facilities in
Tier 1 (assumed to be very close to the facility), which can significantly affect chemical
deposition to the lake.
For Tier 2 assessments, a matrix of meteorological characteristics and lake locations was
developed, representing ranges of wind speed, mixing height, precipitation amount, and lake
distance expected to be found at most U.S. locations. Several hundred runs of TRIM.FaTE and
MIRC then were performed using those characteristics to develop chemical exposure factors
and Tier 2 emission screening thresholds for each possible combination of meteorology and
lake location. All other modeled site characteristics remained unchanged from Tier 1, although
the spatial layout was modified to accommodate the different lake locations (discussed further in
Attachment B of Appendix 4 to the Risk Report). For a facility undergoing a Tier 2 analysis, the
typical meteorological conditions and the locations of potentially fishable lakes are analyzed.
Wind and lake data are subset by the eight cardinal directions ("octants," i.e., north, northeast,
east, etc.), while mixing height and precipitation data do not differ by direction. For each octant,
the facility's combination of meteorology and lake information is matched to the most similar
modeled combination, the frequency of winds blowing into the octant is accounted for, and the
facility emissions are evaluated against the respective Tier 2 emission screening thresholds. For
a given PB-HAP group, the octant having the largest (among all octants) Tier 2 screening
quotient is selected to avoid underestimating exposure and risk. As with the Tier 1 analysis, the
Tier 2 screening methods enable EPA to rank emissions from facilities and confidently screen
out those unlikely to pose human health risks above levels of concern (by avoiding
underestimating exposure and risk).
Summary-level Tier 2 screening results for the ferroalloys production facilities are shown in
Table 2-2.
Table 2-2. Tier 2 Screening Results for the Ferroalloys Production Source Category
Facility
Tier 2 Emission Screening Analysis
Facility
PB-HAP
Group
(a)Emission
Screening
Threshold (TPY)a
(b)Facility-level
Emissions (TPY)b
(c)Screening
Quotient[(b)/(a)]c
NEI11660
Mercury0 (non-cancer)
3.74E-03
3.43E-02
9
Cadmium (non-cancer)
1.40E-01
1.20E-01
0.9
PAHs as BaP (cancer)
6.92E-03
1.48E-01
20
Dioxins as 2,3,7,8-TCDD (cancer)
3.46E-08
2.32E-07
7
NEIWV053FELMAN
Mercury0 (non-cancer)
3.18E-03
3.52E-03
1
Cadmium (non-cancer)
1.00E-01
2.27E-03
0.02
PAHs as BaP (cancer)
7.78E-03
2.05E-02
3
Dioxins as 2,3,7,8-TCDD (cancer)
2.75E-08
4.48E-07
20
aTPY = tons per year
Emissions of PAHs and dioxins in this column were normalized to BaP and 2,3,7,8-TCDD, respectively for oral toxicity and Tier 2
modeled environmental fate and transport.
°Red highlights indicate screening quotients that are greater than 1 (i.e., 2 or larger when rounded; screening quotients rounded to
one significant figure).
dThe emission screening threshold for mercury applies to emissions of divalent mercury, although it was derived based on hazards
associated with ingestion offish contaminated with methyl mercury (i.e., emissions of divalent mercury, which transforms to methyl
mercury within the environment and accumulates in fish tissue).
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Human Health Multipathway Residual Risk Assessment for the Ferroalloys Production Source Category
Table 2-3 contains both Tier 1 and Tier 2 screening results for NEI11660 for each chemical (i.e.,
for emissions of cadmium compounds, divalent mercury, 17 congeners of dioxins, and 18
congeners of PAHs) and chemical group. At NEI11660, emissions of cadmium exceeded the
Tier 1 emission screening threshold but screened out in the Tier 2 analysis, and the same is
true for mercury emissions from NEIWV053FELMAN.
For NEI11660 in Tier 2:
For mercury, the screening quotient (9) was much smaller than in Tier 1 (100) and was
the largest in the source category (same rank as in Tier 1).
Cadmium emissions screened out, although the screening quotient (0.9, down from 10 in
Tier 1) was the largest in the source category (same rank as in Tier 1).
For PAHs, the screening quotient (20) was much smaller than in Tier 1 (200) and was
the largest in the source category (same rank as in Tier 1), driven primarily by
benzo(b)fluoranthene and, to a lesser extent, benzo(k)fluoranthene and benzo(e)pyrene.
For dioxins, the screening quotient (7) was much smaller than in Tier 1 (80), and was the
second largest in the source category (same rank as in in Tier 1), driven primarily by
1,2,3,7,8-PCDD and, to a lesser extent, 2,3,7,8-TCDD.
When the Tier 2 screening quotients are summed across the four PB-HAP groups, the
facility ranked first in the source category (same ranking as in Tier 1).
In the Tier 2 analysis for NEI11660, the meteorological statistics (shown in Table 2-4) were
derived from the Mid-Ohio Valley Regional Airport surface station (Weather Bureau Army Navy
[WBAN] ID 03804; 8 km east-southeast of NEI11660) and from the Pittsburgh International
Airport upper-air station (WBAN ID 94823). This pair of surface and upper-air stations is the
same as that used in the Risk and Technology Review (RTR) inhalation analysis for this facility.
Concentrations of cadmium, mercury, and dioxins were highest in one lake (Veto Lake; 12 km
west-southwest of the facility, was matched with the 10-km lake scenario in Tier 2); therefore,
this lake was analyzed for consumption of fish contaminated by emissions of these chemicals.
Veto Lake was farther from the facility than the generic lake in Tier 1 (which was approx. 1.4 km
from the facility), which reduces modeled chemical deposition to the lake in Tier 2 if all other
parameters are held constant. For PAHs, chemical exposure via fish ingestion was largest with
Wolf Run Lake in the north octant (48 km from the facility, which matched to the 40-km lake
scenario in Tier 2); therefore, this lake was used to assess risk from PAH exposure via fish
ingestion.
The modeled meteorological conditions for NEI11660 (in the west octant for cadmium, mercury,
and dioxins and the north octant for PAHs) are also shown in Table 2-4, along with the values
used for all facilities in Tier 1. Annual precipitation and average mixing height are not modeled
by compass direction, and the modeled Tier 2 mixing height remained unchanged from the Tier
1 analysis, while the modeled Tier 2 precipitation was smaller than in Tier 1. Tier 2 modeled
wind speeds were also unchanged from Tier 1. Winds blew into each Tier 2 octant much less
frequently than into the Tier 1 domain, decreasing modeled chemical deposition to the domain.
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Human Health Multipathway Residual Risk Assessment for the Ferroalloys Production Source Category
Table 2-3. Tiers 1 and 2 Screening Results for NEI11660, by PB-HAP Chemical
and PB-HAP Group Total3
Chemical Information
Tier 1 Emission Screening Analysis
Tier 2 Emission Screening Analysis
PB-HAP
Group
PB-HAP Chemical
Raw
Emissions
(lbs)
Adjusted
Emissions
(lbs)"
Emission
Screening
Threshold
(lbs)
Screening
Quotient
(by
Chem.)
Screening
Quotient (by
PB-HAP
Group)"'0
Adjusted
Emissions
(lbs)"
Emission
Screening
Threshold
(lbs)
Screening
Quotient
(by
Chemical)
Screening
Quotient (by
PB-HAP
Group)"'0
Mercuryd
Divalent mercury
3.43E-02
3.43E-02
3.16E-04
100
100
3.43E-02
3.74E-03
9
9
Cadmium
Cadmium compounds
1.20E-01
1.20E-01
1.18E-02
10
10
1.20E-01
1.40E-01
2-Methyl-naphthalene
6.98E-01
7.64E-04
4.65E-05
Acenaphthene
2.89E-01
4.76E-04
0.18
6.22E-05
0.0090
Acenaphthylene
9.71 E-01
2.51 E-03
0.97
1.41E-04
0.020
Anthracene
3.75E-01
0
0
0
0
Benz(a)anthracene
1.52E-01
2.16E-03
0.84
1.77E-03
0.26
Benzo(a)pyrene
2.58E-02
2.58E-02
10
2.58E-02
3.7
Benzo(b)fluoranthene
1.61 E-01
3.01 E-01
120
4.70E-02
6.8
Benzo(e)pyrene6
2.08E-01
6.37E-02
25
3.20E-02
4.6
PAHs
Benzo(g,h,i)perylene
1.58E-02
4.68E-03
2.58E-03
1.8
200
2.53E-03
6.92E-03
0.37
20
Benzo(k)fluoranthene
4.75E-02
1.40E-01
54
2.71 E-02
3.9
Chrysene
4.30E-01
1.76E-03
0.68
1.11 E-03
0.16
Dibenzo(a,h)anthracene
2.72E-03
1.23E-02
4.8
4.95E-03
0.72
Fluoranthene
1.33E+00
3.42E-03
1.3
1.14E-03
0.16
Fluorene
3.82E-01
7.95E-04
0.31
4.87E-05
0.0070
lndeno(1,2,3-c,d)pyrene
1.01E-02
7.51 E-03
2.9
4.24E-03
0.61
Perylene6
2.79E-03
2.24E-04
0.087
7.07E-05
0.010
Phenanthrene
2.11E+00
0
0
0
0
Pyrene
9.89E-01
0
0
0
0
HeptaCDD, 1,2,3,4,6,7,8-
5.96E-08
5.94E-10
6.46E-10
Dioxins
HeptaCDF, 1,2,3,4,6,7,8-
7.82E-08
1.25E-10
2.81 E-09
0.044
80
2.53E-10
3.46E-08
0.0073
7
HeptaCDF, 1,2,3,4,7,8,9-
3.29E-08
6.19E-11
0.022
9.45E-11
0.0027
HexaCDD, 1,2,3,4,7,8-
3.21 E-08
4.99E-09
1.8
5.03E-09
0.15
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Chemical Information
Tier 1 Emission Screening Analysis
Tier 2 Emission Screening Analysis
PB-HAP
Group
PB-HAP Chemical
Raw
Emissions
(lbs)
Adjusted
Emissions
(lbs)"
Emission
Screening
Threshold
(lbs)
Screening
Quotient
(by
Chem.)
Screening
Quotient (by
PB-HAP
Group)"'0
Adjusted
Emissions
(lbs)"
Emission
Screening
Threshold
(lbs)
Screening
Quotient
(by
Chemical)
Screening
Quotient (by
PB-HAP
Group)"'0
HexaCDD, 1,2,3,6,7,8-
3.11E-08
3.17E-09
3.23E-09
HexaCDD, 1,2,3,7,8,9 -
3.22E-08
1.36E-09
0.48
1.38E-09
0.040
HexaCDF, 1,2,3,4,7,8-
3.69E-08
1.17E-09
0.41
1.46E-09
0.042
HexaCDF, 1,2,3,6,7,8-
2.84E-08
1.33E-09
0.47
1.69E-09
0.049
HexaCDF, 1,2,3,7,8,9-
3.41 E-08
1.65E-09
0.59
2.09E-09
0.060
HexaCDF, 2,3,4,6,7,8-
4.14E-08
8.14E-10
0.29
1.03E-09
0.030
OctaCDD, 1,2,3,4,6,7,8,9-
5.02E-07
1.65E-10
0.059
3.11E-10
0.0090
OctaCDF, 1,2,3,4,6,7,8,9-
8.66E-08
4.35E-12
0.0015
9.57E-12
0.00028
PentaCDD, 1,2,3,7,8-
4.60E-08
1.76E-07
63
1.69E-07
4.9
PentaCDF, 1,2,3,7,8-
3.68E-08
4.68E-10
0.17
6.44E-10
0.019
PentaCDF, 2,3,4,7,8-
5.60E-08
6.15E-09
2.2
7.97E-09
0.23
TetraCDD, 2,3,7,8-
3.57E-08
3.57E-08
13
3.57E-08
1.0
TetraCDF, 2,3,7,8-
5.09E-08
6.47E-10
0.23
7.21 E-10
0.021
aEmissions and emission thresholds in this table are in units of pounds, whereas the units in Table 2-1 and Table 2-2 are in short tons (pounds/2000). All values in the "Screening
Quotient (by PB-HAP Group)" columns, and values for mercury and cadmium in the "Screening Quotient (by Chem.)" columns, are rounded to one significant figure.
b"Adjusted Emissions" of PAHs and dioxins were normalized to BaP and 2,3,7,8-TCDD, respectively, to account for differences in oral toxicity (using toxic equivalency factors) and
Tier 1 (or Tier 2) modeled environmental fate and transport (i.e., exposure equivalency factors).
Red highlights indicate PB-HAP group screening quotients that are greater than 1 (i.e., 2 or larger when rounded; see table footnote (a) regarding rounding). Gray font indicates
screening quotients of 1 or smaller (i.e., not 2 or larger when rounded). Green highlights indicate emissions that screened out in Tier 2 but not in Tier 1. Mercury and cadmium are
evaluated for non-cancer effects, while PAHs and dioxins are evaluated for cancer effects.
dThe emission screening threshold for mercury applies to emissions of divalent mercury, although it was derived based on hazards associ ated with ingestion of fish contaminated
with methyl mercury (i.e., emissions of divalent mercury that transforms to methyl mercury within the environment and accumulates in fish tissue).
These PAH congeners are not currently fully parameterized in TRIM.FaTE to evaluate their specific fate, transport, and transformation properties. Their exposure equivalency
factors (to relate them to the benzo(a)pyrene surrogate used in screening) were based on how their Kow values related to the Kow values of the fully parameterized congeners. See
Attachment 4 to the Risk Report (specifically, Section A.2.7.1) for more information.
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Table 2-4. Meteorological and Lake Location Information Used in Tier 1 (all facilities) and
in Tier 2 (specific to NEI11660)
Parameter
(As Modeled)
Tier 1
Tier 2
All Chemicals
Cadmium, Mercury,
Dioxins
PAHs
Octant Analyzed
-
W
N
Wind Speed (m/s)
2.8
2.8
2.8
Frequency ofWinds Blowing into Modeled
Domain (fraction)
0.43
0.07
0.19
Annual Precipitation (mm), site-wide (same for
all octants)
1,500
1,187
1,187
Mixing Height (m), site-wide (same for all
octants)
710
710
710
Distance to Lake (km)
1.4
10 (Veto Lake)
40 (Wolf Run Lake)
3. Scope of Site-specific Risk Assessment
This site-specific risk assessment evaluated the emissions of four PB-HAP groups (mercury,
cadmium, dioxins, and PAHs) emitted from the selected ferroalloys production facility. This
section describes the conceptual exposure model and overall scope of the site-specific
assessment.
For an inhalation risk assessment, the risk can be approximated (taking into account a range of
assumptions) using modeled long-term average air concentrations associated with a source and
information on where people reside. For an evaluation of non-inhalation exposures, however,
estimating the risk to the "most exposed" individual can be more difficult because chemical
concentrations in environmental media to which people are exposed and individual exposure
patterns associated with ingestion can vary greatly, depending on location, timing, and other
factors. For example, people can be exposed to chemicals that accumulate in the FFC by
consuming a variety of fruits and vegetables, each of which might or might not be grown in the
vicinity of the source. The amount of each type of produce consumed can vary widely among
the individuals in a population, as can the fraction of each type of produce that is actually
impacted by emissions from a source.
To simplify the exposure and risk assessment of PB-HAPs for the selected facility for this risk
assessment, a scenario approach was employed. This approach involved evaluating a
combination of exposure media by which an individual is most likely to be exposed to elevated
concentrations of PB-HAPs (i.e., a set of "exposure scenarios"). The scenario approach
provides a systematic method for evaluating the relative importance of exposure media (e.g.,
consumption of farm food products versus consumption of fish) that are of potential concern for
different chemicals and locations. Typically, only scenarios that are plausible for the situation of
interest are evaluated, and the assessment usually focuses on those scenarios that are
assumed a priori to lead to the highest individual exposure and risks. Risk metrics such as
incremental lifetime cancer risk and chronic non-cancer HQ are calculated as appropriate for
each scenario. If warranted, information regarding the likelihood of a specific exposure scenario
actually occurring could be used to develop estimates of uncertainty for each scenario and the
variations thereof.
For this site-specific residual risk assessment, exposure estimates and corresponding risks
were calculated for two basic exposure scenarios:
. A subsistence farmer scenario, involving an individual living for a 70-year lifetime on a
farm homestead in the vicinity of the source and consuming produce grown on, and
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meat and animal products raised on, the farm. The individual also incidentally ingests
surface soil at the location of the farm homestead.
. An angler scenario, involving an individual who regularly consumes fish caught in a
freshwater lake in the vicinity of the source of interest over the course of a 70-year
lifetime.
Variations of these two scenarios were evaluated using different assumptions regarding food
source (i.e., location of the farm homestead or the water body from which fish are obtained), the
age of the individual exposed (for non-cancer hazards), the assumed ingestion rate of each food
type, and other factors. In particular, a range offish ingestion rates was evaluated to determine
the possible health risks associated with that important medium.
In addition, exposure estimates and risks for infants consuming contaminated breast milk
were evaluated in the case of dioxins, with the assumption that the nursing mother was exposed
to chemicals via one of the two basic scenarios listed above.
The conceptual exposure model for the subsistence farmer scenario is presented in Figure 3-1.
The arrows represent the movement of the chemical of concern through the environment and
FFC. In this exposure scenario, the hypothetical receptors consume produce, meat, animal
products, and incidentally ingested soil. The conceptual exposure model for the angler scenario
is presented in Figure 3-2. The hypothetical receptor consumes fish from a contaminated water
body. Note that the groundwater parts of these conceptual models were not used in this
analysis of the ferroalloys production facility. Chemical transport into a modeled lake via
horizontal groundwater flow and recharge is negligibly small compared to other chemical inputs
into the lake. The vadose soil zone also was not used in this analysis because theoretical
considerations (and sensitivity runs with TRIM.FaTE) suggest that chemical transported into
lower soil layers will not substantially move back into the surface soil. Removing the vadose soil
zone and groundwater compartments improves model runtime and have no appreciable impact
on human chemical exposure in the TRIM.FaTE modeled environment.
These scenarios are expected to cover the highest possible long-term exposures and risks for
the chemicals evaluated. In addition to ingestion, non-inhalation exposure to PB-HAPs also can
occur by the dermal pathway. Risk from dermal exposure, however, was expected to be a small
fraction of the risk from inhalation exposure or ingestion exposure (see Attachment A,
Addendum 3 to Appendix 4 to the Risk Report for more detail). Therefore, the risk from dermal
exposure was not calculated for this site.
These three exposure scenarios (subsistence farmer, angler, and infant consuming breast milk)
were evaluated for each of the four PB-HAP groups as appropriate (note, for example, that for
methyl mercury, only the angler scenario is relevant). As described in Sections 1 and 2 of this
report, the facility evaluated was the Eramet facility near Marietta, Ohio (NEI ID NEI11660).
NEI11660 was selected for this site-specific assessment based on its Tier 2 screening results
and based on the feasibility of parameterizing the environment surrounding the facility for the
TRIM.FaTE and MIRC models. EPA anticipates that the results for this facility and these
scenarios are among the highest that might be encountered for the ferroalloys production
source category.
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Figure 3-1. Conceptual Exposure Model for Subsistence Farmer Scenario3
°The soil vadose zone and groundwater compartments were not modeled in this analysis, which had no
noticeable impact on modeled human chemical exposure.
Figure 3-2. Conceptual Exposure Model for Angler Scenario3
"The soil vadose zone and groundwater compartments were not modeled in this analysis, which had no
noticeable impact on modeled human chemical exposure.
4. Analysis Methods
An overview of the processes analyzed by TRIM.FaTE and MIRC for assessing site-specific
multipathway risks from the assessed ferroalloys production facility is shown in Figure 4-1. The
approach can be divided into the following four steps, which correspond to the green boxes in
Figure 4-1.
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Figure 4-1. Overview of Ingestion Exposure and Risk Screening Evaluation Methods
Chemical Emissions to Air
\
Chemical fate and
transport:
Physical environment
and aquatic ecosystem
TRIM. FaTE
V V
Uptake & transfer
into produce and
livestock
Human
ingestion
exposure
Risk & hazard
estimation
Multimedia Ingestion Risk Calculator (MIRC)
Cancer Risk
Hazard Quotient
1. TRIM.FaTE: Fate and transport modeling of PB-HAPs that are emitted to air by the source
and partition into soil, water, and other environmental media (including fish).
2. MIRC: Modeling of transfer and uptake of PB-HAPs into FFC media (produce, livestock, and
dairy products) from soil and air.
3. MIRC: Estimating ingestion exposures (i.e., average daily ingestion rates) resulting from
contact by a hypothetical human receptor with the various selected media.
4. MIRC: Calculating incremental lifetime cancer risk estimates or chronic non-cancer HQs, as
appropriate, for each PB-HAP and comparing these metrics to health effect levels of
concern used in the RTR.
Site-specific aspects of the fate and transport modeling for this facility are discussed in Section
4.1. The methods used for the exposure and risk modeling are presented in Section 4.2. Further
discussion of TRIM.FaTE and MIRC and their implementation for RTR screening analyses can
be found in Appendix 4 to the Risk Report.6
4.1 Fate and Transport Modeling
This section describes the TRIM.FaTE modeling conducted for this assessment. Most of the
material presented here describes the assumptions and data sources used to develop
TRIM.FaTE inputs and settings related to meteorological inputs used by the model, the spatial
aspects of the modeled region, the characteristics of abiotic environmental compartments and
plants included in the scenario, and the aquatic ecosystems set up in each water body of
interest.
4.1.1 Overview of the Area Surrounding the Facility
Figure 4-2 shows the location of NEI11660, along with satellite imagery, water bodies, terrain,
and land cover in the larger region around the facility. The facility is located in Washington
TRIM.FaTE in Appendix 4 to the Risk Report: Section 2.2.1; Section A.2.5 of Attachment A. MIRC in Appendix 4:
Sections 2.2.2-2.2.4; Section A.2.6 and Addendum 2 of Attachment A. Note: Appendix 4 focuses on development of
Tiers 1 and 2 of the multipathway screening analysis, although general descriptions of TRIM.FaTE and MIRC and
their components and purposes still apply to this site-specific assessment.
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County, OH—approximately 8 km southwest of Marietta, OH arid 5 km northeast of Vienna, WV.
The area around the facility is generally forested with relatively small areas of pasture and
farmland. Some industrial and other development is located along the Ohio River, generally
running northeast to southwest directly past the facility. Most crop farming activities are related
to soybeans and corn (not shown in Figure 4-2) and occur on the Ohio side of the river
(northwest of the facility). The elevation in most nearby areas is between 200 and 300 m above
sea level, with areas farther north in Ohio, northeast in Pennsylvania, and east/southeast in
West Virginia exceeding 300 m. The number of people in Washington County, OH is relatively
small (approx. 62,000 people in 2010; U.S. Census Bureau 2013; not shown in Figure 4-2).
Wood County, WV, directly across the river from the facility, also has a relatively small
population (approx. 87,000 people in 2010). The modeling domain for the facility occupies parts
of seven counties for which the 2010 total population exceeded 236,000 (all of the counties, not
just the parts inside the modeling domain).
Figure 4-2. Location of the Assessed Facility (NE111660)a
Elevation
2006 Land Cover
Aerial Imagery with Water Bodies
Pennsylvania
aTop Left Panel: Basemap: ESRI World Imagery (ESRI 2013); Foreground: ESRI Water Body Types (Specifically, the geospatial file
in the ESRI Data & Maps 2009 Data Update for ArcGIS version 9.3.1; derived by LISGS, EPA, and ESRI from the USGS National
Hydrography Dataset (USGS 2012). Top Right Panel: Shuttle Radar Topography Mission elevation data (see:
http://www.esri.com/news/arcnews/sprina07articles/alobal-eievation.html1. Bottom Panel: 2006 National Land Cover Dataset (MLRC
2006).
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4.1.2 Meteorological Data for Modeling
In the RTR assessments of inhalation risk and Tier 2 ingestion screening (Section 2.2),
NEI11660 was matched to the surface station (WBAN 03804) at the Mid-Ohio Valley Regional
Airport because it was the closest Automated Surface Observing Station (8 km east-southeast
of NEI11660) with relatively few missing data in the data period used in these assessments
(2011). That surface station was matched to the upper-air observing station closest to it—
Pittsburgh International Airport (WBAN ID 94823; approx. 160 km northeast of the surface
station, approx. 175 km northeast of NEI11660).
EPA followed the suggested meteorological data development steps described in Section 3 of
the site-specific assessment protocol provided in Appendix 9 to the Risk Report. For this site-
specific analysis, the same surface and upper-air stations were selected. A recent 4-year period
(2008 through 2011) was used after ensuring that the period-averaged annual precipitation total
was not much larger or much smaller than the 30-year average. Multiple years of meteorological
data often are used in chemical dispersion and advection models to sample a wider range of
meteorological conditions and to reduce the impact of individual extreme weather events on
long-term exposure estimations. For this assessment, TRIM.FaTE was run for 50 years on
hourly data to allow the emitted chemical to bioaccumulate and persist in the environment.
Obtaining, analyzing, and processing 50 years of meteorological data are impractical (based on
cost and availability of such a long period of record). Consequently, 4 years of meteorological
data were used because repeating it over and over to create a 50-year meteorological dataset is
simpler while still representing long-term meteorological patterns on the whole.
TRIM.FaTE requires five meteorological parameters: temperature, wind speed, wind direction,
mixing height, and precipitation amount. EPA obtained hourly mixing height values for the site-
specific assessment by running AERMOD's meteorology processor (AERMET; version 12345,
with modifications to reduce the impact of a known error with friction velocity calculations;
includes processing of 1-minute winds with EPA's AERMINUTE processor) on the surface data,
upper-air data, and the 1992 National Land Cover Dataset (EPA's AERSURFACE tool accepts
only 1992 land cover data, which did not change significantly by 2006, which is the latest data
available from the National Land Cover Database). Approximately 7 percent of the
meteorological data was missing, so EPA used automated methods to replace all missing data
based on reasonable surrounding values. The methods of replacing missing data were based in
part on EPA's guidance for substituting missing meteorological data for regulatory air quality
models (Atkinson and Lee 1992; see also Section 3.5 of the site-specific protocol, Appendix 9 to
the Risk Report). EPA also substituted all calm winds (which occurred 6 percent of the time) and
winds speeds less than 0.75 m/s (which occurred 7 percent of the time) with a wind speed value
of 0.75 m/s, and replaced all mixing heights less than 20 m (which occurred less than 1 percent
of the time) with a value of 20 m. As evident from Figure 4-3 and Table 4-1, winds most often
blew toward the east (19 percent frequency) during the analysis period, and winds blowing
toward the north or northeast also were common (17 and 16 percent frequency, respectively).
Winds with a strong westward or southward wind component were least frequent (8 percent
toward the southwest; 9 percent each toward the south and northwest; 10 percent toward the
west). The average wind speed was 2.7 m/s, although that average varied by ±0.9 m/s
depending on wind direction.
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Figure 4-3. Wind Rose for the Meteorological Data Used in this
Site-specific Assessment3
aWinds are shown "blowing from." That is, the cone in the northeastern quadrant
indicates that winds blow toward the southwest approximately 8 percent of the time.
Table 4-1. Wind and Precipitation Statistics for the Meteorological Data Used in this
Site-specific Assessment
Directional
Octant
All Times
When Precipitating
(9% of the time)
When Not Precipitating
(91% of the time)
Wind
Frequency
Toward
Octant (%)
Average
Wind Speed
(m/s)
Average
Annual
Precipitation
Total (mm)1
Wind
Frequency
Toward
Octant (%)
Average
Wind Speed
(m/s)
Wind
Frequency
Toward
Octant (%)
Average
Wind Speed
(m/s)
N
17%
2.8
204
18%
3.4
17%
2.7
NE
16%
3.2
169
16%
3.8
16%
3.2
E
19%
3.6
157
17%
4.0
19%
3.6
SE
12%
2.4
108
10%
3.0
12%
2.4
S
9%
2.1
100
9%
2.7
9%
2.0
SW
8%
2.2
75
8%
2.9
8%
2.1
W
10%
1.8
124
11%
2.5
9%
1.8
NW
9%
1.8
101
11%
2.7
9%
1.7
Average
Regardless
of Octant
-
2.7
1,038
-
3.2
-
2.6
Calm
Calm winds (<0.75 m/s) occurred 6% of the time in the raw meteorological data. For TRIM.FaTE, calm
values and values less than 0.75 m/s (occurred 7% of the time) were replaced with 0.75 m/s.
Missing
Missing wind data occurred 1% of the time. For TRIM. FaTE, missing values were replaced with
averages of surrounding values.
aThe average annual precipitation totals in the individual octant rows represent only the precipitation that fell when winds were
blowing into that octant. The "Average Regardless of Octant" value is the total precipitation without regard to wind direction.
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Precipitation occurred 9 percent of the time and was usually associated with stronger winds
than times without precipitation. Approximately 46 percent of the annual precipitation occurred
when winds had a northward component. The average annual precipitation total was 1,038 mm
(Table 4-1), which was approximately equal to the 1981-2010 30-year average (not shown).
Hourly mixing heights typically were less than
1,000 m, and more than 64 percent of hours
modeled had mixing heights smaller than 500 m
(Table 4-2). The average of the maximum daily
mixing heights was approximately 1,254 m, and
the average of the minimum daily mixing heights
was approximately 67 m (not shown).
4.1.3 Characteristics of the Modeled
Emission Source Compartment
For TRIM.FaTE, the modeled emissions location,
amounts, and release heights were derived from
the same NEI emissions data used for the RTR
inhalation assessment for the ferroalloys
production source category. The emission source
compartment in TRIM.FaTE was approximately
centered on the facility and generally
encompassed the locations of all of its 16 PB-
HAP-emitting sources.
The height of the source compartment represents
the chemical release height, and the release height's only impact in TRIM.FaTE is to determine
whether the emissions are released within the mixing layer or above it. Chemicals released
above the mixing layer are not available for ground-level exposure (i.e., they are emitted to an
upper-air sink). When determining whether emissions are within the mixing layer, one must
consider not only the physical release height of the emission source but also the buoyancy and
vertical momentum of the emission. TRIM.FaTE does not explicitly model the buoyancy and
vertical momentum characteristics related to the emissions' exit gas temperatures and
velocities, which have the real-world effect of ejecting the chemical vertically beyond exiting the
source (i.e., plume rise). The effect of plume rise, however, can be represented in TRIM.FaTE
by varying the release height with time to account for plume-rise effects appropriately. The use
of time-varying release heights can increase model runtime, so this adjustment was
implemented only when plume-rise estimations indicated that more than 5 percent of the
modeled chemical emissions would be lost to the upper-air sink.
Among the 16 PB-HAP-emitting sources at NEI11660 were 5 unique release height values
ranging from 13 m to 38 m. Other physical stack parameters (exit diameter, exit gas
temperature, and exit gas velocity) also had widely varying values, leading to seven unique
combinations of release height, exit diameter, exit gas temperature, and exit gas velocity. EPA
examined these seven unique combinations at NE111660 and reduced the data to three
modeled sources generally representing three unique combinations of physical characteristics.
These groups, and their emission amounts, are shown in Table 4-3. Plume-rise values were
estimated for each emission source for each hour of modeling, using the hourly meteorology
developed for this site-specific assessment and using methods summarized by Seinfeld and
Pandis (1998) (see also Section 3.7 of the site-specific protocol, Appendix 9 to the Risk Report).
Table 4-2. Mixing Height Statistics for the
Meteorological Data
Range of Hourly Mixing Heights
(exclusive - inclusive) (m)
Frequency
CO
O
CM
II
0.1%
20-418
58%
418-816
21%
816-1214
11%
1214-1612
7%
1612-2010
3%
2010-2408
1%
2408-2806
0.2%
2806-3204
0.1%
3204-3602
0.04%
3602-4000
0.03%
aMixing heights of less than 20 m are set equal to 20 m.
