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# Draft Residual Risk Assessment for the Shipbuilding and Ship Repair Source Category

by EPA’s Office of Air Qualit y Planning and Standards Office of Air and Radiation December 2010

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## Table of Contents

1 Introduction ........................................................................................................................ 4 2 Methods

.............................................................................................................................. 5 2.1 Emissions and source data ......................................................................................... 5 2.2 Dispersion modeling for inha lation exposure assessment ......................................... 5 2.3 Estimating human inhalation exposure ...................................................................... 8 2.4 Acute Risk Screening and Refined Assessments ....................................................... 8 2.5 Multipathway and environmental risk screening ..................................................... 10 2.6 Dose-Response Assessment ..................................................................................... 11 2.6.1 Sources of chronic dose-response information ................................................ 11 2.6.2 Sources of acute dose-response information .................................................... 16 2.7 Risk Characterization ............................................................................................... 19 2.7.1 General ............................................................................................................. 19 2.7.2 Mixtures ........................................................................................................... 21 2.7.3 Facility-wide Risks .......................................................................................... 22 3 Risk Results for the Shipbuilding and Sh ip Repair Operations Source Category ....... 23 3.1 Source Category Description and Results ................................................................ 23 3.2 Risk Characterization ............................................................................................... 25 4 General Discussion of Uncertainties and How They Have Been Addressed ................... 28 4.1 Exposure Modeling Uncertainties ............................................................................ 28 4.2 Uncertainties in the Dose-Response Relationships .................................................. 29 5 References ........................................................................................................................ 38

Index of Tables

Table 2.2-1 AERMOD version 09292 model options for RTR modeling ................................ 6 Table 2.6-1 (a) Dose-Response Values for Chronic Inhalation Exposure to Carcinogens .... 14 Table 2.6-1 (b) Dose-Response Values fo r Chronic Oral Exposure to Carcinogens ............. 15 Table 2.6-1 (c) Dose-Response Values for Ch ronic Inhalation Expos ure to Noncarcinogens

.............................................................................................................................. .................... 15 Table 2.6-2 Dose-Response Values for Acute Exposure ........................................................ 19 Table 3.1-1 Summary of Emissions from the Shipbuilding and Ship Repair Operations Source Category and Availability of Dose-Response Values .................................................. 24 Table 3.2-1 Summary of Source Category Level Inhalation Risk s for Shipbuilding and Ship Repair Operations .................................................................................................................... 26 Table 3.2-2 Summary of NPRM Acute Refined Results for Shipbuilding and Ship Repair Operations Facilities ................................................................................................................ 27 Table 3.2-3 Shipbuilding Source Category Cont ribution to Facility-Wide Cancer Risks ...... 28

Appendices

Appendix 1 Emissions Inventory Support Memorandum Appendix 2 Technical Support Docu ment for HEM-AERMOD Modeling

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Appendix 3 Meteorological Da ta for HEM-AERMOD Modeling Appendix 4 Analysis of data on short-term emission rates relative to long-term emission
rates Appendix 5 Technical Support Document fo r TRIM-Based Multipathway Screening Scenario for RTR: Summary of Approach and Evaluation Appendix 6 Detailed Risk Modeling Results Appendix 7 Acute Impacts Re fined Analysis Figures

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 NATA National Air Toxics Assessment NEI National Emissions Inventory NPRM Notice of Proposed Rulemaking PB-HAP Persistent and Bioaccumulative - HAP POM Polycyclic organic matter REL Reference exposure level RfC Reference concentration RfD Reference dose RTR Risk and Technology TOSHI Target-organ-specific hazard index URE Unit risk estimate

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## 1 Introduction
Section 112 of the Clean Air Act (CAA) establ ishes a two-stage regu latory process for addressing emissions of hazardous air pollutants ( HAPs) from stationary sources. In the first stage, section 112(d) requires the Environmental Protection Agen cy (EPA, or the Agency) to develop technology-based standards for categorie s of sources (e.g., petr oleum refineries, pulp and paper mills, etc.) [ 1 ]. EPA has largely completed the initial Maximum Achievable Control Technology (MACT) standards as re quired under this provision. Under section 112(d)(6), EPA must review each of these tec hnology-based standards at least every eight years and revise a standard, as necessary, “tak ing into account developments in practices, processes and control technologi es.” 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 ar e necessary to protect public health with an ample margin of safety or to prevent an a dverse environmental effect, EPA must develop standards to address these remaining risks. This second stage of th e regulatory process is known as the residual risk stage. For each source category for which EPA issued MACT standards, the residual risk st age 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 (R TR) Assessment Plan” and in June of 2007, we received a letter with the results of that c onsultation. 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) A ssessment 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 assessm ents by incorporating updates based on the SAB recommendations as they are developed and be come available. Our responses to the key recommendations of the SAB are outlined in the memo for this rulemaking docket [ 4 ].

This document contains the methods and the result s of baseline risk assessments (i.e., after the implementation of the respective MACT standard s) performed for the Shipbuilding and Ship Repair Operations source category. The met hods 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-inha lation health risks, and adverse environmental effects. Since none of the screening assessments indicated any significant potential for chronic non-inhalation health effects or environmental impacts, including effects to threatened and endangered species, no further refineme nt of these assessme nts was performed.

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## 2 Methods

2.1 Emissions and source data

The 2005 National-Scale Air Toxics Assessmen t (NATA) National Emissions Inventory (NEI) served as the starting point for this as sessment. The 2005 NEI contains information on actual emissions during the entire 2005 ba se year. Using the process MACT code 1 

### , we developed a subset of this i nventory that contains emissions and facility data for the Shipbuilding and Ship Repair Operations source category. Next , we performed an engineering review of the information using EP A engineers who were directly involved in the development of the MACT standards for the source category, and/or who have extensive knowledge of the characteristics of this i ndustry. NEI data wa s updated with industry supplied data as available. The goal of th e engineering review wa s to identify readilyapparent limitations and issues with the emissions data (particularly those that would have the potential to influence risk estimates) and to ma ke changes to the data set where possible to address these issues and decrease the uncerta inties associated with the assessment.

Details on the development of the emissions a nd source data for this source category are discussed in Section 3. The emissions data and modifications made to the NEI data are discussed in the Appendix 1, entitled “Emi ssions Inventory Support Memorandum.”



## 2.2 Dispersion modeling for inha lation exposure assessment Both long- and short-term inhalation exposure co ncentrations and associated health risk from each facility in the source category of interest were estimated using the Human Exposure Model in combination with the American Meteorological Society/ EPA Regulatory Model dispersion modeling system (HEM-AERMOD). Th e approach used in applying this modeling system is outlined below, and further deta ils are provided in Appendix 2. HEM-AERMOD performs three main operations: atmospheric dispersion modeling, estimation of individual human exposures and health risks, and estimatio n of population risks. This section focuses on the dispersion modeling component. The expos ure and risk characterization components are discussed in other subsecti ons of Sections 2 and 3.

The dispersion model in the HEM-AERMOD sy stem, AERMOD version 09292, is a state-ofthe-science Gaussian plume di spersion model that is prefer red by EPA for modeling point, area, and volume sources of continuous ai r 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 concentra tions 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.

1 The tagging of data with MACT codes allows EPA to de termine reductions attributable to the MACT program. The NEI associates MACT codes corresponding to MACT source categories with stationary major and area source data. MACT codes are assigned at th e process level for the point source.

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The first step in the application of the HEM-AERMOD 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.

Table 2.2-1 AERMOD version 09292 model options for RTR modeling

Modeling Option Selected Parameter for chronic exposure

Type of calculations Hourly Ambient Concentration

Source type Point, area represented as pseudo point source

Receptor orientation Polar (13 rings and 16 radials) Discrete (census block centroids)

Terrain characterization Actual from USGS 1-degree DEM data

Building downwash Not Included

Plume deposition/depletion Not Included

Urban source option No

Meteorology 1 year representative NW S from nearest site (158 stations)



### In HEM-AERMOD, meteorological data are ordinarily select ed from a list of 158 National Weather Service (NWS) surface observation stat ions 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. Idea lly, when considering off-site meteorological data most site specific disp ersion modeling efforts will employ up to five years of data to capture variabil ity in weather patterns from year to year. However, because we had insufficient number of appropriately formatted model input files derived from available meteorological data, we modeled onl y a single year, typica lly 1991. 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-predicti on at others. For each facility identified by its characteristic latitude and long itude coordinates, the closest meteorological station was used in the dispersion modeling. The average dist ance between a modele d facility and the applicable meteorological station was 40 miles (72 km). Appendix 3 (Meteorological Data Processing Using AERMET for HEM-AERMOD) provides a complete listing of stations and assumptions along with further details used in processing the data through AERMET. The sensitivity of model results to the selection of the nearest weather station and the use of one year of meteorological data is discussed in “Risk and Technology Review (RTR) Risk Assessment Methodologies” [ 2 ].

The HEM-AERMOD system estimates ambient con centrations at the ge ographic centroids of census blocks (using the 2000 Census), and at othe r receptor locations that can be specified by the user. The model accounts for the effect s of multiple facili ties when estimating concentration impacts at each block centroid. Typically we combined only the impacts of facilities within the same source category, and assessed chronic exposur e and risk only for census blocks with at least one resident (i .e., locations where people may reasonably be

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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 fi nest resolution availa ble in the census data, are typically comprised of approximately 40 people or about ten households.

In contrast to the developmen t of ambient concentrations fo r evaluating long-term exposures, which was performed only for occupied census blocks, worst-case short-term (one-hour) concentrations were estimated both at the censu s 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 NEI contains only annual emission tota ls, we generally apply the general assumption to all source categories that the maximum one- hour emission rate from any source was 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 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 us ing a short-term emi ssions dataset from a number of sources located in Texa s (originally reported on by Allen et al. 2004)[ 7 ]. In that report, the Texas Environmental Research C onsortium Project compared hourly and annual emissions data for volatile organic compounds fo r all facilities in a heavily-industrialized 4- county area (Harris, Galveston, Chambers, and Brazoria Counties, TX) over an eleven-month time period in 2001. We obtained the dataset an d performed our own analysis, focusing that analysis on sources which reported emitting high quantities of HAP over short periods of time (see Appendix 4, “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 11 times the annual average. Based on these results, we chose the factor of ten for all initial screening; it is in tended to cover routinely- variable emissions as well as startup, shutdown, and malfunction (SSM ) 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% of the short-term peak gaseous or volatile HAP emissions from typical industrial sources.

Census block elevations for HEM-AERMOD modeling were determined nationally from the US Geological Service 1-degree digital elevation model (DEM ) data files, which have a spatial resolution of about 90 meters. Elevations of polar grid points used in estimating shortand long-term ambient concentrations were assu med to be equal to the highest elevation of any census block falling within the polar grid sector corresponding to the grid point. If a sector does not contain any blocks, the model defaults the elevation to that of the nearest block. If an elevation is not provided for the emission source, the model uses the average elevation of all sectors within the innermost model ring.

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In addition to using receptor elevation to determine plume height, AERMOD adjusts the plume’s flow if nearby elevated hills are expected to influence the wind pa tterns. For details on how hill heights were estimated and used in the AERMOD modeling see Appendix 2.



## 2.3 Estimating human in halation exposure We used the annual average ambient air con centration of each HAP at each census block centroid as a surrogate for the lifetime inhalati on exposure concentration of all the people who reside in the census block. That is, the risk analysis did not cons ider either the short-term or long-term behavior (mobility) of the exposed populations and its poten tial influence on their
exposure.

We did not address short-term human activity for two reasons. First, our experience with the 1996, 1999, 2002, and 2005 NATA assessments (which modeled daily activity using EPA’s HAPEM model) suggests that, given our cu rrent understanding of microenvironment concentrations and daily activ ities, modeling short-term ac tivity would, on average, reduce risk estimates about 25% for particulate HAPs; it will also reduce risk estimates for gaseous HAPs, but typically by much less. Second, ba sing 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 be havior 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 assumpti on 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 highe st concentration; the population risk distribution; a nd 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 populati on number remains the same. It does, however, affect the shape of the distribution of individual risks across the affected population, shifting it to ward higher estimated individua l risks at the upper end and reducing the number of people estimated to be at lower risks, thereby biasing the risk estimates high.

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.



## 2.4 Acute Risk Screening and Refined Assessments In establishing a scientifically defensible approa ch for the assessment of potential health risks due to acute exposures to HAP, we followed the same general appr oach that has been used for developing chronic health risk assessments under the residual risk program. That is, we developed a tiered, iterative approach. This a pproach to risk assessment was endorsed by the National Academy of Sciences in its 1993 pub lication “Science and Judgment in Risk

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Assessment” and subsequently was adopted in the EPA’s “Residual Risk Report to Congress” in 1999.

The assessment methodology is designed to el iminate 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 tier ed, iterative approach to the assessment. This means that we began with a screening assessmen t, which relies on readily available data and uses conservative assumptions that in combina tion approximate a worst-case exposure. The result of this screening process is that either the facility be ing assessed poses no acute health risks (i.e., it “screens out”), or that it requires further, more refined assessment. A refined assessment could use industry- or site-specific data on the temporal pa ttern of emissions, the layout of emission points at the facility, th e boundaries of the facil ity, and/or the local meteorology. In some cases, all of these site-s pecific data would be needed to refine the assessment; in others, lesser amounts of site-spe cific data could be used to determine that acute exposures are not a con cern, and significant additional data collection would not be
necessary.

Acute health risk screening was performed as the first step. We used conservative assumptions for emission rates, meteorology, an d exposure location. We used the following worst-case assumptions in our screening approach:

• Peak 1-hour emissions were assumed to equal 10 times the average 1-hour emission
rates.

• For facilities with multiple emission points, peak 1-hour emissions were assumed to occur at all emission points at the same time.

• For facilities with multiple emission points, 1-hour concentrations at each receptor were assumed to be the sum of the maximum concentrations due to each emission point, regardless of whether those maximum concentrations occurred during the same hour.

• Worst-case meteorology (from one year of local meteorology) was assumed to occur at the same time the peak emission rates occur. The recommended EPA local-scale dispersion model, AERMOD, is used for simulating atmospheric dispersion.

• A person was assumed to be located downwind at the point of maximum impact during this same 1-hour period, but no nearer to the source than 100 meters.

• The maximum impact was compared to multiple short-term health benchmarks for the chemical being assessed to determine if a possi ble acute health risk might exist. These benchmarks are described in section 2.6 of this report.

As mentioned above, when we identify acu te impacts which exceed their relevant benchmarks, we pursue refining our acute screeni ng estimates. In some cases, this includes use of a refined emissions multiplier to estimate the peak hourly emission rates from the average rates. In this case, we conducted a review of the layout of emission points at the facility with the facil ity boundaries to determine the maxi mum off-site acute impact for the one facility that did not screen out during the initial model run. Refer to Appendix 7 for the detailed results for this site.

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## 2.5 Multipathway and enviro nmental risk screening The potential for significant hum an health risks due to expos ures 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). The PB-HAP co mpounds or compound classes are identified for the screening from the EPA’s Air Toxics Risk Assessment Library [ 8 ], and they are cadmium compounds, chlordane, chlorinated dibenz odioxins and furans, DDE, heptachlor, hexachlorobenzene, hexachlorocyclohexa ne, lead compounds, mercury compounds, methoxychlor, polychlorinated biphenyls, polycyclic organic matter (POM), toxaphene, and trifluralin. Emissions of 3 di fferent PB HAP were identified in the emissions inventory for the Shipbuilding and Ship Repair Operations source category; they are POM, lead, and cadmium. These emissions were evaluated for potential non-inhalati on risks and adverse environmental impacts using our recently-devel oped screening scenario which was developed for use with the TRIM.FaTE 2 

### model. This screening scenario uses environmental media outputs from the peer-reviewed TRIM.FaTE to estimate the maximum potential ingestion risks for any specified emission scenario by usi ng a generic farming/fish ing exposure scenario that simulates a subsistence environment. The screening scenario retains many of the ingestion and scenario inputs developed for EP A’s Human Health Risk Assessment Protocols (HHRAP) for hazardous wast e combustion facilities. 3 

### In the development of the screening scenario a sensitivity analysis was conducted to ensure that its key design parameters were established such that environmental media c oncentrations were not underestimated, and to also minimize the occurrence of false positives for human health endpoints. See Appendix 5 for a complete discussion of the development a nd testing of the screening scenario, as well as for the values of facility-level de minimis emission rates developed for screening potentially significant multi-pathway impacts. For the purpose of developing de minimis emission rates for our multi-pathway screening, we derived em ission levels for each PB HAP 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. In ev aluating the potential multi-pathway risks from emissions of lead compounds, rather than developing a de minimis emission rate, we compared maximum estimated chronic (3-month average) atmospheric concentrations with the current National Ambient Air Quality Stan dard (NAAQS) for lead. Values below the NAAQS were considered to have a low potentia l for multi-pathway risks of any significance.

Additionally, we evaluated the potential for significan t ecological exposures to non PB-HAP from exceedances of chronic human health inhala tion thresholds in the ambient air near these facilities. Human health dose -response threshold values are generally derived from studies conducted on laboratory animals (such as rodent s) and developed with the inclusions of uncertainty factors that could be as high as 3000. As a result, these human threshold values are often significantly lower than the level expected to cause an adverse effect in an exposed rodent. It should be noted that there is a scarcity of data on th e direct atmospheric impact of these HAPs on other receptors, such as plants, birds, and wildlife. Thus, if the maximum inhalation hazard in an ecosystem is below the level of concern for humans, we have generally concluded that mammalian receptors shoul d be at no risk of adverse effects due to

2 EPA’s Total Risk Integrated Methodology (General Information) http://epa.gov/ttn/fera/trim_gen.html

3 EPA’s Human Health Risk Assessment Protocol (HHRAP) for Hazardous Waste Combustion Facilities; http://www.epa.gov/epaoswer/hazwaste/combust/riskvol.htm#volume1

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inhalation exposures from non PB-HAP, and have assurance that other ecological receptors are also not at any significant ri sk from direct atmospheric imp act. In some isolated cases where we have data indicating po tential adverse impacts on plants, birds, or other wildlife due to the direct atmospheric impacts of specific HAP s, we note that as an uncertainty and, where possible, refine our analysis by comparing our modeled impacts to available threshold values from the scientific literature.



## 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 HAPs reported in the emissions inventory for Shipbuilding and Ship Repair Operations sources were ba sed on the EPA Office of Air Quality Planning and Standards’ existing recommendations for HAPs [ 9 ], also used for NATA 1999, 2002, and 2005 [ 10 ]. This information has been obtaine d from various sources and prioritized according to (1) conceptual consistency with EP A risk assessment guidelines and (2) lev peer review received. The prioritization pr ocess was aimed at incorporating into our assessments the best available science with respect to dose-response information. The recommendations are based on the follo wing sources, in el of

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 stat ement regarding the st rength of scientific data and specify a reference concentration (RfC, for inhalation) or reference dose (RfD, for ingestion) to protect against effects othe r than cancer and/or a unit risk estimate (URE, for inhalation) or slope factor (SF, for ingestion) to estimat e the probability of developing cancer. The RfC is defined as an “estimate (with uncertainty spanning perhaps an order of magnitude) of a continuous inhalation e xposure to the human population (including sensitive subgroups) that is likely to be without an appreciabl e risk of deleterious effects during a lifetime.” The URE is defined as “the upper-bound excess ca ncer risk estimated to result from continuous lifet ime exposure to an agent at a concentration of 1 μg/m 3 

### in air.” The SF is “an upper bound, approximating a 95% confidence limit, on the increased cancer risk from a lifetime exposure to an agen t. This estimate, [is] usually expressed in units of proportion (of a populat ion) affected per mg/kg-da y...” EPA disseminates doseresponse assessment information in several fo rms, based on the level of review. The Integrated Risk Information System (IRIS) [ 11 ] is an EPA database that contains scientific health assessment information, including dose-response information. All IRIS assessments since 1996 have also undergone in dependent external peer review. The current IRIS process includes review by EPA sc ientists, interagency reviewers from o federal agencies, and the public, and peer revi ew by independent scie ntists external to EPA. EPA’s science policy approach, under th e current carcinogen guidelines, is to use linear low-dose extrapolation as a default option for carcinogens for which the mode of action (MOA) has not been iden tified. We expect future EPA dose-response assessme to identify nonlinear MOAs where appropriate, and we will use those analyses (once they are peer reviewed) in our risk assessments. At this time, however, there are no availab ther

nts

le

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carcinogen dose-response assessments for i nhalation 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 Huma n Services, develops and publishes Minimum Risk Levels (MRLs) [ 12 ] for inhalation and oral exposur e to many toxic substances. As stated on the ATSDR web site: “Following di scussions 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 Refere nce 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 subs tance that is likely to be without an appreciable risk of advers e 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 environm ental contaminants for further evaluation. Exposures above an MRL do not necessarily re present 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 ha s developed dose-response assessments for many substances, based both on carcinogenicity a nd health effects other than cancer. The process for developing these assessments is si milar to that used by EPA to develop IRIS values and incorporates signifi cant external scientific peer review. As cited in the CalEPA Technical Support Document fo r developing their chronic assessments 4 

### : “The guidelines for developing chronic inhala tion exposure levels incorporate many recommendations of the U.S. EPA [ 13 ] and NAS [ 14 ].” The non-cancer information includes available inhalation hea lth risk guidance values expr essed as chronic inhalation reference exposure levels (RELs) [ 15 ]. CalEPA defines the R EL as “the concentration level at or below which no health effects are anticipated in the gene ral human population.” CalEPA's quantitative dose-response info rmation on carcinogenicity by inhalation exposure is expressed in terms of the URE [ 16 ], defined similarly to EPA's URE.

In developing chronic risk estimates, we adju sted dose-response values for some HAPs based on professional judgment, as follows:

1) In the case of HAP categories such as glyc ol ethers and cyanide compounds, the most conservative dose-response value of the chemical category is used as a surrogate for other compounds in the group for which dose-response valu es are not available. This is done in order to examine, under conservative assump tions, whether these HAPs that lack doseresponse values may pose an unacceptable risk and require further examination, or screen out from further assessment.

4 Air Toxics Hot Spots Program, Risk Assessment Guidelines, Part III - Technical Support Document for the Determination of Non-cancer Chronic Referen ce Exposure Levels. Air Toxicology and Epidemiology Section, Office of Environmental Health Hazard Assessment, California Environmental Protection Agency. February 2000 (http://www.oehha.ca.gov/air/chronic_rels/pdf/relsP32k.pdf)

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2) Where possible for emissions of unspecified mixtures of HAP categories such as metal compounds and POM, we apply ca tegory-specific chemical sp eciation profiles appropriate to the source category to develop a compos ite dose-response value for the category.

3) In 2004, EPA determined that the CIIT dos e-response value for formaldehyde (5.5 x 10 -9



### per μ g/m 3 

### ) was based on better science than the IRIS dose-response value (1.3 x 10 -5 

### per μ g/m 3 

### ), and we switched from using the IRIS valu e to the CIIT value in risk assessments supporting regulatory actions. This determination was ba sed on a substantial body of research on the inhalation dosimetry for formaldehyde in rodents and primates by the CIIT Centers for Health Research (f ormerly the CIIT), with a focus on use of rodent data for refinement of the quantitative can cer dose-response assessment [ 17

, 18

, 19 ]. The CIIT’s risk assessment of formaldehyde incorporated mechanistic and dosimetric information on formaldehyde. However, recent research published by EPA indicates that, when the CIIT’s two-stage modeling assumptions are va ried, resulting dose-re sponse estimates can vary by several orders of magnitude [ 18, 19, 20, 20 ]. These findings are not supportive of interpreting the CIIT model results as pr oviding a conservative (health-protective) estimate of human risk [ 21 ]. The recent EPA research also examined the contribution of the two-stage modeling for formaldehyde toward s characterizing the relative weights of key events in the mode-of-action of a carcinoge n. For example, in the EPA research, the model-based inference in the published CIIT study that form aldehyde’s direct mutagenic action is not relevant to the compound’s tumorigenicity was found not to hold under variations of modeling assumptions [ 22 ]. Additionally, in 2007, the California Environmental Protection Agency considered a petition to use the CIIT URE value rather than its 1992 cancer risk value of 6 X 10 -6 

### per ug/m 3 

### . After consideration and consultation with their Scientific Review Panel of Toxic Air Contaminants, they decided not to change from their 1992 value [ 23

, 24 ]. EPA no longer considers the CIIT URE value to be health protective, and we are again using the Agen cy’s current value on IRIS, which was last revised in 1991, and which is more than 2000 times greater than the CIIT value. We note that a new IRIS re-assessment has been drafte d and sent to the NAS for review. The NAS review is expected to be completed by March of 2011.

4) A substantial proportion of POM reported to EPA’s national emission inventory (NEI) are not speciated into individual compounds. As a result, it is necessary to apply the same simplifying assumptions to assessments that were used for the 1999 NATA study [ 25 ]. 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 cases where no values are available from our preferred hierarchy but values have been previously developed under EPA’s HEAST progr am, we have used these values in our assessments. We note that these values are only used as a last resort since the program was discontinued in 1997.

The emissions inventory for the shipbuilding and ship repair source category includes emissions of 30 HAP with available chronic quantitative inhalation dose-response values.

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These HAP, their dose-response values, and the s ource of the values are listed in Tables 2.6-1 (a) through (c).

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

URE (unit risk estimate for cancer) 5 = cancer risk per μ g/m 3 of average lifetime exposure. Sources: IRIS = EPA Integrated Risk Informat ion System, CAL = California EPA Office of Environmental Health Hazard Assessment, EPA/OAQPS = interim val ue recommended by the EPA Office of Air Quality Planning and Standards.

Pollutant CAS Number 6 URE 5 (1/ μ g/m3) Source

Acetaldehyde 75070 2.2E-06 IRIS
Benzene 7 71432 7.8E-06 IRIS p-Dichlorobenzene 106467 1.1E-05 CAL Epichlorohydrin 106898 1.2E-06 IRIS Ethyl benzene 100414 2.5E-6 CAL Formaldehyde 50000 1.3E-05 IRIS 4,4-Methylene bis(2- chloroaniline) 101144 4.3E-04 CAL Methylene chloride 75092 4.7E-07 IRIS Naphthalene 91203 3.4E-05 CAL Polycyclic Organic Matter 246 8 - Benzo(a)anthracene 56553 1.1E-04 CAL - Benzo(a)pyrene 50328 1.1E-03 CAL - Benzo(b)fluoranthene 205992 1.1E-04 CAL - Benzo(k)fluoranthene 207089 1.1E-04 CAL - Chrysene 218019 1.1E-05 CAL - Dibenzo(a,h)anthracene 53703 1.2E-03 CAL - Indeno(1,2,3-cd)pyrene 193395 1.1E-04 CAL Trichloroethylene 79016 2.0E-06 CAL

5 The URE is the upper-bound excess cancer risk estimated to result from continuous lifetime exposure to an agent at a concentration of 1 μ g/m 3 in air. URE’s are considered upper bound estimates meaning they represent a plausible upper limit to the true value.

6 Chemical Abstract Services identification number. For groups of compounds that lack a CAS number we have used a surrogate 3-digit identifier corresponding to the group’s position on the CAA list of HAPs.

7 The EPA IRIS assessment for benzene provides a range of plausible UREs. This assessment used the highest value in that range, 7.8E-06 per ug/m3. The low end of the range is 2.2E-06 per ug/m 3

. 8 Assigned the URE associated with a mixture of POM compounds having a similar potency. Details of this method, also used in the 1999 National Air Toxics Assessment, are available at http://www.epa.gov/ttn/atw/nata1 999/99pdfs/pomapproachjan.pdf

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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 aver age lifetime exposure. Sources : IRIS = EPA Integrated Risk Information System, CAL = Ca lifornia EPA Office of Environmental Health Hazard Assessment, EPA/OAQPS = interim value recommended by the EPA Office of Air Quality Planning and Standards.

Pollutant CAS Number 10 SF (1/mg/kg/d) Source Polycyclic organic matter (POM) 246 0.5 - Benzo(a)anthracene 56553 1 CAL - Benzo(a)pyrene 50328 7 CAL - Benzo(b)fluoranthene 205992 1 CAL - Benzo(k)fluoranthene 207089 1 CAL - Chrysene 218019 0.1 CAL - Dibenz(a,h)anthracene 53703 4 CAL - Indeno(1,2,3-cd)pyrene 193395 1 CAL



### Table 2.6-1 (c) 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 (incl uding sensitive subgroups) that is likely to be without an appreciable risk of delet erious effects during a lifetime. Sources: IRIS = EPA Integrated Risk Informatio n System, CAL = California EPA Office of Environmental Human Health Assessment, ATSDR = US Agency for Toxic Substanc es Disease Registry, HEAST = EPA Health Effects Assessment Summary Table
Pollutant CAS Number 6 RfC (mg/m3) Source 9

Acetaldehyde 75070 0.009 IRIS -- L
Acrolein 107-02-8 0.00002 IRIS - H
Benzene 71432 0.03 IRIS – M Chlorobenzene 108907 1 CAL Chloroprene 126998 0.007 HEAST Cumene 98828 0.4 IRIS – L p-Dichlorobenzene 106467 0.8 IRIS – M Epichlorohydrin 106898 0.001 IRIS – M Ethyl benzene 100414 1 IRIS – L Ethylene glycol 107211 0.4 CAL Formaldehyde 50000 0.0098 ATSDR Glycol Ethers 10 181 0.02 IRIS – M - Diethylene glycol monobutyl ether (DEGBE) * 112345 0.02 - Ethylene glycol ethyl ether 110805 0.2 IRIS - M

9 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.

10 The RfC value for ethylene glycol methyl ether (EGME) was used as a surrogate for all glycol ethers without an RfC (denoted with an *).

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Table 2.6-1 (c) 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 (incl uding sensitive subgroups) that is likely to be without an appreciable risk of delet erious effects during a lifetime. Sources: IRIS = EPA Integrated Risk Informatio n System, CAL = California EPA Office of Environmental Human Health Assessment, ATSDR = US Agency for Toxic Substanc es Disease Registry, HEAST = EPA Health Effects Assessment Summary Table
Pollutant CAS Number 6 RfC (mg/m3) Source 9

- Ethylene glycol methyl ether (EGME) 109864 0.02 IRIS - M - 2-Butoxyethyl acetate * 112072 0.02 - Ethylene glycol ethyl ether acetate 111159 0.3 CAL - Methoxytriglycol * 112356 0.02 Hexamethylene-1,6-diisocyanate 822060 0.00001 IRIS - L
n-Hexane 110543 0.7 IRIS – M Hydrochloric acid 7647010 0.02 IRIS – L Hydrofluoric acid 7664393 0.014 CAL
Methanol 67561 4 CAL Methyl isobutyl ketone 108101 3 IRIS – L/M Methylene chloride 75092 1 ATSDR Naphthalene 91203 0.003 IRIS – M
Phenol 108952 0.2 CAL Styrene 100425 1 IRIS – M
Toluene 108883 5 IRIS – H Methyl Chloroform (1,1,1-T richloroethane) 71556 5 IRIS Trichloroethylene 79016 0.6 CAL Vinyl acetate 108054 0.2 IRIS – H Xylenes (mixed) 1330207 0.1 IRIS – M m-Xylene 11 108383 0.1 IRIS – M o-Xylene 11 95476 0.1 IRIS – M p-Xylene 11 106423 0.1 IRIS – M



### 2.6.2 Sources of acute dose-response information Hazard identification and dose-response assessment information for preliminary acute inhalation exposure assessments are based on the existing recommenda tions of OAQPS for HAPs [ 26 ]. Depending on availability, the result s 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 refe rence level depends on the definition of that level and any limitations expressed therei n. Comparisons among different available inhalation health effect refere nce values (both acute and chro nic) for selected HAPs can be

11 The RfC for mixed xylene was used as a surrogate.

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found in a newly released EPA document (Reference: U.S. EPA. Graphical Arrays of Chemical-Specific Health Effect Reference Valu es for Inhalation Exposures [Final Report]. (U.S. Environmental Protection Agen cy, Washington, D.C., EPA/600/R-09/061, 2009. http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=211003.)

California Acute Reference Exposure Levels (RELs ). The California Environmental Protection Agency (CalEPA) has developed acu te dose-response refe rence values for many substances, expressing the results as Acute i nhalation 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. [ 27 ]. RELs are based on the most sensitive, relevant, adverse health effect reported in the medi cal 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 auto matically indicate an adverse health impact.” Acute RELs are developed fo r 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 Guide line 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/op ptintr/aegl/pubs/sop.pdf ), AEGLs “represent threshold exposure limits for the general public and are applicable to emer gency 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 fixe d facilities and from tran sport carriers.” The document states that “the primary purpos e of the AEGL program and the NAC/AEGL Committee is to develop guideline levels for once-in-a-lifetim e, short-term exposures to airborne concentrations of acute ly toxic, high-priority chemi cals.” In detailing the intended application of AEGL values, the document states that, “It is antic ipated 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 c onjunction with chemical emergency response, planning, and prevention programs. More spec ifically, the AEGL values will be used for conducting various risk assessments to aid in the development of emergency preparedness and prevention plans, as well as real-time emerge ncy response actions, for accidental chemical releases at fixed facilities and from transpor t carriers.” The NAC/A EGL defines AEGL-1 and AEGL-2 as:

“AEGL-1 is the airborne concentr ation (expressed as ppm or mg/m 3 

### ) of a substance above which it is predicted that th e general population, including su sceptible individuals, could experience notable discomfort, irritation, or certain asymptomatic nonsensory effects. However, the effects are not disabling and ar e transient and revers ible upon cessation of

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exposure.”

“AEGL-2 is the airborne concentr ation (expressed as ppm or mg/m 3 

### ) of a substance above which it is predicted that th e general population, including su sceptible individuals, could experience irreversible or other serious, long-lasting adverse h ealth effects or an impaired ability to escape.”

“Airborne concentrations below AEGL-1 repr esent exposure levels that can produce mild and progressively increasing but transient and nondisabli ng odor, taste, and sensory irritation or certain asymptomatic, nonsenso ry 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 el derly, persons with asthma, and those with other illnesses, it is recognized that indivi duals, subject to unique or idiosyncratic responses, could experience the effects de scribed at concentr ations below the corresponding AEGL.”

Emergency Response Planning Guidelines (ERPGs). The American Industrial Hygiene Association (AIHA) has developed Emergenc y Response Planning Guidelines (ERPGs) [ 28 ] for acute exposures at three different levels of severity. Thes e guidelines represent concentrations for exposure of the general population (but not pa rticularly sensitive persons) for up to 1 hour associated with effects expected to be mild or transient (ERPG-1), irreversible or serious (ERPG-2), and potentially life-threatening (ERPG-3).

ERPG values ( 

### http://www.aiha.org/1documents /Committees/ERP-erpglevels.pdf ) are described in their supporting documentation as follows: “Emergency Response Planning Guidelines (ERPGs) were developed for emerge ncy planning and are intended as health based guideline concentrations for single exposures to chemicals. These gui delines (i.e., the ERPG Documents and ERPG values) are intended fo r use as planning tools for assessing the adequacy of accident prevention and emergenc y response plans, including transportation emergency planning and for developing community emergency response plans. The emphasis is on ERPGs as planning values: When an actua l chemical emergency occurs there is seldom time to measure airborne concentrations and then to take action.” ERPG-1 and ERPG-2 values are defined by AIHA as follows:

“ERPG-1 is the maximum airborne concentration below which it is believed that nearly all individuals could be exposed for up to 1 hour without experiencing other than mild transient adverse health eff ects 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 h ealth effects or symptoms which could impair an individual's ability to take protective action.”

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The emissions inventory for the Shipbuilding an d Ship Repair Operations source category includes emissions of 23 HAP w ith relevant and available qua ntitative acute dose-response threshold values. These HAPs, their acute thre shold values, and the source of the value are listed below in Table 2.6-2.

Table 2.6-2 Dose-Response Values for Acute Exposure

Pollutant CAS Number AEGL-1 (1-hr) (mg/m 3 ) AEGL-2 (1-hr) (mg/m 3 ) ERPG-1 (mg/m 3 ) ERPG-2 (mg/m 3 ) REL Acetaldehyde 75070 81 490 81 490 0.47
Acrolein 107028 0.069 0.23 0.069 0.23 0.0025
Benzene 71432 170 2600 170 2600 1.3 Chlorobenzene 108907 46 690 Cumene 98828 250 1500 Epichlorohydrin 106898 22 91 22 91 1.3 Ethyl Benzene 100414 140 4800 Formaldehyde 50000 1.1 17 1.1 17 0.055 Glycol ethers 12 0.093 - 2- Butoxyethyl Acetate * 112072 0.093 - Ethylene Glycol Ethyl Ether 110805 0.37 - Ethylene Glycol Ethyl Ether Acetate 111159 0.14 - Ethylene Glycol Methyl Ether 109864 0.093 - Diethylene Glycol Monobutyl Ether * 112345 0.093
Hexane 110543 12000 Hydrochloric acid 7647010 2.7 33 2.7 33 2.1 Hydrogen fluoride 7664393 0.82 20 0.82 20 0.24
Methanol 67561 690 2700 690 2700 28 Methyl Chloroform (1,1,1- Trichloroethane) 71556 1300 3300 1300 3300 68 Methyl Isobutyl Ketone 108101 Methylene Chloride 75092 690 1900 690 1900 14
Phenol 108952 58 89 58 89 5.8 Styrene 100425 85 550 85 550 21
Toluene 108883 750 4500 190 1130 37 Trichloroethylene 79016 700 2400 700 2400 Vinyl Acetate 108054 24 630 18 260 Xylenes (mixed) 1330207 560 4000 22



## 2.7 Risk Characterization

2.7.1 General

12 The acute REL for ethylene glycol methyl ether (EGME) was used as a surrogate for glycol ether compounds without an acute REL.

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The final product of the risk as sessment is the risk characteri zation, in which the information from the previous steps is integrated and an overa ll conclusion about risk is synthesized that is complete, informative, and useful for decision ma kers. In general, the nature of this risk characterization depends on the in formation available, the application of the risk information and the resources available. In all cases, majo r issues associated with determining the nature and extent of the risk are identified and disc ussed. Further, the EPA Administrator’s March 1995 Policy for Risk Characterization [ 29 ] specifies that a risk ch aracterization “be prepared in a manner that is clear, transparent, r easonable, and consistent with other risk characterizations of similar scope prepared acro ss programs in the Agency.” These principles of transparency and consistency have been reinforced by the Agency’s Risk Characterization Handbook [ 30 ], in 2002 by the Agency’s information quality guidelines [ 31 ], and in the OMB/OSTP September 2007 Memorandum on U pdated Principles for Risk Analysis 13 

### , and are incorporated in these assessments.

Estimates of health risk are pres ented in the context of uncertainties and limitations in the data and methodology. Through our tiered, iterative an alytical approach, we have attempted to reduce both uncertainty and bias to the greate st degree possible in these assessments, within the limitations of available time and resour ces. We provide summari es of risk metrics (including maximum individual cancer risks and noncancer hazards, as well as cancer incidence estimates) along with a discussion of the major uncertainties associated with their derivation to provide decision ma kers with the fullest pictur e of the assessment and its limitations.

For each carcinogenic HAP included in an assess ment that has a potency estimate available, individual and population cancer risks were calculated by multiplying the corresponding lifetime average exposure estimate by the approp riate URE. This calculated cancer risk is defined as the upper-bound probability of de veloping cancer over a 70- yr period (i.e., the assumed human lifespan) at that exposure. Because UREs for most HAPs are upper-bound estimates, actual risks at a given exposure le vel may be lower than predicted, and could be

zero.

For EPA’s list of carcinogenic HAPs th at act by a mutagenic mode-of-action [ 32 ], we applied EPA’s Supplemental Guidance for Asse ssing Susceptibility from Early-Life Exposure to Carcinogens [ 33 ]. 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 th is 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 term s 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 [ 34 ] recommends applying the Supplemental Guidance to all carcinogenic PAHs for which risk

13 Memorandum 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 Depu ty Director for Science, Office of Science and Technology Policy (http://www.whitehouse.gov/omb/memoranda/fy2007/m07-24.pdf)

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estimates are based on relative potency. Acco rdingly, we applied the Supplemental Guidance to all unspeciated POM mixtures.

Increased cancer incidence for the entire receptor population with in 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 populati on incidence estimate was divide d by 70 years to obtain an estimate of the number of cancer cases per year.

In the case of benzene, the high end of th e reported cancer URE range was used in our assessments to provide a conservative estimate of potential cancer risks. Use of the high end of the range provides risk estimates that are approximately 3.5 times higher than use of the equally-plausible low end value. If the esti mated benzene–associated risks exceed 1 in a million, we also evaluated the impact of using the low end of the URE range on our risk results.

Unlike linear dose-response assessments for can cer, noncancer health hazards generally are not expressed as a probability of an adverse occurrence. Inst ead, “risk” for noncancer effects is expressed by comparing an exposure to a refe rence level as a ratio. The “hazard quotient” (HQ) is the estimated exposure divided by a re ference 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 th e degree of confidence ascribed to the RfC values for the compound(s) of concern (i.e., high, medium, or lo w confidence) and discusses the impact of this on possible health interpretations.

The risk characterization for chronic effects othe r than cancer is expressed in terms of the HQ for inhalation, calculated for each HAP at each census block centroid. As discussed above, RfCs incorporate generally conservative uncer tainty 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 adve rse effects will occur, and is unlikely to be proportional to advers e health effect outcomes in a population.

Screening for potentially signi ficant 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 valu es relative to the various acute endpoints and thresholds. In general, when none of these HQ values are greater th an 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 a ssociated with all the exposures from a particular source category combined.

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To combine risks across multiple carcinogens, ou r assessments use the mixtures guidelines’ [ 35

, 36 ] 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 expo sures, 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 th ese mode-of-action differences, the mixtures guidelines support aggregating effects of different substances in specif ic and limited ways. To conform to these guidelines, we aggregated non-cancer HQs of HAPs 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 indi vidual HAPs that affect the same organ or organ system. All TOSHI calcu lations presented here were based exclusively on effects occurring at the “critica l dose” (i.e., the lowest dose that pr oduces adverse health effects). Although HQs associated with some pollutants ha ve been aggregated into more than one TOSHI, this has been done only in cases where th e 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. Ra ther, the TOSHI is another measure of the potential for adverse health outcomes associated with pollutant exposur e, and needs to be interpreted carefully by health scientists and risk managers.

Because of the conservative nature of the acute inhalation screening and th e variable nature of emissions and potential exposures, acute impact s were screened on an individual pollutant basis, not using the TOSHI approach.

2.7.3 Facility-wide Risks To help place the source category risks in cont ext, we examined “facility-wide” risks using facility-wide and source category data files developed by EPA thru emission data updates from industry and refined EPA profiles for this source category. For the facilities in the Shipbuilding and Ship Repair Operations sour ce category, we modeled facility-wide risks from these files, to estimate the maximum i nhalation cancer and chronic non-cancer risks for all HAP emissions sources at the facility. These emissions include all sources that are located within a contiguous area and are under common c ontrol by the facility. We analyzed risks due to the inhalation of HAP for the populati ons residing within 50 kilometers of each facility. The results of the facility-wide asse ssments are provided for in Table 2 of Appendix 5.

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## 3 Risk Results for the Shipbuilding and Ship Repair Operations Source Category

3.1 Source Category Descri ption and Results The shipbuilding and ship repair operations industry consists of establishments that build, repair, repaint, convert, and alter ships. In ge neral, activities and processes involved in ship repair and new ship construction are relatively similar. Operations include fabrication of basic components from raw materials, weldi ng to join components and parts together, painting and repainting, overhauls, ship convers ions, and other alterations. Nearly all shipyards that construct new ships also perform major ship re pairs. Marine coatings used on offshore oil and gas well drilling and production platforms are not included in this source category. Emissions of hazardous air pollutant s (HAP) from shipbuilding and ship repair facilities are primarily the result of emi ssions from painting operations and cleaning operations. The HAP emitted include a range of organic compounds used as solvents, including toluene, xylene, ethylbenzene, metha nol, methyl isobutyl ke tone, ethylene glycol, and glycol ethers.

We currently estimate that there are 90 shipbui lding and ship repair operations facilities operating in the U.S. The NPRM data set contai ns 85 facilities identified with a shipbuilding and ship repair operations (surface coating) MACT code in the 2005 National-Scale Air Toxics Assessment (NATA) National Emissions Inventory (NEI). While the NEI identifies 44 of these 85 facilities as area sources of HAP (and 41 as ma jor sources), the uncertainty associated with these major/area classifications is significant. Without ve rification of the area source status of individual facilities, EPA will c onsider all facilities in the NEI to be major sources in this and any future RTR risk assessments.

The emissions for the shipbuilding and ship repa ir operations NPRM data set (of 85 facilities) are summarized in Table 3.1-1. The total HAP emissions for the source category are approximately 940 tons per year. Based on these data, the HAP emitted in largest quantities by the source category are xylenes, ethyl benzen e, toluene, methanol, glycol ethers, and methyl isobutyl ketone. Emissions of thes e six HAP make up 99 percent of the total emissions by mass. Xylenes are reported as an emission at 82 facilitie s (96 percent); ethyl benzene and toluene are reported as emissions at 72 facilities (85 percen t) and 68 facilities (80 percent), respectively. Persistent and bioaccumulative HAP (PB HAP) 14 

### emissions reported in the data set for the shipbuilding and ship repair source category in clude polycyclic organic matter (POM).

14 Persistent and bioaccumulative HAP are defined in the EPA’s Air Toxics Risk Assessment Library , Volume 1, EPA-453K-04-001A, wh ich is accessible from the EPA’s Technolo gy Transfer Network website for Fate, Exposure, and Risk Assessment at http://www.epa.gov/ttn/fera/risk_atra_vol1.html

.

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Table 3.1-1 Summary of Emissions from the Shipbuilding and Ship Repair Operations Source Category and Availability of Dose-Response Values

HAP a Emissions (tpy) Number of Facilities Reporting HAP (85 facilities in

ANPRM data set) Prioritized Inhalation Dose-Response Value Identified by OAQPS b

PBHAP? Unit Risk Estimate for

Cancer? Reference Concentration for Noncancer? Health Benchmark Values for Acute Noncancer?
Xylenes (Mixture of o, m, and p Isomers) 717 82 U U
Ethyl Benzene 138 72 U U Toluene 55 68 U U Styrene 0.3 3 U U Methanol 13 7 U U Glycol Ethers 1 2 U U Methyl Isobutyl Ketone 5 11 U
Hexane 3 3 U U Methylene Chloride 2 1 U U U Dibutylphthalate 1 5
Cumene 0.8 7 U U Phenol 0.2 4 U U Methyl Chloroform 0.03 2 U U Ethylene Glycol 0.2 3 U Naphthalene 0.1 6 U U Diethylene Glycol Monobutyl ether 0.2 1 U

Benzene 0.08 3 U U U Methoxytriglycol 0.1 1 U U Vinyl Acetate 0.08 1 U U Ethylene Glycol Methyl Ether 0.03 1 U U 2-Butoxyethyl Acetate 0.03 1 U U Trichloroethylene 0.05 1 U U U Chlorobenzene 0.04 1 U U Ethylene Glycol Ethyl Ether Acetate 0.02 1 U U
Acetaldehyde 0.01 1 U U U p-Dichlorobenzene 0.008 1 U U Chloroprene 0.007 1 U Formaldehyde 0.007 2 U U U
POM 0.007 2 U Y Hydrochloric Acid 0.006 1 U U 4,4’-Methylene bis(2- chloroaniline) 0.002 1 U
Hydrogen Fluoride 0.0006 1 U U Epichlorohydrin 0.0005 1 U U U Hexamethylene-1,6- 0.0005 1 U

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Table 3.1-1 Summary of Emissions from the Shipbuilding and Ship Repair Operations Source Category and Availability of Dose-Response Values

HAP a Emissions (tpy) Number of Facilities Reporting HAP (85 facilities in

ANPRM data set) Prioritized Inhalation Dose-Response Value Identified by OAQPS b

PBHAP? Unit Risk Estimate for

Cancer? Reference Concentration for Noncancer? Health Benchmark Values for Acute Noncancer?
diisocyanate
Acrolein 0.0003 1 U U

a Notes for how HAP were speciated for risk assessment

• For emissions of any chemicals or chemical groups classified as polycyclic organic matter (POM), emissions were grouped into POM subgroups as found on EPA’s Technology Tr ansfer Network website for the 2002 National-Scale Air Toxics Assessment at http://www.epa.gov/nata2002/methods.html#pom . ( Approach to Modeling POM ).

b Specific dose-response values for each chemical are identified on EPA’s Technology Transfer Network website for air toxics at http://www.epa.gov/ttn/atw/toxsource/summary.html and are also available in Appendix 5 of the Development of Preliminary Risk Estimat es for Source Categories Previously Subj ected to Technology-based Standards document.



## 3.2 Risk Characterization

This section presents the resu lts of the preliminary risk as sessment for the wood furniture source category. The basic chronic inhalation ri sk estimates presented here are the maximum individual lifetime cancer risk, the maximum chr onic hazard index, and the cancer incidence. We also present results from our acute inhalati on impact assessments in the form of maximum hazard quotients, as well as the results of our screen for potential non-inhalation risks from PB HAP. Also presented are the HAP “dri vers,” which are the HAP that collectively contribute 90 percent of the maximum cancer ri sk or maximum hazard at the highest exposure location, as well as a summary of the results of our facility-wide assessments and our analysis of risks associated with the maximum allowed emissions under the current MACT standards. A detailed summary of the facility-specific risk assessment results is av ailable in Appendix 6.

Tables 3.2-1 and 3.2-2 summarize the preliminary chronic and acute inhalation risk results for this source category. The results of the chroni c inhalation cancer risk assessment indicate the maximum lifetime individual cancer risk could be as high as 10 in a million. The maximum cancer risks for this source cate gory exceeded a cancer risk of 1 in a million at 26 facilities, with emissions of ethylbenzene dominating the risk. The total estimated cancer incidence from this source category based on actual em ission levels is 0.003 excess cancer cases per year, or one case in every 330 y ears. Approximately 20 people we re estimated to have cancer risks above 10 in a million and approximately 3,700 people were estimated to have cancer risks above 1 in a million.

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Table 3.2-1 Summary of Source Category Leve l Inhalation Risks for Shipbuilding and Ship Repair Operations

Result HAP “Drivers” Facilities in Source Category Number of Facilities Estimated to be in Source Category 90 n/a

Number of Facilities Identified in the NEI and Modeled in Preliminary Risk Assessment 85 n/a

Cancer Risks Maximum Individual Lifetime Cancer Risk (in 1 million) 10 ethyl benzene
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 1 ethyl benzene
Greater than or equal to 1 in 1 million 26 ethyl benzene, naphthalene, methylene chloride Chronic Noncancer Risks Maximum Hazard Index (Neurological) 0.5 xylenes (REL) Number of Facilities with Maxi mum Reproductive Hazard Index: Greater than 1 0 n/a Acute Noncancer Screening Results Maximum Acute Hazard Quotient 4 glycol ethers a 

### (REL) Number of Facilities With Potential for Acute Effects 1 glycol ethers a



### Refined Acute Noncancer Results Maximum Acute Hazard Quotient 0.1 glycol ethers a 

### (REL) Population Exposure Number of People Living Within 50 Kilometers of Facilities Modeled 63,000,000 n/a
Number of People Exposed to Cancer Risk: Greater than or equal to 100 in 1 million 0 n/a Greater than or equal to 10 in 1 million 20 n/a Greater than or equal to 1 in 1 million 3,700 n/a Number of People Exposed to Noncancer Hazard Index: Greater than 1 0 n/a Estimated Cancer Incidence (excess cancer cases per year) 0.003 n/a
Contribution of HAP to Cancer Incidence: ethyl benzene 99% n/a naphthalene 1% n/a

a Emissions of glycol ethers as sociated with the maximum acute impacts were not speciated into individual glycol ether

compounds. Their impacts were assessed us ing the acute REL for EGME, which is the lowest acute REL for any glycol ether.

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Table 3.2-2 Summary of NPRM Acute Re fined Results for Shipbuilding and Ship Repair Operations Facilities

Refined Results MAXIMUM ACUTE HAZARD QUOTIENTS ACUTE DOSE-RESPONSE VALUES

HAP Max. 1-hr. Air Conc.

(mg/m 3 ) Based on REL Based on AEGL-1/ ERPG-1 Based on AEGL-2/ ERPG-2 REL (mg/m 3 ) AEGL-1 (1-hr) (mg/m 3 ) ERPG-1 (mg/m 3 ) AEGL-2 (1-hr) (mg/m 3 ) ERPG-2 (mg/m 3 )

glycol ethers a 0.0093 0.1 n/a n/a 0.093

a Emissions of glycol ethers asso ciated with the maximum acute impacts were not speciated into individual glycol ether

compounds. Their impacts were assessed us ing the acute REL for EGME, which is the lowest acute REL for any glycol ether.

Notes on Refined Process: 1) The screening was performed for all emitted HAP with availabl e acute dose-response values. Only those pollutants whose screening HQs were greater than 1 for at least one acute threshold value are shown in the table. 2) HAP with available acute dose-response valu es which are not in the table do not carry any potential for posing acute health ris ks, based on an analysis of curren tly available emissions data. Notes on Acute Dose-Response Values: REL – California EPA reference exposure level for no adverse effects. Most, but not all, RELs are for 1-hour exposures. AEGL – Acute exposure guideline levels represent emer gency exposure (1-hour) limits for the general public. AEGL-1 is the exposure level above which it is predicted that the general population, including susceptible individuals, could experience effects that are notable discomfort, but whic h are transient and reversible upon cessation of exposure. AEGL-2 is the exposure level above which it is predicted that the general population, including susceptible individuals, could experience irreversible or other seriou s, long-lasting adverse health effects of an impaired ability to escape. EPRG – Emergency Removal Program gui delines represent emergency exposure (1-hour) limits for the general public. ERPG-1 is the maximum level below which it is believed that nearly all individuals could be exposed for up to 1 hour without experiencing other than mild, tran sient adverse health effects. ERPG-2 is the maximum exposure below which it is believed that nearly all individuals could be exposed for up to 1 hour without experiencing or developing irreversible or other serious health effects or symptoms which could impair an individual’s ability to take protective action.



### The maximum chronic noncancer TOSHI value is less than 0.5, with em issions of xylene(s) dominating those impacts, indicating no significa nt potential for chronic noncancer impacts. The primary process contributing to the sour ce category cancer and chronic non-cancer risks for shipbuilding and repair is painting operations.

The maximum screening acute HQ exceeded a value of 1 based upon the REL for emissions of glycol ethers at 1 facility; this required a refined assessment be conducted at that facility. The result of refining the assessment was th at the maximum off-s ite HQ was 0.1, indicating that there are no concerns for potential acute heal th impacts for this source category. Refer to Appendix 7 for details on the refined assessment.

In performing the screening for potential multi-pa thway exposures and risks of concern, we determined that emissions of cadmium a nd POM were not significant enough to pose multipathway impacts of concern for human health or the environment. We also determined that emissions of lead were not significant enough to cause an exceedance of the lead NAAQS, indicating that potential multipathway impacts fo r lead from this source category are not significant.

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The results of the facility-wide assessments are summarized in Table 3.2-3. The results indicate that 35 facilities contai ning Shipbuilding and Ship RepairOperations have a facilitywide cancer MIR greater than 1 in a million. The maximum f acility-wide cancer MIR is 200 in a million, with the source category contributing less than 1 percent at 1 in a million. There are also 6 facilities with a f acility-wide TOSHI value greater than 1. The maximum facilitywide TOSHI value of 4 (nervous system), has a source category contribu tion of less than 2%. In general, the contribution of the source ca tegory to elevated f acility-wide cancer or noncancer risks is low throughout th e facilities in this source ca tegory. The primary processes driving the facility-wide cancer and noncancer risks are welding and blasting.

Table 3.2-3 Shipbuilding Source Category Cont ribution to Facility-Wide Cancer Risks

Number of Facilities Binned by Facility-Wide MIR (in 1 million) Source Category MIR Contribution to Facility-Wide MIR <1 ≥ 1 <10 ≥ 10 <100 > 100 Total

100% 23 8 1 0 32 90-99% 4 2 0 0 6 50-89% 4 4 0 0 8 10-49% 7 6 0 13

1-9% 1 4 8 0 13

0% 5 4 1 3 13 Total 50 20 12 3 85

Analysis of potential differences between act ual emission levels and the maximum emissions allowable under the MACT standards indicated that MACT allowable em ission levels may be up to 2 times greater than actual emission levels. Considering this differe nce, the risk results from the inhalation risk assessment indicate that the maximum excess lifetime individual cancer risk could be up to 20 in a million, a nd that the maximum chronic noncancer TOSHI value could be as high as1 at the MACT-allowabl e emissions level. For more details about the estimate of the ratio of act ual to MACT-allowable emissions , see the memo in the docket entitled, “Actual vs. Allowable Emissions for Shipbuilding and Ship Repair Operations - Emission Standards Technical Support and Post Promulgation Activities” [ 37 ].



## 4 General Discussion of Uncertaint ies and How They Have Been Addressed

4.1 Exposure Modeling Uncertainties

Although every effort has been made to identify a ll the relevant faciliti es and emission points, as well as to develop accurate estimates of the annual emission rates for all relevant HAP, the uncertainties in our emission inventory likely dominate the uncertainties in our exposure modeling estimates. The chronic exposure modeli ng uncertainties are considered relatively small in comparison, since we are using EPA’ s refined local disper sion model with site-

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specific parameters and reasonably representa tive 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 i ndividual risk (MIR) contain uncertainty, because they are derived at census block centroid locations rather than actual residences. This uncertainty is known to create potential under estimates 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 sour ce category. Finally, we did not factor in the possibility of a source closure occurri ng during the 70-year chronic exposure period, leading to a potential upward bias in both the MIR and populati on 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 populat ion risk estimates.

A sensitivity analysis performed for the 1999 NATA found that th e selection of the meteorology dataset location could result in a ra nge of chronic ambient concentrations which varied from as much as 17% below the predic ted value to as much as 84% higher than the predicted value. This va riability translates dir ectly to the predicted exposures and risks in our assessment, indicating that the actual risks could vary from 17 % lower to 84% higher than the predicted values.

We have purposely biased the acute screening results high, consideri ng that they depend upon the joint occurrence of independent factors, su ch as hourly emissions rates, meteorology and human activity patterns. Furthermore, in cas es where multiple acute threshold values are considered scientifically accep table we have chosen the most conservative of these assessments, erring on the side of overestimating potential health risks from acute exposures. In the cases where these results indicated the potential for exceeding short-term health thresholds, we have refined our assessmen t by developing a better understanding of the geography of the facility relative to potential exposure locations. In this case, refining the assessment for the one facility that did not screen out resulted in lower acute risks.



## 4.2 Uncertainties in the Dose -Response Re lationships

In the sections that follow, separate discussi ons 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% of lifetime) exposure durations, all of which are derived based on an assumption of con tinuous exposure throughout the duration specified. For the purposes of assess ing 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 p eer-reviewed dose-response values for all HAPs 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 acu te effects (inhalation or ingestion). Since exposures to these pollu tants cannot be included in a quantitative risk

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estimate, an understatement of risk for these po llutants at environmental exposure levels is possible. Additionally, chronic dose -response values for certain compo unds included in the assessment may be under EPA IRIS review and revised asse ssments 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 mischaracter ize 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 specifi c 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 sour ce of peer-reviewed cancer potency factors used where EPA-developed values are not yet ava ilable. To place this discussion in context, we provide a quote from the EPA’s Guidelines for Carcinogen Risk Assessment [ 38 ] (herein referred to as Cancer Guidelines ). “The primary goal of EPA actions is prot ection of human health; accordingly, as an Agency policy, ri sk assessment procedures, including default options that are used in the absence of scien tific data to the cont rary, should be health protective.” The approach adopt ed in this document is consis tent with this approach as described in the Cancer Guidelines

.
For cancer endpoints EPA usually derives an oral slope factor for inge stion and a unit risk value for inhalation exposures. These values allow estimation of a lifetime probability of developing cancer given long-term exposures to the pollutant. Depending on the pollutant being evaluated, EPA relies on both animal bioassay and epidemiological studies to characterize cancer risk. As a science policy approach, consistent with the Cancer Guidelines , EPA uses animal cancer bioassays as i ndicators of potential human health risk when other human cancer risk data are unavailable.

Extrapolation of study data to estimate poten tial 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 ri sk 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). 15 

### In some circumstances, the true risk could be as low as zero; howev er, in other circumstances the risk could also be greater. 16 

### 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. 17 

### EPA also

15 IRIS glossary (www.epa.gov/NCEA/iris/help_gloss.htm).

16 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, an d which is based on maximum likelihood estimates.

17 According to the NRC report Science and Judgment in Risk Assessment (NRC, 1994) “[Default] options are generic approaches, based on general scientific knowle dge and policy judgment, that are applied to various

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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 wh ich may be considered quantitatively, and others which generally are ex pressed qualitatively. Uncertainties may vary substantially among cancer risk a ssessments associated with expos ures to different pollutants, since the assessments employ different database s with different strengt hs 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 estimat es demonstrating the impact on the assessment of alternative choices, data, models and assumptions [ 39 ]. Some of the major sources of uncertainty and variability in deriving cancer risk valu es are described more fully below.

(1) The qualitative similarities or differen ces between tumor responses observed in experimental animal bioassays and those wh ich would occur in humans is a source of uncertainty in cancer risk assessment. In ge neral, EPA does not assume that tumor sites observed in an experimental an imal bioassay are necessarily predictive of the sites at which tumors would occur in humans. 18 

### However, unless scientific support is ava ilable to show otherwise, EPA assumes that tumors in animals are relevant in humans, regardless of target organ concordance. For a specific pollutant, qua litative differences in species responses can lead to either under-estimation or over-e stimation of human cancer risks.

(2) Uncertainties regarding the most appropriate dose metric for an assessment can also lead to differences in risk predictions. For exam ple, the measure of dose is commonly expressed in units of mg/kg/d ingested or the inhaled con centration of the pollutant. However, data may support development of a pharmacokinetic mode l for the absorption, distribution, metabolism and excretion of an agent, which may resu lt 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 choi ce of a dose metric is uncertain or when dose metric estimates are themselves uncerta in (e.g., as can occur when alternative pharmacokinetic models are available for a co mpound). Uncertainty in dose estimates may lead to either over or underestimation of risk.