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Table 4-3. Modeled Emissions Amounts for Each Grouped Emission Source
Emissions (tons per year)
Modeled PB-HAP Group
Modeled Chemical
Stack A
Stack B
Stack C
Total
Release Ht.: 28.8 m
Release Ht.: Varies by Hour for Plume-rise Purposes
Mercury
Divalent Mercury
1.58E-03
1.05E-05
3.27E-02
3.43E-02
Elemental Mercury3
6.30E-03
4.20E-05
1.31 E-01
1.37E-01
Cadmium
Cadmium
8.94E-02
4.82E-05
3.06E-02
1.20E-01
2-Methylnaphthalene
5.98E-01
8.11E-03
9.22E-02
6.98E-01
Acenaphthene
2.42E-01
3.18E-03
4.37E-02
2.89E-01
Acenaphthylene
6.62E-01
6.55E-03
3.02E-01
9.71 E-01
Benz(a)anthracene
7.93E-02
2.01 E-04
7.20E-02
1.52E-01
Benzo(a)pyreneD
2.11E-02
7.69E-05
1.91 E-02
4.03E-02
Benzo(b)fluoranthene
7.82E-02
1.66E-04
8.22E-02
1.61 E-01
PAHs
Benzo(g,h,i)perylene
9.23E-03
9.20E-05
6.48E-03
1.58E-02
Benzo(k)fluoranthene
2.37E-02
8.32E-05
2.37E-02
4.75E-02
Chrysene
2.15E-01
3.42E-04
2.15E-01
4.30E-01
Dibenzo(a,h)anthracene
1.43E-03
1.31E-05
1.27E-03
2.72E-03
Fluoranthene
7.04E-01
2.42E-03
6.24E-01
1.33E+00
Fluorene
2.85E-01
3.66E-03
9.34E-02
3.82E-01
lndeno(1,2,3-c,d)pyrene
4.88E-03
3.94E-05
5.16E-03
1.01 E-02
HeptaCDD, 1,2,3,4,6,7,8-
5.24E-08
1.05E-09
6.12E-09
5.96E-08
HeptaCDF, 1,2,3,4,6,7,8-
7.40E-08
1.53E-09
2.70E-09
7.82E-08
HeptaCDF, 1,2,3,4,7,8,9-
2.91 E-08
5.74E-10
3.22E-09
3.29E-08
HexaCDD, 1,2,3,4,7,8-
2.88E-08
5.74E-10
2.73E-09
3.21 E-08
Dioxins
HexaCDD, 1,2,3,6,7,8-
2.79E-08
5.56E-10
2.69E-09
3.11 E-08
HexaCDD, 1,2,3,7,8,9 -
2.84E-08
5.65E-10
3.20E-09
3.22E-08
HexaCDF, 1,2,3,4,7,8-
3.36E-08
6.75E-10
2.66E-09
3.69E-08
HexaCDF, 1,2,3,6,7,8-
2.59E-08
5.21 E-10
2.05E-09
2.84E-08
HexaCDF, 1,2,3,7,8,9-
3.08E-08
6.18E-10
2.65E-09
3.41 E-08
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Emissions (tons per year)
Modeled PB-HAP Group
Modeled Chemical
Stack A
Stack B
Stack C
Total
Release Ht.: 28.8 m
Release Ht.: Varies by Hour for Plume-rise Purposes
HexaCDF, 2,3,4,6,7,8-
3.84E-08
7.84E-10
2.23E-09
4.14E-08
OctaCDD, 1,2,3,4,6,7,8,9-
4.29E-07
8.72E-09
6.41 E-08
5.02E-07
OctaCDF, 1,2,3,4,6,7,8,9-
7.76E-08
1.54E-09
7.50E-09
8.66E-08
PentaCDD, 1,2,3,7,8-
4.20E-08
8.45E-10
3.24E-09
4.60E-08
PentaCDF, 1,2,3,7,8-
3.36E-08
6.79E-10
2.53E-09
3.68E-08
PentaCDF, 2,3,4,7,8-
5.22E-08
1.07E-09
2.74E-09
5.60E-08
TetraCDD, 2,3,7,8-
3.16E-08
6.18E-10
3.53E-09
3.57E-08
TetraCDF, 2,3,7,8-
4.56E-08
9.02E-10
4.42E-09
5.09E-08
Emissions of elemental mercury are included in site-specific modeling because it can transform in the environment to other forms of mercury.
bModeled emissions of BaP shown here include emissions of benzo(e)pyrene and perylene, which are not currently fully parameterized in TRIM.FaTE to evaluate their specific fate, transport,
and transformation properties. Emissions of benzo(e)pyrene and perylene respectively make up 35.37% and <1% of the modeled emissions of BaP.
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Two of the source groups (Sources B and C) had physical release heights, exit gas
temperatures, and exit gas velocities that were large enough to produce large plume-rise values
during some meteorological conditions. Stacks B and C were estimated to emit chemical into
the upper-air sink 17 percent and 31 percent of the time, respectively, so they were modeled
with hourly varying release heights in TRIM.FaTE. The median plume-rise value above the
physical release points was 34 m for Stack B and 101 m for Stack C. Chemical emissions from
Stack A rarely had enough buoyancy or vertical momentum to release into the upper-air sink, so
they were modeled with a static release height of approximately 29 m.
As is evident in Table 4-3, most of the mercury emissions (95 percent) came from Stack C, with
nearly all the remainder coming from Stack A, so approximately 30 percent of total mercury
emissions was released into the upper-air sink and was unavailable for human exposure. Most
of the cadmium emissions (75 percent) came from modeled Stack A, with nearly all the
remainder coming from Stack C, so approximately 8 percent of total cadmium emissions was
released into the upper-air sink and was unavailable for human exposure. The PAH (BaP-
equivalent emissions) were split evenly between Stacks A and C, with less than 1 percent
emitted from Stack B, so approximately 15 percent of total BaP-equivalent PAH emissions was
released into the upper-air sink and was unavailable for human exposure. Nearly all of the
dioxin (2,3,7,8-TCDD-equivalent) emissions (91 percent) came from Stack A, with 2 and 7
percent respectively from Stacks B and C, so approximately 3 percent of total 2,3,7,8-TCDD-
equivalent emissions was released into the upper-air sink and was unavailable for human
exposure. These BaP- and 2,3,7,8-TCDD-equivalent emissions are not shown in Table 4-3.
Two PAH compounds emitted from NEI11660 (benzo(e)pyrene—BeP for simplicity—and
perylene) are not currently parameterized in TRIM.FaTE. Surrogate compounds were selected
to approximate their environmental fate, transport, and transformation characteristics in the
modeling. To select an appropriate surrogate chemical, a brief literature search was conducted
to obtain information on physicochemical and environmental fate properties of these congeners.
These properties included number of benzene rings, molecular weight, Kow, Koc, Henry's Law
Constant, solubility in water, vapor pressure, biotransformation half-life in fish, and
environmental partitioning and persistence. Data sources included the National Institutes of
Health's TOXNET (U.S. NIH 2013b) and ChemID (U.S. NIH 2013a) and British Columbia's
Ministry of Environment (B.C. MOELP 1993), among others. These properties were compared
to those of congeners already parameterized in TRIM.FaTE. The number of benzene rings and
molecular weight were the priority properties; a reasonable surrogate for a chemical would
usually have the same number of benzene rings and a similar molecular weight. For the other
properties, similarity between chemicals was quantified by grouping chemicals with similar
values for each property and noting roughly the number of times each TRIM-parameterized
chemical was grouped with the chemical requiring a surrogate. Additional considerations
included the number and types of additional groups (e.g., methyl, chlorine) attached to one or
more benzene rings and overall configuration of the molecule (e.g., planar, bent). The weight of
evidence, considered along with professional judgment, suggested that BaP was an appropriate
surrogate for both congeners. The compound-to-surrogate ratio of oral pathway toxicities (i.e.,
the oral toxic equivalency factor) was 0.0685 for both congeners. That is, emissions of BeP and
perylene were multiplied by 0.0685 and added to the emissions of BaP. The emissions of BaP
shown in Table 4-3 reflect this process. BeP and perylene emissions make up approximately 35
and less than 1 percent, respectively, of the modeled emissions of BaP.
The properties of the modeled chemicals are shown in various tables in Appendix A to this
report. These properties include diffusivity, Henry's Law Constant, molecular weight, Kow,
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Human Health Multipathway Residual Risk Assessment for the Ferroalloys Production Source Category
deposition velocity, transformation rates, and half-lives, and various assimilation, absorption,
and elimination rates in aquatic species.
4.1.4 Modeling Domain and Parcel Design
The spatial configurations of the TRIM.FaTE surface and air parcels are described below.
These parcels subdivide the surface and air modeling domains in two dimensions. The
modeling compartments associated with a parcel have well-mixed chemical concentrations and
are where chemical transformations and transport take place. The parcels were designed step-
by-step, generally following the design recommendations provided in Section 4 of the site-
specific protocol (Appendix 9 to the Risk Report). These steps first considered the locations of
the modeled features of interest (i.e., farms and lakes), and then considered other parcels
adjacent to (including upwind and downwind from) those features. Finally, the spaces were
methodically filled in between the source, those features of interest, and the desired outer
domain of the modeling, considering important land cover, elevation, soil type, and watershed
qualities where appropriate. Air and surface parcels were generally collocated, except for the
features of interest and areas directly crosswind of those features, and except where important
land characteristics changed significantly beneath a single air parcel. For the parcel design
overall, runtime also was considered because each additional air or surface parcel introduces
additional runtime. Model runtime also increases with increasing number of points (i.e., vertices)
used to draw a parcel, so parcels for features of interest were drawn with as few points as was
reasonable to represent the modeled area. The rationale for this system of parcel designed is
described in Section 4 of the site-specific protocol (Appendix 9 to the Risk Report).
4.1.4.1 Surface Parcels
The surface parcel design for NEI11660 is shown in Figure 4-4. The name of each modeled
water body is shown, and each of the other parcels is labeled based roughly on its location
relative to the source parcel (e.g., "NW1," "SE3," and so on). The parcel shading generally
corresponds to the type of vegetation used in the modeling (or the tilled or unfilled nature of the
soil, in the case of modeled farms and other parcels with heavy farm use). The selection of the
water bodies and farms and the assignment of vegetation and soil types are described below.
This design process is described below. Figure 4-5, Figure 4-6, and Figure 4-7, respectively,
show the surface parcels with land cover, elevation, and county boundary base maps. Figure
4-8 is similar to Figure 4-4 but is zoomed in on farms and lakes near the facility to show
additional resolution.
The characteristics of all potentially fishable lakes7 within approximately 50 km of the facility
were analyzed. Lakes identified included those used in the Tier 2 analysis, plus a small number
of other lakes. Most of the potential lakes, upon examination using satellite data and Internet
searches, appeared unlikely to be fishable or were close to a much larger lake that was more
likely to be open to fishing and to support the larger fish populations that are more likely to be
supportive of a subsistence angler.
7Based on available data, for RTR multipathway emission screening analyses, EPA defines potentially fishable lakes
as those larger than 100 acres, without exceeding 100,000 acres. Even a 100-acre lake is unlikely to be large enough
to sustain harvesting the number of piscivorous fish required for the current screening ingestion rate (i.e., 373 g ww
fillet/day). This is discussed in Section B.3.1 of Attachment B in Appendix 4 to the Risk Report. However, EPA
includes smaller lakes (as small as 25 acres) in site-specific RTR multipathway analyses to be health protective and
to ensure that small lakes that might be more highly contaminated than estimated by the screening analyses are not
eliminated.
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Figure 4-4. Modeled Surface Parcels for NEI11660 arid Their Modeled
Vegetation or Soil Typesa b
aParcel shadings correspond to modeled vegetation or soil types.
bAs explained in the text (Footnote 8), Goodfellows Park Lake ultimately was not included in the risk results.
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Figure 4-5. Modeled Surface Parcels for NEI11660 and Land Cover Base Map3 '3
Surface Parcels
Land Cover (NLCD 2006)
I Open Water
Developed, Open Space
Developed, Low Intensity
Developed, Medium Intensity
Developed. High Intensity
Barren Land
Forest, Deciduous
Forest, Evergreen
Forest, Mixed
Shrub/Scrub
Grassland/Herbaceous
Pasture/Hay
Cultivated Crops
iGdpMello.ws!
.FarrrilSE
Poun|wop]a!
Park Lake
20 Kilometer
aMLRC (2006)
bAs explained in the text (Footnote 8), Goodfellows Park Lake ultimately was not included in the risk results.
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Figure 4-6. Modeled Surface Parcels for NEI11660 and Terrain Base Mapa,b
| | Surface Parcels
STRM Elevation (m)
_ High : 421
Gppdfellpws
Park . &,
iF'arm SE
^ \
^Mountw.Qo!al
FarmIWSW»
Wma
20 Kilometers!'-''
aShuttle Radar Topography Mission elevation data (see: http://www.esri.com/news/arcnews/sprina07articles/alobal-
elevation.htmll
bAs explained in the text (Footnote 8), Goodfellows Park Lake ultimately was not included in the risk results.
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Figure 4-7. Modeled Surface Parcels for NEI11660 and County Base Map3
'As explained in the text (Footnote 8), Goodfeilows Park Lake ultimately was not included in the risk results.
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Figure 4-8. Modeled Surface Parcels for NEI11660, Zoomed on Farms and
Lakes Near the Facility"
aAs explained in the text (Footnote 8), Goodfellows Park Lake ultimately was not included in the risk results.
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Table 4-4. Lake Sizes
Lake Name
Modeled Lake Size
(same as actual size)
km2 (acres)
Veto Lake
0.51 (126)
Mountwood Park Lake
0.16 (39.5)
Wolf Run Lake
0.92 (227)
Three lakes were selected to be included in the site-specific TRIM.FaTE modeling and then
used to evaluate exposures via the fish consumption pathway—Veto Lake (12 km west-
southwest of the facility), Mountwood Park Lake (23 km southeast of the facility), and Wolf Run
Lake (48 km north of the facility).8 Veto Lake and Wolf Run Lake also were used in the Tier 2
emission screening analysis. The modeled surface areas for each lake were nearly identical to
their actual surface areas (Table 4-4). For the other properties needed to model surface water
and sediment, some site-specific values were
available from literature and Internet sources, and
default state, regional, or national values or
estimation methods were used as a last resort.
Three significant rivers (Ohio, Muskingum, and
Little Kanawha) crossed through the modeled
domain. These rivers were not explicitly modeled,
although the water runoff they collect from
surrounding land (carrying chemical out of the
domain) was accounted for.
EPA used recent land cover data (MLRC 2006) and farming data (USDA 2011, USDA 2007) to
identify areas that were within approximately 5 km of the emission source and that appeared to
support farming activities. Most of the crops were soybeans and corn. Local animal husbandry
included cattle, hogs, sheep, and poultry. EPA selected three farming areas close to the
facility—one to the north-northwest (labeled "Farm NNW" in Figure 4-4; see Figure 4-8 for
greater resolution), one to the west-southwest ("Farm WSW"), and one to the southeast ("Farm
SE"). A modeled farm parcel does not necessarily represent a single, entire farm owned by one
entity; instead, the farms were delineated to represent where farming likely would occur in
general. EPA split in half each modeled farming area—one half was modeled with tilled soil
(representing crop farming), the other half with unfilled soil (representing where animals graze).
For each non-lake parcel other than those modeled for subsistence farming activities, EPA
analyzed land cover data (MLRC 2006) to identify the major land cover type and then related
those land cover types to the types used in TRIM.FaTE (i.e., coniferous forest, deciduous forest,
grasses/herbs, tilled soil, unfilled soil, and bare earth). The other properties of soil
compartments unrelated to vegetation were set to site-specific values when readily available
(i.e., from literature, Internet sources, and the U.S. Department of Agriculture Web Soil Survey
[USDA 2012]), and default state, regional, or national values or estimation methods otherwise.
See Appendix A to this report for these values and other modeled properties of soil-related
compartments.
4.1.4.2 Air Parcels
The air parcel design for NEI11660 is shown in Figure 4-9. Each lake and farm surface parcel,
and any "misfit" surface parcels that were included adjacent to those parcels, had a single air
parcel overlaying it. For all other parcels, air parcels were collocated with surface parcels.
Goodfellows Park Lake is present in some of the tables, figures, and setup discussion in this report and
accompanying appendices, but it is not included in the modeling results. This is because it was initially part of the
analysis, but it was later learned that it is likely still owned by a private company, is not for public access, and was dry
in some recent years. Very little data on the lake were available for modeling.
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Figure 4-9. Modeled Air Parcels for NEI11660a b
D 5 10 20 Kilometers
i i i I i i i L
Air Parcels
Surface Parcels
TRIM Vegetation Type
Source
Water
Soil, Tilled (Farm)
Soil, Unfilled (Farm)
Soil, Tilled
Forest, Deciduous
Grasses/Herbs
N
Wolf Run
Goodfellows
NNW
Farm WSW
Mountwood
aFor reference, the base map (the lightly colored parcels) shows the surface parcels shaded as in
Figure 4-4 and without labels.
bAs explained in the text (Footnote 8), Goodfellows Park Lake ultimately was not included in the risk
results.
4.1.5 Abiotic Environment
TRIM.FaTE requires various environmental properties for each abiotic compartment included in
a scenario. Examples of abiotic environmental properties include the depth of surface soil, soil
porosity and water content, erosion and runoff rates for surface soil compartments, and
suspended sediment concentrations in surface water. The values used in this assessment are
shown in Appendix A to this report. Following the site-specific protocol presented in Appendix 9
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to the Risk Report, site-specific inputs were used for this assessment where data supporting
such values were readily available, and these site-specific values are indicated in the tables in
Appendix A using shading and footnotes. State, regional, or national default values or
estimation methods were used for all other inputs, especially for those not expected to strongly
influence chemical concentrations. Following the site-specific protocol, examples of properties
that were at least partially site-specific for at least some parcels included: soil pH; concentration
of suspended sediment in surface water; and surface water pH, depth, algal density,
temperature, and flush rate. USLE (universal soil loss equation) erosion factors were site-
specific because they were readily available. Fraction of precipitation that runs off (i.e., "runoff
fractions") was also site-specific, following the "with sophisticated GIS software" steps in Section
5.3 of the site-specific protocol (Appendix 9 to the Risk Report).
The approach to parameterizing flush rates for lakes in TRIM.FaTE was to rely on measured
site-specific values, where available, from literature or other reliable sources. In the absence of
such values (i.e., for Veto Lake), EPA computes lake flush rate estimates from precipitation
input to the lake or assumptions about the lake's watershed area, the fraction of precipitation
that runs off toward the lake from other non-water surface parcels, lake evaporation rate, lake
volume, and inflow from other water bodies to the lake. Properties of the surface waters of
individual lakes, and of surrounding land parcels, are shown in Appendix A to this report.
4.1.6 Biotic Environment
The TRIM.FaTE biotic environment includes terrestrial plants and aquatic ecosystems.
Examples of biotic environmental properties include leaf litterfall rate and lipid content, water
content of plant parts, chemical transformation rates and transfer factors, and biomass densities
in surface water and sediment. The values used in this assessment are shown in Appendix A to
this report. Because site-specific information is generally lacking, default state, regional, or
national values or estimation methods were used for most biotic properties. Examples of
properties that were at least partially site-specific for at least some parcels included litterfall
period (October 1 through October 30) and the period where leaves are on trees (April 10
through October 1), both estimated based on local frost and freeze data. Site-specific values are
indicated in the tables in Appendix A using shading and footnotes.
4.2 Methods for Exposure and Risk Modeling
This section describes the methods applied to conduct the exposure assessment and risk
characterization for the scenarios described above. Specifically, this section describes the
methods for modeling chemical concentrations in FFC and other media relevant to the selected
exposure scenarios (Section 4.2.1); estimating human exposures associated with ingestion of
FFC media, incidental ingestion of soil, consumption of fish, and infant consumption of breast
milk, where applicable (Section 4.2.2); and estimating human health risk metrics associated with
these exposure media (Section 4.2.3). All calculations were conducted using the MIRC
exposure model.
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4.2.1 Methods for Calculating Exposure Concentrations
Farm Food Chain Media
For foodstuffs that are part of the FFC, MIRC was used to calculate concentrations of PB-HAPs
as described in Attachment 4 to the Risk Report (see Attachment A, Section A.4.1 and
Addendum 2). The FFC media in the evaluated exposure scenarios included
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 2005). 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) were obtained from TRIM.FaTE. TRIM.FaTE outputs included as inputs to MIRC to
calculate exposure concentrations in farm foodstuffs included
PB-HAP concentrations in air,
air-to-surface deposition rates for PB-HAPs in both particle and vapor phases, 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 2005). Except where noted specifically in this report, the
parameter values used in MIRC for this risk assessment matched those used to develop Tier 1
and Tier 2 screening thresholds as described in Appendix 4 to the Risk Report. Surface soil
concentrations were obtained from the estimated unfilled surface soil compartments
representative of hypothetical farm locations.
Fish
For the fish consumption exposure scenarios, PB-HAP concentrations in uncooked fish tissue
were estimated by TRIM.FaTE. All individuals consuming fish were assumed to eat fish
obtained exclusively from one of the modeled water bodies. Anglers consumed fish from two of
the five fish compartments: 50 percent biomass from water column carnivores and 50 percent
from benthic carnivores (as modeled in TRIM.FaTE). The average size of fish in those two
compartments is 2 kg, or approximately 1 pound per fish. Although anglers can and probably do
also consume smaller "pan" fish from many lakes, for the screening-level assessment, we make
the health protective assumption that all fish consumed are top predatory fish in the lake.
Because the fish consumption data are reported "as consumed" and the estimated fish tissue
concentrations are for uncooked fish, we adjusted the concentrations to reflect possible
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Human Health Multipathway Residual Risk Assessment for the Ferroalloys Production Source Category
changes in concentration due to cooking. When cooked, fish tends to lose both water and fat,
with the amount and proportion of each dependent on the cooking method. These losses affect
chemical concentration in the fish as served. For chemicals that bind primarily to muscle
proteins, such as mercury and cadmium, the loss of water and fat will reduce the overall weight
of the fish serving without reducing the mass of chemical retained in the muscle. Cooking,
therefore, effectively increases the concentrations of mercury and cadmium in the fish tissue. A
cooking conversion factor of 1.5, based on data presented in Morgan et al. (1997, as cited in
U.S. EPA 2011a), was used for mercury and cadmium to account for this potential.
Dioxins, however, are lipophilic and have been demonstrated to be lost during cooking. Based
on a literature review, a conversion factor of 0.7 was applied to dioxin concentrations to account
for the losses during the cooking process (Schecter et al., 1998; Reinert et al., 1972; Zabik and
Zabik 1995).
Although assuming losses of lipophilic PAHs during the cooking process might be reasonable,
information is insufficient to distinguish what the net loss (or gain) during cooking might be
because cooking can create PAHs from proteins in the tissue. The literature acknowledges
these competing processes, but does not provide information sufficient to disentangle the gain
and loss mechanisms. As such, a neutral approach was taken, which is to assume an
adjustment factor of 1.0 (i.e., no adjustment) for PAHs. (See Appendix 4 of the Risk Report:
Attachment A, Addendum B, Section 6.4.4 for additional detail on cooking conversion factors.)
Breast Milk
For dioxins, exposure scenarios were evaluated in this assessment to cover exposures to
nursing infants during the first year of life. Only dioxins were evaluated, given the potential for
these lipophilic chemicals to partition to breast milk and the availability of methods to assess
dioxins for this exposure medium. Methods for estimating chemical concentrations in breast milk
based on exposure to the lactating mother are described in Appendix 4 to the Risk Report.
4.2.2 Ingestion Exposure Assessment
The following subsections describe the ingestion exposure scenarios and corresponding
exposure factors used in this risk assessment.
Ingestion Exposure Media and Routes of Uptake
MIRC was used to estimate ingestion rates as average daily doses normalized to body weight
for a range of exposure media. Exposure media included were incidental ingestion of soil and
consumption offish, produce, and farm animals and related products. Specific methods used to
estimate exposures via these media (including calculations for any intermediate exposure media
for farm animals, such as ingestion of forage) are described in detail in Appendix 4 to the Risk
Report.
Summary of Exposure Scenarios
The exposure scenarios described previously were evaluated by defining ingestion activity
patterns (i.e., estimating how much of each medium was consumed and the fraction of the
consumed medium that was 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 medium separately, with variants of three exposure
scenarios evaluated in this assessment:
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1. consumption of farm-grown fruits, vegetables, and animal products, and incidental ingestion
of soil;
2. consumption of self-caught fish from local water bodies; and
3. ingestion of contaminated breast milk by an infant.
For the first of these scenarios, exposed individuals were assumed to consume produce and
livestock products grown at one of the farm locations. Fruits and vegetables were assumed to
be contaminated from air and soil at those farms. Contaminant uptake by plants included
deposition to leaves/plants, vapor transfer, and root uptake. Livestock was assumed to ingest
contaminants in locally grown feed plants (i.e., forage, silage, and grain) and in soil incidentally
ingested during grazing. The selection of the evaluated farm locations is described in the
preceding section on fate and transport modeling. In the angler scenario, the exposed
individuals were assumed to eat freshwater game fish caught in one of the modeled lakes and
included in the TRIM.FaTE evaluation.
In the breast milk scenario, an infant was assumed to be exposed to contaminants ingested in
the fat-content (i.e., lipid phase) of the mother's breast milk. In estimating contaminant
concentrations in breast milk for the subsistence farmer scenario, the mother was assumed to
be exposed through consumption of farm-grown fruits, vegetables, and animal products and
through incidental ingestion of soil. For the angler scenario, maternal exposure was assumed to
include only consumption of self-caught fish from modeled water bodies.
Exposure Scenario Characterization
With the exception of ingestion rates, exposures were estimated for each scenario evaluated
using the same exposure factor assumptions applied in developing the Tiers 1 and 2
multipathway screening thresholds (i.e., as documented in detail in Appendix 4 to the Risk
Report). This approach is likely to result in a relatively high estimate of exposure, given the
assumption that all foods specific to the scenario were obtained from the local farm or lake,
consumption of contaminated foods occurred continuously (i.e., exposure frequency was daily
without interruption), and the exposure duration was long. With respect to ingestion rates, high-
end assumptions equal to those used to derive screening thresholds were used, as were other,
alternative exposure assumptions.
For the subsistence farmer scenario, two variants were evaluated using two sets of ingestion
rates.
. A reasonable maximum exposure (RME) estimate was evaluated that uses farm
foodstuff ingestion rates corresponding to the 90th percentile of consumers who produce
their own food items. Soil ingestion rates corresponded to the 90th percentile of the
general population. These rates are equal to the inputs used to develop the Tiers 1 and
2 screening thresholds.
. A central tendency exposure (CTE) estimate was evaluated that uses ingestion rates
corresponding to the mean of the distribution of consumers who produce their own food.
Soil ingestion rates were set to the mean of the general population.
Farm food chain and soil ingestion rates used for the farmer scenarios are summarized in Table
4-5.
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Table 4-5. Farm Food Chain and Soil Ingestion Rates Used for Farmer Scenario
Product
Child (age in yr)
Adult
(20-70 yrs)
<1
1-2
3-5
6-11
12-19
Mean ingestion rates (g/kg-day)
Beef
N/A
4.14
4.00
3.77
1.72
1.93
Dairy"
N/A
91.6
50.9
27.4
13.6
2.96
Eggs3
N/A
2.46
1.42
0.86
0.578
0.606
Exposed Fruit3
N/A
6.14
2.60
2.52
1.33
1.19
Exposed Vegetable3
N/A
3.48
1.74
1.39
1.07
1.38
Pork3
N/A
2.23
2.15
1.50
1.28
1.10
Poultry3
N/A
3.57
3.35
2.14
1.50
1.37
Protected Fruit3
N/A
16.6
12.4
8.50
2.96
5.19
Protected Vegetable3
N/A
2.46
1.30
1.10
0.78
0.862
Root Vegetable3
N/A
2.52
1.28
1.32
0.94
1.03
Soil (mg/day)c
N/A
50
50
50
50
20
90th percentile ingestion rates (g/kg-day)a
Beef
N/A
9.49
8.83
11.4
3.53
4.41
Dairy"
N/A
185
92.5
57.4
30.9
6.16
Eggs3
N/A
4.90
3.06
1.90
1.30
1.31
Exposed Fruit3
N/A
12.7
5.41
6.98
3.41
2.37
Exposed Vegetable3
N/A
10.7
3.47
3.22
2.35
3.09
Pork3
N/A
4.90
4.83
3.72
3.69
2.23
Poultry3
N/A
7.17
6.52
4.51
3.13
2.69
Protected Fruit3
N/A
44.8
32
23.3
7.44
15.1
Protected Vegetable3
N/A
3.88
2.51
2.14
1.85
1.81
Root Vegetable3
Soil (mg/day)
N/A
7.25
4.26
3.83
2.26
2.49
N/A
200d
200d
201e
201e
201e
aPrimary source for values was the 1987-1988 NFCS survey; compiled results are presented in Chapter 13 of 2011 Exposure
Factors Handbook (U.S. EPA 2011b). 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 (U.S. EPA 2011 b). 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.
°The recommended general population central tendency rates from EPA's 2011 EFH; Table ES-1 Chapter 5 (U.S. EPA 2011 b).
dThe recommended general population "upper percentile" rate for children aged 3 to <6 from EPA's 2011 EFH (U.S. EPA 2011 b).
e90th percentile adult ingestion rate calculated in Stanek et al. 1997; used to represent older children and adults.
For the angler scenario, multiple variants involving fish consumption rates for adults and
children were evaluated given the importance of this parameter to multipathway exposures and
the potentially wide range offish ingestion rates.
. A subsistence angler was evaluated that corresponds to the same fish ingestion rates
used to develop the Tiers 1 and 2 screening thresholds (see Exhibit_Att A 16 in
Appendix 4 of the Risk Report). The adult rate is the 99th percentile value for adult
females from Burger (2002) and is considered representative of male and female
subsistence anglers. Fish ingestion rates for children are based on the 99th percentile,
consumer-only fish ingestion rates from U.S. EPA (2002). Rates were adjusted to be
representative of the age groups modeled in MIRC (see Attachment A, Addendum 2,
Section 6.3.4.1 of Appendix 4 to the Risk Report for a detailed discussion).
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Human Health Multipathway Residual Risk Assessment for the Ferroalloys Production Source Category
• A high-end recreational angler was assessed by using the 95th percentile ingestion
rates from the same two data sets that were used for the subsistence angler (i.e., Burger
[2002] and U.S. EPA [2002]).
Central tendency recreational anglers were evaluated by using mean values from the
aforementioned Burger (2002) and U.S. EPA (2002) sources.
Fish ingestion rates for the general population were represented by three statistics:
mean, 90th percentile, and 99th percentile. These values are from the per-capita
ingestion rates from U.S. EPA (2002) for adults and children. Per-capita ingestion rates
are based on the entire population rather than the subset of the population that ingests
the particular food category (i.e., consumer-only). Thus, the general population includes
anglers and nonanglers, including individuals who rarely or never consume fish.
Fish ingestion rates used in these angler scenarios are summarized in Table 4-6.
Table 4-6. Fish Ingestion Rates Used for Angler Scenarios Evaluated
Scenario
Fish Ingestion
Rates (g/day)
Source
Subsistence Anglera
Adult
373
Female 99th %-ile ingestion rate of wild-caught fish from Burger (2002)
Child 1-2
108
Based on the 99th %-ile, consumer-only ingestion rates from U.S. EPA (2002);
rates were adjusted to represent the age groups used in MIRC
Child 3-5
159
Child 6-11
268
Child 12-19
331
High-end Recreational Angler
Adult
172
Female 95th %-ile ingestion rate of wild-caught fish from Burger (2002)
Child 1-2
65
Based on the 95th %-ile, consumer-only ingestion rates from U.S. EPA (2002);
rates were adjusted to represent the age groups used in MIRC
Child 3-5
96
Child 6-11
181
Child 12-19
167
Central-tendency Recreational Angler
Adult
39
Female mean ingestion rate of wild-caught fish from Burger (2002)
Child 1-2
18
Based on the mean, consumer-only ingestion rates from U.S. EPA (2002); rates
were adjusted to represent the age groups used in MIRC
Child 3-5
27
Child 6-11
44
Child 12-19
58
General Population 99th Percentile
Adult
105
Based on 99th %-ile percentile per-capita consumption rates from U.S. EPA
(2002); rates were adjusted to represent the age groups used in MIRC
Child 1-2
26.23
Child 3-5
38.72
Child 6-11
62.29
Child 12-19
81.39
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Human Health Multipathway Residual Risk Assessment for the Ferroalloys Production Source Category
Scenario
Fish Ingestion
Rates (g/day)
Source
General Population 90th Percentile
Adult
17
Based on 90th %-ile per-capita consumption rates from U.S. EPA (2002); rates
were adjusted to represent the age groups used in MIRC
Child 1-2
3.24
Child 3-5
4.79
Child 6-11
6.9
Child 12-19
8.95
General Population Central-tendency
Adult
6.9
Based on mean per-capita consumption rates from U.S. EPA( 2002); rates
were adjusted to represent the age groups used in MIRC
Child 1-2
1.37
Child 3-5
2.03
Child 6-11
2.71
Child 12-19
3.90
aThis ingestion scenario is the same as that evaluated in the tiered screening assessment
Evaluation of Combined Exposures
In general, exposures and the associated health risks were evaluated separately as described
above for the subsistence farmer and angler scenarios, based on the assumption that a single
individual at this location would be unlikely to obtain primary dietary needs (and especially
protein) simultaneously from both fishing and farming. In other words, exposures were not
combined across these two scenarios in most cases. There was one exception to this approach:
exposures to each PB-HAP from consuming self-caught fish and farm products grown/raised at
home were evaluated at the same ingestion rates used to evaluate the exposure scenario
supporting Tiers 1 and 2 screening thresholds (as described in Appendix 4 to the Risk Report).
4.2.3 Risk Characterization
MIRC was used to calculate estimated incremental lifetime cancer risks and chronic non-cancer
HQs using the calculated average daily doses and ingestion dose-response values. Chemical
dose-response data included cancer slope factors for ingestion and non-cancer oral reference
doses (RfDs) for chronic exposures. The cancer slope factors and RfDs for the PB-HAPs used
to evaluate risks for this assessment are the same as those used to develop screening
thresholds and are presented in the dose-response assessment in Appendix 4 to the Risk
Report, along with equations used to estimate cancer risk and non-cancer hazard.
5. Results
5.1 Media Concentrations
The modeled concentrations in environmental media are presented in Appendix B to this report
for the chemicals assessed. These are annual average concentrations for the 50th year of the
modeling period.
For mercury (Table B-1), the relative speciation results indicate that divalent mercury was the
dominant species in surface water, sediment, and surface soil. Methyl mercury was the
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Human Health Multipathway Residual Risk Assessment for the Ferroalloys Production Source Category
dominant species in the higher trophic levels of the aquatic biota, progressively bioaccumulating
in the food chain until it represents more than 95 percent of total mercury in game fish. These
speciation trends are consistent with literature (Raymond and Rossmann 2009). The media
concentrations for cadmium (Table B-2), PAH congeners (Table B-3), and dioxin congeners
(Table B-4) also are provided in Appendix B.
5.2 Risk Assessment Results
The ingestion human health risk assessment results for NEI11660 are presented in this section.
An overview of the risk assessment results for the exposure scenarios evaluated is provided in
Section 5.2.1, while Sections 5.2.2, 5.2.3, 5.2.4, and 5.2.5 contain the results for each of the
four groups of PB-HAPs. In Section 5.2.6, these results are compared to the screening-level
results.
5.2.1 Risk Assessment Summary
The annual average concentrations estimated by TRIM.FaTE and MIRC for the 50th year of the
modeling period were used to calculate individual, incremental lifetime cancer risks and chronic
non-cancer HQs attributable to source emissions. Exposures to the emitted chemicals were
based on two scenarios: a subsistence farmer who grows and consumes the majority of his food
on a farm near the facility (including animal products and incidental soil ingestion) and an angler
who ingests self-caught fish from nearby lakes. Because dioxin exposure can occur via
consumption of breast milk by nursing infants, this medium was included in the incremental
lifetime cancer risk where indicated.
Exposures to four groups of PB-HAPs were included in this assessment: mercury HQs (divalent
for farm exposure, and methyl for fish exposure), cadmium HQs, dioxin incremental lifetime
cancer risks (with 17 individual congeners), and PAH incremental lifetime cancer risks (with 13
individual congeners; 3 of the 18 emitted PAH congeners were not carcinogenic and not
modeled, and the emissions of 2 other congeners were merged with other congeners because
they are not parameterized in TRIM.FaTE). The cancer risks for dioxin congeners were summed
into a total dioxin risk, and likewise for PAH congeners.
Non-cancer HQs from mercury or cadmium did not exceed 1 (interpreted as values 1.5 or
larger) for any exposure location or scenario exposure. For methyl mercury, the largest HQ was
1 for the subsistence angler. For cadmium, the largest HQ was 0.09 for the subsistence angler.
Cancer results from exposure to PAHs included incremental lifetime cancer risks greater than
1-in-one million for several ingestion scenarios from exposure at Veto Lake (risks up to 3-in-one
million) and from exposure at all modeled farms (risks up to 7-in-one million).
Cancer risks from exposure to dioxins did not exceed 1-in-one million for angler exposure,
although risks from farm exposure were as large as 3-in-one million at 90th percentile ingestion
rates.
For each PB-HAP group, these site-specific assessment hazard and risk estimates were smaller
than the screening-level assessment results presented in Section 2 using the subsistence
farmer at 90th percentile ingestion rate and the subsistence angler fish ingestion rate (which
were the screening assessment exposure scenarios), and using the same lakes as in the
screening. This demonstrates that the screening-level assessments avoided underestimating
risk for this facility.
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Human Health Multipathway Residual Risk Assessment for the Ferroalloys Production Source Category
More detailed results for each PB-HAP chemical or group are presented in the following
subsections, along with a more comprehensive comparison with the screening-level
assessment results.
5.2.2 Hazard Quotients Associated With Mercury Exposure
Non-cancer HQs from exposure to methyl mercury (from fish exposure only) and to divalent
mercury (from subsistence farm exposure only) are presented in Table 5-1 and Table 5-2,
respectively. The HQs from exposure to methyl mercury via fish ingestion did not exceed 1 (i.e.,
were smaller than 1.5). HQs for angler scenarios are not shown here for divalent mercury
because they were several orders of magnitude less than the methyl mercury values. The HQs
from exposure to divalent mercury via the farm were several orders of magnitude less than 1;
HQs for the subsistence farm scenarios are not shown for methyl mercury because they were
over an order of magnitude less than the divalent mercury values.
Figure 5-1 shows the levels at which each ingested farm medium (beef, eggs, pork, etc.)
contributed to the total subsistence farmer HQ for each age group (farm medium-specific hazard
quotients are provided in Table 5-3). No one medium contributed a majority of the HQ value.
Overall, protected fruit, root vegetables, soil, protected vegetables, and exposed fruit each
tended to contribute at least 10 percent of the value and together accounted for most of the
value.
5.2.3 Hazard Quotients Associated With Cadmium Exposure
Non-cancer HQs from exposure to cadmium via fish exposure only and via subsistence farm
exposure only are presented in Table 5-4 and Table 5-5, respectively. All HQs were several
orders of magnitude lower than 1.
Figure 5-2 shows the levels at which each ingested farm medium (beef, eggs, pork, etc.)
contributed to the total subsistence farmer HQ for each age group (farm medium-specific hazard
quotients are provided in Table 5-6). Exposure to protected fruit tended to contribute close to
the majority of the total HQ value, followed distantly by exposed fruit and protected vegetables.
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Human Health Multipathway Residual Risk Assessment for the Ferroalloys Production Source Category
Table 5-1. Hazard Quotients from Exposure to Methyl Mercury for Angler Scenarios3
Angler
Population
Ingestion
Level
Period of
Exposure
(years of age)
Hazard Quotient
Wolf Run
Lake
Veto
Lake
Mountwood
Park Lake
Subsistence
Angler
99th %ile
1-2
1E+00
2E-01
2E-02
3-5
1E+00
2E-01
2E-02
6-11
1E+00
2E-01
1E-02
12-19
7E-01
1E-01
1E-02
20-70 (adult)
6E-01
1E-01
9E-03
Recreational
Angler
Central Tendency
(Mean)
1-2
2E-01
3E-02
3E-03
3-5
2E-01
3E-02
3E-03
6-11
2E-01
3E-02
2E-03
12-19
1E-01
2E-02
2E-03
20-70 ( adult)
6E-02
1E-02
9E-04
High-end
(95th %ile)
1-2
7E-01
1E-01
9E-03
3-5
7E-01
1E-01
9E-03
6-11
7E-01
1E-01
9E-03
12-19
3E-01
6E-02
5E-03
20-70 (adult)
3E-01
5E-02
4E-03
General
Population
Central Tendency
(Mean )
1-2
1E-02
2E-03
2E-04
3-5
1E-02
2E-03
2E-04
6-11
1E-02
2E-03
1E-04
12-19
8E-03
1E-03
1E-04
20-70 (adult)
1E-02
2E-03
2E-04
90th %ile
1-2
3E-02
6E-03
5E-04
3-5
3E-02
6E-03
5E-04
6-11
3E-02
4E-03
4E-04
12-19
2E-02
3E-03
3E-04
20-70 (adult)
3E-02
5E-03
4E-04
99th %ile
1-2
3E-01
5E-02
4E-03
3-5
3E-01
5E-02
4E-03
6-11
2E-01
4E-02
3E-03
12-19
2E-01
3E-02
2E-03
20-70 (adult)
2E-01
3E-02
2E-03
aBolded, underlined cell indicates the largest value in this table. Hazard quotients rounded to one significant figure.
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Human Health Multipathway Residual Risk Assessment for the Ferroalloys Production Source Category
Table 5-2. Hazard Quotients from Exposure to Divalent Mercury for Subsistence Farmer
Scenarios3
Ingestion
Level
Period of Exposure
(years of age)
Hazard Quotient
Farm_SE
Farm_NNW
Farm_WSW
Mean
1-2
4E-03
2E-03
6E-04
3-5
2E-03
1E-03
3E-04
6-11
2E-03
9E-04
3E-04
12-19
1E-03
5E-04
1E-04
20-70 (adult)
1E-03
6E-04
2E-04
90th %ile
1-2
1E-02
5E-03
2E-03
3-5
7E-03
3E-03
9E-04
6-11
5E-03
2E-03
7E-04
12-19
3E-03
1E-03
4E-04
20-70 (adult)
3E-03
2E-03
4E-04
aBolded, underlined cell indicates the largest value in this table. Hazard quotients rounded to one significant figure.
Figure 5-1. Contribution to Hazard Quotients by Ingested Medium, per Age Group, from
Exposure to Divalent Mercury at "Farm_SE"
100%
90%
| 80%
"+¦»
O
a 70%
-a
s_
S 60%
x
o
o
4-»
3
-Q
o
u
50%
40%
30%
5 20%
Q.
10%
0%
1-2 3-5 6-11 12-19 20-70 1-2 3-5 6-11 12-19 20-70
Mean Ingestion
(adult)
90th %ile lngestion'ac'u't'
I Soil
ITotal Dairy
'Eggs
I Beef
Pork
Poultry
I Exposed Vegetable
Protected Vegetable
I Root Vegetable
I Exposed Fruit
Protected Fruit
Age Group (years of age)
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Human Health Multipathway Residual Risk Assessment for the Ferroalloys Production Source Category
Table 5-3. Hazard Quotients by Ingested Medium, per Age Group, from Exposure to Divalent Mercury at "Farm_SE"a
Ingestion
Level
Period of
Exposur
e(years
of age)
Hazard Quotient
Breast
Milk
Soil
Total
Dairy
Eggs
Beef
Pork
Poultry
Exposed
Vegetable
Protected
Vegetable
Root
Vegetable
Exposed
Fruit
Protected
Fruit
Mean
1-2
0
5.0E-04
6.9E-06
1.7E-04
1.5E-05
1.7E-06
1.2E-04
2.3E-04
4.2E-04
8.6E-04
4.6E-04
1.1E-03
3-5
0
3.4E-04
3.8E-06
9.9E-05
1.5E-05
1.7E-06
1.1E-04
1.1E-04
2.2E-04
4.3E-04
1.9E-04
8.4E-04
6-11
0
1.8E-04
2.1E-06
6.0E-05
1.4E-05
1.1E-06
7.2E-05
9.1E-05
1.9E-04
4.5E-04
1.9E-04
5.7E-04
12-19
0
9.9E-05
1.0E-06
4.0E-05
6.4E-06
9.8E-07
5.0E-05
7.0E-05
1.3E-04
3.2E-04
9.9E-05
2.0E-04
20-70
(adult)
0
3.2E-05
2.2E-07
4.2E-05
7.2E-06
8.4E-07
4.6E-05
9.0E-05
1.5E-04
3.5E-04
8.9E-05
3.5E-04
90th %ile
1-2
0
2.0E-03
1.4E-05
3.4E-04
3.5E-05
3.8E-06
2.4E-04
7.0E-04
6.6E-04
2.5E-03
9.5E-04
3.0E-03
3-5
0
1.4E-03
7.0E-06
2.1E-04
3.3E-05
3.7E-06
2.2E-04
2.3E-04
4.3E-04
1.4E-03
4.0E-04
2.2E-03
6-11
0
7.1E-04
4.3E-06
1.3E-04
4.2E-05
2.9E-06
1.5E-04
2.1E-04
3.7E-04
1.3E-03
5.2E-04
1.6E-03
12-19
0
4.0E-04
2.3E-06
9.1E-05
1.3E-05
2.8E-06
1.0E-04
1.5E-04
3.2E-04
7.7E-04
2.5E-04
5.0E-04
20-70
(adult)
0
3.2E-04
4.7E-07
9.1E-05
1.6E-05
1.7E-06
9.0E-05
2.0E-04
3.1E-04
8.4E-04
1.8E-04
1.0E-03
aThe numbers in this table were used to make the cumulative stacked bar chart shown in Figure 5-1. The bolded, underlined cell indicates the largest risk value in this
table. Hazard quotients rounded to two significant figures.
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Human Health Multipathway Residual Risk Assessment for the Ferroalloys Production Source Category
Table 5-4. Hazard Quotients from Exposure to Cadmium for Angler Scenarios3
Angler
Population
Ingestion
Level
Period of
Exposure
Hazard Quotient
Wolf Run
Lake
Veto
Lake
Mountwood
Park Lake
Subsistence
Angler
99th %ile
1-2
5E-02
9E-02
3E-02
3-5
5E-02
9E-02
3E-02
6-11
5E-02
8E-02
3E-02
12-19
3E-02
5E-02
2E-02
20-70 (adult)
3E-02
5E-02
2E-02
Recreational
Angler
Central
Tendency
(Mean)
1-2
9E-03
2E-02
5E-03
3-5
9E-03
2E-02
5E-03
6-11
8E-03
1E-02
4E-03
12-19
6E-03
9E-03
3E-03
20-70 (adult)
3E-03
5E-03
2E-03
High-end
(95th %ile)
1-2
3E-02
5E-02
2E-02
3-5
3E-02
5E-02
2E-02
6-11
3E-02
5E-02
2E-02
12-19
2E-02
3E-02
9E-03
20-70 (adult)
1E-02
2E-02
7E-03
General
Population
Central
Tendency
(Mean )
1-2
7E-04
1E-03
4E-04
3-5
7E-04
1E-03
4E-04
6-11
5E-04
8E-04
3E-04
12-19
4E-04
6E-04
2E-04
20-70 (adult)
5E-04
9E-04
3E-04
90th %ile
1-2
2E-03
3E-03
9E-04
3-5
2E-03
3E-03
9E-04
6-11
1E-03
2E-03
7E-04
12-19
9E-04
1E-03
5E-04
20-70 (adult)
1E-03
2E-03
7E-04
99th %ile
1-2
1E-02
2E-02
7E-03
3-5
1E-02
2E-02
7E-03
6-11
1E-02
2E-02
6E-03
12-19
8E-03
1E-02
4E-03
20-70 (adult)
8E-03
1E-02
5E-03
aBolded, underlined cell indicates the largest value in this table. Hazard quotients rounded to one significant figure.
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Human Health Multipathway Residual Risk Assessment for the Ferroalloys Production Source Category
Table 5-5. Hazard Quotients from Exposure to Cadmium for
Subsistence Farmer Scenarios3
Ingestion Level
Period of Exposure
(years of age)
Hazard Quotient
Farm_SE
Farm_NNW
Farm_WSW
Mean
1-2
2E-02
5E-03
1E-02
3-5
1E-02
3E-03
8E-03
6-11
9E-03
2E-03
6E-03
12-19
5E-03
1E-03
3E-03
20-70 (adult)
6E-03
1E-03
4E-03
90th %ile
1-2
5E-02
1E-02
3E-02
3-5
3E-02
7E-03
2E-02
6-11
2E-02
6E-03
2E-02
12-19
1E-02
3E-03
8E-03
20-70 (adult)
1E-02
3E-03
1E-02
aBolded, underlined cell indicates the largest value in this table. Hazard quotients rounded to one significant figure.
Figure 5-2. Contribution to Hazard Quotients by Ingested Medium, per Age Group, from
Exposure to Cadmium at "Farm_SE"
100%
90%
| 80%
"+¦»
O
a 70%
TS
1_
(0
N
(0
X
o
+¦»
c
o
'+¦»
3
-Q
O
U
20%
Q_
10%
o%
~i 1 1 1 1 1 1 r
1-2 3-5 6-11 12-19 20-70 1-2 3-5 6-11 12-19 20-70
(adult) (adult)
Mean Ingestion 90th %ile Ingestion
Age Group (years of age)
I Soil
I Total Dairy
I Eggs
I Beef
Pork
Poultry
I Exposed Vegetable
Protected Vegetable
I Root Vegetable
I Exposed Fruit
Protected Fruit
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Human Health Multipathway Residual Risk Assessment for the Ferroalloys Production Source Category
Table 5-6. Hazard Quotients by Ingested Medium, per Age Group, from Exposure to Cadmium at "Farm_SE"a
Ingestion
Level
Period of
Exposur
e(years
of age)
Hazard Quotient
Breast
Milk
Soil
Total
Dairy
Eggs
Beef
Pork
Poultry
Exposed
Vegetable
Protected
Vegetable
Root
Vegetable
Exposed
Fruit
Protected
Fruit
Mean
1-2
0
2.8E-04
3.3E-04
1.4E-05
1.0E-04
1.4E-05
4.1E-04
1.9E-03
3.0E-03
1.2E-03
3.9E-03
7.9E-03
3-5
0
1.9E-04
1.8E-04
8.0E-06
1.0E-04
1.3E-05
3.8E-04
9.3E-04
1.6E-03
6.3E-04
1.6E-03
5.9E-03
6-11
0
1.0E-04
9.9E-05
4.8E-06
9.4E-05
9.2E-06
2.4E-04
7.5E-04
1.3E-03
6.5E-04
1.6E-03
4.0E-03
12-19
0
5.6E-05
4.9E-05
3.2E-06
4.3E-05
7.9E-06
1.7E-04
5.7E-04
9.4E-04
4.6E-04
8.4E-04
1.4E-03
20-70
(adult)
0
1.8E-05
1.1E-05
3.4E-06
4.8E-05
6.8E-06
1.6E-04
7.4E-04
1.0E-03
5.1E-04
7.5E-04
2.5E-03
90th %ile
1-2
0
1.1E-03
6.7E-04
2.7E-05
2.4E-04
3.0E-05
8.2E-04
5.7E-03
4.7E-03
3.6E-03
8.0E-03
2.1E-02
3-5
0
7.7E-04
3.3E-04
1.7E-05
2.2E-04
3.0E-05
7.4E-04
1.9E-03
3.0E-03
2.1E-03
3.4E-03
1.5E-02
6-11
0
4.0E-04
2.1E-04
1.1E-05
2.9E-04
2.3E-05
5.2E-04
1.7E-03
2.6E-03
1.9E-03
4.4E-03
1.1E-02
12-19
0
2.2E-04
1.1E-04
7.3E-06
8.8E-05
2.3E-05
3.6E-04
1.3E-03
2.2E-03
1.1E-03
2.2E-03
3.5E-03
20-70
(adult)
0
1.8E-04
2.2E-05
7.3E-06
1.1E-04
1.4E-05
3.1E-04
1.7E-03
2.2E-03
1.2E-03
1.5E-03
7.2E-03
aThe values in this table were used to make the cumulative stacked bar chart shown in Figure 5-2. The bolded, underlined cell indicates the largest risk value in this
table. Hazard quotients rounded to two significant figures.
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5.2.4 Cancer Risks Associated With PAH Exposure
Incremental lifetime cancer risks from exposure to PAHs via fish exposure only and via
subsistence farm exposure only are presented in Table 5-7 and Table 5-8, respectively. The
risks from exposure to PAHs via fish ingestion exceeded 1-in-one million for Veto Lake (3-in-one
million or 2-in-one million, depending on the ingestion scenario) and was driven primarily by
emissions of benzo(b)fluoranthene and secondarily by emissions of benzo(k)fluoranthene. The
risks from exposure to PAHs via the farm exceeded 1-in-one million using the mean ingestion
rate at "Farm_NNW" and "Farm_SE" (3-in-one million for both), and for all farms using the 90th
percentile ingestion rate (7-, 7-, and 2-in-one million respectively at Farm_NNW, Farm_SE, and
Farm_WSW) and were driven primarily by emissions of BaP and benzo(b)fluoranthene and
secondarily by emissions of benzo(k)fluoranthene. HQs for PAH exposure are not shown
because non-cancer RfDs were available only for a small subset of congeners, and the HQs for
these congeners summed to values many orders of magnitude less than 1 (thus, the cancer
assessment was more protective).
Figure 5-3 shows the levels at which each ingested farm medium (beef, eggs, pork, etc.)
contributed to the total incremental lifetime subsistence farmer risk (farm medium-specific risks
are provided in Table 5-9). No one medium contributed a majority of the risk value, although
exposures to dairy and exposed fruits together accounted for most of the risk, followed by
exposed vegetables and beef.
Table 5-7. Incremental Lifetime Cancer Risks from Exposure to PAHs for
Angler Scenarios3'13'0
Period of
Exposure
Incremental Lifetime Cancer Risk
Angler Population
Ingestion Level
Wolf Run
Lake
Veto
Lake
Mountwood
Park Lake
Subsistence Angler
99th %ile
Lifetime
1E-06
3E-06
5E-07
Recreational Angler
Central Tendency (Mean)
Lifetime
2E-07
5E-07
7E-08
95th %ile
Lifetime
6E-07
2E-06
3E-07
General Population
Mean
2E-08
5E-08
7E-09
90th %ile
Lifetime
4E-08
1E-07
2E-08
99th %ile
3E-07
9E-07
1E-07
lifetime is ages 1 through 70. Risks rounded to one significant figure.
bShading corresponds to values that exceed 1-in-one million, rounded (which is defined as at least 1,5-in-one million).
°Bolded, underlined cell indicates the largest value in this table.
Table 5-8. Incremental Lifetime Cancer Risk from Exposure to PAHs for
Subsistence Farmer Scenarios3'13'0
Ingestion Level
Period of
Exposure
Incremental Lifetime Cancer Risk
Farm_SE
Farm_NNW
Farm_WSW
Mean
Lifetime
3E-06
3E-06
1E-06
90th %ile
Lifetime
7E-06
7E-06
2E-06
aLifetime is ages 1 through 70. Risks rounded to one significant figure.
Shading corresponds to values that exceed 1-in-one million, rounded (which is defined as at least 1,5-in-one million).
cBolded, underlined cell indicates the largest value in this table.
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Figure 5-3. Contribution to Incremental Lifetime Cancer Risk by Ingested Medium from
Exposure to PAHs at "Farm_NNW"
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Human Health Multipathway Residual Risk Assessment for the Ferroalloys Production Source Category
Table 5-9. Incremental Lifetime Cancer Risk by Ingested Medium from Exposure to PAHs at "Farm_NNW"a
Ingestion
Level
Period of
Exposur
e
Incremental Lifetime Cancer Risk
Breast
Milk
Soil
Total
Dairy
Eggs
Beef
Pork
Poultry
Exposed
Vegetable
Protected
Vegetable
Root
Vegetable
Exposed
Fruit
Protected
Fruit
Mean
Lifetime
0
6.6E-08
1.3E-06
1.8E-08
2.7E-07
1.2E-08
3.2E-08
4.9E-07
1.6E-08
7.9E-08
9.1E-07
4.3E-08
90th %ile
Lifetime
0
3.0E-07
2.7E-06
3.9E-08
6.6E-07
2.8E-08
6.5E-08
1.2E-06
3.2E-08
2.1E-07
2.0E-06
1.2E-07
aThe values in this table were used to make the cumulative stacked bar chart shown in Figure 5-3. The bolded, underlined cell indicates the largest risk value in this table. Risks rounded to
two significant figures.
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5.2.5 Cancer Risks Associated With Dioxin Exposure
Incremental lifetime cancer risks from exposure to dioxins via fish exposure only and via
subsistence farm exposure only are presented in Table 5-10 and Table 5-11, respectively. The
risks from exposure to dioxins exceeded 1-in-one million for "Farm_SE" and "Farm_NNW" using
the 90th percentile ingestion rate (3-in-one million for both) and were below 1-in-one million for
all angler exposure scenarios. The angler risks were driven primarily by emissions of
1,2,3,7,8-PCDD and secondarily by 2,3,7,8-TCDD. The subsistence farmer risks were driven
primarily by emissions of 1,2,3,7,8-PCDD and secondarily by 2,3,4,7,8-PCDF and 2,3,7,8-
TCDD. HQs for dioxin exposure are not shown because non-cancer RfDs were available only
for one modeled congener, and the HQs for that congener were well below 1 (thus, the cancer
assessment was more protective).
Figure 5-4 shows the levels at which each ingested farm medium (beef, eggs, pork, etc.)
contributed to the total lifetime subsistence farmer risk (farm medium-specific risks are provided
in Table 5-12). The dairy medium contributed most of the risk, followed by infant exposure via
breast milk and beef exposure.
Table 5-10. Incremental Lifetime Cancer Risks from Exposure to Dioxins for
Angler Scenarios3'13
Angler Population
Ingestion
Level
Period of
Exposure
Incremental Lifetime Cancer Risk
Wolf Run
Lake
Veto
Lake
Mountwood
Park Lake
Subsistence Angler
99th %ile
Lifetime
6E-07
1E-06
3E-07
Recreational Angler
Central
Tendency
(Mean)
Lifetime
8E-08
1E-07
3E-08
95th %ile
Lifetime
3E-07
5E-07
1E-07
General Population
Mean
Lifetime
1E-08
2E-08
4E-09
90th %ile
3E-08
4E-08
1E-08
99th %ile
2E-07
3E-07
7E-08
lifetime is ages 1 through 70, including infant exposure to chemical from an adult mother's breast milk, where the adult mother's
exposure period did not include childhood. Risks rounded to one significant figure.
bBolded, underlined cell indicates the largest value in this table.
Table 5-11. Incremental Lifetime Cancer Risk from Exposure to Dioxins for
Subsistence Farmer Scenarios3'13'0
Ingestion Level
Period of Exposure
Incremental Lifetime Cancer Risk
Farm_SE
Farm_NNW
Farm_WSW
Mean
Lifetime
1E-06
1E-06
5E-07
90th %ile
Lifetime
3E-06
3E-06
1E-06
lifetime is ages 1 through 70, including infant exposure to chemical from an adult mother's breast milk, where the adult
mother's exposure period did not include childhood. Risks rounded to one significant figure.
bShading corresponds to values that exceed 1 -in-one million, rounded (which is defined as at least 1,5-in-one million).
°Bolded, underlined cell indicates the largest value in this table.
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Figure 5-4. Contribution to Incremental Lifetime Cancer Risk by Ingested Medium from
Exposure to Dioxins at "Farm_NNW"
Breast Milk
I Soil
I Total Dairy
I Eggs
I Beef
Pork
Poultry
I Exposed Vegetable
Protected Vegetable
I Root Vegetable
I Exposed Fruit
Protected Fruit
Mean Ingestion
90th %ile Ingestion
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Table 5-12. Incremental Lifetime Cancer Risk by Ingested Medium from Exposure to Dioxins at "Farm_NNW"a
Ingestion
Level
Period of
Exposure
Incremental Lifetime Cancer Risk
Breast
Milk
Soil
Total
Dairy
Eggs
Beef
Pork
Poultry
Exposed
Vegetable
Protected
Vegetable
Root
Vegetable
Exposed
Fruit
Protected
Fruit
Mean
Lifetime
4.2E-07
1.5E-
09
7.0E-07
2.9E-10
2.6E-
07
1.7E-
08
5.5E-10
1.0E-08
2.3E-10
4.2E-09
1.5E-08
5.7E-10
90th %ile
Lifetime
9.0E-07
8.9E-
09
1.4E-06
6.3E-10
6.2E-
07
3.7E-
08
1.1E-09
2.3E-08
4.8E-10
1.1E-08
3.2E-08
1.6E-09
aThe values in this table were used to make the cumulative stacked bar chart shown in Figure 5-4. The bolded, underlined cell indicates the largest risk value in
this table. Risks rounded to two significant figures.
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5.2.6 Comparison to Screening-level Assessment Results
Table 5-13 presents the screening-level assessment results (from Section 2) as ratios of the
emissions for this facility to the screening thresholds. Although these values are not intended to
be interpreted as "risk estimates," they can be used to evaluate the impact of site-specific fate
and transport modeling by comparing the screening quotients to the site-specific risk results.
The site-specific HQs and incremental lifetime cancer risks shown here correspond to the same
exposure scenarios used in the screening-level assessment, which represents ingestion of both
farm products and fish.
As discussed in Section 2.2, the Tier 2 screening resulted in lower screening quotients than the
Tier 1 screening, and, as expected (and illustrated by the results shown in Table 5-13), the site-
specific risks and hazards were lower than the Tier 2 screening quotients.
The site-specific HQ for mercury, summed for the highest-risk lake and highest-risk farm,
was approximately 50 times smaller than the Tier 2 screening quotient.
For cadmium, the site-specific HQ was approximately 6 times smaller than the Tier 2
screening quotient.
The site-specific incremental lifetime cancer risk from PAH exposure was approximately
3 times smaller than the Tier 2 screening quotient.
The site-specific incremental lifetime cancer risk from dioxin exposure was
approximately 2 times smaller than the Tier 2 screening quotient.
The sum of the site-specific incremental lifetime cancer risks due to exposures to PAHs
and dioxins was 10-in-one million, which was much smaller than the Tiers 2 and 1
screening quotients (interpreted for comparative purposes as 30-in-one million and
300-in-one million, respectively).
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Table 5-13. Comparison of Site-specific Hazard Quotients and Cancer Risks to the
Results of the Screening-level Assessments3
PB-HAP Group
Screening Quotient
Site-specific
Hazard Quotient
or Riskb
Factor Decrease
in HQ or Risk
Tier 1
Tier 2
Via Fish
Only0
Via Farm
Only"
Combined
Fish +
Farm
Tier 1 to
Tier 2
Tier 2 to
Site-
specific
Tier 1 to
Site-
specific
Mercury®
(non-cancer)
100
9
0.2
0.01
0.2
12
47
550
Cadmium
(non-cancer)
10
0.9
0.09
0.05
0.1
12
6.4
76
PAHs
(cancer)
200
20
1
7
9
10
2.5
26
Dioxins
(cancer)
80
7
1
3
4
12
1.6
20
Total Incremental
Lifetime Cancer Risk
(PAH + Dioxin)
300
30
2
10
10
11
2.2
24
aShading corresponds to values that exceed 1, which is defined as at least 1.5. Rounding artifacts are present.
bSite-specific values are hazard quotients for mercury and cadmium, and lifetime incremental cancer risk as "in-one million" (i.e., divided
by 1 x 10"6) for PAHs and dioxins. To be congruent with the setup of the screening assessments, these site-specific values use the Child
1-2 age group for HQs (the ingestion amount per body weight is largest for the Child 1-2 category), a lifetime defined as age 1 through
age 70 (including infant breast milk for dioxin exposure) for cancer risk, the "subsistence angler" scenario for fish exposure, and the 90th
percentile ingestion rate for subsistence farmer exposure.
cWolf Run Lake for PAHs, Veto Lake for others
dFarm_SE for non-cancer, Farm_NNW for cancer
g
Exposure to methyl mercury was evaluated for fish exposure, while exposure to divalent mercury was evaluated for subsistence farmer
exposure.
6. Discussion of Uncertainties and Limitations
The exposure and risk modeling process attempts to simulate naturally occurring physical,
chemical, and biological processes using mathematical algorithms. For computational
tractability in a risk assessment, the modeling process generally involves simple representations
of many complex real-world processes. The simplification introduces uncertainty. Furthermore,
algorithms that describe the environmental movement of pollutants depend on numerous
environmental parameters for which the values might be naturally variable and for which
available data are limited.
The media concentration and risk results presented in Section 5 therefore must be interpreted in
light of the uncertainties associated with the model assumptions, structure, and input values.
How mercury, cadmium, PAHs, and dioxins behave in the environment is highly complex, and
many natural processes are represented in a simplified manner by TRIM.FaTE, including
gaseous and particulate deposition from air;
biogeochemical cycling in the aquatic environment, and especially mercury
transformations through methylation and demethylation at the sediment-surface
interface;
mixing processes in air, water, and sediment;
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suspended and benthic sediment dynamics in lakes; and
biotic processes such as growth, reproduction, and predation.
In addition, the toxicology of the modeled chemicals is complex and uncertain, as are the
estimates of human exposure to these chemicals given the available empirical data. Note that
surrogate congeners were used for emissions of two PAH congeners that currently are not
parameterized in TRIM.FaTE.
Examples of parameters for which values are variable and uncertain include aquatic food web
structure (e.g., diet of each fish species), biokinetic parameters that influence bioaccumulation
(e.g., assimilation efficiencies and elimination rates), topographic characteristics (e.g., lake
depth, runoff rates, and erosion rates), meteorological parameters (e.g., evaporation and
precipitation rates), chemical transformation rates (e.g., methylation and demethylation rates, in
the case of mercury), human exposure parameters (especially fish consumption rates), and the
reference dose/cancer slope factors used to assess potential adverse health effects.
We have conducted several analyses of the sensitivity of risk estimates to parameter values
over the past decade. For those parameters to which the model is particularly sensitive, we
have continued to collect additional data to better quantify the variability and distribution of
values. Nonetheless, because of the large number of parameters included, considering the
model in total, we did not attempt a probabilistic risk assessment. Instead, this analysis relied on
central tendency values and combinations of values that would lead to estimates of reasonable
maximum exposures to bound risk estimates.
This analysis evaluated exposure to chemicals that could be attributed to the modeled facility;
background levels of the modeled chemicals were not included. Thus, the assessment focuses
on incremental risks attributable to the facility. We did not estimate the number or fraction of
people potentially affected in the modeling zones, nor did we estimate specific endpoints like IQ
decrements or cardiovascular effects. The assessment focused on possible central tendency
and high-end exposures for selected exposure scenarios for individuals that might live near a
facility now or in the future.
Major sources of uncertainty affecting model results are shown in Table 6-1. Table 6-1 provides
the assessment input of concern, a qualitative judgment of the sensitivity of risk results to the
assessment input (in general, not limited just to this facility), and a general comment. The table
also provides a qualitative judgment of the impact on modeled risk based on the sensitivity to
the assessment input and based on the range of values manifested by the assessment input in
the environment. In other words, the second column (Sensitivity of Risk Results) describes the
sensitivity of model results to changes in the assessment input, and the fourth column
(Estimated Impact of Uncertainty on Results) takes into account both the sensitivity (column
two) and the variability of the assessment input. Inputs that exhibit high risk sensitivity could
have a limited impact in terms of overall uncertainty, and vice versa, depending on the range of
values manifested by the input. Each source of uncertainty also is discussed in greater detail
below.
6.1 Uncertainties Related to Fate and Transport Modeling (TRIM.FaTE)
The algorithms representing the transport and eventual fate of the modeled chemicals in air,
surface water, sediment, and biotic media are simplified representations of complex natural
processes. Estimated chemical deposition rates and concentrations will vary across different
environmental models and might be most accurate in specific conditions that meet restrictive
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assumptions. The TRIM.FaTE model represents all fate and transport processes in terms of
first-order differential equations; however, some processes like chemical diffusion are known to
follow second-order dynamics. Other algorithms—like those dealing with methylation and
demethylation in the case of mercury, biotic chemical cycling, and sediment dynamics—do not
consider all the factors known to affect these processes, or the processes might not be well
understood. This section expands on some of the major uncertainties included in Table 6-1 that
are specific to the fate and transport modeling conducted for the case studies.
Table 6-1. Sources of Uncertainty in the Current Site-specific Assessment
Assessment Input
Sensitivity of
Risk Results
Comment
Estimated Impact
of Uncertainty on
Results
Aquatic food web
parameters
High
Limited data on chemical- and species-specific
parameters, such as assimilation efficiency and
elimination rates.
Medium
Depth of lakes
High
Based on limited data. Averaged over lake area and
time. Perfect mixing assumed in estimating
concentrations.
Medium
Toxicity reference
value (RfD) and
cancer slope factors
High
Used EPA-recommended values where possible
(estimated from TEFs for most dioxin congeners), but
estimate includes inherent variability and uncertainty.
Surrogate congeners were used for the emissions of
two PAH congeners that are not currently fully
parameterized in TRIM.FaTE.
Low to Medium
Fate and transport
modeling process
High
TRIM.FaTE model might not capture all natural
processes or describe them precisely for particular
sites.