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 under estimated (although defaults are not intended to overtly overestimate risk). See EPA 2004 An Examination of EPA Risk Asse ssment Principles and Practices, EPA/100/B-04/001 available at: http://w ww.epa.gov/osa/pdfs/ratf-final.pdf.

18 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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(3) For the quantitative extrapolation of cancer risk estimates from experimental animals to humans, EPA uses scaling met hodologies (relating expected response to differences in physical size of the species), which introdu ce another source of uncertainty. These methodologies are based on both biol ogical data on differences in rates of process according to species size and empirical comparisons of toxicity between expe rimental animals and humans. For a particular pollutant, the quant itative 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 calculat ion. Statistical uncertainty in developing a POD using a benchmark dose (BMD) approach is generally addressed though use of the 95% lower confidence limit on the dose at which the sp ecified 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 furt her discussion below).

(5) Extrapolation from high to low doses is an important, and potential ly 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 ep idemiological studies at higher dose) depending on the available data and understa nding 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, bi ologically-based risk models when feasible; however, such models are rarely available. The mode of action for a pollutant (i.e., the manner in which a pollutant causes cancer) is a key considerati on in determining how ri sks 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. Lin ear extrapolation can be suppor ted by both pollutant-specific data and broader scientific cons iderations. For example, EPA’s Cancer Guidelines generally consider a linear dose-response to be appropria te for pollutants that interact with DNA and induce mutations. Pollutants whose effects ar e 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 estimate d by the straight line approach.

EPA most frequently utilizes a linear low-dose extrapolation appr oach as a baseline sciencepolicy choice (a “default”) when available data do not allow a compound-specific determination. This approach is designed to not underestimate risk in the face of uncertainty and variability. EPA believes that linear dose-response models , when appropriately applied as part of EPA’s cancer risk assessment proce ss, provide an upper bound estimate of risk and generally provide a health protec tive approach. Note that anothe r source of uncertainty is the

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characterization of low-dose non linear, non-threshold relationships. The National Academy of Sciences has encouraged the exploration of sigmoidal type functions (e.g., log-probit models) in representing dose res ponse relationships due to the variability in response within human populations. Another National Research Council (NRC) report [ 40 ] 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 in dividual tolerances to vary extensively in humans depending on genetics, co incident 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 respons e in human populations (as opposed to responses in bioassays with genetically more uniform rodents). Note al so that low dose linearity in risk can arise for reasons separate from population variability: du e 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 equiva lent to using an upper confidence limit on the slope of the straight line extr apolation. The impact of the choice of the BMDL on bottom line risk estimates can be quantified by comparing ri sk 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) [ 41 ]. 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 st raight line extrapolation from a POD do not show large differences from those based on the upper confidence limit of the multistage model.

(6) Cancer risk estimates do not generally make specific adjustments to reflect the variability in response within the human population — resu lting 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 fr om 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 ar e designed not to underestimate risk in the face of uncertainty and variability.

Chronic noncancer assessment

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Chronic noncancer reference values represent chronic exposure leve ls that are intended to be health-protective. That is, EPA and other or ganizations 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 informa tion, 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 values 19 

### e.g., factors of 10 or 3, used in the absence of compound-specific data; wh ere data are available, uncertainty factors may also be developed using compound-specific information. When data are limited, more assumptions are needed and more default fact ors are used. Thus there may be a greater tendency to overestimate risk—in the sense th at further study might support development of reference values that are higher (i.e., less pot ent) because fewer default assumptions are needed. However, for some pollutants it is possible that risks ma y be underestimated.

For non-cancer endpoints related to chronic exposures, EPA derives a Reference Dose (RfD) for exposures via ingestion, and a Reference C oncentration (RfC) for inhalation exposures. These values provide an estimate (with uncerta inty spanning perhaps an order of magnitude) of daily oral exposure (RfD) or of a conti nuous inhalation exposure (RfC) to the human population (including sensitive subgroups) that is lik ely to be without an appreciable risk of deleterious effects during a lifetime. 20 

### To derive values that are intended to be “without appreciable risk,” EPA’s methodol ogy relies upon an uncertainty factor (UF) approach (U.S. EPA, 1993, 1994) which includes considerati on of both uncertainty and variability.

EPA begins by evaluating all of the availabl e peer-reviewed literature to determine noncancer endpoints of concern, eval uating the quality, strengths and limitations of the available studies. EPA typically chooses th e relevant endpoint that occu rs at the lowest dose, often using statistical modeling of the available data, and then determines th e 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 dos e (BMD) approach; (2) use of the dose or concentration at which the toxic response was not signi ficantly elevated (no observed adverse effect level— NOAEL); or (3) use of the lowest observed adverse effect level (LOAEL).

19 According to the NRC report Science and Judgment in Risk Assessment (NRC, 1994) “[Default] options are generic approaches, based on general scientific knowle dge 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 under estimated (although defaults are not intended to overtly overestimate risk). See EPA 2004 An examination of EPA Risk Assess ment Principles and Practices, EPA/100/B-04/001 available at: http://w ww.epa.gov/osa/pdfs/ratf-final.pdf.

20 See IRIS glossary

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A series of downward adjustments using defau lt UFs is then applied to the POD to estimate the reference value (U.S. EPA 1994, 2002). Whil e collectively termed “UFs”, these factors account for a number of different quantitative co nsiderations 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 th e members of the human population (i.e., interindividual variability); (2) uncer tainty in extrapolating from experimental animal data to humans (i.e., interspecies diffe rences); (3) uncertainty in extr apolating from data obtained in a study with less-than-lifetime exposure (i.e., extrapolating from subchronic to chronic exposure); (4) uncertainty in extrapolating fr om 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 sele cted 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 da ta do not support the use of a 10-fold factor. If an extrapolation step or adjustment is not re levant 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 sour ce of variability as well as uncertainty. Uncertainty related to human variation is consid ered 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 variatio n may be larger or smaller; however, data to examine the potential magnitude of human va riability are often una vailable. In some situations, a smaller UF of 3 ma y be applied to reflect a known l ack of significant variability among humans.

2) Extrapolation from results of studie s in experimental animals to humans is a necessary step for the majority of chemical ri sk assessments. When interpreting animal data, the concentration at the POD (e.g. NOAEL, BM DL) in an animal model (e.g. rodents) is extrapolated to estimate the human response. While there is long-sta nding scientific support for the use of animal studies as indicators of potential toxicity to humans, there are uncertainties in such extrapolat ions. 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 aver age human. Typically, compound specific data to evaluate relative sensitivity in humans versus rodents are lacki ng, 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 consis tent 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

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toxicodynamic differences between animals a nd 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 as sociated 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 availabl e 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 ch ronic exposures where only data from shorter durations are available (e.g., 90-da y subchronic studies in rodent s) 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 th e 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 pref erred approach to arrive at a POD is to use BMD analysis; however, this approach requires ad equate 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 wh ich 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 a ccount for the potential for deriving an underprotective RfD/RfC due to a data gap pr eventing complete char acterization of the chemical’s toxicity. In the absence of studies for a known or suspecte d endpoint of concern, a UF of 10 or 3-fold is typically applied.

Acute noncancer assessment

Many of the UFs used to account for variability and uncertainty in the development of acute reference values are quite similar to those de veloped for chronic durations, but more often using individual UF values that may be less than 10. UFs are applied based on chemicalspecific or health effect-specific informati on (e.g., simple irritation effects do not vary appreciably between human indivi duals, hence a value of 3 is t ypically used), or based on the purpose for the reference value (see the followi ng paragraph). The UFs applied in acute reference value derivation include: 1) he terogeneity among humans; 2) uncertainty in extrapolating from animals to humans; 3) unc ertainty in LOAEL to NOAEL adjustments; and 4) uncertainty in accounting for an incomplete database on toxic effect s of potential concern. Additional adjustments are often applied to a ccount for uncertainty in extrapolation from observations at one exposure dura tion (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 asse ssment of human health effects relative to the reference value or values being exceeded. Wh ere relevant to the estimated exposures, the

### Draft Risk Assessment for the Shipbuildin g and Ship Repair Operations Source Category -- FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE 37

lack of threshold values at di fferent levels of severity shoul d be factored into the risk characterization as poten tial uncertainties.

### Draft Risk Assessment for the Shipbuildin g and Ship Repair Operations Source Category -- FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE 38



## 5 References

1 . US EPA. National Emission Standa rds for Hazardous Air Pollutants. http://www.epa.gov/ttn /atw/mactfnlalph.html

.
2 US EPA, 2009. Risk and Technology Revi ew (RTR) Risk Assessm ent Methodologies: For

Review by the EPA’s Science Advisory Board wi th Case Studies – MACT I Petroleum Refining Sources and Portland Cement Manufacturing. EPA-452/R-09-006. http://www.epa.gov/ttn/atw/rrisk/rtrpg.html.

3 . US EPA, 2010. SAB’s Response to EPA’s RT R Risk Assessment Methodologies.

http://yosemite.epa.gov/ sab/sabproduct.nsf/4AB3966E263D943A8525771F00668381 /$File/EP A-SAB-10-007-unsigned.pdf

4 . US EPA, 2010. Memorandum from Dave Gu innup to RTR Wood Furniture Docket, entitled, “EPA’s Actions in Response to Key Recomm endations of the SAB Review of RTR Risk Assessment Methodologies.

5 . US EPA, 2005. Revision to the Guideline on Air Quality Models: Adop tion of a Preferred

General Purpose (Flat and Comple x Terrain) Dispersion Model and Other Revisions; Final Rule. 40 CFR Part 51. http://www.epa.gov/EPA-AIR/2005/November/Day-09/a21627.htm

6 . US EPA, 2004. Users’ guide for the AMS/EPA regulatory model – AERMOD. EPA-454/B03-001. http://www.epa.gov/scram001/7thconf/aermod/aermodugb.pdf

.
7 . Allen, D., C. Murphy, Y. Kimura, W. Vizuete, T. Edgar, H. Jeffries, B.-U. Kim, M. Webster,

and M. Symons, 2004. Variable industrial VOC emissi ons and their impact on ozone formation in the Houston Galveston Area. Final Repor t: Texas Environmental Research Consortium Project H-13. http://files.harc.edu/Pro jects/AirQuality/Projects/H013.2003/H13FinalReport.pdf

8 . US EPA, 2004. Air Toxics Risk Assessment Re ference Library, Volume 1. EPA-453-K-04-

001A. http://www.epa.gov/ttn/ fera/risk_atra_vol1.html.

9 . US EPA, 2005. Table 1. Prioritized Chronic Dose-Response Values (2/28/05). Office of

Air Quality Planning and Standards. http://www.epa.gov/ttn/at w/toxsource/table1.pdf

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

http://www.epa.gov/t tn/atw/nata1999

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

.
12. US Agency for Toxic Substances and Disease Registry. 2006. Minimu m Risk Levels (MRLs) for Hazardous Substances. http://www.atsdr.cdc. gov/mrls/index.html

.
13. US EPA, 1994. US Environmental Protection Agency. Methods for Derivation of Inhalation Reference Concentrations a nd Application of Inhalation Dosimetry. EPA/600/8-90/066F. Office of Research and Development. Washington, DC: U.S.EPA.

### Draft Risk Assessment for the Shipbuildin g and Ship Repair Operations Source Category -- FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE 39

14. NRC, 1994. National Research Council. Science and Judgm ent in Risk Assessment. Washington, DC: Nati onal Academy Press.

15. CA Office of Environmental Health H azard Assessment, 2005. Chronic Reference Exposure Levels Adopted by OEHHA as of December 2008. http://www.oehha.ca.gov/air/chronic_rels/AllChrels.html

.

16 . CA Office of Environmental Health Hazard Assessment, 2005. Technical Support Document for Describing Availa ble Cancer Potency Factors, May 2005. http://www.oehha.ca.gov/air/hot_spots/may2005tsd.html

.
17 Conolly, RB, Kimbell, JS, Janszen, D, Schlosser, PM, Kalisak, D, Preston, J, and Miller, FJ.

2003. Biologically Motivated Computational Modeling of Formaldehyde Carcinogencity in the F344 Rat. Tox Sci 75: 432-447.

18 Conolly, RB, Kimbell, JS, Janszen, D, Schlosser, PM, Kalisak, D, Preston, J, and Miller, FJ. 2004. Human Respiratory Tract Cancer Risks of Inhaled Formaldehyde: Dose-Response Predictions Derived from Biologi cally-Motivated Computationa l Modeling of a Combined Rodent and Human Dataset. Tox Sci 82: 279-296.

19 Chemical Industry Institut e of Toxicology (CIIT). 1999. Formaldehyde: Hazard Characterization and Dose-Res ponse Assessment for Carci nogenicity by th e Route of Inhalation. CIIT, September 28, 1999. Re search Triangle Park, NC.

20 Subramaniam, R; Chen, C; Crump, K; et al. (2008). Uncertainties in Biologically-Based Modeling of Formalde hyde-Induced Cancer Risk: Id entification of Key Issues. Risk Anal 28 (4):907-923.

21 Crump, K; Chen, C; Fox, J; et al. (2008). Sensitivity Analysis of Biologically Motivated Model for Formaldehyde-Induced Re spiratory Cancer in Humans. Ann Occup Hyg 52:481- 495.

22 Subramaniam RP; Crump KS; Van Landingham C; White P; Chen C; Schlosser PM (2007). Uncertainties in th e CIIT model for formaldehyde-induced ca rcinogenicity in the rat: A limited sensitivity analysis–I . Risk Anal, 27: 1237–1254.

23 CalEPA. (2005). Letter from JR Froines, Chair of Scientific Review Pa nel to B Jordan, Interim Chairperson, Air Resource s Board, California Enviro nmental Protection Agency.

24 CalEPA. (2007). Final Statement of Reeas ons for Rulemaking Including Summary of Comments and Agency Responses. Public h earing to consider ad option of the proposed airborne toxic control measure to reduce fo rmaldehyde emissions from composite wood products. Public Hear ing Date: April 26, 2007.

25 . US EPA, 2006. Approach for modeling POM. Technical support in formation for the 1999 National Air Toxics Assessment. http://www.epa.gov/ttn/atw/nata1999/99pdfs/pomapproachjan.pdf

.
26 . US EPA, 2005. Table 2. Acute Dose-Respons e Values for Screening Risk Assessments

### Draft Risk Assessment for the Shipbuildin g and Ship Repair Operations Source Category -- FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE 40

(6/02/2005). Office of Air Quality Planni ng and Standards. http://www.epa.gov/ttn/a tw/toxsource/table2.pdf

27 . CA Office of Environmenta l Health Hazard Assessment, 2000. All Acute Reference Exposure Levels developed by OEHHA as of May 2000. http://www.oehha.ca.gov/air/acute_rels/allAcRELs.html

28 . American Industrial Hygi ene Association , 2009. Current AIHA ERPG Values. http://www.aiha.org/insideaiha/volunteergr oups/Documents/ER P-erpglevels.pdf

.
29 . US EPA, 1995. Guidance for Risk Characterization. Science Policy Council. http://www.epa.gov/OSA/spc/pdfs/rcguide.pdf

.
30 . US EPA, 2000. Risk Characte rization Handbook. EPA 100-B-00-002.

31 . US EPA, 2002. EPA’s Guidelines for Ensu ring and Maximizing the Quality, Objectivity, Utility, and Integrity of Inform ation Disseminated by the Envi ronmental Protection Agency. EPA Office of Environmental In formation. EPA/260R-02-008. http://www.epa.gov/quality/inf ormationguidelines/documents/EP A_InfoQualityGuidelines.pdf

32 . US EPA, 2006. Performing risk assessments that include carcinoge ns described in the Supplemental Guidance as having a mutagenic m ode of action. Science Policy Council Cancer Guidelines Implementation Wo rkgroup Communication II: Memo from W.H. Farland dated 14 June 2006. http://www.epa.gov/osa/spc/pdf s/CGIWGCommunication_II.pdf

33 . US EPA, 2005. Supplemental guidance for a ssessing early-life e xposure to carcinogens. EPA/630/R-03003F. http://www.epa.gov/ttn/atw/ch ildrens_supplement_final.pdf

.

34. US EPA, 2005. Science Policy Council Ca ncer Guidelines Implementation Workgroup Communication I: Memo from W.H. Farland dated 4 October 2005 to Science Policy Council. http://www.epa.gov/osa/spc/pdfs/canguid1.pdf

35 . US EPA, 1986. Guidelines for the Health Risk Assessment of Chemical Mixtures. EPA-630- R-98-002. http://www.epa.gov/NCEA/raf/ pdfs/chem_mix/chemmix_1986.pdf

36 . US EPA, 2000. Supplementary Guidance for Conducting Health Risk Assessment of Chemical Mixtures. EPA-630/R-00-002. http://www.epa.gov/ncea/raf/ pdfs/chem_mix/chem_mix_08_2001.pdf

.
37 US EPA, August 5, 2010. Maximum Avai lable Control Technology (MACT) Allowable

Emissions Estimate Memorandum from Kaye Whitfield, Project Le ad to Dave Guinnup, SBAG Leader

38 . US EPA, 2005. Guidelines for Carcinogen Ri sk Assessment (2005). U.S. Environmental Protection Agency, Washington, DC, EPA/630/P-03/001F, 2005. http://cfpub.epa.gov/ncea/raf/recordisplay.cfm?deid=116283

39 . US EPA, 2000. Risk Characteri zation Handbook. EPA 100-B-00-002.

40 . NRC (National Research Council) 2006. Assessing the Human Health Risks of

### Draft Risk Assessment for the Shipbuildin g and Ship Repair Operations Source Category -- FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE 41

Trichloroethylene. National Ac ademies Press, Washington DC.

41 . R.P. Subramaniam, P. White and V.J. Cogl iano. 2006. Comparison of cancer slope factors using different st atistical approaches, Risk Anal. Vol 26, p. 825–830.

### Appendix 1 Emissions Inventory Support Memorandum

### Ship Building And Ship Repair Operations

### 1 MEMORANDUM

TO: Anne Pope, EPA

FROM: Darcy Wilson, ERG

DATE: August 31, 2010

SUBJECT: Emissions Data Used in Residual Risk Modeling for Shipbuilding and Ship Repair (Surface Coating)

The purpose of this memorandum is to provide information on the RTR database used in estimating residual risk for the Shipbuild ing and Ship Repair (Surface Coating) Maximum Achievable Control Tec hnology (MACT) source category.

BACKGROUND

Section 112 of the Clean Air Act (CAA) establ ishes a two-stage regu latory process to address emissions of hazardous air pollutants ( HAPs) from stationary sources. In the first stage, section 112(d) requires the Environmental Protecti on Agency (EPA) to develop technology-based standards for categories of industrial sour ces. In the second stage, known as the residual risk stage, section 112( f)(2) requires EPA to a ssess the health and environmental risks that remain after sour ces come into compliance with MACT. If additional risk reductions are ne cessary to protect public health with an ample margin of safety or to prevent adverse environmenta l effects, EPA must develop standards to address these remaining risks. As part of this second stage, data were gathered to assess the residual risks from the Shipbuilding and Ship Repair MACT source category.

EMISSIONS DATA USED IN RESIDUAL RISK MODELING

The 2002 National Emissions Inventory (NEI) data served as the star ting point for this assessment. Using the process MACT code, 1 

### a subset of the NEI was developed that contained facility, process, and emissions da ta for each facility in the Shipbuilding and Ship Repair (Surface Coating) source category (MACT code 0715-2). The 2002 NEI data for shipbuilding facilities were releas ed through an Advanced Notice of Proposed Rulemaking (ANPRM) in March of 2007, wher e NEI revisions for facilities in the MACT source category were solicited from State and local agencies, industry representatives, and EPA. The revisions that were provided to EPA were then incorporated into the NEI.

Risks associated with facilities covered by the Shipbuilding and Ship Repair MACT standard were then modeled in the initi al 2005 NATA risk assessment in June of 2009. EPA reviewed the results of this analysis, a nd performed a more in-depth review of the

1 Assigning data with MACT codes allows EPA to determine reductions attributable to the MACT program. The NEI associates MACT codes corresp onding to MACT source categories with stationary major and area source data.

### 2 NEI data set for the source category. EPA reviewed the Marine Log publication and facility lists from EPA’s Office of Polic y, Economics, and Innovation (OPEI) and Office of Compliance and Enforcement (OECA) to identify additional major source facilities subject to the Shipbuilding and Ship Repa ir (Surface Coating) NESHAP. The NEI was also closely reviewed to iden tify additional processes with shipbuilding and ship repair Source Classification Codes (SCCs), and the NE I data were compared with data in the EPA’s Toxic Release Inventory (TRI). MACT code and emission revisions were made as a result of these analyses. EPA Regional Offices and State and local agencies were contacted in an effort to collect emissions da ta for facilities thought to be subject to the rule, but not included in the NEI or TRI da tabases; HAP or volatile organic compound (VOC) estimates were provided for a number of facilities. HAP speciation profiles were applied to the VOC estimates as needed. For facilities in the NEI th at have Shipbuilding and Ship Repair SCCs but EPA could not obtain HAP or VOC emission estimates, EPA applied surrogate HAP emission estimates in order to assess the ri sks associated with these facilities.

EPA then re-modeled the NEI dataset for Shi pbuilding and Ship Repair in January of 2010, and made additional refinements to treat emissions as volume sources for modeling, corrected some erroneous locati on coordinates, and contacted two State agencies where high-risk facilities were located to confirm emission estimates and emission processes. Additional MACT code and emission revisions were made as a result of these analyses.

EPA also evaluated the facility-wide risks for Shipbuilding and Ship Repair facilities using the 2005 NEI data and risk assessment re sults that were released in the 2005 NATA Risk Preview with State and local agencies in June of 2010. EPA made revisions to remove all emission records for metals, as these HAPs are not associated with surface coating operations at Shipbuild ing and Ship Repair facilitie s. EPA has also identified revisions (removal of emission estimates for pr ocesses and facilities that are not subject to the Shipbuilding and Ship Repair NESH AP, and addition of su rrogate HAP emission estimates for facilities that are subject to the NESHAP but were not previously modeled. All of these revisions were in cluded in the risk analyses.

### 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 8/7/2008

Technical Support Document

Prepared for:

U. S. Environmental Protection Agency Office of Air and Radiation Office of Air Quality Planning and Standards Emissions Standards Divisi on, Risk and Exposure Group Research Triangle Park, NC 27711

Prepared by:

EC/R Incorporated 501 Eastowne Drive, Suite 250 Chapel Hill, North Carolina 27514

### ii 

## Disclaimer

The research described in this document has been funded by the United States Environmental Pr otection Agency contracts 68-D01-076 and 68-D-06-119 to EC/R Incorporated. Although it has been subject to the Agency's revi ew, it does not nece ssarily reflect the views of the Agency, and no official endorsement should be inferred.

### iii 

# Contents

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

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

2. Overview of the HEM3– AERMOD System.............................................................................. 2 2.1 Preparation of Dispersion Modeling Inputs........................................................................ 3 2.1.1 Compiling Emission Source Data.............................................................................. 3 2.1.2 Defining the Modeling Domain and Receptors ......................................................... 4 Treatment of Nearby Census Blocks and Screening for Overlapping Blocks............... 4 Polar receptor network................................................................................................... 5 Elevations and hill heights for model receptors............................................................. 5 2.1.3 Selection of Meteorological Data .............................................................................. 6 2.2 Running of AERMOD ........................................................................................................ 6 2.2.1 AERMOD Dispersion Options Used by HEM3 ........................................................ 6 2.2.2 Use of Dilution Factors.............................................................................................. 7 2.3 Postprocessing of AERMOD Results in HEM3 ................................................................. 7 2.3.1 Calculation of Impacts at Modeled Receptors........................................................... 8 2.3.2 Interpolation of Impact s at Outer Census Blocks ...................................................... 9 2.3.3 Calculation of Populati on Exposures and Incidence ............................................... 10 2.3.4 Model Outputs ......................................................................................................... 10 2.4 Data Libraries Used in HEM3 .......................................................................................... 11 2.4.1 Chemical Health Effects Information ...................................................................... 11 2.4.2 Census Block Locati ons and Elevation Data........................................................... 12 2.4.3 Meteorological Data................................................................................................. 12 2.4.4 Gaseous Deposition Parameters............................................................................... 13

3. Modeling for the Residua l Risk Technology Review .............................................................. 14 3.1 Emission Source Inputs....................................................................................................

. 14 3.2 Pollutant Cross-Referencing ............................................................................................. 15 3.3 Meteorological Data.......................................................................................................

... 15 3.4 Model Options Selected.................................................................................................... 18 3.4.1 Urban or Rural Dispersion Characteristics .............................................................. 19 3.4.2 Deposition and Plume Depletion ............................................................................. 19 3.4.3 Cutoff Distance for Mode ling of Individual Blocks................................................ 20 3.4.4 Plant Boundary Assumptions................................................................................... 20 3.5 Modeling of Multiple Facilities ........................................................................................ 20

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

........... 21 4.1 Engineering Review........................................................................................................

.. 21 4.2 Geographic Checks .........................................................................................................

.. 21

5. Uncertainties ..............................................................................................................

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

### iv 6. References.................................................................................................................

................ 31



# Figures

Figure 3-1. Meteorological data availa ble in the HEM3 library for AERMOD........................... 18 Figure 4-1. Illustration of a quality assurance test for Census block coordinates located on plant property.......................................................................................................................

....... 23



# Tables

Table 2-1. Parameters Used to Deli neate the Modeling Domain in HEM3 ................................... 4 Table 3-1. Groupings Used for Po lycyclic Organic Matter in the RTR Phase II Modeling Effort

. . ........................................................................................................................... ............. 16 Table 3-2. Modeling Domain and Model Set-Up Options in the HEM-3 Model As Used in the Residual Risk and Technology Review Assessments........................................................ 19 Table 5-1. Summary of Genera l Uncertainties Associated wi th Risk and Technology Review Risk Assessment ................................................................................................................ 26

### 1 

# 1. Introduction

This document describes the modeling appro ach used to estimate the risks to human populations in support of the Residual Risk and Technology Review currently being carried out by the U.S. Environmental Protection Agency (EPA). The model used in these risk assessments was the Human Exposure Model, Version 3 (HEM3) . HEM3 incorporates the AERMOD, a state of the art air dispersion model developed under the direction of the American Meteorological Society / Environmental Protection Agency Regulatory Model Improvement Committee (AERMIC).

Section 2 of this report provides an ov erview of the HEM3–AERMOD system; and Section 3 describes inputs and choices made in implementing the model for the RTR program. Quality assurance efforts undertaken in the m odeling effort are discussed in Section 4, and uncertainties associated with the mode ling effort are discussed in Section 5.

### 2 

# 2. Overview of the HEM3– AERMOD System

HEM3 performs three main operations: di spersion modeling, estimation of population exposure, and estimation of human health risk s. Two options are provided for dispersion modeling. The first is the American Meteor ological Society (AMS) EPA Regulatory Model (AERMOD). 1,2 

### The second option is the Industrial Source Complex - Short Term, Version 3 (ISCST3) model. Both of these dispersion models handle a wide range of different source types which may be associated with an industrial source complex, including stack sources, area sources, and volume sources. AERMOD was used for the RTR modeling effort.

To prepare dispersion modeling inputs and carry out risk calculations, HEM3 draws on four data libraries, which are provided with the model. The first is a library of meteorological data for over 120 stations, which are used for dispersion calculations. A second library of Census block internal point lo cations and populations provides the basis of human exposure calculations (Census, 2000). This Census library also includes the elevation of each Census block, which can also be used in dispersion calcul ations, at the option of the model user. A third library of pollutant unit risk estimates and re ference concentrations is used to calculate population risks. These unit risk estimates and re ference concentrations are based on the latest values recommended by EPA for hazardous air po llutants (HAP) and other toxic air pollutants (EPA, 2007). The fourth data library is used only with AERMOD and only when the user opts to compute deposition or plume depletion. This da tabase provides deposition parameters for gaseous pollutants.

HEM3 has been implemented in two versi ons: a single-plant ve rsion, and a community and sector version. The latter was used in this modeling effort. Both versions operate under the same general principles. In e ssence, the community and sector version provides a platform for running the single plant version multiple times. In both versions, source location and emissions data are input through a set of Ex celTM spreadsheets. The main diffe rence is in the user interface for other model inputs. The single plant incl udes a graphical user in terface (GUI) for the selection of various dispersion modeling options. In the comm unity and sector version of HEM3, a control file replaces many of these GUI inputs.

The model estimates cancer risks and noncancer adverse health effects due to inhalation exposure at Census block internal point locations, and at other receptor lo cations that can be specified by the user. Cancer risks are comput ed using EPA’s recommended unit risk estimates for Hazardous Air Pollutants (HAP) and other to xic air pollutants. The resulting estimates reflect the excess cancer risk for an individual br eathing the ambient air at a given receptor site 24-hours per day over a 70-year lifet ime. The model estimates the numbers of people exposed to various cancer risk levels. In addition, HEM3 estimates the total incremental cancer risks for people living within different distan ces of the modeled emission sources.

### 3 Potential noncancer health e ffects due to chronic exposures are quantified using hazard quotients and hazard indices for various target organs. The “hazard quotient” for a given chemical and receptor site is the ratio of the ambient concentration of the chemical to the reference concentration. The “hazard index” for a given organ is the sum of hazard quotients for substances that affect that organ. HEM3 comp utes target-organ-specific hazard indices (TOSHI) for HAPs and other toxic air polluta nts, and estimates the numbers of people exposed to different hazard index levels. In additi on, maximum short term concentrations are computed for all pollutants, and concentrations are compared with threshold levels for acute health effects.

The following sections outline the methodologi es used in the HEM3–AERMOD system. Section 2.1 describes the prepar ation of dispersion modeling i nputs, Section 2.2 describes the running of AERMOD, Section 2.3 de scribes calculations performe d by HEM3 to calculate risks and exposures, and Section 2.4 details the sources and methods used to produce HEM3’s data libraries. The HEM3 User’s Manual provides add itional details on the input data and algorithms used in the model. 3 

### Specific model options used in the RTR are discussed in Chapter 3.



## 2.1 Preparation of Dispersion Modeling Inputs

HEM3 compiles data that will be needed for dispersion modeling, and prepares an input file suitable for running AERMOD. The disper sion modeling inputs can be divided into three main components: emission source data, inform ation on the modeling domain and receptors for which impacts will be comput ed, and meteorological data.

2.1.1 Compiling Emission Source Data

A series of ExcelTM spreadshee t files are used to specify th e emissions and configuration of the facility to be modeled. At a minimum, tw o files are needed: a po llutant emission file, and an emission location file. The emission file incl udes an emission source identification code for each emission source at the facility, the name s 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 mu st also specify the percentage of each pollutant that is in the form of particulate matter. The bala nce is assumed to be in vapor form.

The emission location file includes the coordi nates of each source, as well as information on the configuration and other characteristics of the source. HEM3 can analyze stack sources, area sources, or volume sources. a 

### For stack sources, the locati on file must provide the stack height, stack diameter, emission ve locity, and emission temperature. The file must also provide dimensions for each area or volume source, as we ll as the height of the source above the ground. For area sources, the angle of rotation from north can also be specified. The user can also

I For definitions of stack, area, and volume sources , please see the AERMOD documentation. AERMOD provides the capability to analyze some other emission sour ce configurations, such as storage piles, however these configurations are not supported by HEM3.

### 4 provide the terrain elevation at the base of each source, and the controlling hill height for each source. (The controlling hill height is used in AERMOD’s flow calculations. Calculation of the hill height is discussed in Section 2.4.2.) If these are not provided, HEM3 will calculate terrain elevations and controlling hill heights based on elevations and hill heights for the Census blocks nearest to the facility.

If particulate deposition or plume depletion are to be cons idered, then HEM-3 requires a third input file to specify the particle size distribution and sc avenging parameters for various particle size ranges. Another opti onal file can be used to specif y building dimensions if building wake effects are to be modeled.

2.1.2 Defining the Modeling Domain and Receptors

HEM3 defines a modeling domain for each faci lity that is analyzed based on parameters specified by the model user. These parameters are summarized in Table 2-1. The modeling domain is circular, and is centered on the facil ity, with a radius specifi ed by the user. For the RTR analysis, the radius of the modeling domain is 50 kilometers (km). HEM3 identifies all of the Census block locations in the modeling domai n 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 fo r modeling individual Census bl ocks is determined by the model user. The model user can also provide an ExcelTM spreadsheet specifying additional locations to be included as model receptors in AERMOD. These additional discrete receptors may include plant boundary locations, monitoring s ites, individual residen ces, schools, or other locations of interest.

For Census blocks in the outer group, beyond this modeling cutoff distance, emissions impacts are interpolated based on modeling results for a polar receptor network. The user also specifies an “overlap” distance, within which Census block coordinates will be considered to be on plant property. The following paragraphs provide more details on the treatment of blocks near the facility, on the polar receptor network, and on the determination of receptor elevations and controlling hill heights to be used in AERMOD.

Treatment of Nearby Census Blocks and Screening for Overlapping Blocks

Census block locations near the facility are modeled as separate receptors Table 2-1. Parameters Used to Delineate the Modeling Domain in HEM3

Parameter Typical value
Modeling domain size – maximum distance to be modeled a 

### 50 km
Cutoff distance for modeling of individual blocks b 

### 2,000 – 3,000 m
Overlap distance – where receptors will be considered to be on plant property b 

### 30 m
Polar receptor network
Distance to the innermost ring a 

### 100 m

N umber of concentric rings 13

N umber of radial directions 16

a 

### Measured from the center of the facility.

b 

### Measured from each stack at the facility, and from the edges of each area or volume source.

### 5 within AERMOD. The cutoff distance for modeli ng of individual Census blocks is chosen by the user, and is tpically between 2000 and 3000 mete rs. This distance is not measured from the center of the facility, but is the minimum distance from any source at the facility. Therefore, any Census block location that is within the cutoff distance from any emission source is treated as a discrete AERMOD receptor.

HEM3 checks Census blocks that are very clos e to the facility in order to assess whether they overlap any area or volume emission sources. In addition, the user can specify an overlap distance, within which receptors will be consider ed to be on plant property. The default value for the overlap distance is 30 meters, or approxima tely equal to the width of a narrow buffer and a roadway. HEM3 tests each nearby receptor to dete rmine whether it is within this distance from any stack or from the perimeter of any area or volume source. If a receptor falls within this distance, HEM-3 will not calculate risks based on the location of that receptor, but will instead assume that the risks associated with the recepto r are the same as the highest predicted value for any receptor that is not overlapping. The locat ion for calculating the default impact may be either another Census block, one of the polar gr id receptors, or one of the additional discrete receptor locations provided by the model user.

Polar receptor network

The polar receptor network used in HEM3 serv es three functions. First, it is used to estimate default impacts if one or more Census lo cations are inside the overlap cutoff distance is used to represent the plant boundary. Second, it is used to evaluate potentia l acute effects that may occur due to short-term exposures in unpopu lated locations outside the plant boundary. Third, the polar receptor network is used to in terpolate long-term and short-term impacts at Census block locations that are outside the cuto ff distance for modeling of individual blocks.

The user defines the inner radius for the pol ar receptor network, th e number of concentric rings to be analyzed, and the number of radial di rections. The inner radius of the polar network should the minimum distance from the facility center that is generally outs ide of plant property. (For complex plant shapes, it is sometimes useful to specify an inner ring that encroaches on facility property in some direc tions.) The default, and also the minimum inner radius for the polar network is 100 meters (m). A typical run includes 13 concentric rings and 16 radial directions. HEM3 will distribut e the radial directions even ly around the plant. For the concentric rings, the model will generate loga rithmic progression of di stances starting at the inner ring radium and ending at the outer radius of the modeling domain. In the single plant version of HEM3, the user can change these ring distances to meet the needs of a specific study.

Elevations and hill heights for model receptors

The user can specify whether HEM3 will in clude the effects of terrain when running AERMOD. If the terrain option is selected, HEM3 obtains elevations and controlling hill heights for Census block receptors from its internal Ce nsus location library. Section 2.4.2 describes the derivation of these elevations and hill heights.

### 6 Elevations and controlling hill heights for th e polar grid receptors are also estimated based on values from the Census library. HEM3 divides the modeling doma in 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 assigne d an elevation based on the highest elevation for any Census block in its sector. The contro lling 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 he ight of the nearest block outside the sector.

2.1.3 Selection of Meteorological Data

In addition to source and receptor information, AERMOD requires surface and upper air meteorological observations in a prescribed format . The model user can select a meteorological station from the HEM3 meteorological data library, or add new files to the library if site-specific data are available. If the user does not spec ify a meteorological station, HEM3 will select the closest station to the cente r of the modeling domain.



## 2.2 Running of AERMOD

Based on the user input data and other data described in the previous section, HEM3 produces an input file suitab le for AERMOD. HEM3 then runs AERMOD as a compiled executable program. No changes have been made from the version of AERMOD released to the public by EPA. The following sections give additional information on how AERMOD is used within HEM3.

2.2.1 AERMOD Dispersion Options Used by HEM3

AERMOD provides a wide array of opti ons for controlling dispersion modeling calculations. In general, HEM3 uses the re gulatory default options when running AERMOD. 1 

### These options include the following:

! consideration of stack-tip downwash (except for Schulman-Scire downwash)

! use of calm processing routines

! use of missing-data processing routines

! consideration of terrain effects

However, the model user can opt to exclude th e effects of terrain when running HEM3. The following additional options are available to the HEM3 user:

! calculation of wet and dr y deposition rates for vapor and particulate mater

! consideration of plume depletion (due to de position) when calculati ng air concentrations

! consideration of bu ilding wake effects

! calculation of short term impacts

As noted in Section 2.1, the calculation of de position or depletion and the consideration of building wake effects require additional user inputs.

### 7 The user can opt to analyze short term impacts on a number of different time scales (e.g. 1 hour, 2 hours, etc.), however only one short term time scale can be selected per run. If the user chooses to analyze short term impacts, a multiplier must be specified to reflect the ratio between the maximum short term emission rate and the long term average emission rate. The default multiplier for short term emissions is a factor of 10. This means that in the default case the maximum short term emission rate is assumed to be 10 times the long term average emission rate. The multiplier can be set to 1.0 if emissions from the facility are known to be constant.

HEM3 uses the AERMOD TOXICS run op tion, which optimizes the area source integration routine to reduce model runtimes. The TOXICS Sampled Chronological Input Model (SCIM) is not used, since the HEM3 AERMOD me teorological library only includes one year of observations for each station. (The SCIM option can be used to skip a portion of the hourly observations when meteorological data are available for multiple years.)

2.2.2 Use of Dilution Factors

To save computer run time when analyz ing the impacts of multiple pollutants, HEM3 does not model each pollutant separately. Instead , AERMOD is used to compute a series of dilution factors, specific to each emission source and receptor. The dilution factor for a particular emission source and receptor is defined as the predicted ambient impact from the given source and at the given receptor, divided by th e emission rate from the given source.

If the user chooses not to analyze deposition or plume depletion, the dilution factor does not vary from pollutant to pollutant. If eith er deposition or depletion is chosen with the AERMOD dispersion model option of HEM3, separa te 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 com ponents do not have the same particle size distribution. In the current ve rsion of HEM3, this can be done by creating a separate emission record for each pollutant emitted by from each source. (Common location data and source configurations can be used for different pol lutant records representing the same emission source.)



## 2.3 Postprocessing of AERMOD Results in HEM3

HEM3 estimates total excess cancer risks and potential chronic noncancer health effects for all Census block locations in the modeling domain, all user-defined receptors, and all points in the polar receptor network. Po tential chronic noncancer health effects are expressed in terms of TOSHI. Based on the results for Census bl ocks and other receptors, HEM-3 estimates the maximum individual risk and maximum TOSHI for populated receptors, and determines the locations of these maximum impacts. The model al so 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 locati ons may differ for the maximum individual cancer risk and for the haza rd indices for different target organs.

### 8 For acute impacts, HEM3 calculates the ma ximum short term concentrations for all pollutants emitted by the facility. These maximum short term concentrations are compared with various thresholds for acute health effects.

At the option of the model user, HEM3 will also compute the long term and short term predicted ambient concentrations of all pollutant s emitted by the plant at all of the receptors in the modeling domain. In addition, pollutant co ntributions from each emission source at the facility are computed under this option.

Section 2.3.1. describes methods used to cal culate cancer risks a nd hazard indices for receptors that are explicitly modeled using AE RMOD. Section 2.3.2 describes the interpolation approach used to estimate cancer risks and hazard i ndices at Census blocks that are not explicitly modeled.

2.3.1 Calculation of Impacts at Modeled Receptors

As noted in Section 2.2.2, HEM3 does not model each pollutant separately unless deposition or depletion is being analyzed. Instead, AERMOD is used to compute a series of dilution factors, specific to each emission source and receptor. HEM3 also conserves computer memory by computing cancer risks and hazard indices directly, without recording the concentration of each pollutant at each receptor. The following algorithms are used to compute cancer risks and TOSHI for chr onic noncancer health effects.

For cancer risk:
CR T 

### = 

## G i, j 

### CR i, j



### CR i, j 

### = DF i, j 

### H CF H 

## G k 

## [ E i, k 

### H URE k 

## ]

For TOSHI:
TOSHI T 

### = 

## G i, j 

### TOSHI i, j



### TOSHI i, j 

### = DF i, j 

### H CF H 

## G k 

## [ E i, k 

### ' RfC k 

## ]

where: CR T 

### = total cancer risk at a given receptor (probability for one person) 

## G i, j 

### = the sum over all sources i and pollu tant types j (parti culate or gas) CR i, j 

### = cancer risk at the given recepto r for source i and pollutant type j DF i, j 

### = dilution factor [( F g/m 3 

### ) / (g/sec)] at the given receptor for source i and pollutant type j CF = conversion factor, 0.028 77 [(g/sec) / (ton/year)] 

## G k 

### = sum over all pollutants k within pol lutant group j (particulate or gas)
E i, k 

### = emissions of pollutant k from source i and in pollutant type j URE k 

### = cancer unit risk estimate for pollutant k

### 9 TOSHI T 

### = total target-organ -specific hazard index at a given receptor TOSHI i, j 

### = target-organ-specific hazard index at the given recepto r for source i and pollutant type j RfC k 

### = noncancer health effect reference concentration for pollutant k

If the cancer unit risk estimate is not availabl e 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 chem ical is not included in the calculation of hazard indices. Note also that separa te reference concentrations are used for acute and chronic hazard indices.

HEM3 computes short term concentrati ons 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:
AC T, k 

### = 

## G i 

### AC i, k



### AC i, k 

### = DF i, j 

### H CF H E i, k



### Short term concentrations:
AC T 

### = 

## G i 

### AC i, k



### AC i, k 

### = DF i, j 

### H CF H E i, k 

### H M

where: AC T, k 

### = total estimated ambient concentratio n for pollutant k at a given receptor 

## G i 

### = the sum over all sources i ( F g/m 3 

### ) AC i, k 

### = estimated ambient concentration of po llutant k at the given receptor as a

result of emissions from source i ( F g/m 3 

### ) M = ratio between the estimated maximum s hort 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 distan ce cutoff for individual block modeling, HEM-3 estimates cancer risks and hazard indices by inte rpolation from the polar receptor network. Impacts at the polar grid receptors are es timated using AERMOD modeling results and the algorithms described in Section 2.3.1. The impact s at each outer Census block are interpolated from the four nearest polar grid receptors. Th e interpolation is linear in the angular direction, and logarithmic in the radi al direction, as summarized in the following equations:

I a, r 

### = I 1, r 

### + (I 2, r 

### – I 1, r 

### ) H (a – A1) ' (A2 – A1)

### 10 I A1, r 

### = exp{ln(I A1,R1 

### ) + [ln(I A1,R2 

### ) – ln(I A1,R1 

### )] H [ln r) – ln(R1)] ' [ln(R2) – ln(R1)]}
I A2, r 

### = exp{ln(I A2,R1 

### ) + [ln(I A2,R2 

### ) – ln(I A2,R1 

### )] H [ln r) – ln(R1)] ' [ln(R2) – ln(R1)]}

where: I a, r 

### = the impact (cancer risk, h azard index, or concentration) at an angle, a, from north, and radius, r, from the cen ter of the modeling domain a = the angle of the ta rget receptor, from north r = the radius of the target receptor, from the center of the modeling domain A1 = the angle of the polar network recep tors immediately counterclockwise from the target receptors A2 = the angle of the polar network recepto rs immediately clockwise from the target receptor R1 = the radius of the polar network recept ors immediately inside the target receptor R2 = the radius of the polar network recept ors immediately outside the target receptor

2.3.3 Calculation of Population Exposures and Incidence

Using the predicted impacts for Census bl ocks, HEM3 estimates the numbers of people exposed to various cancer ri sk levels and TOSHI levels. This is done by adding up the populations for receptors that have predicted can cer risks or TOSHI above the given threshold.

The model also estimates the total annual ex cess cancer risk (incidence) for the entire modeling region. The following equation is used:

TCR = 

## G

m 

## [ CR m 

### H P m 

## ] ' LT where: TCR = the estimated total annual cancer risk, or incidence, (cancers/year) to the population living within the modeling domain 

## G

m 

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



### = the total lifetime cancer risk (from all modeled pollutants and emission sources) at Census block m P m 

### = the population at Census block m LT = the average lifetime used to develop the cancer unit risk factor, 70 years

2.3.4 Model Outputs

The following is a summary of the outputs pr oduced by HEM3. These are written to a collection of files in ExcelTM and dBaseTM format.

! Maximum long term impacts at populated locations

" maximum lifetime individual cancer risk

" maximum TOSHI for the following health effects - respiratory system effects - liver effects

### 11 - neurological system effects - developmental effects - reproductive system effects - kidney effects - ocular system effects - endocrine system effects - hematological system effects - immunological system effects - skeletal system effects - spleen effects - thyroid effects

" locations of the maximum cancer risk and TOSHI

" Census block identification codes for the maximum cancer risk and TOSHI, and number of people in the Census block

" contributions of different chemicals a nd emission sources to the maximum risk and TOSHI

! Maximum acute impacts

" maximum short term ambient concentration for each chemical

" threshold levels for acute health eff ects of each chemical (compared with the maximum short term concentrations)

" locations of the maximum impacts for different chemicals

" Census block identificati on codes at the locations of maximum concentration, and number of people in the block

" contribution of each emission source at the facility to the maximum short term concentration of each chemical

! Outputs for all receptors

" maximum individual cancer risk and TOSHI (all target organs) for each Census block and each user-specified discre te receptor (monitoring sites, etc.)

" maximum individual cancer risk and TOSHI (all target organs) for each polar grid receptor

" estimated deposition (optional)

" predicted ambient concen tration resulting from each emission source at each Census block (optional)

! Population exposures and tota l cancer risk, or incidence

" estimated numbers of people exposed to different levels of lifetime individual cancer risk (1 in a million, 1 in 100,000, etc.)

" estimated numbers of people exposed to different levels of TOSHI (1, 2, 10, etc.)

" total cancer risk, or incidence, in estima ted cancer deaths per year, over the entire modeling domain



## 2.4 Data Libraries Used in HEM3

2.4.1 Chemical Health Effects Information

### 12 HEM3 includes a library of available health effects data for HAPs. For each pollutant, the library includes the following parameters, where available:

! unit risk estimate for cancer

! reference concentration for chronic noncancer health effects

! reference concentrations for acute health effects

! target organs affected by the ch emical for chronic health effects

Unit risk estimates and reference concentrations included in the HEM3 chemical library have been taken from EPA’s database of recommended dose-response factors for HAPs. 4



### 2.4.2 Census Block Locations and Elevation Data

The HEM3 Census library includes Census block identification codes, locations, populations, elevations, and controlling hill height s for all of the over 5 million Census blocks identified in the 2000 Census. The location coordi nates reflect the intern al point of the block, which is a point selected by the Census to be ro ughly 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 stat es and Puerto Rico were extracted from the LandView® database. 5 

### Locations and populations for blocks in the Virgin Islands were obtained from the Census web site.

The elevation of each Census block in the continental U.S. and Hawaii was estimated by interpolating the elevation at the block’s inte rnal point from the U.S. Geological Service’s 1:250,000 Digital Elevation Model (DEM) data. 6 

### These data have a resolution of 3 arc seconds, or about 90 meters. The estimation of the elevati on of each Census block in Alaska and the U.S. Virgin Islands was performed with analysis tool s in the ArcGis 9.1 software application. The point locations of the Census bloc k centroids in Alaska and the Virg in Islands were overlaid with a raster layer of North American Digital Elevation Model elevati ons (in meters). Each Census block point was assigned an elev ation value based on the closest point in the ArcGis elevation raster file.

Hill heights were determined with base on the algorithm used in AERMAP, the AERMOD terrain processor. 7 

### In order to save run time and resources, the HEM3 Census block elevation database was substituted for the DEM data generally used in AERMAP. As noted above, the Census block elevations were orig inally derived from the DEM database. To determine the controlling hill height for each Cens us block, a cone was projected away from the block location, representing a 10% elevation grade. The controlling hill height was selected based on the highest elevati on above that 10% grade (in accordance with the AERMAP methodology). The distance cutoff for this calcu lation was 100 km. (This corresponds to an elevation difference, at a 10% grade, of 10,000 m, which cons iderably exceeds the maximum elevation difference in North America.)

2.4.3 Meteorological Data

### 13 HEM3 includes an extensive library of meteorological data to support both the AERMOD and ISCST3 dispersion models. 150 mete orological stations ha ve been preprocessed for AERMOD as part of the RTR effort. In addi tion, 87 stations have previously been processed for ISCST3. Section 3.3 discusses the prepar ation of meteorologica l data for the RTR.

2.4.4 Gaseous Deposition Parameters

HEM3 provides options to compute the deposi tion of air pollutants, and to take into account the impacts of plume depletion due to depos ition of gaseous and particulate pollutants. If either of these options is selected by the model user for gaseous pollutants, a number of pollutant properties are required by AERMOD. Thes e 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 uptak e of the pollutant by leaves r CL 

### ). 8 

### HEM3 includes a library of these parameters for most gaseous HAPs. This library is based on an compendium of gaseous deposition parameters develope d by Argonne National Laboratories. 9 

### It should be noted, however, that the deposition and depletion options of HEM3 and AERMOD were not used in the this component of the RTR analysis.

### 14 

# 3. Modeling for the Residual Risk Technology Review

This section discusses the general approach used to implement the AERMOD version of HEM3 for the RTR modeling analysis. Separate repor ts have been prepared for all of the emission source categories included in the RTR analys is. These reports provide information on the emissions inputs and results for specific emission categories.



## 3.1 Emission Source Inputs

HEM3 and AERMOD require detailed data on emissions from each emission source included in the modeling analysis. These data include:

! pollutants emitted

! emission rate for each pollutant

! emission source coordinates

! stack height (or emission height for fugitive sources)

! stack diameter (or configuration of fugitive sources)

! emission velocity

! emission temperature

Emissions data for the modeling effort were taken from the 2002 National Emissions Inventory (NEI) Final Version 1. 10 

### These data include HAP emission rates, emission source coordinates, stack heights, stack diameters, flow rates, and exit temperatures. EPA performed an engineering review of the NEI da ta. In cases where new or bette r data were known to exist for a particular source category, that info rmation was integrated into the data used in modeling for that category. For each source category, the emissions ar e summarized in the source category specific report. Detailed computer files containing all emission and release characteristics are available on the RTR website.

As noted in the previous section, industri al emission sources can be characterized in AERMOD and other dispersion models as point, area, or volume sources. The NEI includes a framework for characterizing area and volume sources ; however, these fields are not filled in the 2002 Final Version 1 of the NEI. Therefore, fug itive emissions are generally characterized as low point sources with minimal exit velocities. Fo r some categories, additional information was available on the configuration of fugitive emission sources. This information was incorporated into the emissions database as part of the engineering review. Thus, fugitive emission sources were characterized as area or volume sources when sufficient configuration information was available.

### 15 

## 3.2 Pollutant Cross-Referencing

Because the NEI is developed from a number of different data sources, a single chemical may be listed in the inventory under different names (i.e. a “common name” and one or more structure-based names). In addition, pollutant groupings such as polycyclic organic matter (POM), can be listed in the NEI under the na mes of individual member compounds, and under different synonyms (e.g. polynuclear aromatic hydrocarbons). HEM3 requires an exact match in the chemical name in order to link emissions to the appropriate dose-response factors. The model will not process any pollutant that is not specifically listed in the chemical library (see Table 2-1). Therefore, all of the HAP names used in the NEI were linked to the appropriate chemical names in the HEM3 reference file.

The individual POM chemicals listed in the NEI were grouped into seven subclasses. Table 3-1 shows these groupings, and gives the can cer unit risk estimate used for each subclass. Emissions of metal compounds were also adjusted using algorith ms developed for the Emissions Modeling System for Hazardous Air Pollutants (E MS-HAP) under the National-scale Air Toxics Assessment (NATA). A mass adjustment factor wa s applied to the emissions of metal compounds to account for a particular portion (e .g., the lead portion of lead sulfat e) or to partition them among multiple pollutant categories (e.g. chromium arse nate into chromium VI compounds and arsenic compounds). In addition, where no specific compound information was available, metals were speciated into appropriate oxida tion states (e.g. chromium compounds into chrome VI and chrome III) based on factors that have been developed for specific source categories and average factors that have been developed for the inventory as a whole. The adjustment factors and speciation factors were taken from the HAP Table module of EMS-HAP. 11 ,12



## 3.3 Meteorological Data

AERMOD meteorological data for the RTR anal ysis were derived from three sources: (1) AERMET processing of 122 National Weather Serv ice (NWS) surface observ ation stations, (2) State agencies, and (3) industry (o n-site). The 122 NWS sites we re selected to provide thorough coverage of the U.S. For the RTR analysis, th e average distance between a modeled facility and the applicable meteorological st ation was 72 km. One year of hour ly data for these sites were processed through the AERMET program, and this is documented in “Meteorological Data Processing Using AERMET.” 13 

### Four states provided AERMOD -ready meteorology files. These states were: Alabama (5 stations), Florida (4 stations), North Caro lina (6 stations), and Wisconsin (22 stations). Eleven of the state supplied meteor ology sites were co-located with sites in the list of 122. The data at the state suppl ied sites were more recent, so th e state data for the 11 co-located sites were used. In addition, for the RTR petrol eum refinery modeling, two refineries (Fairbanks, Alaska and the Virgin Islands) provided on-site AERMOD-ready meteorology files. These on-site meteorological data files were used with their respective refinery f acilities for the HEM3 refinery modeling. Figure 3-1 shows the location of the 1 50 surface meteorological stations used in the RTR analysis.

As noted above, 122 NWS stations were processed through the AERMET program. To estimate the parameters needed by AERM OD, AERMET requires hourly surface weather

### 16 observations and the full twice-daily upper air soundings (i.e., meteor ological variables reported at all levels). The surface and upper air stations are paired to pr oduce the required input data for AERMOD. To simplify processing and minimize the amount of quality assurance needed, the analysis was restricted to meteor ological data collected prior to the installation of the Automated Surface Observation System (ASOS). The ASOS has previously been found to omit the ceiling height for a large percentage of the observat ions at a number of me teorological stations. Installation and operatio n of ASOS equipment began in 1992; therefore, data for 1991 were processed with AERMET (note; the most recent ve rsion of AERMET has corrected this issue and is now compatible with ASOS data, processing of me teorological data in the future will utilize this revised version of AERMET and more recent years of meteorology). Data were retrieved from products available from the National

Table 3-1. Groupings Used for Polycy clic Organic Matter in the RTR Phase II Modeling Effort

POM subgroup Names used in the NEI Cancer URE used for the

subgroup (m 3 

### /g)

POM 71002 16-PAH 0.000055 PAH, total 0.000055 Polycyclic Organic Matter 0.000055
POM72002 2-Chlorona p hthalene 0.000055 2-Methylnaphthalene 0.000055 Acenaphthene 0.000055 Acenaphthylene 0.000055
Anthracene 0.000055 Benzo(c)phenanthrene 0.000055 Benzo[e]Pyrene 0.000055 Benzo[g,h,i,]Perylene 0.000055
Fluoranthene 0.000055
Fluorene 0.000055 Perylene 0.000055
Phenanthrene 0.000055 Pyrene 0.000055
POM 73002 7

, 12-Dimeth y lbenz [ a ] Anthracene 0.1
POM 74002 3-Meth y lcholanthrene 0.01
POM 75002 5-Meth y lchr y sene 0.001 Benzo[a]Pyrene 0.001 Dibenzo[a,h]Anthracene 0.001
POM 76002 B [j] Fluoranthen 0.0001 Benz[a]Anthracene 0.0001 Benzo[b]Fluoranthene 0.0001 Benzo[k]Fluoranthene 0.0001 Indeno[1,2,3-c,d]Pyrene 0.0001

### 17 POM 77002 Chr y sene 0.00001



### Climatic Data Center (NCDC). The surface data for 1991 were retrieved from the Hourly United States Weather Observation (HUSWO) CD. Up per air soundings were obtained from the Radiosonde Data of North America CDs produced by NCDC and the Forecast Systems Laboratory (FSL).

The input to and output from AERMET were examined for indications of missing input data. The 1200 Greenwich Mean Time (GMT) s ounding is required for AERMET to calculate the convective boundary layer height and several asso ciated parameters. There were many isolated days in which the 1200 GMT sounding was missing for many of the stations. However there also were several stations for which there were tw o or more consecutive days of missing 1200 GMT soundings. To minimize the impact on the out put from AERMET, upper air data from a representative upper air stati on were substituted for those periods for which the 1200 GMT sounding was missing for two or more consecutive days.

One of the important requirements of applyi ng the AERMOD model is the specification of surface characteristics for use in processing the me teorological data using AERMET. Application of AERMET requires specificati on of the surface roughness length, the Bowen ratio (an indicator of surface moisture), and the albedo (an indi cator of surface reflec tivity). These surface characteristics are used by AERMET to calculate the level of shear-induced mechanical turbulence generated by flow over the surface and for th e energy balance calculations used in the determination of the Monin-Obukhov stability parame ter and the convective ve locity scale. For this application, the following surface characteristics were used:

! Surface roughness length = 0.25 m. At the airport meteorological site, the surface roughness Includes runways, terminal buildings and other airport structures. In addition, off-airport structures often are within 3 kilometers of the measurement site. his combination of land covers suggests a va lue of 0.2 – 0.3 meters is appropriate.

! Bowen ratio = 1.0. Representing an equal partition of the heat fluxes

! Albedo = 0.15. Representing conditions fo r all seasons, including winter without continuous snow cover.

The file STNS.TXT located on th e HUSWO CD was used for the an emometer heights required by AERMET. These heights are to the nearest meter and were deemed appropriate for use in this application.

Two problems in the AERMET computer prog ram were fixed before completing this application. The first pertained to hourly surface obser vations. Incorrect missi ng indicators in the code for wind speed and direction were output when the data were extracted from the raw input file in Stage 1, which caused th e program to stop running in Stage 3. The AERMET program was changed to use correct missing indicators. The second problem was associated with the upper air soundings. AERMET would not process an entire station’s data if th e surface pressure for the first sounding was less than 850 millibars (mb). Surface pressures less than 850 mb are not uncommon for stations at high elevations . The AERMET program was modified to expand the allowable surface pressures.

### 18 Figure 3.1. Locations of RTR AE RMOD Meteorological Stations

In early 2007, AERMOD was modified to version 07026 which checks the header record of the surface meteorological input file to verify that its version number is 06341. As a result of this modification, the 122 NWS stations were re-processed through a ne wer version of AERMET (version 06431) to create meteor ological data files compatible with AERMOD version 07026. The meteorology data received from the States were alrea dy at version 06341.



## 3.4 Model Options Selected

HEM3 presents a number of options for characterizing the modeling domain and data sources. As many sources were modeled in this assessment, established defaults and common practices were relied on to make these choices. The choices available to a HEM3 user and the selections that were made in this assessment are presented in Table 3-2. Some of the key selections are discussed in more detail in the paragraphs below.

Although routine emissions are not expected to vary significantly with time, nonroutine (upset) emissions can be significant relative to routine emissions. Upset emissions occur during periods of startup, shutdown, a nd malfunction. Upset emissions ar e not likely for equipment or storage tanks, but do result from malfunctioning c ontrol devices and leaks in cooling tower heat exchangers. There is some limite d data on upset emissions available, 14 

### but no facility-specific

### 19 analyses of these data were performed to char acterize short-term emissions from these emission sources, and upset emissions were not modeled for the RTR risk assessments.

Table 3-2. Modeling Domain and Model Set-Up Options in the HEM-3 Model As Used in the Residual Risk and Tec hnology Review Assessments
Option Selection Dispersion model: AERMOD or ISCST3 AERMOD Type of analysis: chronic, acute, or both Both Averaging time for short term impacts 1-hour Multiplier for short term emissions 10 a 

### Urban or rural dispersion characteristics Rural Include terrain impacts Yes Include building wake effects No Calculate deposition (wet, dry, or both) No Include impacts of plume depletion due to deposition No User-specified receptor locations (for plant bou ndary sites or other sites of interest) No Modeling domain size – maximum distance to be modeled 50 km Cutoff distance for modeling of individual blocks 3 km b


### Overlap distance where receptors are considered to be on plant property – measured from each source 30 m c



### Polar receptor network specifications Distance from the plant center to the innermost ring 100 m c 

### Number of rings 13

Number of directions 16 Meteorology data Closest site

a 

### For the secondary aluminum production category, th e ratio of maximum short-term emissions to long-term average emissions was set to 100, based on the engineering review.

b 

### The individual block modeling cutoff was increased for categories and for so me facilities to ensure that the maximum individual risk values were not interpolated.

c 

### The overlap distance and inner ring distance were adjusted for some facilities to avoid modeling locations that are on plant property (see section 4.2).



### 3.4.1 Urban or Rural Dispersion Characteristics

Since 51 source categories were considered in these screening-level RTR assessments, the rural option was chosen to be most conservative (i.e., more likely to overe stimate risk results).

3.4.2 Deposition and Plume Depletion

This modeling analysis did not take into acc ount the depletion of po llutant concentrations in the plume due to wet or dry deposition. In ad dition, reactivity and decay were not considered.