Low to Medium
Ingestion exposure
parameters
High
There is a great deal of variability and uncertainty in
ingestion rates, which is compounded when assessing
exposures from multiple food groups. In general, used
EPA-recommended health-protective values.
Medium
Methylation and
demethylation rates in
sediment, wetlands,
and surface water
(mercury only)
High
Model uses fixed rate constants for methylation and
demethylation in abiotic media. Model does not
capture complex dependence of rate constants on
environmental conditions, except for differences
between wetlands and unsaturated land.
Medium
Retention time/flush
rate
Medium-High
From literature (for Mountwood Park Lake and Wolf
Run Lake) or calculated from lake dimensions and flow
formulas. Independent data would help validate inflows
and runoff fractions.
Low to Medium
Modeling resolution
and layout
Medium
Resolution of compartments in modeling zone is
relatively coarse. Larger area averaging could dilute
exposure point concentrations.
Low
Erosion and runoff
flow directions
Medium
Estimated based on topography, watersheds, and
streamflows. Could not validate owing to lack of field
measurements.
Low to Medium
Erosion rates
Medium
Based on site-specific universal soil loss equation
factors. May not be reflective of small areas with
higher or lower erosion rates.
Low to Medium
Precipitation rate
Medium
Average annual precipitation total for the 4-year period
used in modeling can differ from the 30-year average.
Affects deposition quantity and type.
Low to Medium
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Assessment Input
Sensitivity of
Risk Results
Comment
Estimated Impact
of Uncertainty on
Results
Evaporation rate
Medium
Not a direct input to TRIM.FaTE—used in offline
estimations of chemical movement (e.g., runoff rates)
and flush rate. Limited site-specific data. Impacts
media concentrations.
Low to Medium
Wind speed and
direction
Medium
Data for 4-year period might not represent average
conditions . Excessive diffusion could underestimate
risk.
Low to Medium
Sediment dynamics
Medium
Model uses simplistic algorithms to describe
deposition and resuspension of sediment.
Low to Medium
Other soil, surface
water, air, and
vegetation
physicochemical
parameters
Medium
Limited site-specific data available. Default
assumptions often used.
Low to Medium
Cooking correction
factor
Medium
Limited studies available. Single value does not
account for different styles of cooking.
Medium
Breast milk pathway
parameters
Medium
All dioxins were assumed to exhibit the same
partitioning behavior as 2,3,7,8-TCDD.
Medium
Aquatic Food Web Parameters. Estimates of pollutant concentrations in fish that people
consume are sensitive to aquatic food web parameters. Chemical-, site- and species-specific
data on food web structure (e.g., species, diets, biomass per species) and chemical- and
species-specific biokinetic and biodynamic parameters that influence bioaccumulation (e.g., gill
absorption from water, assimilation efficiency from food, elimination rates) are limited. We
developed a simplified food web to include both benthic and water column organisms that is
consistent with data reported for many lake food webs (Great Lakes excluded) and identified
central tendency values for chemical biokinetic/dynamic parameters.
Lake Modeling Assumptions. The concentration of pollutants in modeled lakes (and thereafter
up the aquatic food chain) depends on the depth of lake assumed in the model. The
assessment used depth data specific to each lake. Each lake is modeled as a single water
column compartment with a surface sediment layer and a sediment sink. That approach
assumes perfect and instantaneous mixing of chemical in the water body at each modeling time
step. The approach does not, therefore, capture possible spatial variation in chemical
concentration within the lake (e.g., would not identify local pockets of high concentrations).
Similarly, the screening-level simulation does not simulate possible short-term high
concentrations that might follow a snow melt or strong storm event.
Surface Water Retention Time/Flush Rate. Retention time, which is inversely proportional to
flush rate, determines how quickly pollutants are passed out of a lake. A flush rate that is too
high (or retention time that is too low) could result in an underestimate of pollutant
concentrations in surface water and in the aquatic food chain. For this assessment, site-specific
retention times were available for Mountwood Park Lake and for Wolf Run Lake from literature
sources. For Veto Lake, retention time was calculated from estimated values for inflow to the
lake and lake volume.
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Methylation and Demethylation Rates (Mercury). Methylation and demethylation in sediment
and surface water are key processes governing biogeochemical cycling of mercury in the
aquatic environment. Saturated wetlands also facilitate mercury transformations at rates higher
than unsaturated soils do, and the rate constants used in the model were increased slightly to
account for this. How prevalent these processes are greatly influences sensitivity of an aquatic
ecosystem to mercury inputs and specifically influences the amount of methyl mercury available
for accumulation. Some water bodies that are efficient at methylating inorganic mercury can
show significant methyl mercury concentrations in biota despite a relatively small mercury input
into the system. The representation of these processes in the TRIM.FaTE model does not
explicitly account for known dependencies of transformation rates on redox potential, pH, sulfite
concentration, dissolved organic carbon content, and hydrodynamics at the sediment-surface
water interface. Both methylation and demethylation can occur either biotically or abiotically.
Certain conditions, like specific ranges of chloride, sulfide, and dissolved organic matter
concentrations, can increase the bioavailability of divalent mercury for methylation. Redox
conditions can influence the rate of abiotic demethylation. These process mechanics, potentially
antagonistic interactions with heavy metals like selenium, and the potential for photodegradation
of methyl mercury are not captured in the TRIM.FaTE model. Instead, user-supplied first-order
rate constants were used to model methylation and demethylation in surface water and
sediment. Site-specific data were limited and the regional default rate constants used might not
represent conditions at the modeled lakes.
Sediment Dynamics. The suspension and burial of sediment can have a significant impact on
surface water concentration and mercury speciation by influencing the methylation and
demethylation process. Suspended solid concentrations also affect the amount of chemical
transported from the water body during flushing. Resuspension of buried sediment could
remobilize previously deposited contaminants into the water body. In the TRIM.FaTE model,
these processes are simplistically represented by default sediment deposition rates and
suspended solids concentrations, which do not account for hydrodynamic sediment cycling
processes. These parameters were not based on site-specific data.
Erosion and Runoff Flow Directions. For pollutants for which the risks are transmitted chiefly
by the fish consumption pathway, the amount of pollutant entering lakes is a significant variable.
Because erosion and runoff can account for a significant portion of the pollutant transported into
the lake in some locations, erosion and runoff flows from the watershed are a potentially
sensitive parameter in the model. These flow directions were estimated based on information
about the surrounding topography.
Erosion Rates. Similar to runoff rates, erosion rates can affect the quantity of pollutants
transported into a water body. Erosion rates were estimated using the USLE, which is a
generalized estimate that depends on local topography, land use, and climate. Site-specific
USLE factors were used in this assessment. Local erosion rates could differ from the USLE
estimate.
Precipitation Rate. The precipitation rate in the model affects the rate at which pollutants are
transported between surface soil compartments and water bodies and also the rate at which
pollutants are flushed from water bodies. This assessment used precipitation data for a 4-year
period. Although the annualized average precipitation total for that 4-year period was
approximately equal to the 30-year annual average, it contained periods (e.g., months, seasons,
and individual years) where precipitation was considerably smaller or larger than the 30-year
average.
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Evaporation Rate. Evaporation rate is not directly input to TRIM.FaTE; it is used in offline
estimations of chemical movement (e.g., chemical runoff rates from land) and flush rates.
Consequently, evaporation rate affects lake concentration and aquatic biota concentration
estimates. Limited site-specific data were available and regional estimates had to be used.
Wind Speed and Direction. Wind speed and direction affect advection and diffusion of the
pollutant in the model. Because these data were derived from a single, 4-year period, they might
not be representative of average or future conditions.
Other Soil, Surface Water, Air, and Vegetation Physicochemical Parameters. Default or
national-average values or estimation methods were used for some soil, surface water, and air
and vegetation physical and chemical parameters due to a lack of easily accessible site-specific
data.
Algorithm Uncertainty. The TRIM.FaTE model represents all fate and transport processes in
terms of first-order differential equations. Some processes like diffusion, however, are known to
follow second-order dynamics. TRIM.FaTE also does not explicitly deal with lateral or vertical
dispersion in the air compartments. As noted earlier, some algorithms like methylation and
sediment transport do not consider all the factors known to affect the process. Biotic processes
including chemical absorption, chemical elimination, growth, reproduction, predation, and death
have been represented relatively simplistically in the model. Although the model's algorithms
have been validated and are based on professional judgment, some level of uncertainty results
from such simplifications.
6.2 Uncertainties Related to Exposure Modeling and Risk Characterization
(MIRC)
Ingestion Exposure Parameters. We evaluated all the surveys and data analyses EPA
presented in its 2011 Exposure Factors Handbook (Chapter 10) (U.S. EPA 2011b). For possible
subsistence populations and high-end recreational fish consumers, numerous studies have
reported catch and ingestion rates, predominantly for adults, but some for children as well. All of
these studies can be characterized as having relatively small sample sizes (tens to a few
hundred) and as applying to a localized population with varying lakes and rivers available for
fishing. Many of the studies also focus on specific racial or ethnic groups with culturally higher
fish harvesting practices. As a consequence, extrapolating fish ingestion rates from one
population or location to another or using any single study to represent a cultural group is
difficult. We therefore used one of the studies reporting high-end consumption rates by avid
sports anglers (Burger 2002) to represent subsistence anglers. One advantage of the Burger
(2002) data is that all ingestion rates were reported for self- or wild-caught fish (many fish
ingestion surveys do not distinguish wild-caught from store-bought fish). A difficulty with all of
the studies, however, is that given the small sample sizes, the certainty associated with upper-
percentile values is relatively low.
To represent fish consumption for children and for the general population, two large (i.e.,
hundreds to thousands surveyed) data sets are available. We used EPA's 2002 analysis of the
U.S. Department of Agriculture's Continuing Survey of Food Intakes by Individuals (CSFII) for
1994-1996 and 1998 (USDA 2000). Another more recent large data set is available from the
2003-2006 National Health and Nutrition Examination Survey (NHANES) conducted by the
Centers for Disease Control and Prevention. EPA presented its analysis of those data by
consumers and non-consumers and by age categories in its 2011 Exposure Factors Handbook.
Total sample size for each age category from the NHANES data set is somewhat smaller at this
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Human Health Multipathway Residual Risk Assessment for the Ferroalloys Production Source Category
time than from the CSFII data set (U.S. EPA 2011b). The overall fish ingestion rates from the
CSFII data set are somewhat higher than for the NHANES data set. Several factors might
contribute to that trend (e.g., lower fish ingestion rates for more recent data as people heed fish
consumption warnings, shellfish included as a separate category in the NHANES data set but
included with fish in the CSFII data set). We used EPA's 2002 analysis of the CSFII data set for
this risk assessment to be somewhat conservative, given the difficulty in evaluating temporal
trends in fish consumption at this time, and because the data for adults as summarized by EPA
(2002) were more similar to the data reported for adults in the Burger (2002) study.
For site-specific risk assessment, using data on actual fish harvesting and consumption
practices by people living in the vicinity of the specific facility would be preferable. Such data,
however, were not available for the facility evaluated in this report. High-end values from
nationwide surveys should be protective of possible future populations around any given facility.
The likelihood of any families or residents in the vicinity of a given facility being subsistence
fishers now or in the future is low.
Cooking Correction Factor. As described in Section 4.2.1, a cooking conversion factor of 1.5
was used for mercury and cadmium to account for the fact that cooking fish reduces the overall
mass of the fish without reducing the amount of the chemical bound to proteins, which
effectively increases the chemical concentration in the cooked fish. The cooking conversion
factor assumption was developed based on information presented by Morgan et al. (1997, as
cited in U.S. EPA 2011a) and might overestimate the loss of fat and water from fish when
steamed or poached and underestimate the loss of water by some dry-cooking methods (e.g.,
broiling, grilling).
A conversion factor of 0.7 was applied to dioxin concentrations to account for loss of the
chemical along with loss of fats during cooking methods that include grilling, broiling, and
steaming and poaching. The one value was assumed based on data from several sources
(Schecter et al., 1998; Reinert et al., 1972; Zabik and Zabik 1995) and might not apply to all
species of fish or cooking methods.
Although PAHs in fish taken up from their environment also might be lost along with lipids during
cooking, PAHs also can be created by cooking, particularly at higher heats often associated with
broiling or grilling. Although one could argue for reducing the PAH concentration in fish due to
loss of lipids during cooking, we decided not to adjust the TRIM.FaTE-estimated PAH
concentrations in fish because of the uncertainty associated with both the loss and gain
processes.
Breast Milk Pathway Parameters. Exposure to dioxins via the breast milk pathway was
estimated using algorithms and assumptions to simulate the partitioning behavior of the
chemical in breast milk and the rate and duration of breast milk consumption. All dioxins were
assumed to exhibit the same partitioning behavior as 2,3,7,8-TCDD, owing to a lack of data.
Although the methods and assumptions are supported by scientific literature, they have not
been evaluated against empirical biomonitoring data or other models.
Toxicity Reference Value. Incremental hazard quotients and incremental lifetime cancer risks
were calculated using EPA's recommended oral reference doses and cancer slope factors
where available (for most dioxins, cancer slope factors were estimated from toxicity equivalency
factors). Reference doses and cancer slope factors are typically estimated after building in
uncertainty factors for pharmacokinetic variability and uncertainty, pharmacodynamic variability
and uncertainty, inter- or intra-species variability, and potentially other factors. An awareness of
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Human Health Multipathway Residual Risk Assessment for the Ferroalloys Production Source Category
the values and ranges of these uncertainty factors (documented in EPA's IRIS data base) could
help inform risk management decisions.
7. References
Atkinson, D., and R.F. Lee. 1992. Procedures for Substituting Values for Missing NWS
Meteorological Data for Use in Regulatory Air Quality Models. Available at:
http://www.epa.gov/ttn/scram/surface/missdata.txt
B.C. MOELP (Province of British Columbia Ministry of Environment, Lands and Parks). 1993.
Ambient Water Quality Criteria for Polycyclic Aromatic Hydrocarbons (PAHs). Available at:
http://www.env.gov.bc.ca/wat/wq/BCguidelines/pahs/index. htm !#TopQfPage.
Burger, J. 2002. Daily consumption of wild fish and game: Exposures of high end
recreationalists. Environmental Health Research 12(4):343-354.
ESRI (Environmental Systems Research Institute). 2013. World Imagery. Accessed January 15,
2013. Available at:
http://www.arcgis. com/home/item. html?id=10df2279f9684e4a9f6a7f08febac2a9.
MLRC. 2006. National Land Cover Database. Available at:
http://www.mlrc.gov/nlcd06 data.php.
Morgan, J.N., M.R. Berry, and R.L. Graves. (1997). Effects of commonly used cooking practices
on total mercury concentration in fish and their impact on exposure assessments. Journal of
Exposure Analysis and Environmental Epidemiology 7(1): 119—133.
Raymond, B., and R. Rossmann. 2009. Total and methyl mercury accumulation in 1994-1995
Lake Michigan lake trout and forage fish. Journal of Great Lakes Research 35:438-446.
Reinert, R.E., D. Stewart, and H.L. Seagran. 1972. Effects of dressing and cooking on DDT
concentrations in certain fish from Lake Michigan. Journal of the Fisheries Research Board
of Canada 29:525-529.
SchecterA., M. Dellarco, O. Papke, and J. Olson. 1998. A comparison of dioxins, dibenzofurans
and coplanar PCBS in uncooked and broiled ground beef, catfish, and bacon. Chemosphere
37(9-12): 1723-1730.
Seinfeld, J.H., and S.N. Pandis. 1998. Atmospheric Chemistry and Physics: From Air Pollution
to Climate Change, Wiley-lnterscience, New York, pp. 931-933.
Stanek, E.J., E.J. Calabrese, R. Barnes, P. Pekow. 1997. Soil ingestion in adults - results of a
second pilot study. Toxicol. Environ. Safety 36:249-257.
U.S. Census Bureau. 2013. Available at: http://www.census.gov/.
U.S. EPA (U.S. Environmental Protection Agency). 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.
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U.S. EPA. 2005. Human Health Risk Assessment Protocol for Hazardous Waste Combustion
Facilities. Office of Solid Waste and Emergency Response, Washington, DC. EPA-530-R-
05-006. September. Available at: http://www.epa.gov/osw/hazard/tsd/td/combust/risk.htm.
U.S. EPA. 2011a. Revised Technical Support Document: National-Scale Assessment of
Mercury Risk to Populations with High Consumption of Self-caught Freshwater Fish, In
Support of the Appropriate and Necessary Finding for Coal- and Oil-Fired Electric
Generating Units. Office of Air Quality Planning and Standards, Health and Environmental
Impacts Division. Research Triangle Park, North Carolina. Available at:
http://www.epa.gov/mats/pdfs/20111216MercuryRiskAssessment.pdf.
U.S. EPA. 2011b. Exposure Factors Handbook: 2011 Edition. Office of Research and
Development, Washington, D.C. EPA/600/R-090/052F. September. Available at:
http://cfpub.epa. gov/ncea/risk/recordisplay.cfm?deid=236252.
USDA (U.S. Department of Agriculture). 2000. 1994-96, 1998 Continuing Survey of Food
Intakes by Individuals (CSFII). CD-ROM. Agricultural Research Service, Beltsville Human
Nutrition Research Center, Beltsville, MD. Available from the National Technical Information
Service, Springfield, VA, Accession Number PB-2000500027.
USDA. 2007. Census of Agriculture. Available at: http://www.agcensus.usda.gov/.
USDA. 2011. Cropland Data Layer. Available at: http://nassgeodata.gmu.edu/CropScape/.
USDA. 2012. Web Soil Survey. Available at:
http://websoilsurvey.nrcs.usda.gov/app/HomePage.htm).
U.S. NIH (U.S. National Institutes of Health). 2013a. ChemlDplus Advanced. Available at:
http://chem.sis.nlm.nih.gov/chemidplus/.
U.S. NIH. 2013b. Toxicology Data Network (TOXNET). Available at: http://toxnet.nlm.nih.gov/.
USGS (U.S. Geological Survey). 2012. National Hydrography Dataset. Available online at
http://nhd.usgs.gov/.
Zabik, M.E., and M.J. Zabik. 1995. Tetradiocholorodibenzo-p-dioxin residue reduction by
cooking/processing offish fillets harvested from the Great Lakes. Bulletin of Environmental
Contamination and Toxicology 55:264-269.
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Human Health Multipathway Residual Risk Assessment for the Ferroalloys Production Source Category
Appendix A
User-Specified Values for TRIM.FaTE Properties
The properties of the TRIM.FaTE scenario developed to model the environmental fate and
transport of mercury, cadmium, dioxins, and particulate organic matter (POM) for this risk
assessment are shown in the following tables. Shaded cells in these tables indicate where site-
specific values were developed for this site-specific assessment, with additional information
provided for some values in table notes. In some instances where an entire table shows site-
specific values (such as the values for estimated erosion rates for modeled surface soil
compartments), table notes are used and cells are left unshaded. All values presented in
unshaded and otherwise unmarked tables were unchanged from the Tiers 1 and 2 screening
assessments.
A-1
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Human Health Multipathway Residual Risk Assessment for the Ferroalloys Production Source Category
Table A-1. TRIM.FaTE Simulation Parameters3
Parameter Name
Units
Value
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 stepb
hr
4
Selected value.
aAII values were unchanged from the Tiers 1 and 2 Screening assessments (no site-specific values used).
bOutput time step is set in TRIM.FaTE using the scenario properties "simulationStepsPerOutputStep" and "simulationTimeStep."
A-3
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Human Health Multipathway Residual Risk Assessment for the Ferroalloys Production Source Category
Table A-2. Meteorological Inputs3
Parameter Name
Units
Value
Reference
Meteorological Inputs
Air temperature
degrees K
Varies by hour
Hourly surface meteorological data
from NOAA; see Section 4 of main
report for details on station and data
processing.
Horizontal wind speed
m/sec
Varies by hour
Hourly surface meteorological data
from NOAA; see Section 4 of main
report for details on station and data
processing.
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 the
ferroalloy analysis.
Wind direction
degrees clockwise
from N (blowing
from)
Varies by hour
Hourly surface meteorological data
from NOAA; see Section 4 of main
report for details on station and data
processing.
Rainfall rate
m3[rain]/m2[surface
area]-day
Varies by hour
Hourly surface meteorological data
from NOAA; see Section 4 of main
report for details on station and data
processing.
Mixing height (used to set air
VE property named "top")
m
Varies by hour
Twice-daily upper-air meteorological
data from NOAA; see Section 4 of
main report for details on station
and data processing.
isDay_SteadyState_forAir
unitless
-
Value not used in current dynamic
runs (would need to be reevaluated
if steady-state runs are needed).
isDay_SteadyState_forOther
unitless
-
aShaded values indicate where refined values were developed for this site-specific assessment. All other values (unshaded) were
unchanged from the Tiers 1 and 2 screening assessments.
A-4
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Human Health Multipathway Residual Risk Assessment for the Ferroalloys Production Source Category
Table A-3. Air Parameters3
Parameter Name
Units
Value
Reference
Atmospheric dust load
kg[dust]/m3[air]
6.15E-08
Bidleman 1988.
Density of air
g/cm3
0.0012
U.S. EPA 1997.
Dust density
kg[dust]/m3[dust]
1,400
Bidleman 1988.
Fraction organic matter on
particulates
unitless
0.2
Harnerand Bidleman 1998.
aAII values were unchanged from the Tiers 1 and 2 screening assessments (no site-specific values used).
A-5
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Human Health Multipathway Residual Risk Assessment for the Ferroalloys Production Source Category
Table A-4. Soil and Ground Water Parameters*
Parameter
Name
Units
Value
Soil-Tilled
(incl.
farms)
Soil-
Untilled
(incl.
farms)
Grasses/
Herbs
Deciduous
Forest
References
Surface Soil Compartment Type
Air content
volume[air]/vo
lume
[compartment
]
0.25
varies2
varies2
varies2
See additional Soil
table.
Average
vertical
velocity of
water
(percolation)
m/day
0.000624
0.000624
0.000624
0.000624
= Average Annual
Precipitation / 365 *
Fraction of Precipitation
that Percolates, where
365 = days per year;
percolation fraction is
professional judgment
= 0.20
Boundary
layer
thickness
above
surface soil
m
0.006
0.006
0.006
0.006
Thibodeaux 1996;
McKone et al. 2001
(Table 3).
Density of soil
solids (dry
weight)
kg[soil]/m3[soi
I]
2600
2600
2600
2600
Default in McKone et
al. 2001 (Table 3)
Thickness1
m
0.2a
0.01b
0.01b
0.01b
aU.S. EPA 2005b
bMcKone et al. 2001 (p.
30)
Erosion
fraction
unitless
varies2
varies2
varies2
varies2
See Erosion and
Runoff Fraction table.
Fraction of
area
available for
erosion
m2[area
available]/m2[t
otal]
1
1
1
1
Professional judgment;
area is rural.
Fraction of
area
available for
runoff
m2[area
available]/m2[t
otal]
1
1
1
1
Professional judgment;
area is rural.
Fraction of
area
available for
vertical
diffusion
m2[area
available]/m2[t
otal]
1
1
1
1
Professional judgment;
area is rural.
A-6
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Human Health Multipathway Residual Risk Assessment for the Ferroalloys Production Source Category
Table A-4. Soil and Ground Water Parameters (Cont.)*
Parameter
Name
Units
Value
Soil-
Tilled
(incl.
farms)
Soil-
Untilled
(incl.
farms)
Grasses/
Herbs
Deciduous
Forest
References
Surface Soil Compartment Type
Fraction
sand
unitless
0.181
varies2
0.181
varies2
USDA Web Soil
Survey
Organic
carbon
fraction
unitless
varies2
varies2
varies2
varies2
See additional Soil
table
PH
unitless
varies2
varies2
varies2
varies2
See additional Soil
table
Runoff
fraction
unitless
varies2
varies2
varies2
varies2
See Erosion and
Runoff Fraction table.
Total
erosion rate
^2
[soil]/m /day
varies2
varies2
varies2
varies2
See Total Erosion
Rates table.
Total runoff
rate
m3[water]/m2
/day
0.00125
0.00125
0.00125
0.00125
Calculated using
scenario-specific
precipitation rate and
assumptions
associated with water
balance.(= Average
Annual Precipitation /
365 x Fraction of
Precipitation that
Runs Off, where 365
= days per year;
runoff fraction is
professional
judgment = 0.4)
Water
content
volume[wate
r]/vol-
ume[compar
tment]
0.22
varies2
varies2
varies2
McKone et al. 2001
(Table A-3).
Root Zone Soil Compartment Type
Air content
volume[air]/v
ol-
ume[compar
tment]
0.25
varies2
varies2
varies2
See additional Soil
table
A-7
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Human Health Multipathway Residual Risk Assessment for the Ferroalloys Production Source Category
Table A-4. Soil and Ground Water Parameters (Cont.)*
Parameter
Name
Units
Value
Soil-
Tilled
(incl.
farms)
Soil-
Untilled
(incl.
farms)
Grasses/
Herbs
Deciduous
Forest
References
Root Zone Soil Compartment Type
Average
vertical
velocity of
water
(percolation)
m/day
0.000624
0.000624
0.000624
0.000624
= Average Annual
Precipitation / 365 *
Fraction of
Precipitation that
Percolates, where
365 = days per year;
percolation fraction is
professional
judgment = 0.20
Density of
soil solids
(dry weight)
kg[soil]/m3[s
oil]
2601
2601
2600
2601
Default in McKone et
al. 2001 (Table 3)
Fraction
sand
unitless
0.181
varies2
0.181
varies2
See additional Soil
table
Thickness1
m
0.6a
0.79a
0.79a
0.79a
aMcKone et al. 2001
(Table 16 directly or
adjusted).
Organic
carbon
fraction
unitless
varies2
varies2
varies2
varies2
See additional Soil
table
PH
unitless
varies2
varies2
varies2
varies2
See additional Soil
table
Water
content
volume[wate
r]/vol-
ume[compar
tmentl
0.22
varies2
varies2
varies2
See additional Soil
table
Shaded values indicate where refined values were developed for this site-specific assessment. All other values
(unshaded) were unchanged from the Tiers 1 and 2 screening assessments.
Set using the volume element properties file.
2See separate tables for values of these parameters.
A-8
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Human Health Multipathway Residual Risk Assessment for the Ferroalloys Production Source Category
Table A-5. Additional Soil Parameters*
Surface Parcel
Surface Soil Compartment Type
Root Zone Compartment Type
Air
Content0
Fraction
Organic
Carbon3
/
Water
Content15
/
PHC
Fraction
Organic
Carbon3
Fraction
Sandc
Water
Content15
PHC
Farm NNW Tilled
0.25
0.007267
0.22
5.3
0.007267
0.181
0.22
5.3
Farm NNW Unfilled
0.25
0.007267
0.22
5.3
0.007267
0.181
0.22
5.3
Farm SE Tilled
0.25
0.010174
0.22
5.5
0.010174
0.181
0.22
5.5
Farm SE Unfilled
0.25
0.010174
0.22
5.5
0.010174
0.181
0.22
5.5
Farm WSW Tilled
0.25
0.007267
0.22
5.3
0.007267
0.181
0.22
5.3
Farm WSW Untilled
0.25
0.007267
0.22
5.3
0.007267
0.181
0.22
5.3
E1
0.3
0.010174
0.2
5.5
0.010174
0.181
0.2
5.5
E2
0.3
0.010174
0.2
5.5
0.010174
0.181
0.2
5.5
N1
0.25
0.010174
0.22
5.3
0.010174
0.181
0.22
5.3
N2
0.25
0.014535
0.22
5.5
0.014535
0.367
0.22
5.5
N3e
0.25
0.010174
0.22
5.1
0.010174
0.304
0.22
5.1
N3w
0.25
0.010174
0.22
5.1
0.010174
0.304
0.22
5.1
N4
0.25
0.010174
0.22
5.1
0.010174
0.304
0.22
5.1
NE1
0.25
0.010174
0.22
5.3
0.010174
0.181
0.22
5.3
NNW1
0.25
0.007267
0.22
5.3
0.007267
0.181
0.22
5.3
NNW2e
0.25
0.007267
0.22
5.3
0.007267
0.181
0.22
5.3
NNW2w
0.25
0.007267
0.22
5.3
0.007267
0.181
0.22
5.3
NNW3
0.25
0.010174
0.22
5.3
0.010174
0.181
0.22
5.3
NNW4
0.25
0.007267
0.22
5.3
0.007267
0.181
0.22
5.3
NW1
0.25
0.010174
0.22
5.3
0.010174
0.181
0.22
5.3
NW2
0.25
0.010174
0.22
5.3
0.010174
0.181
0.22
5.3
NW3
0.25
0.010174
0.22
5.3
0.010174
0.181
0.22
5.3
S1
0.3
0.010174
0.2
5.5
0.010174
0.181
0.2
5.5
S2
0.3
0.010174
0.2
5.5
0.010174
0.181
0.2
5.5
S3
0.3
0.010174
0.2
5.5
0.010174
0.181
0.2
5.5
SE1
0.3
0.010174
0.2
5.5
0.010174
0.181
0.2
5.5
SE2
0.3
0.010174
0.2
5.5
0.010174
0.181
0.2
5.5
A-9
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Human Health Multipathway Residual Risk Assessment for the Ferroalloys Production Source Category
Table A-5. Additional Soil Parameters (Cont.)*
Surface Parcel
Surface Soil Compartment Type
Root Zone Compartment Type
Air
Content0
Fraction
Organic
Carbon3
Water
Content"
PHC
Fraction
Organic
Carbon3
Fraction
Sandc
Water
Content15
PHC
SE3
0.3
0.010174
CM
O
5.5
0.010174
0.181
0.2
5.5
SE4n
0.3
0.010174
0.2
5.5
0.010174
0.181
0.2
5.5
SE4s
0.3
0.010174
0.2
5.5
0.010174
0.181
0.2
5.5
SE5
0.3
0.010174
0.2
5.5
0.010174
0.181
0.2
5.5
Source
0.28
0.008
0.19
6.9
0.008
0.25
0.21
6.9
SW1
0.25
0.010174
0.22
5.3
0.010174
0.181
0.22
5.3
SW2
0.25
0.010174
0.22
5.3
0.010174
0.181
0.22
5.3
SW3
0.25
0.010174
0.22
5.3
0.010174
0.181
0.22
5.3
W1
0.25
0.007267
0.22
5.3
0.007267
0.181
0.22
5.3
W2
0.25
0.01017
0.22
5.3
0.01017
0.181
0.22
5.3
W3n
0.25
0.01017
0.22
5.3
0.01017
0.181
0.22
5.3
W3s
0.25
0.010174
0.22
5.3
0.010174
0.181
0.22
5.3
W4
0.25
0.01017
0.22
5.3
0.01017
0.181
0.22
5.3
W5
0.25
0.01
0.22
5.3
0.01
0.181
0.22
5.3
WNW1
0.25
0.010174
0.22
5.3
0.010174
0.181
0.22
5.3
WNW2n
0.25
0.010174
0.22
5.3
0.010174
0.181
0.22
5.3
WNW2s
0.25
0.010174
0.22
5.3
0.010174
0.181
0.22
5.3
WNW3
0.25
0.01017
0.22
5.3
0.01017
0.181
0.22
5.3
WNW4
0.25
0.01017
0.22
5.3
0.01017
0.181
0.22
5.3
WNW5
0.25
0.010174
0.22
5.3
0.010174
0.181
0.22
5.3
WSW1
0.25
0.00727
0.22
5.3
0.00727
0.181
0.22
5.3
WSW2
0.25
0.007
0.22
5.3
0.007
0.181
0.22
5.3
WSW3n
0.25
0.007267
0.22
5.3
0.007267
0.181
0.22
5.3
WSW3s
0.25
0.007267
0.22
5.3
0.007267
0.181
0.22
5.3
WSW4
0.25
0.01017
0.22
5.3
0.01017
0.181
0.22
5.3
WSW5
0.25
0.010174
0.22
5.3
0.010174
0.181
0.22
5.3
WSW6
0.25
0.0102
0.22
5.3
0.0102
0.181
0.22
5.3
All values shown were refined for this site-specific assessment (versus the values used in Tiers 1 and 2 screening assessments).
a=fraction organic matter /1.72, as per McKone et al. 2001.
"McKone et al. 2001 (Table A-3).
°USDA Web Soil Survey.
A-10
-------
Human Health Multipathway Residual Risk Assessment for the Ferroalloys Production Source Category
Table A-6. Runoff Fractions3
Receiving parcel
Sending parcel
Sink
LU
CV|
LLJ
Farm_NNW_Tilled
Farm_NNW_Untilled
Farm_SE_Tilled
Farm_SE_Untilled
Farm_WSW_Tilled
Farm_WSW_Untilled
Goodfellows_Park_Lake
Mountwood_Park_Lake
2
CV|
z
N3e
N3w
z
NE1
NNW1
NNW2e
NNW2W
NNW3
NNW4
NW1
NW2
NW3
5i
CV|
(/>
CO
(/>
SE1
SE2
E1
.20
.24
.35
.20
.01
E2
.48
.01
.41
.01
Farm NNW Tilled
.94
.02
.04
Farm NNW Unfilled
.13
.40
.46
Farm SE Tilled
.06
.88
.06
Farm SE Unfilled
.02
.96
.02
Farm WSW Tilled
.33
Farm WSW Unfilled
.11
.02
Goodfellows_Park_L
ake
0
0
0
0
0
0
0
0
0
1.00
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Mountwood_Park_L
ake
0
0
0
0
0
0
0
0
0
0
1.00
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
N1
.04
.92
.01
.01
N2
.05
.03
.01
.78
.02
.11
N3e
.01
.80
.10
.08
N3w
.10
.04
.69
.07
N4
.45
.32
.23
NE1
.47
.01
.01
.51
NNW1
.24
.04
.68
NNW2e
.76
.04
.19
NNW2w
.47
.11
.02
.02
.38
NNW3
.07
.19
.26
.25
.23
NNW4
.24
.59
.03
.01
.13
NW1
.16
.29
NW2
.12
.02
.42
NW3
.17
.18
.51
S1
.16
.06
.17
.15
S2
.63
.18
.12
S3
.59
.19
SE1
.23
.74
.02
SE2
.33
.01
.59
SE3
.35
.43
.01
SE4n
.04
.01
SE4s
.05
.05
SE5
.91
Source
1
A-11
-------
Human Health Multipathway Residual Risk Assessment for the Ferroalloys Production Source Category
Table A-6. Runoff Fractions (Cont.)a
Receiving parcel
Sending parcel
Sink
LU
cvl
LLJ
Farm_NNW_Tilled
Farm_NNW_Untilled
Farm_SE_Tilled
Farm_SE_Untilled
Farm_WSW_Tilled
Farm_WSW_Untilled
Goodfellows_Park_Lake
Mountwood_Park_Lake
2
CV|
z
N3e
N3w
NE1
NNW1
NNW2e
NNW2W
NNW3
NNW4
NW1
NW2
NW3
5i
CV|
(0
CO
(O
SE1
SE2
SW1
.43
.02
.10
SW2
.18
SW3
.93
.02
Veto Lake
W1
W2
W3n
.67
W3s
.66
W4
W5
.30
WNW1
.03
.04
WNW2n
.53
.31
WNW2s
.15
.43
WNW3
.27
WNW4
.28
WNW5
.23
.13
Wolf Run Lake
WSW1
WSW2
WSW3n
.79
.01
.11
WSW3s
.01
.41
WSW4
WSW5
WSW6
.19
A-12
-------
Human Health Multipathway Residual Risk Assessment for the Ferroalloys Production Source Category
Table A-6. Runoff Fractions (Cont.)a
Receiving parcel
Sending parcel
c
tn
LLJ
LLJ
(/>
(/>
m
-------
Human Health Multipathway Residual Risk Assessment for the Ferroalloys Production Source Category
Table A-6. Runoff Fractions (Cont.)a
Receiving parcel
Sending parcel
SE3
SE4n
SE4s
SE5
Source
SW1
SW2
SW3
a>
_l
o'
aJ
>
i
W2
W3n
W3s
W4
W5
WNW1
WNW2I1
WNW2S
WNW3
WNW4
WNW5
V
j*
(U
_l
I
c
3
a:
o
£
WSW1
WSW2
WSW3I1
WSW3S
WSW4
WSW5
WSW6
SW1
.01
.22
.08
.09
.01
.03
SW2
.05
.10
.42
.03
.23
SW3
.02
.02
Veto Lake
1.00
W1
.77
.03
.06
.09
.04
W2
.35
.04
.02
.10
.49
W3n
.22
.01
.01
.08
W3s
.07
.26
W4
.06
.02
.19
.01
.72
W5
.31
.18
.21
WNW1
.73
.17
.03
WNW2n
.06
.07
.04
WNW2s
.25
.04
.12
.01
WNW3
.59
.07
.02
.05
WNW4
.28
.12
.32
WNW5
.24
.17
.22
Wolf Run Lake
1.00
WSW1
.02
.42
.20
.36
WSW2
.31
.17
.05
.35
.11
WSW3n
.04
.06
WSW3s
.10
.04
.45
WSW4
.39
.26
.34
WSW5
.54
.25
.09
.12
WSW6
.02
.42
.15
.06
.16
aAII values shown were refined for this site-specific assessment (versus the values used in the Tiers 1 and 2 screening assessments).
A-14
-------
Human Health Multipathway Residual Risk Assessment for the Ferroalloys Production Source Category
Table A-7. USLE Erosion Parameters*
Area
Rainfall/
Erosivity
Index3
Soil
Erodibility
Index3
Length-
Slope
Factor3
Cover
Mgmt
Factor3
Supporting
Practices
Factor3
Unit Soil Lossb
Sediment
Delivery
Ratioc
Calculated
(Adjusted)
Erosion Rate'1
Soil Parcel
R
(100 ft-
ton/ac)
K
(ton/ac/(100
ft-ton/acre))
A
calculated
m
LS
(USCS)
C
(USCS)
P
M
(ton/ac/yr
)
A
(kg/in /d)
SDR
(adjusted)
erosion rate
(kg/in /d)
E1
1,312,959
125
0.43
—
0.003
1
0.115e
0.0000706
0.32657
2.31 E-05
E2
76,917,882
125
0.43
...
0.001
1
0.25e
0.0001535
0.12400
1.90E-05
Farm NNW Tilled
24,200
125
0.32
5.4
0.11
1
23.76
0.0145926
0.59463
8.68E-03
Farm NNW Untilled
24,200
125
0.32
5.4
0.11
1
23.76
0.0145926
0.59463
8.68E-03
Farm SE Tilled
152,680
125
0.43
...
0.11
1
25e
0.0153541
0.47233
7.25E-03
Farm SE Untilled
152,682
125
0.43
...
0.11
1
25e
0.0153541
0.47233
7.25E-03
Farm WSW Tilled
49,600
125
0.32
5.4
0.11
1
23.76
0.0145926
0.54361
7.93E-03
Farm WSW Untilled
49,600
125
0.32
5.4
0.11
1
23.76
0.0145926
0.54361
7.93E-03
N1
1,622,459
125
0.37
3.39
0.001
1
0.156788
0.0000963
0.31804
3.06E-05
N2
371,817,129
125
0.37
5.4
0.001
1
0.24975
0.0001534
0.05092
7.81 E-06
N3e
36,817,614
125
0.17
5.4
0.001
1
0.11475
0.0000705
0.13596
9.58E-06
N3w
7,353,631
125
0.17
5.4
0.001
1
0.11475
0.0000705
0.19401
1.37E-05
N4
92,484,526
125
0.17
5.4
0.001
1
0.11475
0.0000705
0.12118
8.54E-06
NE1
216,092,437
125
0.37
3.39
0.001
1
0.156788
0.0000963
0.10898
1.05E-05
NNW1
1,195,820
125
0.32
5.4
0.001
1
0.216
0.0001327
0.33040
4.38E-05
NNW2e
340,031
125
0.32
5.4
0.001
1
0.216
0.0001327
0.38665
5.13E-05
NNW2w
38,196
125
0.32
5.4
0.001
1
0.216
0.0001327
0.56165
7.45E-05
NNW3
9,611,600
125
0.037
2.2
0.11
1
1.11925
0.0006874
0.18762
1.29E-04
A-15
-------
Human Health Multipathway Residual Risk Assessment for the Ferroalloys Production Source Category
Table A-7. USLE Erosion Parameters (Cont.)*
Soil Parcel
Area
Rainfall/
Erosivity
Index3
Soil
Erodibility
Index3
Length-
Slope
Factor3
Cover
Mgmt
Factor3
Supporting
Practices
Factor3
Unit Soil Lossb
Sediment
Delivery
Ratioc
Calculated
(Adjusted)
Erosion Rate'1
m
R
(100 ft-
ton/ac)
K
(ton/ac/(100
ft-ton/acre))
LS
(USCS)
C
(USCS)
P
A
(ton/ac/yr
)
< E
O)
SDR
calculated
(adjusted)
erosion rate
(kg/m /d)
NNW4
165,843,092
125
0.032
5.4
0.001
1
0.0216
0.0000133
0.11265
1.49E-06
NW1
3,049,789
125
0.037
2.2
0.001
1
0.010175
0.0000062
0.21657
1.35E-06
NW2
7,387,449
125
0.037
2.2
0.001
1
0.010175
0.0000062
0.19389
1.21E-06
NW3
126,334,123
125
0.037
2.2
0.001
1
0.010175
0.0000062
0.11654
7.28E-07
S1
1,768,932
125
0.43
—
0.001
1
0.25e
0.0001535
0.31462
4.83E-05
S2
142,826,053
125
0.43
—
0.001
1
0.25e
0.0001535
0.11477
1.76E-05
S3
28,312,955
125
0.43
—
0.003
1
0.115e
0.0000706
0.14050
9.92E-06
SE1
393,244
125
0.43
—
0.001
1
0.25e
0.0001535
0.37968
5.83E-05
SE2
7,835,857
125
0.43
—
0.001
1
0.25e
0.0001535
0.19247
2.96E-05
SE3
84,872,112
125
0.43
—
0.001
1
0.25e
0.0001535
0.12249
1.88E-05
SE4n
4,347,596
125
0.43
—
0.001
1
0.25e
0.0001535
0.20718
3.18E-05
SE4s
2,097,360
125
0.43
—
0.001
1
0.25e
0.0001535
0.30800
4.73E-05
SE5
45,631,552
125
0.43
—
0.001
1
0.25e
0.0001535
0.13236
2.03E-05
Source
714,066
125
0.39
1.5
0.2
1
35.1
0.02156
0.35240
0.00E+00
SW1
10,272,513
125
0.37
3.32
0.001
1
0.15355
0.0000943
0.18607
1.75E-05
SW2
32,293,781
125
0.37
3.32
0.003
1
0.46065
0.0002829
0.13821
3.91 E-05
SW3
55,275,322
125
0.37
3.32
0.001
1
0.15355
0.0000943
0.12923
1.22E-05
W1
5,516,937
125
0.32
5.4
0.001
1
0.216
0.0001327
0.20110
2.67E-05
A-16
-------
Human Health Multipathway Residual Risk Assessment for the Ferroalloys Production Source Category
Table A-7. USLE Erosion Parameters (Cont.)*
Soil Parcel
Area
Rainfall/
Erosivity
Index3
Soil
Erodibility
Index"
Length-
Slope
Factor3
Cover
Mgmt
Factor3
Supporting
Practices
Factor3
Unit Soil Lossb
Sediment
Delivery
Ratioc
Calculated
(Adjusted)
Erosion Rate'1
in
R
(100 ft-
ton/ac)
K
(ton/ac/(100
ft-ton/acre))
LS
(USCS)
C
(USCS)
P
A
(ton/ac/yr
)
A
(kg/m /d)
SDR
calculated
(adjusted)
erosion rate
(kg/m /d)
W2
14,501,479
125
0.37
2.2
0.001
1
0.10175
0.0000625
0.17822
1.11E-05
W3n
5,333,202
125
0.37
2.2
0.001
1
0.10175
0.0000625
0.20195
1.26E-05
W3s
2,168,442
125
0.37
2.2
0.001
1
0.10175
0.0000625
0.30672
1.92E-05
W4
27,245,681
125
0.37
2.2
0.001
1
0.10175
0.0000625
0.14118
8.82E-06
W5
34,264,723
125
0.37
2.2
0.001
1
0.10175
0.0000625
0.13719
8.57E-06
WNW1
4,208,581
125
0.37
2.2
0.003
1
0.30525
0.0001875
0.20802
3.90E-05
WNW2n
235,254
125
0.37
2.2
0.001
1
0.10175
0.0000625
0.44748
2.80E-05
WNW2s
991,985
125
0.37
2.2
0.001
1
0.10175
0.0000625
0.33821
2.11E-05
WNW3
14,464,874
125
0.37
2.2
0.003
1
0.30525
0.0001875
0.17827
3.34E-05
WNW4
8,391,775
125
0.37
2.2
0.001
1
0.10175
0.0000625
0.19083
1.19E-05
WNW5
52,557,694
125
0.37
2.2
0.003
1
0.30525
0.0001875
0.13005
2.44E-05
WSW1
1,162,068
125
0.32
5.4
0.001
1
0.216
0.0001327
0.33159
4.40E-05
WSW2
3,051,907
125
0.32
5.4
0.003
1
0.648
0.0003980
0.21655
8.62E-05
WSW3n
459,880
125
0.32
5.4
0.001
1
0.216
0.0001327
0.37233
4.94E-05
WSW3s
707,648
125
0.32
5.4
0.001
1
0.216
0.0001327
0.35280
4.68E-05
WSW4
14,537,125
125
0.37
2.2
0.003
1
0.30525
0.0001875
0.17816
3.34E-05
WSW5
7,870,758
125
0.37
2.2
0.001
1
0.10175
0.0000625
0.19236
1.20E-05
WSW6
48,298,321
125
0.37
2.2
0.001
1
0.10175
0.0000625
0.13143
8.21 E-06
*AII values shown were refined for this site-specific assessment (versus the values used in the Tiers 1 and 2 screening assessments)
aUSDA Web Soil Survey, unless otherwise noted.
b=R*K*LS*C*P, with proper unit and time conversions, unless otherwise noted.
Calculated using SD = 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).
Calculated as A*SDR*E, where E = enrichment ratio for inorganics = 1.
6Derived using Ohio EPA (2009) weighted average KSLP for Beech Fork in the absence of LS data in USDA Web Soil Survey Data
A-17
-------
Human Health Multipathway Residual Risk Assessment for the Ferroalloys Production Source Category
Table A-8. Terrestrial Plant Placement
Surface Soil Volume Element
Grasses/
Herbs
Deciduous
Forest
None
E1
X
E2
X
Farm NNW Tilled
X
Farm NNW Unfilled
X
Farm SE Tilled
X
Farm SE Unfilled
X
Farm WSW Tilled
X
Farm WSW Unfilled
X
N1
X
N2
X
N3e
X
N3w
X
N4
X
NE1
X
NNW1
X
NNW2e
X
NNW2w
X
NNW3
X
NNW4
X
NW1
X
NW2
X
NW3
X
S1
X
S2
X
S3
X
SE1
X
SE2
X
SE3
X
SE4n
X
A-18
-------
Human Health Multipathway Residual Risk Assessment for the Ferroalloys Production Source Category
Table A-8. Terrestrial Plant Placement (Cont.)
Surface Soil Volume Element
Grasses/
Herbs
Deciduous
Forest
None
SE4s
X
SE5
X
Source
X
SW1
X
SW2
X
SW3
X
W1
X
W2
X
W3n
X
W3s
X
W4
X
W5
X
WNW1
X
WNW2n
X
WNW2s
X
WNW3
X
WNW4
X
WNW5
X
WSW1
X
WSW2
X
WSW3n
X
WSW3s
X
WSW4
X
WSW5
X
WSW6
X
A-19
-------
Human Health Multipathway Residual Risk Assessment for the Ferroalloys Production Source Category
Table A-9. Terrestrial Plant Parameters*
Parameter Name
Units
Grass/Herbs1
Deciduous
Forest
Reference
Leaf Compartment Type
Allow exchange
1=yes, 0=no
seasonal2
seasonal2
-
Average leaf area index
m2[leaf]/ m2[area]
5a
.a
CO
aMid-range of 4-6 for old
fields, Scurlock et al. 2001
bCDIAC 2010 (Harvard
Forest, dom. red oak and red
maple).
Calculate wet dep interception
fraction (Boolean)
1=yes, 0=no
0
0
Professional judgment.
Correction exponent, octanal to
lipid
unitless
0.76
0.76
Trapp 1995 (from roots).
Degree stomatal opening
unitless
1
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/mJ
820
820
Paterson et al. 1991.
Leaf wetting factor
m
3.00E-04
3.00E-04
Muller and Prohl 1993 (1E-04
to 6E-04 for different crops
and elements).
Length of leaf
m
0.05
0.1
Professional judgment.
Lipid content
kg/kg wet weight
0.00224
0.00224
Riederer 1995 (European
beech).
Litterfall rate
1/day
seasonal"
seasonal"
-
A-20
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Human Health Multipathway Residual Risk Assessment for the Ferroalloys Production Source Category
Table A-9. Terrestrial Plant Parameters (Cont.)*
Parameter Name
Units
Grass/Herbs1
Deciduous
Forest1
Reference
Leaf Compartment Type
Stomatal area normalized
effective diffusion path length
1/m
200
200
Wilmerand Fricker 1996.
Vegetation attenuation factor
m2/kg
2.9
2.9
Baes et al. 1984
(Grass/hay).
Water content
unitless
0.8
0.8
Paterson et al. 1991.
Wet dep interception fraction
unitless
Calculated within
TRIM.FaTE
Calculated
within
TRIM.FaTE
Calculated based on the
meteorology data used
within TRIM.FaTE
Wet mass of leaf per soil area
kg [fresh
leaf]/m2[area]
0.6a
.a
CD
O
Calculated from leaf area
index and Leith 1975.
bSimonich and Hites 1994
(Calculated from leaf area
index, leaf thickness,
density of wet foliage).
Particle on Leaf Compartment Type
Allow exchange
1=yes, 0=no
seasonal2
seasonal2
-
Volume particle per area leaf
m3[leaf
particles]/m2[leaf]
1.00E-09
1.00E-09
Coe and Lindberg 1987
(based on particle density
and size distribution for
atmospheric particles
measured on an adhesive
surface).
A-21
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Human Health Multipathway Residual Risk Assessment for the Ferroalloys Production Source Category
Table A-9. Terrestrial Plant Parameters (Cont.)*
Parameter Name
Units
Grass/Herbs1
Deciduous
Forest1
Reference
Root Compartment Type - Nonwoody Only
Allow exchange
1=yes, 0=no
seasonal2
seasonal2
-
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/m3
820
"
Paterson et al. 1991
(soybean).
Wet mass per soil area
kg/m2
1.4
"
Jackson et al. 1996
(temperate grassland).
Stem Compartment Type - Nonwoody Only
Allow exchange
1=yes, 0=no
seasonal2
seasonal2
-
Correction exponent, octanol to
lipid
unitless
0.76
~
Trapp 1995.
Density of phloem fluid
kg/mJ
1,000
-
Professional judgment.
Density of xylem fluid
kg/cmJ
900
-
Professional judgment.
Flow rate of transpired water per
leaf area
m3[water]/
m2[leaf]
0.0048
~
Crank et al. 1981.
Fraction of transpiration flow rate
that is phloem rate
unitless
0.05
-
Paterson et al. 1991.
Lipid content of stem
kg/kg wet weight
0.00224
-
Riederer 1995 (European
beech).
Water content of stem
unitless
0.8
-
Paterson et al. 1991.
Wet density of stem
kg/mJ
830
-
Professional judgment.
Wet mass per soil area
kg/m2
0.24a
Calculated from leaf and root
biomass density based on
professional judgment.
Shaded values indicate where refined values were developed for this site-specific assessment. All other values (unshaded) were unchanged from the Tiers 1
and 2 screening assessments.
1See separate table for assignment of plant types to surface soil compartments.
2Begins April 10 (set to 1), ends after October 1 (set to 0). Based on local frost/freeze data.
3Begins October 1, ends after October 30; rate = 0.15/day during this time (value assumes 99 percent of leaves fall in the 30 days beginning with the last day
of allow-exchange).
A-22
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Human Health Multipathway Residual Risk Assessment for the Ferroalloys Production Source Category
Table A-10. Surface Water Parameters*
Parameter Name
Units
Value
Reference
Goodfellows
Park Lake
Mountwood Park
Lake
Veto Lake
Wolf Run Lake
Algal carbon content
(fraction)
unitless
0.465
0.465
0.465
0.465
APHA 1995.
Algal density in water
column
g[algae]/L
[water]
0.0025
0.0025
0.0025
0.0025
Millard et al. 1996 as
cited in ICF 2005.
Algal growth rate
1/day
0.7
0.7
0.7
0.7
Hudson et al. 1994 as
cited in Mason et al.
1995b.
Algal radius
jjm
2.5
2.5
2.5
2.5
Mason et al. 1995b.
Algal water content
(fraction)
unitless
0.9
0.9
0.9
0.9
APHA 1995.
Average algal cell
density (per vol cell,
not water)
g[algae]/m3
[algae]
1,000,000
1,000,000
1,000,000
1,000,000
Mason et al. 1995b;
Mason et al. 1996.
Boundary layer
thickness above
sediment
m
0.02
0.02
0.02
0.02
Cal EPA 1993.
Chloride concentration
mg/L
13
12
13
11
USGS Water Alert
Data.
A-23
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Human Health Multipathway Residual Risk Assessment for the Ferroalloys Production Source Category
Table A-10. Surface Water Parameters (Cont.)*
Parameter Name
Units
Value
Reference
Goodfellows Park
Lake
Mountwood Park
Lake
Veto Lake
Wolf Run Lake
Chlorophyll
concentration
mg/L
0.0197
0.0075944
0.0197
0.0017
USGS 1984; Fulmer
1990.
Depth
m
2.23a
4.57b
2.23a
10.5°
aUSGS 1984 (Veto
used as surrogate for
Goodfellows)
bWest Virginia DNR
cFulmer 1990
Dimensionless viscous
sublayer thickness
unitless
4
4
4
4
Ambrose et al. 1995.
Drag coefficient for
water body
unitless
0.0011
0.0011
0.0011
0.0011
Ambrose et al. 1995.
Flush rate
1/year
4.87
9.358976
19.022
0.595238
Calculated.
Fraction sand
unitless
0.25
0.25
0.25
0.25
Professional judgment.
Organic carbon
fraction in suspended
sediments
unitless
0.02
0.02
0.02
0.02
Professional judgment.
A-24
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Human Health Multipathway Residual Risk Assessment for the Ferroalloys Production Source Category
Table A-10. Surface Water Parameters (Cont.)*
Parameter Name
Units
Value
Reference
Goodfellows Park
Lake
Mountwood Park
Lake
Veto Lake
Wolf Run Lake
PH
unitless
7.33a
.a
CO
7.33a
7.85a
aUSGS Water Alert
Data
bDefault; professional
judgment
Suspended sediment
deposition velocity
m/day
2
2
2
2
U.S. EPA 1997.
Total suspended
sediment
concentration
kg[sedime
nt]/
m3[water
column]
0.0423
0.05b
0.0423
0.001c
aUSGS 1984 and
USGS Water Alert Data
bU.S. EPA 2005b
CUSGS Water Alert
Data
Water temperature
degrees K
291.853
298b
291.853
288.05°
aUSGS 1984
bU.S. EPA 2005b
CUSGS WaterAlert Data
Shaded values indicate where refined values were developed for this site-specific assessment. All other values (unshaded) were unchanged from the Tiers 1 and 2 screening
assessments.
A-25
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Human Health Multipathway Residual Risk Assessment for the Ferroalloys Production Source Category
Table A-11. Sediment Parameters*
Parameter Name
Units
Value
Reference
Goodfellows
Park Lake
Mountwood
Park Lake
Veto Lake
Wolf Run Lake
<
Q_
CD
Q
m
0.05a
1.09728b
0.05a
0.05a
aMcKone et al. 2001
(Table 3).
bEPA 1998b
Fraction Sand
unitless
0.25
0.25
0.25
0.25
Professional
judgment.
Organic carbon
fraction
unitless
0.02
0.02
0.02
0.02
Professional
judgment.
Porosity of the
sediment zone
volume[total pore
space]/volume[se
diment
compartment]
0.6
0.6
0.6
0.6
U.S. EPA 1998a.
Solid material density
in sediment
kg[sediment]/m3[s
ediment]
2600
2600
2600
2600
McKone et al. 2001
(Table 3)
PH
unitless
7.33
7.2
7.33
7.85
Same as surface
water.
Sediment
resuspension velocity
m/day
8.28388E-05
9.6426E-05
8.2424E-05
1.9265E-06
Calculated from water
balance model.
Shaded values indicate where refined values were developed for this site-specific assessment. All other values (unshaded) were unchanged from the Tiers 1 and 2 screening
assessments.
1Set using the volume element properties named "top" and "bottom."
A-26
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Human Health Multipathway Residual Risk Assessment for the Ferroalloys Production Source Category
Table A-12. Aquatic Animals Food Chain, Density, and Mass*
Aquatic Biota
(Consuming
Organism)
Fraction Diet
Biomass
(kg/m2)
Body
Weight (kg)
Reference
Algae
Zooplankton
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
0.25
Professional judgment.
Water column
omnivore
0%
0%
0%
100%
0%
0%
0%
0%
0. 0005
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.
All values were unchanged from the Tiers 1 and 2 Screening assessments (no site-specific values used).
A-27
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Human Health Multipathway Residual Risk Assessment for the Ferroalloys Production Source Category
Table A-13. Cadmium Chemical-Specific Parameters*
Parameter Name3
Units
Value
Reference
CAS numberb
unitless
7440-43-9
-
Diffusion coefficient in pure
air
m2[air]/day
0.71
U.S. EPA 1999 (Table A-2-35).
Diffusion coefficient in pure
water
m2[water]/day
8.16E-05
U.S. EPA 1999 (Table A-2-35).
Henry's Law constant
Pa-m3/mol
1.00E-37
U.S. EPA 1999 (Table A-2-35; assumed
to be zero).
Melting point
degrees K
594
ATSDR 1999.
Molecular weight
g/mol
112.41
ATSDR 1999.
Octanol-air partition
coefficient (Koa)
m3[air]/m3[octanol]
-
-
Octanol-carbon partition
coefficient (Koc)
m3[carbon]/m3[octanol]
-
-
Octanol-water partition
coefficient (Kow)
L[water]/kg[octanol]
-
-
*AII values were unchanged from the Tiers 1 and 2 Screening assessments (no site-specific values used).
aAII parameters in this table are TRIM.FaTE chemical properties.
bCAS numbers apply to elemental Cd; however, the cations of cadmium are being modeled.
A-28
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Human Health Multipathway Residual Risk Assessment for the Ferroalloys Production Source Category
Table A-14. Mercury Chemical-Specific Parameters*
Parameter Name3
Units
Value
Reference
Hg(0)b
Hg(2)b
MHgb
CAS number
unitless
7439-97-6
14302-87-5
22967-92-6
-
Diffusion coefficient in pure
air
m2[air]/day
0.478
0.478
0.456
U.S. EPA 1997.
Diffusion coefficient in pure
water
m2[water]/day
5.54E-05
5.54E-05
5.28E-05
U.S. EPA 1997.
Henry's Law constant
Pa-m3/mol
719
7.19E-05
0.0477
U.S. EPA 1997.
Melting point
degrees K
234
5.50E+02
443
CARB 1994.
Molecular weight
g/mol
201
201
216
U.S. EPA 1997.
Octanol-water partition
coefficient (Kow)
L[water]/kg[octanol]
4.15
3.33
1.7
Mason et al. 1996.
Vapor washout ratio
m3[air]/m3[rairi]
1,200
1.6E+06
0
U.S. EPA 1997,
based on Petersen et
al. 1995.
*AII values were unchanged from the Tiers 1 and 2 Screening assessments (no site-specific values used).
aAII 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.
A-29
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Human Health Multipathway Residual Risk Assessment for the Ferroalloys Production Source Category
Table A-15. PAH Chemical-Specific Parameters*
Parameter
Name3
Units
Value
2Methyl
Acenaphthene
Acenaphthylene
BaA
BaP
BbF
BghiP
CAS number
unitless
91-57-6
83-32-9
208-96-8
56-55-3
50-32-8
205-99-2
191-24-2
Diffusion
coefficient in
pure air
m2/day
0.451
0.009
0.388
0.441
0.372
0.009
0.190
Diffusion
coefficient in
pure water
m2/day
6.70E-05
8.64E-05
6.03E-05
7.78E-05
7.78E-05
8.64E-05
4.54E-05
Henry's Law
constant
Pa-m3/mol
50.56
18.50
12.70
1.22
0.07
0.05
0.03
Melting point
degrees K
307.75
366.15
365.65
433
452
441
550.15
Molecular
weight
g/mol
142.20
154.21
152.20
228.29
252.32
252.32
276.34
Octanol-water
partition
coefficient
(Kow)
L[water]/L[
octanol]
7.24E+03
8.32E+03
1.00E+04
6.17E+05
9.33E+05
6.03E+05
4.27E+06
A-30
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Human Health Multipathway Residual Risk Assessment for the Ferroalloys Production Source Category
Table A-15. PAH Chemical-Specific Parameters (Cont.)*
Parameter
Name3
Units
Value
BkF
Chr
DahA
Fluoranthene
Fluorene
IcdP
CAS number
unitless
207-08-9
218-01-9
53-70-3
206-44-0
86-73-7
193-39-5
Diffusion
coefficient in
pure air
m2/day
0.009
0.009
0.009
0.009
0.009
0.009
Diffusion
coefficient in
pure water
m2/day
8.64E-05
8.64E-05
8.64E-05
8.64E-05
8.64E-05
8.64E-05
Henry's Law
constant
Pa-m3/mol
0.04
0.53
0.01
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]
8.71 E+0S
5.37E+05
3.16E+06
1.45E+05
1.51E+04
5.25E+06
A-31
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Human Health Multipathway Residual Risk Assessment for the Ferroalloys Production Source Category
Table A-15. PAH Chemical-Specific Parameters (Cont.)*
Parameter
Name3
Units
Reference
CAS number
unitless
-
Diffusion
coefficient in
pure air
m2/day
U.S. EPA 2005b. Exceptions include Siemens 2007 (2-Methylnaphthalene, Acenaphthylene, and
Benzo(g,h,i)perylene).
Diffusion
coefficient in
pure water
m2/day
U.S. EPA 2005b. Exceptions include Siemens 2007 (2-Methylnaphthalene, Acenaphthylene, and
Benzo(g,h,i)perylene).
Henry's Law
constant
Pa-m3/mol
U.S. EPA 2005b. Exceptions include U.S. EPA 2003 (2-Methylnaphthalene), HSDB 2001a (Acenaphthylene), and
HSDB 2001b (Benzo(g,h,i)perylene).
Melting point
degrees K
Budavari 1996. Exceptions include U.S. EPA 2003 (2-Methylnaphthalene), HSDB 2001a (Acenaphthylene), HSDB
2001b (Benzo(g,h,i)perylene), and U.S. EPA 2005b (Acenaphthene, Fluoranthene, and Fluorene).
Molecular
weight
g/mol
Budavari 1996. Exceptions include U.S. EPA 2003 (2-Methylnaphthalene), HSDB 2001a (Acenaphthylene), HSDB
2001b (Benzo(g,h,i)perylene), and U.S. EPA 2005b (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
lndeno(1,2,3-cd)pyrene), and Sangster 1993 (Benzo(b)fluoranthene).
*AII values were unchanged from the Tiers 1 and 2 Screening assessments (no site-specific values used).
aAII parameters in this table are TRIM.FaTE chemical properties.
A-32
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Human Health Multipathway Residual Risk Assessment for the Ferroalloys Production Source Category
Table A-16. Dioxin Chemical-Specific Parameters*
Parameter
Name1
Units
Value
1,2,3,4,6,7,8
-HpCDD
1,2,3,4,7,8-
HxCDF
1,2,3,7,8-
PeCDF
2,3,7,8-
TCDD
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
-HpCDF
CAS number
unitless
35822-46-9
70648-26-9
57117-41-6
1746-01-6
3268-87-9
39001-02-0
67562-39-4
Diffusion
coefficient in
pure air
m2/day
0.782
0.183
0.192
0.899
0.0883
0.123
0.129
Diffusion
coefficient in
pure water
m2/day
6.91 E-05
6.91 E-05
6.91 E-05
4.84E-05
3.08E-06
3.15E-05
3.33E-05
Henry's Law
constant
Pa-m3/mol
1.22
1.45
0.507
3.33
0.68
0.19
1.43
Melting point
degrees K
538a
499a
499b
578a
603
259
236.5
Molecular
weight
g/mol
425.2a
374.87a
340.42b
322a
460.0
443.76
409.31
Octanol-water
partition
coefficient (Kow)
L[water]/
L[octanol]
1.00E+08
1.00E+07
6.17E+06
6.31 E+06
1.58E+08
1.00E+08
2.51 E+07
A-33
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Human Health Multipathway Residual Risk Assessment for the Ferroalloys Production Source Category
Table A-16. Dioxin Chemical-Specific Parameters (Cont.)*
Parameter
Name1
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
2,3,4,6,7,8-
HxCDF
1,2,3,7,8-
PeCDD
CAS number
unitless
57653-85-7
57117-44-9
19408-74-3
72918-21-9
60851-34-5
40321-76-4
Diffusion
coefficient in
pure air
m2/day
0.0958
0.135
0.0958
0.135
0.135
0.101
Diffusion
coefficient in
pure water
m2/day
3.43E-05
3.53E-05
3.43E-05
3.53E-05
3.53E-05
3.65E-05
Henry's Law
constant
Pa-m3/mol
1.08
0.74
1.08
0.74
0.74
3.33
Melting point
degrees K
558.0
506.0
517.0
509.0
512.5
513.0
Molecular
weight
g/mol
390.84
374.9
390.8
374.9
374.9
356.4
Octanol-water
partition
coefficient (Kow)
L[water]/L[octanol
]
1.62E+08
8.24E+07
1.62E+08
3.80E+07
8.31 E+07
4.37E+06
A-34
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Human Health Multipathway Residual Risk Assessment for the Ferroalloys Production Source Category
Table A-16. Dioxin Chemical-Specific Parameters (Cont.)*
Parameter
Name1
Units
Value
Reference
2,3,4,7,8-PeCDF
1,2,3,4,7,8,9
-HpCDF
1,2,3,4,7,8-
HxCDD
2,3,7,8-
TCDF
CAS number
unitless
57117-31-4
55673-89-7
39227-28-6
51207-31-9
-
Diffusion
coefficient in
pure air
m2/day
0.142
0.129
0.0958
0.149
U.S. EPA 2005b.
Diffusion
coefficient in
pure water
m2/day
3.76E-05
3.33E-05
3.43E-05
4.04E-05
U.S. EPA 2005b.
Henry's Law
constant
Pa-m3/mol
0.5
1.43
1.08
1.46
U.S. EPA 2005.
Melting point
degrees K
469.3
222
546
500.0
aMackay et al. 2000.
bATSDR 1998.
Molecular
weight
g/mol
340.4
409.31
391.0
306.0
aMackay et al. 2000.
bATSDR 1998.
Octanol-water
partition
coefficient (Kow)
L[water]/L[octanol
]
3.16E+06
7.94E+06
6.31 E+07
1.26E+06
Mackay et al. 1992 as cited in U.S.
EPA 2000.
*AII values were unchanged from the Tiers 1 and 2 Screening assessments (no site-specific values used).
1AII parameters in this table are TRIM.FaTE chemical properties.