### 20 It is possible that this approach, may overestimat e air concentrations and therefore risk. However, one of the main metrics used by EPA in the residual risk program is the risk to the individual most exposed (maximum risk). Because the maximum ri sk usually occurs at a receptor very close to the emission source, it is unlikely to be influe nced by altered plume disp ersion characteristics of this type. For more refined, multipathway assessments, EPA will consider deposition and depletion.

3.4.3 Cutoff Distance for Modeling of Individual Blocks

The cutoff distance for modeling individual Census blocks was in itially set to 3 km. This distance generally ensures that the maximum indi vidual cancer risk and the maximum TOSHI are modeled explicitly and not interpol ated. Following the modeling run, the results for each facility were checked to determine whether the maximum impacts were located inside the modeling cutoff distance. If the maximum impacts were outside the cutoff distance any of the impacts were significant, then HEM3 was rerun for the facility with an expanded modeling distance cutoff. In general, this was done if the cancer risk ex ceeded 1 in 10 million or any TOSHI exceeded 0.1. However, the risks for these facilities were gene rally very low, since the maximum impacts were generally only interpolated when the nearest Census block was more than 3 km from the facility.

3.4.4 Plant Boundary Assumptions

The main input mechanisms for incorporati ng plant boundary information in HEM3 are the overlap distance, the distance to the innermost polar receptor ring, and user-specified receptor locations. The NEI does not provide informati on on plant boundaries. Therefore, the userspecified receptor file was not us ed in this analysis. Conservative default assumptions were used for the overlap distance and the innermost polar r eceptor ring. However, these were adjusted for some categories where plant sites are known to be la rge. In addition, satellite imagery was used to check the plant boundary assumptions for facilities with large projected impacts. These checks are discussed further in the section on Quality Assurance (Section 4).



## 3.5 Modeling of Multiple Facilities

HEM3 models one facility at a time. However, clusters of nearby facilities may impact the same people, resulting in higher risk to those pe ople. To account for this situation, risks were summed at each Census block for all f acilities affecting the Census block.

As described earlier (Section 2.3.4), HEM3 produces detailed output tables containing the risk and population for every Census block in th e modeling domain. Thes e detailed tables were combined for all facilities in a source categor y and the risk for each Census block was summed. Thus, the effect of multiple facilities in the sa me source category on the same receptor could be estimated. The resulting “combined facility” or “cluster-effect” Ce nsus block risks were used to calculate population exposure to different cance r risk levels and sour ce category incidence.

### 21 

# 4. Quality Assurance

The National Emissions Inventory is subject to an extensive program of quality assurance (QA) and quality control (QC). The QA/QC prog ram for the point source component of the NEI is documented in a separate report, available from the NEI website. 15 

### This section describes QA activities carried out under the RTR modeling analysis.



## 4.1 Engineering Review

In addition to the standardized QA steps ta ken for the entire NE I, EPA performed an engineering review of NEI data for the emission s ource categories included in the RTR analysis. This engineering review included two main compone nts. The first component addressed the list of facilities included in each source category. EP A engineers reviewed independent sources of information to identify all source s in the category that are include d in the NEI. In addition, EPA reviewed the list of sources repres ented as part of each category in the NEI to make sure that the facilities actually manufacture products char acteristic of the source category.

The second component of the e ngineering review focused on the appropriateness of facility emissions. EPA reviewed the list of HAPs em itted by each facility to make sure that the pollutants were appropriate to the source cate gory. In addition, EPA engineers review the magnitude of those HAP emissions. In cases wher e new or better data were known to exist for a particular source category, that info rmation was integrated into the data used in modeling for that category. In these cases, the source category sp ecific documents provide additional details on the emissions inputs used.



## 4.2 Geographic Checks

The NEI QA process includes some basic check s on location data for point sources. The coordinates for each source are checked to ensure that they are in the county that has been specified for the source. If this is not the case, or if no geographic coordinates are available for the emission source, then the coordinates are set to a default location based on the nature of the emission source category. 15 

### In addition, coordinates for all emi ssion sources at a given facility are checked to ensure that they ar e withing 3 km of one another.

As part of the RTR modeling analysis, addi tional geographical QA checks were made for some facilities. Facilities subjecte d to these additional checks include:

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

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

" any maximum TOSHI above 10

! cases where no Census blocks were id entified within 3 km of the facility

The emission source coordinates were reviewed for each of these facilities, and compared with the address reported for the facility. Both the coordinates and the facility address were

### 22 entered into Internet mapping sites which provid e the option of viewing aerial or satellite photography. These sites include Google Earth® ( earth.google.com/ ), Google Maps® ( local.google.com/ ), MapQuest® ( www.mapquest.com ) and Microsoft Terraserver® ( www.terraserver.com/ ). If the address and the coordinates represented the same location, then the coordinates were taken to be correct. If the lo cations were different, th en the aerial photographs for the address and the coordinate location were reviewed to de termine whether either photograph included an industrial facility. Generally, where the two locations were different, the facility address was found to be correct. In some cases, this comparison could not be made because the reported address was a Post Office box or a headquart ers address. Where this occurred, the aerial photograph for the coordinate loca tion was reviewed to determine whether an industrial facility was located at or near the lo cation. If emission source coordinates were found to be incorrect, HEM3 was rerun using corrected coordinates. Th ese changes are described in the source category documents.

For the high-risk facilities, the coordinates used to represent the most impacted Census blocks were also reviewed. Th is review drew on detailed Census block boundary maps and aerial photographs. Large industrial facilities will frequen tly occupy one or more entire Census blocks. However, these blocks may also include one or more residences on the periphery of the industrial land. Generally, the coordinates listed for a Cens us block are near the center of the block. In these cases of mixed industrial a nd residential blocks, the coordinates may be on plant property.

The potential for overlap between an industria l facility and a Census block coordinate is illustrated in Figure 4-1. The figure shows Census blocks divisions around a large industrial facility, with the coordinates of emission sources at the facility illustrated by red squares. Census block boundaries are shown, and th e coordinates given in the Ce nsus data base for populated blocks are indicated by blue circles. As the figur e shows, the facility is spread over five Census blocks. A number of populated bl ocks can be seen to the Southwes t of the facility, and most of the blocks covering the facility are not populated . However, the block containing most of the facility’s emission points also includes at least one residence (the total population given for the block is 5). The location of the residence is at the periphery of the block, near the one of the adjacent populated blocks. However, the coordinates given for the block are near its center, illustrated by the blue star, clearly between various emission sources at the facility.

In general, block coordinates were considered to be on plant property if they were located between the different emission source locations listed for the facil ity. In these situations, HEM3 was rerun with an expanded overlap distance, in order to exclude the Ce nsus block coordinates that appeared to be located on plant property. The distance to the innermost polar receptor ring was also adjusted to ensure that this ring was not on plant property.

### 23 Figure 4-1. Illustration of a qualit y assurance test for Census block coordinates located on plant property

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### 24 

# 5. Uncertainties

This analysis is subject to a number of un certainties. For instance, model verification studies for AERMOD showed predicted maximu m 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 m easured values, with an average of 1.0. 16



### In addition, a number of simplifying assump tions have been made in this modeling analysis. First, the c oordinates reported by the Department of Census for Census block “internal points” have been used as a surrogate for l ong-term population exposures . Locations of actual residences have not been modeled. In addition, the current version of HEM3 does not take into account the movement of people from one Census block to another dur ing the course of their lives, or commuting patterns during a given day. Nor doe s the model take into account the attenuation of pollutant from outside emission sources in indoor air. Ideall y, 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 e xpected that long-term exposures will be overstated for high-end estimate s (as most individuals will not spend all their time at their highly affected residences), but ma y understate the total popul ation exposed (as some individuals living outside the m odeled area may regularly commute into the area for work or school).

When considering long-term or lifetime expos ures, it should be noted that relatively few people in the United States reside in one place for their entire liv es. For the purposes of this assessment, cancer risk estimates are based on a lifetime exposure at the 2000 Census-identified place of residence. While it is impossible to know how this assumption affects the risk experiences by a particular individual (as people ca n 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 ar eas). However, this assumption will also tend to underestimate the total number of people exposed and population risk (i.e., incidence) because population levels are generally increasing.

In the current analysis, only direct inhalati on has been modeled. Other pathways such as the deposition of pollutants to dri nking water, and to bioaccumula tion of deposited pollutants in the food supply may be a significant source of exposure for persistent and bioaccumulative pollutants. These exposures will be modeled in subsequent anal yses. Because this analysis is restricted to the inhalation pathway, the impact s of plume depletion due to deposition have not been into account. Thus, inha lation impacts may be overestimat ed for some pollutants, but exposures through other pathwa ys would be underestimated.

A number of other simplifications have been made in the dispersion modeling analysis, as has been noted in Table 3-1. For instance, building wake effects have not been considered. Rural dispersi on conditions have also been used for a ll facilities. This is viewed as a conservative assumption, since rural conditions resu lt in less mixing close to the emission source.

### 25 In addition, meteorological observations are based on the closest station in the HEM3 meteorological library (see Figure 2-1). Altern ative meteorological st ations may be more appropriate for some facilities. Ideally, facility-specific meteor ological observations would be used. A single year of meteorological data (1 991) was selected for dispersion modeling. When considering off-site meteorological data most s ite specific dispersion modeling efforts will employ up to five years of data to capture variability in weathe r patterns from year to year. However, because of the large number of f acilities in the analysis and the extent of the dispersion modeling analysis (national scale), it was not practical to m odel five years of data. Other national studies such as NATA have also considered only a single year of meteorological data. A sensitivity analyses performed by the NATA assessment found that variability attr ibutable 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 17



### .
Finally, risk and exposure fact ors are also subject to uncer tainty. Not all individuals experience the same degree of e xposure or internal dos e of a given pollutantdue to individualspecific parameters such as weight, age, and gend er. While the health benchmarks used in the analysis crudely account for sensitive populat ions, 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 resu lt in a degree of uncertainty in the resulting risk estimate.

Table 5-1 summarizes the general sources of uncertainty for the RTR modeling analysis. The table also gives a qualitative indication of the potential direction of bias on risk estimates. The sources of uncertainty in Table 5-1 are di vided into four categories, based on the major components of the analysis:

! emissions inventory

! fate and transport modeling

! exposure assessment

! toxicity assessment

It must also be noted that individual source catego ries may be subject to additional uncertainties. These are discussed in separate reports which have been prepared for each emission source category included in the RTR.26Table 5-1. Summary of General Uncertainties Associatedwith Risk and Technology Review Risk AssessmentParameterAssumptionUncertainty/Variability DiscussionPotential Direction of Biason Risk EstimatesEmissions InventoryIndividual HAPemissions rates andfacility characteristics(stack parameters,property boundaries)Emissions and facilitycharacteristics from the NEIprovide an accuratecharacterization of actual sourceemissions.Our emissions inventory is based on the 2002NEI, our internal review, and public commentsreceived. The degree to which the data in ourinventory represents actual emissions is likely tovary across sources. Nearly half of the sourcesin the source category submitted a review oftheir emissions and facility characteristics data.Some detailed data, such as property boundaryinformation was not available for most facility.This is an important consideration indetermining acute impacts.Unbiased overall, magnitudevariableMultiplier for short-termemission ratesMaximum short term emissionrates are estimated by applying asimple multiplier (a factor of 10)to average annual emissions.The ratio between short-term and long-termaverage emission rates may vary from amongthe different emission sources at a facility. Inaddition, the use of a simple multiplier meansthat impacts of maximum short term emissionsare modeled for all meteorological conditions,including the worst-case conditions forpopulation exposure.Potential overestimate due tothe fact that worst-caseemissions are assumed tooccasionally coincide withworst-case meteorology.Overestimate due to lack ofactual information on shortterm emission rates.Fate and Transport ModelingAtmospheric dispersionmodel choiceAERMOD is one of EPA'srecommended models forassessing pollutant concentrationsfrom industrial facilitiesField testing of these models have shown resultsto generally be within a factor of 2 of measuredconcentrations.OverestimateTable 5-1. Summary of General Uncertainties Associatedwith Risk and Technology Review Risk Assessment(continued)27ParameterAssumptionUncertainty/Variability DiscussionPotential Direction of Biason Risk EstimatesBuilding downwash Not included in assessmentUse of this algorithm in AERMOD couldimprove the dispersion calculations at individualfacilities. However, data were not readilyavailable to utilize this option.Potential underestimate ofmaximum risks near facility.No effect on risks furtherout.Plume depletion anddepositionNot included in assessmentIgnoring these impacts for pollutants thatdeposit minimally and whose risks derivepredominantly from inhalation should haveminimal effect on risk estimates.Unbiased or minimaloverestimate.MeteorologyOne year of meteorological datafrom the nearest weather station(selected from 122 nationwide) isrepresentative of long-termweather conditions at the facility.The use of one year of data rather than the fiveor more adds uncertainty based on whether thatyear is representative of each location’sclimatology. Use of weather station data ratherthan on-site data can add to uncertainty.Additionally, the use of default surfaceparameters in the generation of themeteorological datasets imparts uncertainty tothe results from any individual facility.Minimal underestimate oroverestimate.Reactivity Not included in the assessment.Chemical reactions and transformations ofindividual HAP into other compounds due tosolar radiation and reactions with otherchemicals happens in the atmosphere. However,in general, the HAP in this assessment do notreact quickly enough for these transformationsto be important near the sources, where thehighest individual risks are estimated. Further,most of the HAP do not react quickly enoughfor these transformations to be important to riskestimates in the entire modeled domain (i.e.,within 50 km of the source).No impact on maximum riskestimates. Minimal impacton population risks andincidence.Table 5-1. Summary of General Uncertainties Associatedwith Risk and Technology Review Risk Assessment(continued)28ParameterAssumptionUncertainty/Variability DiscussionPotential Direction of Biason Risk EstimatesMaximum modelingdistance50 kilometers from center offacilityThis distance is considered to be the maximumdownwind distance for a Gaussian plume modelsuch as AERMOD. This is because, in general,winds cannot be considered to follow straightline trajectories beyond this distance.No effect on maximumindividual risks. Minimalunderestimation ofincidence.Exposure AssessmentLocations and shortterm movements ofindividualsAmbient concentration at centroidof each off-site census block isequal to the exposureconcentration for all people livingin that census block.Effect of human activity patternson exposures is not included in theassessment.People live at different areas within block thatmay have higher or lower exposures than at thecentroid. Individuals also move from outdoorsto indoors and from home to school/work torecreation, etc., and this can affect their totalexposure from these sources.Unbiased across populationfor most pollutants andindividuals, likelyoverestimate for mostexposed and underestimatefor least exposed persons.Long-term movementsof individualsMIR individual is exposedcontinuously to the highestexposure concentration for a 70-year lifetime.Population moves into and out ofexposure area consistent withnational frequency distribution ofresidency.MIR (maximum individual risk) is defined inthis way to be a maximum theoretical risk at apoint where a person can actually reside.Individuals may move into or out of areasimpacted by the facilities of interest in ways thatdon’t correspond exactly with national norms orlocal demographic patterns, and this could leadto localized biases. In general, source categorieswith larger numbers of facilities should carryless potential for bias.Unbiased for mostindividuals, likelyoverestimate for the actualindividual most exposed andlikely underestimate for theleast exposed. Incidenceremains unbiased unlesspopulation around facilitiesincreases or decreases over70 years.Toxicity AssessmentTable 5-1. Summary of General Uncertainties Associatedwith Risk and Technology Review Risk Assessment(continued)29ParameterAssumptionUncertainty/Variability DiscussionPotential Direction of Biason Risk EstimatesReferenceconcentrations (RfC)Consistent with EPA guidance,RfCs are developed includinguncertainty factors to be protectiveof sensitive subpopulations.Additionally, RfCs are developedbased on the level producing aneffect in the most sensitive targetorgan or system.While other organ systems may be impacted atconcentrations above the RfC, these are notincluded in the calculation of target organspecific hazard indices.In general, EPA derivesRfCs using procedureswhose goal is to avoidunderestimating risks in lightof uncertainty andvariability. The greater theuncertainties, the greater thepotential for overestimatingrisks.Unit Risk Estimate(URE)Use of unit risk estimatesdeveloped from dose-responsemodels such as linear low-doseextrapolation.Uncertainty in extrapolating the impacts fromshort-duration, high-dose animal or work-relatedexposures to longer duration, lower-doseenvironmental impacts.Overestimate of risks fornonlinear carcinogens andfor linear carcinogens withsparse health effects data. Ingeneral, EPA derives UREvalues using proceduresaimed at overestimatingrisks in light of uncertaintyand variability.Toxicity of mixturesCancer risks and noncancer hazardquotients were calculated for eachHAP individually and thensummed into a total risk or hazardindex (assumption of additivity).Concurrent exposures to multiple chemicalsmay result in either increased or decreasedtoxicity due to chemical interactions but the dataneeded to quantify these effects are generallynot available.Unbiased overall. Somemixtures may haveunderestimated risks, someoverestimated, and somecorrectly estimated.Table 5-1. Summary of General Uncertainties Associatedwith Risk and Technology Review Risk Assessment(continued)30ParameterAssumptionUncertainty/Variability DiscussionPotential Direction of Biason Risk EstimatesSurrogate doseresponse values forHAPs without valuesIn the case of groups of HAPssuch as glycol ethers, the mostconservative dose-response valueof the chemical group was used asa surrogate for missing doseresponse values in the group. Forothers, such as unspeciated metals,we have applied speciationprofiles appropriate to the sourcecategory to develop a compositedose-response value for the group.For HAP which are not in a groupand for which no URE’s or RfC’sare available from crediblesources, no assessment of risk ismade.Rather than neglecting the assessment of risksfrom some HAPs lacking dose response values,conservative assumptions allow the examinationof whether these HAPs may pose anunacceptable risk and require furtherexamination, or whether the conservative levelexamination with surrogates screens out theHAPs from further assessment.Overestimate where mostconservative values used.Unbiased where categoryspecific profiles applied.There is the potential tounderestimate risks forpollutants which are notincluded 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. www.epa.gov/scram001/dispersion_prefrec.htm#aermod

2. EPA. 2004. User's Guide for the AMS/EPA Regulatory Model - AERMOD . EPA454/B-03-001, U.S. Environmental Protection Ag ency, Research Triangle Park, NC. www.epa.gov/scram001/dispersion_prefrec.htm#aermod

3. EC/R Incorporated. 2006. The HEM-3 User’s Guide . Prepared for the U.S. Environmental Protection Agency, Sector-B ased Assessment Group, Research Triangle Park, NC.

4. EPA. 2007. OAQPS Prioritized Chronic Dose-Response Values (6/12/2007). U.S. Environmental Protection Agency, Resear ch Triangle Park, NC. Available at: http://www.epa.gov/ttn/atw/toxsource/summary.html

5. Census. 2000. LandView® 5 on DVD . U.S. Census Bureau, Washington, DC.

6. USGS. 2003. 1:250,000 Scale Digital Elevation Models (DEM) . U.S. Geologic Survey, Washington, DC. http://edcsgs9.cr.usgs.gov/glis/hype r/guide/1_dgr_demfig/states.html

7. EPA. 2004. User's Guide for the AERMOD Terrain Preprocessor (AERMAP) . EPA454/B-03-003, U.S. Environmental Protection Agency, Research Triangle Park, NC. http://www.epa.gov/scram001/di spersion_related.htm#aermap

8. EPA. 2004. Addendum: User's Guide for th e AMS/EPA Regulatory Model - AERMOD (related to deposition). U.S. Environmenta l Protection Agency, Research Triangle Park, NC. http://www.epa.gov/scram001/dispersion_prefrec.htm#aermod

9. Wesely, M.L., P.V. Doskey, and J.D. Shannon. 2002. Deposition Parameterizations for the Industrial Source Complex (ISC3) Model . Argonne National Laboratory, Argonne, Illinois. http://www.epa.gov/scram001/di spersion_prefrec.htm#aermod

10. EPA. 2006. 2002 National Emissions Inventory Data & Documentation . U.S. Environmental Protection Agency, Research Triangle Park, NC. http://www.epa.gov/ttn/chie f/net/2002inventory.html

11. EPA. 2002. EMS-HAP Modeling System . Environmental Protection Agency, Research Triangle Park, NC. http://www.epa.gov/scram001/disp ersion_related.htm#ems-hap

### 32 12. Strum, Madeleine, Richard Mason, and Diane Linderman. 2002. User's Guide for the Emissions Modeling System for Hazar dous Air Pollutants (EMS-HAP) Version 2.0

. EPA-454/B-02-001, Environmental Protection Ag ency, Research Triangle Park, NC. http://www.epa.gov/scram001/di spersion_related.htm#ems-hap

13. MACTEC. 2006. Meteorological Data Processing Using Aermet for HEM-AERMOD

. Prepared by MACTEC Federal Programs, Inc., Durham, NC for EC/R Incorporated, Chapel Hill, NC.

14. Texas Commission on Environmental Qualit y. 2006. Reports on Air Emission Events. http://www.tceq.state.tx.us /compliance/field_ops/eer/

15. EPA. 2006. NEI Quality Assurance and Data Augmentation for Point Sources . U.S. Environmental Protection Agency, Emission Inventory Group, Res earch Triangle Park, NC.

16. EPA. 2003. AERMOD: Latest Features and Evaluation Results. EPA-454/R-03-003, U.S. Environmental Protection Agency, Research Triangle Park, NC. http://www.epa.gov/scram001/7thconf/aermod/eval.pdf

17 

### EPA, 2001, National-Scale Air Toxics Assessment for 1996; EPA-453/R-01-003; EPA454/B-03-003, U.S. Environmental Protection Agency, Research Triangle Park, NC. http://www.epa.gov/ttn/atw/nata/peer.html

### Appendix 3 Meteorological Data for HEM-AERMOD Modeling

### 2 

## DRAFT

METEOROLOGICAL DATA PROCESSING USING AERMET

FOR

HEM-AERMOD

Submitted by:

MACTEC Federal Programs, Inc.

5001 S. Miami Blvd., Suite 300 Durham, NC 27703 919-941-0333

July 28, 2006Meteorological Data Processing using AERMET July 28, 2006 For HEM-AERMOD



### i TABLE OF CONTENTS

PROJECT BACKGROUND ...............................................................................................1

METEOROLOGICAL DATA PROCESSING ...................................................................1

MISSING DATA.................................................................................................................5

ANEMOMETER HEIGHTS ...............................................................................................6

SURFACE CHARACTERISTICS ......................................................................................6

MODIFICATIONS TO AERMET CODE ..........................................................................7

RESULTS ........................................................................................................................ ....7

LIST OF TABLES

Table

1 SURFACE STATIONS PAIRED WITH UPPER AIR STATION.........................1

2 UPPER AIR STATIONS WITH MISSING 1200 GMT SOUNDINGS AND STATION USED FOR DATA SUBSTITUTION.........................................5

3 SURFACE CHARACTERISTICS ..........................................................................7

4 PERCENT CALM AND MISSING BY STATION ...............................................8

LIST OF FIGURES Figure

1 SURFACE AND UPPE R AIR STATIONS ............................................................4Meteorological Data Processing using AERMET July 28, 2006 For HEM-AERMOD



### 1 PROJECT DESCRIPTION AND BACKGROUND

The AERMOD meteorological processor, AERM ET, was used to process one year of hourly data from 122 National Weather Serv ice (NWS) surface observation stations across the continental United States, Alaska, Hawaii, and Puerto Rico.

METEOROLOGICAL DATA PROCESSING

To estimate the boundary layer parameters required by AERMOD, AERMET requires hourly surface weather observations and the fu ll (i.e., meteorological variables reported at all levels) twice-daily upper air soundings. The surface and upper air stations are paired to produce the required input data for AERMOD.

One of the requirements of the project was to use surface observations prior to the introduction of the Automated Surface Observation System (ASOS). Installation and operation of ASOS equipment began in 1992; therefore, data for 1991 were processed with AERMET.

A list of 86 previously paired surface and upper air stations was provided to MACTEC. On review of these stations, it was determined that data for 1991 for three of the stations – Galveston, TX, Houston-Hobby, TX and B oulder, CO – was not available. The remaining 83 stations did not provide adequa te coverage for the MACT sources to be modeled with AERMOD. As a result, an additi onal 39 surface stations were identified to include for processing. These additional surf ace stations were paired with representative upper air stations. A list of the surface a nd paired upper air stations are in Table 1.

Data were retrieved from products availabl e from the National Climatic Data Center (NCDC). The surface data for 1991 were retr ieved from the Hourly United States Weather Observation (HUSWO) CD. Uppe r air soundings were obtained from the Radiosonde Data of North America CDs pr oduced by NCDC and the Forecast Systems Laboratory (FSL).

TABLE 1

SURFACE STATIONS WITH PA IRED UPPER AIR STATION

SURFACE WBAN LAT LON UPPER AIR WBAN LAT LON Paducah, KY 03816 37.07 -88.77 Paducah, KY 03816 37.07 -88.77 Huntsville, AL 03856 34.65 -86.78 Nashville, TN 13897 36.13 -86.68 Huntington, WV 03860 38.37 -82.55 Huntington, WV 03860 38.37 -82.55 Greenville, SC 03870 34.90 -82.22 Athens, GA 13873 33.95 -83.32 Fort Worth, TX 03927 32.90 -97.02 Stephenville, TX 13901 32.22 -98.18 Wichita, KS 03928 37.65 -97.45 Topeka, KS 13996 39.07 -95.63 Lake Charles, LA 03937 30.12 -93.23 Lake Charles, LA 03937 30.12 -93.23 Jackson, MS 03940 32.32 -90.08 Jackson, MS 03940 32.32 -90.08 Kansas City, MO 03947 39.30 -94. 73 Topeka, KS 13996 39.07 -95.63Meteorological Data Processing using AERMET July 28, 2006 For HEM-AERMOD



### 2 SURFACE WBAN LAT LON UPPER AIR WBAN LAT LON San Juan, PR 11641 18.43 -66.02 San Juan, PR 11641 18.43 -66.00 Orlando, FL 12815 28.45 -81.32 Tampa, FL 12842 27.70 -82.40 Gainesville, FL 12816 29.68 -82.27 Waycross, GA 13861 31.25 -82.40 Tampa, FL 12842 27.97 -82.53 Ta mpa Bay, FL 12842 27.97 -82.53 W. Palm Beach, FL 12844 26.68 -80.10 W. Palm Beach, FL 12844 26.68 -80.12 Victoria, TX 12912 28.85 -96.92 Corp us Christi, TX 12924 29.77 -97.50 New Orleans, LA 12916 30.00 -90.25 Slidell, LA 53813 30.33 -89.82 Port Arthur, TX 12917 29.95 -94.02 Lake Charles, LA 03937 30.12 -93.23 Brownsville, TX 12919 25.90 -97.43 Brownsville, TX 12919 25.90 -97.43 San Antonio, TX 12921 29.53 -98.47 Del Rio, TX 22010 29.37 -100.92 Corpus Christi, TX 12924 27.77 -97.52 Corpus Christi, TX 12924 27.77 -97.52 Houston, TX 12960 29.98 -95.37 Lak e Charles, LA 03937 30.12 -93.23 Raleigh, NC 13722 35.87 -78.78 Greensboro, NC 13723 36.10 -79.95 Greensboro, NC 13723 36.08 -79.95 Greensboro, NC 13723 36.08 -79.95 Norfolk, VA 13737 36.90 -76.20 Wa llops Island, VA 93739 37.93 -75.47 Philadelphia, PA 13739 39.87 -75.23 Atlantic City, NJ 93755 39.75 -74.67 Richmond, VA 13740 37.50 -77.32 Sterling, VA 93734 38.98 -77.47 Roanoke, VA 13741 37.32 -79.97 Greensboro, NC 13723 36.10 -79.95 Wilmington, NC 13748 34.27 -77.90 Charleston, SC 13880 32.90 -80.03 Wilmington, DE 13781 39.67 -75.60 At lantic City, NJ 93755 39.75 -74.67 Meridian, MS 13865 32.33 -88.75 Jackson, MS 03940 32.32 -90.08 Charleston, WV 13866 38.38 -81.58 Huntington, WV 03860 38.37 -82.55 Atlanta, GA 13874 33.63 -84.45 Athens, GA 13873 33.95 -83.33 Birmingham, AL 13876 33.57 -86.75 C enterville, AL 03881 32.90 -87.25 Bristol, TN 13877 36.47 -82.40 Huntington, WV 03860 38.37 -82.55 Charleston, SC 13880 32.90 -80.03 Charleston, SC 13880 32.90 -80.03 Charlotte, NC 13881 35.22 -80.93 Greensboro, NC 13723 36.08 -79.95 Columbia, SC 13883 33.95 -81.12 Athens, GA 13873 33.95 -83.33 Jacksonville, FL 13889 30.50 -81.70 Waycross, GA 13861 31.25 -82.40 Memphis, TN 13893 35.05 -89.98 N. Little Rock, AR 03952 34.83 -92.27 Mobile, AL 13894 30.68 -88.25 Slidell, LA 53813 30.33 -89.82 Montgomery, AL 13895 32.30 -86.40 Centerville, AL 03881 32.90 -87.25 Nashville, TN 13897 36.13 -86.68 Nashville, TN 13897 36.13 -86.68 Shreveport, LA 13957 32.45 -93.82 Longview, TX 03951 32.35 -94.65 Little Rock, AR 13963 34.73 -92.23 N. Little Rock, AR 03952 34.83 -92.27 Wichita Falls, TX 13966 33.98 -98.50 Stephenville, TX 13901 32.22 -98.18 Oklahoma City, OK 13967 35.40 -97. 60 Norman, OK 03948 35.23 -97.47 Baton Rouge, LA 13970 30.53 -91.15 Slidell, LA 53813 30.33 -89.82 Dodge City, KS 13985 37.77 -99.97 Dodge City, KS 13985 37.77 -99.97 St. Louis, MO 13994 38.75 -90.37 Peoria, IL 14842 40.67 -89.68 Springfield, MO 13995 38.82 -92. 27 Monet, MO 03946 36.87 -93.97 Buffalo, NY 14733 42.93 -78.73 Buffalo, NY 14733 42.93 -78.73 Newark, NJ 14734 41.72 -75.17 Atl antic City, NJ 93755 39.75 -74.67 Albany, NY 14735 42.75 -73.82 Albany, NY 14735 42.75 -73.82 Allentown, PA 14737 40.65 -75.45 Albany, NY 14735 42.75 -73.82 Boston, MA 14739 42.37 -71.02 Chatham, MA 14684 41.67 -69.97 Hartford, CN 14740 41.93 -72.68 Albany, NY 14735 42.75 -73.80 Burlington, VT 14742 44.47 -73.15 Albany, NY 14735 42.75 -73.82Meteorological Data Processing using AERMET July 28, 2006 For HEM-AERMOD



### 3 SURFACE WBAN LAT LON UPPER AIR WBAN LAT LON Portland, ME 14764 43.65 -70.30 Portland, ME 14764 43.65 -70.30 Providence, RI 14765 41.73 -71.43 Chatham, MA 14684 41.67 -69.97 Syracuse, NY 14771 43.12 -76.12 Buffalo, NY 14733 42.93 -78.73 Williamsport, PA 14778 41.27 -77.05 Pittsburgh, PA 94823 40.53 -80.23 Cleveland, OH 14820 41.40 -81.85 Pittsburgh, PA 94823 40.53 -80.23 Columbus, OH 14821 39.98 -82.88 Dayton, OH 13840 39.83 -84.05 Fort Wayne, IN 14827 41.00 -85. 20 Dayton, OH 13840 39.83 -84.05 Lansing, MI 14836 42.78 -84.58 Flint, MI 14826 42.97 -83.75 Madison, WI 14837 43.13 -89.33 Green Bay, WI 14898 44.48 -88.13 Erie, PA 14860 42.08 -80.18 Bu ffalo, NY 14733 42.93 -78.73 Akron, OH 14895 40.92 -81.43 Pittsburgh, PA 94823 40.50 -80.23 Green Bay, WI 14898 44.50 -88.12 Green Bay, WI 14898 44.50 -88.12 Duluth, MN 14913 46.83 -92.22 Int'l Falls, MN 14918 48.57 -93.40 Minneapolis, MN 14922 44.88 -93.23 St. Cloud, MN 14926 45.55 -94.05 Moline, IL 14923 41.45 -90.52 Peoria, IL 14842 40.67 -89.68 Rochester, MN 14925 43.92 -92.50 St. Cloud, MN 14926 45.55 -94.08 Sioux Falls, SD 14944 43.57 -96. 73 Huron, SD 14936 44.38 -98.22 Eau Claire, WI 14991 44.87 -91.48 St. Cloud, MN 14926 45.55 -94.08 Lihue, HI 22536 21.98 -159.33 Lihue, HI 22536 21.98 -159.33 Midland, TX 23023 31.95 -102.18 Midland, TX 23023 31.95 -102.18 El Paso, TX 23044 31.82 -106.38 El Paso, TX 23044 31.82 -106.38 Amarillo, TX 23047 35.22 -101.70 Amarillo, TX 23047 35.22 -101.70 Albuquerque, NM 23050 35.03 -106.62 Albuquerque, NM 23050 35.03 -106.62 Denver, CO 23062 39.77 -104.87 Denver, CO 23062 39.77 -104.88 Goodland, KS 23065 39.37 -101.70 Dodge City, KS 13985 37.77 -99.97 Grand Junction, CO 23066 39.12 -108.53 Grand Junction, CO 23066 39.12 -108.53 Tonopah, NV 23153 38.05 -117.08 Ely, NV 23154 39.30 -114.85 Tucson, AZ 23160 32.12 -110.93 Tucson, AZ 23160 32.12 -110.93 Daggett, CA 23161 34.87 -116.78 De sert Rock, NV 03160 36.62 -116.02 Los Angeles, CA 23174 33.93 -118.38 Miramar, CA 03190 32.87 -117.15 Prescott, AZ 23184 34.65 -112.43 Tucson, AZ 231 60 32.12 -110.93 San Diego, CA 23188 32.73 -117.17 Miramar, CA 03190 32.87 -117.15 Sacramento, CA 23232 38.52 -121.50 Oakland, CA 23230 37.75 -122.22 San Francisco, CA 23234 37.62 -122.40 Oakland, CA 23230 37.72 -122.22 Bismarck, ND 24011 46.78 -100.75 Bismarck, ND 24011 46.78 -100.75 Cheyenne, WY 24018 41.15 -104.77 Denver, CO 23062 39.77 -104.88 Billings, MT 24033 45.80 -108.55 Gr eat Falls, MT 24143 47.47 -111.38 Casper, WY 24089 42.90 -106.47 Lander, WY 24021 42.82 -108.73 Rapid City, SD 24090 44.05 -103.07 Rapid City, SD 24090 44.05 -103.07 Salt Lake City, UT 24127 40.78 -111.97 Salt Lake City, UT 24127 40.78 -111.97 Great Falls, MT 24143 47.47 -111.38 Great Falls, MT 24143 47.47 -111.38 Spokane, WA 24157 47.62 -117.53 Spokane, WA 24157 47.62 -117.53 Medford, OR 24225 42.37 -122.87 Medford, OR 24225 42.37 -122.87 Portland, OR 24229 45.60 -122.60 Salem, OR 24232 44.90 -123.00 Salem, OR 24232 44.90 -123.00 Salem, OR 24232 44.90 -123.00 Seattle, WA 24233 47.45 -122.32 Quillayute, WA 94240 47.93 -124.55 Juneau, AK 25309 58.37 -134.58 A nnette Is., AK 25308 55.03 -131.57 Cold Bay, AK 25624 55.20 -162.72 Cold Bay, AK 25624 55.20 -162.72Meteorological Data Processing using AERMET July 28, 2006 For HEM-AERMOD



### 4 SURFACE WBAN LAT LON UPPER AIR WBAN LAT LON Fairbanks, AK 26411 64.80 -147.88 Fairbanks, AK 26411 64.80 -147.88 Valdez, AK 26442 61.13 -146.35 An chorage, AK 26409 61.22 -149.85 Anchorage, AK 26451 61.20 -150.00 Anchorage, AK 26409 61.22 -149.85 Nome, AK 26617 64.50 -165.43 Nome, AK 26617 64.50 -165.43 Colo. Springs 93037 38.82 -104.72 Denver, CO 23062 39.77 -104.88 Fresno, CA 93193 36.78 -119.72 Oakland, CA 23230 37.72 -122.22 Baltimore, MD 93721 39.18 -76.67 Sterling, VA 93734 38.98 -77.47 Sterling, VA 93738 38.95 -77.45 Sterling, VA 93734 38.98 -77.47 Evansville, IN 93817 38.05 -87.52 Paducah, KY 03816 37.05 -88.77 Indianapolis, IN 93819 39.73 -86. 28 Dayton, OH 13840 39.83 -84.05 Lexington, KY 93820 38.03 -84.60 Dayton, OH 13840 39.83 -84.05 Louisville, KY 93821 38.18 -85.73 Dayton, OH 13840 39.83 -84.05 Springfield, IL 93822 39.85 -89. 68 Peoria, IL 14842 40.67 -89.68 Pittsburgh, PA 94823 40.53 -80.23 Pittsburgh, PA 94823 40.50 -80.23 Toledo, OH 94830 41.58 -83.80 Flint, MI 14826 42.97 -83.73 Chicago, IL 94846 42.00 -87.93 Peoria, IL 14842 40.67 -89.68 Detroit, MI 94847 42.23 -83.33 Flint, MI 14826 42.97 -83.75



### Figure 1 shows the surface stations (green circ les) and upper air stations (red pentagons) used in this study.

FIGURE 1. SURFACE AND UPPER AIR STATIONSMeteorological Data Processing using AERMET July 28, 2006 For HEM-AERMOD



### 5 MISSING DATA

While AERMET will run if input observations are missing, the output will be incomplete, with missing data indicators in the output file. The input to and output from AERMET were examined for indications of missing input data.

The 1200 Greenwich Mean Time (GMT) sounding is required for AERMET to calculate the convective boundary layer height and seve ral associated parameters. There were many isolated days in which the 1200 GMT sounding was missing for many of the stations. However there also were several stations for which there were two or more consecutive days of missing 1200 GMT sounding s. The longest period of consecutive missing 1200 GMT soundings was at Dayton, Ohio with seven days of missing soundings. To minimize the impact on th e output from AERMET and not consume too much time and effort, MACTEC substituted upper air data from a representative upper air station for those periods for which the 1200 GMT sounding was missing for two or more consecutive days. Table 2 s hows the station substitutions.

TABLE 2

UPPER AIR STATIONS WITH MISSI NG 1200 GMT SOUNDINGS AND STATION USED FOR DATA SUBSTITUTION

Upper Air Station With Missing Data WBAN Station Used for Data Substitution WBAN

Amarillo, TX 23047 Norman, OK 03948 Athens, GA 13873 Greensboro, NC 13723 Brownsville, TX 12929 Corpus Christi, TX 12924 Centerville, AL 03881 Jackson, MS 03940 Chatham, MA 14684 Portland, ME 14764

Dayton, OH 13840 Paducah, KY 03826

Ely, NV 23154 Winnemucca, NV 24128 Grand Junction, CO 23066 Salt Lake City, UT 24127

Lake Charles, LA 03937 Slidell, LA 53813 Paducah, KY 03826 Nashville, TN 13897

Peoria, IL 14842 Omaha, NE 94918 Pittsburgh, PA 94823 Huntington, WV 03860 Salt Lake City, UT 24127 Ely, NV 23154 St. Cloud, MN 14926 Huron, SD 14936Meteorological Data Processing using AERMET July 28, 2006 For HEM-AERMOD



### 6 ANEMOMETER HEIGHTS

The file STNS.TXT located on the HUSWO CD was used for the anemometer heights required by AERMET. These heights are to the nearest meter and were deemed appropriate for use in this application.

SURFACE CHARACTERISTICS

One of the important requirements of appl ying the AERMOD model is the specification of surface characteristics for use in processing the meteorological data using AERMET. Application of AERMET requires specifi cation of the surfac e roughness length, the Bowen ratio (an indicator of surface moisture ), and the albedo (an indicator of surface reflectivity). These surface characteristics ar e used by AERMET to calculate the level of shear-induced mechanical turbulence genera ted by flow over the surface and for the energy balance calculations used in the de termination of the Monin-Obukhov stability parameter and the convective velocity scale.

The surface characteristics can be varied by wi nd direction sector (upwind) and by month or season. Current guidance sp ecifies defining the characteri stics for the meteorological measurement site and extending outward fr om the measurement location for three kilometers. In an application of this magnit ude, it is impractical to evaluate and vary the surface characteristics for each of the 122 st ations, therefore, MACTEC used annual values for one sector, as shown in Table 3.

The albedo used here is repres entative of conditions for all seasons which includes winter without continuous snow cover. Most stati ons fall into this category and continuous snow coverage for several months even at northern stations in the continental United States is unlikely.

Without a detailed and individual analysis for each of the 122 station locations and for this large an area for an entire year, a value representing an equal partition of the fluxes, or a Bowen ratio of 1.0, is the best represen tation to use in this type of analysis.

The surface roughness not only includes runways and areas between runways, but usually terminal buildings and other airport structur es. In addition, in urban environments, offairport structures often are within 3 kilometers of the measurement site. This combination of land covers suggests a valu e of 0.2 – 0.3 meters is appropriate. MACTEC used 0.25 for the roughness length.Meteorological Data Processing using AERMET July 28, 2006 For HEM-AERMOD



### 7 TABLE 3

SURFACE CHARACTERISTICS

MODIFICATIONS TO AERMET CODE

Recent use of AERMET identified two ‘bugs’ in the code that required fixing before running AERMET. The first had to do with the hourly surface observations. Incorrect missing indicators in the code for wind speed and direction were output when the data were extracted from the raw input file in Stage 1. When AERMET encountered these flags in Stage 3, AERMET simply stopped proce ssing data, resulting in files with less data than requested. MACTEC modified th e code to use correct missing indicators. MACTEC also checked other meteorological variables to be sure the correct missing indicators were used. No other missing indicators were in error.

The second ‘bug’ was associated with th e upper air soundings. AERMET would not process an entire station’s data if the surface pressu re for the first sounding was less than 850 millibars (mb). Surface pressures less than 850 mb is not uncommon for stations at high elevations such as Denver, CO and Al buquerque, NM. MACTEC modified the code to expand the allowable surface pressures.

Both ‘bugs’ were fixed prior to runni ng AERMET with the result that AERMET successfully ran to completion for all files.

RESULTS

AERMET successfully ran for all 122 stations . While the reports and output from AERMET provide an indication of the success of the runs, MACTEC ran AERMOD with a single source and single receptor to obtai n information on the number of hours with calm winds or missing data. Table 4 shows these values by surface station. Albedo 0.15

Bowen Ratio 1.0

Surface Roughness (m) 0.25Meteorological Data Processing using AERMET July 28, 2006 For HEM-AERMOD



### 8 TABLE 4

PERCENT CALM AND MISSING BY STATION

WBAN Station Calms % Calm Missing % Missing Total % Total 03816 Paducah, KY 820 9.4% 66 0.75% 886 10.1% 03856 Huntsville, AL 719 8.2% 0 0.00% 719 8.2% 03860 Huntington, WV 340 3.9% 28 0.32% 368 4.2% 03870 Greenville, SC 810 9.2% 20 0.23% 830 9.5% 03927 Fort Worth, TX 508 5.8% 24 0.27% 532 6.1% 03928 Wichita, KS 215 2.5% 0 0.00% 215 2.5% 03937 Lake Charles, LA 616 7.0% 15 0.17% 631 7.2% 03940 Jackson, MS 1228 14.0% 58 0.66% 1286 14.7% 03947 Kansas City, MO 231 2.6% 0 0.00% 231 2.6% 11641 San Juan, PR 670 7.6% 11 0.13% 681 7.8% 12815 Orlando, FL 144 1.6% 0 0.00% 144 1.6% 12816 Gainesville, FL 952 10.9% 11 0.13% 963 11.0% 12842 Tampa, FL 572 6.5% 0 0.00% 572 6.5% 12844 W. Palm Beach, FL 566 6.5% 8 0.09% 574 6.6% 12912 Victoria, TX 320 3.7% 0 0.00% 320 3.7% 12916 New Orleans, LA 721 8.2% 8 0.09% 729 8.3% 12917 Port Arthur, TX 736 8.4% 10 0.11% 746 8.5% 12919 Brownsville, TX 418 4.8% 36 0.41% 454 5.2% 12921 San Antonio, TX 913 10.4% 11 0.13% 924 10.5% 12924 Corpus Christi, TX 371 4.2% 0 0.00% 371 4.2% 12960 Houston, TX 1097 12.5% 13 0.15% 1110 12.7% 13722 Raleigh, NC 810 9.2% 0 0.00% 810 9.2% 13723 Greensboro, NC 542 6.2% 24 0.27% 566 6.5% 13737 Norfolk, VA 598 6.8% 39 0.45% 637 7.3% 13739 Philadelphia, PA 154 1.8% 2 0.02% 156 1.8% 13740 Richmond, VA 605 6.9% 0 0.00% 605 6.9% 13741 Roanoke, VA 1287 14.7% 1 0.01% 1288 14.7% 13748 Wilmington, NC 976 11.1% 0 0.00% 976 11.1% 13781 Wilmington, DE 837 9.6% 0 0.00% 837 9.6% 13865 Meridian, MS 1567 17.9% 56 0.64% 1623 18.5% 13866 Charleston, WV 1257 14.3% 29 0.33% 1286 14.7% 13874 Atlanta, GA 309 3.5% 21 0.24% 330 3.8% 13876 Birmingham, AL 1228 14.0% 15 0.17% 1243 14.2% 13877 Bristol, TN 2541 29.0% 28 0.32% 2569 29.3% 13880 Charleston, SC 487 5.6% 0 0.00% 487 5.6% 13881 Charlotte, NC 985 11.2% 0 0.00% 985 11.2% 13883 Columbia, SC 1340 15.3% 20 0.23% 1360 15.5% 13889 Jacksonville, FL 1134 12.9% 12 0.14% 1146 13.1% 13893 Memphis, TN 809 9.2% 0 0.00% 809 9.2% 13894 Mobile, AL 527 6.0% 8 0.09% 535 6.1% 13895 Montgomery, AL 1027 11.7% 19 0.22% 1046 11.9% 13897 Nashville, TN 87 1.0% 0 0.00% 87 1.0% 13957 Shreveport, LA 1088 12.4% 11 0.13% 1099 12.5% 13963 Little Rock, AR 1090 12.4% 0 0.00% 1090 12.4%Meteorological Data Processing using AERMET July 28, 2006 For HEM-AERMOD



### 9 WBAN Station Calms % Calm Missing % Missing Total % Total 13966 Wichita Falls, TX 622 7.1% 23 0.26% 645 7.4% 13967 Oklahoma City, OK 158 1.8% 0 0.00% 158 1.8% 13970 Baton Rouge, LA 787 9.0% 9 0.10% 796 9.1% 13985 Dodge City, KS 30 0.3% 32 0.37% 62 0.7% 13994 St. Louis, MO 416 4.7% 26 0.30% 442 5.0% 13995 Springfield, MO 212 2.4% 39 0.45% 251 2.9% 14733 Buffalo, NY 181 2.1% 0 0.00% 181 2.1% 14734 Newark, NJ 197 2.2% 0 0.00% 197 2.2% 14735 Albany, NY 1217 13.9% 8 0.09% 1225 14.0% 14737 Allentown, PA 465 5.3% 9 0.10% 474 5.4% 14739 Boston, MA 36 0.4% 38 0.43% 74 0.8% 14740 Hartford, CN 204 2.3% 8 0.09% 212 2.4% 14742 Burlington, VT 155 1.8% 9 0.10% 164 1.9% 14764 Portland, ME 381 4.3% 25 0.29% 406 4.6% 14765 Providence, RI 215 2.5% 39 0.45% 254 2.9% 14771 Syracuse, NY 481 5.5% 0 0.00% 481 5.5% 14778 Williamsport, PA 1403 16.0% 24 0.27% 1427 16.3% 14820 Cleveland, OH 271 3.1% 27 0.31% 298 3.4% 14821 Columbus, OH 734 8.4% 0 0.00% 734 8.4% 14827 Fort Wayne, IN 440 5.0% 0 0.00% 440 5.0% 14836 Lansing, MI 632 7.2% 10 0.11% 642 7.3% 14837 Madison, WI 685 7.8% 30 0.34% 715 8.2% 14860 Erie, PA 286 3.3% 4 0.05% 290 3.3% 14895 Akron, OH 232 2.6% 28 0.32% 260 3.0% 14898 Green Bay, WI 414 4.7% 28 0.32% 442 5.0% 14913 Duluth, MN 336 3.8% 0 0.00% 336 3.8% 14922 Minneapolis, MN 311 3.6% 3 0.03% 314 3.6% 14923 Moline, IL 812 9.3% 24 0.27% 836 9.5% 14925 Rochester, MN 10 0.1% 0 0.00% 10 0.1% 14944 Sioux Falls, SD 492 5.6% 0 0.00% 492 5.6% 14991 Eau Claire, WI 1193 13.6% 0 0.00% 1193 13.6% 22536 Lihue, HI 6 0.1% 0 0.00% 6 0.1% 23023 Midland, TX 207 2.4% 19 0.22% 226 2.6% 23044 El Paso, TX 262 3.0% 0 0.00% 262 3.0% 23047 Amarillo, TX 113 1.3% 6 0.07% 119 1.4% 23050 Albuquerque, NM 606 6.9% 0 0.00% 606 6.9% 23062 Denver, CO 530 6.1% 0 0.00% 530 6.1% 23065 Goodland, KS 176 2.0% 41 0.47% 217 2.5% 23066 Grand Junction, CO 295 3.4% 15 0.17% 310 3.5% 23153 Tonopah, NV 341 3.9% 11 0.13% 352 4.0% 23160 Tucson, AZ 154 1.8% 24 0.27% 178 2.0% 23161 Daggett, CA 616 7.0% 11 0.13% 627 7.2% 23174 Los Angeles, CA 944 10.8% 21 0.24% 965 11.0% 23184 Prescott, AZ 376 4.3% 23 0.26% 399 4.6% 23188 San Diego, CA 473 5.4% 21 0.24% 494 5.6% 23232 Sacramento, CA 1324 15.1% 77 0.88% 1401 16.0% 23234 San Francisco, CA 376 4.3% 77 0.88% 453 5.2% 24011 Bismarck, ND 676 7.7% 22 0.25% 698 8.0%Meteorological Data Processing using AERMET July 28, 2006 For HEM-AERMOD



### 10 WBAN Station Calms % Calm Missing % Missing Total % Total 24018 Cheyenne, WY 110 1.3% 0 0.00% 110 1.3% 24033 Billings, MT 273 3.1% 25 0.29% 298 3.4% 24089 Casper, WY 138 1.6% 37 0.42% 175 2.0% 24090 Rapid City, SD 736 8.4% 33 0.38% 769 8.8% 24127 Salt Lake City, UT 581 6.6% 22 0.25% 603 6.9% 24143 Great Falls, MT 111 1.3% 26 0.30% 137 1.6% 24157 Spokane, WA 537 6.1% 36 0.41% 573 6.5% 24225 Medford, OR 1937 22.1% 0 0.00% 1937 22.1% 24229 Portland, OR 1180 13.5% 0 0.00% 1180 13.5% 24232 Salem, OR 1799 20.5% 0 0.00% 1799 20.5% 24233 Seattle, WA 66 0.8% 14 0.16% 80 0.9% 25309 Juneau, AK 1781 20.3% 6 0.07% 1787 20.4% 25624 Cold Bay, AK 119 1.4% 41 0.47% 160 1.8% 26411 Fairbanks, AK 1393 15.9% 0 0.00% 1393 15.9% 26442 Valdez, AK 661 7.5% 2 0.02% 663 7.6% 26451 Anchorage, AK 724 8.3% 0 0.00% 724 8.3% 26617 Nome, AK 358 4.1% 24 0.27% 382 4.4% 93037 Colo. Springs 195 2.2% 0 0.00% 195 2.2% 93193 Fresno, CA 1578 18.0% 76 0.87% 1654 18.9% 93721 Baltimore, MD 361 4.1% 3 0.03% 364 4.2% 93738 Sterling, VA 995 11.4% 0 0.00% 995 11.4% 93817 Evansville, IN 1434 16.4% 66 0.75% 1500 17.1% 93819 Indianapolis, IN 190 2.2% 0 0.00% 190 2.2% 93820 Lexington, KY 234 2.7% 0 0.00% 234 2.7% 93821 Louisville, KY 385 4.4% 0 0.00% 385 4.4% 93822 Springfield, IL 287 3.3% 25 0.29% 312 3.6% 94823 Pittsburgh, PA 493 5.6% 26 0.30% 519 5.9% 94830 Toledo, OH 898 10.3% 13 0.15% 911 10.4% 94846 Chicago, IL 343 3.9% 38 0.43% 381 4.3% 94847 Detroit, MI 468 5.3% 11 0.13% 479 5.5%

### 1 

## Appendix 4:

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

### 2 

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

Ted Palma Roy Smith EPA/OAQPS/SBAG

1. Introduction

1.1. The problem

The process of listing hazardous air pollutants (HAPs) provided by the Clean Air Act (CAA, section 112(b)(2)) explicitly includ es acute toxicity as a listing criterion. For this reason, in addition to chronic exposures, EPA considers ac ute exposures in risk-based decision-making for the HAP regulatory program. Estimating acute exposures via dispersion modeling requires input data on hourly meteorological c onditions (available for most ar eas of the US) and short-term emission rates of individual facilities (almost universally absent from the National Emissions Inventory (NEI), the Toxic Release Invent ory (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 Ri sk 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 fu rther assumed that this tenfold emission spike could coincide with worst-case meteorological conditions and the presen ce of a human receptor at the facility boundary, as a means of screen ing for potentially signifi cant acute exposures.

In a consultation on the “RTR Assessment Plan”, a panel of the EPA’s Science Advisory Board (SAB), several reviewers questioned the appropri ateness of the factor of ten; some even suggested that this tenfold a ssumption may underestimate actual maximum short-term emissions for some facilities, and thereby also unde restimate 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 th e protectiveness of the tenfold assumption using a database of “event emissions” collected from facilities in the Houston-Galveston area, to compare events repres entative 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 Qu ality 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-r outine event. The TCEQ data are intended to improve the state’s knowledge of how short-term releases affect tropospheric ozone levels in that area. The database we utilized in our analysis was a subset of the TCEQ data covering emission events that

### 3 occurred in an eight-county area in eastern Texas during a 756-day period between January 31, 2003 and February 25, 2005.

The complete emissions event data were obtained in April 2007 from Cynthia Folsom Murphy, a research scientists with the University of Texas at Austin (UTA) Center for Energy and Environmental Resources. The da ta were provided in four Excel spreadsheets generated from an original MS Access file. We used these Excel files to recons truct a MS Access database in order to facilitate selection of a representati ve subset of record s for this analysis.

Although some of the released s ubstances 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 ma ss was ethene and propene, neither of which are HAPs. The database included release events caused by accidents, equipment fa ilures, maintenance, startup, and shutdown. It also contained facility names, information on amounts of individual compounds released. To provide a basis for co mparing 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 experien ce any reportable events during this period.

2.2. Data filtering

Because the event release data were intended for modeling short-term releases of ozoneproducing VOCs, the database includes releas es 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 ar e 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 sche duled startups, shutdowns, and maintenance, relative to long-term release rates. Because the full emi ssion events database was not representative of likely HAP em issions 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 droppe d, except that all HAPs (including non-VOCs) were retained regardless of molecular structure;

2. Accidental releases were dropped, but all others (including st artup, 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 populat ion 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 number s did not link to any facility in the database;

5. A few facilities had to be dropped because the database did not include their 2004 TRI VOC release information.

2.3. Analysis

### 4 Annual VOC emissions and emission event release data were both converted to lb/hr. In order to conform to our atmospheric dispersion models, which estimate ambient concentrations for periods of 1 hour or more, amounts released duri ng 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 norm ally, e.g., a release of 100 lb in 120 minutes was converted to 50 lb/hr. The ev ent release rates for individual compounds we re summed, yielding a total release rate for each event. This to tal 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 re solve 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 even t release rate to long-te rm release rate varied from 0.00000004 to 74. Distribution statistics appear in Tables 3 and 4. The 99 th 

### 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 hour s, 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 to tal period covered by the database. Figure 4 shows the relationship betwee n ratio and total amount released, and suggests that the highest ratios were pr oduced 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 te nfold ratio assumption for short-te rm 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 followi ng ways by the nature of the database and the filtering that we applied:

### 5 1. The only long-term release data available fr om the database were total VOC emissions for 2004. Ideally, we would have preferred to have routine release rates for each individual contaminant. However, retrievi ng 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 ra tios. 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 li kely to be less than for (2) above.