A-35
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Human Health Multipathway Residual Risk Assessment for the Ferroalloys Production Source Category
Table A-17. Cadmium Chemical-Specific Parameters for Abiotic Compartments*
Parameter Name
Units
Value
Reference
Air Compartment Type
Particle dry deposition velocity
m/day
260
Calculated from Muhlbaier
and Tisue 1981.
Washout ratio
m3[air]/m3[rairi]
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.
Surface Water Compartment Type
Ratio of concentration in water to
concentration in algae to
concentration dissolved in water
L[water]/g[algal wet wt]
1.87
McGeer et al. 2003.
*AII values were unchanged from the Tiers 1 and 2 Screening assessments (no site-specific values used).
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Human Health Multipathway Residual Risk Assessment for the Ferroalloys Production Source Category
Table A-18. Mercury Chemical-Specific Parameters for Abiotic Compartments
Parameter Name
Units
Value
Reference
Hg(0)
Hg(2)
MHg
Air Compartment Type
Particle dry deposition
velocity
m/day
500
500
500
McKone et al. 2001 (CalTOX value).
Demethylation rate
1/day
N/A
N/A
0
Professional judgment.
Methylation rate
1/day
0
0
0
Professional judgment.
Oxidation rate
1/day
0.00385
0
0
U.S. EPA 1997 (low end of half-life range (6
months to 2 years)).
Reduction rate
1/day
0
0
0
Professional judgment.
Washout ratio
mJ[air]/mJ[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
U.S. EPA 1997.
Vapor dry deposition velocity
m/day
50a
2500b
0
aLindberg et al. 1992 .
bEstimate by U.S. EPA using the Industrial Source
Complex (ISC) Model [See Vol. Ill, App. A of the
Mercury Study Report (U.S. EPA 1997)].
Demethylation rate
1/day
N/A
N/A
0.06
Range reported in Porvari, P. and M. Verta. 1995.
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Human Health Multipathway Residual Risk Assessment for the Ferroalloys Production Source Category
Table A-18. Mercury Chemical-Specific Parameters for Abiotic Compartments (Cont.)
Parameter Name
Units
Value
Reference
Hg(0)
Hg(2)
MHg
Surface Soil Compartment Type
Methylation rate
1/day
0
0.001
0
Professional judgment.
Oxidation rate
1/day
0
0
0
U.S. EPA 1997.
Reduction rate
1/day
0
1.25E-5
0
Professional judgment.
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
U.S. EPA 1997.
Demethylation rate
1/day
N/A
N/A
0.06
Range reported in Porvari and Verta 1995 is 3E-2
to 6E-2 /day; value is average maximum potential
demethylation rate constant under anaerobic
conditions.
Methylation rate
1/day
0
0.001
0
Range reported in Porvari and Verta 1995 is 2E-4
to 1E-3 /day; value is average maximum potential
methylation rate constant under anaerobic
conditions.
Oxidation rate
1/day
0
0
0
U.S. EPA 1997.
Reduction rate
1/day
0
3.25E-06
0
U.S. EPA 2005a
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Human Health Multipathway Residual Risk Assessment for the Ferroalloys Production Source Category
Table A-18. Mercury Chemical-Specific Parameters for Abiotic Compartments (Cont.)
Parameter Name
Units
Value
Reference
Hg(0)
Hg(2)
MHg
Surface Water Compartment Type
Algal surface area-specific
uptake rate constant
nmol/[|jm2-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[octa
nol]
0
1
2
Mason et al. 1996.
Solids-water partition
coefficient
L[water]/kg[solid
s wet wt]
1,000
100,000
100,000
U.S. EPA 1997.
Vapor dry deposition velocity
m/day
N/A
2500
U.S. EPA 1997 (Vol. Ill, App. A).
Demethylation rate
1/day
N/A
N/A
0.013
Gilmour and Henry 1991 (average of range of 1E-3
to 2.5E-2/day).
Methylation rate
1/day
0
0.001
0
U.S. EPA 1997; Gilmour and Henry 1991 (range is
from 1E-4 to 3E-4/day).
Oxidation rate
1/day
0
0
0
Professional judgment.
Reduction rate
1/day
0
0.0075
0
U.S. EPA 1997; reported values range from less
than 5E-3/day for depths greater than 17m, up to
3.5/day (Xiao et al. 1995; Vandal et al. 1995;
Mason et al. 1995a; Amyot et al. 1997).
Sediment Compartment Type
Solids-water partition
coefficient
L[water]/kg[solid
s wet wt]
3,000
50,000
3,000
U.S. EPA 1997.
Demethylation rate
1/day
N/A
N/A
0.0501
Gilmour and Henry 1991 (average of range of2E-4
to 1E-1/day).
Methylation rate
1/day
0
1.0E-4
0
U.S. EPA 1997; Gilmour and Henry 1991 (range is
from 1E-5 to 1 E-3/day).
Oxidation rate
1/day
0
0
0
Professional judgment.
Reduction rate
1/day
0
1.00E-06
0
U.S. EPA 1997; Vandal et al. 1995; (inferred value
based on presence of Hg(0) in sediment
porewater).
TRIM.FaTE Formula Property, which varies from 0.025 to 1.625 depending on pH and chloride concentration.
2TRIM.FaTE Formula Property, which varies from 0.075 to 1.7 depending on pH and chloride concentration.
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Human Health Multipathway Residual Risk Assessment for the Ferroalloys Production Source Category
Table A-19. PAH Chemical-Specific Parameters for Abiotic Compartments*
Parameter Name
Units
Values
2Methyl
Acenaphthene
Acenaphthylene
BaA
BaP
BbF
BghiP
BkF
Air Compartment Type
Particle dry
deposition velocity
m/day
500
500
500
500
500
500
500
500
Half-life
day
0.154
0.3
0.208
0.125
0.046
0.596
0.215
0.458
Washout ratio
m3[air]/m3
[rain]
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
Half-life
day
18
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
Half-life
day
18
56
66.5
680
530
610
415
2140
Surface Water Compartment Type
Ratio of
concentration in
algae to
concentration
dissolved in water
(g[chem]/kg
[algae]) /
(g[chem]/
L[water])
2.6
3
3.7
325
510
317
1539
473
Half-life
day
78
25
184
0.375
0.138
90
1670
62.4
Sediment Compartment Type
Half-life
day
2290
2290
2290
2290
2290
2290
2290
2290
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Human Health Multipathway Residual Risk Assessment for the Ferroalloys Production Source Category
Table A-19. PAH Chemical-Specific Parameters for Abiotic Compartments (Cont.)*
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), U.S. EPA 2005b
(Benzo(g,h,i)perylene, and Fluoranthene) /
average of range, HSDB 2001c (Acenaphthene),
HSDB 2001a (Acenaphthylene), and Spero et al.
2000 (Fluorene).
Washout ratio
m3[air]/m3
[rain]
20000
0
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), U.S. EPA 2005b
(Benzo(g,h,i)perylene, and Fluoranthene) /
average of range, HSDB 2001c (Acenaphthene),
HSDB 2001a (Acenaphthylene), and HSDB
2001 d (Fluorene).
Root Zone Soil Compartment Type
User input
characteristic
depth (Boolean)
0 = No,
Else = Yes
0
0
0
0
0
Professional judgment.
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Human Health Multipathway Residual Risk Assessment for the Ferroalloys Production Source Category
Table A-19. PAH Chemical-Specific Parameters for Abiotic Compartments (Cont.)*
Parameter Name
Units
Value
Reference
Chr
DahA
Fluoranthene
Fluorene
IcdP
Root Zone Soil Compartment Type
Half-life
day
1000
940
275
33
730
Howard et al. 1991 / upper bound measured or
estimated value. Exceptions include ATSDR
2005 (2-Methylnaphthalene), U.S. EPA 2005b
(Benzo(g,h,i)perylene, and Fluoranthene) /
average of range, HSDB 2001c (Acenaphthene),
HSDB 2001a (Acenaphthylene), and HSDB
2001 d (Fluorene).
Surface Water Compartment Type
Ratio of
concentration in
algae to
concentration
dissolved in water
(g[chem]/k
g[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 2001c
(Acenaphthene), HSDB 2001a
(Acenaphthylene), and HSDB 2001b
(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 on Table 2.3).
*AII values were unchanged from the Tiers 1 and 2 Screening assessments (no site-specific values used).
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Human Health Multipathway Residual Risk Assessment for the Ferroalloys Production Source Category
Table A-20. Dioxin Chemical-Specific Parameters for Abiotic Compartments*
Parameter Name
Units
Value
1,2,3,4,6,7,
8,9-OCDD
1,2,3,4,6,
7,8,9-
OCDF
1,2,3,4,6,7,8
-HpCDD
1,2,3,4,6,7,8
-HpCDF
1,2,3,4,7,8,
9-HpCDF
1,2,3,4,7,8-
HxCDD
Air Compartment Type
Deposition velocity
m/day
500
500
500
500
500
500
Half-life
day
162
321
64
137
122
42
Washout ratio
m3[air]/m3[rairi]
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
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
Half-life
day
1008
1008
1008
1008
1008
1008
Surface Water Compartment Type
Ratio of concentration in
algae to concentration
dissolved in water
(g[chem]/g
[algae])/(g[chem]/L[wate
r])
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
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Human Health Multipathway Residual Risk Assessment for the Ferroalloys Production Source Category
Table A-20. Dioxin Chemical-Specific Parameters for Abiotic Compartments (Cont.)*
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
m3[air]/m3[rairi]
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
Half-life
day
1008
1008
1008
1008
1008
1008
Surface Water Compartment Type
Ratio of concentration in
algae to concentration
dissolved in water
(g[chem]/g[algae])/(g[ch
em]/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
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Human Health Multipathway Residual Risk Assessment for the Ferroalloys Production Source Category
Table A-20. Dioxin Chemical-Specific Parameters for Abiotic Compartments (Cont.)*
Parameter Name
Units
Value
1,2,3,7,8-
PeCDF
2,3,4,6,7,8-
HxCDF
2,3,4,7,8-
PeCDF
2,3,7,8-
TCDD
2,3,7,8-TCDF
Air Compartment Type
Deposition velocity
m/day
500
500
500
500
500
Half-life
day
31
59
33
12
19
Washout ratio
m3[air]/m3[rairi]
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
Surface Water Compartment Type
Ratio of concentration in
algae to concentration
dissolved in water
(g[chem]/g[algae])/(g[ch
em]/
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
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Human Health Multipathway Residual Risk Assessment for the Ferroalloys Production Source Category
Table A-20. Dioxin Chemical-Specific Parameters for Abiotic Compartments (Cont.)*
Parameter Name
References
Air Compartment Type
Deposition Velocity
McKone et al. 2001.
Half-life
Atkinson 1996 as cited in U.S. EPA 2000; 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 for2,3,7,8-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 for2,3,7,8-TCDD.
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 concentration in
algae to concentration
dissolved in water
Estimated from Kow value using model from Del Vento and Dachs 2002.
Half-life
Kim and O'Keefe 1998 as cited in U.S. EPA 2000.
Sediment Compartment Type
Half-life
Estimation based on Adriaens and Grbic-Galic 1992,1993 and Adriaens et al. 1995 as cited in U.S. EPA 2000.
*AII values were unchanged from the Tiers 1 and 2 Screening assessments (no site-specific values used)
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Human Health Multipathway Residual Risk Assessment for the Ferroalloys Production Source Category
Table A-21. Cadmium Chemical-Specific Parameters for Plant Compartments*
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/Herbs3
Root to root soil partition - alpha of steady state
unitless
0.95
Henning et al. 2001.
Root to root soil partition - partitioning coefficient
m3[bulk root soil]/m3[root]
0.23
Nriagu 1980 (based on average value
calculated from various agricultural plant
species).
Root to root soil partition - time to reach alpha
day
28
Henning et al. 2001.
Stem Compartment Type - Grasses/Herbs3
Transpiration stream concentration factor (TSCF)
m3[soil pore water]/m3[xylem fluid]
0.45
Tsiros et al. 1999.
*AII values were unchanged from the Tiers 1 and 2 Screening assessments (no site-specific values used).
aRoots and stems are not modeled for deciduous or coniferous forest in the current version of TRIM.FaTE.
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Human Health Multipathway Residual Risk Assessment for the Ferroalloys Production Source Category
Table A-22. Mercury Chemical-Specific Parameters for Plant Compartments*
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
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/Herbs3
Alpha for root-root zone bulk soil
unitless
0.95
0.95
0.95
Selected value.
Root/root-zone-soil-water partition
coefficient
m3[bulk root soil]/
m3[root]
0
0.18a
1.2b
aGeometric mean Leonard et al. 1998, John
1972, Hogg et al. 1978
bMHg- assumed, based on Hogg et al. 1978.
t-alpha for root-root zone bulk soil
day
21
21
21
Professional judgment.
Demethylation rate
1/day
N/A
N/A
0
Professional judgment.
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Human Health Multipathway Residual Risk Assessment for the Ferroalloys Production Source Category
Table A-22. Mercury Chemical-Specific Parameters for Plant Compartments (Cont.)*
Parameter Name
Units
Value
Reference
Hg(0)
Hg(2)
MHg
Root Compartment Type - Grasses/Herbs3
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/Herbs3
Transpiration stream concentration
factor (TSCF)
m3[soil pore water]/
m3[xylem fluid]
0
0.5
0.2
Bishop et al. 1998 (Norway spruce, Scots
pine).
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.
*AII values were unchanged from the Tiers 1 and 2 Screening assessments (no site-specific values used).
aRoots and stems are not modeled for deciduous or coniferous forest in the current version of TRIM.FaTE.
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Human Health Multipathway Residual Risk Assessment for the Ferroalloys Production Source Category
Table A-23. PAH Chemical-Specific Parameters for Plant Compartments*
Parameter
Name
Units
Value
2Methyl
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
Half-life
day
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
Half-life
day
2.31
2.31
2.31
1.84
2.31
3.56
2.31
17.80
Root Compartment Type - Grasses/Herbs3
Half-life
day
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
Stem Compartment Type - Grasses/Herbs3
Half-life
day
3.50
3.50
3.50
3.50
3.50
3.50
3.50
3.50
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Human Health Multipathway Residual Risk Assessment for the Ferroalloys Production Source Category
Table A-23. PAH Chemical-Specific Parameters for Plant Compartments (Cont.)*
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 (as cited in Efroymson 1997)/
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; Acenaphthene;
Acenaphthylene; Benzo(ghi)perylene;
Fluoranthene; and Fluorene)
Root Compartment Type - Grasses/Herbs3
Half-life
day
34.60
34.60
34.60
34.60
34.60
Edwards 1988 (as cited in Efroymson 1997)/
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/Herbs3
Half-life
day
3.50
3.50
3.50
3.50
3.50
Edwards 1988 (as cited in Efroymson 1997)/
calculated from metabolic rate constant.
*AII values were unchanged from the Tiers 1 and 2 Screening assessments (no site-specific values used).
aRoots and stems are not modeled for deciduous or coniferous forest in the current version of TRIM.FaTE.
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Human Health Multipathway Residual Risk Assessment for the Ferroalloys Production Source Category
Table A-24. Dioxin Chemical-Specific Parameters for Plant Compartments*
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 U.S. EPA 2000 (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/Herbs3
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/Herbs3
Half-life
day
70
Arjmand and Sandermann 1985, as cited in Komoba, et al. 1995 (soybean
root cell culture metabolism test data for DDE).
a Roots and stems are not modeled for deciduous or coniferous forest in the current version of TRIM.FaTE.
*AII values were unchanged from the Tiers 1 and 2 Screening assessments (no site-specific values used).
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Human Health Multipathway Residual Risk Assessment for the Ferroalloys Production Source Category
Table A-25. Cadmium Chemical-Specific Parameters for Aquatic Species*
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 & 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 & Heikens 2001.
Elimination rate constant
unitless
1.68E-03
Professional judgment.
Water-column Herbivore Compartment Type
Assimilation efficiency from food
unitless
0.1
Professional judgment based on Yan and Wang
2002.
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Human Health Multipathway Residual Risk Assessment for the Ferroalloys Production Source Category
Table A-25. Cadmium Chemical-Specific Parameters for Aquatic Species (Cont.)A
Parameter Name
Units
Value
Reference
Water-column Herbivore Compartment Type
Assimilation efficiency from plants
unitless
0.1
Professional judgment based on Yan and Wang
2002.
Absorption rate constant
unitless
2.46
Calculated based on body weight from regression
in Hendriks & Heikens 2001.
Elimination rate constant
unitless
1.73E-02
Professional judgmentr.
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 & 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 & Heikens 2001.
Elimination rate constant
unitless
1.73E-02
Professional judgment.
*AII values were unchanged from the Tiers 1 and 2 Screening assessments (no site-specific values used).
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Human Health Multipathway Residual Risk Assessment for the Ferroalloys Production Source Category
Table A-26. Mercury Chemical-Specific Parameters for Aquatic Species*
Parameter Name
Units
Value
Reference
Hg(0)
Hg(2)
MHg
Zooplankton Compartment Type
AssimilationEfficiencyFromAlgae
unitless
0.2
0.015
0.5
Environment Canada 2002.
Half-life
day
1.0E+09
1.0E+09
1.0E+09
Professional judgment.
HowMuchFasterHgEliminationlsThanForMHg
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.
Benthic invertebrate-bulk sediment partition
coefficient
kg[bulk sediment]/
kg[invertebrate wet wt]
0.08243
0.0824b
5.04b
aAssumed based on Hg(2) value.
bSaouter et al. 1991.
t-alpha for equilibrium for sediment
partitioning
day
14
14
14
Saouteretal. 1991 (experiment
duration).
All Fish Compartments Types1
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. The 0.5
value was used for MHg (instead
of 0.8) to calibrate the model to
match the ratio of Hg
concentrations at different trophic
levels within the same food web
from published literature.
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Human Health Multipathway Residual Risk Assessment for the Ferroalloys Production Source Category
Table A-26. Mercury Chemical-Specific Parameters for Aquatic Species (Cont.)*
Parameter Name
Units
Value
Reference
Hg(0)
Hg(2)
MHg
All Fish Compartments Types1
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. The 0.5
value was used for MHg (instead
of 0.8) to calibrate the model to
match the ratio of Hg
concentrations at different trophic
levels within the same food web
from published literature.
*AII values were unchanged from the Tiers 1 and 2 Screening assessments (no site-specific values used).
1Benthic Omnivore, Benthic Carnivore, Water-column Herbivore, Water-column Omnivore, and Water-column Carnivore.
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Human Health Multipathway Residual Risk Assessment for the Ferroalloys Production Source Category
Table A-27. PAH Chemical-Specific Parameters for Aquatic Species*
Parameter Name
Units
Value
2Methyl
Acenaphthene
Acenaphthylene
BaA
BaP
BbF
BghiP
BkF
Zooplankton Compartment Type
Absorption rate
constant
L[water]/
kg [fish wet wt]-
day
790
42231
42302.2
42650.8
42652.8
42650.7
42655.8
42653
Assimilation
efficiency from
algae
unitless
0.5
0.5
0.5
0.46
0.25
0.25
0.25
0.25
Elimination rate
constant
1/day
169.68
148.07
123.44
2.073
1.3864
2.12
0.33
1.48
Half-life
day
0.00779
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
Vd (ratio of
concentration in
benthic
invertebrates to
concentration in
water)
mL/g
7235
7235
7235
7235
7235
7235
7235
7235
Half-life
day
0.722
0.722
0.722
1.284
16.5
17
17
17
All Fish Compartment Types3
Gamma fish
unitless
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.2
Assimilation
efficiency from
food
unitless
0.5
0.5
0.32
0.15
0.15
0.15
0.15
0.15
Half-life
day
0.2
0.2
0.2
0.408
1.925
2
2
2
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Human Health Multipathway Residual Risk Assessment for the Ferroalloys Production Source Category
Table A-27. PAH Chemical-Specific Parameters for Aquatic Species (Cont.)*
Parameter Name
Units
Value
Reference
Chr
DahA
Fluoranthene
Fluorene
IcdP
Zooplankton Compartment Type
Absorption rate
constant
L[water]/
kg [fish wet wt]-
day
42650
42655.5
142000
15000
42655.9
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
(Benzo(a)pyrene, Benzo(b)fluoranthene,
Benzo(g,h,i)perylene, Benzo(k)fluoranthene,
Dibenz(a,h)anthracene, and lndeno(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
0.000248
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).
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Human Health Multipathway Residual Risk Assessment for the Ferroalloys Production Source Category
Table A-27. PAH Chemical-Specific Parameters for Aquatic Species (Cont.)*
Parameter Name
Units
Value
Reference
Chr
DahA
Fluoranthene
Fluorene
IcdP
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.
All Fish Compartment Types3
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
Lemairetal. 1992. Exceptions include Barber
2008 & Wang and Wang 2006 (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.
*AII values were unchanged from the Tiers 1 and 2 Screening assessments (no site-specific values used).
aBenthic Omnivore, Benthic Carnivore, Water-column Herbivore, Water-column Omnivore, and Water-column Carnivore.
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Human Health Multipathway Residual Risk Assessment for the Ferroalloys Production Source Category
Table A-28. Dioxin Chemical-Specific Parameters for Aquatic Species*
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
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
Zooplankton Compartment Type
Assimilation efficiency from food
unitless
0.41
0.41
0.41
0.41
0.41
0.41
0.41
0.41
0.41
Absorption rate constant
L/kg(ww)/day
380.0
380.0
380.0
380.0
380.0
380.0
380.0
380.0
380.0
Elimination rate constant
/day
5.5E-4
5.5E-4
5.5E-4
5.5E-4
5.5E-4
5.5E-4
5.5E-4
5.5E-4
5.5E-4
Half life
day
1E09
1E09
1E09
1E09
1E09
1E09
1E09
1E09
1E09
All Fish Compartments3
Assimilation efficiency from food
unitless
0.03
0.03
0.5
0.5
0.5
0.5
0.5
0.5
0.5
Chemical uptake rate via gill
L[water]/kg[fish wet
wt]/day
142
N/Ab
14
N/Ab
N/Ab
127
N/Ab
127
N/Ab
Gamma_fish
unitless
N/Ab
0.2
N/Ab
0.2
0.2
N/Ab
0.2
N/Ab
0.2
Half-life
day
693.15
346.57
346.57
346.57
346.57
495.11
495.11
495.11
495.11
Water Column Herbivore Compartment
Assimilation efficiency from plants
unitless
0.03
0.03
0.5
0.5
0.5
0.5
0.5
0.5
0.5
Water Column Omnivore Compartment
Assimilation efficiency from plants unitless
0.03
0.03
0.5
0.5
0.5
0.5
0.5
0.5
0.5
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Human Health Multipathway Residual Risk Assessment for the Ferroalloys Production Source Category
Table A-28. Dioxin Chemical-Specific Parameters for Aquatic Species (Cont.)*
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
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 Compartments3
Assimilation efficiency from food
unitless
0.5
0.5
0.5
0.5
0.5
0.5
0.5
0.5
Chemical uptake rate via gill
L[water]/kg[fish wet
wt]/day
127
N/Ab
700
N/Ab
N/Ab
N/Ab
380
N/Ab
Gamma_fish
unitless
N/Ab
0.2
N/Ab
0.2
0.2
0.2
N/Ab
0.2
Half-life
day
495.11
495.11
420.09
420.09
495.11
420.09
5251.1
5251.1
Water Column Herbivore Compartment
Assimilation efficiency from plants
unitless
0.5
0.5
0.5
0.5
0.5
0.5
0.5
0.5
Water Column Omnivore Compartment
Assimilation efficiency from plants
unitless
0.5
0.5
0.5
0.5
0.5
0.5
0.5
0.5
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Table A-28. Dioxin Chemical-Specific Parameters for Aquatic Species (Cont.)*
Parameter Name
Units
Reference
Benthic Invertebrate Compartment
Clearance constant
unitless
Professional judgment.
Sediment partitioning partition
coefficient
TCDD data for sandworm in Rubenstein et al. 1990; dry weight sediment. PeCDF,
kg/kg
multiplied TCDD partition coefficient for sandworm by congener-specific
bioaccumulation equivalency factor in GLWQI from U.S. EPA 1999.
Sediment partitioning alpha of
equilibrium
unitless
Professional judgment.
Sediment partitioning time to reach
alpha of equilibrium
days
TCDD: professional judgment; PeCDF: Rubinstein et al. 1990; data forTCDF in
sandworm.
Vd (ratio of concentration in benthic
invertebrates to concentration in
mL/g
Professional judgment.
water)
Half-life
day
Change source to f-pass
Zooplankton Compartment Type
Assimilation efficiency from food
unitless
Morrison et al. 1999.
Absorption rate constant
L/kg(ww)-day
Based on fish value in Muir et al. 1986
Elimination rate constant
/day
Professional judgment based on water column herbivore value.
Half Life
day
Professional judgment.
All Fish Compartments3
Assimilation efficiency from food
unitless
TCDD: calculated from data in Kleeman et al. 1986b trout data as cited in U.S.
EPA 1993; PeCDF: used assimilation efficiency for TCDD in trout.
Chemical uptake rate via gill
L[water]/kg[fish wet
wt]-day
Muir et al. 1986.
Gamma_fish
unitless
Thomann 1989
Half-life
day
Change source to f-pass
Water Column Herbivore Compartment
Assimilation efficiency from plants
unitless
TCDD: calculated from data in Kleeman et al. 1986b trout data as cited in U.S.
EPA 1993; PeCDF: used assimilation efficiency for TCDD in trout.
Water Column Omnivore Compartment
Assimilation efficiency from plants
unitless
TCDD: calculated from data in Kleeman et al. 1986b trout data as cited in U.S.
EPA 1993; PeCDF: used assimilation efficiency for TCDD in trout.
*AII values were unchanged from the Tiers 1 and 2 screening assessments (no site-specific values used).
aFerroalloys scenario includes: Benthic Omnivore, Benthic Carnivore, Water-column Herbivore, Water-column Omnivore, and Water-column Carnivore.
b N/A = not applicable. This parameter is used in calculating the uptake when measured data are unavailable.
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Human Health Multipathway Residual Risk Assessment for the Ferroalloys Production Source Category
References
Citation
Bibliography
Adriaens and
Grbic-Galic 1992
Adriaens, P.; Grbic-Galic, D. 1992. Effect of cocontaminants and concentration on the anaerobic biotransformation of
PCDD/F in methanogenic river sediments. Organohalogen Compounds 8:209-212.
Adriaens and
Grbic-Galic 1993
Adriaens, P.; Grbic-Galic, D. 1993. Reductive dechlorination of PCDD/F by anaerobic cultures and sediments.
Organohalogen Compounds 12:107-110.
Adriaens et al.
1995
Adriaens, P.; Fu, Q.; Grbic-Galic, D. 1995. Bioavailability and transformation of highly chlorinated dibenzo-p-dioxins and
dibenzofurans in anaerobic soils and sediments. Environ. Sci. Technol. 29(9):2252 2260.
Ambrose et al.
1995
Ambrose, R.A., Jr., T.A. Wool, and J.L. Martin. 1995. The water quality analysis simulation program, WASP5, Part A:
Model documentation. Athens, GA: U.S. EPA National Exposure Research Laboratory, Ecosystems Division.
Amyot et al. 1997
Amyot, M., D. Lean, and G. Mierle. 1997. Photochemical formation of volatile mercury in high arctic lakes. Environmental
Toxicology and Chemistry. 16(10):2054-2063.
APHA 1995
American Public Health Association (APHA). 1995. Standard methods for the examination of water and waste water.
Washington, DC.
Arjmand and
Sandermann 1985
Arjmand, M., and H. Sandermann. 1985. Metabolism of DDT and related compounds in cell suspension cultures of
soybean (Glycine max L.) and wheat (Tritucum aestivum L.) Pestic. Biochem. Physiol. 23: 389.
Arnot et al. 2004
Arnot J., and Gobas, F. 2004. A Food Web Bioaccumulation Model For Organic Chemicals In Aquatic Ecosystems.
Environmental Toxicology and Chemistry, Vol. 23, No. 10, pp. 2343-2355, 2004.
Atkinson 1996
Atkinson, R. 1996. Atmospheric chemistry of PCBs, PCDDs and PCDFs. Issues in Environmental Science and
Technology. 6: 53-72.
ATSDR 1998
Agency for Toxic Substances and Disease Registry (ATSDR). 1998. Toxicological profile for chlorinated dibenzo-p-
dioxins. Atlanta, GA: U.S. Department of Health and Human Services, Public Health Service.
ATSDR 1999
Agency for Toxic Substances and Disease Registry (ATSDR). 1999. Toxicological profile for Mercury. Atlanta, GA: U.S.
Department of Health and Human Services, Public Health Service.
ATSDR 2005
Agency for Toxic Substances and Disease Registry (ATSDR). 2005. Toxicological profile for naphthalene, 1-
methylnaphthalene, and 2-methylnaphthalene. Atlanta, GA: U.S. Department of Health and Human Services, Public
Health Service.
Baes et al. 1984
Baes, C.F., III, R.D. Sharp, A.L. Sjoreen, and R.W. Shor. 1984. A review and analysis of parameters for assessing
transport of environmentally released radionuclides through agriculture. ORNL-5786. Oak Ridge National Laboratory, Oak
Ridge, TN.
Bache et al. 1973
Bache, C.A., W.J. Gutenmann, L.E. St. John, Jr., R.D. Sweet, H.H. Hatfield, and D.J. Lisk. 1973. Mercury and
methylmercury content of agricultural crops grown on soils treated with various mercury compounds. J. Agr. Food Chem.
21:607-613.
Barber 2008
Barber, M.C. 2008. Dietary uptake models used for modeling the bioaccumulation of organic contaminants in fish. Environ
Toxicol & Chem 27(4): 755-777.
A-63
-------
Human Health Multipathway Residual Risk Assessment for the Ferroalloys Production Source Category
Citation
Bibliography
Berrojalbiz et al.
2009
Berrojalbiz, N., Lacorte, S., Calbet, A., Saiz, E., Barata, C., Dachs, Jordi, 2009. Accumulation and cycling of polycyclic
aromatic hydrocarbons in zooplankton. Environ. Sci. Technol. 47(7), 2295-2301.
Bidleman 1988
Bidleman, T.F. 1988. "Atmospheric Processes." Environmental Science and Technology. Volume 22. Number 4. Pages
361-367.
Bishop et al. 1998
Bishop, K.H., Y.H. Lee, J. Munthe, and E. Dambrine. 1998. Xylem sap as a pathway for total mercury and methylmercury
transport from soils to tree canopy in the boreal forest. Biogeochemistry. 40:101-113.
Boyle 1985
Boyle, T. 1985. Validation and predictability of laboratory methods for assessing the fate and effects of contaminants in
aquatic ecosystems. Baltimore, MD: ASTM International.
Budavari 1996
Budavari, S. [ed.]. 1996. The Merck Index - An Encyclopedia of Chemicals, Drugs, and Biologicals. Whitehouse Station,
NJ: Merck and Co., Inc., p. 178.
Cal EPA 1993
California Environmental Protection Agency (Cal EPA). 1993. CalTOX, A Multimedia Total-Exposure Model for
Hazardous-Waste Sites, Part II: The Dynamic Multimedia Transport and Transformation. Model Prepared for: The Office
of Scientific Affairs. Department of Toxic Substances Control. Sacramento, California. December. Draft Final.
CARB 1994
California Air Resources Board (CARB). 1994. Development of intermedia transfer factors for toxic air pollutants. Volume
II: Metals and non-volatile organic compounds. PB95-260691. March.
CDIAC 2010
CDIAC (2010). Leaf Area Index. Carbon Dioxide Information Analysis Center. Available at: http://cdiac.ornl.gov/
Coe and Lindberg
1987
Coe, J.M., and S.E. Lindberg. 1987. The morphology and size distribution of atmospheric particles deposited on foliage
and inert surfaces. JAPCA. 37:237-243.
Crank et al. 1981
Crank, J., N.R. McFarlane, J.C. Newby, G.D. Paterson, and J.B. Pedley. 1981. Diffusion processes in environmental
systems. In: Paterson et al. 1991. London: Macmillan Press, Ltd.
Del Vento and
Dachs 2002
Del Vento, S., Dachs, J., 2002. Prediction of uptake dynamics of persistent organic pollutants by bacteria and
phytoplankton. Environmental Toxicology and Chemistry 21, 2099-2107.
Edwards 1988
Edwards, N.T. 1988. Assimilation and metabolism of polycyclic aromatic hydrocarbons by vegetation - an approach to
this controversial issue and suggestions for future research, pp. 211- 229. In M. Cooke and A.J. Dennis,eds. Polynuclear
Aromatic Hydrocarbons: A Decade of Progress. Tenth International Symposium. Batelle Press, Columbus, OH.
Efroymson et al.
1997
Efroymson, R.A., M.E. Will, and G.W. Suter II. 1997. Toxicological benchmarks for contaminants of potential concern for
effects on soil and litter invertebrates and heterotrophic process: 1997 revision. Prepared for the U.S. Department of
Energy. ES/ER/TM-126/R2.
Environment
Canada 2002
Environment Canada. 2002. Ecosystem Health Science-Based Solutions: Canadian Tissue Residue Guidelines for the
Protection of Wildlife Consumers of Aquatic Biota: Methylmercury. Report No. 1-4.
A-64
-------
Human Health Multipathway Residual Risk Assessment for the Ferroalloys Production Source Category
Citation
Bibliography
Fulmer 1990.
Fulmer, DG; Cooke, GD. 1990. Evaluating the restoration potential of 19 Ohio reservoirs. Lake and Reservoir
Management. 6(2):197-206.
Gay 1975
Gay, D.D. 1975. Biotransformation and chemical form of mercury in plants. International Conference on Heavy Metals in
the Environment, pp. 87-95. Vol. II, Part 1. October.
Gilmour and Henry
1991
Gilmour, C.C. and E.A. Henry. 1991. Mercury methylation in aquatic systems affected by acid deposition. Environmental
Pollution. 71:131-169.
Goulet 2007
Goulet R., Krack S., Doyle, P., et al. 2007. Dynamic multipathway modeling of Cd bioaccumulation in Daphnia magna
using waterborne and dietborne exposures. Aquat Toxicol. 2007 Feb 28;81(2):117-25.
Hansch et al. 1995
Hansch, C., A. Leo, and D. Hoekman. 1995. Exploring QSAR - Hydrophobic, Electronic, and Steric Constants.
Washington, DC: American Chemical Society, 154.
Hare et al. 2001
Hare, L., Tessier, A., Warren, L., 2001. Cadmium accumulation by invertebrates living at the sediment-water interface.
Environ. Toxicol. Chem. 20, 880-889
Harner and
Bidleman 1998
Harner, T., and T. F. Bidleman, 1998: Octanol-air partition coefficient for describing particle/gas partitioning of aromatic
compounds in urban air. Environ. Sci. Technol., 32, 1494-1502.
Hendriks &
Heikens 2001
Hendriks, A.J., Heikens, A., 2001. The power of size: II. Rate constants and equilibrium ratios for accumulation of
inorganic substances. Environ. Toxicol. Chem. 20, 1421-1437.
Henning et al. 2001
Henning, B.J., Snyman, H.G., Aveling, T.A.S., 2001. Plant-soil interactions of sludge-borne heavy metals and the effect
on maize (Zea Mays L.) seedling growth. Water SA 27 (1), 71-78.
Hogg et al. 1978
Hogg, T.J., J.R. Bettany, and J.W.B. Stewart. 1978. The uptake of 203Hg-labeled mercury compounds by bromegrass
from irrigated undisturbed soil columns. J. Environ. Qual. 7:445- 450.
Howard et al. 1991
Howard, P.H., R.S. Boethling, W.F. Jarvis, W.M. Meylan, and E.M. Michalenko. 1991. Handbook of Environmental
Degredation Rates. Chelsea, Michigan: Lewis Publishers.
HSDB 2001a
Hazardous Substances Data Bank (HSDB). 2001b. Bethesda, MD: National Library of Medicine, U.S. [Last Revision Date
08/09/2001], Acenaphthylene; Hazardous Substances Databank Number: 2661. Available at:
http://toxnet.nlm.nih.gov/cgi-bin/sis/htmlgen7HSDB
HSDB 2001b
Hazardous Substances Data Bank (HSDB). 2001c. Bethesda, MD: National Library of Medicine, U.S. [Last Revision Date
08/09/2001], Benzo(ghi)perylene; Hazardous Substances Databank Number: 6177. Available at:
http://toxnet.nlm.nih.gov/cgi-bin/sis/htmlgen7HSDB
HSDB 2001c
Hazardous Substances Data Bank (HSDB). 2001 d. Bethesda, MD: National Library of Medicine, U.S. [Last Revision Date
08/09/2001], Acenaphthene; Hazardous Substances Databank Number: 2659. Available at: http://toxnet.nlm.nih.gov/cgi-
bin/sis/htmlgen?HSDB
HSDB 2001d
Hazardous Substances Data Bank (HSDB). 2001 e. Bethesda, MD: National Library of Medicine, U.S. [Last Revision Date
08/09/2001], Fluorene; Hazardous Substances Databank Number: 2165. Available at: http://toxnet.nlm.nih.gov/cgi-
bin/sis/htmlgen?HSDB
A-65
-------
Human Health Multipathway Residual Risk Assessment for the Ferroalloys Production Source Category
Citation
Bibliography
HSDB 2005
Hazardous Substances Data Bank (HSDB). 2005. Bethesda, MD: National Library of Medicine, U.S. [Last Revision Date
10/27/2005], Acenaphthylene; Hazardous Substances Databank Number: 5274. Available at:
http://toxnet.nlm.nih.gov/cgi-bin/sis/htmlgen7HSDB
Hudson et al. 1994
Hudson, R., S.A. Gherini, C.J. Watras, and D. Porcella. 1994. Modeling the biogeochemical cycle of mercury in lakes:
The Mercury Cycling Model (MCM) and its application to the MTL Study Lakes. In: C.J. Watras and J.W. Huckabee, eds.
Mercury pollution integration and synthesis. Lewis Publishers, pp. 473-523.
ICF 2005
ICF International (ICF). 2005. Memorandum: TRIM.FaTE screening scenario: Aquatic food web analysis; submitted to
Deirdre Murphy and Terri Hollingsworth, U.S. EPA, from Margaret McVey and Rebecca Kauffman, ICF Consulting.
October 18.
Jackson et al. 1996
Jackson, R.B., J. Canadell, J.R. Ehleringer, H.A. Mooney, O.E. Sala and E.D. Schulze. 1996. A global analysis of root
distributions for terrestrial biomes. Oecologia. 108:389-411.
John 1972
John, M.K. 1972. Mercury uptake from soil by various plant species. Bull. Environ. Contam. Toxicol. 8:77-80.
Kim and O'Keefe.
1998
Kim, M., and P. O'Keefe. 1998. The role of natural organic compounds in photosensitized degradation of polychlorinated
dibenzo-p-dioxins and dibenzofurans. Organohalogen Compounds 36: 377-380.
Kleeman et al.
1986
Kleeman, J.M., J.R. Olson, S.M. Chen, et al. 1986. 2,3,7,8-Tetrachlorodibenzo-p-dioxin metabolism and disposition in
yellow perch. Toxicol Appl Pharmacol. 83: 402-411.
Komoba et al. 1995
Komoba, D., C. Langebartels, and H. Sandermann. 1995. Metabolic processes for organic chemicals in plants. In: Plant
contamination modeling and simulation of organic chemical processes. Trapp, S., and Mc Farlane, J.C., eds., CRC Press,
Boca Raton, FL. Pages 69-103.
Leith 1975
Leith, H. 1975. Primary productivity in the biosphere. In: H. Leith and R.W. Whitaker. Ecological studies, volume 14.
Springer-Verlag.
Lemair et al. 1992
Lemaire, P., Berhaut, J., Lemaire, G. S. and Lefaurie, M. (1992). Ultrastructure changes induced by benzo(a)pyrene in
sea bass (Dicentrarchus labrax) liver and intestine: importance of the intoxication route. Env. Res. 57, 59-72.
Leonard et al. 1998
Leonard, T.L., M.S. Gustin, G.C.J. Fernandez, and G.E. Taylor, Jr. 1998. Mercury and plants in contaminated soil. 1:
Uptake, partitioning, and emission to the atmosphere. Env. Tox. & Chem. 17(10): 2063-2071.
Lindberg et al.
1992
Lindberg, S. E., T. P. Meyers, G. E. Taylor, R. R. Turner, and W. H. Schroeder. 1992. Atmosphere-Surface Exchange of
Mercury to a Forest: Results of Modeling and Gradient Approaches. J. of Geophy. Res. 97(D2):2519-2528.
MacKay et al. 1986
Mackay, D., Paterson, S., Schroeder, W.H. 1986. Model describing the rates of transfer processes of organic chemicals
between atmosphere and water. Environmental Science and Technology 20(8):810-816.
Mackay et al. 1992
MacKay, D., W.Y. Shiu, K.C. Ma. 1992. Illustrated Handbook of Physical-Chemical Properties and Environmental Fate for
Organic Chemicals, Volume II, Polynuclear Aromatic Hydrocarbons, Polychlorinated Dioxins, and Dibenzofurans.
Chelsea, Ml: Lewis Publishers.
Mackay et al. 2000
Mackay, D., W.Y. Shiu, and K.C. Ma. 2000. Physical-chemical properties and environmental fate handbook. Boca Raton,
FL: CRC Press LLC.
A-66
-------
Human Health Multipathway Residual Risk Assessment for the Ferroalloys Production Source Category
Citation
Bibliography
Mason et al. 1995a
Mason, R.P., J.R. Reinfelder, and F.M.M. Morel. 1995. Bioaccumulation of mercury and methylmercury. Water Air and
Soil Pollution. 80(1-4):915-921.
Mason et al. 1995b
Mason, R.P., K.R. Rolfhus, and W.F. Fitzgerald. 1995. Methylated and elemental mercury cycling in surface and deep
ocean waters of the North Atlantic. Wat Air Soil Pollut 80:665-677.
Mason et al. 1996
Mason RP, Reinfelder JR, Morel FMM. 1996. Uptake, toxicity, and trophic transfer of mercury in a coastal diatom.
Environ. Sci. Technol. 30:1835-45
McCrady and
Maggard 1993
McCrady, J.K., and S.P. Maggard. 1993. Update and photodegradation of 2,3,7,8-tetrachloro-p-dioxin sorbed to grass
foliage. Environ. Sci. Tech. 27: 343-350.
McElroy 1990
McElroy, A.E. 1990. Polycyclic aromatic hydrocarbon metabolism in the polychaete Nereis virens. Aquat Toxicol 18: 35-
50.
McGeer et al. 2003
McGeer J., Brix K.V., Skeaff J.M., De Forest D.K., Bingham S.I., Adams W.J. and Green A. (2003). Inverse relationship
between bioconcentration factors and exposure concentration of metals: implications for hazard assessment of metals in
the aquatic environment. Env. Tox. & Chem. 22, nr 5, 1017-1037.
McKone et al. 2001
McKone, T., Bodnar, A.B., and Hertwich, E.G. 2001. Development and Evaluation of State-Specific Landscape Data Sets
for Multimedia Source-to-Dose Models. University of California, Berkeley School of Public Health. Available online:
http://repositories.cdlib.org/lbnl/LBNL-43722/
Millard et al. 1996
Millard, E.S., Myles, D.D., Johannsson, O.E., and Ralph, K.M. 1996. Phytoplankton photosynthesis at two index stations
in Lake Ontario 1987-199s: assessment of the long-term response to phosphorus control. Canadian Journal of Fisheries
and Aquatic Sciences 53:1092-1111.
Moermond et al.
2007
Moermond C., Traas, T., Roessink, I. 2007. Modeling Decreased Food Chain Accumulation of PAHs Due to Strong
Sorption to Carbonaceous Materials and Metabolic Transformation. Environ. Sci. Technol. 2007, 41, 6185-6191.
Montgomery 2000
Montgomery, J. 2000. Groundwater chemicals desk reference. Boca Raton, FL: CRC Press LLC, p. 1701. Morrison, H.A.,
D.M. Whittle, C.D. Metcalfe, and A.J. Niimi. 1999. Application of a food web bioaccumulation model for the prediction of
polychlorinated biphenyl, dioxin, and furan congener concentrations in Lake Ontario aquatic biota. Canadian Journal of
Fisheries and Aquatic Sciences 56(8):1389-1400.
Morrison et al.
1999
Morrison, H., Whittle, D., et al. 1999. Application of a food web bioaccumulation model for the prediction of
polychlorinated biphenyl, dioxin, and furan congener concentrations in Lake Ontario aquatic biota. Can. J. Fish. Aquat.
Sci. 56: 1389-1400 (1999).
Muir et al. 1986
Muir, D.C.G., Yarechewski, A.L., Knoll, A, and Webster, G.R.B. 1986. Bioconcentration and disposition of 1,3,6,8-
tetrachlorodibenzo-p-dioxin and octachlorodibenzo-p-dioxin by rainbow trout and fathead minnows. Environ. Toxicol.
Chem. 5: 261-272.
Muhlbaier and
Tisue 1981
Muhlbaier, J., and G.T. Tissue. 1980. Cadmium ion the southern basin of Lake Michigan. Water, Air and Soil Pollution
(15):45-49.
Muller and Prohl
1993
Muller, H. and G. Prohl. 1993. Ecosys-87: A dynamic model for assessing radiological consequences of nuclear
accidents. Health Phys. 64:232-252.
A-67
-------
Human Health Multipathway Residual Risk Assessment for the Ferroalloys Production Source Category
Citation
Bibliography
Niimi and Palazzo
1986
Niimi, A.J., Palazzo, V., 1986. Biological half-lives of eight polycyclic aromatic hydrocarbons (PAHs) in rainbow trout
(Salmo Gairdneri). Wat. Res. 20, 503-507.
Nriagu 1980
Nriagu, J.O. 1980. Cadmium in the Environment, Wiley, New York, Vol. 1
Ohio EPA 2009
Ohio EPA. 2009. Appendix C: Nutrient Model Development for Upper Salt Creek and Beech Fork. Available online at:
http://www.epa.state.oh.us/portals/35/tmdl/SaltSciotoTMDL_finalJun09_appC.pdf
Passivirta et al.
1999
Passivirta, J., Sinkkonen, S., Mikkelson, P., Rantio, T., Wania, F. (1999) Estimation of vapor pressures, solubilities and
Henry's law constants of selected persistent organic pollutants as functions of temperatures. Chemosphere 39, 811-832.
Paterson et al.
1991
Paterson, S., Mackay, D., and Gladman, A. 1991. A fugacity model of chemical uptake by plants from soil and air.
Chemosphere. 23:539-565.
Petersen et al.
1995.
Petersen, G., A. Iverfeldt, and J. Munthe. 1995. Atmospheric mercury species over Central and Northern Europe. Model
calculations and comparison with observations from the Nordic Air and Precipitation Network for 1987 and 1988.
Atmospheric Environment 29:47-68.
Porvari and Verta
1995
Porvari, P. and M. Verta. 1995. Methylmercury production in flooded soils: A laboratory study. Water, Air, and Soil
Pollution. 80:765-773.
Riederer 1995
Riederer, M. 1995. Partitioning and transport of organic chemicals between the atmospheric environment and leaves. In:
Trapp, S. and J. C. McFarlane, eds. Plant contamination: Modeling and simulation of organic chemical processes. Boca
Raton, FL: Lewis Publishers, pp. 153-190.
Rubinstein et al.
1990
Rubinstein, N.I., Pruell, R.J., Taplin, B.K., LiVoIsi, J.A., and Norwood, C.B. 1990. Bioavailability of 2,3,7,8-TCDD, 2,3,7,8-
TCDF and PCBsto marine benthos from Passaic river sediments. Chemosphere. 20: 1097-1102.
Sangster 1993
Sangster, J. 1993. LOGKOW - A databank of evaluated octanol-water partition coefficients. Sangster Research
Laboratories, Montreal, 1993.
Saouter et al. 1991
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. Environ. Pollut. 69:51-67.
Scurlock et al.
2001
Scurlock, J. M. O., G. P. Asner, and S. T. Gower. 2001. Global Leaf Area Index from Field Measurements, 1932-2000.
Data set. Available on-line [http://www.daac.ornl.gov] from Oak Ridge National Laboratory Distributed Active Archive
Center, Oak Ridge, Tennessee, U.S.A. doi:10.3334/ORNLDAAC/584
Siemens 2007
Siemens Water Technologies Corp. 2007. Draft Risk Assessment for the Siemens Water Technologies Corp. Carbon
Reactivation Facility, Parker, Arizona. Prepared by CPF Associates. July 2007.
Simonich and Hites
1994
Simonich, S.L., and R.A. Hites. 1994. Importance of vegetation in removing polycyclic aromatic hydrocarbons from the
atmosphere. Nature 370:49-51.
Spero et al. 2000
Spero, J., Devito, B., and Theodore, L. 2000. Regulatory chemicals handbook. Dekker, Inc, New York, NY.
Stehly et al. 1990
Stehly, G.R., P.F. Landrum, M.G. Henry, C. Klemm. 1990. Toxicolkinetics of PAHs in Hexagenia Environmental
Toxicology and Chemistry 9: 167-174.
A-68
-------
Human Health Multipathway Residual Risk Assessment for the Ferroalloys Production Source Category
Citation
Bibliography
Thibodeaux 1996
Thibodeaux, L.J. 1996. Environmental chemodynamics: Movement of chemicals in air, water, and soil. New York, NY:
John Wiley and Sons, Inc.
Thomann 1989
Thomann, R.V. 1989. Bioaccumulation model of organic-chemical distribution in aquatic food chains. Environmental
Science & Technology. 23(6):699-707.
Trapp 1995
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 and
Rasmussen 1997
Trudel, M. and J.B. Rasmussen. 1997. Modeling the elimination of mercury by fish. Environmental Science and
Technology. 31:1716-1722.
Tsiros et al. 1999
Tsiros, I. X., Ambrose, R.B., Chronopoulou-Sereli, A. 1999. Air-vegetation-soil partitioning Of toxic chemicals in
environmental Simulation modeling Global nest: he Int. J. Vol 1, no 3, pp 177-184.
USDA Web Soil
Survey
U.S. Department of Agriculture (USDA), Natural Resources Conservation Service. Web Soil Survey Version 2.3. Available
online at: http://websoilsurvey.nrcs.usda.gov/.
U.S. EPA 1997
U.S. Environmental Protection Agency (EPA). 1997. Mercury Study Report to Congress. Volume III: Fate and Transport
of Mercury in the Environment. Office of Air Quality and Planning and Standards and Office of Research and
Development. EPA 452/R-97-005. December.
U.S. EPA 1999
U.S. Environmental Protection Agency (EPA). 1999. Screening level ecological risk assessment protocol for hazardous
waste combustion facilities. Office of Solid Waste and Emergency Response. EPA530-D-99-001A.
U.S. EPA 1998a
U.S. EPA. 1998a. Methodology for assessing health risks associated with multiple pathways of exposure to combustor
emissions. Office of Research and Development, NCEA. EPA-600-R- 98-137. (Update to EPA600-6-90-003)
U.S. EPA 1998b
U.S. EPA 1998. TMDL Development for Mountwood Park Lake, Wood County, West Virginia. Available online at:
httD://www.deD.wv.aov/WWE/watershed/TMDL/arDd/Documents/Little%20Kanawha/7457 Mountwood Park TMDL.Ddf.
U.S. EPA 2000
U.S. Environmental Protection Agency (EPA). 2000. Draft exposure and human health reassessment of 2,3,7,8-
tetrachlorodibenzo-p-dioxin (TCDD) and related compounds, Part II: Health assessment for 2,3,7,8-tetrachlorodibenzo-p-
dioxin (TCDD) and related compounds. Chapters 1 through 7. EPA/600/P-00/001Be.
http://www.epa.gov/ncea/pdfs/dioxin/part2/dritoc.pdf.
U.S. EPA 2003
U.S. Environmental Protection Agency (EPA). 2003. Toxicological Review of 2-Methylnaphthalene. December, 2003. EPA
635/R-03/010.
U.S. EPA 2005a
U.S. Environmental Protection Agency (EPA). 2005a. Evaluation of TRIM.FaTE, Volume II: Model Comparison Focusing
on Mercury Test Case. EPA-453/R-05-002. Office of Air Quality and Planning Standards: Research Triangle Park, NC.
July.
U.S. EPA 2005b
U.S. Environmental Protection Agency (EPA). 2005b. Human Health Risk Assessment Protocol for Hazardous Waste
Combustion Facilities. Office of Solid Waste and Emergency Response, Washington, DC. EPA-530-R-05-006.
September. Available at: http://www.epa.gov/osw/hazard/tsd/td/combust/risk.htm.
A-69
-------
Human Health Multipathway Residual Risk Assessment for the Ferroalloys Production Source Category
Citation
Bibliography
USGS 1984
USGS 1984. Chemical and Biological Quality of Selected Lakes in Ohio, 1978 and 1979. USGS Open-file Report 84-249.
Available online at: httD://Dubs.usas.aov/of/1984/0249/reDort.Ddf.
USGS Water Alert
Data
USGS. 2013. Water Alert Data. Available online at: httD://maDs.waterdata.usas.aov/maDDer/wateralert/. Accessed Julv
19, 2013. Database last updated October 1, 2013.
Vandal et al. 1995
Vandal, G.M., W.F. Fitzgerald, K.R. Rolfhus, and C.H.Lamborg. 1995. Modeling the elemental mercury cycle in Pallette
Lake, Wisconsin, USA. Water, Air, and Soil Pollution. 80:789-798.
Vulykh et al. 2001
Vulykh, N. and V. Shatalov. 2001. Investigation of Dioxin/Furan Composition in Emissions and in Environmental Media.
Selection of Congeners for Modeling. Meteorological Synthesizing Centre - E. MSC-E Technical Note 6/2001. Accessed
at http://www.msceast.org/reps/TN6-2001 .pdf.
Wang and Wang
2006
Wang, X., Wang, W-X. 2006. Bioaccumulation and transfer of benzo(a)pyrene in a simplified marine food chain. Mar Ecol
Prog Ser 312: 101-111.
West Virginia DNR
West Virginia Department of Natural Resources (WV DNR). 2003. Lakes: Wood County: Mountwood Lake. Available
online at: httD://www.wvdnr.aov/fishina/Dublic access.asD?countv=Wood&tvDe=Lakes&Doint=l71.
Williams et al. 2010
Williams, J.J., Dutton, J., Chen, C.Y., Fisher, N.S., 2010. Metal (As, Cd, Hg, and CH(3)Hg) bioaccumulation from water
and food by the benthic amphipod Leptocheirus plumulosus. Environ. Toxicol. Chem. 29, 1755-1761.
Wilmer and Fricker
1996
Wilmer, C. and M. Fricker. 1996. Stomata. Second ed. New York, NY: Chapman and Hall. p. 121.
Xiao et al. 1995
Xiao, Z., D. Stromberg, and O. Lindqvist 1995. Influence of Humic Substances on photolysis of divalent mercury in
aqueous solution. WASP 80:789-798.
Yan and Wang
2002
Yan Q-L, Wang W-X. 2002. Metal exposure and bioavailability to a marine deposit-feeding sipuncula Sipunculus nudus.
Environmental Science and Technology 36: 40-47.
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Appendix B
Modeled Media Concentrations
This appendix provides the concentrations of PB-HAPs estimated in TRIM.FaTE compartments.
The values are the annual average for the 50th year of the simulation; these outputs were used
in the risk characterization estimates.
B-1
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Human Health Multipathway Residual Risk Assessment for the Ferroalloys Production Source Category
Table B-1. Concentration Estimates and Speciations for Mercury
Total Hg
Divalent Hg
Elemental Hg
Methyl Hga
Location and Medium
Units
Value
Value
% Total Hg
Value
% Total Hg
Value
% Total
Hg
Tilled Surface Soil
M9 g" [dry
weight]
9.87E-03
9.62E-03
97.49%
8.75E-05
0.89%
1.60E-04
1.62%
Farm_NNW
Unfilled Surface
Soil
M9 9"' [dry
weight]
1.96E-02
1.93E-02
98.36%
3.67E-06
0.02%
3.18E-04
1.62%
Tilled Surface Soil
Mg g" [dry
weight]
2.01 E-02
1.96E-02
97.58%
1.60E-04
0.80%
3.27E-04
1.63%
Farm_SE
Unfilled Surface
Soil
Mg g" [dry
weight]
3.88E-02
3.81 E-02
98.35%
4.68E-06
0.01%
6.34E-04
1.64%
Tilled Surface Soil
Mg g" [dry
weight]
2.62E-03
2.55E-03
97.41%
2.54E-05
0.97%
4.26E-05
1.62%
Farm_WSW
Unfilled Surface
Soil
Mg g" [dry
weight]
7.06E-03
6.94E-03
98.35%
1.74E-06
0.02%
1.15E-04
1.63%
Surface water
mg L"1
2.97E-08
2.45E-08
82.54%
4.57E-09
15.40%
6.12E-10
2.06%
Wolf Run
Macrophyte
mg kg"1 [wet
weight]
4.30E-08
4.09E-08
95.17%
4.30E-17
0.00%
2.08E-09
4.83%
Lake
Zooplankton
mg kg"1 [wet
weight]
3.17E-04
1.48E-04
46.63%
0.00E+00
0.00%
1.69E-04
53.37%
Water Column
Herbivore
mg kg"1 [wet
weight]
6.84E-04
5.99E-05
8.76%
0.00E+00
0.00%
6.24E-04
91.24%
B-3
-------
Human Health Multipathway Residual Risk Assessment for the Ferroalloys Production Source Category
Table B-1. Concentration Estimates and Speciations for Mercury (Cont.)
Location and Medium
Units
Total Hg
Divalent Hg
Elemental Hg
Methyl Hga
Value
Value
% Total Hg
Value
% Total Hg
Value
% Total
Hg
Wolf Run
Lake
Water Column
Omnivore
mg kg"1 [wet
weight]
3.11E-03
2.57E-05
0.82%
0.00E+00
0.00%
3.09E-03
99.18%
Water Column
Carnivore
mg kg"1 [wet
weight]
1.73E-02
1.41E-05
0.08%
0.00E+00
0.00%
1.73E-02
99.92%
Sediment
M9 a"1 [dry
weight]
1.01 E-03
1.01 E-03
99.19%
6.13E-06
0.60%
2.07E-06
0.20%
Benthic
Invertebrate
mg kg"1 [wet
weight]
5.92E-05
5.24E-05
88.51%
3.19E-07
0.54%
6.49E-06
10.95%
Benthic Omnivore
mg kg"1 [wet
weight]
4.97E-05
2.51 E-05
50.46%
3.23E-12
0.00%
2.46E-05
49.54%
Benthic Carnivore
mg kg"1 [wet
weight]
2.35E-04
2.22E-05
9.43%
2.05E-12
0.00%
2.13E-04
90.57%
Veto Lake
Surface water
mg L"'
1.06E-07
1.02E-07
96.79%
3.02E-09
2.86%
3.70E-10
0.35%
Macrophyte
mg kg"' [wet
weight]
3.14E-08
3.11E-08
99.04%
4.77E-17
0.00%
3.02E-10
0.96%
Zooplankton
mg kg"1 [wet
weight]
1.55E-04
1.31E-04
84.32%
0.00E+00
0.00%
2.43E-05
15.68%
Water Column
Herbivore
mg kg"1 [wet
weight]
1.43E-04
5.29E-05
36.92%
0.00E+00
0.00%
9.03E-05
63.08%
Water Column
Omnivore
mg kg"1 [wet
weight]
4.78E-04
2.27E-05
4.75%
0.00E+00
0.00%
4.55E-04
95.25%
Water Column
Carnivore
mg kg"1 [wet
weight]
2.51 E-03
1.25E-05
0.50%
0.00E+00
0.00%
2.50E-03
99.50%
B-4
-------
Human Health Multipathway Residual Risk Assessment for the Ferroalloys Production Source Category
Table B-1. Concentration Estimates and Speciations for Mercury (Cont.)
Location and Medium
Units
Total Hg
Divalent Hg
Elemental Hg
Methyl Hga
Value
Value
% Total Hg
Value
% Total Hg
Value
% Total
Hg
Veto Lake
Sediment
M9 g"1 [dry
weight]
1.90E-03
1.90E-03
99.63%
3.10E-06
0.16%
3.89E-06
0.20%
Benthic
Invertebrate
mg kg"1 [wet
weight]
1.11 E-04
9.88E-05
88.91%
1.61E-07
0.15%
1.22E-05
10.94%
Benthic Omnivore
mg kg"1 [wet
weight]
9.15E-05
4.54E-05
49.60%
2.80E-12
0.00%
4.61 E-05
50.40%
Benthic Carnivore
mg kg"1 [wet
weight]
4.40E-04
4.00E-05
9.10%
1.05E-12
0.00%
4.00E-04
90.90%
Mountwood
Park Lake
Surface water
mg L"1
1.10E-08
1.01E-08
92.37%
7.94E-10
7.25%
4.24E-11
0.39%
Macrophyte
mg kg"1 [wet
weight]
5.12E-09
5.09E-09
99.41%
9.92E-18
0.00%
3.02E-11
0.59%
Zooplankton
mg kg"1 [wet
weight]
1.32E-05
1.08E-05
82.11%
0.00E+00
0.00%
2.36E-06
17.89%
B-5
-------
Human Health Multipathway Residual Risk Assessment for the Ferroalloys Production Source Category
Table B-1. Concentration Estimates and Speciations for Mercury (Cont.)
Location and Medium
Units
Total Hg
Divalent Hg
Elemental Hg
Methyl Hga
Value
Value
% Total Hg
Value
% Total Hg
Value
% Total
Hg
Mountwood
Park Lake
Water Column
Herbivore
mg kg"1 [wet
weight]
1.30E-05
4.31 E-06
33.07%
0.00E+00
0.00%
8.73E-06
66.93%
Water Column
Omnivore
mg kg"1 [wet
weight]
4.55E-05
1.83E-06
4.02%
0.00E+00
0.00%
4.37E-05
95.98%
Water Column
Carnivore
mg kg"1 [wet
weight]
2.28E-04
9.74E-07
0.43%
0.00E+00
0.00%
2.27E-04
99.57%
Sediment
M9 a"1 [dry
weight]
1.04E-04
1.03E-04
99.33%
4.83E-07
0.47%
2.06E-07
0.20%
Benthic Invertebrate
mg kg"1 [wet
weight]
6.02E-06
5.36E-06
88.99%
2.51 E-08
0.42%
6.37E-07
10.59%
Benthic Omnivore
mg kg"1 [wet
weight]
4.80E-06
2.42E-06
50.35%
7.70E-13
0.00%
2.38E-06
49.65%
Benthic Carnivore
mg kg"1 [wet
weight]
2.18E-05
2.10E-06
9.64%
7.57E-13
0.00%
1.97E-05
90.36%
aMethyl mercury concentrations represent the mass of mercury as methyl mercury.
B-6
-------
Human Health Multipathway Residual Risk Assessment for the Ferroalloys Production Source Category
Table B-2. Concentration Estimates for Cadmium
Location and Medium
Units
Values
Farm_NNW
Tilled Surface Soil
|jg g"1 [dry weight]
1.10E-02
Unfilled Surface Soil
|jg g"1 [dry weight]
9.09E-03
Farm_SE
Tilled Surface Soil
|jg g"1 [dry weight]
5.33E-02
Unfilled Surface Soil
|jg g"1 [dry weight]
7.18E-02
Farm_WSW
Tilled Surface Soil
|jg g"1 [dry weight]
3.94E-02
Unfilled Surface Soil
|jg g"1 [dry weight]
2.94E-02
Surface water
mg L"1
1.84E-06
Macrophyte
mg kg"1 [wet weight]
1.83E-04
Zooplankton
mg kg"1 [wet weight]
1.17E-01
Water Column Herbivore
mg kg"1 [wet weight]
5.82E-02
Wolf Run Lake
Water Column Omnivore
mg kg"1 [wet weight]
2.02E-02
Water Column Carnivore
mg kg"1 [wet weight]
5.77E-03
Sediment
|jg g"1 [dry weight]
4.55E-03
Benthic Invertebrate
mg kg"1 [wet weight]
7.75E-04
Benthic Omnivore
mg kg"1 [wet weight]
3.99E-04
Benthic Carnivore
mg kg"1 [wet weight]
2.38E-03
B-7
-------
Human Health Multipathway Residual Risk Assessment for the Ferroalloys Production Source Category
Table B-2. Concentration Estimates for Cadmium (Cont.)
Location and Medium
Units
Values
Surface water
mg L"1
1.98E-06
Macrophyte
mg kg"1 [wet weight]
1.89E-04
Zooplankton
mg kg"1 [wet weight]
1.27E-01
Water Column Herbivore
mg kg"1 [wet weight]
7.73E-02
Veto Lake
Water Column Omnivore
mg kg"1 [wet weight]
3.28E-02
Water Column Carnivore
mg kg"1 [wet weight]
1.17E-02
Sediment
|jg g"' [dry weight]
2.32E-03
Benthic Invertebrate
mg kg"1 [wet weight]
3.95E-04
Benthic Omnivore
mg kg"1 [wet weight]
2.98E-04
Benthic Carnivore
mg kg"1 [wet weight]
2.02E-03
Surface water
mg L"1
2.38E-07
Macrophyte
mg kg"1 [wet weight]
2.26E-05
Zooplankton
mg kg"1 [wet weight]
1.69E-02
Water Column Herbivore
mg kg"1 [wet weight]
1.45E-02
Mountwood Park
Water Column Omnivore
mg kg"1 [wet weight]
8.50E-03
Lake
Water Column Carnivore
mg kg"1 [wet weight]
4.39E-03
Sediment
|jg g"' [dry weight]
1.73E-04
Benthic Invertebrate
mg kg"1 [wet weight]
2.93E-05
Benthic Omnivore
mg kg"1 [wet weight]
3.26E-05
Benthic Carnivore
mg kg"1 [wet weight]
2.49E-04
B-8
-------
Human Health Multipathway Residual Risk Assessment for the Ferroalloys Production Source Category
Table B-3. Concentration Estimates for PAH Congeners
Location and Medium
Units
Values
Acenaphthene
Acenaphthylene
Benzo(A)anthracene
Benzo(A)pyrene
Benzo(B)flouranthene
Benzo(g,h,i)perylene
Benzo(K)fluoranthene
Farm_NNW
Tilled Surface Soil
M9 g"1
1.17E-07
1.04E-06
5.57E-05
1.14E-04
8.12E-04
9.62E-05
1.10E-03
[dry weight]
Unfilled Surface
Soil
M9 g"1
2.25E-06
5.12E-05
7.07E-04
1.47E-03
9.72E-03
1.35E-03
6.88E-03
[dry weight]
Farm_SE
Tilled Surface Soil
Mg g"1
1.56E-07
1.65E-06
6.97E-05
1.45E-04
9.93E-04
1.23E-04
1.56E-03
[dry weight]
Unfilled Surface
Soil
Mg g"1
3.56E-06
6.34E-05
8.80E-04
1.81E-03
1.16E-02
1.65E-03
9.34E-03
[dry weight]
Farm_WSW
Tilled Surface Soil
Mg g"1
9.56E-08
1.00E-06
2.15E-05
2.79E-05
3.03E-04
2.63E-05
4.26E-04
[dry weight]
Unfilled Surface
Soil
Mg g"1
1.05E-06
1.81E-05
2.72E-04
3.89E-04
3.82E-03
4.02E-04
2.86E-03
[dry weight]
Wolf Run Lake
Surface water
mg L"1
1.53E-09
9.30E-09
1.96E-10
2.66E-11
2.51 E-08
1.72E-09
6.77E-09
Macrophyte
mg kg"1
3.11E-08
2.28E-07
1.31E-07
1.74E-08
1.68E-05
7.69E-07
4.47E-06
[wet weight]
B-9
-------
Human Health Multipathway Residual Risk Assessment for the Ferroalloys Production Source Category
Table B-3. Concentration Estimates for PAH Congeners (Cont.)