4. The database contains only facilities that had at least one re lease event during the reporting period. The number of facilities in the statistical popul ation 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 descript ive 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 t he original database, which included multiple redundancies that did not affect the analysis and were left intact.)
Contaminant HAP CAS SAROAD 2-Methyloctane No 3221-61-2 90008 2-Methylpentane No 107-83-5 43229 2-methylhexane No 591-76-4 43263 2-Methylpentane No 107-83-5 43229 2,2,3-Trimethylpentane No 564-02-3 2,2,4-Trimethylpentane Yes 540-84-1 43250 dimethyl butane No 75-83-2 43291 2,3-Dimethylbutane No 79-29-8 43276 2,3,4-Trimethylpentane No 565-75-3 43252 2,3-Dimethylbutane No 79-29-8 43276 2,4-Dimethylpentane No 108-08-7 43247 2-methylheptane No 592-27-8 43296 2-methylhexane No 591-76-4 43263 2-Methylpentane No 107-83-5 43229 3-Methylhexane No 589-34-4 43295 3-Methylpentane No 96-14-0 43230 3-Methylhexane No 589-34-4 43295 3-Methylpentane No 96-14-0 43230 3-Methylheptane No 589-81-1 43253 3-Methylhexane No 589-34-4 43295 3-Methylpentane No 96-14-0 43230

### 6 Table 1. Event emissions in the Houston-Galveston area. Representative contaminants included in the analysis, selected because they are either HAPs or VOCs with more than 5 carbon atoms. (These data were retrieved directly from t he original database, which included multiple redundancies that did not affect the analysis and were left intact.)
Contaminant HAP CAS SAROAD Acetaldehyde Yes 75-07-0 43503
Acetic Acid No 64-19-7 43404
Acetonitrile Yes 75-05-8 70016 Acetophenone Yes 98-86-2
Acrolein Yes 107-02-8 43505 Acrylic acid Yes 79-10-7 43407 Acrylonitrile Yes 107-13-1 43704 alkylphenol No none Benzene Yes 71-43-2 45201 Benzo[a]anthracene Yes 56-55-3 46716 Benzo[a]pyrene Yes 50-32-8 46719 Benzo[b]fluoranthene Yes 205-99-2 46717 Biphenyl Yes 92-52-4 45226
Butanol No 35296-72-1 Butyl Acrylate No 141-32-2 43440 t-Butyl Alcohol No 75-65-0 43309 butylcyclohexane No 1678-93-9 90101 Butyraldehyde No 123-72-8 43510
C9 Aromatics No none Naphthalene Yes 91-20-3 46701
Nonane No 111-84-2 43235
C9+ No none Carbon tetrachloride Yes 56-23-5 43804 Carbonyl Sulfide Yes 463-58-1 43933
Chloral No 75-87-6
Trichloromethane Yes 67-66-3 43803
Chlorothalonil No 1897-45-6
Petroleum No 8002-05-9
Petroleum No 8002-05-9
Cumene Yes 98-82-8 45210 Cyclohexane No 110-82-7 43248 Cyclohexanol No 108-93-0 43317 Cyclohexanone No 108-94-1 43561 Cyclohexanone No 108-94-1 43561
Decane No 124-18-5 43238
Decane No 124-18-5 43238 1,2-Dichloroethane No 107-06-2 43815 Diethylbenzene (mixture) No 25340-17-4 45106 Methyl Ether No 115-10-6 43350 Dimethylcyclohexane No 27195-67-1 98059 Dimethylcyclopentane No 28729-52-4 90064

### 7 Table 1. Event emissions in the Houston-Galveston area. Representative contaminants included in the analysis, selected because they are either HAPs or VOCs with more than 5 carbon atoms. (These data were retrieved directly from t he original database, which included multiple redundancies that did not affect the analysis and were left intact.)
Contaminant HAP CAS SAROAD Dimethylcyclopentane No 28729-52-4 90064 Dimethyl formamide Yes 68-12-2 43450 Dimethylhexane No 28777-67-5 90067 Dimethyl pentane No 38815-29-1 90063 Epichlorohydrin Yes 106-89-8 43863 Ethyl Alcohol No 64-17-5 43302 Ethyl Acrylate Yes 140-88-5 43438 Ethyl Alcohol No 64-17-5 43302 Ethyl Benzene Yes 100-41-4 45203 Ethyl Chloride Yes 75-00-3 43812 Ethylcyclohexane No 1678-91-7 43288 ethylacetylene No 107-00-6 43281 Ethyl Benzene Yes 100-41-4 45203 Ethylene Oxide Yes 75-21-8 43601 ethylmethylbenzene No 25550-14-5 45104 formaldehyde Yes 50-00-0 43502
Furfural No 98-01-1 45503 straight-run mi ddle distillate No 64741-44-2
Gasoline No 86290-81-5
Gasoline No 86290-81-5 Heavy Olefins No none n-Heptane No 142-82-5 43232 n-Heptane No 142-82-5 43232 Heptylene No 25339-56-4
hexane Yes 110-54-3 43231
hexane Yes 110-54-3 43231 2-Methylpentane No 107-83-5 43229
hexane Yes 110-54-3 43231
Hexene No 25264-93-1 43289 Indeno[1,2,3-cd]pyrene Yes 193-39-5 46720 Isobutyraldehyde No 78-84-2 43511 2-Methyl-1-propanol No 78-83-1 43306 2-Methyl-1-propanol No 78-83-1 43306 Isobutyraldehyde No 78-84-2 43511 Isoheptanes (mixture) No 31394-54-4 43106 2-Methylpentane No 107-83-5 43229 2,2,4-Trimethylpentane No 540-84-1 43250 2,2,4-Trimethylpentane No 540-84-1 43250 Isopar E No Isoprene No 78-79-5 43243 2-Propanol No 67-63-0 43304

### 8 Table 1. Event emissions in the Houston-Galveston area. Representative contaminants included in the analysis, selected because they are either HAPs or VOCs with more than 5 carbon atoms. (These data were retrieved directly from t he original database, which included multiple redundancies that did not affect the analysis and were left intact.)
Contaminant HAP CAS SAROAD 2-Propanol No 67-63-0 43304
Cumene Yes 98-82-8 45210 Isopropylcyclohexane No 696-29-7 90128 Diisopropyl ether No 108-20-3 85005
Kerosene No 64742-81-0 Methyl ethyl ketone No 78-93-3 43552 Methyl isobutenyl ketone Yes 141-79-7
Methanol Yes 67-56-1 43301 Methyl Acetylene No 74-99-7 43209
Cresol Yes 1319-77-3 45605 Methyl Chloride Yes 74-87-3 43801 methyl cyclohexane No 108-87-2 43261 Methyl ethyl ketone No 78-93-3 43552
Iodomethane No 74-88-4 86025 Methyl Mercaptan No 74-93-1 43901 methyl cyclohexane No 108-87-2 43261 Methylcyclopentane No 96-37-7 43262 2-Methyldecane No 6975-98-0 98155 Methylheptane No 50985-84-7 90045 2-methylheptane No 592-27-8 43296 2-Methyl nonane No 871-83-0 90047 Tert-butyl methyl ether No 1634-04-4 43376 meta-xylene No 108-38-3 45205
Nonane No 111-84-2 43235 Naphtha No 8030-30-6 45101 Naphthalene Yes 91-20-3 46701 Naphtha No 8030-30-6 45101 Naphthalene No 91-20-3 46701 Butyl acetate No 123-86-4 43435 Butyraldehyde No 123-72-8 43510
Nonane No 111-84-2 43235
Nonane No 111-84-2 43235
Octadecene No 27070-58-2
n-Octane No 111-65-9 43233 Octene (mixed isomers) No 25377-83-7 ortho-xylene No 95-47-6 45204
Parathion Yes 56-38-2 4-Aminohippuric Acid No 61-78-9
Phenol Yes 108-95-2 45300
Silicone No 63148-62-9 Naphtha No 8030-30-6 45101

### 9 Table 1. Event emissions in the Houston-Galveston area. Representative contaminants included in the analysis, selected because they are either HAPs or VOCs with more than 5 carbon atoms. (These data were retrieved directly from t he original database, which included multiple redundancies that did not affect the analysis and were left intact.)
Contaminant HAP CAS SAROAD Naphtha No 8030-30-6 45101 Polyethylene No 9002-88-4 Poly(Isobutylene) No 9003-27-4 Chloromethyl pivalate No 18997-19-8 Process fuel gas No none Propionic Acid No 79-09-4 43405 Propylene oxide No 75-56-9 43602 para-xylene No 106-42-3 45206 Styrene Yes 100-42-5 45220
Sulfolane No 126-33-0 t-Butyl Alcohol No 75-65-0 43309 t-Butyl Alcohol No 75-65-0 43309 tert-butyl hydroperoxide No 75-91-2
Toluene Yes 108-88-3 45202 Aqualyte(TM), LSC cocktail No 25551-13-7 45107 1,3,4-Trimethylbenzene No 95-63-6 45208 trimethylcyclopentane No 30498-64-7 98058 trimethylpentane No 29222-48-8 90092
Undecane No 1120-21-4 43241 Vinyl acetate Yes 108-05-4 43453 Vinyl acetate Yes 108-05-4 43453 Vinyl chloride Yes 75-01-4 43860 vinyl resin No none Vinylcyclohexane No 695-12-5 xylenes Yes 1330-20-7 45102 xylenes Yes 1330-20-7 45102 meta-xylene Yes 108-38-3 45205 ortho-xylene Yes 95-47-6 45204 para-xylene Yes 106-42-3 45206 Mineral spirits No 64475-85-0 43118 Propylene glycol No 57-55-6 43369 Vinyl chloride Yes 75-01-4 43860
1-Decene No 872-05-9 90014 2-Ethyl-1-hexanol No 104-76-7 43318 2-Pyrrolidone No 616-45-5
Aromatic No none Decene No 25339-53-1 90014 2-N,N-Dibutylaminoethanol No 102-81-8 86007 Diisopropanolamine No 110-97-4 86004 N,N-Dimethylethanolamine No 108-01-0 84004
trifluoroethane No 27987-06-0

### 10 Table 1. Event emissions in the Houston-Galveston area. Representative contaminants included in the analysis, selected because they are either HAPs or VOCs with more than 5 carbon atoms. (These data were retrieved directly from t he original database, which included multiple redundancies that did not affect the analysis and were left intact.)
Contaminant HAP CAS SAROAD 2,2'-Oxybisethanol No 111-46-6 43367 Hydrocarbons No none Methyl Formate No 107-31-3 43430 Isopropylamine No 75-31-0 86014
n-Butanol No 71-36-3 43305 Polypropylene glycol ether No N-Vinyl-2-Pyrrolidinone No 88-12-0 1,1-Di(t-Amylperoxy) Cyclohexane No 15667-10-4 1,2,3-Trimethyl-4-ethylbenzene No none 2-Methyldecane No 6975-98-0 98155 2-methylheptane No 592-27-8 43296 2-Methyl nonane No 871-83-0 90047 2,5-Dimethylhexane-2,5- dihydroperoxide No 3025-88-5 Butyl ether No 142-96-1 43372 1,2-Dichloroethane Yes 107-06-2 43815 Hydrindene No 496-11-7 98044 Methylheptane No 50985-84-7 90045 methyl methacrylate No 80-62-6 43441 Naphtha No 8030-30-6 45101
hexane Yes 110-54-3 43231 tert-amyl hydroperoxide No 3425-61-4 1,3,4-Trimethylbenzene No 95-63-6 45208
n-Butanol No 71-36-3 43305 2-Butoxy ethanol Yes 111-76-2 43308
hexane Yes 110-54-3 43231 cycloheptane No 291-64-5 43115 n-Heptane No 142-82-5 43232
n-Octane No 111-65-9 43233 Hexyl Carbitol No 112-59-4
Nonene No 27215-95-8 Silane, ethenyltrimethoxy No 2768-02-7 tetrahydrofuran No 109-99-9 70014 Vinyl chloride Yes 75-01-4 43860 Methyl Formate No 107-31-3 43430 Phenyl ether No 101-84-8 phosgene Yes 75-44-5 1,2-Dichloroethane No 107-06-2 43815 2-Butoxy ethanol Yes 111-76-2 43308
Gasoline No 86290-81-5
1-Tridecanol No 112-70-9

### 11 Table 1. Event emissions in the Houston-Galveston area. Representative contaminants included in the analysis, selected because they are either HAPs or VOCs with more than 5 carbon atoms. (These data were retrieved directly from t he original database, which included multiple redundancies that did not affect the analysis and were left intact.)
Contaminant HAP CAS SAROAD 1,2,4-Trichlorobenzene Yes 120-82-1 45208 2-(2-Butoxyethoxy)ethanol Yes 112-34-5 43312 2,3,4-trihydroxybenzophenone Ester No 1143-72-2 Methyl n-amyl ketone No 110-43-0 43562 4,4-Cyclohexylidenebis[phenol] No 843-55-0
Anisole No 100-66-3 2-Butoxy ethanol Yes 111-76-2 43308 Cresol-Formaldehyde novolac Resin No proprietary
Decane No 124-18-5 43238 gamma-Butyrolactone No 96-48-0 Dimethyl pentane No 38815-29-1 90063 Dodecyl Benzenesulfonic Acid No 27176-87-0
Ethanol Amine No 141-43-5 43777 ethyl lactate No 687-47-8 Hexamethyldisilazane No 999-97-3 Methyl ethyl ketone No 78-93-3 43552
Cresol Yes 1319-77-3 45605 Naphthalene Sulfonic Acid Resin No Naphthalene Sulfonic Acid Resin No
n-Butanol No 71-36-3 43305
Decane No 124-18-5 43238 1-Methyl-2-pyrrolidinone No 872-50-4 70008 Pentyl Ester Acetic Acid No Phenol Formaldehyde Resin, Novolac No Phenol Formaldehyde Resin, Novolac No Propylene Glycol Monomethyl Ether No 107-98-2 70011 Pyrocatechol No 120-80-9
Carbon Disulfide Yes 75-15-0 43934
Hexene No 592-41-6 43245
VOC No none Methacrylic acid No 79-41-4 84009 Methyl 3-hydroxybutyrate No 1487-49-6 t-Butyl Alcohol No 75-65-0 43309 methyl valeraldehyde No 123-15-9 Butyl Methacrylate No 97-88-1 85008 dipropyl ether No 111-43-3 n-Propanol No 71-23-8 43303 Propyl propionate No 106-36-5 86052

### 12 Table 1. Event emissions in the Houston-Galveston area. Representative contaminants included in the analysis, selected because they are either HAPs or VOCs with more than 5 carbon atoms. (These data were retrieved directly from t he 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 mi ddle distillate No 64741-44-2 4-Vinylcyclohexene No 100-40-3 straight-run mi ddle distillate No 64741-44-2 Allyl alcohol No 107-18-6 xylenes Yes 1330-20-7 45102 Naphthalene Yes 91-20-3 46701 3-Methylethylcyclohexane No
VOC No none Gasoline No 86290-81-5 Butyl ether No 142-96-1 dimethyl butane No 75-83-2
Dodecene No 25378-22-7 Styrene Yes 100-42-5 45220 tetrahydrofuran No 109-99-9 70014
hexane Yes 110-54-3 43231 2-Propanol No 67-63-0 43304 liquified petroleum gas No 68476-85-7 Methyl acetylene propadiene No methyl isobutyl ketone Yes 108-10-1 Methyl n-amyl ketone No 110-43-0 43562 Methylpentane No 43133-95-5 Tert-butyl methyl ether Yes 1634-04-4 43376
Toluene Yes 108-88-3 45202
Mineral oil No 8012-95-1
Gasoline No 86290-81-5 2,2-Dimethylpropane No 463-82-1 43222 n-propylbenzene No 103-65-1

### 13 Table 1. Event emissions in the Houston-Galveston area. Representative contaminants included in the analysis, selected because they are either HAPs or VOCs with more than 5 carbon atoms. (These data were retrieved directly from t he original database, which included multiple redundancies that did not affect the analysis and were left intact.)
Contaminant HAP CAS SAROAD propylcyclohexane No 1678-92-8
n-Octane No 111-65-9 43233 ortho-xylene No 95-47-6 45204
Gasoline No 86290-81-5 propylenimine No 75-55-8
Gasoline No 86290-81-5
Technical White Oil No Total Alkylate - non-speciated No Trichloroethylene Yes 79-01-6 Di(2-ethylhexyl) peroxydicarbonate No 16111-62-9 trimethylcyclopentane No 30498-64-7 98058
Ultraformate No 4-Vinylcyclohexene No 100-40-3

### 14 Table 2. Event emissions in the Houston-Galveston area. Major emitters reporting at least one release event of a representative substance.

Company Name 2004 VOC Emission Rate (lb/h) ATOFINA PETROCHEMICALS LA PORTE PLANT 47.88 BALL METAL BEVERAGE CONTAINER CONROE FACILITY 24.18

BASF FREEPORT SITE 46.47
BELVIEU ENVIRONMENTAL FUELS 112.3
BOC GROUP CLEAR LAKE BOC GASES PLANT 9.52
BP AMOCO CHEMICAL CHOCOLATE BAYOU PLANT 130.4
BP AMOCO CHEMICAL PASADENA PLANT 36.92
BP AMOCO POLYMERS 57.18
BP PRODUCTS NORTH AMERICA TEXAS CITY 737.4
BP TEXAS CITY CHEMICAL PLANT B 112.2
CELANESE BAY CITY PLANT 17.12
CELANESE CLEAR LAKE PLANT 53.11
CELANESE PASADENA PLANT 5.934
CHEVRON PHILLIPS CEDAR BAYOU PLANT 105.3
CHEVRON PHILLIPS CHEMICAL SWEENY COMPLEX 106.7
CHEVRON PHILLIPS HOUSTON CHEMICAL COMPLEX 215.7
CROWN BEVERAGE PACKAGING 18.05
CROWN CENTRAL PETROLEUM PASADENA PLANT 114.3 CROWN CORK & SEAL 18.10 DEER PARK LIQUID STORAGE TERMINAL 124.8
DOW CHEMICAL LA PORTE SITE 5.902
DOW TEXAS OPERATIONS FREEPORT 203.2
E I DUPONT DE NEMOURS AND COMPANY - LA PORTE PLANT 51.30

EQUISTAR CHEMICALS CHANNELVIEW COMPLEX 275.4 EQUISTAR CHEMICAL S CHOCOLATE BAYOU COMPLEX 84.87

EQUISTAR CHEMICALS LA PORTE COMPLEX 90.97
EXXON MOBIL CHEMICAL BAYTOWN OLEFINS PLANT 84.73
EXXONMOBIL CHEMICAL BAYTOWN CHEMICAL PLANT 313.7

EXXONMOBIL CHEMICAL MONT BELVIEU PLASTICS PLANT 40.64

GOODYEAR HOUSTON CHEMICAL PLANT 85.68
ISP TECHNOLOGIES TEXAS CITY PLANT 22.12
KANEKA TEXAS CORPORATION 20.55 KINDER MORGAN LIQUID TERMINALS PASADENA 913.9 KINDER MORGAN LIQUIDS TERMINALS 132.7
LBC HOUSTON BAYPORT TERMINAL 12.83
LYONDELL CHEMICAL BAYPORT PLANT 30.04
LYONDELL CHEMICAL CHANNELVIEW 74.15
MARATHON ASHLAND PETROLEUM TEXAS CITY REFINERY 111.8

MOBIL CHEMICAL HOUSTON OLEFINS PLANT 26.29
MORGANS POINT PLANT 31.03
PASADENA PLANT 13.40

### 15 Table 2. Event emissions in the Houston-Galveston area. Major emitters reporting at least one release event of a representative substance.

Company Name 2004 VOC Emission Rate (lb/h) SHELL OIL DEER PARK 405.2 SOLUTIA CHOCOLATE BAYOU PLANT 53.09
STOLTHAVEN HOUSTON TERMINAL 7.347
SWEENY COMPLEX 157.1
UNION CARBIDE TEXAS CITY OPERATIONS 174.4
VALERO REFINING TEXAS CITY 260.1
WHARTON GAS PLANT 7.552

Table 3. Frequency distribution for ratio of event emission rate to long-term emission rate

Bin Frequency Cumulative Frequency 1.00E-08 0 0 3.16E-08 0 0 1.00E-07 2 2 3.16E-07 1 3 1.00E-06 0 3 3.16E-06 2 5 1.00E-05 1 6 3.16E-05 2 8 1.00E-04 5 13 3.16E-04 9 22 1.00E-03 15 37 3.16E-03 28 65 1.00E-02 33 98 3.16E-02 41 139 1.00E-01 59 198 3.16E-01 38 236 1.00E+00 33 269 3.16E+00 31 300 1.00E+01 16 316 3.16E+01 2 318 1.00E+02 1 319 3.16E+02 0 319

### 16 Table 4. Statistics for ratio of event emission rate to long-term emission rate Statistic for Ratio Value Median 0.043923 75th %ile 0.342655 90th %ile 2.204754 95th %ile 3.344422 96th %ile 3.400832 97th %ile 3.8126 98th %ile 4.790098 99th %ile 8.973897
Max 74.37138 Average 0.815352



### Figure 1. Cumulative probability density for ratio of event to routine emission rates.

Cumulative probability of event ratios

0 50 100 150 200 250 300 350

1.E-07 1.E-06 1.E-05 1.E-04 1.E-03 1.E-02 1.E-01 1.E+00 1.E+01 1.E+02 1.E+03 1.E+04
Ratio of event emission rate to long-term emission raten

### 17 Figure 2. Relationship between ratio of ev ent to duration emissi on rate and emission duration.

Event ratio vs. duration

1.E-08 1.E-07 1.E-06 1.E-05 1.E-04 1.E-03 1.E-02 1.E-01 1.E+00 1.E+01 1.E+02

1 10 100 1,000 10,000 100,000 Event duration (min)Ratio -- event emission rate to long-term emission rate



### Figure 3. Relationship between ratio of event to duration emission rate and emission duration, as percentage of total time.

Event ratio vs. duration

1.E-08 1.E-07 1.E-06 1.E-05 1.E-04 1.E-03 1.E-02 1.E-01 1.E+00 1.E+01 1.E+02

0% 1% 2% 3% 4% 5% 6% 7% 8% Event duration (as % of total time)Ratio -- event emission rate to long-term emission rate

### 18 Figure 4. Relationship between ratio of event to duration emission rate and total amount emitted during the event.

Event ratio vs. 2004 VOC releases -- by event

1.E-08 1.E-07 1.E-06 1.E-05 1.E-04 1.E-03 1.E-02 1.E-01 1.E+00 1.E+01 1.E+02

1 10 100 1,000 Long-term VOC releases (lb/hr)Ratio -- event emission rate to long-term emission rate

## APPENDIX 5: Technical Support Document for TRIM-Based Multipathway Screening Scenario fo r RTR: Summary of Approach and EvaluationAppendix 5 i 

### [This page intentionally left blank.]Appendix 5 ii 

### TABLE OF CONTENTS

EXECUTIVE SUMMARY ............................................................................................................. vii

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

1.1 Background .................................................................................................................... ... 1

1.2 Organization of this Document .......................................................................................... 2

2 Approach for Screening Evaluation of Multipathway Exposures .................................. 2

2.1 Overview ...................................................................................................................... ..... 2

2.2 Chemicals of Potential Concern ........................................................................................ 5

2.3 Use of De Minimis Emission Levels .................................................................................. 6

2.4 The TRIM-Based Screening Scenario: Basis for De Minimis Emission Rates ................. 7 2.4.1 Exposure Routes Evaluated .................................................................................. 8 2.4.2 Approach to Configuration and Parameterization.................................................. 9 2.4.3 Modeling Framework ........................................................................................... 10 2.4.3.1 Fate and Transport Modeling ................................................................ 11 2.4.3.2 Exposure Modeling and Risk Characterization ..................................... 12

2.5 Refined Analyses ............................................................................................................ 1 3

3 Description of Modeling Scenario ................................................................................... 13

3.1 TRIM.FaTE Scenario Configuration and Parameterization ............................................. 13 3.1.1 Chemical Properties ............................................................................................ 13 3.1.2 Spatial Layout ...................................................................................................... 13 3.1.3 Watershed and Water Body Parameterization .................................................... 15 3.1.3.1 Water Balance....................................................................................... 15 3.1.3.2 Sediment Balance ................................................................................. 16 3.1.4 Meteorology ......................................................................................................... 17 3.1.5 Aquatic Food Web ............................................................................................... 20 3.1.6 Using TRIM.FaTE Media Concentrations ............................................................ 20

3.2 Exposure and Risk Calculations ..................................................................................... 22 3.2.1 Calculating Concentrations in Farm Food Chain Media ...................................... 22 3.2.2 Ingestion Exposure Assessment ......................................................................... 23 3.2.2.1 Ingestion Exposure Pathways and Routes of Uptake ........................... 23 3.2.2.2 Exposure Scenarios and Corresponding Inputs .................................... 23 3.2.2.3 Calculating Average Daily Doses .......................................................... 25 3.2.2.4 Infant Ingestion of Breast Milk ............................................................... 25 3.2.3 Risk Characterization .......................................................................................... 26 3.2.4 Dermal Risk Screening ........................................................................................ 26 3.2.4.1 Hazard Identification and Dose Response Assessment ....................... 27 3.2.4.2 Dermal Exposure Estimation ................................................................. 28 3.2.4.3 Screening-Level Cancer Risks and Non-Cancer Hazard Quotients ..... 32 3.2.4.4 Dermal Screening Results .................................................................... 33

3.3 Summary of Scenario Assumptions ................................................................................ 34Appendix 5 iii 

### 4 Evaluation of Screening Scenario ................................................................................... 37

4.1 Overview ...................................................................................................................... ... 37

4.2 Overall Chemical Mass Partitioning ................................................................................ 38

4.3 Comparison to Measured Concentrations ...................................................................... 40 4.3.1 Scope of the Evaluation ...................................................................................... 40 4.3.2 Methods and Organization of this Section ........................................................... 41 4.3.3 Chemical-Specific Comparisons.......................................................................... 42 4.3.3.1 Cadmium ............................................................................................... 42 4.3.3.2 Mercury ................................................................................................. 48 4.3.3.3 Dioxins (2,3,7,8-TCDD) ......................................................................... 56 4.3.3.4 PAHs (Benzo[a]pyrene) ........................................................................ 62 4.3.4 Summary ............................................................................................................. 69

4.4 Sensitivity Analyses ........................................................................................................ 7 0 4.4.1 Systematic Sensitivity Analysis ........................................................................... 71 4.4.1.1 Methods ................................................................................................ 71 4.4.1.2 Results .................................................................................................. 73 4.4.2 Evaluation of Ingestion Rate Assumptions .......................................................... 81 4.4.3 Evaluation of Body Weight Assumptions ............................................................. 83 4.4.4 Sensitivity When Accounting for Temporally Correlated Body Weight and Ingestion Rates ................................................................................................... 84 4.4.5 Comparison of Scenarios Using Site-Specific and de minimis Meteorological Data ..................................................................................................................... 86

4.5 Comparison to Other Model Results ............................................................................... 88 4.5.1 Comparison to Preliminary RTR Screening Runs (HHRAP Approach) ............... 88 4.5.2 Comparison of Results for Screening Scenario and Previous TRIM.FaTE Applications ......................................................................................................... 88

5 References .................................................................................................................... .... 94

Attachment 1 – TRIM.FaTE Inputs Attachment 2 – RTR Access-based Exposure and Risk Calculation Tool - Multimedia Ingestion Risk Calculator (MIRC) Attachment 3 – Systematic Sensitivity Analysis Variables and ResultsAppendix 5 iv 

### LIST OF EXHIBITS

Exhibit 2-1. Conceptual Decision Tree for Evaluat ion of Non-Inhalation Ex posures of PB-HAPs .............. 4

Exhibit 2-2. OAQ PS PB-HAP Compounds .......................................................................................... ........ 5

Exhibit 2-3. De Minimis Thresholds for Screening of Multipathway Exposures ........................................... 6
Exhibit 2-4. Overview of Ingestion Expos ure and Risk Screening Evaluation Method .............................. 11

Exhibit 2-5. Overview of Process Carried Out in the Multimedia Ingesti on Risk Calculator ...................... 12

Exhibit 3-1. TRIM.FaTE Surface Parc el Layout ................................................................................. ........ 14
Exhibit 3-2. Summary of Key Meteorologi cal Inputs ............................................................................ ...... 19
Exhibit 3-3. Parameters for Aquatic Biota for the Screening Scenar io of TRIM.FaTE .............................. 21
Exhibit 3-4. Spatial Considerations – TRIM.FaT E Results Selected for Calculating Farm Food Chain Media Concentrations and Receptor Exposures ......................................................... 22
Exhibit 3-5. Summary of Ingestion Expos ure Pathways and Rout es of Uptake ........................................ 24
Exhibit 3-6. Overview of Exposure Factor s Used for RTR Multipat hway Screening ................................. 25
Exhibit 3-7. Dose-response Values for PB-HAPs Addressed by the Screening Scenario ........................ 26
Exhibit 3-8. Cancer Slope Factors and Re ference Doses Based on Absorbed Dose ............................... 28
Exhibit 3-9. Receptor-Specifi c Body Surface Area Assumed to be Exposed to Chemicals ...................... 30
Exhibit 3-10. Scenario-Specific Exposur e Values for Water and Soil Cont act .......................................... 31
Exhibit 3-11. Chemical-Specific Dermal Ex posure Values for Wate r and Soil Co ntact ............................. 32
Exhibit 3-12. Summary of De rmal Non-Cancer Hazards ........................................................................... 33
Exhibit 3-13. Summary of Dermal Cancer Risks.................................................................................. ...... 34
Exhibit 3-14. Summary of RTR Screening Scenar io Assumptions and Associated Cons ervatism ........... 35

Exhibit 4-1. Distribution of Chem ical Mass in Screening Scenario ............................................................ 40
Exhibit 4-2. Summary of Modeled and Observed Concentrations of Cadmium in Environmental Media ......................................................................................................................... ............. 44

Exhibit 4-3. TRIM.FaTE Cadmium Concentratio ns in Fish and Calculated Bioaccumulation Factors with Respect to To tal Water Conc entration .............................................................. 45

Exhibit 4-4. Fraction of Cadmium Mass Sorbed vs. Dissolved in TRIM.F aTE Compartments .................. 46
Exhibit 4-5. Summary of Modeled and Observed Conc entrations of Cadmium in Ingestible Media ......... 47

Exhibit 4-6. Estimated Contribution of Modeled Food Types to Cadmium Ingestion Exposures and Hazard Q uotient

........................................................................................................... ... 48
Exhibit 4-7. Summary of Modeled and Obse rved Concentrations of Total Mercury in Environmenta l Media............................................................................................................ .. 50
Exhibit 4-8. TRIM.FaTE Mercury Concentrations, Speciation, and Calculated Methyl Mercury Bioaccumulation Factors (in white box es) in Fish Co mpartments ......................................... 51
Exhibit 4-9. TRIM.FaTE Mercury Speciati on and Partitioning in Environmental Media Compartments .................................................................................................................. ...... 52
Exhibit 4-10. Summary of Modeled and Observ ed Concentrations of Total Mercury in Ingestible Media ......................................................................................................................... ............. 54

Exhibit 4-11. Estimated Contribution of Modeled Food Types to Divalent Mercury and Methyl Mercury Ingesti on Exposures ................................................................................................. 5 5Appendix 5 v Exhibit 4-12. Estimated Contribution of Summ ed Modeled Food Types to Divalent Mercury and Methyl Mercury Hazard Quotients .......................................................................................... 55
Exhibit 4-13. Summary of Modeled 2,3,7,8-TC DD Concentrations and Observed Total Dioxin TEQ Concentrations in Environmental Media ........................................................................ 58
Exhibit 4-14. TRIM.FaTE 2,3,7,8-TCDD Concentr ations in Fish and Calculated Bioaccumulation Factors with Respect to To tal Water Conc entration .............................................................. 59
Exhibit 4-15. Fraction of 2,3,7,8 - TCDD Mass Sorbed vs. Dissolved in TRIM.FaTE Compartments .................................................................................................................. ...... 59
Exhibit 4-16. Summary of Modeled 2,3,7,8-TC DD Concentrations and Observed Total Dioxin TEQ Concentrations in Ingestible Media................................................................................ 61
Exhibit 4-17. Contribution of Modeled Food Ty pes to 2,3,7,8-TCDD Ingestion Exposures (mg/kg/day) ................................................................................................................... ......... 62
Exhibit 4-18. Summary of Modeled and Obse rved Concentrations of Benzo[a]pyrene in Environmenta l Media............................................................................................................ .. 64
Exhibit 4-19. TRIM.FaTE Benzo[a]Pyrene Concentrations in Fish and Calculated Bioaccumulation Factors with Respect to Total Water Co ncentration ................................... 65
Exhibit 4-20. Fraction of Benzo[a]Pyrene Mass Sorbed vs. Dissolved in TRIM.FaTE Compartments .................................................................................................................. ...... 66
Exhibit 4-21. Summary of Modeled and Obse rved Concentrations of Benzo[a]pyrene in Ingestible Media .............................................................................................................. ....... 68
Exhibit 4-22. Contribution of Modeled Food Ty pes to Benzo[a]pyrene Ingestion Exposures (mg/kg/day) ................................................................................................................... ......... 69
Exhibit 4-23. The 26 Variables with the Highest Elas ticities for Benzo[a]Pyrene Lifetime Risk (-5% Perturbation of Variable) ..................................................................................................... ... 76
Exhibit 4-24. The 25 Variables with the Highest El asticities for 2,3,7,8-TCDD Lifetime Risk (-5% Perturbation of Variable) ..................................................................................................... ... 77
Exhibit 4-25. The 28 Variables with the Highest Elasticities for Cadmium Hazard Quotient for Child 1-2 (-5% Perturbat ion of Vari able) ................................................................................ 78
Exhibit 4-26. The 25 Variables with the Highest Elas ticities for Divalent Mercury Hazard Quotient for Child 1-2 (-5% Pertur bation of Va riable) ........................................................................... 79
Exhibit 4-27. The 26 Variables with the Highest El asticities for Methyl Mercury Hazard Quotient for Child 1-2 (-5% Pertur bation of Va riable) ........................................................................... 80
Exhibit 4-28. Ratio of the Modeled Total Inges tion Rates and the USEPA Total Ingestion Rates ............ 82
Exhibit 4-29. Comparison of the Risks and Hazard Quotients in the de minimis and Alternate Ingestion Cases ...................................................................................................................... 83

Exhibit 4-30. The Risk or Hazard Quotient Estima tes Using Alternate Body Weight Percentiles .............. 84
Exhibit 4-31. Comparison in the Elasticities In Lifetime Risk in the Correlated and Uncorrelated Analyses Assuming a 5% Decrease in the Input Variables ................................................... 85
Exhibit 4-32. Summary of Site-specifi c Meteorological Data Parameters .................................................. 86
Exhibit 4-33. Percent Change in Risk or Hazard Q uotient Using Site-specific Meteorological Data .......... 87
Exhibit 4-34. The Wind Speed and the Direction Toward Which the Wind is Blowing for All Conditions fo r Site 1 ......................................................................................................... ...... 87
Exhibit 4-35. Emission Thresholds Derived in Preliminary HHRAP Screening Runs and in Current Analyses .............................................................................................................. ...... 88Appendix 5 vi Exhibit 4-36. Meteorological Data Parameters for TRIM.FaTE Secondary Lead Smelting Application ................................................................................................................... ........... 89
Exhibit 4-37. Surface Soil Parcel Spatial Lay outs for New York Site Lead Smelting TRIM.FaTE Application and Scr eening Scena rio ...................................................................................... 90
Exhibit 4-38. Air Parcel Spatial Layouts for Ne w York Site Lead Smelting TRIM.FaTE Application and Screening Scenario ........................................................................................................ . 91
Exhibit 4-39. Comparison of Concentration Outputs: NY Site Refined TRIM.FaTE Application vs. Screening Scenario ............................................................................................................ .... 92
Exhibit 4-40. Comparison of Concentration Ou tputs Grouped By Chemical: New York Site Refined TRIM.FaTE Application vs. Screening Scenario ...................................................... 93Appendix 5 vii 

## EXECUTIVE SUMMARY

This report describes the methods used by EPA to evaluate multipathway exposures to hazardous air pollutants and the associated human health risks. In particular, the report explains the methods, assumptions, and input data used to develop a screening scenario that is used in the first tier of Risk and Technology Review II (RTR II) assessments of such emissions. This report describes the development of the screening scenario and its application in generating qualitative, screening-level estimates of human exposure to PB-HAPs and consequent risk.

BACKGROUND

The Clean Air Act directs the U.S. Environmental Protection Agency (EPA) to assess the residual risk from hazardous air pollutants (HAPs ) emitted by sources regulated by technologybased standards. To evaluate multipathway exposures and human health risks, a two-tiered approach was developed. In the first tier of t he approach, a screening evaluation is conducted that uses the identity and magnitude of HAP emissions from a source to determine whether that source meets certain human health risk-based criteria with respect to multipathway exposures. The purpose of this first-tier screening is to elimi nate facilities from further analysis that pose no unacceptable risk to human health, while identifying those facilities that warrant a second-tier, more refined, site-specific analysis of residual risk.

PURPOSE OF THE SCENARIO

The approach described here for evaluating human mu ltipathway exposures and risks consists of an initial, screening-level tier that can be conducted quickly and efficiently to determine those facilities for which multipathway risks are expected to be below levels of concern. The key component of the first tier of this approach is a multipathway screening scenario based on EPA’s Total Risk Integrated Methodology (TRIM). The TRIM-based modeling scenario provides a means for quickly and efficiently completing an initial non-inhalation exposure and risk screening analysis of a facility. The scenario is applied for use in RTR evaluations by calculating de minimis emission rates for selected PB-HAPs that correspond to a cancer risk of 1 in 1 million or a chronic non-cancer hazard quotient (HQ) of 1. These de minimis or threshold emission rates then can be used in the first risk screening step without requiring additional model runs. Sources whose emissions exceed the de minimis emission rate for any PB-HAP would be subjected to refined evaluation(s) in a second tier analysis.

The scenario has been used to calculate numerical exposure and risk values for 4 of the 14 HAPs that OAQPS has identified as candidates for multipathway risk assessments: cadmium, mercury, dioxins (i.e., chlorinated dibenzo- p -dioxins and -furans), and polycyclic organic matter. These compounds were selected because they are expected, based on current knowledge of relative emissions and toxicity, to pose a substantial share of the non-inhalation risks to humans from air emissions at sources subject to resi dual risk provisions of the Clean Air Act. The scenario is not intended to be used to produce quantitative estimates of actual or potential risk. Rather, it provides a basis for determining if residual human health risks are of potential concern. Such determinations can then be used to support decisions to proceed with or forego more definitive analyses of non-inhalation expos ures to HAPs and the associated risks. The scenario does, however, provide a technically defensible starting point for additional fate and transport and exposure/risk analyses of facility em issions that are not “screened out” in the first tier evaluation.Appendix 5 viii 

### OVERVIEW OF SCREENING SCENARIO

This screening scenario is intended to reduce the possibility that EPA will fail to identify unacceptable risks. Thus, the scenario’s conservative approach most likely overestimates risk, which is appropriate for a screening assessment. Parameter values were defined carefully, and properties having more uncertainty were assigned greater conservative bias to prevent underestimating potential risks. The screening scenario is designed to estimate the upper end of the range of individual, long-term, non-inhalation exposures for situations likely to be encountered in the United States. The result of reviewing a broad range of conditions and selecting values representative of higher exposures in conceptualizing and building the screening tool is that the scenario is unlikely to occur at any one location but has a high likelihood of representing the upper end of any potential exposures.

The screening scenario addresses non-inhalation exposures, which can occur through both dermal and ingestion exposure pathways. Pathways examined include incidental ingestion of soil; ingestion of homegrown produce, beef, cows’ milk, poultry and eggs, and pork; and ingestion of fish. Dermal absorption of chemicals that are originally airborne is generally relatively minor, and this pathway was not included in the scenario used to calculate de minimis emission thresholds. A highly conservative estimate of dermal exposures and risks was calculated for comparison to ingestion exposures and risks. In addition, exposure to nursing infants via consumption of contaminated breast milk was evaluated for dioxins as a separate scenario.

For this approach, chemicals were modeled separately to evaluate the potential for risks, with exposures for each PB-HAP summed across all ingestion exposure pathways. Exposures were modeled for a hypothetical farm homestead and fishable lake near an emissions source. For this setting, exposures were estimated for a hypothetical individual assuming subsistence consumption of all potentially contaminated foods tuffs from the farm or lake. The scenario was purposely designed to produce conservative (i.e., health-protective) results, and certain critical exposure/activity assumptions, such as food ingestion rates, were selected from the upper ends (e.g., the 90 th 

### percentile) of representative exposure parameter distributions. The physical/chemical environment was parameterized with a mix of typical and health-protective values. The scenario’s spatial/temporal aspects and the components that influence air concentrations were also chosen so that concentrations in environmental media would not be underestimated given the range of possible settings and meteorological conditions that might be encountered. Properties of the environmental medi a were parameterized with either typical or conservative values, with a more protective bias introduced for properties having greater uncertainty.

MODELING FRAMEWORK

The approach for risk evaluation of ingestion exposures and risk screening has four components:



### 1. fate and transport modeling of PB-HAPs emitted to air that partition into soil, water, and other environmental media (including fish); 

### 2. modeling of transfer and uptake of PB-HAPs by farm food chain media from soil and air; 

### 3. estimating ingestion exposures for the selected media contact scenarios and average daily ingestion doses for a hypothetical human receptor; and 

### 4. calculating lifetime cancer risk estimates or chronic non-cancer HQs for each HAP.Appendix 5 ix 

### TRIM.FATE SCENARIO CONFIGURATION AND PARAMETERIZATION

To model chemical fate and transport in the environment, EPA’s Fate, Transport, and Ecological Exposure (TRIM.FaTE) module of the TRIM system was used. The physical configuration ofthe scenario was designed to be generally conservative and the environmental and chemicalspecific properties were parameterized with ei ther conservative or central-tendency values. Chemical/physical properties were obtai ned from peer-reviewed and standard reference sources. The spatial layout represents a fa rm homestead and a fishable lake near an emissions source. The predominant wind direction is toward the farm and lake watershed, and the downwind modeling area is symmetrical around a 10-kilometer east-west line and divided into five pairs of parcels. The aquatic food web in the scenario is meant to represent a generic aquatic ecosystem within a 47-hectare lake.

Fate and transport modeling outputs include average PB-HAP concentrations and deposition rates for various media (air, soil, surface water, and fish) for each year and for each parcel ofthe model scenario. TRIM.FaTE can output instantaneous chemical concentrations for a userspecified time step and also can be configured to calculate temporal averages. For the screening scenario, the model outputs results on a daily basis, and daily concentration results are averaged to obtain annual average concentrations . The source is assumed to emit for 50
years.

EXPOSURE AND RISK CALCULATIONS

The Multimedia Ingestion Risk Calculator (MIRC) was developed to carry out required farm food chain transfer, ingestion exposure, and risk calculations. Concentrations in farm food chain media are calculated using empirical biotransfer factors (e.g., soil-to-plant factors, which are the ratios of the concentrations in plants to concentrations in soil). Ingestion exposures based on exposure factors, including food-type-specific inge stion rates, are calculated for a hypothetical exposed individual. Lifetime cancer risks and the potential for chronic non-cancer effects are estimated using chemical-specific ingestion cancer slope factors and reference doses. Exposure pathways evaluated include incidental ingestion of soil and consumption of fish, produce, and farm animals and related products. Cancer risk estimates and HQs are calculated separately for each PB-HAP included in an analysis.

CALCULATION OF DE MINIMIS EMISSION THRESHOLDS

After the configuration of the TRIM.FaTE and MIRC modeling scenarios was completed, de minimis emission rate thresholds were calculated by conducting iterative model simulations to determine emission rates for cadmium, mercury, dioxins, and polycyclic organic matter that correspond to a cancer risk of 1 in 1 million or a chronic non-cancer hazard quotient (HQ) of 1. Given the generally conservative nature of the scenario inputs, these thresholds are assumed to be appropriate for screening sources emitting these HAPs.

EVALUATION OF SCREENING SCENARIO

Model evaluations serve as an important aspect of environmental risk assessments by illustrating the performance of the model under different conditions and assumptions and facilitating the comparison of model outputs to measurement data and other modeling results. Evaluations thereby provide an opportunity to gain confidence in model performance and identify and better characterize uncertainties associated with model construct and inputs. The screening scenario was analyzed through comparisons to the literature and sensitivity analyses.Appendix 5 x 

### Outputs from the screening scenario based on de minimis emission values were compared to literature values for cadmium, mercury, 2,3,7,8-TCDD, and benzo[a]pyrene. In general, taking into account the limitations of this type of comparison, the configuration of the models for screening appear to be reasonable. Chemical par titioning predicted by the model is generally consistent with information on environmental parti tioning presented in the literature for media of concern. Where results are not consistent with literature values, a more detailed investigation of underlying assumptions may help to identify means of adjusting the scenario configuration.

Sensitivity analyses were performed to eval uate the influence of model parameters and to provide information on which parameters are likely to be most influential in dictating the uncertainty associated with the results. The sensitivity analyses conducted on the RTR screening modeling scenario encompassed the fate and transport modeling carried out using TRIM.FaTE and the farm food chain and ingestion exposure calculations performed using MIRC. A systematic sensitivity analysis was conducted by varying each input independently and calculating the resulting effect on the risk or hazard quotient estimates in order to rank the variables from most to least sensitive. The analysis suggests that several TRIM.FaTE variables (including wind speed, mixing height, emission ra te and, for methyl mercury, sediment deposition rate) have the largest effect on the risk and hazard estimates. The estimates of hazard and risk are also highly sensitive to ke y parameters in the primary exposure pathway (i.e., ingestion of food types resulting in the highest exposures) for each PB-HAP. Other analyses performed indicated that accounting for temporal correlations in ingestion rates and body weights and varying the body weight and i ngestion rate percentiles used in the model scenario have a limited effect on the risk and hazard estimates. The use of site-specific meteorological variable values (as opposed to the generic screening scenario values) resulted in a decrease in the risk and hazard estimates of approximately one order of magnitude.

In addition, media concentrations estimated using the screening scenario were compared to analogous outputs estimated using site-specific TRIM.FaTE model applications configured for two secondary lead smelting sources. The same emission rates for benzo[a]pyrene, 2,3,7,8- TCDD, elemental mercury, and divalent mercury were entered into the screening scenario and the site-specific model scenarios. Model results were compared for soil, water, and sediment compartment types. In all media, the screening scenario produced higher concentrations for all chemicals than the site-specific model applications, the expected result given that the screening scenario is conservative (and therefore tends to result in higher media concentrations).Appendix 5 xi 

### [This page intentionally left blank.]Appendix 5 1 

## 1 Introduction

1.1 Background

Section 112(f)(2)(A) of the Clean Air Act (CAA) directs the U.S. Environmental Protection Agency (EPA) to assess the risk remaining (residual risk) from hazardous air pollutants (HAPs) that continue to be emitted from sources after application of maximum achievable control technology (MACT) standards under section 112(d) of the CAA. Under these requirements, EPA will promulgate additional emission standards for a source category if the MACT standards do not provide an “ample margin of safety” for human health. One aspect of human health that EPA must consider is the potential for exposures to HAPs via non-inhalation pathways and the risks associated with such exposures.

As described in EPA’s Risk and Technology Review (RTR) Assessment Plan (EPA 2006a), multipathway human health risks were prelim inarily evaluated in 2006. The evaluation used draft National Emissions Inventory (NEI) data for RTR Phase II (RTR II) source categories and a simplified multipathway exposure modeling approach (see Appendix 5 in EPA 2006a). 1 

### However, as noted in the RTR Assessment Plan , EPA’s intention was to develop an approach that would supersede the preliminary methods used in 2006 and involve the use of EPA’s Total Risk Integrated Methodology (TRIM), a risk assessment modeling system for air toxics developed by OAQPS. The TRIM system can be used to predict the local impacts of persistent and bioaccumulative HAPs (PB-HAPs) from an em issions source to estimate associated human health risk.

EPA will implement a two-tiered approach to evaluate multipathway exposures and human health risks for RTR II. In the first tier, a screening evaluation is conducted that focuses on the identity and magnitude of HAP emissions from a given facility to determine whether a facility passes certain human health risk-based criteria. Sources that are “screened out” are assumed to pose no unacceptable risks to human health and are not considered in further analyses. For sources that do not pass the screen, more refined, site-specific multipathway assessments are conducted as appropriate. These human health risk results are considered, in combination with estimated inhalation human health risks, potential ecological risks, and other factors, to support decisions about residual risk for RTR II source categories.

This current document describes the technical ba sis for the first, screening-level tier of EPA’s multipathway human health evaluation of RTR em ission sources. Specifically, the models, configurations, and inputs used to derive de minimis emission thresholds in the first tier of the approach are described in detail here. 2 

### Analyses of the screening scenario conducted to evaluate the scenario’s defensibility are also disc ussed. EPA expects that refined multipathway risk assessment methods (when required) will rely on the same TRIM-based modeling approach used to derive the de minimis thresholds used in the first screening tier. However, the details of refined assessments will vary depending on the facility location, source category, chemicals emitted, and other parameters, and the specific methods and processes involved in multipathway evaluations beyond the first tier are not explored in depth in this document.

1 The preliminary evaluation conducted in 2006 relied on a simpler modeling approach that did not utilize TRIM and involved a less rigorous analysis of parameter input values.

2 De minimis is a Latin phrase that translates to “regarding mi nimal things.” In the current context, the term de minimis is used in reference to human health risk that is belo w a level of concern (or, more specifically, a chemical emission rate that is not expect ed to result in unacceptable risks). See also the definition for “risk de minimis ” in the “Glossary for Chemists of Terms Used in Toxicology” (IUPAC 1993).Appendix 5 2 

### 1.2 Organization of this Document

This document presents the methods, assumptions, and inputs used to develop a method for evaluation of human multipathway exposures and health risks for RTR II risk assessments. Section 2 presents an overview of the methods for evaluating multipathway exposures andrisks, a conceptual description of the TRIM-based scenario that is the basis for the screeninglevel de minimis emission thresholds, and a brief description of how refined analyses will be conducted for facility emissions that cannot be screened out in the first step. Section 3 presents a technical description of the screening-level, TRIM-based modeling scenario and the configuration of the models used to estimate the de minimis levels. Section 4 discusses evaluation activities conducted for this screening scenario and summarizes uncertainty. References cited in this report are listed in Section 5.



## 2 Approach for Screening Eval uation of Multipathway Exposures

2.1 Overview

As described above, EPA’s method for evaluating multipathway exposures for RTR risk assessments consists of a two-tiered process. Exhibit 2-1 diagrams the approach for evaluating non-inhalation, multipathway exposures to PB-HAPs. The first tier of this approach is the screening evaluation that relies on the TRIM-based “screening scenario” as the technical basis for decisions regarding whether a facility passes the screen. Air toxics emitted by a source under consideration are reviewed to determine fi rst whether emissions of any PB-HAPs are reported. If such emissions are reported, the emission rates are compared to available de minimis threshold emission levels that hav e been derived using the TRIM-based screening scenario. 3 

### The list of chemicals that are PB-HAPs is discussed in Section 2.2, and the use of de minimis emission thresholds is discussed in Section 2.3.

The TRIM-based multipathway modeling configuration, referred to in this document as the “screening scenario,” is a key component of the first tier of this approach, as this modeling application is the technical basis for determining the levels of PB-HAP de minimis emission thresholds. The term “screening scenario” is used in this document to refer collectively to the specific TRIM.FaTE and exposure modeling configuration described here, including the set of assumptions and input values associated with a hypothetical watershed and the exposure and risk scenarios evaluated for this watershed. The screening scenario is a static configuration, and its primary purpose is as a modeling tool to calculate the de minimis emission rates for PBHAPs of concern. Descriptions of the components of the screening scenario are presented in Section 2.4.

The two potential outcomes of the screening human health evaluation are:

• 

### Non-inhalation exposures are unlikely to pose a human health problem; or

• 

### The potential for unacceptable non-inhalation exposures cannot be ruled out and further assessment is required to determine the potential for unacceptable risk.

An ideal screening approach strikes a balance between being conservative – to ensure that unacceptable risks are identified, and being accurate , to minimize results suggesting that additional assessment is required when in fact the actual risk is low. Typically, gains in

3 As described later in this report, to date, TRIM-based modeling has been used to calculate de minimis emission rates only for those PB-HAPs consid ered as those of highest concern.Appendix 5 3 

### accuracy in environmental modeling are accom panied 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.

Facilities whose emissions exceed the established de minimis emission rate for any PB-HAP would be subjected to more refined evaluations. Because the initial screening evaluationenables EPA to confidently eliminate from consideration those facilities where risks from noninhalation exposures are projected to be minimal, resources can be targeted toward those facilities that do not “pass” the screening test. An overview of the anticipated approach to refined evaluation is described in Section 2.5.Appendix 5 4 

### Exhibit 2-1. Conceptual Decision Tree for Evaluation of Non-Inhalation Exposures of PB-HAPsAppendix 5 5 

### 2.2 Chemicals of Potential Concern

EPA’s assessment of multipathway human expo sures for RTR focuses on PB-HAPs that 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:

• 

### their presence on three existing EPA lists of persistent, bioaccumulative, and toxic substances, and

• 

### a semi-quantitative ranking of toxicity and bioaccumulation potential of the entire list of HAPs.

The list’s development and utility in hazard identif ication for multipathway risk assessment are further explained in Chapter 14 and Appendix D of Volume I of EPA’s Air Toxics Risk Assessment (ATRA) Reference Library (EPA 2004a). Exhibit 2-2 presents the 14 chemicals and groups that are PB-HAPs.

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 be used to quantitatively estimate exposures and risks for four PB-HAP compounds (indicated in bold in Exhibit 2-2). These compounds are the focus of the current scenario because, based on current emissions and toxicity considerations, they are expected to pose the vast majority of the non-inhalation risks to humans from air emissions at sources subject to residual risk provisions of the CAA. 4



### Exhibit 2-2. OAQPS PB-HAP Compounds a PB-HAP Compound Addressed by Screening Scenario? Cadmium compounds Yes
Chlordane No
Chlorinated dibenzodioxins and furans Yes
DDE No Heptachlor No

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

a Source of list: EPA 2004a. Compounds in bold text can be evaluated using the current version of the TRIM-based screening scenario.

4 Potential impacts on human healt h from non-inhalation exposures to lead are evaluated for RTR using the National Ambient Air Quality Standard for lead, which takes into acco unt multipathway risks. Non-i nhalation exposures to the other nine PB-HAPs not addressed by the modeling scena rio discussed in this report will be evaluated on an individual facility or source category basis as needed.Appendix 5 6 

### 2.3 Use of De Minimis Emission Levels

The multipathway screening evaluation for RTR compares reported air emission rates of PBHAPs (summed by PB-HAP for each facility) to de minimis emission rates derived using the screening scenario described here. A de minimis emission rate is the level that, when input to a risk model that uses emissions as a parameter , corresponds to a specified cancer risk or noncancer HQ that, for the purposes of the evaluation being conducted, is assumed to be below a level of concern. De minimis rates were calculated for the screening scenario for a cancer risk of 1 in 1 million or an HQ of 1.0 (Exhibit 2-3). 5 

### Conceptually, a de minimis 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 le vel, taking into account the exposure and fate and transport calculations included in the model. Be cause the models used in this assessment are not designed to run “backwards,” these rates were derived from regression equations established following a series of TRIM.FaTE and exposure/risk model runs spanning a wide range of emission rates for each chemical.

Exhibit 2-3. De Minimis Thresholds for Screening of Multipathway Exposures

Chemical De Minimis Emission Rate (TPY) Basis of Threshold (Type of Health Endpoint) POM (as Benzo[a]pyrene toxic equivalents) 2.3E-03 Cancer Dioxins (as 2,3,7,8-TCDD TEQ) 3.2E-08 Cancer Divalent Mercury 1.6E-01 Non-cancer

Cadmium 6.5E-01 Non-cancer



### The more probable risk for each emission rate would be lower than the level corresponding to the de minimis risk quantities in nearly all circumstances given the conservative and highly general nature of the screening scenario configuration. This conservatism ensures that a facility with cancer risk greater than 1 in 1 million or a chronic HQ greater than 1.0 is very unlikely to be omitted from refined evaluation.

Evaluation of Chemical Groups

In the screening evaluation, emissions of PB-HAPs are summed by chemical group for each facility. The summed emission rates for each group are then compared to the de minimis threshold corresponding to the appropriate chemical.

Emissions of polycyclic organic matter (POM, a HAP chemical group that includes polyaromatic hydrocarbons or PAHs) are often reported in NE I as unspeciated or partially speciated groups (such as “total PAHs” or “16-PAH”) rather than as specific PAH compounds. In addition, quantitative data are lacking for some POM compounds and groups that are suspected carcinogens. To evaluate risks associated with exposure to emissions of the various POM species, EPA has grouped each POM species included in NEI into categories and then defined a cancer slope factor (CSF) for each group that can be used to estimate lifetime cancer risks.

5 For chemicals that are known to cause bot h cancer and chronic non-cancer impacts, and for which acceptable quantitative dose-response values are avai lable for both cancer and non-cancer endpoints, the endpoi nt that results in the lower de minimis level will be used for screening (i.e., the thresh old will be based on the effect that occurs at the lower exposure level). For the set of PB-HAPs for which de minimis levels have been derived, only chlorinated dibenzo-dioxins and -furans meet both of these criteria. Because the canc er dose-response value is lower than that for non-cancer effects, the de minimis value is based on the cancer endpoint.Appendix 5 7 

### The CSFs used to evaluate risk for emissions of POM compounds and groups are listed in Attachment 2.

Similarly, polychlorinated dioxins and furans are reported in NEI as individual congeners, congener groups, or as toxic equivalents (TEQs) that are scaled relative to the toxicity of 2,3,7,8-TCDD. To evaluate cancer risks and non-cancer hazards from these compounds, EPA has developed CSFs and reference doses (RfDs) that apply to congener groups. These values are also presented in Attachment 2.

In the development of the RTR screening thresholds, we determined the de minimis levels for one compound from each of the POM and dioxin groups (i.e., benzo[a]pyrene and 2,3,7,8- TCDD). Then, to use this de minimis threshold in an evaluation of facility emissions of POMs or dioxins, a toxicity-weighted emissions sum is calc ulated for each group using the toxicity of each modeled chemical – benzo[a]pyr ene and 2,3,7,8-TCDD – to scale the total POM or dioxin group emissions. This approach avoids the need to develop de minimis emission rates for every POM and dioxin congener (some of which are not in cluded in the existing TRIM.FaTE algorithm library). A consequence of this approach is that benzo[a]pyrene and 2,3,7,8-TCDD serve as fate and transport surrogates. That is, the behavior of these two compounds is assumed toadequately represent the behavior of all other compounds included in the POM and dioxin PBHAP groups, respectively.

De minimis 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. 6 

### However, only speciated emissions of divalent mercury are screened because the sum of elemental mercury emissions across all NEI facilities is less than the elemental mercury de minimis level.


### 2.4 The TRIM-Based Screening Scenario: Basis for De Minimis Emission Rates

The TRIM-based modeling screening scenario descri bed in this document was used to provide a means to qualitatively estimate the potential for unacceptable non-inhalation risks for PBHAPs emissions from facilities in the context of residual risk assessments conducted as part of RTR II. The screening scenario used to derive de minimis emission rates is not intended to be representative of any particular situation. Rather, it was developed for the purpose of RTR to portray an exposure scenario at least as conserva tive as any situation that might plausibly be encountered in the United States. The range of conditions considered when conceptualizing and building the screening scenario was chosen so that any given individual, long-term exposure condition for a given geographic region would be reasonably likely to be captured. These criteria were met by constructing a hypothetical scenario that would be 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 conservatism and accuracy called for in the ideal screening approach.

For this approach, exposures were modeled for a hypothetical farm homestead and fishable lake near an emissions source. The hypothetical individual exposed to PB-HAPs in this

6 Note that TRIM.FaTE models the transformation of mercury within the environment; thus, emissions of only divalent mercury will likewise result in multipathway exposures to bo th elemental and methyl mercury also. Emissions of only elemental mercury will result in multipathway exposur es to both divalent and methyl mercury also.Appendix 5 8 

### scenario was assumed to derive all potentially cont aminated foodstuffs from the farm or lake. Many of the exposure/activity assumptions were selected from the upper ends of representative exposure parameter distributions. The physical/ chemical environment was parameterized with a mix of typical values (such as national averages ) and health-protective values (i.e., values that would tend to overestimate concentrations in media). The spatial and temporal aspects of the scenario and the components of the scenario that influence air concentrations were chosen from the upper ends of their possible ranges so that concentrations in the environmental media would not be underestimated given the wide range of possible settings and meteorological conditions that might be encountered. Chemical -specific and non-chemical-specific properties of the environmental media were parameterized with either typical or conservative values (with a greater conservative bias introduced for properties having greater uncertainty).

The development and application of the screening scenario for residual risk evaluations considered EPA’s technical and policy guidelines presented in the R esidual Risk Report to Congress (EPA 1999); Volumes I and II of the Air Toxics Risk Assessment Reference Library (EPA 2004a, 2005); and other EPA publications. The sc enario described in this document is the culmination of analyses completed over the past 5 years; it provides the basis for an efficient and scientifically defensible method for screening multipathway human health risk and is a solid foundation for conducting more refined analyses when necessary. Nevertheless, this scenario should not be considered “final” but rather a product that can continue to evolve based on feedback from the scientific community and Agency reviewers, lessons learned as the scenario is further applied for RTR, variations in EPA’s needs and requirements, and other factors.

2.4.1 Exposure Routes Evaluated

The screening scenario is intended to address non-inhalation exposures (inhalation exposures are being evaluated separately for RTR II using a dispersion modeling approach to estimate ambient air concentrations). The quantitativ e aspects of this non-inhalation screening evaluation for human exposures focus primarily on human exposures via the following ingestion pathways:

• 

### Incidental ingestion of soil,

• 

### Ingestion of homegrown produce,

• 

### Ingestion of homegrown beef,

• 

### Ingestion of milk from homegrown cows,

• 

### Ingestion of homegrown poultry and eggs,

• 

### Ingestion of homegrown pork, and

• 

### Ingestion of fish.

Non-inhalation exposure to PB-HAPs also can occur by way of the dermal pathway (e.g., through incidental contact with PB-HAP-contaminated soil). However, dermal absorption of chemicals that are originally airborne is gener ally a relatively minor pathway of exposure compared to other exposure pathways (EPA 2006, CalEPA 2000). The risk from dermal exposure in the environmental setti ng from airborne toxicants is expected to be a fraction of the risk from inhalation exposure or exposure via in gestion of contaminated crops, soil, or breast milk, for example (CalEPA 2000). Preliminary ca lculations of estimated dermal exposures and risk of PB-HAPs, presented in Section 3.2.4, showed that the dermal exposure route is not a significant risk pathway relative to ingesti on exposures. Assessment of dermal exposure through incidental contact with soil could be conducted on facilities that require refined evaluation following the screening evaluation if deemed necessary. Procedures for estimating dermal absorption from soil would be based on EPA’s dermal exposure assessment principlesAppendix 5 9 

### and applications (EPA1992b) and EPA’s Superfund Human Health Evaluation Manual (EPA 2004c).

Another ingestion pathway – through breast milk by nursing infants – can also be of concern for chlorinated dibenzo-dioxins and -furans (typically referred to collectively as “dioxins,” nomenclature that is used elsewhere in this do cument when referencing the collective chemical category), and may also be of concern for mercury. Algorithms have been developed for calculating the exposure and risk associated with dioxin contamination of breast milk and are used to evaluate the likelihood of developmental e ffects resulting from exposure to dioxins via this pathway. Assessment of breast milk expos ure for nursing infants will be assessed when refined evaluations are conducted; this exposure pathway is not incorporated in the calculation of the de minimis levels.

One other non-inhalation exposure route discussed in ATRA Volume I of possible concern for PB-HAPs is ingestion of drinking water from su rface water sources. This exposure route, however, is not evaluated in the current asse ssment. 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. 7



### 2.4.2 Approach to Configuration and Parameterization

This screening scenario is intended to reduce the possibility that EPA would not mis-identify unacceptable risks. Although the “conservativ e” approach likely overestimates risk, EPA determined that conservatism is appropriate for the purposes of screening assessments. As in the preliminary multipathway screening for RTR conducted in 2006 (EPA 2006a), 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 these 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 typica l values, such as national averages. The second type is health-protective, conservative 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 relatively conservative. That is, they were chosen from the upper ends of their respective possible ranges so that the wide range of possiblephysical settings and meteorological conditions would be captured. Chemical-specific and nonchemical-specific properties of the environmental media were parameterized with either typical or conservative values; properties having greater uncertainty were assigned greater conservative bias.

The spatial layout of the scenario and the meteorological data (or a combination of these two factors) are generally more influential than phy sical/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

7 An exception to this generality would be reservoirs used for drinking water supplies. This situation may be worthy of additional analysis, if warranted by the characterist ics of a given assessment (e.g., to estimate PB-HAP concentrations in treated drinking water derived from reservoirs).Appendix 5 10 

### wind direction can dramatically affect the concentrations in air, thereby driving estimated concentrations of PB-HAPs in soil, water, and biota. In contrast, a relatively large change in soil characteristics within the range of possible values (e.g., organic carbon content, water content) might result in relatively small changes in outputs.

The mix of conservative and typical approaches and values is expected to result in a scenario configuration that, on average, is likely to over-predict environmental concentrations of PBHAPs in media of interest for this evaluation. Given the intended application of this scenario as a screening tool, this conservative bias was deliberate, because of the desire to ensure that unacceptable risks are not overlooked (i.e., to minimize false negatives). Although the inclusion of typical values where warranted is intended to minimize the number of false positives, some false positives are to be expected from a scr eening scenario. These false positives would be addressed in iterations of the refined evaluation for a particular source.

2.4.3 Modeling Framework

The approach for risk screening (and ingestion exposure) evaluation described here can be divided into four steps:

1. Fate and transport modeling of PB-HAPs emitted to air by the source that partition into soil, water, and other environmental media (including fish); 8



### 2. Modeling of transfer and uptake of PB-HAPs into farm food chain media (produce, livestock, dairy products) from soil and air;

3. Estimating ingestion exposures as a result of contact with the various 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 metrics to selected risk management points of departure used in the RTR II.

The relationship among these four processes is shown in Exhibit 2-4.

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. 9 

### Currently, however, only one component corresponding to the first step included in Exhi bit 2-4 – the fate and transport module – is available for application in an ingestion risk assessment. EPA has completed some development activities for TRIM.Expo-Ingesti on and TRIM.Risk-Human Health, two additional modules that cover the other three steps. Modeli ng software, however, is not currently available for these modules. For the RTR screening scenario, the Multimedia Ingestion Risk Calculator (MIRC), a Microsoft Access-based computer framework, was constructed to complete the calculations required for estimating PB-HAP conc entrations in farm food chain media, average

8 As discussed below, concentrations in fish calculated by the TRIM.FaTE model were used in the current approach to estimate ingestion exposures for humans consuming fish. Modeling of fish concentratio ns is therefore discussed in this document as part of the fate and transport modeling. Up take of PB-HAPs into all other biotic media assumed to be ingested is modeled in the second step.

9 Information regarding the current status of TRIM modules as well as comprehensive docu mentation of modules that have been developed thus far can be accessed on EPA’s Technology Transfer Network (TTN) on the Fate, Exposure, and Risk Analysis web site (http://www.epa.gov/ttn/fera/).Appendix 5 11 

### daily ingestion doses, and cancer risks and chronic non-cancer HQs. This framework is conceptually identical to the ingestion expos ure and risk analyses that TRIM is intended to

cover.

2.4.3.1 Fate and Transport Modeling

The fate and transport modeling step depicted in the first box in Exhibit 2-4 is implemented for RTR using the Fate, Transport, and Ecological Exposure module of the TRIM modeling system (TRIM.FaTE). 10 

### In developing the screening scenario, Version 3.6.2 of TRIM.FaTE was used to model the fate and transport of emitted PB-HAPs and to estimate HAP concentrations in relevant media. Additional information about TRIM.FaTE, including support documentation, software, and the TRIM.FaTE public reference library, is available on EPA’s TTN at http://www.epa.gov/ttn/fera/.

The algorithms used to model mercury species and 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 T RIM.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 2004b). Parameterization of the TRIM.FaTE library used for RTR analyses with regard to

10 TRIM.FaTE is a spatially explicit, compartmental ma ss balance model that describes the movement and transformation of pollutants over time, through a user-defi ned, bounded system that includes both biotic and abiotic compartments. Outputs include pollutant concentrations in multiple environmental m edia and biota, which provide exposure estimates for ecological receptors (i.e., plants and animals). The output concentrations from TRIM.FaTE are also intended to be used as inputs to a human ingestion exposure model to estimate human exposures. Significant features of TRIM.FaTE incl ude: (1) a fully coupled multimedia model; (2) user flexibility in defining scenarios, in terms of the links among compartments, and nu mber and types of compartments, as appropriate for the application spatial and temporal scale; (3) transparent, user-acce ssible algorithm and input library that allows the user to review and modify how environmental transfer and transf ormation processes are modeled; (4) a full accounting of all of the pollutant as it moves am ong environmental compartments during simu lation; (5) an embedded procedure to characterize uncertainty and variability; and (6) the capabi lity to provide exposure estimates for ecological receptors. 

### Exhibit 2-4. Overview of Ingestion Exposure and Risk Screening Evaluation MethodAppendix 5 12 

### dioxins is identical to the configuration described in that third evaluation report. More recently (largely as part of this current project), the TRIM.FaTE public reference library has been 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 proper ties used by TRIM.FaTE to model cadmium (as well as PAHs, mercury, and dioxins) are documented in Attachment 1 to this document. Parameterization of the TRIM.FaTE scenario used for RTR screening is described in more detail in Section 3.

2.4.3.2 Exposure Modeling and Risk Characterization

The algorithms included in MIRC that calculate chemical concentrations in farm food chain media and ingestion exposures for hypothetical individuals were generally obtained from EPA’s Human Health Risk Assessment Protocol for Hazardous Waste Combustion Facilities, or HHRAP (EPA 2005a). 11 

### 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 2-5. This exhibit demonstrates the general relationships between the relevant TRIM.FaTE outputs (i.e., chemical concentrations in environmental m edia and fish) and the ingestion exposure and risk calculations carried out using MIRC. Additional discussion of exposure and risk calculations for this screening scenario is presented in Section 3.2 and Attachment 2, and all inputs required by these calculations are documented in Attachment 2.

Exhibit 2-5. Overview of Process Carried Out in the Multimedia Ingestion Risk Calculator

Farm Food Chain

Concentration

Calculator Ingestion Exposure Calculator Risk and Hazard Calculator TRIM.FaTE

outputs

Human activity/ exposure factors Plant- and animal-specific

parametersChemicalspecific uptake/ transfer factors Ingestion dose-response values FFC media concentrations Average daily doses Cancer risks and hazard quotients

Access db

process Tool output Input data Key to symbols:

11 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.Appendix 5 13 

### 2.5 Refined Analyses

For facilities that are not screened out by comparison to the de minimis emission thresholds, additional analysis is required to evaluate the risks via multipathway exposures. EPA envisions that the screening scenario provides a technically defensible “starting point” for additional fate and transport and exposure/risk analyses, by substitu ting site-specific values for key parameters used in the modeling scenario. As appropriate, the scenario can be further refined in iterative steps until either the risks predicted are not of concern or sufficient information has been gathered to inform a risk management decision. Based on the model performance evaluations completed on the screening scenario, settings and parameters that might be appropriate for revision in a more refined analysis include meteorological inputs and spatial configuration assumed for TRIM.FaTE modeling, relev ant ingestion exposure scenarios (based on surrounding land-use or other characteristics), and exposure factors such as ingestion rate for individual food types, among other inputs. The refined evaluation could eventually involve developing a site-specific TRIM.FaTE application that incorporates significant site-specific data (e.g., a model application that includes a site-specific spatial layout taking into account local geographic features and environmental parameter va lues selected from the best available data for that location).



## 3 Description of Modeling Scenario

3.1 TRIM.FaTE Scenario Confi guration and Parameterization

As described in Section 2.4.2, the physical configuration of the RTR Screening Scenario was designed to be generally conservative, and the environmental and chemical-specific properties were parameterized with either conservative or central-tendency values. Information regarding the scenario configuration and important aspects of the parameterization process, justifications for selecting particular property values, and certain uncertainties is presented in the sections that follow. Comprehensive documentation of TRIM.FaTE property values for this scenario is provided in Attachment 1.

3.1.1 Chemical Properties

The chemical/physical properties that TRIM.FaTE requires, such as Henry’s law constant, molecular weight, and other “general” parameters, 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 Attachment 1.

3.1.2 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 3-1. The source parcel is parameterized as a square with sides of 250 m, which is assumed to be a fair estimation for the size of a relatively small-to-medium facility at the fence line. With a predominant wind direction toward the east, the modeled layout is generally symmetric about an east-west line and is wedge-shaped to reflect Gaussian dispersion of the emission plume.Appendix 5 14 

### Exhibit 3-1. TRIM.FaTE Surface Parcel Layout

A lateral, downwind distance of 10 km was established for the watershed included in the scenario. Based on the results of dispersion modeling (results not presented here), the location of the maximum air concentration and deposition rate would be 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 would be expected to decrease by about two orders of magnitude relative to the predicted maximum rate within a 10-km radius. Extending the modeling lay out 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 wa tershed diminishes rapidly with distance. In addition, the impact of this additional deposited mass on ingestion exposures is expected to be negligible. 12 

### 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 ( σ y 

### , measured from the centerline) at a certain distance from the source can be estimated using the Pasquill-Gifford curves. Turner (1970) derived the equations for these curves, which can be found in the ISC3 Dispersion Model Manual (among other sources). 13 

### For a relatively neutral atmosphere (stability class D), σ at 10 km is about 550 m using this estimation. In a Gaussian distribution, about 99.6 percent of the plume spread area is contained within 3 σ of the median line. Therefore, the plume σ was set at 3 times 550 m, or approximately 1.75 km from the centerline at 10 km distance. The plume width for these conditions is expected to be about twice this dist ance, or 3.5 km. These dimensions were used to define the dimensions of the overall air and surface parcel layouts for the screening scenario.

12 Mass deposited at the outer e dge 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 la ke would attenuate transport of chemical mass by erosion and runoff, dampening the effect of including additional de position beyond 10 km. (Other exposure pathways would be largely unaffected; the soil concentr ations used to calculate exposures fo r 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.)

13 http://www.epa.gov/scram001 /userg/regmod/isc3v2.pdfAppendix 5 15 

### The surface (land and surface water) modeling area was initially divided into five pairs of parcels whose areas 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 further divided into two parts, one of them tilled soil (Parcel N6) to represent agricultural conditions.

The depth of the surface soil compartments was set to 1 centimeter (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 sub-soil depth) were typically set to represent typical or national averages as summarized by McKone et al. (2001), for example.

Initial considerations when the layout was configured included the presence of a stream that ran along the bisecting east-west line from the southwestern corner of Parcel N3 through the eastern edge of the layout. In that configuration, the eastern extent of the pond was restricted by a parcel (S3) directly south of Parcel N3. The stream received chemical mass from Parcels S3, S4, and S5 and flowed directly into the pond. Preliminary modeling runs showed that the existence of a stream somewhat decreased t he concentrations of 2,3,7,8-TCDD in the pond parcel and significantly decreased concentrations in Parcels S3 and S4. These results indicated that the pond was receiving more 2,3,7,8-TCDD mass through surface soil transfer than through the stream, perhaps due to a chemical sink into stream sediment. Given the goal of creating a conservative scenario, a stream was not included in the final layout.

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).

3.1.3 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 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 seepage, 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 TR IM.FaTE) and the configuration of terrestrial plants included in the scenario.

3.1.3.1 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 screening 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:Appendix 5 16 

### [ total precipitation] = [evapotranspiration] + [total runoff]

In this equation, total runoff is equal to the sum of overland runoff to the lake and seepage to the lake via groundwater.

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

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

Note that TRIM.FaTE actually uses only some of these properties (e.g., precipitation rate and surface runoff, but not evapotranspiration). The water characteristics assumed for the screening scenario are meant to represent a relatively wet and moderately warm location in the United States (USGS 1987). Following are the assumptions for this scenario:

• 

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

• 

### 65 percent of total precipitation remains in the modeled system and contributes to total runoff.

• 

### Total runoff is divided between overland runoff and seepage to groundwater as follows:

o 40 percent of total precipitation contributes to overland runoff.

o 25 percent of total precipitation seeps into the groundwater and eventually flows into the lake.

For these calculations, the source parcel was considered to be outside the watershed and therefore was not included in the water balance. The evaporation rate from the lake was assumed to be 700 millimeters per year (mm/yr) based on data reported by Morton (1986) for various lakes. This estimate is probably more representative of cooler locations [by comparison, the overall average of evaporation ra tes from various reservoirs is reported by McKone et al. (2001) to be close to 1,200 mm/yr]. The runoff rate was defined to be spatially constant and temporally constant (i.e., it is not linked to precipitation events) throughout the modeled domain. Based on these assumptions, the outflow of water from the lake is about 18 million m 3 

### /yr, which translates to a volumetric turnover rate of about 12.2 lake volumes per year.

Other quantitative water body and watershed characteristics TRIM.FaTE uses are listed in Attachment 1.

3.1.3.2 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 setti ng 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 th ird term (sediment burial) is the transfer of sediment from the unconsolidated benthic sediment compartment to the consolidated sediment layer.Appendix 5 17 

### To maintain the sediment balance, erosion rates were calculated for each surface soil compartment using the universal soil loss equation (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 flush 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 di fference 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 bur ial rate is a formula property calculated by the model according to the difference between user-specified values for sediment deposition velocity (from the water column to the benthic sediment) and sediment resuspension velocity (back into the water column from the benthic sediments). These formula properties assume a constant volume of particles in the sediment layer (because the densities for benthic and suspended sediment particles were defined to the same value, the mass of particles in the sediment is also constant).

For the screening 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 kilograms per square meter per day (kg/m 2 

### -day), based on calculations using the universal soil loss equation (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 this screening scenario, however, this approach was considered to be appropriate in that conservatism is a goal of the screening scenario. 14 

### 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 3-1). Erosion and runoff from the source parcel are linked directly to a sink and do not enter the screening scenario lake. The transport of sediment to the la ke via overland flow (e.g., by streams) 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 model.

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.

3.1.4 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, as well as 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

14 Based on sensitivity analysis, a higher erosion rate wi ll both increase surface water concentrations and decrease surface soil concentrations; however, the relative impact on resulting concentrations will be proportionally greater in the waterbody.Appendix 5 18 

### frequency of false negatives, a conservative 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 loca tions impacted by chemicals emitted from the local source). However, ensuring that the meteorological parameters were not overly conservative, such as always having the wind blow toward the location of interest, 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 3-2; however, an artificial data set was compiled (for example, temporally variable meteorological parameters were made to vary only on a daily basis). This simplified approach allowed for greater control (relative to selecting a data set for an actual location) so that desired trends or outcomes could be specified. Also, using a meteorological data set with values varying on a daily basis rather than a shorter period (such as hourly, which is the typical temporal interval for meteorological measurements) reduced required model run time. Meteorological inputs are summarized in Exhibit 3-2.

The sensitivity of modeled 2,3,7,8-TCDD concent rations 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 into the soil and lower mixing heights should reduce the volume through which pollutants disperse. The wind speed used for the screening scenario was 2.8 m/s, the 5 th 

### percentile of annual average among 239 stations ; by comparison, the mean annual average wind speed is 4.0 m/s in the contiguous United States). The mixing height (mean heights from four states) used was 710 m (the 5 th 

### percentile of annual average among all 40 states in the SCRAM database).Appendix 5 19 

### Exhibit 3-2. Summary of Key Meteorological Inputs Meteorological Property Selected Value Justification

Air temperature Constant at 2 98 Kelvin Recommended default val ue listed in HHRAP (EPA 2005a). Value is similar to the mean daily June temperature in the U.S. Deep South and to the mean daily July temperature in the U.S. Central Plains. a Mixing height Constant at 710 meters Value is 5 th percentile of annual average mixing heights for 463 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 one direction (eastward). d 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 three days out of seven.
Horizontal wind speed Constant at 2.8 meters per second Set to 5 th percentile of annual average speed for 239 stations across the contiguou s United States (about 50 years of data per station). Value is similar to the annual average wind speeds of the U.S. Deep South. c 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 precip itation occurs 35 – 40% of the time (Holzworth 1972). These locations include parts of the U.S. Northeast and Northwest. c

Total Precipitation 1.5 meters per year Assumed to represen t 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. d Conditional precipitation rate (rainfall rate when precipitation is occurring) is 9.59 mm per day, which is similar to conditions along the U.S. East Coast and Midwest. c

a National Climatic Data Center Historical Climate Series (NCDC-HCS) (2007). http://www5.ncdc.noaa.gov/climatenormals/hcs/HCS_MAP_7100.pdf
b Support Center for Regulatory Atmospheric Modeling; http://ww w.epa.gov/scram001 /tt24.htm.

c National Climatic Data Center CliMap s (NCDC-CliMaps) (2007). http://cdo.nc dc.noaa.gov/cgi-bin/climaps/climaps.plAppendix 5 20 

### 3.1.5 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 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. The scenario includes a benthic invertebrate compartment to represent the primary invertebrate consumers (second trophic level) in the benthic environment, and the fish compartments represent the higher tropic levels in the aquatic system. For TRIM.FaTE to provi de reasonable predictions of the distribution of a chemical across biotic and abiotic compartments in aquatic systems, the biomass of the aquatic biotic compartments must represent all biota in the system and the distribution of biomass among the trophic levels and groups must be as realistic as possible.

To support the development of a relatively generic freshwater aquatic ecosystem in which to model bioaccumulation in fish, a literature search, review, and analysis was conducted in support of developing and parameterizing aquatic biotic compartments for TRIM.FaTE (ICF 2005). This research demonstrated that the diversity of species and food webs across U.S. aquatic ecosystems is substantial, reflecting the wide range of sizes, locations, and physical/chemical attributes of both flowing (rivers, streams) and low-flow (ponds, lakes, reservoirs) waterbodies. In general, lotic bodies of water (lakes and ponds) are at a higher risk of accumulating contaminants in both sediment s and biota than are flowing systems (rivers, streams). Also, the previous research (ICF 2005) suggested that a lake of at least 60 hectares (ha) likely would be sufficient to support higher trophic level predatory fish, with some fraction of their diet comprising smaller fish.

The RTR screening scenario includes a generic aquatic ecosystem with a 47-ha 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 the appropriate conditions. Also, this size was compatible with the overall size of the defined watershed. In the lake ecosystem defined for the screening scenario, benthic invertebrates are an important food source for a large proportion of the total fish biomass. The fish types, biom ass, diet fractions, and body weights recommended for fish compartments for the screening scenario of TRIM.FaTE are listed in Exhibit 3-3. Biomass is based on an assumption that the total fish biomass for the aquatic ecosystem is 5.4 grams per square meter expressed as a wet weight (g w 

### /m 2 

### , ICF 2005).

A strict piscivore compartment was not selected for the screening scenario because such species are rare in lakes of small to moderate size. In general, the food web implemented in the screening scenario is intended to be generally applicable across the United States and is intended to be generally conservative (to simulate a food web that maximizes bioaccumulation).

3.1.6 Using TRIM.FaTE Media Concentrations

The screening 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, soil concentrations are provided for the surface, root, and vadose zones and grass or leaf concentrations as appropriate for the plants. Groundwater concentrations and deposition rates to the soil are also provided. 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 included. For the ingestion exposure calculations, some concentrations are used to calculate direct exposure, and some are used to perform the farm food chain concentration calculations in the various media that humans can ingest (see Exhibit 2-4).Appendix 5 21 

### Exhibit 3-3. Parameters for Aquatic Biota for the Screening Scenario of TRIM.FaTE

TRIM.FaTE Compartment Type Organisms Represented by

Compartment Biomass

Diet Average Body Weight (kg) Areal density (g w /m 2 ) Fraction of Total Fish Biomass Macrophyte hydrilla, m ilfoil 500 -- -- --

Benthic invertebrate aquatic insects, crustacean, mollusks a 20 -- Feeds in sediment 0.000255

Water column (WC) herbivore young-of-theyear, minnows 1.0 18.5% 100% algae b 0.025

Water column omnivore sucker, carp 1.0 18.5% 40% WC herbivore 30% benthic invert. 30% macrophyte 0.25

Water column carnivore largemouth bass, walleye 0.4 7.5% 60% WC omnivore 20% WC herbivore 20% benthic omniv. 2.0

Benthic omnivore small catfish, rock bass 2.0 37% 100% benthic invert. 0.25

Benthic carnivore large catfish 1.0 18.5% 70% benthic invert. 30% benthic omniv. 2.0

Total Fish Biomass c 5.4

a Benthic invertebrates include aquatic insects (e.g., nymphs of mayflies, caddisflies, dragonflies, and other species that emerge from the water when they become adults), crustacea (e.g., amphipods, crayfish), and mollusks (e.g., snails, mussels).
b Algae is modeled as a phase of surface water in TRIM.FaTE.

c Total fish biomass does not include macrophytes or benthic invertebrates.



### Regardless of whether the concentration and deposition values are used to calculate ingestion directly or are used in farm food chain calculations, selecting the parcel that is the source of the values used as inputs to succeeding calculations is necessary. The locations that determine direct and indirect exposures were selected assuming generally conservative assumptions. In general, decisions regarding which TRIM.FaTE outputs to use in calculating exposures for the hypothetical scenario assume exposure at locati ons near the modeled source, thereby resulting in higher exposures to emitted chemicals. These assumptions are summarized in Exhibit 3-4.

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 screening 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 (most of the chemicals modeled for RTR approach steady state well before 50 years).Appendix 5 22 

### Exhibit 3-4. Spatial Considerations – TRIM.FaTE Results Selected for Calculating Farm Food Chain Media Concentrations and Receptor Exposures TRIM.FaTE Output Used in Exposure Calculations Representative Compartment

Concentration in air, for uptake by plants via vapor transfer Air compartment in air Parcel N2 (air over tilled soil)

Deposition rates, for uptake by farm produce Deposition to surface soil compartment in surface Parcel N6 (tilled soil) Concentration in surface soil, for incidental ingestion by humans and farm animals Surface soil compartment in surface Parcel N1 (untilled soil, closest to facility) Concentration in soil, for uptake by farm produce and animal feed Surface soil compartment in surface Parcel N6 (tilled soil)

Concentration in fish consumed by angler Water column carnivore compartment in lake (50% of fish consumed) and benthic omnivore in lake (50% of fish consumed)



### 3.2 Exposure and Risk Calculations

This section describes the approach for modeling chemical concentrations in farm food chain (FFC) media (Section 3.2.1); estimating human exposures associated with ingestion of FFC media, incidental ingestion of soil, consumption of fish, and infant consumption of breast milk (Section 3.2.2); and calculating human health screening risk metrics associated with these exposure pathways (Section 3.2.3). All of these calculations are conducted using the MIRC modeling software. For this multipathway screeni ng evaluation, partitioning into FFC media is calculated with the same data set used to model exposure and risk, rather than as a component of the TRIM.FaTE modeling scenario. Cons equently, processes and inputs related to estimating chemical levels in FFC media are summarized in this section and discussed in detail in Attachment 2.

3.2.1 Calculating Concentrations in Farm Food Chain Media

As was shown in Exhibit 2-5, 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 are as follows:

• 

### exposed and protected fruit,

• 

### exposed and protected vegetables,

• 

### root vegetables,

• 

### beef,

• 

### total 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 empirical biotransfer factors. As noted in Section 1 this report, the algorithms involving ingestion exposure to be included eventually in TRIM are expected to be ve ry similar to those presented in HHRAP. Environmental media concentrations (i.e., the chem ical source terms in these algorithms) wereAppendix 5 23 

### obtained from TRIM.FaTE. As noted in Section 3.1.6, 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 groundwater (used as drinking water);

• 

### PB-HAP concentrations in fish tissue for fish in trophic levels three and four [T3 and T4]); and

• 

### PB-HAP concentrations in surface soil and root zone soil.

In general, plant- and animal-specific parameter va lues, including chemical-specific transfer factors for FFC media, were obtained from the Hazardous Waste Companion Database included in HHRAP (EPA 2005a). A list of variables and PB-HAP-specific input parameters, along with the input values used in this screening scenario, are provided in Attachment 2.

3.2.2 Ingestion Exposure Assessment

3.2.2.1 Ingestion Exposure Pathways and Routes of Uptake

MIRC was used to estimate ingestion rates as average daily doses (ADDs) normalized to body weight for a range of exposure pathways. Ex posure pathways included are incidental ingestion of soil and consumption of fish, produce, and farm animals and related products. The ingestion exposure pathways included in the screening ev aluation and the environmental media through which these exposures occur are summarized in Exhibit 3-5.

3.2.2.2 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 1997a).

For the screening scenario described here, exposure characteristics were selected that result in a highly conservative estimate of total exposure. The ingestion rate for each medium was set equal to the 90 th 

### percentile of the distribution of national data for that medium. All media were assumed to be obtained from locations impacted by the modeled source. Although this approach results in an overestimate of total chemical exposure for a hypothetical exposure scenario (for example, note that the total food ingestion rate that results is extremely high for a hypothetical consumer with ingestion rates in the 90 th 

### percentile for every farm food type), it was selected to avoid underestimating exposure for any single farm food type . The exposure characteristics selected for the ingestion screening scenario are summarized in Exhibit 3-6. Quantitative input values corresponding to thes e parameters are presented in Attachment 2.Appendix 5 24 

### Exhibit 3-5. Summary of Ingestion Exposure Pathways and Routes of Uptake

Ingestion Exposure Pathway Medium Ingested Intermediate Exposure Pathway – Farm Animals a Environmental Uptake Route

Medium Process b

Incidental ingestion of soil Surface soil N/A Surface soil Deposition; transfer through plants; transfer via erosion and runoff c

Consumption of fish Fish from local water body N/A Fish tissue Direct uptake from water and consumption of food compartments modeled in TRIM.FaTE c Consumption of breast milk d Breast milk N/A Breast milk Ingested by mother and then partition to breast milk

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

Belowground produce N/A Soil Root uptake

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

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

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

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

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

a Calculation of intermediate exposur e concentrations were required only for the farm animal/animal product ingestion pathways.
b Process by which HAP enters medium ingested by humans.

c Modeled in TRIM.FaTE.
d The consumption of breast milk exposure sc enario is discussed in Section 3.2.2.4.Appendix 5 25 

### Exhibit 3-6. Overview of Exposure Factors Used for RTR Multipathway Screening a,b
Exposure Factor Selection for Screening Assessment

Age group evaluated Infants under 1 year (breast milk only) Children 1 to 2 years of age Children 3 to 5 years of age Children 6 to 11 years of age Children 12 to 19 years of age Adult (20 to 70 years) Body weight (BW; varies by age) Weighted mean of national distribution Intake rate and ingestion rate (IR) for farm produce and animal products (varies by age and media consumed) 90 th percentile of distribution of consumers who produce own food

Ingestion rate for fish 17 g/day (approximately 90 th percentile of general population; also equal to mean value for anglers); lower for children Exposure frequency (EF) 365 days/year

Exposure duration Lifetime, for estimating ca ncer risk; varies by chemical for chronic non-cancer evaluation Fraction contaminated (FC) (varies by media consumed) c 1

Cooking loss d Assumed to be “typical;” varies depending on food product (see Attachment 2)

a Values for exposure characteristics are presented in Atta chment 2. Exposure parameter values were based on data obtained from the Exposure Factors Handbook (EPA 1997a). See Atta chment 2 for details.
b Exposure factor inputs are used in calculating average daily dose (ADD) es timates for each ex posure pathway. ADD equations for each pathway evaluated in this scree ning assessment are provided in Attachment 2.

c Fraction contaminated represents the fr action 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.
d Cooking loss inputs were included to simulate the amount of a food product that is not ingested due to loss during preparation or cooking, or after cooking.



### 3.2.2.3 Calculating Average Daily Doses

MIRC calculates chemical-specific ADDs of c hemicals 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 from the Exposure Factors Handbook (EPA 1997a). The ingestion exposure modeling approach embodied by 3MRA is conceptua lly similar to that presented in HHRAP. A discussion of exposure dose estimation is provided in Attachment 2. The equations to calculate ADDs for each ingestion pathway are provided in Attachment 2.

3.2.2.4 Infant Ingestion of Breast Milk

A nursing mother exposed to contaminants by any ingestion pathway described above can pass the contaminants on to her infant through breast milk (EPA 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. Exposures can occur for infants via this pathway for dioxins and possibly also for mercury.

Exposure to dioxin and mercury via breast milk c onsumption during the first year of life is expected to have a small effect on the estima ted lifetime ADD and on the individual’s excess lifetime cancer risk for dioxins or the highest chronic HQ for either chemical. Therefore,Appendix 5 26 

### exposures to these chemicals via the breast milk pathway were not considered in developing the de minimis emission thresholds for dioxins and mercury. The potential for non-cancer health effects (e.g., developmental effects) is of greater concern for nursing infants exposed to either chemical during the first year of life. These exposures will be considered in more detail for facilities and emissions that do not pass the initial screen.

3.2.3 Risk Characterization

MIRC was used to calculate excess lifetime cancer risk (ELCR) and non-cancer hazard (expressed as an HQ) using the calculated ADDs and ingestion dose-response values. Chemical dose-response data include CSFs for ingestion and non-cancer oral RfDs for chronic exposures. The CSFs and RfDs for the PB-HAPs included in this screening scenario are presented in Exhibit 3-7 and are discussed in more detail in Attachment 2. Equations used to estimate cancer risk and non-cancer hazard are provided in Attachment 2.

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

Exhibit 3-7. 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 not available 5E-4 IRIS Elemental Mercury NA not available Divalent Mercury not available 3E-4 IRIS Methyl Mercury not available 1E-4 IRIS Organics a Benzo[a]pyrene 10 IRIS not available 2,3,7,8-TCDD 1.5E+5 EPA ORD 1E-9 ATSDR

Source: EPA OAQPS 2007 CSF = cancer slope factor; RfD = reference dose; IRIS = Integrated Risk In formation System; CalEPA = California Environmental Protection Agency; AST DR = Agency for Toxic Substances and Disease Registry; EPA ORD = EPA’s Office of Research and Development NA = not applicable

a The CSF listed in IRIS for benzo[a]pyrene was adjust ed for consistency with the overall approach for dose-response assessment of PAHs; see Attachment 2.



### 3.2.4 Dermal Risk Screening

Non-inhalation exposure to PB-HAPs can occur by way of the dermal pathway through contact with PB-HAP-contaminated soil and water. However, dermal absorption of chemicals that are originally airborne is generally a relatively minor pathway of exposure compared to other exposure pathways (EPA 2006, CalEPA 2000). This section demonstrates that for theAppendix 5 27 

### conservative 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 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 2004c).

3.2.4.1 Hazard Identification and Dose Response Assessment

To assess the potential contribution of dermal exposure to non-inhalation exposure, we evaluated the potential for cancer and chronic non-cancer effects for the four PB-HAPs currently assessed in the multipathway screening evaluation for RTR: cadmium, divalent mercury, 2,3,7,8-TCDD, and benzo(a)pyrene. EPA has not developed carcinogenic potency slope factors (CSFs) and non-cancer reference doses (RfDs) specifically for evaluating potential human health concerns associated with dermal exposure to PB-HAPs. Instead, dermal toxicity values can be derived from oral toxicity values via route-to-route extrapolation by adjusting for gastrointestinal (GI) absorption. EPA reco mmends making this adjustment only when GI absorption of the chemical is significantly less than 100% (i.e., less than 50 percent). Otherwise, a default value of complete (100 percent) oral absorption is assumed, and no adjustment is made (EPA 2004c).

The absorbed cancer slope factor (CSF ABS 

### ) is based on the oral cancer slope factor (CSF O 

### ) and the fraction of the contaminant absorbed in the gastrointestinal track (ABS GI 

### ), as follows:

GI o ABS 

### ABS CSF CSF =

where:

CSF ABS = Absorbed slope factor (mg/kg-day) -1

CSF o = Oral slope factor (mg/kg-day) -1

ABS GI = Fraction of chemical absorbed in gastrointestinal tract (unitless)



### The absorbed reference dose (RfD ABS 

### ) is based on the oral reference dose (RFD O 

### ) and the fraction of the contaminant absorbed in the gastrointestinal track (ABS GI 

### ), as shown below.

GI o ABS 

### ABS RfD RfD × =

where:

RfD ABS = Absorbed reference dose (mg/kg-day)

RfD o = Oral reference dose (mg/kg-day)

ABSGI = Fraction of chemical absorbed in gastrointestinal tract (unitless)



### GI absorptions for 2,3,7,8-TCDD and all polyc yclic aromatic hydrocarbons (PAHs) (which includes benzo[a]pyrene) were estimated to be greater than 50 percent. Therefore, as shown in Exhibit 3-8, no adjustments to the available oral CSFs were required. Similarly, no adjustment to the oral RfD for 2,3,7,8-TCDD was required. For cadmium and divalent mercury, adjustmentsAppendix 5 28 

### 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 3-8.

Exhibit 3-8. Cancer Slope Factors and Reference Doses Based on Absorbed Dose

PB-HAP ABS GI (unitless) CSF ABS a (mg/kg-day) -1 RfD ABS a (mg/kg-day)
Cadmium Compounds 0.05 NA 2.5E-05

Divalent Mercury 0.07 NA 2.1E-05

2,3,7,8-TCDD No adjustment required b 1.5E+05 1.0E-09

Benzo[a]pyrene No adjustment required b 1.0E+01 NA

NA = Not applicable

a Oral dose response values are presented in Exhibit 3-7. Only the resulting adjusted dose response values are presented in this table.
b According to RAGS Part E, Exhibit 4-1, GI absorption is expected to be greater than 50%.



### 3.2.4.2 Dermal Exposure Estimation

Dermal exposures and risks resulting from absor ption 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.

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). DAD is calculated separately for the water and soil pathways.

AT BW SA EF ED EV DA DAD event



### × × × × ×

=



### where:

DA event 

### = Absorbed dose per event; chemical-specific; equation for DA event also differs depending on water or soil contact (mg/cm 2 -event)

EV 

### = Event frequency (events/day)

ED 

### = Exposure duration (years)

EF 

### = Exposure frequency (days/year)

SA 

### = Skin surface area available for contact (cm 2 )

BW 

### = Body weight (kg)

AT 

### = Averaging time; for non-cancer effects, equals ED x 365 days/year; for cancer effects, equals 70 years x 365 days/year (days)Appendix 5 29 

### DA event 

### 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 DA event 

### values for water contact are provided in RAGS Part E, Chapter 3 (see Equations 3.2 – 3.4). For soil, the equation for calculating these chemical-specific DA event 

### values is provided in RAGS Part E, Chapter 3 (see Equation 3.12).

Water – Organic Chemicals:



### π τ event

event p w event 

### t 6 K FA 2 C DA × × × × × =

Water – Inorganic Chemicals: event p w event 

### t K C DA × × =

Soil – All Chemicals: 

### CF ABS AF C DA
s event 

### × × × =
where:

DA event = Absorbed dose per event (mg/cm 2 -event)
C w C s = Chemical concentration in water (mg/cm 3 ) or soil (mg/kg)

K

p = Chemical-specific dermal permeability c oefficient 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 in to skin (unitless)



# τ event = Chemical-specific lag time per event (hr/event)

t event = Receptor-specific event duration (hr/event)

AF = Receptor- and activity-specific adherence factor of soil to skin (mg/cm 2 -event)

ABS = Chemical-specific dermal absorption fraction (unitless)

CF = Conversion factor (10 -6 kg/mg)



### Exposure Factors and Assumptions The exposure parameters included in this assess ment and their default and other value options are summarized in this subsection. Default values were selected to result in a highly conservative estimated of exposure (i.e., expos ures are likely overestimated). Parameter values were primarily obtained or estimated from RAGS Part E (EPA 2004c) and the CSEFH (EPA 2008). Receptor-and scenario-specific exposure assumptions are discussed first, and a discussion of chemical-specific parameters valu es follows. Estimated water and soil exposure concentrations are presented at the end of this subsection.

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 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 onAppendix 5 30 

### 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 3-9.

Exhibit 3-9. Receptor-Specific Body Surface Area Assumed to be Exposed to Chemicals Age Group a (years) Surface Area for Water Exposure (cm 2 ) Surface Area for Soil Exposure (cm 2 ) Adult 20-70 18,150 g 6,878 h
Child <1 b 3,992 1,772 Child 1-2 c 5,700 2,405 Child 3-5 d 7,600 3,354 Child 6-11 e 10,800 4,501 Child 12-19 f 17,150 6,906

a Sources for the child groups included Table 7-1 (total body surface area for SA-Water), and Table 7-2 (fraction of total body surf ace area for SA-Soil) of the 2008 CSEFH.
b Represents a time-weighted average for age groups birth to <1 month, 1 to <3 months, 3 to <6 months, and 6 to <12 months.

c Represents a time-weighted average for age grou ps 1 to <2 years and 2 to <3 years.
d Values for age group 3 to <6 years in the 2008 CSEFH.

e Values for age group 6 to <11 years in the 2008 CSEFH. Represents a conservative (i.e., slightly low) estimate for ages 6 through 11 years since 11-year olds are not included in this CSEFH age group.
f Represents a time-weighted average for age groups 11 to <16 years and 16 to <21 years. Note that estimated values include 11-year-olds and individuals through age 20, which contributes to uncertainty in the estimates for 12 to 19 years.

g Represents the average total surface area of adults from Table C-1 of RAGS Part E.
h Represents the average surface area of adults for head, forearms, hands, lower legs, and feet from Table C-1 of RAGS Part E.



### Scenario-Specific Parameters

Exhibit 3-10 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.Appendix 5 31 

### Exhibit 3-10. Scenario-Specific Exposure Values for Water and Soil Contact Exposure Parameter Receptor Value Source Water Contact

Event Duration (t event ) (hr/event) Child 1 Reasonable maximum exposure scenario for showering/bathing from RAGS Part E, Exhibit 3-2 Adult 0.58
Soil Contact

Soil Adherence Factor (AF) (mg/cm 2 ) Child 0.2 For children, value is geometric mean value for children playing (wet soil) and for adults, value is geometric mean value for an adult farmer from RAGS Part E, Exhibit 3-3 Adult 0.1

Both Media

Event Frequency (EV) (events/day) All 1 Reasonable maximum exposure scenario from RAGS Part E, Exhibits 3-2 & 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) x 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 va ry by receptor group. Same value used in ingestion exposure calculations.



### Chemical-Specific Parameters The chemical-specific parameters required to q uantitatively evaluate dermal pathway exposures are listed in Exhibit 3-11. For the water concentration in the dermal analysis, the modeled TRIM.FaTE chemical concentration in the screening scenario pond at the 

### de minimis emission rate was used. For the soil concentration, t he modeled TRIM.FaTE chemical concentration in surface soil in parcel N1 of the screening scenario at 

### de minimis emission rate. This same soil concentration was also used in ingestion exposure calculations for soil ingestion.

Dermal absorption of chemicals in water is based on the use of a dermal permeability coefficient (K p 

### ), which measures the rate that a chemical penetrates the skin. Dermal absorption of soilbound chemicals is based on the use of a dermal absorption fraction (ABS), which is a measure of how much of a chemical the skin absorbs through contact with soil.Appendix 5 32 

### Exhibit 3-11. 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) 4.77E-07 1.81E-07 6 .20E-17 2.03E-12 TRIM.FaTE modeled concentration in screening scenario pond

Chemical concentration in Soil (Cs) (mg/kg) 1.37E+00 5.94E+00 5 .36E-09 1.21E-03 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 ( τ event) (hr/event) NA NA 6.82 2.69 Values from RAGS Part E, Exhibits B-3; only used for organic chemicals Dermal absorption fraction (ABS) from soil (unitless) 0.001 0.045 a 0.03 0.13 Values from RAGS Part E, Exhibit 3-4, unless otherwise noted

a Value obtained from Bioavailability in Environmental Risk Assessment (Hrudey et al. 1996).



### 3.2.4.3 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.

Dermal Cancer Risk Cancer risk for the dermal route was calculated as the product of the age-specific 

### DAD s and the absorbed CSF for each chemical, as follows:



### Dermal Cancer Risk = DAD x CSF ABS 

### where:

DAD = Dermal Absorbed Dose (mg/kg-day)

CSF ABS = 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 indi vidual might receive from these PB-HAPs in water plus soil over his or her lifetime (70 years).

Dermal Hazard Quotient Dermal hazard quotient (HQ) was estimated as the ratio of age-specific 

### DAD s to the absorbed RfD for each chemical, as shown below:



### Dermal HQ = DAD / RfD ABS 

### where:

DAD = Dermal Absorbed Dose (mg/kg-day)

RfD ABS = Absorbed reference dose (mg/kg-day)Appendix 5 33 

### 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.

3.2.4.4 Dermal Screening Results

Exhibit 3-12 presents a summary of estimated dermal non-cancer hazards by age group. A summary of estimated lifetime dermal cancer risks is provided in Exhibit 3-13. All HQ values

were 0.5 (representing divalent mercury exposure for children less than 1 year of age) or less, a factor of at least two smaller than the potential ingestion hazard quotients associated with the screening scenario. The highest estimated individual lifetime cancer risk associated with potential dermal exposures was 3.33E-8 for benzo[a]pyrene; this value is a factor of 30 times smaller than the ingestion risk estimated for the same 

### de minimis emission rate.

Exhibit 3-12. Summary of Dermal Non-Cancer Hazards

2,3,7,8-TCDD

0 0.00002 0.00004 0.00006 0.00008

0.0001 0.00012

Child <1 Child 1-2 Child 3-5 Child 6-11 Child 12-19 Adult 20-70 ReceptorHazard Quotient Water
Soil

Total Cadmium

0 0.002 0.004 0.006 0.008

0.01 0.012

Child <1 Child 1-2 Child 3-5 Child 6-11 Child 12-19 Adult 20-70 ReceptorHazard Quotient Water
Soil

Total

Divalent Mercury

0 0.1 0.2 0.3 0.4 0.5 0.6

Child <1 Child 1- 2 Child 3- 5 Child 6- 11 Child 12-19 Adult 20-70
ReceptorHazard Quotient Water
Soil

TotalAppendix 5 34 

### Exhibit 3-13. Summary of Dermal Cancer Risks

PB-HAP Dermal Lifetime Cancer Risk Magnitude of Difference
2,3,7,8-TCDD

Water 6.14E-09 >160 Soil 3.48E-10 >2800 Total 6.49E-09 >150 Benzo[a]pyrene

Water 1.06E-08 >90 Soil 2.27E-08 >40 Total 3.33E-08 >30



### 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 

### de minimis 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 swi mming 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.



### 3.3 Summary of Scenario Assumptions

As emphasized previously, the screening scena rio created for evaluating PB-HAP emissions from RTR facilities is intended to be generally conservative to prevent underestimating risk. The overall degree of conservatism of the scenario is the sum total of the multiple assumptions that affect the outputs of the fate and transport, exposure, and risk modeling. Exhibit 3-14 summarizes important characteristics that influence exposure and risk estimates for this scenario and indicates the general degree of conservatism 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 qualitativ ely that the scenario generally overestimates exposure and thus favors a health-protective risk output.Appendix 5 35 

### Exhibit 3-14. Summary of RTR Screening Scenario Assumptions and Associated Conservatism

Characteristic Value Conservatism Comments on Assumptions General Spatial Attributes Farm location 375 m from source; generally downwind Conservative 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 Conservative Location dictates where impacted fish population is located.
Surface soil properties Typical values or national averages Neutral Based on existing EPA documentation and other references. Size of farm parcel About 4 ha Conservative Re latively small parcel size results in higher chemical concentration. Size of lake 46 ha; about 3 m average depth Conservative 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 Conservative Intended to represent rainy U.S. location; set to highest state-wide average for the contiguous U.S. Precipitation frequency (with respect to impacted farm/lake) 2/3 of total precipitation fall on farm/lake and watershed Conservative Most of total precipitation occurs when the farm/lake are downwind of the source.

Wind direction Farm/lake are downwind 40% of the time Conservative Farm/lake located in the predominantly downwind direction. Temporal dominance of wind direction based on data from Yakima, WA, where wind is predominantly from the west. Wind speed 2.8 m/sec Conservative Low wind speed (5 th percentile of long-term averages for contiguous U.S.); increases net deposition to lake/watershed. Air temperature 298 K Neutral Typical for summer temperatures in central and southern U.S. Mixing height 710 m Conservative Relatively low long-term average mixing height (5 th percentile of long-term averages for contiguous U.S.); increases estimated air concentration. Watershed and Water Body Characteristics Evaporation of lake surface water 700 mm/yr Neutral Value is repres entative of cooler climates.

Surface runoff into lake Equal to 40% of total precipitation Conservative Based on typical water flow in wetter U.S. locations; higher runoff results in greater transfer of chemical to lake.Appendix 5 36 

### Exhibit 3-14. Summary of RTR Screening Scenario Assumptions and Associated Conservatism

Characteristic Value Conservatism Comments on Assumptions
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. May overestimate flush rate if water inputs are also overestimated. Note that after evapotranspiration, remaining water volume added via precipitation is assumed to flow into or through lake.
Soil erosion from surface soil into lake Varies by parcel; ranges from 0.002 to 0.01 kg/m 2 -day Neutral Erosion rates were calculated using the universal soil loss equ ation (USLE); inputs to USLE were selected to be generally conservative with regard to concentration in the pond (i.e. higher erosion rates were favored). May 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 Multi-level; includes large, upper trophic level fish Conservative 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. 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) Conservative Assumes livestock feed sources (including grains and silage) are all derived from most highly impacted locations.

Soil- and air-to-plant transfer factors for produce and related parameters Typical (see Attachment 2 for details) Neutral Obtained from peer-reviewed and standard EPA reference sources.

Biotransfer factors for efficiency of uptake by animal of chemical in food/soil Typical (see Attachment 2 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) Conservative Probably overes timates bioavailability in soil; many chemicals are less bioavailable in soil than in plants.

Ingestion Exposure Assumptions Ingestion rates for all farm produce/livestock types Person obtains all food sources from local farm; ingestion rate is 90 th percentileof rates for homeproduced food items Conservative All food derived from impacted farm; total food ingestion rate would exceed expected body weight-normalized ingestion rates (prevents underestimating any individual food type).Appendix 5 37 

### Exhibit 3-14. Summary of RTR Screening Scenario Assumptions and Associated Conservatism

Characteristic Value Conservatism Comments on Assumptions Fish ingestion rate 0.24 g/kg-day for adult; between 0.14 and 0.26 g/kg-day for children age d 1-19 Conservative Rates are based on EPA’s analysis of freshwater and estuarine fish consumption from the USDA’s Continuing Survey of Food Intake by Individuals (2002d). This likely overestimates long-term fish consumption rates for the general population. See Attachment 2 for a detailed discussion. Exposure frequency Consumption of contaminated food items occurs 365 days/yr Conservative 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, K ow , etc.) Varies Neutral Obtained from peer-reviewed sources; intended to be representative of typical behavior and characteristics.

"General" physical properties (plant matter density, aquatic life biomass, 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 conservative Values used are those determined to be appropriate for risk asse ssment by OAQPS; some values may be health-protective.



## 4 Evaluation of Screening Scenario

4.1 Overview

To evidence our understanding of the models used, their configuration, and the total uncertainty associated with this model application, the screening scenario developed for RTR was subjected to a series of evaluations. These analyses were somewhat more focused on TRIM.FaTE, given the complexity and variability associated with the fate and transport modeling phase relative to other aspects of the screening calculations. These evaluations emphasized the application of this approach (and especially TRIM.FaTE) in the context of ingestion exposure and risk screening for RTR. The analyses were not intended to be general model performance evaluations of TRIM.FaTE. EPA has conducted such analyses, which are documented in detail in Volumes I and II of the 

### TRIM.FaTE Evaluation Report (EPA 2002b, 2005b).Appendix 5 38 

### The purpose of the current evaluations, however, is similar to that of the previous model evaluations, in that both are intended to increase the confidence associated with model performance. Although the focus of the screening scenario evaluation (assessing the utility of the constructed modeling scenario) differs from the purpose of TRIM.FaTE model evaluations, the underlying objectives are comparable. In summary, the current evaluations are intended to achieve several objectives:

• 

### enhance understanding of how the scenario operates;

• 

### better characterize the uncertainty of model results;

• 

### measure model sensitivity to changes in parameter values and scenario configuration; and

• 

### strengthen the defensibility of the scenario’s application as a component of air toxics residual risk assessment.

That these analyses do not attempt to validate or “prove” the accuracy of model results is important to note. As specified in the TRIM .FaTE mercury test case evaluation report (EPA 2005b):

‘Validation’ of such models, in the classic sense (e.g., proving the model produces accurate results across a range of input conditions), is not generally possible, in part because there are no comprehensive data sets of measured chemical concentrations (and associated contributing pollutant sources) for use in such comprehensive studies, nor are there other fully validated multimedia models against which TRIM.FaTE can be benchmarked. Thus, evaluation of TRIM.FaTE is not a yes/no exercise but a continuing accumulation of evidence leading to model refinement and eventually to increasing levels of confidence in the model results.

The screening scenario was evaluated through a series of analyses:

• 

### a general evaluation of TRIM.FaTE fate and transport modeling outputs;

• 

### comparisons of model outputs to the literature (e.g., measured concentrations of the chemicals evaluated, information on the expected distribution of chemical mass in the environment);

• 

### sensitivity analyses of TRIM.FaTE and MIRC inputs and model configurations on endpoints of interest; and

• 

### an evaluation of related information.

As noted previously, most of these evaluations focused on the TRIM.FaTE modeling scenario. Methods and results are discussed in the following sections.



### 4.2 Overall Chemical Mass Partitioning

A general evaluation of TRIM.FaTE outputs from simulations run using the screening scenario was conducted to inform the set-up and parameterization of the scenario and confirm that the model outputs were generally reasonable. One approach for evaluating the overall performance of the model is to review the distribution of chemical mass among the various compartment types and chemical sinks included in the scenario. In general, many persistent and bioaccumulative chemicals emitted to the air do not readily deposit within close range of a source but instead are transported in the air away from the source (sometimes great distances) before depositing on land or water surfaces or being transformed to other chemical species.Appendix 5 39 

### This is true of the PB-HAPs that can be evaluated using the RTR screening scenario, and thus much of the chemical mass emitted by the mode led sources, regardless of chemical, was not expected to accumulate in the soil, water, or sediment compartments. Instead, most of the chemical mass emitted by the source was expec ted to be removed through advective transport in the air and to end up in the air sinks (the compartments that capture chemical mass blown outside of the modeling domain). This outcome al so would be expected, given the relatively small modeling domain. For chemical mass t hat remains in the domain (is deposited and does not degrade or leave the domain by other means , such as sediment burial), the overall distribution would be expected to approximate environmental observations, with much of the mass remaining in the soil or benthic sediment.

Exhibit 4-1 illustrates the distribution of chemical mass for the screening scenario for the PBHAPs of primary concern (one dioxin, one PAH, cadmium, and mercury). Results show the distribution at the end of a 50-year simulation (or similar duration) for each chemical performed at the 

### de minimis emission rate (the overall distribution fo r each chemical is not expected to be substantially different for other emission rates, given a relatively long simulation period). As anticipated, much of the chemical mass emitted to the screening scenario is largely removed from the scenario via air advection processes and transported to air sinks (blown out of the modeled domain of the scenario). The amount remaining in the scenario varies by chemical, with a larger fraction of mass deposited for mercury (which deposits relatively quickly as a divalent species) and much lower deposition for dioxin. Of the chemical mass deposited to plants, land, or water and remaining in the modeled domain, most accumulates in the soil. For the two metals evaluated (mercury and cadmium) , the sediment compartment also contains a significant amount of mass.

The mass distribution for mercury presented in Exhibit 4-1 was calculated from a single simulation with emissions split evenly by mass between divalent mercury and elemental mercury. Recall that TRIM.FaTE models mercury transformation among elemental, divalent, and methyl mercury species. Consequently, the distribution of mercury mass shown in the middle and lower parts of the table accounts for inter-species transformation within media as well as movement between media (and all methyl mercury in the scenario was derived from emitted divalent or elemental mercury).Appendix 5 40 

### Exhibit 4-1. Distribution of Chemical Mass in Screening Scenario2,3,7,8-TCDDBenzo[a]pyreneCadmium Mercury (Divalent Mercury Emitted)DivalentMercuryElementalMercuryMethylMercuryTotalMercury

Distribution of Total Mass Added to Scenario from Modeled Source

Emitted chemical mass removed from scenario and deposited into air sinks 99.5% 81.6% 97.3% 85.4%

– b

– b

– b

Emitted chemical mass remaining in scenario (not in air sinks) a 0.5% 18.4% 2.7% 14.6%

– b

– b

– b

Distribution of Mass Remaining in Scenario c

Air 0.30% 0.2% 0.01% 0. 001% 0.002% 0.0% 0.001%
Soil 89.2% 97.6% 96.5% 86. 0% 91.1% 98.0% 86.3%
Plants 6.0% 1.6% 0.1% 0.7% 0.0% 0.0002% 0.7%
Surface Water 0.02% 0.01% 0. 2% 0.0% 0.5% 0.03% 0.05%
Sediment 4.4% 0.6% 3.2% 13.2% 8.5% 2.0% 12.9% Aquatic Biota 0.03% 0.001% 0.003% 0.0003% 0.0002% 0.005% 0.0004%
Groundwater 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Distribution of Mass in Soil

Surface Soil 98.8% 98.9% 30.1% 98.9% 9.3% 98.9% 97.9%
Root Zone Soil 1.2% 1.1% 69. 9% 1.1% 90.7% 1.1% 2.1%
Vadose Zone Soil 0.0% 0.0% 0. 0% 0.0% 0.0% 0.0% 0.0%

a Fraction includes mass transferred from air to soil or other media, but subsequently removed from the modeling domain (e.g., accumulated in sediment burial or degradation/reaction sink).
b To calculate the de minimis level for mercury, neither elemental mercury or methyl mercury is emitted by the modeled source.

c Fractions represent amount of mass remaining in non-sink compartments.



### 4.3 Comparison to Measured Concentrations

4.3.1 Scope of the Evaluation

This section presents environmental measurements of the PB-HAPs evaluated for RTR and compares them to modeled concentrations es timated by TRIM.FaTE and the Multimedia Ingestion Risk Calculator (MIRC). Model outputs correspond to the estimates associated with emission rates at the 

### de minimis thresholds for each representative PB-HAP calculated for RTR multipathway screening. We emphasize that this analysis is not intended to “validate” the accuracy of the models used for RTR or the specific model configurations used to calculate 

### de minimis emission thresholds. In general, although the comparison of environmental data to model results is often a central component of model evaluation, deriving useful conclusions from such comparisons is complicated by a range factors, including: fundamental differences between the modeling scenario and the environmental system in which samples were collected; temporal and spatial issues (e.g., model re sults representative of long-term averageAppendix 5 41 

### concentrations vs. measured point estimates); data quality issues; and other factors. In the current evaluation, it is difficult to derive conclusions from model-to-measurement comparisons given the use of a modeling scenario that is intended to be representative but hypothetical and conservative (i.e., possessing characteristics that lead to higher exposures while not representing any real location or specific site). In addition, the RTR modeling scenario is intended to calculate incremental environmental concentrations and exposures without accounting for any “background” concentrations from natural or anthropogenic sources.

In spite these complications, this comparat ive analysis is an appropriate—and necessary— aspect of the model evaluation process because environmental data provide a general frame of reference for the model outputs. Basic differences between the modeling scenario and the data sets collected preclude the verification or validation of model configuration, performance, or results, and the comparisons should be viewed in light of the complicating issues listed above. However, we believe that this comparative analysis, considered in conjunction with other aspects of the evaluation, is a useful tool in gauging the effectiveness of the screening scenario in informing regulatory decision-making.

4.3.2 Methods and Organization of this Section

The TRIM.FaTE and MIRC outputs presented in this section are those used to calculate the 

### de minimis emission levels in the screening scenario (see Section 2 for discussion of 

### de minimis emission rates). Model outputs from the source parcel are not used for comparisons; instead, concentrations from the land parcel closest to the source (N1) are used in comparisons for soil results, while outputs from the pond parcel are used for comparing results for water, sediment, and fish (See Exhibit 3-1 for Surface Parcel Layout). Chemical concentrations in air are not evaluated in this section for two reasons. First, the TRIM.FaTE model was not designed to estimate air concentrations for the assessment of inhalation exposure, as noted in Section 2.4.1. Second, chemical concentrations in t he air compartment affect exposure concentrations less directly than deposition of the chemical. Therefore, the comparison of chemical concentrations in air to observed values is not included in this evaluation.

The purpose of the 50-year duration of the scenario is to represent long-term input of source emissions over a hypothetical facility lifetime (many compartments also attain an approximate steady state by this time). For all environmental media compartments, the maximum annual average concentrations, taken from the final year of the simulation, are used in MIRC to determine the maximum average concentrations in ingestible products. These concentrations, along with ingestion rates for each food product, are used to calculate average daily doses (ADDs) for each chemical, from which the lif etime cancer risk or age-group-specific hazard quotient (HQ) is then calculated. The model results presented in this section are the environmental media concentrations used to calculate the 

### de minimis emission rates (i.e., those concentrations calculated for the 50th year of the TRIM.FaTE simulation) and the ingestible media concentrations calculated in MIRC using those environmental media concentrations (see Exhibit 3-5 for TRIM.FaTE outputs used in exposure calculations).

The evaluations described in this section are presented separately for each chemical for which a screening threshold has been derived. Each chemical-specific section provides a review of the emission profiles used in the modeling proc ess, and a summary of the expected chemical behavior in the environment. This is follow ed by comparisons between model results and ranges of observed concentrations in environmen tal media, partitioning behavior, and observed concentrations in ingestible products.Appendix 5 42 

### In the charts displaying the comparisons bet ween model results and observed concentrations, the horizontal bars in the graph represent measured concentrations reported in the literature for various environmental media. The green bars show ranges of measured concentrations that were reported as less than a maximum concentration or detection limit, with the upper bound of the bar representing the limit of detection. Blue bars indicate a range of values reported by a particular source. The vertical red lines in the graph represent the modeled TRIM.FaTE concentrations obtained using the de minimis threshold emission rates. Observed data from areas exposed to varying ranges of chemical emissions were chosen for the comparison in order to represent a variety of scenarios. In general, the low end of the range represents concentrations from remote areas not located near a considerable pollution source, while the high end of the range represents concentrations observed near industrial sources (e.g., metal smelters, chlor-alkali plants, incineration/co mbustion facilities) and/or in urban areas.

The observed values provided as examples in this section include data collected from a range of sampling techniques, locations, emissions profiles, historical contributions, and other factors influencing concentration. They do not represent the entire range of possible measured values, and therefore should not be combined to create a contiguous range of concentrations representative of all patterns in the United States. Model outputs were derived using a generally conservative set of fate and transport inputs, and they reflect the estimated environmental levels assuming concentrations from only one source and no ambient background concentrations.

4.3.3 Chemical-Specific Comparisons

4.3.3.1 Cadmium

Behavior in the Environment

Based upon reviewed literature, some of the largest anthropogenic sources of cadmium to air are facilities that process, mine, or smelt cadm ium-zinc or cadmium-zinc-lead ores; coal and oilfired boilers; other urban and industrial facilities; phosphate fertilizer manufacturing facilities; road dust; and municipal sewage sludge incinerator s (ATSDR 2008). These facilities can emit airborne cadmium particles that are capable of traveling long distances before depositing onto soil or water bodies via dust, rain, or snow. Cadmium adsorbs to soil particles in the surface layers of the soil profile, but to a lesser degree than many other heavy metals (HSDB 2005a). It may enter surface waters through atmospheric fallout, runoff, or wastewater streams, but most cadmium compounds will be removed from the surface water compartment through adsorption to organic matter in sediment or to other suspended compounds. Concentrations in bed sediment are expected to be roughly an order of magnitude higher than those in overlying surface water (HSDB 2005a).

Freshwater fish accumulate cadmium primarily through direct uptake of the dissolved form through the gills and secondarily through diet, which plays a variable role in total cadmium uptake (Reinfelder et al. 1998, Chen et al. 2000, Saiki et al. 1995). The degree to which cadmium bioaccumulates in fish is largely dependent upon water pH and humus content (ATSDR 2008). Reported bioaccumulation factors (BAFs), or bioconcentration factors (BCFs), of 3 to 4,190 (ATSDR 2008) and 907 (EPA 2005a) have been reported for freshwater organisms and fish. Although some biomagnification of cadmium has been reported for aquatic food chains involving fish in saltwater systems, bi omagnification 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)Appendix 5 43 

### of less than 1 have generally 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).

Emission Profile

The cadmium 

### de minimis emission rate derived from the RTR PB-HAP screening is 0.65 TPY (based on an HQ of 1 in children aged 1-2). Although the annual concentration from year 50 is used for comparison, modeled concentrations of cadmium in most compartments generally leveled off after year 20. One notable excepti on was the soil compartment in parcel N6, where the soil in the screening scenario is tilled and no plants are modeled in the TRIM.FaTE scenario. Cadmium concentrations in this compartment continued to rise through year 50. The steady build-up of cadmium in this parcel is likely due to the thickness of the surface soil layer in the N6 parcel. Because this parcel is tilled, the surface soil layer is thicker than in the other parcels, and the rate of exchange between the surface soil and the root zone soil is correspondingly lower.

Concentrations in Environmental Media

Exhibit 4-2 displays measured concentrations in environmental media with TRIM.FaTE outputs for the screening scenario. Modeled concentrations and BAFs in fish compartments are shown in Exhibit 4-3. Because relationships betw een fish compartments in the screening scenario are established within a food web structure, there is no explicit delineation to specific trophic levels. However, for evaluation of trophic level patterns, the water column herbivore (WCH) can be considered to represent the lowest trophic level among fish, while the water column carnivore (WCC) represents the highest trophic level.

The cadmium concentrations output by the RTR screening scenario were consistent with reported values in all environmental media compartments. Consistent with trends noted in the literature, modeled cadmium concentrations were highest in fish at the lowest trophic level evaluated in the scenario (i.e., WCH). Though modeled concentrations did not uniformly decrease with an increase in trophic level, the concentrations for all but the benthic carnivore (BC) compartment were markedly lower than those in the WCH compartment. It should be noted that BAFs for the other chemicals evaluated in this section are several orders of magnitude greater—and span much wider ranges—than the BAFs reported for cadmium in fish. Thus, in the context of comparison between chem icals, the difference between cadmium BAFs in fish compartments is quite small.

The BAFs calculated using model outputs from the RTR screening scenario ranged from approximately 200 to 1,400, which is consistent with the range of values presented in the literature for freshwater systems. Overall, modeled concentrations and BAFs in all fish compartments were reasonably consistent with observed trends, with cadmium levels generally diminishing from lower to higher trophic levels, and consistent with the assumption that diet plays a variable role in the bioaccumulation of this substance in fish.Appendix 344Exhibit 4-2. Summary of Modeled and Observed Concentrations of Cadmium in Environmental Media0.0001 0.001 0.01 0.1 1 10 100 1000Northeastern U.S. Lakes (d)Sacramento River System Acid-Mine Drainage (c)U.S. Average (a)Spring River Basin Mining District, KS, MO & OK (a)Unpolluted U.S. Soils (a)Superfund Site, KS (a)Near Smelter, MT (a)Patroon Creek Reservoir, NY, Avg. (a)Spring River Basin Mining District, KS, MO, & OK (c)Great Lakes (b)Trout, Remote Stream, MD (a)Catfish, Mining-Contaminated Water, OK (a)Redear Sunfish, SWP, FL, Avg. (a)Salmon Flesh (a)Largemouth Bass, SWP, FL, Avg. (a)

Surface Water (μg/L)
Surface Soil (mg/kg dry wt.)
Sediment (mg/kg dry wt.)
Fish (mg/kg fresh wt.)Cadmium Concentration (see units at left)Reported value was less than limit of detection (LOD = upper bound of bar)Value or range from literatureModeled concentration corresponding to de minimis threshold emission rateWCCBCTSUSWCC = Water Column Carnivore, BC = Benthic Carnivore, SWP = Stormwater Pond, TS = Tilled Soil, US = Untilled SoilaSource:ATSDR Toxicological Profile for Cadmium(ATSDR 2008)bSource:Hazardous Substances Databank Record for Cadmium Compounds(HSDB 2005a)cSource:Copper, Cadmium, and Zinc Concentrations in Aquatic Food Chainsfrom the Upper Sacramento River and Selected Tributaries (Saikiet al. 1995)dSource: Accumulation of Heavy Metals in Food Web Components Across a Gradient of Lakes (Chen et al. 2000)Appendix 5 45 

### Exhibit 4-3. TRIM.FaTE Cadmium Concentrations in Fish and Calculated Bioaccumulation Factors with Respect to Total Water Concentration

0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70

Water Column Herbivore Benthic Omnivore Water Column Omnivore Benthic Carnivore Water Column CarnivoreConcentration in Fish(mg/kg wet weight)

0 222 444 666 888 1,110 1,332 1,554Bioaccumulation Factors(BAF)



### Partitioning Behavior

Because most cadmium emitted to the air w ill eventually deposit onto soils, cadmium has been observed to partition primarily to soil when released to the environment (ATSDR 2008). Cadmium mobility 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 binds strongly to organic matter, rendering the metal immobile; however, some plants are able to efficiently take up cadmium, thus providing an entry point for cadmium into the food chain (ATSDR 2008).

Most cadmium in a natural water column is dissolved, but some adsorption to humic substances and other organic complexing agents can occur. This behavior can be especially important in polluted or organic-rich waters, playing a dominant role in cadmium transport (ATSDR 2008). Concentrations in surface water tend to be lower than those in bed sediment because cadmium readily adsorbs to mineral surfaces, hydrous metal oxides, and organic materials present in sediment. Cadmium that has adsorbed to mineral surfaces in the sediment is not easily bioaccumulated in the aquatic food web unless the sediment is disturbed, and the metal is redissolved.