Values
Location and Medium
Units
Acenaphthene
Acenaphthylene
Benzo(A)anthracene
Benzo(A)pyrene
Benzo(B)flouranthene
Benzo(g,h,i)perylene
Benzo(K)fluoranthene
Zooplankton
mg kg"1
1.50E-07
9.70E-07
5.87E-06
1.04E-06
6.12E-04
2.76E-04
2.44E-04
[wet weight]
Water Column
mg kg"1
1.38E-06
9.27E-06
8.26E-07
4.21 E-07
4.87E-04
1.70E-05
1.14E-04
Herbivore
[wet weight]
Water Column
mg kg"1
9.68E-07
5.93E-06
1.55E-07
8.66E-08
1.07E-04
2.13E-06
2.39E-05
Omnivore
[wet weight]
Water Column
mg kg"1
7.94E-07
4.85E-06
1.23E-07
6.32E-08
7.83E-05
1.39E-06
1.74E-05
Carnivore
[wet weight]
Wolf Run Lake
Sediment
Mg g"1
3.47E-08
3.78E-07
2.81 E-06
5.82E-07
4.35E-04
5.54E-05
1.44E-04
[dry weight]
Benthic
mg kg"1
6.54E-07
4.10E-06
1.09E-06
1.93E-07
2.91 E-04
5.41 E-06
6.71 E-05
Invertebrate
[wet weight]
Benthic Omnivore
mg kg"1
9.63E-07
5.90E-06
1.57E-07
8.07E-08
1.01E-04
1.80E-06
2.26E-05
[wet weight]
Benthic Carnivore
mg kg"1
7.93E-07
4.85E-06
1.25E-07
6.43E-08
8.02E-05
1.42E-06
1.78E-05
[wet weight]
B-10
-------
Human Health Multipathway Residual Risk Assessment for the Ferroalloys Production Source Category
Table B-3. Concentration Estimates for PAH Congeners (Cont.)
Values
Location and Medium
Units
Acenaphthene
Acenaphthylene
Benzo(A)anthracene
Benzo(A)pyrene
Benzo(B)flouranthene
Benzo(g,h,i)perylene
Benzo(K)fluoranthene
Surface water
mg L"1
4.69E-09
2.01 E-08
2.40E-09
4.31E-10
8.53E-08
7.68E-09
2.67E-08
Macrophyte
mg kg"1
9.54E-08
4.90E-07
1.41 E-06
2.13E-07
4.71 E-05
2.24E-06
1.42E-05
[wet weight]
Zooplankton
mg kg"1
4.60E-07
2.09E-06
7.26E-05
1.45E-05
1.95E-03
9.75E-04
8.95E-04
[wet weight]
Water Column
mg kg"1
4.22E-06
1.99E-05
8.94E-06
5.34E-06
1.39E-03
6.65E-05
3.73E-04
Herbivore
[wet weight]
Veto Lake
Water Column
mg kg"1
2.97E-06
1.28E-05
1.65E-06
1.10E-06
3.10E-04
7.15E-06
7.84E-05
Omnivore
[wet weight]
Water Column
mg kg"1
2.43E-06
1.04E-05
1.29E-06
7.78E-07
2.22E-04
4.09E-06
5.54E-05
Carnivore
[wet weight]
Mg g"1
Sediment
[dry weight]
3.47E-07
2.91 E-06
3.76E-05
8.81 E-06
1.39E-03
1.91E-04
5.12E-04
Benthic Invertebrate
mg kg"1
6.54E-06
3.16E-05
1.45E-05
2.91 E-06
9.25E-04
1.87E-05
2.38E-04
[wet weight]
B-11
-------
Human Health Multipathway Residual Risk Assessment for the Ferroalloys Production Source Category
Table B-3. Concentration Estimates for PAH Congeners (Cont.)
Values
Location and Medium
Units
Acenaphthene
Acenaphthylene
Benzo(A)anthracene
Benzo(A)pyrene
Benzo(B)flouranthene
Benzo(g,h,i)perylene
Benzo(K)fluoranthene
Benthic Omnivore
mg kg"1
3.00E-06
1.29E-05
1.69E-06
1.02E-06
2.94E-04
5.46E-06
7.38E-05
Veto Lake
[wet weight]
mg kg"1
Benthic Carnivore
2.45E-06
1.05E-05
1.33E-06
8.00E-07
2.30E-04
4.22E-06
5.75E-05
[wet weight]
Surface water
mg L"1
1.50E-09
7.03E-09
4.05E-10
6.98E-11
1.22E-08
9.52E-10
3.56E-09
Macrophyte
mg kg"1
3.06E-08
1.72E-07
2.31 E-07
3.26E-08
6.54E-06
2.61 E-07
1.82E-06
[wet weight]
Zooplankton
mg kg"1
1.49E-07
7.43E-07
1.59E-05
2.94E-06
3.42E-04
1.57E-04
1.50E-04
[wet weight]
Mountwood
Water Column
mg kg"1
1.35E-06
6.95E-06
1.58E-06
9.07E-07
2.03E-04
1.35E-05
5.27E-05
Park Lake
Herbivore
[wet weight]
Water Column
Omnivore
mg kg"1
[wet weight]
9.56E-07
4.48E-06
2.76E-07
1.84E-07
4.64E-05
1.25E-06
1.10E-05
Water Column
mg kg"1
7.83E-07
3.66E-06
2.13E-07
1.21 E-07
3.14E-05
5.01 E-07
7.27E-06
Carnivore
[wet weight]
B-12
-------
Human Health Multipathway Residual Risk Assessment for the Ferroalloys Production Source Category
Table B-3. Concentration Estimates for PAH Congeners (Cont.)
Values
Location and Medium
Units
Acenaphthene
Acenaphthylene
Benzo(A)anthracene
Benzo(A)pyrene
Benzo(B)flouranthene
Benzo(g,h,i)perylene
Benzo(K)fluoranthene
Sediment
M9 g"1
5.69E-08
4.34E-07
1.01E-06
2.00E-07
3.06E-05
3.65E-06
1.05E-05
[dry weight]
Benthic Invertebrate
mg kg"1
1.07E-06
4.70E-06
3.89E-07
6.58E-08
2.04E-05
3.55E-07
4.86E-06
Mountwood
[wet weight]
mg kg"1
Park Lake
Benthic Omnivore
9.53E-07
4.46E-06
2.63E-07
1.43E-07
3.72E-05
5.72E-07
8.61 E-06
[wet weight]
mg kg"1
Benthic Carnivore
[wet weight]
7.83E-07
3.66E-06
2.13E-07
1.18E-07
3.07E-05
4.71 E-07
7.10E-06
B-13
-------
Human Health Multipathway Residual Risk Assessment for the Ferroalloys Production Source Category
Table B-3. Concentration Estimates for PAH Congeners (Cont.)
Location and Medium
Units
Values
Chrysene
Dibenz[a,h]anthracene
Fluoranthene
Fluorene
lndeno(1,2,3-cd)pyrene
Methylnaphthalene, 2-
Farm_NNW
Tilled Surface Soil
M9 g"1
5.14E-04
4.44E-05
4.64E-05
1.78E-07
1.21 E-04
1.11 E-07
[dry weight]
Unfilled Surface Soil
M9 g"1
4.90E-03
4.46E-04
6.69E-04
3.70E-06
1.37E-03
8.30E-06
[dry weight]
Farm_SE
Tilled Surface Soil
Mg g"1
6.35E-04
5.85E-05
5.52E-05
2.01 E-07
1.54E-04
1.23E-07
[dry weight]
Unfilled Surface Soil
Mg g"1
5.94E-03
5.55E-04
8.78E-04
4.52E-06
1.65E-03
8.30E-06
[dry weight]
Farm_WSW
Tilled Surface Soil
Mg g"1
2.21 E-04
1.27E-05
2.80E-05
1.01 E-07
3.55E-05
7.00E-08
[dry weight]
Unfilled Surface Soil
Mg g"1
2.19E-03
1.46E-04
3.17E-04
1.62E-06
4.60E-04
2.63E-06
[dry weight]
Wolf Run
Lake
Surface water
mg L"1
2.15E-09
3.23E-10
1.56E-08
2.01 E-09
1.35E-09
2.59E-09
Macrophyte
mg kg"1
1.41E-06
1.51E-07
5.22E-06
7.51 E-08
5.82E-07
4.58E-08
[wet weight]
B-14
-------
Human Health Multipathway Residual Risk Assessment for the Ferroalloys Production Source Category
Table B-3. Concentration Estimates for PAH Congeners (Cont.)
Values
0)
£
0)
(J
re
0>
£
0)
Q_
Methylnaphthalene, 2-
Location and Medium
Units
0)
c
0)
CO
>*
I.
.£
£
re,
IE1
0)
.c
+¦»
c
<0
I.
o
c
0)
o
3
S"
U
CO
cs
o
.re"
£
-------
Human Health Multipathway Residual Risk Assessment for the Ferroalloys Production Source Category
Table B-3. Concentration Estimates for PAH Congeners (Cont.)
Values
Location and Medium
Units
Chrysene
Dibenz[a,h]anthracene
Fluoranthene
Fluorene
lndeno(1,2,3-cd)pyrene
Methylnaphthalene, 2-
Surface water
mg L"1
1.89E-08
1.39E-09
3.39E-08
9.44E-09
5.61 E-09
6.00E-09
Macrophyte
mg kg"1
1.05E-05
4.93E-07
1.11 E-05
3.52E-07
1.50E-06
1.06E-07
[wet weight]
Zooplankton
mg kg"1
3.65E-04
1.61E-04
1.59E-05
5.59E-08
8.09E-04
4.29E-08
[wet weight]
Water Column Herbivore
mg kg"1
9.10E-05
1.27E-05
8.08E-05
1.17E-05
5.11 E-05
4.99E-06
[wet weight]
Veto Lake
Water Column Omnivore
mg kg"1
1.76E-05
1.63E-06
1.59E-05
6.08E-06
4.79E-06
3.75E-06
[wet weight]
Water Column Carnivore
mg kg"1
1.37E-05
1.01E-06
1.28E-05
4.96E-06
2.54E-06
3.08E-06
[wet weight]
Sediment
Mg g"1
2.76E-04
3.53E-05
1.36E-04
1.72E-06
1.40E-04
5.07E-07
[dry weight]
Benthic Invertebrate
mg kg"1
4.47E-05
6.33E-06
2.58E-04
2.07E-05
1.11 E-05
7.52E-06
[wet weight]
B-16
-------
Human Health Multipathway Residual Risk Assessment for the Ferroalloys Production Source Category
Table B-3. Concentration Estimates for PAH Congeners (Cont.)
Location and Medium
Units
Values
Chrysene
Dibenz[a,h]anthracene
Fluoranthene
Fluorene
lndeno(1,2,3-cd)pyrene
Methylnaphthalene, 2-
Veto Lake
Benthic Omnivore
mg kg"1
1.72E-05
1.41E-06
1.64E-05
6.11E-06
3.36E-06
3.78E-06
[wet weight]
Benthic Carnivore
mg kg"1
1.38E-05
1.07E-06
1.30E-05
4.97E-06
2.61 E-06
3.10E-06
[wet weight]
Mountwood
Park Lake
Surface water
mg L"1
3.09E-09
1.88E-10
1.04E-08
2.58E-09
7.19E-10
2.01 E-09
Macrophyte
mg kg"1
1.65E-06
6.39E-08
3.37E-06
9.64E-08
1.79E-07
3.55E-08
[wet weight]
Zooplankton
mg kg"1
7.59E-05
2.87E-05
5.18E-06
1.62E-08
1.34E-04
1.81E-08
[wet weight]
Water Column Herbivore
mg kg"1
1.50E-05
2.68E-06
2.45E-05
3.19E-06
1.10E-05
1.67E-06
[wet weight]
Water Column Omnivore
mg kg"1
2.83E-06
2.91 E-07
4.87E-06
1.67E-06
9.19E-07
1.26E-06
[wet weight]
Water Column Carnivore
mg kg"1
2.17E-06
1.36E-07
3.89E-06
1.36E-06
3.23E-07
1.04E-06
[wet weight]
B-17
-------
Human Health Multipathway Residual Risk Assessment for the Ferroalloys Production Source Category
Table B-3. Concentration Estimates for PAH Congeners (Cont.)
Values
0)
c
0)
o
<0
*
I.
.c
+¦»
c
.2i
IE1
0)
£
c
I.
0)
c
0)
I.
o
3
o
CO
ci
O
.
-------
Human Health Multipathway Residual Risk Assessment for the Ferroalloys Production Source Category
Table B-4. Concentration Estimates for Dioxin Congeners
Location and Medium
Units
Values
HeptaCDD,
1,2,3,4,6,7,8-
HexaCDF,
1,2,3,4,7,8-
PentaCDF,
1,2,3,7,8-
TetraCDD,
2,3,7,8-
OctaCDF,
1,2,3,4,6,7,8,
9-
TetraCDF,
2,3,7,8-
Farm_NNW
Tilled Surface Soil
M9 g" [dry
weight]
3.00E-09
5.99E-10
8.70E-10
2.03E-10
5.47E-09
1.55E-10
Unfilled Surface Soil
M9 9"' [dry
weight]
1.39E-08
2.77E-09
4.01 E-09
1.01 E-09
2.52E-08
7.30E-10
Farm_SE
Tilled Surface Soil
Mg g" [dry
weight]
4.61 E-09
9.04E-10
1.34E-09
3.35E-10
8.30E-09
2.96E-10
Unfilled Surface Soil
Mg g" [dry
weight]
1.92E-08
3.85E-09
5.78E-09
1.59E-09
3.44E-08
1.51 E-09
Farm_WSW
Tilled Surface Soil
Mg g"1 [dry
weight]
9.03E-10
2.28E-10
3.26E-10
9.24E-11
1.61 E-09
1.15E-10
Unfilled Surface Soil
Mg g" [dry
weight]
5.39E-09
1.29E-09
1.80E-09
5.06E-10
9.60E-09
4.55E-10
Wolf Run
Lake
Surface water
mg L"1
4.50E-15
2.04E-16
9.49E-17
3.65E-16
7.67E-16
5.85E-17
Macrophyte
mg kg"1 [wet
weight]
8.76E-11
5.06E-12
1.95E-12
7.62E-12
1.48E-11
3.99E-13
Zooplankton
mg kg"1 [wet
weightl
7.94E-10
1.03E-11
3.27E-12
1.27E-11
1.61E-10
4.88E-13
Water Column
Herbivore
mg kg"1 [wet
weight]
1.13E-09
2.13E-11
8.68E-12
3.94E-11
6.73E-11
2.36E-12
Water Column
Omnivore
mg kg"1 [wet
weight]
1.24E-09
2.90E-11
1.44E-11
6.93E-11
2.00E-11
4.99E-12
Water Column
Carnivore
mg kg"1 [wet
weight]
7.83E-10
4.58E-11
3.10E-11
1.53E-10
4.96E-12
1.37E-11
Sediment
Mg g"1 [dry
weight]
1.24E-10
1.13E-12
3.45E-13
1.36E-12
2.05E-11
5.00E-14
Benthic Invertebrate
mg kg"1 [wet
weight]
4.04E-13
5.61E-15
5.00E-15
1.56E-13
2.20E-14
1.68E-15
Benthic Omnivore
mg kg"1 [wet
weight]
9.56E-12
2.49E-12
1.65E-12
1.30E-11
1.86E-13
1.34E-12
Benthic Carnivore
mg kg"1 [wet
weight]
1.35E-11
5.21E-12
4.09E-12
3.17E-11
2.14E-13
3.67E-12
B-19
-------
Human Health Multipathway Residual Risk Assessment for the Ferroalloys Production Source Category
Table B-4. Concentration Estimates for Dioxin Congeners (Cont.)
Location and Medium
Units
Values
HeptaCDD,
1,2,3,4,6,7,8-
HexaCDF,
1,2,3,4,7,8-
PentaCDF,
1,2,3,7,8-
TetraCDD,
2,3,7,8-
OctaCDF,
1,2,3,4,6,7,8,
9-
TetraCDF,
2,3,7,8-
Veto Lake
Surface water
mg L"1
2.98E-14
2.35E-15
1.27E-15
2.75E-15
1.07E-14
6.79E-16
Macrophyte
mg kg"1 [wet
weight]
2.54E-11
1.05E-11
6.66E-12
1.47E-11
8.70E-12
2.90E-12
Zooplankton
mg kg"1 [wet
weight]
2.18E-10
2.21 E-11
1.15E-11
2.47E-11
9.41 E-11
3.50E-12
Water Column
Herbivore
mg kg"1 [wet
weight]
3.67E-10
4.99E-11
3.24E-11
8.13E-11
4.71 E-11
1.70E-11
Water Column
Omnivore
mg kg"1 [wet
weight]
4.91E-10
7.41 E-11
5.65E-11
1.53E-10
1.77E-11
3.72E-11
Water Column
Carnivore
mg kg"1 [wet
weight]
4.06E-10
1.48E-10
1.49E-10
4.18E-10
5.82E-12
1.24E-10
Sediment
M9 9"' [dry
weight]
4.76E-10
3.11 E-11
1.49E-11
3.42E-11
1.63E-10
4.30E-12
Benthic Invertebrate
mg kg"1 [wet
weight]
1.53E-12
1.53E-13
2.14E-13
3.81E-12
1.73E-13
1.42E-13
Benthic Omnivore
mg kg"1 [wet
weightl
4.82E-12
5.09E-12
5.58E-12
3.07E-11
1.72E-13
8.93E-12
Benthic Carnivore
mg kg"1 [wet
weight]
5.59E-12
1.18E-11
1.51 E-11
7.91 E-11
1.65E-13
2.78E-11
Mountwood
Park Lake
Surface water
mg L"1
3.53E-15
3.30E-16
1.83E-16
3.60E-16
1.52E-15
1.07E-16
Macrophyte
mg kg"1 [wet
weight]
2.94E-12
1.55E-12
9.88E-13
1.95E-12
1.29E-12
4.44E-13
Zooplankton
mg kg"1 [wet
weightl
2.12E-11
2.68E-12
1.44E-12
2.85E-12
1.25E-11
5.14E-13
Water Column
Herbivore
mg kg"1 [wet
weight]
5.04E-11
8.76E-12
5.57E-12
1.20E-11
1.03E-11
2.92E-12
Water Column
Omnivore
mg kg"1 [wet
weight]
9.21 E-11
1.76E-11
1.28E-11
2.82E-11
5.57E-12
7.70E-12
Water Column
Carnivore
mg kg"1 [wet
weight]
1.09E-10
5.38E-11
5.33E-11
1.17E-10
2.56E-12
4.01 E-11
B-20
-------
Human Health Multipathway Residual Risk Assessment for the Ferroalloys Production Source Category
Table B-4. Concentration Estimates for Dioxin Congeners (Cont.)
Location and Medium
Units
Values
HeptaCDD,
1,2,3,4,6,7,8-
HexaCDF,
1,2,3,4,7,8-
PentaCDF,
1,2,3,7,8-
TetraCDD,
2,3,7,8-
OctaCDF,
1,2,3,4,6,7,8,
9-
TetraCDF,
2,3,7,8-
Mountwood
Park Lake
Sediment
M9 g" [dry
weight]
9.10E-12
8.45E-13
4.22E-13
8.39E-13
4.13E-12
1.35E-13
Benthic Invertebrate
mg kg"1 [wet
weight]
2.84E-14
4.09E-15
5.92E-15
8.81 E-14
4.40E-15
4.35E-15
Benthic Omnivore
mg kg"1 [wet
weight]
3.26E-13
6.30E-13
6.76E-13
2.83E-12
1.73E-14
1.24E-12
Benthic Carnivore
mg kg"1 [wet
weight]
5.69E-13
2.1 OE-12
2.79E-12
1.12E-11
2.17E-14
5.84E-12
B-21
-------
Human Health Multipathway Residual Risk Assessment for the Ferroalloys Production Source Category
Table B-4. Concentration Estimates for Dioxin Congeners (Cont.)
Location and Medium
Units
Values
PentaCDD,
1,2,3,7,8-
PentaCDF,
2,3,4,7,8-
HexaCDD,
1,2,3,4,7,8-
HexaCDD,
1,2,3,6,7,8-
HexaCDF,
1,2,3,6,7,8-
HexaCDF,
1,2,3,7,8,9-
Farm_NNW
Tilled Surface Soil
M9 g" [dry
weight]
2.10E-09
8.46E-10
1.41 E-09
1.63E-09
1.52E-09
1.34E-09
Unfilled Surface Soil
M9 9"' [dry
weight]
9.75E-09
3.89E-09
6.52E-09
7.57E-09
6.99E-09
6.17E-09
Farm_SE
Tilled Surface Soil
Mg g" [dry
weight]
3.35E-09
1.36E-09
2.19E-09
2.55E-09
2.31 E-09
2.02E-09
Unfilled Surface Soil
Mg g" [dry
weight]
1.42E-08
6.06E-09
9.18E-09
1.06E-08
9.57E-09
8.41 E-09
Farm_WSW
Tilled Surface Soil
Mg g" [dry
weight]
6.54E-10
3.71E-10
4.32E-10
4.84E-10
4.60E-10
4.32E-10
Unfilled Surface Soil
Mg g" [dry
weight]
3.81 E-09
1.89E-09
2.57E-09
2.90E-09
2.74E-09
2.55E-09
Wolf Run
Lake
Surface water
mg L"1
1.55E-15
1.39E-16
1.39E-15
1.23E-15
2.36E-16
2.57E-16
Macrophyte
mg kg"1 [wet
weight]
4.25E-11
1.96E-12
3.28E-11
1.82E-11
5.00E-12
6.87E-12
Zooplankton
mg kg"1 [wet
weight]
1.17E-10
2.71 E-12
1.95E-10
1.97E-10
3.70E-11
3.10E-11
Water Column
Herbivore
mg kg"1 [wet
weight]
3.08E-10
8.90E-12
4.38E-10
3.75E-10
7.02E-11
5.96E-11
Water Column
Omnivore
mg kg"1 [wet
weightl
5.09E-10
1.61E-11
5.58E-10
4.81E-10
8.95E-11
7.69E-11
Water Column
Carnivore
mg kg"1 [wet
weight]
1.10E-09
3.66E-11
8.33E-10
7.23E-10
1.33E-10
1.16E-10
Sediment
Mg g"1 [dry
weight]
1.44E-11
2.78E-13
3.02E-11
4.04E-11
5.79E-12
4.01 E-12
Benthic Invertebrate
mg kg"1 [wet
weight]
7.84E-13
2.86E-14
5.71E-13
3.01E-13
7.07E-14
1.64E-13
Benthic Omnivore
mg kg"1 [wet
weight]
5.54E-11
3.37E-12
6.27E-12
8.80E-12
1.69E-12
2.62E-12
Benthic Carnivore
mg kg"1 [wet
weight]
1.36E-10
8.28E-12
1.25E-11
1.81E-11
3.45E-12
5.27E-12
B-22
-------
Human Health Multipathway Residual Risk Assessment for the Ferroalloys Production Source Category
Table B-4. Concentration Estimates for Dioxin Congeners (Cont.)
Location and Medium
Units
Values
PentaCDD,
1,2,3,7,8-
PentaCDF,
2,3,4,7,8-
HexaCDD,
1,2,3,4,7,8-
HexaCDD,
1,2,3,6,7,8-
HexaCDF,
1,2,3,6,7,8-
HexaCDF,
1,2,3,7,8,9-
Veto Lake
Surface water
mg L"1
1.40E-14
1.75E-15
1.14E-14
1.18E-14
3.25E-15
3.35E-15
Macrophyte
mg kg"1 [wet
weight]
4.51E-11
9.82E-12
1.44E-11
6.33E-12
3.13E-12
6.29E-12
Zooplankton
mg kg"1 [wet
weight]
1.23E-10
1.37E-11
8.19E-11
6.26E-11
2.26E-11
2.87E-11
Water Column
Herbivore
mg kg"1 [wet
weight]
3.55E-10
4.66E-11
2.10E-10
1.37E-10
4.72E-11
6.06E-11
Water Column
Omnivore
mg kg"1 [wet
weight]
6.35E-10
8.74E-11
3.05E-10
2.01E-10
6.75E-11
8.68E-11
Water Column
Carnivore
mg kg"1 [wet
weight]
1.71E-09
2.40E-10
5.95E-10
3.97E-10
1.32E-10
1.70E-10
Sediment
M9 9"' [dry
weight]
2.02E-10
1.70E-11
1.77E-10
1.87E-10
4.87E-11
4.93E-11
Benthic Invertebrate
mg kg"1 [wet
weight]
1.07E-11
1.72E-12
3.28E-12
1.37E-12
5.90E-13
2.00E-12
Benthic Omnivore
mg kg"1 [wet
weight]
7.61 E-11
1.87E-11
8.07E-12
5.14E-12
1.98E-12
5.59E-12
Benthic Carnivore
mg kg"1 [wet
weight]
1.96E-10
4.87E-11
1.42E-11
1.02E-11
3.78E-12
1.02E-11
Mountwood
Park Lake
Surface water
mg L"1
1.78E-15
2.62E-16
1.34E-15
1.40E-15
4.51E-16
4.59E-16
Macrophyte
mg kg"1 [wet
weight]
5.87E-12
1.46E-12
1.75E-12
7.68E-13
4.63E-13
9.22E-13
Zooplankton
mg kg"1 [wet
weight]
1.32E-11
1.83E-12
7.79E-12
5.92E-12
2.58E-12
3.29E-12
Water Column
Herbivore
mg kg"1 [wet
weight]
4.96E-11
8.06E-12
2.82E-11
1.79E-11
7.83E-12
1.02E-11
Water Column
Omnivore
mg kg"1 [wet
weight]
1.12E-10
1.92E-11
5.41 E-11
3.39E-11
1.50E-11
1.99E-11
Water Column
Carnivore
mg kg"1 [wet
weight]
4.59E-10
8.18E-11
1.58E-10
9.85E-11
4.42E-11
5.92E-11
B-23
-------
Human Health Multipathway Residual Risk Assessment for the Ferroalloys Production Source Category
Table B-4. Concentration Estimates for Dioxin Congeners (Cont.)
Values
Location and Medium
Units
PentaCDD,
1,2,3,7,8-
PentaCDF,
2,3,4,7,8-
HexaCDD,
1,2,3,4,7,8-
HexaCDD,
1,2,3,6,7,8-
HexaCDF,
1,2,3,6,7,8-
HexaCDF,
1,2,3,7,8,9-
Sediment
M9 g" [dry
weight]
4.54E-12
5.00E-13
3.69E-12
3.89E-12
1.26E-12
1.28E-12
Mountwood
Benthic Invertebrate
mg kg"1 [wet
weight]
2.27E-13
4.97E-14
6.54E-14
2.71 E-14
1.50E-14
5.11 E-14
Park Lake
Benthic Omnivore
mg kg"1 [wet
weight]
6.27E-12
2.17E-12
3.72E-13
3.34E-13
1.48E-13
3.64E-13
Benthic Carnivore
mg kg"1 [wet
weight]
2.48E-11
8.75E-12
1.04E-12
1.02E-12
4.45E-13
1.06E-12
B-24
-------
Human Health Multipathway Residual Risk Assessment for the Ferroalloys Production Source Category
Table B-4. Concentration Estimates for Dioxin Congeners (Cont.)
Location and Medium
Units
Values
HexaCDD,
1,2,3,7,8,9 -
HexaCDF,
2,3,4,6,7,8-
HeptaCDF,
1,2,3,4,7,8,9-
OctaCDD,
1,2,3,4,6,7,8,9-
HeptaCDF,
1,2,3,4,6,7,8-
Farm_NNW
Tilled Surface Soil
|jg g"' [dry weight]
1.67E-09
2.05E-09
4.75E-10
2.76E-08
2.33E-09
Unfilled Surface Soil
|jg g"' [dry weight]
7.73E-09
9.44E-09
2.19E-09
1.27E-07
1.07E-08
Farm_SE
Tilled Surface Soil
|jg g"' [dry weight]
2.61 E-09
3.11 E-09
7.14E-10
4.28E-08
3.48E-09
Unfilled Surface Soil
|jg g"' [dry weight]
1.09E-08
1.29E-08
3.06E-09
1.78E-07
1.45E-08
Farm_WSW
Tilled Surface Soil
|jg g"' [dry weight]
4.95E-10
6.32E-10
1.87E-10
8.11 E-09
7.95E-10
Unfilled Surface Soil
|jg g"' [dry weight]
2.96E-09
3.76E-09
1.04E-09
4.84E-08
4.66E-09
Wolf Run
Lake
Surface water
mg L"1
1.27E-15
3.34E-16
1.79E-16
4.61E-15
5.48E-16
Macrophyte
mg kg"1 [wet
weight]
1.88E-11
7.04E-12
4.11E-12
6.85E-11
1.53E-11
Zooplankton
mg kg"1 [wet
weight]
2.02E-10
5.24E-11
7.58E-12
1.01 E-09
5.55E-11
Water Column
Herbivore
mg kg"1 [wet
weight]
3.86E-10
9.94E-11
1.08E-11
6.49E-10
3.98E-11
Water Column
Omnivore
mg kg"1 [wet
weight]
4.95E-10
1.27E-10
1.06E-11
2.93E-10
2.05E-11
Water Column
Carnivore
mg kg"1 [wet
weight]
7.43E-10
1.89E-10
1.08E-11
1.14E-10
9.74E-12
Sediment
|jg g"1 [dry weight]
4.15E-11
8.20E-12
8.15E-13
1.48E-10
6.40E-12
Benthic Invertebrate
mg kg"1 [wet
weight]
3.57E-13
3.61E-13
2.12E-14
1.18E-13
4.81E-15
Benthic Omnivore
mg kg"1 [wet
weight]
9.12E-12
2.77E-12
6.39E-13
1.49E-12
8.78E-13
Benthic Carnivore
mg kg"1 [wet
weight]
1.87E-11
5.35E-12
1.07E-12
1.87E-12
1.16E-12
B-25
-------
Human Health Multipathway Residual Risk Assessment for the Ferroalloys Production Source Category
Table B-4. Concentration Estimates for Dioxin Congeners (Cont.)
Values
Location and Medium
Units
HexaCDD,
1,2,3,7,8,9 -
HexaCDF,
2,3,4,6,7,8-
HeptaCDF,
1,2,3,4,7,8,9-
OctaCDD,
1,2,3,4,6,7,8,9-
HeptaCDF,
1,2,3,4,6,7,8-
Surface water
mg L"1
1.21E-14
4.54E-15
2.03E-15
6.33E-14
6.77E-15
Macrophyte
mg kg"1 [wet weight]
6.48E-12
4.34E-12
9.99E-12
3.33E-11
1.75E-11
Zooplankton
mg kg"1 [wet weight]
6.42E-11
3.15E-11
1.89E-11
4.77E-10
6.55E-11
Water Column
Herbivore
mg kg"1 [wet weight]
1.40E-10
6.59E-11
3.03E-11
3.67E-10
5.44E-11
Veto Lake
Water Column
Omnivore
mg kg"1 [wet weight]
2.06E-10
9.43E-11
3.39E-11
2.09E-10
3.35E-11
Water Column
Carnivore
mg kg"1 [wet weight]
4.06E-10
1.85E-10
4.41 E-11
1.09E-10
2.02E-11
Sediment
|jg g"' [dry weight]
1.92E-10
6.83E-11
2.59E-11
9.66E-10
9.79E-11
Benthic Invertebrate
mg kg"1 [wet weight]
1.61E-12
2.97E-12
6.69E-13
7.65E-13
7.32E-14
Benthic Omnivore
mg kg"1 [wet weight]
5.65E-12
6.58E-12
2.29E-12
1.15E-12
1.03E-12
Benthic Carnivore
mg kg"1 [wet weight]
1.10E-11
1.11E-11
3.64E-12
1.24E-12
1.40E-12
Surface water
mg L"1
1.45E-15
6.30E-16
2.92E-16
9.66E-15
9.60E-16
Macrophyte
mg kg"1 [wet weight]
7.91E-13
6.44E-13
1.47E-12
5.18E-12
2.61E-12
Zooplankton
mg kg"1 [wet weight]
6.10E-12
3.60E-12
2.35E-12
6.64E-11
8.05E-12
Mountwood
Park Lake
Water Column
Herbivore
mg kg"1 [wet weight]
1.84E-11
1.09E-11
5.85E-12
7.82E-11
1.13E-11
Water Column
Omnivore
mg kg"1 [wet weight]
3.49E-11
2.1 OE-11
9.19E-12
6.08E-11
1.02E-11
Water Column
Carnivore
mg kg"1 [wet weight]
1.01E-10
6.19E-11
1.77E-11
4.35E-11
8.68E-12
B-26
-------
Human Health Multipathway Residual Risk Assessment for the Ferroalloys Production Source Category
Table B-4. Concentration Estimates for Dioxin Congeners (Cont.)
Values
Location and Medium
Units
HexaCDD,
1,2,3,7,8,9 -
HexaCDF,
2,3,4,6,7,8-
HeptaCDF,
1,2,3,4,7,8,9-
OctaCDD,
1,2,3,4,6,7,8,9-
HeptaCDF,
1,2,3,4,6,7,8-
Sediment
|jg g"' [dry weight]
4.00E-12
1.77E-12
7.01E-13
2.44E-11
2.56E-12
Mountwood
Benthic Invertebrate
mg kg"1 [wet weight]
3.22E-14
7.59E-14
1.79E-14
1.91E-14
1.91E-15
Park Lake
Benthic Omnivore
mg kg"1 [wet weight]
3.54E-13
3.29E-13
2.27E-13
1.16E-13
1.41E-13
Benthic Carnivore
mg kg"1 [wet weight]
1.07E-12
8.67E-13
5.31E-13
1.70E-13
2.33E-13
B-27
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