For the RTR screening scenario, 5.7 percent of cadmium in the pond compartment partitioned to surface water, while 94.3 percent partitioned to sediment. The percentages of cadmium in sorbed and dissolved states in surface water, sediment, and surface soil are presented in Exhibit 4-4. The partitioning behavior modeled in TRIM.FaTE is consistent with the behavior of cadmium in the natural environment. The presence of cadmium primarily in the dissolved state in surface water and sorbed to sediment and surface soil is consistent with trends noted in supporting literature.Appendix 5 46 

### Exhibit 4-4. Fraction of Cadmium Mass Sorbed vs. Dissolved in TRIM.FaTE Compartments Compartment Sorbed Dissolved

Surface Soil 100.0% 0.0%

Surface Water 4.6% 95.4%

Sediment 99.9% 0.1%



### Concentrations in Ingestible Products

The major source of non-inhalation exposure to cadmium outside of occupational settings is through dietary intake. Available data indicate that cadmium accumulates in plants, aquatic organisms, and animals, offering multiple i ngestion exposure pathways (ATSDR 2008). However, actual cadmium levels in ingestible products can vary based upon type of food, agricultural and cultivation practices, atmospher ic deposition rates, conditions in environmental media, and presence of other anthropogenic pollutants. General trends indicate that high levels of cadmium can be found in green, leafy vegetables; peanuts; soybeans; and sunflower seeds. Meat and fish generally contain lower amounts of cadmium, overall, but cadmium can be found 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).

Modeled cadmium concentrations in ingestible products are displayed in Exhibit 4-5 along with cadmium concentrations reported in literature. In the screening scenario we assume that individuals consume equal amounts of benthic carnivores and water column carnivores, so the concentration for fish is given as the average of these two fish compartments. 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 highest modeled concentrations.

To determine media types most relevant to exposure and risk, the estimated media concentrations must be combined with ingestion exposure factors (i.e., higher concentrations do not necessarily equal higher risk. The contribut ion of ingestion exposure pathways to the ADD (and thus the HQ) for the different age categories are displayed in Exhibit 4-6. The HQ of 1 for children aged 1-2 was used to determine the 

### de minimis level for cadmium in the screening scenario. The dominant exposure pathway for this age group was consumption of fruit (protected and exposed), which comprises almost half of the total ADD. Fruit was a dominant exposure pathway for all age groups, though less so for adults. The other dominant exposure pathways were fish, vegetables, and soil. This trend is consistent with observed trends and representative of the preferential bioaccumulative behavior of cadmium in the natural environment.Appendix 5 47 

### Exhibit 4-5. Summary of Modeled and Observed Concentrations of Cadmium in Ingestible Media

1.E-05 1.E-04 1.E-03 1.E-02 1.E-011.E+001.E+011.E+021.E+03 Meat, Poultry & Fish, Urban Areas (b) Beef (Prepared) (c) Urban Areas (b) Eggs (Prepared) (c) Apples (c) All Fruits, Urban Areas (b) Strawberries (c) All Fruits, Silver Mine-Contaminated Gardens, CO (c) All Vegetables, Urban Areas (b) Spinach (Prepared) (c) Broccoli, Lead-Zinc Mining Area, England (a) Trout, Remote Stream, MD (b) Catfish, Mining-Contaminated Water, OK (a) Redear Sunfish, SWP, FL, Avg. (b) Largemouth Bass, SWP, FL, Avg. (b) Salmon Flesh (b) Meat, Poultry & Fish, Urban Areas (b) Pork Products (Prepared) (c) Poultry (Prepared) (c) Meat, Poultry & Fish, Urban Areas (b) Mean & Max, Meat & Poultry, Urban Areas, Canada (c ) Oranges (c) All Fruits, Urban Areas (b) Cantaloupe (c) All Fruits, Silver Mine-Contaminated Gardens, CO (c) Winter Squash (Prepared) (c) All Vegetables, Urban Areas (b) Potatoes (Prepared) (c ) Carrots, Lead-Zinc Mining Area, England (a) Unpolluted U.S. Soils (b)

Superfund Site, KS (b)

Near Smelter, MT (b) Dairy Products, U.S. (c)

Urban Areas, U.S. (b) Mean & Max, Urban Areas, Canada (a)Beef Eggs Exposed FruitExposedVegetableFish Pork Poultry Prot. FruitProt.Veg.RootSoil(mg/kgdry wt.)Dairy

Cadmium Concentration (mg/kg wet wt.*) Reported value was less than limit of detection (LOD = upper bound of bar)
Value or range from literature
Modeled concentration corresponding to de minimis threshold emission rate

*except soil, which is expressed as mg/kg dry weight. SWP = Stormwater Pond

a Source: Hazardous Substances Databank for Cadmium Compounds (HSDB 2005a)
b Source: ATSDR Toxicological Profile for Cadmium (ATSDR 2008)

c Source: Total Diet Study, Market Baskets 1991-3 through 2005-4 (FDA 2007)Appendix 5 48 

### Exhibit 4-6. Estimated Contribution of Modeled Food Types to Cadmium Ingestion Exposures and Hazard Quotient

0 0.0001 0.0002 0.0003 0.0004 0.0005

Children 1-2 Children 3-5 Children 6-11 Children 12-19 Adults 20-70ADD(mg/kg/day)

0.0E+00 2.0E-01 4.0E-01 6.0E-01 8.0E-01 1.0E+00HazardQuotient Fruits Vegetables Fish Soil Meat, Dairy, & Eggs



### 4.3.3.2 Mercury

Behavior in the Environment

Mercury emitted by anthropogenic sources undergoes changes in form and species as it moves through environmental media. The three most rele vant forms of mercury in the environment are elemental mercury, divalent inorganic mercury, and methyl mercury. Based upon reviewed literature, 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 (ASTDR 1999). Stack emissions can include a mixture of elemental and divalent forms, mostly in the gaseous phase, with some divalent forms in particlebound phases (EPA 1997b). Although elemental mercury is more prevalent in the atmosphere, divalent mercury can be found at higher concentrations near emissions sources. Elemental mercury has a very long residence time in the atmosphere and will thus be distributed relatively evenly on a global scale, resulting in negligible impacts from any single source on local and regional scales. Divalent mercury, however, is removed from the atmosphere at a faster rate than elemental mercury, and may be transferred to the surface near the emission source via wet or dry deposition, where it appears to adsorb tightly to soil particles (EPA 1997b). Some adsorbed divalent mercury can be reduced to elemental mercury and be revolatilized back into the atmosphere. Methylation to methyl mercury by microbes can also occur in the soil; however, divalent mercury is generally the dominant species in soil (EPA 1997b). Small amounts of divalent and methyl mercury in soil can be transported to surface water through runoff and leaching. Direct deposition to water can occur through atmospheric fallout. Once in the water body, divalent mercury may be methylated through microbial activity, and both divalent and methyl mercury may be further reduced to elemental mercury, which will reenter the atmosphere. But, as in soil, divalent mercury will generally be the dominant species in bothAppendix 5 49 

### surface water and sediment. Methyl mercury is readily bioaccumulated and efficiently biomagnified in aquatic organisms and is typically the species of greatest concern for mercury exposure via the food chain.

Emission Profile

TRIM.FaTE assumes that only elemental mercury and divalent mercury are emitted from the source (see Section 2.3); but once in the environment, these species may change form, resulting in ingestion exposure to methyl mercury as well as divalent mercury. Relative to these two species, elemental mercury does not comprise a significant amount of total mercury in environmental media other than air. Because hu man health effects from exposure to methyl mercury and divalent mercury are not additive, two 

### de minimis rates for emissions of divalent mercury were calculated for exposure to methyl mercury and divalent mercury, respectively, to determine the most health-protective level. The emission rate calculated for the HQ of divalent mercury was lower, and thus was used to define the 

### de minimis emission threshold. At the 

### de minimis rate, the HQ for divalent mercury in children aged 1-2 is approximately twice as high as that calculated for methyl mercury.

For the scale of this evaluation, we compared predicted concentrations of total mercury to measured concentrations of total mercury in environmental media compartments and ingestible products. In TRIM.FaTE, divalent mercury compri ses from 83 to 100 percent of total mercury in all environmental and ingestible media compartment s except fish. Because methyl mercury comprises approximately 90 percent of total mercury in fish, and this pathway is recognized as a significant contributor to mercury exposure in humans, we also examine methyl mercury bioaccumulation in fish and the contribution of this pathway to methyl mercury ingestion exposure and HQ.

The 

### de minimis emission rate used for mercury in the RTR screening scenario is 1.6E-01 TPY of divalent mercury (based on an HQ of 1 for divalent mercury exposure for children aged 1-2). Annual concentrations from year 50 used to calculate the 

### de minimis threshold are presented here, and these values are used for comparison. Modeled concentrations of mercury in all compartments but untilled soil continued to rise up to the end of the simulation, with the rate of increase steadily diminishing over time.

Concentrations in Environmental Media

Exhibit 4-7 displays measured ranges of total mercury concentrations in environmental media with TRIM.FaTE outputs for the screening scenario. The total mercury concentrations estimated by TRIM.FaTE were generally consistent with reported values in all environmental media compartments. Overall, modeled total mercury concentrations were generally within therange of values reported for areas near a significant pollution point source (e.g., smelter, chloralkali facility).

Methyl mercury is formed via microbial transform ation of inorganic mercury in sediment, surface water, and soil and readily accumulates in the ti ssues of planktivorous and piscivorous fish. There seems to be a relationship between methyl mercury levels in fish and water pH, showing higher levels of methyl mercury in fish found in acidic lakes (EPA 1997b). Lake alkalinity and dissolved oxygen content may also influence the abilit y of fish to bioaccumulate methyl mercury (EPA 1997b). BAFs on the order of 10 5 

### to 10 7 

### have been reported for methyl mercury concentrations in fish (Alpers et al. 2008). Modeled concentrations of mercury species in fish compartments along with estimated methyl me rcury BAFs are shown in Exhibit 4-8. TheAppendix 550Exhibit 4-7. Summary of Modeled and Observed Concentrations of Total Mercury in Environmental Media0.0001 0.001 0.01 0.1 1 10 100 1000 1Lake Cresent, WA (b)U.S. Samples (b)Crab Orchard Lake, IL (a)Typical U.S. (b)U.S. Chlor-alkali Facility, Avg. (b)Near Mine/Smelter, Spain (b)Little Rock Lake, WI (b)U.S. Lakes, Avg. (b)80 MN Lakes (b)Canadian Lakes Near Smelters (a)Predatory Fish, ME (b)Paper Mills Using Chlorine (c)42 Lakes and Rivers, NJ (b)U.S.Min/Max, 1990–1995 (a)

Surface Water (ng/L)
Surface Soil (mg/kg dry wt.)
Sediment (ng/g dry wt.)
Fish (μg/g fresh wt.)Total Mercury Concentration (see units at left)Reported value was less than limit of detection (LOD = upper bound of bar)Value or range from literatureModeled concentration corresponding to de minimis threshold emission rateWCCBCTSUSBC = Benthic Carnivore, WCC = Water Column Carnivore, TS = Tilled Soil, US = Untilled SoilaSource:ATSDR Toxicological Profile for Mercury(ATSDR 1999)bSource:Mercury Study Report to Congress(EPA 1997b)cSource:National Study of Chemical Residues in Fish(EPA 1992a)Appendix 5 51 

### Exhibit 4-8. TRIM.FaTE Mercury Concentrations, Speciation, and Calculated Methyl Mercury Bioaccumulation Factors (in white boxes) in Fish Compartments

22,269 39,148 33,759 112,637 172,120

0.00 0.05 0.10 0.15 0.20 0.25 0.30

Water Column Herbivore Benthic Omnivore Water Column Omnivore Benthic Carnivore Water Column CarnivoreConcentration in Fish(mg/kg wet weight)

Elemental

Divalent
Methyl



### TRIM.FaTE outputs for the fish compartments mirror a trend noted in the literature in which divalent mercury is observed more prominently in lower trophic level organisms, while methyl mercury is the more prominent species in higher trophic level organisms (ATSDR 1999). Calculated BAFs are consistent with reported values, with the higher trophic levels exhibiting BAFs of approximately 10 5 

### . The biomagnification of the methylated species in the aquatic ecosystem can be observed in Exhibit 4-8, with modeled methyl mercury concentrations increasing substantially from the lower to higher trophic levels.

Partitioning Behavior

Soil acts as the primary reservoir for divalent mercury emitted from anthropogenic sources. In some cases, divalent mercury will be adsorbed onto forms of dissolved organic carbons (DOCs), which are susceptible to runoff. However, most divalent mercury from atmospheric deposition remains in the soil profile in the form of inorganic compounds bound to soil organic matter. The ability of mercury to form complexes with soil organic matter is highly dependent 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). Although inorganic compounds containing divalent mercury are relatively soluble, this complexing behavior with organic matter significantly limits mercury transport. Only very small amounts of mercury present in soil are partitioned to runoff. Elemental mercury present in soil (e.g., as a result of the reduction of divalent mercury) will readily volatilize, especially in acidic soils, and this species therefore comprises very little of the total mercury content in soil. Typically, methyl mercury comprises 1 to 3 percent of total mercury in soil (EPA 1997b). For the TRIM. FaTE results in the RTR screening scenario, divalent mercury comprised approximately 95 percent of total mercury in the surface soil compartment; this result is consistent with environmental trends.Appendix 5 52 

### Solubility of mercury in water depends on the species and form of mercury, water pH, chloride ion concentrations, and other factors (ATSDR 1999). Solid forms of inorganic mercury compounds will partition to particulates in the water column and then partition to the sediment bed (EPA 1997b). Low pH favors the methylation of mercury in the water column, typically performed by sulfur-reducing bacteria in anaerobic conditions. Methyl mercury generally comprises 10 percent of total mercury observed in surface water (EPA 1997b). A considerable amount (25 to 60 percent) of both divalent mercury compounds and methyl mercury will be strongly particle-bound in the water column (EPA 1997b). The remaining mercury will be in a dissolved state. Most of the elemental mercury produced as a result of reduction of divalent mercury by humic acid will volatilize.

Screening scenario modeling results for mercury speciation in environmental media compartments are displayed in Exhibit 4-9 along with the percent of each species that was present in both adsorbed and dissolved states in surface soil, surface water, and sediment. Consistent with observed trends, 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. For the RTR screening scenario, 99 percent of total mercury in the pond (i.e., surface water and sediment) was divalent mercury, while 0.7 percent was methyl mercury and 0.2 percent was elemental mercury. The TRIM.FaTE outputs of approximately 3 percent methyl mercury in soil and 9 percent methyl mercury in surface water are also consistent with trends noted in the literature (EPA 1997b).

Exhibit 4-9. TRIM.FaTE Mercury Speciation and Partitioning in Environmental Media Compartments

0% 25% 50% 75% 100%

Surface Soil Surface Water SedimentPercent of Total Mercury Divalent Elemental Methyl

.

Chemical Methyl Mercury Elemental Mercury Divalent Mercury
Sorbed Dissolved Sorbed Dissolved Sorbed Dissolved

Surface soil 100.0% 0.0% 100.0% 0.0% 100.0% 0.0% Surface Water 80.7% 19.3% 4.8% 80.7% 83.0% 17.0% Sediment 100.0% 0.0% 100.0% 0.0% 100.0% 0.0%



### Overall, the partitioning behavior in the RTR screening scenario is consistent with the behavior of mercury in the natural environment. Within the pond compartment of the model, 99.6 percent of divalent mercury, 98.6 percent methyl me rcury, and 94.5 percent of elemental mercury partitioned to the sediment. Though all mercury in natural soils and sediment may not be in theAppendix 5 53 

### sorbed state, the percentage of mercury that does not complex with organic matter would likely be negligible. The behavior in the TRIM.FaTE surface water compartment is consistent with trends observed in natural waters in which divalent and methyl mercury species exhibit a strong preference for adsorption to suspended matter in the water column. These species may also form complexes with dissolved organic carbon and thus be considered as dissolved in the water column; however, this is not as prevalent as complexation with suspended solids. Conversely, elemental mercury does not adsorb readily to suspended particles, and is therefore found primarily in the dissolved form in natural waters, where it is highly susceptible to revolatilization into the atmosphere. The high percent dissolved elemental mercury estimated by TRIM.FaTE (when compared to percent dissolved divalent or methyl mercury) in surface water is consistent with this trend. It has been estimated that more than 97 percent of dissolved gaseous mercury in the water column is elemental mercury (EPA 1997b).

Concentrations in Ingestible Products

Available data indicate that mercury bioaccumulates in plants, aquatic organisms, and terrestrial animals, providing multiple ingestion exposure pathways (EPA 1997b, ASTDR 1999). Low levels of mercury are found in plants, with leafy vegetables containing higher concentrations than potatoes, grains, legumes, and other vegetables and fruits (ASTDR 1999, EPA 1997b). Cattle are capable of demethylating mercury in the rumen, and therefore store little of the small amount of mercury that is transferred to livestock via foraging or silage/grain consumption. Mercury content in meat and cow’s milk is therefore low (ASTDR 1999). Concentrations of methyl mercury in fish are generally highest in larger, older specimens at the higher trophic levels (EPA 1997b).

Concentrations of total mercury in ingestible products are presented in Exhibit 4-9. Though 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 influence the mercury HQs in the model can be seen in Exhibit 4-10, which displays the cont ributions of ingestion exposure pathways to the divalent mercury and methyl mercury ADDs for all age groups analyzed in the screening scenario. The dominant exposure pathway for divalent mercury in children aged 1-2 was ingestion of contaminated soil. Divalent mercury accumulates readily in the upper 20 cm of the soil profile, where it is accessible to children that may frequently ingest soil (EPA 1997b, EPA 2008). Exposures to children aged 1 to 2 are 4 to 5 times higher than exposures to children and adults aged 12 to 70 years. This is driven by a ve ry high soil ingestion rate in children, and the assumption that the soil consumed is next to the emission source. In older children and adults who do not frequently ingest soil, fruits and veget ables provide the greatest exposure pathway for divalent mercury. Though divalent mercury is not considered to be readily taken up by plants from the soil, atmospheric deposition may figure strongly into elevated plant concentrations. In addition to the deposition of divalent mercury directly onto plant surfaces, divalent mercury may accumulate in edible portions of plants due to the transformation of elemental mercury following deposition. The relatively low contributions by meat and fish consumption are consistent with observed trends i ndicating that the divalent mercury biotransfer is not high between plants and animals, and that the most persistent mercury species in fish is methyl mercury, rather than diva lent mercury (EPA 1997b, ATSDR 1999).Appendix 5 54 

### Exhibit 4-10. Summary of Modeled and Observed Concentrations of Total Mercury in Ingestible Media

1.E-6 1.E-5 1.E-4 1.E-3 1.E-2 1.E-1 1.E+0 1.E+1 1.E+2 Germany (b) Background Level, Japan (c) Raw Beef (c) Natural and Abnormal (a) Denmark, Germany & U.K. (a) Average, Germany (b) Exposed Fruits (e) Apples, Background Mean, Netherlands (c) All Fruits, Belgium, Germany & U.K. (a) Background Means, Netherlands (c) U.S. Chlor-alkali Plant (c) Alfalfa (Above-Ground Parts), Near Mine/Smelter, Spain (c) Predatory Fish, ME (c) Near Paper Mills Using Chlorine (d) 42 Lakes and Rivers, NJ (c) U.S. Min/Max, 1990

– 1995 (b) Raw & Sausage Hg2 (c) Background Level, Japan (c) Chicken, Background Level, Japan (c) Average, Germany (b) Chicken, Raw & Lunch Meat, 33 – 80% Hg2 (c) Garden Fruits, U.S. (a) All Fruits, U.S. (a) All Fruits/Vegetables, Belgium, Germany & U.K. (a) Legume Vegetables, U.K. (a) Maize (c) All Fruits/Vegetables, Belgium, Germany & U.K. (a) Potatoes, U.S. (a) Root Vegetable, U.S. Avg.(a) Carrot/Potato Background Means, Netherlands (b) Typical U.S. Soils (c) U.S. Chlor-alkali Facility (c) Mine/Smelter, Spain (c) Dairy Products, U.S. (a) Milk, Germany (b) Germany and U.K. (a)Beef EggsExposedFruitExposedVegetableFish Pork PoultryProtectedFruitProt. Veg. RootSoil(mg/kg drywt.)Dairy

Mercury Concentration (mg/kg wet wt.*) Reported value was less than limit of detection (LOD = upper bound of bar) Value or range from literature Modeled concentration corresponding to de minimis threshold emission rate Non-Detect With No Limit of Detection Specified

*except soil, which is expressed as mg/kg dry weight

a Source: Hazardous Substances Databank Report for Mercury Compounds (HSDB 2005b)
b Source: ATSDR Toxicological Profile for Mercury (ATSDR 1999)

c Source: Mercury Study Report to Congress (EPA 1997b)
d Source: National Study of Chemical Residues in Fish (EPA 1992a)

e Source: Total Diet Study, Market Baskets 1991-3 through 2005-4 (FDA 2007)Appendix 5 55 

### The dominant exposure pathway for methyl mercury in all age groups was ingestion of fish (Exhibit 4-11). Though methyl mercury concentrations in fish were very high (approximately 88 percent of total mercury) in Exhibit 4-11, and fish consumption can contribute substantially to methyl mercury exposure, the HQ for this species (Exhibit 4-12) is likely lower than that of divalent mercury in children aged 1-2 due to a relatively low fish consumption rate in combination with high exposure to divalent me rcury from multiple pathways, but primarily through ingestion of soil. The divalent and methyl mercury age-group-specific HQs are displayed in Exhibit 4-12. In humans aged 6-70, the methyl mercury HQ is higher than that for divalent mercury, which is likely the result of lower soil consumption rates and higher fish consumption rates.

Exhibit 4-12. Estimated Contribution of Summed Modeled Food Types to Divalent Mercury and Methyl Mercury Hazard Quotients

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

Children 1-2 Children 3-5 Children 6-11 Children 12-19 Adults 20-70HazardQuotient Divalent Mercury Methyl Mercury 

### Exhibit 4-11. Estimated Contribution of Modeled Food Types to Divalent Mercury and Methyl Mercury Ingestion Exposures

0 0.00006 0.00012 0.00018 0.00024 0.0003

Children 1-2 Children 3-5 Children 6-11 Children 12-19 Adults 20-70Divalent Mercury ADD (mg/kg/day) Soil Fruits & Vegetables Eggs, Pork, & Poultry Fish Beef & Dairy

0 0.00006 0.00012 0.00018 0.00024 0.0003

Children 1-2 Children 3-5 Children 6-11 Children 12-19 Adults 20-70Methyl Mercury ADD (mg/kg/day) Fish Fruits & Vegetables Soil Eggs, Pork, & Poultry Beef & DairyAppendix 5 56 

### 4.3.3.3 Dioxins (2,3,7,8-TCDD)

Behavior in the Environment

Polychlorinated benzo-dioxins and -furans 15 

### are reported in the National Emission Inventory (NEI) as individual congeners, congener groups, or toxic equivalents (TEQs). The dioxincongener 2,3,7,8-TCDD was used as a surrogate to evaluate risks for chlorinated dibenzo-pdioxins in the screening scenario. Of the chlorinated dioxin congeners, this compound has been the most widely studied, and is considered to be one of the two most toxic congeners to vertebrates (ASTDR 1998). As a result, the World Health Organization (WHO) assigned a toxic equivalency factor (TEF) of 1 to this congener, meaning that the concentrations of the other congeners are scaled relative to the toxicity of 2,3,7,8-TCDD (See Attachment 2 for TEFs). We deemed this species an appropriate surrogate to represent the fate and transport behavior of the dioxin group.

Incineration/combustion processes are believed to be the primary emission sources for chlorinated dioxins (ASTDR 1998). The five stationary source categories that generate the vast majority of 2,3,7,8-TCDD emissions in the Unit ed States are municipal waste incineration, medical waste incineration, hazardous waste kilns from Portland cement manufacturing, secondary aluminum smelting, and biological inciner ation. Dioxins emitted to the atmosphere may be transported long-range as vapors or bound to particulates, depositing in soils and water bodies in otherwise pristine locations far from the source. Though 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 (ASTDR 1998).

Dioxins strongly adsorb to organic matter in so il and show very little vertical movement, particularly in soils with a high organic carbon content (ASTDR 1998). Most surface water deposition occurs through dry deposition from the atmosphere and from wind-transported eroded soil particles contaminated with dioxins. Most dioxins entering the water column will partition to bed sediment, which appears to be the primary environmental sink for this chemical group (EPA 2004b). Although dioxins bound to aquatic sediment will primarily be buried in the sediment compartment, some resuspension and remobilization of congeners may occur as a result of disturbance of sediments by benthic organisms (ASTDR 1998).

Bioaccumulation factors in fish are high for 2,3,7,8-TCDD as a result of the lipophilic nature of chlorinated dioxins. Though the processes by which freshwater fish accumulate dioxins are not well understood, it has been shown that fish and invertebrates both bioaccumulate congeners when exposed to contaminated sediments, and bioconcentrate congeners dissolved in water (EPA 2004b). However, because most dioxins in the aquatic environment are adsorbed to suspended particles, it is unlikely that direct uptake from the water is the primary route of exposure for most aquatic organisms at higher trophic levels (ASTDR 1998). At the lower trophic levels, the primary route of exposure appears to be through uptake of water in contaminated sediment pores, whereas the prim ary route of exposure in the higher trophic levels appears to be through food chain transfer. Following ingestion, some fish are capable of slowly metabolizing certain congeners, such as 2,3,7,8-TCDD, and releasing the polar metabolites in bile. This may ultimately limit bioaccumulation at higher trophic levels (ASTDR 1998). Measured bioaccumulation factors for 2,3,7,8-TCDD are not widely reported due to the difficulty of detecting trace levels of congeners in ambient water. Reported bioconcentration factors, which can be measured in laboratory conditions, but do not account for exposure

15 Group is commonly referred to as “dioxins;” this short hand is used in this section.Appendix 5 57 

### pathways other than direct uptake from water, range from 200-100,000, depending on the species and medium (ASTDR 1998).

Emission Profile

The 2,3,7,8-TCDD 

### de minimis emission rate used for the RTR screening scenario is 3.2E-08 TPY (based on lifetime cancer risk of 1-in-a-million). Although the annual concentration from year 50 is used for comparison, modeled concentrations of 2,3,7,8-TCDD either reached an approximate steady state or had begun to level off in all compartments by year 20.

Concentrations in Environmental Media

Measured ranges of total dioxin TEQ concentrations 16 

### in environmental media are displayed with modeled TEQ concentrations of 2,3,7,8-T CDD in Exhibit 4-13. Modeled concentrations and BAFs in fish compartments are shown in Ex hibit 4-14. The 2,3,7,8-TCDD concentrations output by the RTR screening scenario were consistent with reported dioxin TEQs in all environmental media compartments (Exhibit 4-13) . Because dioxin congeners are present at trace levels, analytical instruments must be very sensitive if congeners are to be detected in environmental media. As a result, limits of detec tion in many instruments may not be sufficiently sensitive to produce quantifiable results for very low levels of dioxin congeners. Many of the concentrations output by TRIM.FaTE are low enough to be considered nondetects by most analytical instruments.

Though the 2,3,7,8-TCDD concentrations in environm ental media estimated by TRIM.FaTE are generally low, they are not outside the range of plausible values when considered as a surrogate for the dioxin profile. In the screening scenario, the amount of chemical mass in the water column is small relative to the amount in the sediment. This is consistent with the trend that most congeners are removed from water bodies through adsorption to organic matter in soil and sediment. The screening scenario BAFs are also consistent with observed trends that indicate that 2,3,7,8-TCDD accumulates much less readily in herbivorous fish than in carnivorous fish that consume other contaminated organisms (ATSDR 1998).

16 It was not possible to confirm that TEQ concentrations reported in the literature an d summarized here were all estimated using the most recent TE F scheme adopted by the WHO in 1998. TEQ concentrations reported here may represent values determined using TEFs from EPA’ s 1989 scheme, WHO’s 1994 scheme, or WHO’s 1998 scheme (EPA 1997b).Appendix 558Exhibit 4-13. Summary of Modeled 2,3,7,8-TCDD Concentrations and Observed Total Dioxin TEQ Concentrations inEnvironmental Media0.00001 0.0001 0.001 0.01 0.1 1 10 100 1000North America (a)Stormwater Runoff (a)Rural, North America (a)Urban, North America (a)Industrial, Austria (a)Waste-to-Energy Facility, OH (a)Polluted River, Japan (a)North America (a)Manmade Lakes, MS (a)Industrial Area, Venice Lagoon (a)Rural NY (b)Southern MS Lakes (a)Non-background Sites, U.S. (a)Great Lakes (a)

Surface Water (ppq) Surface Soil (ppt dry wt.)
Sediment (ppt dry wt.)
Fish (ppt fresh wt.)2,3,7,8-TCDD Concentration (see units at left)Reported value was less than limit of detection (LOD = upper bound of bar)Value or range from literatureModeled concentration corresponding to de minimis threshold emission rateWCCBCTSUSBC = Benthic Carnivore, WCC = Water Column Carnivore, TS = Tilled Soil, US = Untilled SoilaSource:Exposure and Human Health Reassessment of 2,3,7,8-Tetrachlorodibenzo-p-Dioxin (TCDD) and Related Compounds(EPA 2004b)bSource:Toxicological Profile for Chlorinated Dibenzo-p-dioxins(ATSDR 1998)Appendix 5 59 

### Exhibit 4-14. TRIM.FaTE 2,3,7,8-TCDD Concentrations in Fish and Calculated Bioaccumulation Factors with Respect to Total Water Concentration

0.0E+00 5.0E-09 1.0E-08 1.5E-08 2.0E-08 2.5E-08 3.0E-08

Water Column Herbivore Benthic Omnivore Water Column Omnivore Benthic Carnivore Water Column CarnivoreConcentration in Fish(mg/kg wet weight)

0 80,650 161,300 241,950 322,600 403,250 483,900Bioaccumulation Factors(BAF)



### Partitioning Behavior

Chlorinated dioxins have been observed to partition mostly to soil when released to the environment, and sorbed congeners are unlikely to leach or volatilize out of the soil profile. In soils with a high organic carbon content, congeners below the top few millimeters of the soil are very strongly adsorbed to organic matter and exhibit very little migration. Most dioxins deposited onto soil are expected to remain buried in the soil profile, and erosion of contaminated soil particles is the only signific ant mechanism for transport to water bodies.

Because of the hydrophobic nature of the 2,3,7,8-TCDD congener, and its affinity for organic carbon, 70 to 80 percent of the congener is expected to bind to suspended organic particles in natural waters. The remainder in the water column is associated with dissolved organic substances (ASTDR 1998). Because most of the dioxins in water are in a sorbed state, the ultimate fate of most congener-laden particles is in the bed sediment. For the RTR screening scenario, more than 99.5 percent of 2,3,7,8-TCDD in the pond compartment was found in the sediment. The percentages of 2,3,7,8-TCDD in sorbed and dissolved states in surface water, sediment, and soil are presented in Exhibit 4-15.

Exhibit 4-15. Fraction of 2,3,7,8 - TCDD Mass Sorbed vs. Dissolved in TRIM.FaTE Compartments Compartment Sorbed Dissolved

Surface Soil 100.0% 0.0%

Surface Water 75.1% 24.9%

Sediment 100.0% 0.0%Appendix 5 60 

### The partitioning behavior exhibited 2,3,7,8-TCDD in TRIM.FaTE is consistent with observed trends. In the surface water compartment, approximately 75 percent of 2,3,7,8-TCDD was present in the sorbed state, which is within the reported 70-80 percent range reported in the literature. In the sediment and soil compartments, 100 percent of the congener is in the sorbed state, which is representative of the strong preference of dioxins for adsorption to soil and sediment organic matter.

Concentrations in Ingestible Products

The primary source of non-inhalation exposure to dioxins outside of occupational settings is through dietary intake, which accounts for over 90 percent of daily dioxin intake (ATSDR 1998). Available data indicate that dioxins concentra te in plants, aquatic organisms, and animals, offering multiple ingestion exposure pathways. However, actual congener levels in ingestible products can vary based upon type of food, agricultural and cultivation practices, atmospheric deposition rates, conditions in environmental media, and presence of other anthropogenic pollutants. Dioxins appear to enter the terre strial food chain prim arily through vapor phase deposition onto plant surfaces, 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 are the dominant exposure pathways, comprising 90 percent of dioxin dietary intake (ATSDR 1998). Though concentrations in vegetables are generally exceptionally low, root vegetables normally contain slightly higher concentrations of dioxins than vegetables that ar e affected primarily by atmospheric deposition, such as lettuce and peas (ATSDR 1998). The 2, 3,7,8-TCDD concentrations in ingestible product compartments are displayed in Exhibit 4-16.

Data for concentrations of dioxin congeners in ingestible products are not abundant and sophisticated analytical instruments with sufficiently sensitive limits of detection were not widely available for older studies, which likely resulted in a greater number of nondetects in samples. As a result, the data available for comparison was limited, but concentrations of 2,3,7,8-TCDD in ingestible products were generally consistent wi th the available dioxin TEQ values (Exhibit 4-16). As noted in the literature, the concentration in the fish compartment for the screening scenario was at least one order of magnitude greater than those 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 in the environment. The percent contributions of ingestion exposure pathways to the Lifetime ADD are displayed in Exhibit 4-17.

Consistent with trends reported in the literature, consumption of meat, fish, and dairy products contribute to over 90 percent of lifetime dioxin exposure in the screening scenario. Daily intakes of 2,3,7,8-TCDD from 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. However, some studies note that specific subpopulations, such as subsistence farmers and fishers, may have very different ex posure 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, dairy, and beef are appropriate within the context of this analysis.Appendix 5 61 

### Exhibit 4-16. Summary of Modeled 2,3,7,8-TCDD Concentrations and Observed Total Dioxin TEQ Concentrations in Ingestible Media

1.E-13 1.E-12 1.E-11 1.E-10 1.E-9 1.E-8 1.E-7 1.E-6 1.E-5 1.E-4 1.E-3 U.S. Samples (a) North America (a) Beef Products, U.S. (b) North America (a) Southern MS Samples (a) U.K. Samples (a) Fresh Fruit, U.K. (a) All Fruits, Average, Germany (a) Green Vegetables, U.K. (a) Non-green Vegetables, U.K. (a) Above-ground Vegetables, Germany (a) Rural NY (b) Southern MS Lakes (a) Non-background Sites, U.S. (a) Great Lakes (a) Pork Sausage, MS (a) U.S. Samples (a) North America (a)

Chicken, MS (a) Chicken, U.S. (a) North America (a) Fresh Fruit, U.K. (a) All Fruits, Average, Germany (a) Non-green Vegetables, U.K. (a) Above-ground Vegetables, Germany (a) Potatoes, UK (a) Average, Germany (a) Rural, North America (a) Urban, North America (a) Industrial, Austria (a) Waste-to-Energy Facility, OH (a) Dairy Products, Rural NY (b) Milk and Dairy, North America (a) Dairy Products, Southern MS (a)Beef EggsExp.FruitExposedVegetableFish Pork PoultryProt.FruitProt.Veg.RootSoil(mg/kg dry wt.)Dairy

2,3,7,8-TCDD Concentration (mg/kg wet wt.*) Reported value was less than limit of detection (LOD = upper bound of bar)
Value or range from literature
Modeled concentration corresponding to de minimis threshold emission rate

*except for soil, which is ex pressed as mg/kg dry weight.

a Source: Exposure and Human Health Reassessment of 2,3,7,8-Tetrachlorodibenzo-p-Dioxin (TCDD) and Related Compounds (EPA 2004b) b Source: Toxicological Profile for Chlorinated Dibenzo-p-dioxins (ATSDR 1998)Appendix 5 62 

### Exhibit 4-17. Contribution of Modeled Food Types to 2,3,7,8-TCDD Ingestion Exposures (mg/kg/day)

Eggs, Pork, & Poultry, 1.1E-13,

2% Soil, 2.5E-14,

<1% Fruits & Vegetables, 4.7E-14,

1%

Beef, 6.2E-13,

9%

Fish, 3.7E-12,

56% Dairy, 2.1E-12,

32%



### 4.3.3.4 PAHs (Benzo[a]pyrene)

Behavior in the Environment

Emissions of POMs are often reported in terms of unspeciated or partially speciated groups, such as total PAHs, because they are often found in the environment as mixtures of two or more compounds exhibiting comparable behavior and toxicity . Because there are relatively few data concerning the behavior and toxicity of individual PAH compounds, EPA has proposed assigning a relative potency factor to PAHs based on the relative toxicity of these compounds to the most carcinogenic PAH compound(s) (EPA 1993). Like TEFs, this allows for risks from exposure to certain PAH compounds that are likely carcinogens to be determined relative to the toxicity of other PAH compounds that have been identified as probable carcinogens. Benzo[a]pyrene, a high molecular weight PAH identified by EPA as a probable carcinogenic compound (possibly the most potent carcinogen of the PAH group), was used to represent PAHs in the screening scenario. A relative potency factor of 1 has been proposed by EPA for this compound (EPA 1993). However, because the relative potency index has not been widely adopted by the scientific community, modeled c oncentrations of benzo[a]pyrene are mostly compared to reported concentrations of this species, rather than to total PAHs. Data for benzo[a]pyrene comprises much of the avail able exposure information on carcinogenic PAHs for the last few decades.

PAHs can enter the atmosphere as a result of a variety of combustion processes, both natural and anthropogenic. Based upon reviewed literature, stationary emission sources account for approximately 80 percent of total annual PAH emissions. Though the primary producer of stationary source PAH emissions is thought to be residential wood burning, other processesAppendix 5 63 

### such as power generation; incineration; coal tar, coke, and asphalt production; and petroleum catalytic cracking are also major contributors (ASTDR 1995). PAHs emitted to the atmosphere can travel long distances in vapor form or atta ched to particles, or they can deposit relatively close to an emission source via wet or dry deposition onto water, soil, and vegetation. In the atmosphere, PAHs are found primarily in the particle-bound phase, and atmospheric residence time and transport distances are highly influenced by climatic conditions and the size of the particles to which they are bound (ASTDR 1995).

As a result of sustained input from anthropogenic combustion sources and other sources, PAHs are ubiquitous in soil. Like other high molecular weight PAHs, benzo[a]pyrene strongly adsorbs to organic carbon in soil, indicating that adsorption to soil particles will limit the mobility of these compounds following deposition to soil. Most PAHs enter the water column directly through atmospheric fallout (ASTDR 1995). Following deposition onto surface waters, approximately two-thirds of PAHs adsorb strongly to sediment and suspended particles, while only small amounts revolatilize back to the atmosphere (ASTDR 1995). Aquatic organisms may accumulate PAHs via uptake of water, sediment, or food. Though fish and other organisms readily take up PAHs from contaminated f ood, biomagnification generally does not occur because many organisms are capable of rapidly metabolizing them (ASTDR 1995). As a result, concentrations of PAHs have generally been observed to decrease with increasing trophic levels (ASTDR 1995). Based upon observed data, bioaccumulation factors in fish are also not expected to be especially high because fish are able to readily metabolize the compound. BCFs in the range of 10-10,000 have been reported for fish and crustaceans, with the higher end of the range attributable to greater accumulation of the higher molecular weight compounds, such as benzo[a]pyrene (ASTDR 1995). Higher BCFs have also been observed in species at lower trophic levels, and BAFs will likely be higher in fish as a result of increased exposure from diet. Additionally, sediment -dwelling organisms may experience increased exposure to PAHs through association (e.g., direct uptake and/or consumption) with contaminated sediment (ASTDR 1995).

Emission Profile

The benzo[a]pyrene 

### de minimis emission rate used for the RTR screening scenario is 2.3E-03 TPY (based on lifetime cancer risk of 1-in-a-million). Although the annual concentration from year 50 is used for comparison, modeled conc entrations benzo[a]pyrene either reached a steady state or had begun to level off in all compartments by year 10.

Concentrations in Environmental Media

Measured ranges of benzo[a]pyrene (and occasionally PAH-group concentrations) in environmental media are presented in Exhibit 4-18 with concentrations from the screening scenario. Modeled concentrations and BAFs in fish compartments are shown in Exhibit 4-19. The benzo[a]pyrene concentrations output by the RTR screening scenario were consistently lower than values reported in the literature. Three main factors likely contributed to this trend. These are the high background values resulting from ubiquitous nature of PAHs in the environment, limited availability of benzo[a]pyrene-specific data, and use of conservative exposure factors to calculate the 

### de minimis threshold. Firstly, due to the quantity of PAHs that are emitted from mobile sources (~20 percent), as well as stationary synthetic and natural sources, PAHs are typically present in the env ironment at relatively high background levels.Appendix 564Exhibit 4-18. Summary of Modeled and Observed Concentrations of Benzo[a]pyrene in Environmental Media1.E-05 1.E-04 1.E-03 1.E-02 1.E-01 1.E+00 1.E+01 1.E+02 1.E+03 1.E+04 1.E+05 1.E+06Great LakesUrban Runoff(Individual PAHs)Industrial Effluent(Individual PAHs)RuralAgriculturalUrbanContaminated SitesGreat LakesBoston Harbor(High Molecular Mass PAHs)Various Aquatic Organisms

Surface Water (ng/L)
Surface Soil (μg/kg dry wt.)
Sediment (μg/kg dry wt.)

Fish (mg/kg w wt)Benzo[a]pyrene Concentration* (see units at left)Reported value was less than limit of detection (LOD = upper bound of bar)Value or range from literatureModeled concentration corresponding to de minimis threshold emission rateWCCBCTSUS* Source of literature data:ATSDR Toxicological Profile for PolycyclicAromatic Compounds (PAHs) (ATSDR 1995). Data are for benzo[a]pyrene (BaP)exclusively, unless otherwise noted.Appendix 5 65 

### The concentrations output from the screening scenar io consider only emissions from a single facility. As a result, it is not unreasonable that some of the TRIM.FaTE outputs were several orders of magnitude smaller than those reported in the literature, as background exposure will be higher than incremental exposure. Secondly, though measured data for benzo[a]pyrene were used when available, concentrations in many media were available only for groups of PAHs or total PAHs. Thus, those ranges are not representative of benzo[a]pyrene alone and may contain values that are higher— and ranges that are wider— than those for a single chemical. Thirdly, it should be noted that in order to maintain a health-protective approach to screening emissions, a collection of moderately conservative exposure factors were used, which likely resulted in low, but not implausible values. For these reasons, we believe that the TRIM.FaTE outputs for concentrations of benzo[a] pyrene in environmental media are within the range of plausible values for this chemical. However, because of this discrepancy between the reported data and the screening scenario outputs, further investigation is necessary regarding site-appropriate biotransfer factors for facilities that do not pass the screen as a result of PAH emissions.

The screening scenario BAFs for benzo[a]pyrene in fish compartments were consistent with trends reported in the literature. BAFs for all but the WCH compartment were in the range of 5,500 to 11,500. No biomagnification of this chemical was exhibited in the fish compartments in the screening scenario, and the highest concentration of benzo[a]pyrene was in the WCH compartment, which represents the fish with the lowest trophic level evaluated in the scenario. The BAF for the WCH compartment in the screening scenario is approximately 28,000.

Partitioning Behavior Exhibit 4-19. TRIM.FaTE Benzo[a]Pyrene Concentrations in Fish and Calculated Bioaccumulation Factors with Respect to Total Water Concentration

0.0E+00 5.0E-06 1.0E-05 1.5E-05 2.0E-05 2.5E-05 3.0E-05 3.5E-05 4.0E-05 4.5E-05

Water Column Herbivore Benthic Omnivore Water Column Omnivore Benthic Carnivore Water Column CarnivoreConcentration in Fish(mg/kg wet weight)

0 3,372 6,744 10,116 13,488 16,860 20,232 23,604 26,976 30,348Bioaccumulation Factors(BAF)Appendix 5 66 

### Data suggest that benzo[a]pyrene partitions mainly to soil (82 percent) and sediment (~17 percent) following deposition to a 1km 2 

### area adjacent to an emission source (ASTDR 1995). Once in soil, PAHs can volatilize, undergo abiotic or biotic degradation, accumulate in plants, or remain sorbed to soil organic matter. High molecular weight PAHs, such as benzo[a]pyrene, tend to adsorb more strongly to organic carbon than PAHs having lower molecular weights (ASTDR 1995). As a result of this affinity for organic carbon, volatilization is not an important loss mechanism for benzo[a]pyrene from soils (ASTDR 1995). Because of its low solubility and affinity for organic carbon, most benzo[a]pyrene is expected to be particle-bound in natural waters. Less than one-third of PAHs in aquatic systems are generally present in the dissolved phase (ASTDR 1995). The remainder may be associated with suspended particles in the water column or particles that have settled on the bed sediment. Because most of the benzo[a]pyrene found in natural water is in a sorbed state, the ultimate fate of most contaminant-laden particles is burial in the bed sediment. For the RTR screening scenario, >98.8 percent of benzo[a]pyrene in the pond compartment partitioned to the benthic sediment. The percentage of benzo[a]pyrene in sorbed and dissolved states in soil, surface water, and sediment are presented in Exhibit 4-19.

Exhibit 4-20. Fraction of Benzo[a]Pyrene Mass Sorbed vs. Dissolved in TRIM.FaTE Compartments Compartment Sorbed Dissolved

Surface Soil 100.0% 0.0%

Surface Water 30.7% 69.3%

Sediment 100.0% 0.0%



### The partitioning behavior of benzo[a]pyrene in the screening scenario is generally consistent with trends reported in the literature. The presence of this chemical in the sorbed state in the soil and sediment compartments is consistent with reported trends. A different trend was observed in the surface water compartment where more of the chemical was estimated by TRIM.FaTE to be dissolved. The dissolved c oncentration in TRIM.FaTE is affected by suspended sediment concentration, organic carbon content, and suspended sediment deposition and resuspension rates. Additional evaluation may be required to determine the specific factors affecting this behavior.

Concentrations in Ingestible Products

The primary source of non-inhalation exposure to benzo[a]pyrene outside of occupational settings is through dietary intake. Exposure may depend equally on the origin of the food (higher values often recorded at contaminated sites) and the method of food preparation (higher values 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. However, these compounds are readily metabolized by higher trophic level organisms, including humans, so biomagnification is not cons idered to be significant (ASTDR 1995). Plants accumulate PAHs primarily through atmospheric deposition, but chemical concentrations tend to be below detection levels. In general, grains and cereals may contain slightly higher concentrations of benzo[a]pyrene than fruits and vegetables. PAHs in meat have also 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 concentrationsAppendix 5 67 

### 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 may be significantly higher than those output by MIRC. Measured concentrations of benzo[a]pyrene in ingestible products are presented in Exhibit 4-21 along with RTR screening scenario concentrations.

The RTR screening scenario concentrations were generally lower than—or at the low end of— 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 is 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 benzo[a]pyrene in ingestible products. The perc ent contributions of ingestion exposure pathways to the lifetime ADD for benzo[a]pyrene are displayed in Exhibit 4-22.

No single exposure pathway in the RTR screening scenario appears to drive human exposure to benzo[a]pyrene, but dairy, vegetables, and fruits are the three most dominant pathways. This is consistent with observations indicating that only low concentrations of this chemical are present in most ingestible products due to the ability of most plants and animals to metabolizing it. It is also consistent with data suggesting that biotransfer factors between soil and plants/animals are relatively low.Appendix 5 68 

### Exhibit 4-21. Summary of Modeled and Observed Concentrations of Benzo[a]pyrene in Ingestible Media

1.E-8 1.E-7 1.E-6 1.E-5 1.E-4 1.E-3 1.E-2 1.E-1 1.E+0 1.E+1 1.E+2 Cooked (Not Smoked or Grilled), U.S. (b) Grilled/Barbequed, U.S. (b) Beef Sausage (c) U.S. Samples (b) Italian Samples (a)

Various, U.S. (b) Apples (a) Leafy Greens, U.S. (b) Various, Italy (a) Spinach/Kale, Near Airport Runway (a)

Various (No Leafy Greens), U.S. (b) Smoked Fish, France (c) Trout and Cod (Unsmoked), Italy (a) Smoked Salmon (c) Smoked Pork, France (c) Pork, Italy (a) Pork Sausage (c ) Smoked Poultry, France (c) Chicken, Italy (a) Various, U.S. (b) Citrus Fruits, Italy (a) Corn, Italy (a) Various, U.S. (b) Various, U.S. (b) Potatoes, Italy (a)

Agricultural (c) Rural (c) Urban (c) Contaminated Sites (c) Dairy Products, U.S. (b) Dairy Products, Italy (a) Cheese (a)Beef EggsExp.FruitExposedVegetableFish Pork PoultryProt.FruitProt.Veg.RootSoil(mg/kg dry wt.)Dairy

Benzo[a]pyrene Concentration (mg/kg wet wt.*) Reported value was less than limit of detection (LOD = upper bound of bar)
Value or range from literature
Modeled concentration corresponding to de minimis threshold emission rate

* except for soil, which is ex pressed as mg/kg dry weight

a Source: Hazardous Substances Databank Record for Benzo[a]pyren e (HSDB 2005c) b Source: Analysis of 200 Food items for Benzo[a]pyrene and Estimati on of its Intake in an Epidemiologic Study (Kazerouni et al. 2001)

c Source: ATSDR Toxicological Profile for Pol ycyclic Aromatic Compounds (PAHs) (ATSDR 1995)Appendix 5 69 

### Exhibit 4-22. Contribution of Modeled Food Types to Benzo[a]pyrene Ingestion Exposures (mg/kg/day)

Soil, 4.2E-09,

8% Fish, 2.8E-09,

5% Eggs, Pork, & Poultry, 1.8E-09,

3%

Beef, 4.2E-09,

8%

Dairy, 1.6E-08,

29%

Vegetables, 1.1E-08,

20% Fruit, 1.5E-08,

27%



### 4.3.4 Summary

This analysis compared outputs from the RTR screening scenario (using 

### de minimis emission values) to observed data reported in the literature for cadmium, mercury, 2,3,7,8-TCDD, and benzo[a]pyrene. In general, most results from TRIM.FaTE do not appear to be unreasonable for a screening modeling approach based on this com parison to measured values. Briefly, the results of the evaluation are as follows:

• 

### Cadmium: Modeled concentrations in environmental media and ingestible products and behavior with respect to partitioning and bioaccumulation appear to be reasonable.

• 

### Mercury: Modeled concentrations in environmental media are comparable to levels for contaminated sites, and modeled concentrations in ingestible products are generally consistent with reported values. Speciation of mercury appears to be consistent with observed patterns in the environment.

• 

### 2,3,7,8-TCDD : Modeled concentrations in environmental media are consistent with observed TEQ values, and modeled concentrations in ingestible products are slightly lower than reported values, but still within a reasonable range. Both modeled values and measured values were primarily located in the noise around the limit of detection, which increases the uncertainty of the data.

• 

### Benzo[a]pyrene: Modeled concentrations in both environmental media and ingestible products are generally lower than those reported in the literature, in some cases by more than two orders of magnitude. Because this chemical is found in the environment at background levels that would far exceed concentrations resulting from single facility-Appendix 5 70 

### emissions, a comparison to measured concentrations is not as informative for this chemical.

Note that the exposure factors driving farm food chain ingestion rates are also conservative, and that these parameters were not considered in this evaluation, meaning that the “actual” concentration associated with a given risk leve l (if such a value could be calculated) would be higher than the level associated with the modeled 

### de minimis rate. Also, even “correct” model results may mask incorrect model assumptions that combine to produce compensating errors. Where results differ significantly from reported values, such as with benzo[a]pyrene, a more detailed investigation of underlying assumptions may be necessary to determine appropriate adjustments to the scenario configuration. As noted in the overview of this section, this type of evaluation cannot be used to verify model results.



### 4.4 Sensitivity Analyses

The hypothetical subsistence farmer/fisher expos ure scenario used to screen emissions of PBHAPs described in this document was parameterized using generally conservative inputs. The goal was to construct a modeling scenario that is sufficiently health-protective (i.e., conservative) such that it can be used with confidence to screen out emissions that do not pose unacceptable multipathway risks, while also avoiding overly conservative characteristics that diminish the functionality of the scenario (i.e., by allowing too many “false positives,” or facilities that fail the screen for which the risks are actually acceptable). The level of conservatism of the scenario is dictated largely by the values selected by the user for model inputs. A sensitivity analysis is a useful method for evaluating the influence of model parameters and user selections for parameter values. By provid ing quantitative information on the importance of model parameters to a selected output, a sensitivit y analysis thus also provides information on which parameters may be most influential in dictating the uncertainty associated with the results.

The sensitivity analyses conducted on the RTR screening modeling scenario encompassed the fate and transport modeling carried out using TRIM.FaTE and the farm food chain (FFC) and ingestion exposure modeling performed using MIRC. 17 

### A systematic sensitivity analysis was conducted on these parameters to obtain information regarding the relative importance of user inputs (Section 4.4.1). In addition, several other analyses were performed to evaluate model performance with respect to parameterization of ingestion rates (Section 4.4.2), body weight (Section 0), assumed relationships between ingestion and body weight over time (Section 4.4.4), and meteorological conditions (Section 4.4.5). Each section presents the methods employed to conduct the sensitivity analyses, evaluation results, and notes regarding interpretation of the results. Results of these sensitivity analyses are useful in informing the level of uncertainty in screening results, highlighting parameters and aspects of the modeling scenario worthy of additional research if refinement is appropriate (e.g., for evaluating sources of PB-HAPs that do not pass the screen), and suggesting variables that are likely to be worthy of more detailed examination if a more quantitative uncertainty analysis is desired (e.g., using probabilistic methods).

17 PB-HAP dose-response values used for risk calculation (i .e., oral cancer slope factors and oral reference doses, plus the mutagenicity correction factor applied to certain a ge groups) were not included in the sensitivity analysis.Appendix 5 71 

### 4.4.1 Systematic Sensitivity Analysis

4.4.1.1 Methods

As with all predictive models, the user-specified inputs for TRIM.FaTE and MIRC influence model outputs and thus estimation of 

### de minimis screening thresholds. The degree to which model outputs diverge from a nominally “correct ” answer (e.g., the actual incremental risks for an individual exposed to PB-HAP emissions) enc ompasses both uncertainty (because values are not known with perfect accuracy) and variability (because each value selected represents a discrete choice out of a range of possible parameter values within a distribution, such as variability in body weights among individuals in an exposed population). In this analysis, we did not attempt to separately evaluate uncertainty and variability, and variation in model outputs is assumed to encompass both uncertainty and variability. Representation of each parameter with a single fixed value without representing uncertainty or variability in model calculations is a limitation of deterministic model application.

As a first step to quantifying the combined uncertainty and variability in model outputs and the 

### de minimis thresholds, an analysis was performed to identify the variables to which the risk or hazard quotient calculations are most sensitive. Model sensitivity was evaluated separately for each of the PB-HAPs. The model output to which sensitivity was measured varied by chemical depending on the health impacts of greatest concern. Parameter influence on cancer risk was measured for benzo[a]pyrene and 2,3,7,8-TCDD, and influence on non-cancer hazard quotient was measured for cadmium, divalent mercury, an d methyl mercury. This part of the analysis was conducted systematically by “perturbing,” or changing, the value of each selected parameter independently (i.e., one at a time, holding all other inputs at their base value) by a certain percentage and calculating the corresponding percent change in the risk or hazard quotient value. This metric is referred to as elasticity (i.e., ratio of the percent change of the model output to percent change in the input variable), with higher elasticities corresponding to greater influence.

This type of systematic sensitivity analysis has the advantage of focusing on a wide range of inputs at once so that the variables can be ranked in order of importance. However, by perturbing the variables by fixed percentages, the analysis does not necessarily focus on the most physically relevant values of each parameter. An alternate analysis could examine each variable independently (taking into account the plausible range of input values) and look at the effects of using different plausible variable values on the risk or hazard quotient estimates. We focus on a systematic sensitivity analysis here with the goal of prioritizing variables; additional examinations may be helpful in the future to better define the uncertainty.

In the systematic sensitivity analysis, we estimated both local sensitivity, quantified as the elasticity when a parameter value is perturbed by a small percentage of its base (or default) value, and range sensitivity, for which parameters were varied by a larger percentage of the base value. These two elasticities can be different if the relationship between input and output is nonlinear (e.g., if the perturbed parameter is in the denominator of an equation in the calculation). For the evaluation of risks associated with exposures to PB-HAPs, a large difference between local and range sensitivity could indicate that variables that are less important in the base scenario used to calculate 

### de minimis thresholds scenario assumptions may be more important in a less conservative site-specific evaluation. In this case, following recommendations in the 

### Risk Assessment Guidance for Superfund (EPA 2001; see Volume III, Part A, Appendix A), we perturbed parameter values by +/-5% of the base case default value in the local sensitivity analysis and +/-50% in the range sensitivity analysis. Thus, with someAppendix 5 72 

### exceptions noted below, this resulted in four model simulations and corresponding elasticities for each parameter included in the analysis for each of the PB-HAPs.

Typically, a range sensitivity analysis (also re ferred to as “nominal sensitivity analysis”) measures the effect on model outputs across the entire expected or plausible range of values for a given input. This provides more compr ehensive information on the behavior of the model with respect to the input being varied by demonstrating the maximum potential influence of that input on model outputs. In the case of the RTR modeling scenario, information on the expected or most likely input ranges is available on only a few of the parameters defined by the user, and there is a relatively large number of parameters to be evaluated. The selection of both 5 and 50 percent as perturbation increments was intended to be an efficient compromise between conducting an authentic range sensitivity analysis and evaluating only more localized sensitivity.

To obtain a comprehensive estimate of the relative sensitivity of risk results across the range of numerical user inputs, as many TRIM.FaTE and MIRC inputs as possible were included in the analysis. To that end, all the MIRC ingestion and exposure variables were included. For TRIM.FaTE, properties assumed 

### a priori to have greater influence on model outputs were included. The large number of user-specified numerical inputs to TRIM.FaTE and computational limitations made inclusion of all TRIM.FaTE inputs in this sensitivity analysis impractical. TRIM.FaTE inputs for this sensit ivity evaluation were selected based on results obtained from previous TRIM.FaTE evaluations (including the TRIM.FaTE mercury test case; EPA 2005b) and professional judgment/intuition drawing on experience with the model.

The full set of inputs included in the systematic analys is is shown in Attachment 3, Exhibit 1. Parameters are grouped by the model in which they are used, with the MIRC variables further divided into farm food chain and ingestion/body weight categories. In some cases, inputs included in the analysis could not be increased by either 5% or 50% or both, since the variable has a physical upper bound which cannot be exc eeded (e.g., the number of days of exposure per year was already set to 365, so it cannot be increased by 5% or 50%). The footnotes in Attachment 3, Exhibit 1 indicate the variables that could not be increased by 5% and/or 50%. For some inputs, the ability to perturb the input depends on the PB-HAP being modeled, as indicated. In addition, some inputs were assigned a baseline value of zero for estimating 

### de minimis threshold (e.g., the empirical correction factor for protected vegetables and the exposure variables related to the water pathway). These variables are not included in the systematic sensitivity analysis. In all, approximately 240 variables were examined.

Two types of inputs included in the systematic analysis, pH and the moisture adjustment factor (MAF), received special treatment.

• 

### pH, which is a user input for the surface soil, root zone soil, surface water, and sediment compartments, is measured on a logarithmic scale, and this input is (in some cases) used in the exponent of TRIM.FaTE algorithms. To obtain sensitivity metrics for pH inputs that can be compared to results for other inputs with more linear relationships with risk, the values for pH were changed to the equivalent hydrogen ion concentration (that is, 10 pH 

### ) and that value was varied by 5 or 50%. Then, the log base ten of the varied hydrogen ion concentration was calculated to serve as the pH to use in TRIM.FaTE for the sensitivity

case.

• 

### The moisture adjustment factor indicates the percent of a produce item that is water; this factor is used to convert from wet weight to dry weight concentration in MIRC. This calculation is made by finding the percent of the produce that is not water (i.e., 100 minus MAF) and multiplying by the wet-weight basis concentration. Because the physicalAppendix 5 73 

### variable of interest is really the fraction of produce that is NOT water, this value (100 minus MAF) was used in the sensitivity anal ysis as the base value (rather than MAF itself).

In addition, in two cases, known or assumed correlations between inputs were taken into account in the systematic sensitivity analysis.

• 

### The fractions of T3 and T4 fish that are consumed must add to one in MIRC; thus, when one was changed by a given percentage, the other was also changed to ensure the sum was still one.

• 

### In parameterizing several sediment and water inputs used in TRIM.FaTE, soil/sediment and runoff/surface water balance is assumed. These relationships are not explicitly accounted for by TRIM.FaTE but rather were calculated off-line prior to setting the input value in the model. TRIM.FaTE variables s ubject to soil or sediment balances include erosion rate, precipitation rate, runoff rate, sediment deposition velocity, suspended sediment concentration, sediment porosity, water retention time in the pond, and the suspended sediment concentration. For exam ple, increasing the sediment deposition velocity results in an increase in the sediment resuspension velocity to ensure the sediment mass balance is preserved (the specific relationships assumed between inputs are described elsewhere). These correlations were respected in the systematic sensitivity analysis such that when an independent input was perturbed, any dependent input variables were altered correspondingly to preserve the water and sediment balances.

Other TRIM.FaTE and MIRC inputs are also related. Some of the relationships involving ingestion rates and body weight were evaluated in the context of the sensitivity analysis, as described in the sections that follow. In general, however, the current analysis did not endeavor to determine additional correlations or account for them in the sensitivity calculations conducted.

4.4.1.2 Results

Exhibit 4-23 through Exhibit 4-27 display the vari ables with the highest elasticities for each PBHAP. The inputs were sorted according to the elasticities estimated for a decrease in input by 5%. This case (as opposed to the case where variables were increased by 5%) was chosen because all inputs, including those for which the base case value was set at the maximum, could be part of the sorting procedure. Parameters were sorted by absolute value of elasticity, and the bars in the charts are color coded to distinguish whether the input is used in TRIM.FaTE, MIRC farm food chain calculations, or MIRC ingestion exposure calculations. The top 25 inputs are shown for all PB-HAPs; for some chemicals, additional inputs are included if more than one input corresponded to the 25 th 

### highest elasticity.

In general, mixing height, emission rate, and horizontal wind speed always appear near the top of the ranking for all PB-HAPs analyzed. The emission rate elasticity is always 1.00, indicating that a 5% reduction in the emission rate gives a 5% reduction in the risk or hazard quotient. The mixing height and horizontal wind speed elasticities are always negative and are either above or below 1.00. When the mixing height is decreased, the PB-HAP emissions spread over a smaller volume and more of the PB-HAP mass remains near the surface for deposition. When the horizontal velocity decreases, the PB-HAP remains within the model domain for a longer time and the concentrations in the model compartments increase accordingly.

A fourth input, sediment deposition/resuspension rate, has an elasticity greater than one in magnitude for methyl mercury. This sensitivity ca se represents a decrease in the user-specifiedAppendix 5 74 

### input for sediment deposition velocity by 5% and a concurrent 7% decrease in the resuspension velocity (calculated to maintain the sediment balance 18 

### ). This value is particularly important for methyl mercury HQ because methylation of mercur y occurs to a large degree in the sediment, and a decrease in deposition velocity results in a larger, proportionate decrease in the resuspension velocity, indicating a longer residence time in the sediment and a higher degree of methylation.

Only the above four inputs (mixing height, emis sion rate, horizontal wind speed, and sediment deposition) have absolute elasticities greater than or equal to one. Beyond these, the inputs with the highest elasticities are those that influence the primary exposure pathways for each PBHAP. Primary ingestion exposure pathways (in this case defined as the food categories that account for 75% to 85% of the total risk or hazard) are indicated at the top of each of the charts.

• 

### For benzo[a]pyrene (Exhibit 4-23), the primary pathways are ingestion of fruits, vegetables, and dairy products, and most of the inputs with the highest elasticities are used in farm food chain and ingestion exposure calculations. Because wet deposition is of particular importance, the exposed fruits and vegetables represent the more sensitive pathway compared with the protected fruits and vegetables. In addition, the analysis reveals that within the food chain diet of the dairy cows, risk is most sensitive to the inputs used to estimate chemical transfer via the cow’s forage pathway (as opposed to the silage or grain pathways).

• 

### Sensitivity results obtained for 2,3,7,8-TCDD (Exhibit 4-24) are consistent with the observation that the primary pathways are fish, beef, and dairy ingestion. Results also reinforce the conclusion that cancer risk is more sensitive to the forage pathway for dairy and beef than the grain or silage pathways.

• 

### Sensitivity results for cadmium (Exhibit 4-25) reflect the primary pathways of fruits, vegetables, and fish, but in this case the hazard quotient is more sensitive to the protected fruits and vegetables (and thus also the soil variables) than the exposed fruits and vegetables. The HQ is also more sensitive to variables affecting the T3 fish than the T4 fish, consistent with higher cadmium concentrations estimated by TRIM.FaTE for that fish type.

• 

### Elasticities for divalent mercury (Exhibit 4-26) reflect the primary pathways of soil, fruits, and vegetables through the importance of the rain and erosion variables and the protected fruit and vegetable variables. Finally, results for methyl mercury (Exhibit 4-27) are consistent with fish consumption as the primary pathway, with inputs specific to T4 fish more important than those specific to T3 fish due to the higher bioaccumulation potential (and therefore exposures) associated with higher trophic level fish.

Looking across the different PB-HAPs, several of t he TRIM.FaTE variables with high elasticities are highly dependent on assumptions made in configuring the TRIM.FaTE modeling scenario, including mixing height, horizontal wind speed, rain rate, dry deposition velocity, surface water retention time (correlated directly with pond depth and other inputs given the water balance that is assumed), and surface water temperature. These inputs, which would likely be set differently

18 The sediment balance maintains a zero net flux of sedi ment mass into the sediment at all times (i.e., a steady state) by balancing the deposition, resuspension, and burial fluxes. The burial flux is calculated by subtracting the amount of sediment flushed from the pond from the amount introduced into the pond via erosion at every time step. The deposition rate is specified. Then, t he resuspension rate is calculated by adding the burial and deposition fluxes to ensure no net flux into or out of the sediment.Appendix 5 75 

### for a site-specific analysis, represent influential parameters worthy of additional evaluation to quantify the conservative nature of the scenario. The area of the parcels and the temporal pattern of the precipitation may also be influential; however, these were not evaluated quantitatively in the systematic sensitivity analys is given the complexity involved with adjusting these inputs.

Differences between the results of the local and range sensitivity analyses are presented in Attachment 3, Exhibits 2 through 6. These tables show elasticities and elasticity rankings by PB-HAP for each input that appears in the top 25 elasticities for all four analyses (i.e., perturbation by -50%, -5%, 5%, and 50% of the base case value). The rankings for some inputs are somewhat different across the four cases, but no drastic differences are noted when comparing the range and local sensitivity analyses for any of the PB-HAPs. This suggests the local sensitivity analysis may be sufficient for drawing some conclusions about the relative influence of user-specified inputs on the risk and hazard quotient estimates.

The results of this sensitivity analysis indicate the 25-30 variables to which the risk and hazard quotient estimates are most sensitive. In proceeding with a probabilistic analysis which would quantify the uncertainty in the model estimates, these variables would be of primary importance and should be the focus when developing input variable distributions. Further research could also confirm that these variables are set in an appropriately conservative fashion for the purposes of developing a screening scenario.Appendix 576Exhibit 4-23. The 26 Variables with the Highest Elasticities for Benzo[a]Pyrene Lifetime Risk (-5% Perturbation of Variable)Primary Pathways: Fruits, Vegetables, and DairyMxng Ht, -1.05Emission Rate, 1.00Horiz Wind Spd, -0.97Annual Rain, 0.63Avg Wet Dep Particle, 0.45Frac Wet Dep, 0.45Mammal Metab Fctr, 0.41Yield (Frge), -0.36Intercept Frac (Frge), 0.34Biotrans Fctr (Dairy), 0.34Exp Fctr (Dairy), 0.34Frac Contam (Dairy), 0.34Avg Dry Dep Particle, 0.30Qty Frge Eaten (Dairy), 0.29Frac Frge Contm (Dairy), 0.29Plnt Srf Loss (Frge), -0.25Yield (Exp Frt), -0.23Exp Fctr (Exp Frt), 0.22Frac Contam (Exp Frt), 0.22Intercept Frac (Exp Frt), 0.21Plnt Srf Loss (Exp Frt), -0.19Yield (Exp Veg), -0.17100-Mst Adj Fctr (Exp Veg), 0.17Exp Fctr (Exp Veg), 0.17Frac Contam (Exp Veg), 0.17100-Mst Adj Fctr (Exp Frt), 0.22-2 -1.5 -1 -0.5 0 0.5 1 1.5 2Farm Food ChainTRIMIngestion/Body WeightAppendix 577Exhibit 4-24. The 25 Variables with the Highest Elasticities for 2,3,7,8-TCDD Lifetime Risk (-5% Perturbation of Variable)Primary Pathways: Fish, Beef, and DairyMxng Ht, -1.05Emission Rate, 1.00Horiz Wind Spd, -0.96Frac Contam (Fish), 0.56Exp Fctr (Fish), 0.56Body Weight, -0.45Chem Conc T4 Fish, 0.43Ing Rate (Fish), 0.43Mammal Metab Fctr, 0.43Water Temp, 0.39Water Org Carb Frac, -0.38Sed Dep and Resusp, 0.36Biotrans Fctr (Dairy), 0.32Exp Fctr (Dairy), 0.32Frac Contam (Dairy), 0.32Frac Fish T3, -0.30Frac Fish T4, 0.30Yield (Frge), -0.30Intercept Frac (Frge), 0.28Avg Dry Dep Particle, 0.28Suspended Sed Conc, -0.25Qty Frge Eaten (Dairy), 0.23Frac Frge Contm (Dairy), 0.23Plnt Srf Loss (Frge), -0.21Ing Rate (Dairy), 0.20Annual Rain, 0.20-2 -1.5 -1 -0.5 0 0.5 1 1.5 2Farm Food ChainTRIMIngestion/Body WeightAppendix 578Exhibit 4-25. The 28 Variables with the Highest Elasticities for Cadmium Hazard Quotient for Child 1-2 (-5% Perturbation ofVariable)Primary Pathways: Fruits, Vegetables, and FishMxng Ht, -1.03Horiz Wind Spd, -1.03Emission Rate, 1.00Annual Rain, 0.75Soil Conc RootZn Produce, 0.63Bioconc Fctr (Pro Frt), 0.39Ing Rate (Pro Frt), 0.39Exp Fctr (Pro Fruit), 0.39Frac Contam (Pro Frt), 0.39100-Mst Adj Fctr (Pro Frt), 0.39Body Weight, -0.28Erosion Rate, -0.27Water Temp, 0.27Ing Rate (Fish), 0.19Exp Fctr (Fish), 0.19Frac Contam (Fish), 0.19Surf Soil Frac Air, 0.17Loss 1 (Pro Frt), -0.16Chem Conc T3 Fish, 0.15Water Retention Time, 0.15Sed Dep and Resusp, 0.13100-Mst Adj Fctr (Exp Veg), 0.11Ing Rate (Exp Veg), 0.11Exp Fctr (Exp Veg), 0.11Frac Contam (Exp Veg), 0.11Frac Fish T3, 0.11Frac Fish T4, -0.11Surf Soil Org Carb Frac, 0.10-2 -1.5 -1 -0.5 0 0.5 1 1.5 2Farm Food ChainTRIMIngestion/Body WeightAppendix 579Exhibit 4-26. The 25 Variables with the Highest Elasticities for Divalent Mercury Hazard Quotient for Child 1-2 (-5%Perturbation of Variable)Primary Pathways: Soil, Fruits and VegetablesEmission Rate, 1.00Mxng Ht, -0.97Horiz Wind Spd, -0.97Erosion Rate, -0.77Annual Rain, 0.74Body Weight, -0.61Soil Conc Human Ing, 0.56Frac Contam (Soil), 0.56Ing Rate (Soil), 0.56Soil Conc RootZn Produce, 0.33Bioconc Fctr (Pro Frt), 0.15Ing Rate (Pro Frt), 0.15100-Mst Adj Fctr (Pro Frt), 0.15Exp Fctr (Pro Fruit), 0.15Frac Contam (Pro Frt), 0.15Surf Soil Frac Air, 0.13Bioconc Fctr (Root Veg), 0.11Ing Rate (Root Veg), 0.11100-Mst Adj Fctr (Root Veg),0.11Exp Fctr (Root Veg), 0.11Frac Contam (Root Veg), 0.11Correct Fctr (Root Veg), 0.11Soil Conc Animal Ing, 0.08Soil Bioav Fctr (Livestock), 0.08Exp Fctr (Soil), 0.56-2 -1.5 -1 -0.5 0 0.5 1 1.5 2Farm Food ChainTRIMIngestion/Body WeightAppendix 580Exhibit 4-27. The 26 Variables with the Highest Elasticities for Methyl Mercury Hazard Quotient for Child 1-2 (-5%Perturbation of Variable)Primary Pathway: FishBody Weight, -1.00Emission Rate, 1.00Ing Rate (Fish), 0.90Frac Contam (Fish), 0.90Exp Fctr (Fish), 0.90Mxng Ht, -0.70Horiz Wind Spd, -0.70Annual Rain, 0.64Chem Conc T4 Fish, 0.55Erosion Rate, -0.45Sed Porosity, -0.40Chem Conc T3 Fish, 0.36Water Temp, -0.22Frac Fish T3, -0.19Frac Fish T4, 0.19Suspended Sed Conc, -0.18Surf Soil Frac Air, 0.14Water Retention Time, 0.14Fish Mass All Species, 0.07Total Runoff, 0.07Soil Conc Human Ing, 0.05Ing Rate (Soil), 0.05Exp Fctr (Soil), 0.05Frac Contam (Soil), 0.05Soil Conc RootZn Prod, 0.04Sed Dep andResusp, 1.56-2 -1.5 -1 -0.5 0 0.5 1 1.5 2Farm Food ChainTRIMIngestion/Body WeightAppendix 5 81 

### 4.4.2 Evaluation of Ingestion Rate Assumptions

To calculate the 

### de minimis levels, the conservative assumption was made that a person ingests food at the 90 th 

### percentile level for each food type included in the analysis. These ingestion rates can then be added to get the assumed total ingestion rate. 19 

### USEPA 2008 and 2005d provide estimates of the 90 th 

### percentile total ingestion rate for different age groups (that is, percentiles for total amount of food ingested, rather than a sum of individual percentiles) based on CSFII data 1994-96 and 1998 (see Exhibit 6-12 of Attachment 2). When comparing the two, the 

### de minimis total ingestion rates tend to be a factor of two to three times higher than the 90 th 

### percentile total ingestion rate (see Exhibit 7-1 of Attachment 2). An alternative approach that still captures the high-end would be to use the 90 th 

### percentile ingestion rates for food types associated with highest risks for a given PB-HAP (to account for the individual who may happen to eat higher-than-typical amounts for each of those food categories) while setting ingestion rates for other food types to a lower percentile in order to bring the total ingestion rate to a more realistic level (e.g., to something closer to the 90 th 

### percentile total ingestion rate).

In order to determine the effect of lowering the ingestion rates for the non-primary ingestion pathways (and provide information on whether this refinement would be worth the additional complexity in the model set-up), ingestion rates were kept at the 90 th 

### percentile for the primary (risk-driving) food types for each PB-HAP but were reduced to median ingestion rates for the same population (subsistence famers) for the other types of food. This scenario, referred to here as the “alternate ingestion case,” was evaluated for each PB-HAP. Specifically, the total food ingestion rates was calculated for the alternate ingestion cases for each PB-HAP, and these values were compared with the USEPA 2008 and 2005d estimates of 90 th 

### percentile total ingestion (referred to as the “EPA total ingestion rate”) via a ratio (Exhibit 4-28). Because the EPA total ingestion rate represents the total amount of 

### cooked food consumed, preparation losses were applied to each of the food category-specific ingestion rates input into MIRC before the ratio was calculated. Soil consumption was not included in these calculations. In addition, the estimated 

### de minimis cancer risk or HQ for each PB-HAP was compared to the recalculated risk/HQ for the alternate ingestion cases (Exhibit 4-29).

As expected, the ratios presented in Exhibit 4-28 demonstrate that using the alternate ingestion rate combination tends to result in lower estimated total ingestion rates, approaching the (more realistic) USEPA total ingestion rates for the child age groups. The degree to which the total ingestion rate is decreased depends on the number of pathways labeled “primary pathways” for risk (as well as the mass of food represented by a given pathway). Thus, it is reasonable that lower total ingestion rates (and lower ratios with respect to EPA total ingestion rate) are calculated for methyl mercury, for which only one pathway (fish) drives risk, and higher rates and ratios are calculated for benzo[a]pyrene, for which several food types are important.

Exhibit 4-29 compares the lifetime risk or child age 1-2 HQ for the alternate ingestion rate case to the corresponding risk/HQ for the 

### de minimis case. Overall, the changes in risk and HQ were relatively modest, ranging between 7% and 20%. This result indicates that relaxing the conservative assumption of high ingestion rates for all the pathways does not significantly change the risk and hazard calculations but does drive the total ingestion rates closer to the USEPA 90 th 

### percentile total ingestion rates. However, implementing this multi-percentile technique requires prior knowledge of the dominant pathways for each chemical, which is determined primarily from the model estimates themselves. Assuming 90 th 

### percentile ingestion

19 This calculated total ingestion rate will not include any other food consumed by the homestead family outside of the MIRC food categories. In making the comparison with the to tal ingestion rates, then, the assumption is made that the entire diet of the family is c aptured by the MIRC food categories.Appendix 5 82 

### rates for all food categories has the advantage of simplicity (using the same set of ingestion rates for all chemicals) and does not dramatically impact the risk and hazard quotient estimates.

Exhibit 4-28. Ratio of the Modeled Total Ingestion Rates and the USEPA Total Ingestion Rates Ratio of Ingestion Rates
Child (1-2) Child (3-5) Child (6-11) Child (12-19) Adult (20-70)

Benzo[a]Pyrene Alternate Ingestion 1.7 1.3 1.4 1.4 2.6

2,3,7,8 - TCDD Alternate Ingestion 1.3 1.0 1.0 1.0 1.8

Cadmium Alternate Ingestion 1.3 1.1 1.2 1.0 1.6

Divalent Mercury Alternate Ingestion 1.3 1.1 1.1 1.0 1.6

Methyl Mercury Alternate Ingestion 0.9 0.8 0.8 0.6 0.8

de minimis Ingestion 1.8 1.4 1.6 1.5 2.8Appendix 5 83 

### Exhibit 4-29. Comparison of the Risks and Hazard Quotients in the de minimis and Alternate Ingestion Cases

PB-HAP Categories with 90th Percentile

Ingestion in Alternate Case a Categories with Median Ingestion Rates in Alternate Case a Case Value

Benzo[a]pyrene Pro. fruit, pro. veg., exp. fruit, exp. veg., root veg., and dairy Soil, fish, beef, pork poultry, and eggs de minimis lifetime risk 1.00E-06
Alternate ingestion case 7.98E-07
Percent Change in Risk -20.2%

2,3,7,8 - TCDD Fish and dairy Soil, pro. fruit, pro. veg., exp. fruit, exp. veg., root veg., beef, pork, poultry, and eggs de minimis lifetime risk 1.00E-06
Alternate ingestion case 9.14E-07
Percent Change in Risk -8.6%

Cadmium Fish, pro. fruit,
pro. veg., exp. fruit, exp. veg., and root veg. Soil, beef, dairy, pork, poultry, and
eggs de minimis child (1-2) hazard quotient 1.00E+00

Alternate ingestion case 9.05E-01
Percent Change in Risk -9.5%

Divalent Mercury Soil, pro. fruit,
pro. veg., exp. fruit, exp. veg., and root veg. Fish, beef, dairy, pork, poultry, and eggs de minimis child (1-2) hazard quotient 1.00E+00

Alternate ingestion case 9.26E-01
Percent Change in Risk -7.4%

Methyl Mercury Fish Soil, pro. fruit, pro. veg., exp. fruit, exp. veg., root veg., beef, dairy, pork, poultry, and eggs de minimis child (1-2) hazard quotient 5.62E-01

Alternate ingestion case 5.19E-01
Percent Change in Risk -7.8%

a Pro. fruit is protected fruits, pro. veg. is protected vegetables, exp. fr uit is exposed fruits, exp. veg. is exposed vegetables, and root v eg. is root vegetables.



### 4.4.3 Evaluation of Body Weight Assumptions

As stated in Attachment 2, Section 6.1.3, the 

### de minimis rates were calculated using mean body weight following EPA’s default screening recommendation. This assumption does not represent the most conservative assumption, however, since lower percentile body weights will give higher risk and hazard quotient estimates. In contrast, the ingestion rates were set to the more conservative 90 th 

### percentile level. To investigate the sensitivity of using alternate body weight percentiles, an alternate MIRC run was performed using the 5 th 

### , 50 th 

### , 90 th 

### , and 95 th 

### percentiles for body weight while keeping all other variable values as specified in the 

### de minimis calculations. These alternate risk or hazard quotient model estimates and the percent change relative to the 

### de minimis calculations are shown in Exhibit 4-30. Changing from the mean to the median body weights produces only very modest changes to the risks/hazard quotients. Changing to the 5 th 

### percentile body weights gives the most conservative model estimates, with percent changes of up to 22% for the PB-HAPs used to set the 

### de minimis rates and a 26% change in methyl mercury. Using upper perc entiles for body weights produces modest decreases in risk or hazard quotient of 3% to 16% for the PB-HAPs used to set the 

### de minimis rates. Thus, changing the body weight to either a more conservative or less conservative percentile produces changes in the risk or hazard quotient which are appreciable but notAppendix 5 84 

### dramatic (i.e., typically less than 25%), suggesting the mean body weight assumption may be an appropriate choice for the screening scenario.

Exhibit 4-30. The Risk or Hazard Quotient Estimates Using Alternate Body Weight Percentiles

de minimis 5th Percentile Median 90th Percentile 95th Percentile
Risk or

HQ a % Ch b Risk or HQ a % Ch b Risk or HQ a % Ch b Risk or HQ a % Ch b
Benzo[a]pyrene c 1.0E-06 1.1E-06 6% 1.0E-06 0% 9.7E-07 -3% 9.6E-07 -4% 2,3,7,8-TCDD c 1.0E-06 1.2E-06 22% 1.0E-06 2% 8.8E-07 -12% 8.4E-07 -16% Cadmium d 1.00 1.07 7% 1.00 0% 0.96 -4% 0.95 -5% Divalent Mercury d 1.00 1.16 16% 1.00 0% 0.91 -9% 0.89 -11% Methyl Mercury d 0.56 0.71 26% 0.57 1% 0.48 -15% 0.46 -18%

a HQ is the hazard quotient for a child aged 1-2.
b % Ch Is the percent change in the ri sk or hazard quotient relative to the de minimis calculations.

c Percent change in lifetime risk.
d Percent change in the hazard quotient for a child age 1 to 2.



### 4.4.4 Sensitivity When Accounting for Temporally Correlated Body Weight and Ingestion Rates

In calculating lifetime cancer risks, age-spec ific ingestion rates and body weights are used to estimate the lifetime average daily dose. In the systematic sensitivity analysis, the body weights and ingestion rates for each of the five age categories were changed independently to assess the sensitivity of lifetime risk to each input separately (for example, body weight was first perturbed only for a child 1-2 yrs old, leaving body weight unchanged for other age groups). However, in actuality, ingestion rates (and body weights) for an individual during different age groups will likely be correlated across that individual’s lifetime. For example, a person who eats higher-than-average amounts of poultry when they are 11 can be reasonably expected to eat higher-than-average amounts when they are 50 due to lifetime dietary preferences. To estimate the effect this correlation has on sensitivity of cancer risk to ingestion rate, a perturbation of -5% for a given food type was applied to ingestion rate for all age categories and the resulting elasticity was compared to the previous result (obtained ignoring temporal/age-group correlations). This assumption was applied separately to each ingestion rate category, and separately to body weight, to estimate the effect of age group correlations for these inputs (Exhibit 4-31). Because only benzo[a]pyrene and 2,3,7,8-TCDD were evaluated for cancer risks, this analysis was only performed for these two PB-HAPs (the other PB-HAPs use HQs calculated separately for each age group, and the correlation analysis is not applicable).Appendix 5 85 

### Exhibit 4-31. Comparison in the Elasticities In Lifetime Risk in the Correlated and Uncorrelated Analyses Assuming a 5% Decrease in the Input Variables

Elasticity in the Correlated

Analysis Maximum Elasticity in Uncorrelated

Analysis Age Group of Maximum Elasticity in Uncorrelated

Analysis

Benzo[a]pyrene Dairy Ingestion 0.34 0.11 Child 1-2 Exp. Fruit Ingestion 0.22 0.10 Adult Body Weight -0.17 -0.06 Child 1-2 Exp. Veg. Ingestion 0.17 0.09 Adult Soil Ingestion 0.12 0.05 Child 1-2 Beef Ingestion 0.07 0.03 Adult Fish Ingestion 0.04 0.02 Adult Poultry Ingestion 0.02 0.01 Adult Pro. Fruit Ingestion 0.01 < 0.01 Adult Egg Ingestion 0.01 < 0.01 Adult Pork Ingestion < 0.01 < 0.01 Adult Prot. Veg. Ingestion < 0.01 < 0.01 Adult Root Veg. Ingestion < 0.01 < 0.01 Adult

2,3,7,8 - TCDD Body Weight -0.59 -0.45 Adult Fish Ingestion 0.56 0.43 Adult Dairy Ingestion 0.32 0.20 Child 1-2 Beef Ingestion 0.09 0.06 Adult Pork Ingestion 0.01 0.01 Adult Soil Ingestion < 0.01 < 0.01 Adult Exp. Fruit Ingestion < 0.01 < 0.01 Adult Exp. Veg. Ingestion < 0.01 < 0.01 Adult Root Veg. Ingestion < 0.01 < 0.01 Adult Poultry Ingestion < 0.01 < 0.01 Adult Egg Ingestion < 0.01 < 0.01 Adult Pro. Fruit Ingestion < 0.01 < 0.01 Adult Prot. Veg. Ingestion < 0.01 < 0.01 Adult



### The systematic sensitivity analysis suggested that cancer risks associated with benzo[a]pyrene are relatively insensitive to body weight and any individual food ingestion rate (where both inputs were varied for a single age group at a time). When body weight or ingestion rate for a given food type is decreased by 5% for all age groups in concert, the corresponding elasticity increases by a factor of two to three. This increase is significant enough to cause the dairy ingestion, exposed fruit ingestion, body weight, and exposed vegetable ingestion variables to be ranked among the most influential variables on the basis of absolute elasticity.

For 2,3,7,8-TCDD, the systematic sensitivity analys is illustrated that cancer risk is sensitive to body weight, fish ingestion rate, and dairy ingestion rate. Elasticities for these inputs increase by a factor of 1.3 to 1.6 when the inputs are kept constant across age groups. This difference results in rise in the systematic sensitivity ranking of these variables.Appendix 5 86 

### Overall, however, these differences represent a modest percent change in the lifetime risk values and indicate that accounting for the assumed correlation between the body weights and ingestion rates throughout an individual's lifetime does not significantly influence the model's predicted risks. For the other ingestion rates, th e lifetime risks are not very sensitive to the parameters when the inputs are decreased by 5%, and accounting for the correlations between the variables does not affect this conclusion.

4.4.5 Comparison of Scenarios Using Site-Specific and de minimis Meteorological Data

For RTR, the 

### de minimis thresholds were calculated using a conservative and hypothetical exposure scenario for a farm homestead. If the modeling scenario has been configured as intended, the substitution of site-specific data will nearly always result in a lower (or equal) exposure and risk estimate. To inform the degree of conservatism associated with the baselinemeteorological inputs, three additional analyses were conducted in which observed (sitespecific) meteorology data were used in place of the meteorological values set for the 

### de minimis scenario. The three locations were selected to take advantage of readily-available TRIM.FaTE meteorological data. Exhibit 4-32 com pares some of the summary statistics for the three meteorological data sets with those used for the 

### de minimis screening scenario. 20



### Exhibit 4-32. Summary of Site-specific Meteorological Data Parameters

Parameter (units a ) de minimis Scenario b Site 1 c Site 2 c Site 3 c

Average air temperature (K) 298 291.9 284.7 283.5 Average horizontal wind speed (m/s) 2.8 4.4 4 3

Annual precipitation (m /yr) 1.5 1.1 1.1 1.1 Average urban mixing height (m) 710 1,087 1,225 861

a K = Kelvin, m/s = meters/second, m/yr = meters per year, m = meters b Values listed for the baseline scenario indicate the fixed value used in the baseline screening scenario.

c Site-specific values are arithmetic averages of single or multiyear data se ts; Site 1 = 1989–1993; Site 2 = 1994; Site 3 = 1990–1995.



### The results of using these three site-specific meteorological data sets on the risk or hazard quotient for each PB-HAP are summarized in Exhibit 4-33. The results indicate that the wind direction – and specifically how often the wind blows from the source toward the hypothetical watershed, or toward due east – is an important influence on the estimated media concentrations and ingestion exposures. For the three data sets used in this analysis, the largest decrease in risk or hazard quotient was observed for Site 1. This finding is consistent with the underlying patterns in wind direction and their relationship to the locations where exposure is assumed to occur. Exhibit 4-34 shows that wind direction towards due east occurs less than two percent of the time for this location, reinforcing the conclusion that a limited wind flow directly from the source to the wate rshed will decrease contamination. These results suggest that using meteorological data more representative of a specific site will decrease the estimated risk or hazard quotient by as much as an order of magnitude for the hypothetical receptor represented.

20 Exhibit 4-33 does not include a measure of the frequenc y and average of wind directions, which can be illustrated with a wind rose but cannot be effectively characterized with a single value.Appendix 5 87 

### Exhibit 4-33. Percent Change in Risk or Hazard Quotient Using Site-specific Meteorological Data

de minimis Site 1 Site 2 Site 3

Risk or

HQ a % Ch b Risk or HQ a % Ch b Risk or HQ a % Ch b

Benzo[a]pyrene c 1.00E-06 1.05E-07 -89% 2. 43E-07 -76% 2.19E-07 -78%
2,3,7,8-TCDD c 1.00E-06 8.39E-08 -92% 3. 76E-07 -62% 2.56E-07 -74%

Cadmium d 1.00 0.07 -93% 0.14 -86% 0.15 -85% Divalent Mercury d 1.00 0.08 -92% 0.19 -81% 0.18 -82% Methyl Mercury d 0.56 0.02 -97% 0.06 -90% 0.05 -91%

a HQ is the hazard quotient for a child aged 1-2.
b % Ch Is the percent change in the ri sk or hazard quotient relative to the de minimis calculations.

c Percent change in lifetime risk.
d Percent change in the hazard quoti ent for a child age 1 to 2.



### Exhibit 4-34. The Wind Speed and the Direction Toward Which the Wind is Blowing for All Conditions for Site 1

0% 2% 4% 6% 8% 10% 12% 14% 16% 18% 20%

0 - 22.5 22.5 - 45 45 - 67.5 67.5 - 90 90 - 112.5 112.5 - 135 135 - 157.5 157.5 - 180 180 - 202.5 202.5 - 225 225 - 247.5 247.5 - 270 270 - 292.5 292.5 - 315 315 - 337.5 337.5 - 360
Direction (degrees)Frequency 21+

17-21

11-16

7-10

4-6

1-3

0-1Appendix 5 88 

### 4.5 Comparison to Other Model Results

4.5.1 Comparison to Preliminary RTR Screening Runs (HHRAP Approach)

As another component of this evaluation, 

### de minimis emission rates calculated using the screening scenario were compared to those calculated for the preliminary RTR screening analyses carried out by EPA in the fall of 2006 (EPA 2006a) that use fate and transport algorithms presented in the HHRAP documentation (EPA 2005a). Similar to the current TRIMbased scenario, the modeling scenario evaluated in the preliminary RTR analyses was based on a hypothetical working homestead. The homestead was adjacent to the modeled source, with a 16-acre lake and 100 acres of tillable farm and pasture land. The lake was placed at the location of highest total deposition and the total watershed area was just over 200 acres.

Exhibit 4-35 summarizes a comparison of the emission thresholds for each approach. Thresholds from the current analysis are presented to two significant figures for the purposes of comparison only. The emission threshold for benzo[a]pyrene is lower for the current scenario than for the previous evaluation (i.e., the current scenario is more conservative), while those of cadmium and divalent mercury are higher for t he current runs. Because elemental mercury does not readily deposit and is largely blown out of the modeling domain into the air sinks in TRIM.FaTE, the threshold for divalent mercury is mo re relevant to this analysis. No threshold was available for dioxins for the preliminary HHRAP analysis. Additional refinements, analyses and comparisons described in this section support the current 

### de minimis emission thresholds.

Exhibit 4-35. Emission Thresholds Derived in Preliminary HHRAP Screening Runs and in Current Analyses

Chemical Basis of Threshold Emission Thresholds (TPY) Comparison Current Analysis Preliminary RTR Analysis
2,3,7,8-TCDD Cancer 3.18E-08 NA NA Benzo[a]pyrene Cancer 2.26E-03 2.2E-02 Preliminary threshold higher by ~10x Cadmium Non-cancer 6.54E-01 1.7E-01 Current threshold higher by ~4x Divalent Mercury Non-cancer 1.64E-01 5.4E -03 Current threshold higher by ~30x
NA = not available; threshold was not calculated for dioxins in 2006.



### 4.5.2 Comparison of Results for Screening Scenario and Previous TRIM.FaTE Applications

To obtain another estimate of the degree of conservatism associated with results from the screening scenario, a comparison run was perfo rmed using the TRIM.FaTE scenario developed for a secondary lead smelting facility pr eviously evaluated for a TRIM to IEM 21 

### model comparison in the state of New York (ICF 2004). Results for the current comparison were obtained for benzo[a]pyrene; 2,3,7,8-tetrachlorodibenzo-p-dioxin; and elemental, divalent, and methyl mercury by running the screening scenario with the emission rates for these chemicals that were used in the previous secondary lead smelting application. The annually averaged results for the 30 th 

### year of the screening scenario were compared to annual average concentrations using results from years 28 through 32 from the secondary lead smelting

21 IEM is the Indirect Exposure Methodology that is no w referred to as the Multip le Pathways of Exposure Methodology (U.S. EPA 1999d)Appendix 5 89 

### application. 22 

### No adjustments to the screening or the New York (refined) scenario were implemented; thus, it should be recognized that the results of this comparison are particular to the inputs selected for the New York site TRIM.FaTE application.

Specifications of the New York site application are presented in the model comparison report and are not included here. In general, no comparisons of the values used for the screening and New York site TRIM.FaTE scenarios were conducted. However, because meteorological properties are highly influential on concentrations in all compartments, the characteristics of the New York meteorological inputs are summarized here. Five years of meteorological data collected at a station in Allentown, Pennsylvania were used for the New York application; average values for key properties are summarized in Exhibit 4-36.

Exhibit 4-36. Meteorological Data Parameters for TRIM.FaTE Secondary Lead Smelting Application
Meteorological Property Average Value Used for New York Site Application
Air temperature 284.69 K Horizontal wind speed 3.97 m/s Precipitation rate 1.14 m/yr Urban mixing height 1,224 m Wind direction (overall) Blows pr edominantly from the southeast Wind direction (during rain events) Bl ows predominantly from the southwest



### Media concentrations for air, surface soil, lake surface water, lake benthic sediment, and water column fish from the two applications were compared collectively (i.e., using the mean of all compartments of a single type) and, for soil and air, according to approximate distance from the source. For the latter comparisons, air and soil parcels were grouped into “nearby” and “distant” subgroups. Nearby parcels were those situated within about 2 km of the source, and the remaining parcels are distant. Parcel groupings for air and surface soil output comparisons are presented in the figures that follow. Results for a lake near the source in the New York application were used for comparison to results for water, sediment, and fish in the screening scenario.

22 Averaging results over 5 years of data minimizes bias in troduced by any 1 year of meteorological conditions (5 years of repeating data were used for the New York site application).Appendix 5 90 

### Exhibit 4-37. Surface Soil Parcel Spatial Layouts for New York Site Lead Smelting TRIM.FaTE Application and Screening ScenarioAppendix 5 91 

### Exhibit 4-38. Air Parcel Spatial Layouts for New York Site Lead Smelting TRIM.FaTE Application and Screening ScenarioAppendix 5 92 

### The screening and refined results are compared in Exhibit 4-39. This table shows the ratios of the concentrations for the screening scenario to the concentrations from the New York site application; thus, ratios greater than 1 indicate that screening scenario concentrations are higher than the lead smelting application concentra tions. Exhibit 4-40 graphically illustrates selected concentration ratios.

Exhibit 4-39. Comparison of Concentration Outputs: NY Site Refined TRIM.FaTE Application vs. Screening Scenario

Output a Concentration Ratio of Screening Scenario to Full Lead Smelting Run Benzo[a]

Pyrene 2,3,7,8- TCDD Divalent Mercury Elemental Mercury b Methyl Mercury

Overall Air Mean 15.1 12.8 13.0 12.6 16.4 Nearby Air Mean 15.8 13.8 13.8 13.7 17.0
Distant Air Mean 6.6 6.2 5.7 6.1 8.2
Overall Surface Soil Mean 19.4 3.9 10.4 - 10.3 Nearby Surface Soil Mean 18.9 3.6 9.1 - 9.0
Distant Surface Soil Mean 8.6 2.7 7.6 - 7.6
Lake Surface Water 17.0 6.0 12.1 28.7 5.7
Lake Sediment 52.2 10.3 6.6 12.2 7.0
Lake WC Carnivore 19.4 12.6 2.4 - 1.9
Lake WC Herbivore 10.5 0.8 2.1 - 1.4
Lake WC Omnivore 18.0 2.1 2.3 - 1.6

a “Overall Air” and “Overall Surface Soil” include all air an d surface soil parcels, except for the source parcel. “Nearby” parcels include those within about 2 km of the s ource; “Distant” parcels inclu de those greater than about 2 – 3 km from the source (i.e., all non-source parcels not classified as Nearby). Refer to Exhibit 7-14 and Exhibit 7-15 for details. The source parcel was excluded from all co mparisons. “Lake” is the lake for the New York site application, and the pond for the screening scenario.
b Because elemental mercury is largely blown out of the modeling domain into the air sinks in TRIM.FaTE, concentrations of elemental mercury are not examined here in soil or in water column fish.



### In all media categories, the screening scenario produces greater concentrations than does the lead smelting application. This result is expected because the screening scenario is designed to be health-protective and therefore tends more towards a high-end scenario with regard to media concentrations. The differences are generally larger for air results and smaller for concentrations in fish.

On the whole, the greatest difference between the concentrations in the screening scenario and those in the lead smelting application occurs with benzo[a]pyrene, especially with regard to concentrations in fish and lake sediment. The largest difference observed across all comparisons is the benzo[a]pyrene in lake sedimen t. Outputs were also substantially different for elemental mercury in the surface water and benzo[a]pyrene in water column fish.Appendix 5 93 

### Exhibit 4-40. Comparison of Concentration Outputs Grouped By Chemical: New York Site Refined TRIM.FaTE Application vs. Screening Scenario

0 10 20 30 40 50 60

BaP TCDD Divalent Mercury Elemental Mercury Methyl Mercury

ChemicalConcentration Ratio

Overall Air Mean Overall Surface Soil Mean Lake Surface Water
Lake Sediment Lake WC Carnivore Lake WC Herbivore Lake WC OmnivoreAppendix 5 94 

## 5 References

Agency for Toxic Substances and Disease Regist ry (ATSDR). 1995. Toxicological profile for polycyclic aromatic hydrocarbons. Atlanta, GA: U.S. Department of Health and Human Services, Public Health Service.

Agency for Toxic Substances and Disease Regist ry (ATSDR). 1998. Toxicological profile for chlorinated dibenzo-p-dioxins. Atlanta, GA: U. S. Department of Health and Human Services, Public Health Service.

Agency for Toxic Substances and Disease Regist ry (ATSDR). 1999b. Toxicological profile for Mercury. Atlanta, GA: U.S. Department of He alth and Human Services, Public Health Service.

Agency for Toxic Substances and Disease Regist ry (ATSDR). 2007. Toxicological profile for Lead. Atlanta, GA: U.S. Department of Health and Human Services, Public Health Service.

Agency for Toxic Substances and Disease Regist ry (ATSDR). 2008. Toxicological profile for Cadmium. Atlanta, GA: U.S. Department of Health and Human Services, Public Health Service.

Alpers, C.N., A.R. Stewart, M.K. Saiki, M.C. Marvin-DiPasquale, B.R. Topping, K.M. Rider, S.K. Gallanthine, C.A. Kester, R.O. Rye, R.C. Antweiler, and J.F De Wild. 2008. Environmental factors affecting mercury in Camp Far West Reservoir, California, 2001–03. U.S. Geological Survey Scientific Investigations Report 2006-5008, 358p.

Calabrese, E.J., E.J. Stanek, P. Pekow, R.M. Barnes. 1997. Soil ingestion estimates for children residing on a Superfund site. Ecotox icology and Environmental Safety. 36:258-268.

California Air Resources Board (CARB). 2007. CADAMP Monitoring Sites. http://www.arb.ca.gov/pub/dioxin/cadamp.php

California Environmental Protection Agency (CalEPA) Office of Environmental Health Hazard Assessment (OEHHA). 2000. Air Toxics Hot S pots 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.

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 bi omagnification in nature. Limnology and Oceanography 50(5):1511-1519.

Hazardous Substances Data Bank (HSDB). 2005a. Bethesda, MD: National Library of Medicine, U.S. [Last Revision Date 06/23/2005]. Cadmium Compounds; Hazardous Substances Databank Number: 6922. Available at: http://toxne t.nlm.nih.gov/cgi-bin/sis/htmlgen?HSDB

Hazardous Substances Data Bank (HSDB). 2005b. Bethesda, MD: National Library of Medicine, U.S. [Last Revision Date 06/23/2005]. Mercury Compounds; Hazardous Substances Databank Number: 6943. Available at: http://toxne t.nlm.nih.gov/cgi-bin/sis/htmlgen?HSDBAppendix 5 95 

### Hazardous Substances Data Bank (HSDB). 2005c. Bethesda, MD: National Library of Medicine, U.S. [Last Revision Date 06/23/2005]. Benzo(a)pyrene; Hazardous Substances Databank Number: 2554. Available at: http://toxne t.nlm.nih.gov/cgi-bin/sis/htmlgen?HSDB

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. Regul atory Toxicology and Pharmacology 33:60-65.

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.

Hrudey, S.E., W. Chen and C.G. Roussex, 1996. Bioavailability in environmental risk assessment. CRC Press, Inc, Lewis publishers.

ICF International (ICF). 2004. TRIM.FaTE Application for Two Secondary Lead Smelting Facilities and TRIM.FaTE-IEM Model Comparison. Revised Draft Report. Submitted to Terri Hollingsworth, Work Assignment Manager, EPA/OAQPS. June.

ICF International (ICF). 2005. Memorandum: TRIM.FaTE Screening Scenario: Aquatic Food Web Analysis; submitted to Deirdre Murphy and Te rri Hollingsworth, U.S. EPA, from Margaret McVey and Rebecca Kauffman, ICF Consulting. October 18.

International Union of Pure and Applied Chemistr y (IUPAC). 1993. Glossary for Chemists of Terms Used in Toxicology. IUPAC, Clinical Chemistry Division, Commission on Toxicology (IUPAC Recommendations 1993). Accessed online via the U.S. National Library of Medicine website at http://sis.nlm.nih.gov/enviro/glo ssarymain.html; last accessed October 2008.

Kazerouni, N., R. Sinha, C.-H. Hsu, A. Greenberg, and N. Rothman. 2001. Analysis of 200 food items for benzo[a]pyrene and estimation of its intake in an epidemiologic study. Food and Chemical Toxicology 39: 423-436.

Lorber, M.N., D.L.Winters, J. Griggs, R. Cook, S. Baker, J. Ferrario, C. Byrne, A. Dupuy, and J. Schaum. 1998. A national survey of dioxin-like compounds in the United States milk supply. Organohalogen Compounds 38:125-129.

Mason RP, 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 statespecific landscape data sets for multimedia source-to-dose models. University of California at Berkeley. Supported by U.S. Environment al 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.

National Climatic Data Center (NCDC). 1995. Hourly United States Weather Observations (HUSWO) 1990-1995.Appendix 5 96 

### Ramos, L., E. Eljarrat, L.M. Hernandez, L. Alonso, J. Rivera, and M.J. Gonzalez. 1997. Levels of PCDDs and PCDFs in farm cow’s milk located near potential contaminant sources in Asturias (Spain). Comparison with levels found in control, rural farms and commercial pasteurized cow’s milk. Chemosphere 35(10):2167-2179.

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, 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.

Schmid, P., E. Gujer, M. Zennegg, and C. Studer. 2003. Temporal and local trends of PCDD/F levels in cow’s milk in Switzerland. Chemosphere 53:129-136.

Stull, R.B. 1988. An Introduction to Boundary Layer Meteorology. Kluwer Academic Publishers, The Netherlands. 452 pp.

U.S. Environmental Protection Agency (EPA). 1989. Review of National Ambient Air Quality Standard for Lead: Exposure Analysis Methodology and Validation. EPA-450/2-89-011. Research Triangle Park, NC: Office of Air Quality Planning and Standards; June. Available at: http://nepis.epa.gov/

U.S. Environmental Protection Agency (EPA). 1 992a. National Study of Chemical Residues in Fish, Volume I. EPA 823-R-92-008a. Washington, D.C.: Office of Science and Technology; September. Available at: http://www.epa.gov /waterscience/library/fish/residuevol1.pdf.

U.S. Environmental Protection Agency (EPA) 19 92b. Dermal Exposure Assessment: Principles and Applications. EPA/8-91/011B. Available at: http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=12188.

U.S. Environmental protection Agency (EPA). 1 993. Provisional Guidance for Quantitative risk Assessment of Polycyclic Aromat ic Hydrocarbons. Office of Research and Development, Office of Health and Environmental Assessment, Washington, DC. EPA/600/R-93/089. Available at: http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=49732.

U.S. Environmental Protection Agency (EPA). 1994. Technical Support Document: Parameters and Equations Used in the Integrated Exposure Uptake Biokinetic Model for Lead in Children (v.099d). EPA 540/R-94/040; OSWER-9285.7-22. Office of Solid Waste and Emergency Response; December. Available at: http://www.epa.gov/superfund/lead/products/tsd.pdf.

U.S. Environmental Protection Agency (EPA). 1997a. Exposure Factors Handbook, Volume II, Food Ingestion Factors. EPA/600/P-95/002Fb. Office of Research and Development, Washington, DC. August.

U.S. Environmental Protection Agency (EPA). 1997b. 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.

U.S. Environmental Protection Agency (EPA). 1999. Short Sheet: IEUBK Model Soil/Dust Ingestion Rates. EPA-540-F-00-007; OSWER-9285.7-33. Washington, D.C.: Office of SolidAppendix 5 97 

### Waste and Emergency Response; December. Available at: http://www.epa.gov/superfund/lead/products/ssircolo.pdf.

U.S. Environmental Protection Agency (EPA). 2001. Risk Assessment Guidance for Superfund: Volume III - Part A, Process for Conducting Probabilistic Risk Assessment. Appendix A: Sensitivity Analysis: How Do We Know What’s Important? EPA 540-R-02-002. December. http://www.epa.gov/oswer/riskassessment/rags3adt/pdf/appendixa.pdf.

U.S. Environmental Protection Agency (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.

U.S. Environmental Protection Agency (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.

U.S. Environmental Protection Agency (EPA). 2002b. Evaluation of TRIM.FaTE, Volume III: Model Comparison Focusing on Dioxin Test Case. EPA-453/R-04-002. Office of Air Quality and Planning Standards: Research Triangle Park, NC. December.

U.S. Environmental Protection Agency (EPA). 2002d. 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.

U.S. Environmental Protection Agency (EPA) . 2003. Multimedia, Multipathway, and Multireceptor Risk Assessment (3MRA) Modeling System, Volume II: Site-based, Regional, and National Data. SAB Review Draft. EP-530/D-03-001b. Office of Research and Development, Athens, GA, and Research Triangle Park, NC, and Office of Solid Waste, Washington, DC. July. Available at: http://www.epa.gov/epaoswer/hazwaste/id/hwirwste/risk03.htm.

U.S. Environmental Protection Agency (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.

U.S. Environmental Protection Agency ( EPA). 2004b. 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/

U.S. Environmental Agency (EPA). 2004c. Risk Assessment Guidance for Superfund Volume 1: Human Health Evaluation Manual (Part E, Supplemental Guidance for Dermal Risk Assessment). EPA/540/R99/005. Available at: http://www.epa.gov/oswer/riskassessment/ragse/index.htm.

U.S. Environmental Protection Agency (EPA) . 2005a. Human Health Risk Assessment Protocol for Hazardous Waste Combustion Facilities (including the Hazardous WasteAppendix 5 98 

### Companion Database of chemical-specific paramete r values). U.S. Environmental Protection Agency, Office of Solid Waste and Emergency Response, Washington, DC. EPA-530-R-05- 006. September.

U.S. Environmental Protection Agency (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.

U.S. Environmental Protection Agency (EPA). 2005c. Supplemental Guidance for Assessing Susceptibility from Early-Life Exposure to Carcinogens. EPA-630/R-03-003F. Risk Assessment Forum: Washington, DC. March.

U.S. Environmental Protection Agency (EPA). 2005d. 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 andDevelopment, National Center for Environmental Assessment, Washington, D.C. EPA/600/R05/062F. Available at: http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=132173.

U.S. Environmental Protection Agency (EPA) . 2006a. Risk and Technology Review (RTR) Assessment Plan. Office of Air and Radiation. November 20, 2006. Available at: http://www.epa.gov/sab/panels/consul_ri sk_and_tech_assessment_plan.htm.

U.S. Environmental Protection Agency (EPA). 2006b. Air Quality Criteria for Lead (Final). Office of Research and Development. EPA-600/R-05-44aF and -bF. Washington, DC. October.

U.S. Environmental Protection Agency (EPA). 2006c. Air Toxics Risk Assessment Reference Library; Volume 3 - Community Scale Assessment; Part II, Multisource Cumulative Human Health Assessment: Inhalation. Chapter 3, Overview of a Human Health Multisource Cumulative Inhalation Assessment. Office of Air Quality Planning and Standards, Research Triangle Park, NC. April. Available at: http://www.epa.gov/ttn/fera/data/risk/vol_3/Chapter_03_April_2006.pdf.

U.S. Environmental Protection Agency (EPA) . 2006d. Child-Specific Exposure Factors Handbook, External Review Draft. EPA-600-R-06-096A. National Center for Environmental Assessment, Office of Research and Development; September. Available at: http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=56747

U.S. Environmental Protection Agency (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 .

U.S. Food and Drug Administration (FDA). 2007. Total Diet Study Statistics on Elemental Results (original samples), Revision 4.1, Market Baskets 1991-3 through 2005-4. Center for Food Safety and Applied Nutrition, College Park, MD. December 11, 2007.

U.S. Geological Survey (USGS). 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.

Wischmeier, W.H., and D. Smith. 1978. Predicting Rainfall Erosion Losses: A Guide to Conservation Planning. USDA-ARS Agriculture Handbook No. 537, Washington, D.C. 58 pp.

### Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR: Summary of Approach and Evaluation



# ATTACHMENT 1: TRIM.FaTE InputsAttachment 1 to Technical Support Document for TR IM-Based Multipathway Screening Scenario for RTR 1-i 

### [This page intentionally left blank.]Attachment 1 to Technical Support Document for TR IM-Based Multipathway Screening Scenario for RTR 1-ii 

### TABLE OF CONTENTS

1 TRIM.FaTE Inputs ............................................................................................................ .........1

1.1 Introduction ............................................................................................................. ..............1

Exhibit 1. TRIM.FaTE Simulation Parameters ............................................................................. 2

Exhibit 2. Meteorological Inputs ............................................................................................... .....3

Exhibit 3. Air Parameters ..................................................................................................... ........4

Exhibit 4. Soil and Groundwater Parameters ...............................................................................5

Exhibit 5. Runoff Assumptions ................................................................................................. ....8

Exhibit 6. USLE Erosion Parameters ...........................................................................................9

Exhibit 7. Terrestrial Plant Placement......................................................................................... 10

Exhibit 8. Terrestrial Plant Parameters ......................................................................................1 1

Exhibit 9. Surface Water Parameters .........................................................................................14

Exhibit 10. Sediment Parameters ..............................................................................................1 5

Exhibit 11. Aquatic Plant Parameters ........................................................................................16

Exhibit 12. Aquatic Animals Food Chain, Density, and Mass ....................................................17

Exhibit 13. Cadmium Chemical-Specific ....................................................................................18

Exhibit 14. Mercury Chemical-Specific Properties .....................................................................29

Exhibit 15. PAH Chemical-Specific Properties ...........................................................................20

Exhibit 16. Dioxin Chemical-Specific Properties ........................................................................21

Exhibit 17. Cadmium Chemical-Specific Properties for Abiotic Compartments .........................24

Exhibit 18. Mercury Chemical-Specific Properties for Abiotic Compartments ............................25

Exhibit 19. PAH Chemical-Specific Properties for Abiotic Compartments .................................29

Exhibit 20. Dioxin Chemical-Specific Properties for Abiotic Compartments ...............................31

Exhibit 21. Cadmium Chemical-Specific Properties for Plant Compartments ............................35

Exhibit 22. Mercury Chemical-Specific Properties for Plant Compartments ..............................36

Exhibit 23. PAH Chemical-Specific Properties for Plant Compartments ....................................38Attachment 1 to Technical Support Document for TR IM-Based Multipathway Screening Scenario for RTR 1-iii 

### Exhibit 24. Dioxin Chemical-Specific Properties for Plant Compartments .................................39

Exhibit 25. Cadmium Chemical-Specific Properties for Aquatic Species ...................................40

Exhibit 26. Mercury Chemical-Specific Properties for Aquatic Species .....................................41

Exhibit 27. PAH Chemical-Specific Properties for Aquatic Species ...........................................42

Exhibit 28. Dioxin Chemical-Specific Properties for Aquatic Species ........................................43Attachment 1 to Technical Support Document for TR IM-Based Multipathway Screening Scenario for RTR 1-1 

## 1 TRIM.FaTE Inputs

1.1 Introduction
This attachment provides the tables of the detailed modeling inputs for the TRIM.FaTE screening scenario. Exhibit 1 presents runtim e settings for TRIM.FaTE. Exhibits 2 and 3 present air parameters entered into the model. Exhibits 3 through 8 present the terrestrial parameters. Exhibits 9 through 12 present the lake parameters, and 13 through 28 present the chemical specific parameters.Parameter Name Units V alue 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.

Out p ut time ste p a hr 4 Selected value. Exhibit 1. TRIM.FaTE Simulation Parameters for the TRIM.FaTE Screening Scenario

a Output time step is set in TRIM.FaTE using the scenario properties "simulationStepsPerOutputStep" and "simulationTimeStep."Parameter Name Units Value Used Reference

Air temperature degrees K 298 USEPA 2005.

Horizontal wind speed m/sec 2.8 5th percentile annual average value for contiguous US, calculated from 30 yrs of annual normal temperature values.

Vertical wind speed m/sec 0.0 Professional judgment; vertical wind speed not used by any of the algorithms in the version of the TRIM.FaTE library used for screening.

Wind direction degrees clockwise from N (blowing from) 3-days-on 4-days-off On is defined as time during which wind is blowing into the model domain. A conservative estimate of time during which wind should blow into the modeling domain was determined by evaluating HUSWO; it was concluded that a conservative estimate would be approximately 42% of the time.

Rainfall Rate m 3 [rain]/m 2 [surface area]-day varies daily 1.5 m/yr is the maximum statewide 30-year (1971-2000) average for the contiguous United States, excluding Rhode 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_SteadySta te_forAir unitless --

isDay_SteadySta te_forOther unitless -- Exhibit 2. Meteorological Inputs for the TRIM.FaTE Screening Scenario

Meteorological Inputs (all TRIM.FaTE scenario properties, except mixing height)

Value not used in current dynamic runs (would need to be reevaluated if steady-state runs are needed).Parameter Name Units V alue Used Reference
Atmospheric dust load kg[dust]/m 3 [air] 6.15E-08 Bidleman 1988

Density of air g/cm 3 0.0012 U.S. EPA 1997

Dust density kg[dust]/m 3 [dust] 1,400 Bidleman 1988
Fraction organic matter on particulates unitless 0.2 Harner and Bidleman 1998

Height m 800 5th percentile for United States 

### Exhibit 3. Air Parameters for the TRIM.FaTE Screening ScenarioParameter Name Units Value Used Reference

Air content volume[air]/volume[compartment] 0.28 McKone et al. 2001. A verage vertical velocity o f 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]/m 3 [soil] 2600 Default in McKone et al. 2001 (Table 3)

Thickness - untilled a m 0.01 McKone et al. 2001 (p. 30).

Thickness - tilled a m 0.20 USEPA 2005.

Erosion fraction unitless varies b See Erosion and Runoff Fraction table. Fraction of area available for erosion m 2 [area available]/m 2 [total] 1 Professional judgment; area assumed rural. Fraction of area available for runoff m 2 [area available]/m 2 [total] 1 Professional judgment; area assumed rural. Fraction of area availabe for vertical diffusion m 2 [area available]/m 2 [total] 1 Professional judgment; area assumed rural.
Fraction Sand unitless 0.25 Professional judgment.

Organic carbon fraction unitless 0.008 U.S. average in McKone et al. 2001 (Table 16 and A-3).
pH unitless 6.8 Professional judgment.

Runoff fraction unitless varies b See Erosion and Runoff Fraction table.

Total erosion rate kg [soil]/m 2 /day varies b See Total Erosion Rates table.

Total runoff rate m 3 [water]/m 2 /day 1.64E-03 Calculated using scenario-specific precipitation rate and assumptions associated with water balance.
Water content volume[water]/volume[compartment] 0.19 McKone et al 2001 (Table 15). 

### Exhibit 4. Soil and Groundwater Parameters for the TRIM.FaTE Screening Scenario

Surface Soil Compartment TypeParameter Name Units Value Used Reference 

### Exhibit 4. Soil and Groundwater Parameters for the TRIM.FaTE Screening Scenario

Air content volume[air]/volume[compartment] 0.25 McKone et al 2001 (Table 16).

A verage vertical velocity o f water (percolation) m/day 8.22E-04 Assumed as 0.2 times average precipitation for New England in McKone et al. 2001. Density of soil solids (dry weight) kg[soil]/m 3 [soil] 2,600 McKone et al. 2001 (Table 3).

Fraction Sand unitless 0.25 Professional judgment.

Thickness - untilled a m 0.79 McKone et al. 2001 (Tabel 16 - U.S. Average).

Thickness - tilled a m 0.6 Adjusted from McKone et al. 2001 (Table 16).
Organic carbon fraction unitless 0.008 McKone et al. 2001 (Table 16 and A3, U.S. Average).
pH unitless 6.8 Professional judgment.

Water content volume[water]/volume[compartment] 0.21 McKone et al. 2001 (Table 16).

Air content volume[air]/volume[compartment] 0.22 McKone et al. 2001 (Table 17).

A verage vertical velocity o f water (percolation) m/day 8.22E-04 Assumed as 0.2 times average precipitation for New England in McKone et al. 2001. Density of soil solids (dry weight) kg[soil]/m 3 [soil] 2,600 Default in McKone et al. 2001 (Table 3).
Fraction Sand unitless 0.35 Pofessional judgment.

Thickness a m 1.4 McKone et al. 2001 (Table 17).

Organic carbon fraction unitless 0.003 McKone et al. 2001 (Table 16 and A3, U.S. Average).
pH unitless 6.8 Professional judgment.

Water content volume[water]/volume[compartment] 0.21 McKone et al. 2001 (Table 17 - national average). Root Zone Soil Compartment Type

Vadose Zone Soil Compartment TypeParameter Name Units Value Used Reference 

### Exhibit 4. Soil and Groundwater Parameters for the TRIM.FaTE Screening Scenario

Thickness a m 3 McKone et al. 2001 (Table 3).
Fraction Sand unitless 0.4 Professional judgment.
Organic carbon fraction unitless 0.004 Professional judgment.

pH unitless 6.8 Professional judgment.

Porosity volume[total pore space]/volume[compartment] 0.2 Default in McKone et al. 2001 (Table 3). Density of Solid material in aquifer kg[soil]/m 3 [soil] 2,600 Default in McKone et al. 2001 (Table 3).

b See separate tables for erosion/runoff fractions and total erosion rates. a Set using the volume element properties file Ground Water Compartment TypeOri g inatin g Compartment Destination Compartment Runoff/Erosion Fraction SurfSoil_N1 0.0 SurfSoil_S1 0.0 sink 1.0 SW_Pond 1.0 SurfSoil_Source 0.0

SurfSoil_N6 0.0 SurfSoil_S1 0.0
sink 0.0 SW_Pond 1.0 SurfSoil_Source 0.0

SurfSoil_N1 0.0 sink 0.0 SW_Pond 1.0 SurfSoil_N1 0.0 SurfSoil_N7 0.0 sink 0.0 SW_Pond 1.0 SurfSoil_N6 0.0 SurfSoil_N3 0.0 sink 0.0 SW_Pond 1.0 SurfSoil_N7 0.0 SurfSoil_N4 0.0 sink 0.0 SW_Pond 1.0 SurfSoil_N3 0.0 SurfSoil_N5 0.0 SurfSoil_S4 0.0 sink 0.0 SW_Pond 1.0 SurfSoil_N4 0.0 SurfSoil_S5 0.0 sink 0.0 SW_Pond 0.0 SurfSoil_N4 0.5 SurfSoil_S5 0.5 sink 0.0 SW_Pond 0.0 SurfSoil_N5 0.0 SurfSoil_S4 1.0 sink 0.0

a Assumes that N5 is higher ground that S5, and half of the runoff flows into N4, and the other half in S5. Assumes all runoff from S5 flows into S4. SurfSoil_N7

SurfSoil_N3

SurfSoil_S5 a SurfSoil_N5 a SurfSoil_S4 SurfSoil_N4 SurfSoil_N6 SurfSoil_S1 

### Exhibit 5. Runoff Assumptions for the TRIM.FaTE Screening Scenario

SurfSoil_Source

SurfSoil_N1SoilParcelAreaRainfall/ErosivityIndexSoilErodibilityIndexLengthSlopeFactorLand UseCoverMgmtFactorSupportingPracticesFactorSedimentDeliveryRatioaCalculated(Adjusted)Erosion Ratem2R (100 ft-ton/ac)K (ton/ac/(100ft-ton/acre))LS(USCS)typeC(USCS)PA(ton/ac/yr)A(kg/m2/d)SDRacalculated(adjusted)erosion rate(kg/m2/d)N15.8E+04300 0.39 1.5 grass 0.1 1 17.55 0.010779 0.533 0.005740N64.1E+04300 0.39 1.5 crops 0.2 1 35.1 0.021557 0.557 0.012014N77.3E+04300 0.39 1.5 grass 0.1 1 17.55 0.010779 0.518 0.005580N33.5E+05300 0.39 1.5 grass 0.1 1 17.55 0.010779 0.385 0.004151N42.0E+06300 0.39 1.5 forest 0.1 1 17.55 0.010779 0.309 0.003331N56.7E+06300 0.39 1.5 forest 0.1 1 17.55 0.010779 0.196 0.002116S15.8E+04300 0.39 1.5 grass 0.1 1 17.55 0.010779 0.533 0.005740S42.0E+06300 0.39 1.5 forest 0.1 1 17.55 0.010779 0.309 0.003331S56.7E+06300 0.39 1.5 forest 0.1 1 17.55 0.010779 0.196 0.002116Unit Soil LossExhibit 6. USLE Erosion Parametersfor the TRIM.FaTE Screening ScenarioaCalculated 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 receivingdeposition (m2), and b is the empirical slope coefficient (always 0.125).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) 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 

### Exhibit 7. Terrestial Plant Placement for the TRIM.FaTE Screening ScenarioValue Used Reference Value Used ReferenceAllow exchange 1=yes, 0=no 1 -seasonalb-Average leaf area indexm2[leaf]/ m2[area]5.0Harvard Forest, dom. red oak andred maple, CDIAC website5.0Mid-range of 4-6 for old fields, R.J.Luxmoore, ORNL.Calculate wet dep interceptionfraction (boolean)1=yes, 0=no 0 Professional judgment. 0 Professional judgment.Correction exponent, octanal tolipidunitless 0.76 From roots, Trapp 1995. 0.76 From roots, Trapp 1995.Degree stomatal opening unitless 1Set to 1 for daytime based onprofessional judgment (stomataldiffusion is turned off at night using adifferent property, IsDay).1Set to 1 for daytime based onprofessional judgment (stomataldiffusion is turned off at night using adifferent property, IsDay).Density of wet leafkg/m3820 Paterson et al. 1991. 820 Paterson et al. 1991.Leaf wetting factor m 3.00E-041E-04 to 6E-04 for different cropsand elements, Muller and Prohl 1993.3.00E-041E-04 to 6E-04 for different cropsand elements, Muller and Prohl 1993.Length of leaf m 0.01 Professional judgment. 0.05 Professional judgment.Lipid content kg/kg wet weight 0.00224 European beech, Riederer 1995. 0.00224 European beech, Riederer 1995.Litter fall rate 1/day 0.0021value assumes 1st-order relationshipand that 99% of leaves fall over 6yearsseasonalc-Stomatal area normalizedeffective diffusion path length1/m 200 Wilmer and Fricker 1996. 200 Wilmer and Fricker 1996.Vegetation attenuation factorm2/kg2.9 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 dep interception fraction unitless 0.2Calculated based on 5 years of localmet data, 1987-1991.0.2Calculated based on 5 years of localmet data, 1987-1991.Exhibit 8. Terrestrial Plant Parametersfor the TRIM.FaTE Screening ScenarioConiferousaGrass/HerbaParameter Name UnitsLeaf Compartment TypeValue Used Reference Value Used ReferenceExhibit 8. Terrestrial Plant Parametersfor the TRIM.FaTE Screening ScenarioConiferousaGrass/HerbaParameter Name UnitsWet mass of leaf per soil areakg[freshleaf]/m2[area]2.0Calculated from leaf area index, leafthickness (Simonich & Hites, 1994),density of wet foliage.0.6Calculated from leaf area index andLeith 1975.Allow exchange 1=yes, 0=no 1 -seasonalb-Volume particle per area leafm3[leafparticles]/m2[leaf]1.00E-09Based on particle density and sizedistribution for atmospheric particlesmeasured on an adhesive surface,Coe and Lindberg 1987.1.00E-09Based on particle density and sizedistribution for atmospheric particlesmeasured on an adhesive surface,Coe and Lindberg 1987.Allow exchange 1=yes, 0=noseasonalb-Correction exponent, octanol tolipidunitless 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 rootkg/m3820 Soybean, Paterson et al. 1991.Wet mass per soil areakg/m21.4Temperate grassland, Jackson et al.1996.Allow exchange 1=yes, 0=noseasonalb-Correction exponent, octanol tolipidunitless 0.76 Trapp 1995Density of phloem fluidkg/m31,000 Professional judgment.Density of xylem fluidkg/cm3900 Professional judgment.Flow rate of transpired waterper leaf aream3[water]/m2[leaf]0.0048 Crank et al. 1981.Fraction of transpiration flowrate that is phloem rateunitless 0.05 Paterson et al. 1991.Lipid content of stem kg/kg wet weight 0.00224Leaves of European beech, Riederer1995.Root Compartment Type - Nonwoody OnlyStem Compartment Type - Nonwoody OnlyParticle on Leaf Compartment TypeValue Used Reference Value Used ReferenceExhibit 8. Terrestrial Plant Parametersfor the TRIM.FaTE Screening ScenarioConiferousaGrass/HerbaParameter Name UnitsWater content of stem unitless 0.8 Paterson et al. 1991Wet density of stemkg/m3830 Professional judgment.Wet mass per soil areakg/m20.24Calculated from leaf and rootbiomass density based onprofessional judgment.cBegins November 7, ends December 6; rate = 0.15/day during this time (value assumes 99 percent of leaves fall in 30 days).aSee separate table for assignment of plant types to surface soil compartments.bBegins March 9 (set to 1), ends November 7 (set to 0). Nation-wide 80th percentile.Parameter Name Units Value Used Reference
Algae carbon content (fraction) unitless 0.465 APHA 1995.

Algae density in water column g[algae]/L[water] 0.0025 Millard et al. 1996 as cited in ICF 2005.

Algae growth rate 1/day 0.7 Hudson et al. 1994 as cited in Mason et al. 1995b
Algae radius um 2.5 Mason et al. 1995b.

Algae water content (fraction) unitless 0.9 APHA 1995.
Average algae cell density (per vol cell, not water) g[algae]/m 3 [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.

Depth a m 3.18 WI DNR 2005 - calculation based on relationship between drainage basin and lake area size.

Dimensionless viscous sublayer thickness unitless 4 Ambrose et al. 1995.

Drag coefficient for water body unitless 0.0011 Ambrose et al. 1995.

Flush rate 1/year 12.17 Calculated based on pond dimensions and flow calculations.
Fraction Sand unitless 0.25 Professional judgment.
Organic carbon fraction in suspended sediments unitless 0.02 Professional judgment.
pH unitless 7.3 Professional judgment. Suspended sediment de p osition velocit y m/day 2 USEPA 1997.
Total suspended sediment concentration kg[sediment]/m3[water column] 0.05 USEPA 2005.

Water temperature degrees K 298 USEPA 2005.

a Set using the volume element properties named "top" and "bottom." 

### Exhibit 9. Surface Water Parameters for the TRIM.FaTE Screening ScenarioParameter Name Units Value Used Reference

Depth a m 0.05 McKone et al. 2001 (Table 3).

Fraction Sand unitless 0.25 Professional judgment.

Organic carbon fraction unitless 0.02 McKone et al. 2001 (Table 3).

Porosity of the sediment

zone volume[total pore space]/volume[sediment compartment] 0.6 USEPA 1998.

Solid material density in sediment kg[sediment]/m 3 [sediment] 2,600 McKone et al. 2001 (Table 3). 

### Exhibit 10. Sediment Parameters for the TRIM.FaTE Screening Scenario

a 

### Set using the volume element properties named "top" and "bottom."Parameter Name Units V alue Used Reference Macrophyte biomass per water area k g /m 2 0.5 Professional judgment. Density of macrophytes kg/L 1 Professional judgment. 

### Exhibit 11. Aquatic Plants for the TRIM.FaTE Screening ScenarioAlgae Macrophyte Bethic Invertebrate Water Column Herbivore Benthic Omnivore Water Column Omnivore Benthic Carnivore Water Column CarnivoreBenthicInvertebrate0% 0% 0% 0% 0% 0% 0% 0% 0.020 2.55E-04Professionaljudgment.Water ColumnHerbivore100% 0% 0% 0% 0% 0% 0% 0% 0.001 0.025Professionaljudgment.Benthic Omnivore 0% 0% 100% 0% 0% 0% 0% 0% 0.002 2.50E-01Professionaljudgment.Water ColumnOmnivore0% 30% 30% 40% 0% 0% 0% 0% 0.001 0.25Professionaljudgment.Benthic Carnivore 0% 0% 70% 0% 30% 0% 0% 0% 0.001 2.0Professionaljudgment.Water ColumnCarnivore0% 0% 0% 20% 20% 60% 0% 0% 0.0004 2.0Professionaljudgment.Exhibit 12. Aquatic Animals Food Chain, Density, and Massfor the TRIM.FaTE Screening ScenarioAquatic Biota(ConsumingOrganism)Biomass(kg/m2)BodyWeightReferenceFraction DietCAS numberbunitless 7440-43-9 -Diffusion coefficient in pure airm2[air]/day0.71 USEPA 1999 (Table A-2-35).Diffusion coefficient in purewaterm2[water]/day8.16E-05 USEPA 1999 (Table A-2-35).Henry's Law ConstantPa-m3/mol1.00E-37USEPA 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 partitioncoefficient (Koc)- -Octanol-water partitioncoefficient (Kow)L[water]/kg[octanol] - -Exhibit 13. Cadmium Chemical-Specific PropertiesDocumentation for the TRIM.FaTE Screening ScenariobThese CAS numbers apply to elemental Cd; however, the cations of cadmium are being modeled.Parameter NameaUnits ReferenceValueaAll parameters in this table are TRIM.FaTE chemical properties.Hg(0)bHg(2)bMHgbCAS number unitless 7439-97-6 14302-87-5 22967-92-6-Diffusion coefficient inpure airm2[air]/day0.478 0.478 0.456 USEPA 1997.Diffusion coefficient inpure waterm2[water]/day5.54E-05 5.54E-05 5.28E-05 USEPA 1997.Henry's Law constantPa-m3/mol719 7.19E-05 0.0477 USEPA 1997.Melting Point degrees K 234 5.50E+02 443 CARB 1994.Molecular weight g/mol 201 201 216 USEPA 1997.Octanol-waterpartition coefficient(Kow)L[water]/kg[octanol] 4.15 3.33 1.7 Mason et al. 1996.Vapor washout ratiom3[air]/m3[rain]1,200 1.6E+06 0USEPA 1997, based onPetersen et al. 1995.aAll parameters in this table are TRIM.FaTE chemical properties.Exhibit 14. Mercury Chemical-Specific PropertiesDocumentation for the TRIM.FaTE Screening ScenariobOn this and all following tables, Hg(0) = elemental mercury, Hg(2) = divalent mercury, and MHg = methyl mercury.Parameter Name UnitsValueReferenceBaP BaABbF BkF Chr DahAIcdPCAS number unitless 50-32-8 56-55-3 205-99-2 207-08-9 218-01-9 53-70-3 193-39-5-Diffusion coefficient inpure airm2/day0.188 0.213 0.197 0.197 0.214 0.156 0.164 USEPA 1998.Diffusion coefficient inpure waterm2/day5.05E-05 5.37E-05 4.74E-05 4.74E-05 5.37E-05 5.19E-05 4.89E-05 USEPA 1998.Henry's Law constantPa-m3/mol8.50E-02 3.67E-01 6.26E-01 4.20E-02 1.23E-01 1.13E-03 4.92E-04 USEPA 1998.Melting point degrees K 452 433 441 490 531 539 437 Budavari 1996.Molecular weight g/mol 252.32 228.29 252.32 252.32 228.29 278.33 276.34 Budavari 1996.Octanol-water partitioncoefficient (Kow)L[water]/L[octanol] 9.33E+05 6.17E+05 3.98E+06 6.92E+06 5.37E+05 3.16E+06 4.57E+07 Hansch et al. 1995.Exhibit 15. PAH Chemical-Specific PropertiesDocumentation for the TRIM.FaTE Screening ScenarioParameter Name UnitsValueReference1,2,3,4,6,7,8, 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- HxCDFCAS number unitless 3268-87-9 39001-02-0 35822-46-9 67562-39-4 55673-89-7 39227-28-6 70648-26-9Diffusion coefficient in pureairm2/day0.0883 0.123 0.0925 0.129 0.129 0.0958 0.135Diffusion coefficient in purewaterm2/day3.08E-06 3.15E-05 3.24E-05 3.33E-05 3.33E-05 3.43E-05 3.53E-05Henry's Law constantPa-m3/mol0.68 0.19 1.28 1.43 1.43 1.08 1.45Melting Point degrees K 603 259 538 236.5 222 546 499.0Molecular weight g/mol 460.0 443.76 425.2 409.31 409.31 391.0 374.87Octanol-water partitioncoefficient (Kow)L[water]/L[octanol] 1.58E+08 1.00E+08 1.00E+08 2.51E+07 7.94E+06 6.31E+07 1.00E+07ValueParameter Name UnitsExhibit 16. Dioxin Chemical-Specific PropertiesDocumentation for the TRIM.FaTE Screening Scenario1,2,3,6,7,8- 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- HxCDFCAS number unitless 57653-85-7 57117-44-9 19408-74-3 72918-21-9 40321-76-4 57117-41-6 60851-34-5Diffusion coefficient in pureairm2/day0.0958 0.135 0.0958 0.135 0.101 0.142 0.135Diffusion coefficient in purewaterm2/day3.43E-05 3.53E-05 3.43E-05 3.53E-05 3.65E-05 3.76E-05 3.53E-05Henry's Law constantPa-m3/mol1.08 0.74 1.08 0.74 3.33 0.5 0.74Melting point degrees K 558.0 506.0 517.0 509.0 513.0 499.0 512.5Molecular weight g/mol 390.84 374.9 390.8 374.9 356.4 340.4 374.9Octanol-water partitioncoefficient (Kow)L[water]/L[octanol] 1.62E+08 8.24E+07 1.62E+08 3.80E+07 4.37E+06 6.17E+06 8.31E+07ValueExhibit 16. Dioxin Chemical-Specific PropertiesDocumentation for the TRIM.FaTE Screening ScenarioParameter Name Units2,3,4,7,8- PeCDF 2,3,7,8-TCDD 2,3,7,8-TCDFCAS number unitless 57117-31-4 1746-01-6 51207-31-9 -Diffusion coefficient in pureairm2/day0.142 0.106 0.149 USEPA 1999.Diffusion coefficient in purewaterm2/day3.76E-05 5.68E-05 4.04E-05 USEPA 1999.Henry's Law constantPa-m3/mol0.5 3.33 1.46Mackay et al. 1992 as cited in USEPA 2000a. Exceptions include1,2,3,4,6,7,8,9-OCDF; 1,2,3,4,7,8-HxCDF; and 1,2,3,6,7,8-HxCDF whichare calculated by the VP/WS Ratio Technique (USEPA 2000a)Melting point degrees K 469.3 578.0 500.0Mackay et al. 2000, exceptions include USEPA 2000b (1,2,3,6,7,8-HxCDD;1,2,3,7,8,9-HxCDF; 1,2,3,7,8--PeCDD), ATSDR 1998(1,2,3,6,7,8-HxCDF;1,2,3,7,8-PeCDF; 2,3,4,6,7,8-HxCDF) and NLM 2002 (1,2,3,7,8,9-HxCDD)Molecular weight g/mol 340.4 322.0 306.0Mackay 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; 2,3,4,6,7,8-HxCDF) and NLM 2002 (1,2,3,6,7,8-HxCDD; 1,2,3,7,8,9-HxCDD)Octanol-water partitioncoefficient (Kow)L[water]/L[octanol] 3.16E+06 6.31E+06 1.26E+06Mackay et al. 1992a as cited in USEPA 2000a, exceptions include: USEPA2000b (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) and Sijm et al. 1989 (1,2,3,7,8-PeCDD)ValueUnitsExhibit 16. Dioxin Chemical-Specific PropertiesDocumentation for the TRIM.FaTE Screening ScenarioParameter NameReferenceParticle dry deposition velocity m/day 260 Calculated from Mulbaier and Tisue 1980.

Washout Ratio m3[air]/m3[rai n] 200,000 MacKay et al. 1986.

Use input characteristic depth (boolean) 0 = no, Else =
yes 0 Professional judgment.

Use input characteristic depth (boolean) 0 = no, Else =
yes 0 Professional judgment.

Use input characteristic depth (boolean) 0 = no, Else =
yes 0 Professional judgment.

Ratio of concentration in water to concentration in algae to concentration dissolved in water L[water]/g[alga e wet wt] 1.87 McGeer et al. 2003. 

### Exhibit 17. Cadmium Chemical-Specific Properties for Abiotic Compartments Documentation for the TRIM.FaTE Screening Scenario

Root Zone Soil Compartment Type Reference Value

Vadose Zone Soil Compartment Type Units

Surface Water Compartment Type Parameter Name
Air Compartment Type

Surface Soil Compartment TypeHg(0) Hg(2) MHgParticle dry deposition velocity m/day 500 500 500 CalTOX value cited in McKone et al. 2001.Demethylation rate 1/day N/A N/A 0 Professional judgment.Methylation rate 1/day 0 0 0 Professional judgment.Oxidation Rate 1/day 0.00385 0 0Low end of half-life range (6 months to 2 years) inUSEPA 1997.Reduction rate 1/day 0 0 0 Professional judgment.Washout Ratio m3[air]/m3[rain] 200,000 200,000 200,000 Professional judgment.Input characteristic depth (user supplied) m 0.08 0.08 0.08 Not used (model set to calculate value).Use input characteristic depth (boolean) 0 = no, Else = yes 0 0 0 Professional judgment.Soil-water partition coefficientL[water]/kg[soilwet wt]1,000 58,000 7,000 USEPA 1997.Vapor dry deposition velocity m/day 50 2500 0Hg(0) - from Lindberg et al. 1992 Hg(2) - estimate byUSEPA using the Industrial Source Complex (ISC)Model - [See Vol. III, App. A of the Mercury StudyReport (USEPA, 1997)].Demethylation rate 1/day N/A N/A 0.06Range reported in Porvari and Verta 1995 is 3E-2 to6E-2 /day; value is average maximum potentialdemethylation rate constant under anaerobicconditions.Methylation rate 1/day 0 0.001 0Range reported in Porvari and Verta 1995 is 2E-4 to1E-3 /day; value is average maximum potentialmethylation rate constant under anaerobicconditions.Oxidation rate 1/day 0 0 0 Value assumed in USEPA 1997.Reduction rate 1/day 0 1.25E-05 0Value used for untilled surface soil (2cm), 10%moisture content, in USEPA 1997; general range is(0.0013/day)*moisture content to(0.0001/day)*moisture content for forested region(Lindberg 1996; Carpi and Lindberg 1997).UnitsValueReferenceParameter NameAir Compartment TypeSurface Soil Compartment TypeExhibit 18. Mercury Chemical-Specific Properties for Abiotic CompartmentsDocumentation for the TRIM.FaTE Screening ScenarioHg(0) Hg(2) MHgUnitsValueReferenceParameter NameExhibit 18. Mercury Chemical-Specific Properties for Abiotic CompartmentsDocumentation for the TRIM.FaTE Screening ScenarioInput characteristic depth (user supplied) m 0.08 0.08 0.08 Not used (model set to calculate value).Use input characteristic depth (boolean) 0 = no, Else = yes 0 0 0 Professional judgment.Soil-water partition coefficientL[water]/kg[soilwet wt]1,000 58,000 7,000 USEPA 1997Demethylation rate 1/day N/A N/A 0.06Range reported in Porvari and Verta 1995 is 3E-2 to6E-2 /day; value is average maximum potentialdemethylation rate constant under anaerobicconditions.Methylation rate 1/day 0 0.001 0Range reported in Porvari and Verta 1995 is 2E-4 to1E-3 /day; value is average maximum potentialmethylation rate constant under anaerobicconditions.Oxidation rate 1/day 0 0 0 Value assumed in USEPA 1997.Reduction rate 1/day 0 3.25E-06 0Value used for tilled surface soil (20cm), 10%moisture content, in USEPA 1997 (Lindberg 1996;Carpi and Lindberg, 1997).Input characteristic depth (user supplied) m 0.08 0.08 0.08 Not used (model set to calculate value),Use input characteristic depth (boolean) 0 = no, Else = yes 0 0 0 Professional judgment.Soil-water partition coefficientL[water]/kg[soilwet wt]1,000 58,000 7,000 USEPA 1997.Demethylation rate 1/day N/A N/A 0.06Range reported in Porvari and Verta 1995 is 3E-2 to6E-2 /day; value is average maximum potentialdemethylation rate constant under anaerobicconditions.Methylation rate 1/day 0 0.001 0Range reported in Porvari and Verta 1995 is 2E-4 to1E-3 /day; value is average maximum potentialmethylation rate constant under anaerobicconditions.Oxidation rate 1/day 0 0 0 Value assumed in USEPA 1997.Reduction rate 1/day 0 3.25E-06 0Value used for tilled surface soil (20cm), 10%moisture content, in USEPA 1997 (Lindberg 1996;Carpi and Lindberg, 1997).Vadose Zone Soil Compartment TypeRoot Zone Soil Compartment TypeHg(0) Hg(2) MHgUnitsValueReferenceParameter NameExhibit 18. Mercury Chemical-Specific Properties for Abiotic CompartmentsDocumentation for the TRIM.FaTE Screening ScenarioSoil-water partition coefficientL[water]/kg[soilwet wt]1,000 58,000 7,000 USEPA 1997.Demethylation rate 1/day N/A N/A 0.06Range reported in Porvari and Verta 1995 is 3E-2 to6E-2 /day; value is average maximum potentialdemethylation rate constant under anaerobicconditions.Methylation rate 1/day 0 0.001 0Range reported in Porvari and Verta 1995 is 2E-4 to1E-3 /day; value is average maximum potentialmethylation rate constant under anaerobicconditions.Oxidation rate 1/day 1.00E-08 0 0Small default nonzero value (0 assumed in USEPA1997).Reduction rate 1/day 0 3.25E-06 0Value used for tilled surface soil (20cm), 10%moisture content, in USEPA 1997 (Lindberg 1996;Carpi and Lindberg 1997).Algal surface area-specific uptake rateconstantnmol/[μm2-daynmol]0 2.04E-10 3.60E-10Assumes 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-bMason et al. 1996.Solids-water partition coefficientL[water]/kg[solidswet wt]1,000 100,000 100,000 USEPA 1997.Vapor dry deposition velocity m/day N/A 2500 USEPA 1997 (Vol. III, App. A).Demethylation rate 1/day N/A N/A 0.013Average of range of 1E-3 to 2.5E-2/day fromGilmour and Henry 1991.Methylation rate 1/day 0 0.001 0Value used in EPA 1997; range is from 1E-4 to 3E-4/day (Gilmour and Henry 1991).Oxidation rate 1/day 0 0 0 Professional judgment.Reduction rate 1/day 0 0.0075 0Value used in USEPA 1997; reported values rangefrom less than 5E-3/day for depths greater than17m, up to 3.5/day (Xiao et al. 1995; Vandal et al.1995; Mason et al. 1995a; Amyot et al. 1997).Ground Water Compartment TypeSurface Water Compartment TypeHg(0) Hg(2) MHgUnitsValueReferenceParameter NameExhibit 18. Mercury Chemical-Specific Properties for Abiotic CompartmentsDocumentation for the TRIM.FaTE Screening ScenarioSolids-water partition coefficientL[water]/kg[solidswet wt]3,000 50,000 3,000 USEPA 1997.Demethylation rate 1/day N/A N/A 0.0501Average of range of 2E-4 to 1E-1/day from Gilmourand Henry 1991.Methylation rate 1/day 0 1.00E-04 0Value used in EPA 1997; range is from 1E-5 to 1E-3/day,Gilmour and Henry 1991.Oxidation rate 1/day 0 0 0 Professional judgment.Reduction rate 1/day 0 1.00E-06 0Inferred value based on presence of Hg(0) insediment porewater (USEPA 1997; Vandal et al.1995).bTRIM.FaTE Formula Property, which varies from 0.075 to 1.7 depending on pH and chloride concentration.Sediment Compartment TypeaTRIM.FaTE Formula Property, which varies from 0.025 to 1.625 depending on pH and chloride concentration.BaP BaABbF BkF Chr DahAIcdPParticle dry depositionvelocitym/day 500 500 500 500 500 500 500 McKone et al. 2001.Half-life day 0.046 0.125 0.596 0.458 0.334 0.178 0.262Howard et al. 1991 / upper boundmeasured or estimated value.Washout Ratio 200,000 200,000 200,000 200,000 200,000 200,000 200,000 Mackay et al. 1986.Input characteristic depth m 0.08 0.08 0.08 0.08 0.08 0.08 0.08 Not used (model set to calculate value).Use input characteristicdepth (boolean)0 = No, Else = Yes0000000Professional judgment.Halflife day 530 680 610 2140 1000 940 730 MacKay et al. 2000.Input characteristic depth m 0.08 0.08 0.08 0.08 0.08 0.08 0.08 Not used (model set to calculate value).Use input characteristicdepth0 = No, Else = Yes0000000Professional judgment.Half-life day 530 680 610 2140 1000 940 730 MacKay et al. 2000.Input characteristic depth m 0.08 0.08 0.08 0.08 0.08 0.08 0.08 Not used (model set to calculate value).Use input characteristicdepth (boolean)0 = No, Else = Yes0000000Professional judgment.Half-life day 1060 1360 1220 4280 2000 1880 1460Howard et al. 1991 / upper boundmeasured or estimated value forgroundwater.Half-life day 1060 1360 1220 4280 2000 1880 1460Howard et al. 1991 / upper boundmeasured or estimated value forgroundwater.Parameter Name UnitsValueGroundwater Compartment TypeAir Compartment TypeRoot Zone Soil Compartment TypeSurface Soil Compartment TypeVadose Zone Soil Compartment TypeReferenceExhibit 19. PAH Chemical-Specific Properties for Abiotic CompartmentsDocumentation for the TRIM.FaTE Screening ScenarioBaP BaABbF BkF Chr DahAIcdPParameter Name UnitsValueReferenceExhibit 19. PAH Chemical-Specific Properties for Abiotic CompartmentsDocumentation for the TRIM.FaTE Screening ScenarioRatioOfConcInAlgaeToConcDissolvedInWater(g[chem]/kg[algae]) /(g[chem]/L[water])3610 3610 3610 3610 3610 3610 3610BCF data for green algae for BaP from Luet al. 1977Half-life day 0.138 0.375 90 62.4 1.626 97.8 750Howard et al. 1991 / upper boundmeasured or estimated value.Half-life day 2290 2290 2290 2290 2290 2290 2290Mackay et al. 1992 / PAH values are themean half-life of the log class that Mackayet al. assigned for sediment, except forBbF and IcdP, which were not on Table2.3.Sediment Compartment TypeSurface Water Compartment Type1,2,3,4,6,7,8,9-OCDD1,2,3,4,6,7,8,9-OCDF1,2,3,4,6,7,8-HpCDD1,2,3,4,6,7,8-HpCDF1,2,3,4,7,8,9-HpCDF1,2,3,4,7,8-HxCDD

Deposition Velocity m/day 500 500 500 500 500 500
Halflife day 162 321 64 137 122 42

Washout Ratio m 3 [ air ] /m 3 [ rain ] 91000 22000 64000 32000 32000 9000

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 000000

Halflife day 3650 3650 3650 3650 3650 3650

Input characteristic depth m 0.08 0.08 0.08 0.08 0.08 0.08
Use input characteristic depth 0 = No, Else = Yes 000000

Halflife day 3650 3650 3650 3650 3650 3650

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 000000

Halflife day 1008 1008 1008 1008 1008 1008

Half-life day 1008 1008 1008 1008 1008 1008

Ratio Of Conc In Algae To Conc Dissolved In Water (g[chem]/g[algae])/ (g[chem]/L[water]) 1.025 1.025 1.025 1.025 1.025 1.025
Half-life day 0.67 0.58 47 0.58 0.58 6.3

Half-life day 1095 1095 1095 1095 1095 1095 

### Sediment Com p artment T yp e Surface Water Com p artment T yp e V adose Zone Soil Com p artment T yp e Root Zone Soil Com p artment T yp e

Groundwater Com p artment T yp e Surface Soil Com p artment T yp e Air Compartment Type Value 

### Exhibit 20. Dioxin Chemical-Specific Properties for Abiotic Compartments Documentation for the TRIM.FaTE Screening Scenario

Parameter Name Units1,2,3,4,7,8-HxCDF1,2,3,6,7,8-HxCDD1,2,3,6,7,8-HxCDF1,2,3,7,8,9-HxCDD1,2,3,7,8,9-HxCDF1,2,3,7,8-PeCDD

Deposition Velocity m/day 500 500 500 500 500 500
Halflife day 78 28 55 28 51 18

Washout Ratio m 3 [ air ] /m 3 [ rain ] 10000 9000 10000 9000 10000 18000

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 000000

Halflife day 3650 3650 3650 3650 3650 3650

Input characteristic depth m 0.08 0.08 0.08 0.08 0.08 0.08
Use input characteristic depth 0 = No, Else = Yes 000000

Halflife day 3650 3650 3650 3650 3650 3650

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 000000

Halflife day 1008 1008 1008 1008 1008 1008

Half-life day 1008 1008 1008 1008 1008 1008

Ratio Of Conc In Algae To Conc Dissolved In Water (g[chem]/g[algae])/ (g[chem]/L[water]) 1.025 1.025 1.025 1.025 1.025 1.025
Half-life day 0.58 6.3 0.58 6.3 0.58 2.7

Half-life day 1095 1095 1095 1095 1095 1095 

### Root Zone Soil Com p artment T yp e

V adose Zone Soil Com p artment T yp e

Groundwater Com p artment T yp e

Surface Water Com p artment T yp e

Sediment Com p artment T yp e Value



### Surface Soil Com p artment T yp e Air Compartment Type Exhibit 20. Dioxin Chemical-Specific Properties for Abiotic Compartments Documentation for the TRIM.FaTE Screening Scenario

Parameter Name Units1,2,3,7,8-PeCDF2,3,4,6,7,8-HxCDF2,3,4,7,8-PeCDF2,3,7,8-TCDD2,3,7,8-TCDF

Deposition Velocity m/day 500 500 500 500 500
Halflife day 31 59 33 12 19

Washout Ratio m 3 [ air ] /m 3 [ rain ] 13000 10000 14000 18000 19000

Input characteristic depth m 0.08 0.08 0.08 0.08 0.08 Use input characteristic depth (boolean) 0 = No, Else = Yes 00000

Halflife day 3650 3650 3650 3650 3650

Input characteristic depth m 0.08 0.08 0.08 0.08 0.08
Use input characteristic depth 0 = No, Else = Yes 00000

Halflife day 3650 3650 3650 3650 3650

Input characteristic depth m 0.08 0.08 0.08 0.08 0.08 Use input characteristic depth (boolean) 0 = No, Else = Yes 00000

Halflife day 1008 1008 1008 1008 1008

Half-life day 1008 1008 1008 1008 1008

Ratio Of Conc In Algae To Conc Dissolved In Water (g[chem]/g[algae])/ (g[chem]/L[water]) 1.025 1.025 1.025 1.025 1.025
Half-life day 0.19 0.58 0.19 2.7 0.18

Half-life day 1095 1095 1095 1095 1095 

### Root Zone Soil Com p artment T yp e

V adose Zone Soil Com p artment T yp e

Groundwater Com p artment T yp e Parameter Name Value 

### Exhibit 20. Dioxin Chemical-Specific Properties for Abiotic Compartments Documentation for the TRIM.FaTE Screening Scenario

Units



### Sediment Com p artment T yp e Surface Water Com p artment T yp e Surface Soil Com p artment T yp e Air Compartment TypeDeposition Velocity McKone et al. 2001

Halflife Atkinson 1996 as cited in USEPA 2000; vapor phase reaction with hydroxyl radical Washout Ratio Vulykh et al. 2001 Surface Soil Compartment T y pe Input characteristic depth Not used (model set to calculate value)
Use input characteristic depth (boolean) Professional judgment

Halflife Mackay et al. 2000; the degradation rate was cited by multiple authors, value is for 2,3,7,8-TCDD Root Zone Soil Compartment T y pe Input characteristic depth not used (model set to calculate value)
Use input characteristic depth professional judgment

Halflife Mackay et al. 2000; the degradation rate was cited by multiple authors, value is for 2,3,7,8-TCDD V adose Zone Soil Compartment T y pe Input characteristic depth Not used (model set to calculate value).
Use input characteristic depth (boolean) Professional judgment.

Halflife 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 T y pe

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 T y pe Ratio Of Conc In Algae To Conc Dissolved In Water BCF data for green algae for 2,3,7,8-TCDD from Isense 1978, at 32 days. Half-life Kim and O’Keefe. 1998, as cited in USEPA. 2000.

Half-life Estimation based on Adriaens and Grbic-Galic 1992,1993 and Adriaens et al. 1995 as cited in USEPA 2000. 

### Air Compartment Type Exhibit 20. Dioxin Chemical-Specific Properties for Abiotic Compartments Documentation for the TRIM.FaTE Screening Scenario

Sediment Compartment Type Parameter Name ReferenceTransfer factor to leaf particle 1/day 0.002 Professional judgment.

Transfer factor to leaf 1/day 0.200 Professional judgment.

Root to Root Soil Partition- Alpha of Steady State unitless 0.95 Henning et al. 2001.

Root to Root Soil Partition- Partitioning Coefficient m 3 [bulk root soil]/m 3 [root] 0.23 Nriagu 1980; based on average value calculated from various agricultural plant species.

Root to Root Soil Partition- Time to Reach Alpha day 28 Henning et al. 2001.

Transpiration stream concentration factor (TSCF) m 3 [soil pore water]/m 3 [xylem fluid ] 0.45 Tsiros et al. 1999.

Water ColumnDissolved PartitionAlpha of Equilibrium unitless 0.95 Maine et al. 2001; based on assumption that equilibrium was nearly reached during 21 day experiment. Water ColumnDissolved PartitionPartition Coefficient L[water]/kg[macrop hyte wet wt] 100 Maine et al. 2001; based on calculations from an average of four macrophyte species.

Water ColumnDissolved PartitionTime to Reach Equilibrium day 21 Maine et al. 2001.

a Roots and stems are not modeled for deciduous or coniferous forest in the current version of TRIM.FaTE. 

### Macrophyte Compartment Type Leaf Compartment Type

Particle on Leaf Compartment Type Exhibit 21. Cadmium Chemical-Specific Properties for Plant Compartments Documentation for the TRIM.FaTE Screening Scenario

Root Compartment T y pe - Grasses and Herbs a



### Stem Compartment T y pe - Grasses and Herbs a



### Aquatic Plants Reference Units Parameter Name ValueHg(0)Hg(2)MHgTransfer factor to leaf particle 1/day 0.002 0.002 0.002Professional judgment (assumed 1% oftransfer 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 0Professional judgment; assumed close toinstantaneousReduction rate 1/day 0 0 0 Professional judgment.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.Alpha for root-root zone bulk soil unitless 0.95 0.95 0.95 Selected value.Root/root-zone-soil-water partitioncoefficientm3[bulk root soil]/m3[root]0 0.18 1.2Hg2- geometric mean Leonard et al. 1998,John 1972, Hogg et al. 1978; MHgassumed, based on Hogg et al. 1978.t-alpha for root-root zone bulk soil day 21 21 21 Professional judgment.Demethylation rate 1/day N/A N/A 0 Professional judgment.Methylation rate 1/day 0 0 0 Professional judgment.Leaf Compartment TypeParticle on Leaf Compartment TypeRoot Compartment Type - Grasses and HerbsaReferenceExhibit 22. Mercury Chemical-Specific Properties for Plant Compartments Documentation for the TRIM.FaTEScreening ScenarioValueParameter Name UnitsHg(0)Hg(2)MHgReferenceExhibit 22. Mercury Chemical-Specific Properties for Plant Compartments Documentation for the TRIM.FaTEScreening ScenarioValueParameter Name UnitsOxidation rate 1/day 0 0 0 Professional judgment.Reduction rate 1/day 0 0 0 Professional judgment.Transpiration stream concentrationfactor (TSCF)m3[soil pore water]/m3[xylem fluid]0 0.5 0.2Calculation from Norway spruce, Scots pine,Bishop et al. 1998.Demethylation rate 1/day N/A N/A 0.03 Calculated from Bache et al. 1973.Methylation rate 1/day 0 0 0 Professional judgment.Oxidation rate 1/day 0 0 0 Professional judgment.Reduction rate 1/day 0 0 0 Professional judgment.Water Column Dissolved Partition-Alphaof Equilibriumunitless 0.95 0.95 0.95 Selected value.Water Column Dissolved PartitionPartition CoefficientL[water]/ kg[macrophytewet wt]0.883 0.883 4.4Elodea densa,Ribeyre and Boudou 1994.Water Column Dissolved Partition-Timeto Reach Equilibriumunitless 0.95 0.95 0.95 Selected value.Oxidation rate 1/day 1.00E+09 0 0 Professional judgment.t-alpha day 18 18 18Experiment duration from Ribeyre andBoudou 1994.aRoots and stems are not modeled for deciduous or coniferous forest in the current version of TRIM.FaTE.Aquatic PlantsMacrophyte Compartment TypeStem Compartment Type - Grasses and HerbsaBaP BaABbF BkF Chr DahAIcdPTransfer factor to leafparticle1/day 1.00E-04 1.00E-04 1.00E-04 1.00E-04 1.00E-04 1.00E-04 1.00E-04 Professional judgment.Half-life day 3.5 3.5 3.5 3.5 3.5 3.5 3.5Edwards 1988 (as cited in Efroymson 1997)/calculated from metabolic rate constant.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 Professional judgment.Half-life day 2.31 1.84 3.56 17.8 4.12 17.8 17.8Edwards 1988 (as cited in Efroymson 1997)/calculated from metabolic rate constantHalf-life day 34.6 34.6 34.6 34.6 34.6 34.6 34.6Edwards 1988 (as cited in Efroymson 1997)/calculated from metabolic rate constant.Half-life day 3.5 3.5 3.5 3.5 3.5 3.5 3.5Edwards 1988 (as cited in Efroymson 1997)/calculated from metabolic rate constant.Half-life days 3.5 3.5 3.5 3.5 3.5 3.5 3.5Edwards 1988 (as cited in Efroymson 1997)/calculated from metabolic rate constant.Leaf Compartment TypeRoot Compartment Type - Grasses and HerbsaStem Compartment Type - Grasses and HerbsaaRoots and stems are not modeled for deciduous or coniferous forest in the current version of TRIM.FaTE.Macrophyte Compartment TypeAquatic PlantsParticle on Leaf Compartment TypeParameter NameValueExhibit 23. PAH Chemical-Specific Properties for Plant Compartments Documentation for the TRIM.FaTE ScreeningScenarioTerrestrial PlantsReferenceUnitsValueAll DioxinsTransfer factor to leaf particle 1/day 0.003Calculated as 1 percent of transfer factor to leaf; highlyuncertain.Half-life day 70Arjmand and Sandermann 1985, as cited in Komoba et al. 1995;soybean root cell culture metabolism test data for DDE.Transfer factor to leaf 1/day 0.3Professional judgment based on USEPA 2000c (an estimate formercury) and Trapp 1995; highly uncertain.Half-life day 4.4McCrady and Maggard 1993; photodegradation sorbed to grassfoliage in sunlight; assumed 10 sunlight per day.Half-life day 70Arjmand 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.Half-life day 70Arjmand and Sandermann 1985, as cited in Komoba, et al.1995; soybean root cell culture metabolism test data for DDE.Half-life days 70Arjmand and Sandermann 1985, as cited in Komoba et al. 1995;soybean root cell culture metabolism test data for DDE.Stem Compartment Type - Grasses and HerbsaParticle on Leaf Compartment TypeAquatic PlantsMacrophyte Compartment TypeaRoots and stems are not modeled for deciduous or coniferous forest in the current version of TRIM.FaTE.Exhibit 24. Doixin Chemical-Specific Properties for Plant Compartments Documentation for theTRIM.FaTE Screening ScenarioTerrestrial PlantsLeaf Compartment TypeReferenceUnitsParameter NameRoot Compartment Type - Grasses and HerbsaSediment Partitioning - Alpha of Equilibrium unitless 0.95 Professional judgment

Sediment Partitioning - Partition Coefficient kg[bulk sed/kg[inverte brate wet wt] 0.27 Professional judgment

Sediment Partitioning - Time to Reach Alpha of Equilibrium day 21 Hare et al. 2001

Absorption Rate Constant unitless 1.23 Calculated based on body weight from regression in Hendriks & Heikens 2000 Elimination Rate Constant unitless 2.82E-03 Professional judgment

Assimilation efficiency from food unitless 0.1 Professional judgment based on Yan and Wang 2002.
Absorption Rate Constant unitless 1.23E+00 Calculated based on body weight from regression in Hendriks & Heikens 2000 Elimination Rate Constant unitless 2.82E-03 Professional judgment

Assimilation efficiency from food unitless 0.1 Professional judgment based on Yan and Wang 2002.
Absorption Rate Constant unitless 6.60E-01 Calculated based on body weight from regression in Hendriks & Heikens 2000 Elimination Rate Constant unitless 1.68E-03 Professional judgment

Assimilation efficiency from food unitless 0.1 Professional judgment based on Yan and Wang 2002.
Assimilation efficiency from plants 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 2000 Elimination Rate Constant unitless 5.02E-03 Professional judgment

Assimilation efficiency from food unitless 0.1 Professional judgment based on Yan and Wang 2002.
Assimilation efficiency from plants 0.1 Professional judgment based on Yan and Wang 2002.
Absorption Rate Constant unitless 1.232020679 Calculated based on body weight from regression in Hendriks & Heikens 2000 Elimination Rate Constant unitless 2.82E-03 Professional judgment

Assimilation efficiency from food unitless 0.1 Professional judgment based on Yan and Wang 2002.
Absorption Rate Constant unitless 0.660223535 Calculated based on body weight from regression in Hendriks & Heikens 2000 Elimination Rate Constant unitless 1.68E-03 Professional judgment 

### Exhibit 25. Cadmium Chemical-Specific Properties for Aquatic Species Documentation for the TRIM.FaTE Screening Scenario

Benthic Invertebrate Compartment Type Parameter Name Units Reference Value



### Water-column Carnivore Compartment Type Benthic Omnivore Compartment Type

Water-column Herbivore Compartment Type

Water-column Omnivore Compartment Type Benthic Carnivore Compartment TypeHg(0)Hg(2)MHgAlpha of equilibrium for sedimentpartitioningunitless 0.95 0.95 0.95 Selected value.Benthic invertebrate-bulk sedimentpartition coefficientkg[bulksediment]/kg[invertebrate wet wt]0.0824 0.0824 5.04Hg(0) - assumed based on Hg(2) value;Hg(2) and MHg - Saouter et al. 1991.t-alpha for equilibrium for sedimentpartitioningday 14 14 14Experiment duration from Saouter et al.1991.Elimination adjustment factor unitless 3 3 1 Trudel and Rasmussen 1997.Assimilation efficiency from food unitless 0.04 0.04 0.2 Phillips and Gregory 1979.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.Assimilation efficiency from plants unitless 1 1 1 Phillips and Gregory 1979.Assimilation efficiency from food unitless 1 1 1 Phillips and Gregory 1979.aScreening scenario includes: Benthic Omnivore, Benthic Carnivore, Water-column Herbivore, Water-column Omnivore, and Water-column Carnivore.Exhibit 26. Mercury Chemical-Specific Properties for Aquatic SpeciesDocumentation for the TRIM.FaTE Screening ScenarioWater-column Omnivore Compartment TypeBenthic Invertebrate Compartment TypeAll Fish Compartments TypesaValueParameter Name Units ReferenceWater-column Herbivore Compartment TypeBaP BaABbF BkF Chr DahAIcdPClearance constant unitless 157.6 157.6 157.6 157.6 157.6 157.6 157.6Stehly et al. 1990 / estimated for mayfly,120-day-old nymphs.alpha of equilibrium forsediment partitioningunitless 0.95 0.95 0.95 0.95 0.95 0.95 0.95 Professional judgment.talpha for equilibrium forsediment partitioningdays 14 14 14 14 14 14 14 Professional judgment.Vd(ratio of concentrationin benthic invertebrates toconcentration in water)ml/g 7235.0 7235.0 7235.0 7235.0 7235 7235 7235Stehly et al. 1990 / estimated for mayfly,120-day-old nymphs.Half-life day 1.5 1.5 1.5 1.5 1.5 1.5 1.5Stehly et al. 1990 / calculated fromestimated elimination/depuration rateconstant estimated for mayfly, 120-dayold nymphs.Gamma_fish unitless 0.2 0.2 0.2 0.2 0.2 0.2 0.2 Thomann 1989.Assimilation efficiency fromfoodbunitless 1.0 1.0 1.0 1.0 1.0 1.0 1.0 Professional judgment.Half-life day 2.8 2.8 2.8 2.8 2.8 2.8 2.8Spacie et al. 1983, as cited in MacKayet al. 1992 (bluegill sunfish) forbenzo(a)pyrene.Assimilation efficiency fromplantsbunitless 1.0 1.0 1.0 1.0 1.0 1.0 1.0 Professional judgment.Assimilation efficiency fromplantsbunitless 1.0 1.0 1.0 1.0 1.0 1.0 1.0 Professional judgment.bAll ingestion assimilation efficiencies set to 1 to be consistent with excretion rate calculations.Benthic Omnivore Compartment TypeWater Column Omnivore Compartment TypeBenthic Invertebrate Compartment TypeAll Fish Compartment TypesaaScreening scenario includes: Benthic Omnivore, Benthic Carnivore, Water-column Herbivore, Water-column Omnivore, and Water-column Carnivore.Parameter Name UnitsValueExhibit 27. PAH Chemical-Specific Properties for Aquatic SpeciesDocumentation for the TRIM.FaTE Screening ScenarioReference1,2,3,4,6,7,8, 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- HxCDFClearance constant unitless000000000Sediment Partitioning PartitionCoefficientkg/kg 0.0013 0.0017 0.0055 0.0012 0.042 0.033 0.0081 0.013 0.02Sediment Partitioning Alpha ofEquilibriumunitless 0.95 0.95 0.95 0.95 0.95 0.95 0.95 0.95 0.95Sediment Partitioning Time toReach Alpha of Equilibriumdays 120 42 120 42 42 120 42 120 42Vd(ratio of concentrationin benthic invertebrates toconcentration in water)ml/g000000000Half-life day 5776.2 5776.2 5776.2 5776.2 5776.2 5776.2 5776.2 5776.2 5776.2Assimilation efficiency from food unitless 0.03 0.03 0.5 0.5 0.5 0.5 0.5 0.5 0.5Chemical Uptake Rate Via GillL[water]/kg[fish wetwt]-day142N/Ab14N/AbN/Ab127N/Ab127N/AbGamma_fish unitlessN/Ab0.2N/Ab0.2 0.2N/Ab0.2N/Ab0.2Half-life day 693.15 346.57 346.57 346.57 346.57 495.11 495.11 495.11 495.11Assimilation efficiency from plants unitless 0.03 0.03 0.5 0.5 0.5 0.5 0.5 0.5 0.5Assimilation efficiency from plants unitless 0.03 0.03 0.5 0.5 0.5 0.5 0.5 0.5 0.5Exhibit 28. Dioxin Chemical-Specific Properties for Aquatic SpeciesDocumentation for the TRIM.FaTE Screening ScenarioValueBenthic Invertebrate CompartmentParameter Name UnitsAll Fish CompartmentsaWater Column Omnivore CompartmentbN/A = not applicable. This parameter is used in calculating the uptake when measured data are unavailable.aScreening scenario includes: Benthic Omnivore, Benthic Carnivore, Water-column Herbivore, Water-column Omnivore, and Water-column Carnivore.Water Column Herbivore Compartment1,2,3,7,8,9- 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- TCDFClearance constant unitless00000000Sediment Partitioning PartitionCoefficientkg/kg 0.015 0.067 0.098 0.024 0.072 0.17 0.205 0.056Sediment Partitioning Alpha ofEquilibriumunitless 0.95 0.95 0.95 0.95 0.95 0.95 0.95 0.95Sediment Partitioning Time toReach Alpha of Equilibriumdays 120 42 120 42 42 42 120 42V_d (ratio of concentration inbenthic invertebrates toconcentration in water)ml/g00000000Half-life day 5776.2 5776.2 5776.2 5776.2 5776.2 5776.2 5776.2 5776.2Assimilation efficiency from food unitless 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5Chemical Uptake Rate Via GillL[water]/kg[fish wetwt]-day127N/Ab700N/AbN/AbN/Ab380N/AbGamma_fish unitlessN/Ab0.2N/Ab0.2 0.2 0.2N/Ab0.2Half-life day 495.11 495.11 420.09 420.09 495.11 420.09 5251.1 5251.1Assimilation efficiency from plants unitless 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5Assimilation efficiency from plants unitless 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5Exhibit 28. Dioxin Chemical-Specific Properties for Aquatic SpeciesDocumentation for the TRIM.FaTE Screening ScenarioParameter NameWater Column Omnivore CompartmentaScreening scenario includes: Benthic Omnivore, Benthic Carnivore, Water-column Herbivore, Water-column Omnivore, and Water-columnbN/A = not applicable. This parameter is used in calculating the uptake when measured data are unavailable.ValueUnitsBenthic Invertebrate CompartmentWater Column Herbivore CompartmentAll Fish CompartmentsaClearance constant unitless Professional judgment.Sediment Partitioning PartitionCoefficientkg/kgTCDD data for sandworm in Rubenstein et al. 1990; dry weight sediment.PeCDF: multiplied TCDD partition coefficient for sandworm by congener-specificbioaccumulation equivalency factor in GLWQI from USEPA 1999.Sediment Partitioning Alpha ofEquilibriumunitless Professional judgment.Sediment Partitioning Time toReach Alpha of EquilibriumdaysTCDD: professional judgment; PeCDF: Rubinstein et al. 1990; data for TCDF insandworm.V_d (ratio of concentration inbenthic invertebrates toconcentration in water)ml/g Professional judgment.Half-life day Change source to f-passAssimilation efficiency from food unitlessTCDD: calculated from data in Kleeman et al. 1986b trout data as cited inUSEPA 1993; PeCDF: used assimilation efficiency for TCDD in trout.Chemical Uptake Rate Via GillL[water]/kg[fish wetwt]-dayMuir et al. 1986.Gamma_fish unitless Thomann 1989Half-life day Change source to f-passAssimilation efficiency from plants unitlessTCDD: calculated from data in Kleeman et al. 1986b trout data as cited inUSEPA 1993; PeCDF: used assimilation efficiency for TCDD in trout.Assimilation efficiency from plants unitlessTCDD: calculated from data in Kleeman et al. 1986b trout data as cited inUSEPA 1993; PeCDF: used assimilation efficiency for TCDD in trout.Parameter Name Units ReferencebN/A = not applicable. This parameter is used in calculating the uptake when measured data are unavailable.aScreening scenario includes: Benthic Omnivore, Benthic Carnivore, Water-column Herbivore, Water-column Omnivore, and Water-column Carnivore.Water Column Omnivore CompartmentBenthic Invertebrate CompartmentExhibit 28. Dioxin Chemical-Specific Properties for Aquatic SpeciesDocumentation for the TRIM.FaTE Screening ScenarioWater Column Herbivore CompartmentAll Fish Compartmentsa

### Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR: Summary of Approach and Evaluation



# ATTACHMENT 2: Description of Multimedia Ingestion Risk Calculator (MIRC) Used for RTR Exposure and Risk EstimatesAttachment 2 to Technical Support Document for TR IM-Based Multipathway Screening Scenario for RTR 2-i 

### [This page intentionally left blank.]Attachment 2 to Technical Support Document for TR IM-Based Multipathway Screening Scenario for RTR 2-ii 

### TABLE OF CONTENTS

1 Introduction ................................................................................................................... ......1
1.1 Purpose and Overview..................................................................................................1
1.2 Scope of MIRC.............................................................................................................. 1
1.3 Use in EPA’s Air Toxics Program..................................................................................2
1.4 MIRC Highlights ............................................................................................................ 3 1.5 Organization of This Document.....................................................................................3

2 MIRC Overview.................................................................................................................. ..5

2.1 Software ................................................................................................................... .....5
2.2 Exposure Pathways.......................................................................................................7
2.3 Receptor Groups...........................................................................................................8

3 Exposure Algorithms........................................................................................................11
3.1 Farm Food Chain Algorithms ......................................................................................11
3.1.1 Estimating Chemical Concentrations in Produce ................................................11 3.1.2 Estimating Chemical Concentrations in Animal Products....................................18

3.2 Chemical Intake Calculations for Adults and Non-Infant Children...............................22
3.2.1 Chemical Intake from Soil Ingestion....................................................................24 3.2.2 Chemical Intake from Fish Ingestion ...................................................................24 3.2.3 Chemical Intake from Fruit Ingestion...................................................................26 3.2.4 Chemical Intake from Vegetable Ingestion..........................................................26 3.2.5 Chemical Intake from Animal Product Ingestion..................................................28 3.2.6 Chemical Intake from Drinking Water Ingestion ..................................................30

3.3 Total Chemical Intake..................................................................................................30
3.4 Chemical Intake Calculations for Nursing Infants........................................................32
3.4.1 Infant Average Daily Absorbed Dose ..................................................................32 3.4.2 Chemical Concentration in Breast Milk Fat .........................................................33 3.4.3 Chemical Concentration in Aqueous Phase of Breast Milk .................................37 3.4.4 Alternative Model for Infant Intake of Methyl Mercury .........................................39

4 Dose-Response Values Used for Assessment...............................................................41

5 Risk Characterization .......................................................................................................46

5.1 Cancer Risks............................................................................................................... 46

5.2 Non-cancer Hazard Quotients.....................................................................................48

5.2.1 Hazard Quotients for Chemicals with a Chronic RfD...........................................48 5.2.2 Hazard Quotients for Chemicals with RfD Based on Developmental Effects......48 5.2.3 Hazard Index for Chemicals with RfDs................................................................48

6 Model Input Options .........................................................................................................50

6.1 Environmental Concentrations ....................................................................................50Attachment 2 to Technical Support Document for TR IM-Based Multipathway Screening Scenario for RTR 2-iii 

### 6.2 Farm-Food-Chain Parameter Values ..........................................................................51
6.2.1 List of Farm-Food-Chain (FFC) Parameters........................................................51 6.2.2 Produce Parameter Values .................................................................................52 6.2.3 Animal Product Parameter Values ......................................................................59
6.3 Adult and Non-Infant Exposure Parameter Values .....................................................61
6.3.1 Body Weights ......................................................................................................61 6.3.2 Water Ingestion Rates.........................................................................................62 6.3.3 Local Food Ingestion Rates.................................................................................63 6.3.4 Local Fish Ingestion Rates ..................................................................................67 6.3.5 Soil Ingestion Rates ............................................................................................71 6.3.6 Total Food Ingestion Rates .................................................................................72
6.4 Other Exposure Factor Values....................................................................................73
6.4.1 Exposure Frequency ...........................................................................................73 6.4.2 Fraction Contaminated ........................................................................................74 6.4.3 Preparation and Cooking Losses ........................................................................74
6.5 Breast-Milk Infant Exposure Pathway Parameter Values............................................76
6.5.1 Receptor-specific Parameters .............................................................................76 6.5.2 Chemical-Specific Parameter Values..................................................................80

7 Summary of MIRC Default Exposure Parameter Settings.............................................84
7.1 Default Ingestion Rates...............................................................................................84
7.2 Default Screening-Level Population-Specific Parameter Values ................................86
7.3 Default Chemical-Specific Parameter Values for Screening Analysis.........................86
7.4 Screening-Level Parameter Values for Nursing Infant Exposure................................88

7.4.1 Dioxins.................................................................................................................8 8 7.4.2 Methyl Mercury....................................................................................................89

8 REFERENCES....................................................................................................................9 0Attachment 2 to Technical Support Document for TR IM-Based Multipathway Screening Scenario for RTR 2-iv 

### LIST OF EXHIBITS

Exhibit 2-1. Overview of Access-based MIRC So ftware Application for Performing Farm-Food-Chain Ingestion Exposure and Risk Calcul ations .........................................................................6
Exhibit 2-2. Transfer Pathways for Modeled Farm Food Ch ain (FFC) Media .............................................8

Exhibit 3-1. Chemical Tran sfer Pathways for Produce ........................................................................... ...12
Exhibit 3-2. Estimating Chemical Concentration in A boveground Pr oduce...............................................12

Exhibit 3-3. Chemical Transfer Pathways for Anim al Products .................................................................19
Exhibit 4-1. Dose-response Va lues for Chemicals Addressed by the Screening Scenario.......................44

Exhibit 6-1. MIRC Parameters Used to Estimate Chemical Concentrations in Farm Foods.....................51
Exhibit 6-2. Chemical-Specific Inputs for Produc e Parameters.................................................................5 3
Exhibit 6-3. Chemical-Specific Inputs by Plant Type for Chemicals Included in MIRC ... Error! Bookmark not defined.

Exhibit 6-4. Non-Chemical -Specific Pro duce Inputs .................................... Error! Bookmark not defined.

Exhibit 6-5. Animal Produc t Chemical-specific Inputs for Chemicals Include d in MIRC............................59

Exhibit 6-6. Soil and Plant Ingestion Rates for Anim als....................................................................... ......60
Exhibit 6-7. Mean and Percentile Body We ight Estimates for Adults and Ch ildren..................................62
Exhibit 6-8. Estimated Daily Per capita Mean and Percentile Water Ingestion Rates for Children and Adults ......................................................................................................................... .......63
Exhibit 6-9. Summary of Age-Group-Specific Food Ingestion Rates for Farm F ood Items.......................64
Exhibit 6-10. Daily Mean and Percentile Per Capita Fish Ingestion Rates.. Error! Bookmark not defined.
Exhibit 6-11. Daily Mean and Percentile Soil Ingestion Rates for Children and Adults............................72
Exhibit 6-12. Daily Mean and Percentile Per Capita Total Food Intake.....................................................73
Exhibit 6-13. Fraction Weight Losses from Preparation of Various Foods ................................................75
Exhibit 6-14. Scenario- and Receptor-Specific I nput Parameter Values Used to Estimate Infant Exposures via Br east Milk.................................................................................................76
Exhibit 6-15. Average Body Weight fo r Infants ................................................................................. .........77
Exhibit 6-16. Time-weighted Aver age Body Weight for Mothers ...............................................................78

Exhibit 6-17. Infant Br east Milk Inta ke Rates................................................................................. ............79
Exhibit 6-18. Chemical-specific Input Paramete r Values for Breast M ilk Exposure Pathway ...................81
Exhibit 7-1. Farm Food Category Ingestion Rates for Conservative Screening Scenario for Farming Households ..................................................................................................................... ..85
Exhibit 7-2. Mean Body Weight Es timates for Adults and Child ren ..........................................................86
Exhibit 7-3. Chemical-Specific Para meter Values for Input to MIRC ........................................................87

Exhibit 7-4. Chemical and Animal-T ype Specific Biotransfer Factor (Ba) Values for Input to MIRC .........88Attachment 2 to Technical Support Document for TR IM-Based Multipathway Screening Scenario for RTR 2-v 

### LIST OF EQUATIONS

Equation 3-1. Chemical Concentra tion in Abovegr ound Prod uce .............................................................13
Equation 3-2. Chemical Concentration in Aboveground Prod uce Due to R oot Upta ke ............................13
Equation 3-3. Chemical Concentration in Abovegr ound Produce Due to Deposition of Particle-phase Chemical ....................................................................................................................... ....14
Equation 3-4. Chemical Concentrati on in Aboveground Produce D ue to..................................................15
Equation 3-5. Conversion of Aboveground Produce Chem ical Concentra tion from..................................16
Equation 3-6. Chemical Concentration in Belo wground Produce: Nonionic Organi c Chemicals.............17
Equation 3-7. Chemical Concentration in Be lowground Produce: I norganic Chem icals..........................18
Equation 3-8. Conversion of Belowgrou nd Produce Chemical Concentrati on from..................................18
Equation 3-9. Chemical Concentration in Beef, Pork, or Total Da iry.........................................................19
Equation 3-10. Chemical Concen tration in Poul try or Eggs...................................................................... .20
Equation 3-11. Incidental Ingestion of Chemical in Soil by Livestock........................................................20
Equation 3-12. Ingestion of Chem ical in Feed by Livestock..................................................................... .21
Equation 3-13. Chemical Concentration in Lifestock Feed (A ll Aboveground)..........................................21
Equation 3-14. Chemical Concentration in Livestock Feed Due to Root Uptake ......................................22
Equation 3-15. Average Daily Dose fo r Specified Age Gr oup and Food Type..........................................22
Equation 3-16. Chemical In take from So il Ingestion............................................................................ ......24
Equation 3-17. Chemical Inta ke from Fish Ingestion ............................................................................ .....25
Equation 3-18. Consumption-weighted Chemical Concentr ation in Fish ..................................................25
Equation 3-19. Chemical Intake fr om Consumption of Exposed Fr uits .....................................................26
Equation 3-20. Chemical Intake from Consumption of Pr otected Fr uits....................................................26
Equation 3-21. Chemical Inta ke from Exposed Vegetabl es ......................................................................27
Equation 3-22. Chemical Intake from Protect ed Vegetabl es.....................................................................2 7
Equation 3-23. Chemical In take from R oot Veget ables........................................................................... ..27
Equation 3-24. Chemical Inta ke from Ingesti on of Beef ......................................................................... ...28
Equation 3-25. Chemical In take from Da iry Ingestion ........................................................................... ....28
Equation 3-26. Chemical Inta ke from Pork Ingestion ............................................................................ ....29
Equation 3-27. Chemical Inta ke from Poul try Inges tion......................................................................... ....29
Equation 3-28. Chemical In take from E gg Ingest ion ............................................................................. ....30
Equation 3-29. Chemical Intake fr om Drinking Wate r Ingest ion................................................................30
Equations 3-30 to 3-35. Total Average Daily Dose of a Chemical for Di fferent Age Groups ....................31
Equation 3-36. Lifetime Average Daily Do se (L ADD) ............................................................................. ...31Attachment 2 to Technical Support Document for TR IM-Based Multipathway Screening Scenario for RTR 2-vi Equation 3-37. Average Daily Dose of Chemical to the Nursing In fant .....................................................33
Equation 3-38. Chemical Concen tration in Brea st Milk Fat ...................................................................... .34
Equation 3-39. Daily Mate rnal Absorb ed Intake ................................................................................. .......35
Equation 3-40. Biological Elimination Rate Cons tant for Chemicals for Non-lactating Women ................36
Equation 3-41. Biological Elimination Constant for Lipophilic Chemicals fo r Lactating Women ...............36
Equation 3-42. Chemical Concentratio n in Aqueous Phase of Breast Milk...............................................37
Equation 3-43. Fraction of Total Chemical in Body in the Blood Plasma Comp artment ...........................38
Equation 3-44. Biological Elimination Rate Constant for Hydr ophilic Chem icals ......................................39
Equation 3-45. Calculation of Infant Average Daily Absorbed Dose of Methyl Mercury............................40
Equation 5-1. Calculation of Excess Lifetime Cancer Risk...................................................................... ..46
Equations 5-2 to 5-8. Lifetime Cancer Risk: Chemicals with a Muta genic MOA for Cancer .....................47
Equation 5-9. Hazard Quotient fo r Chemicals with a Chronic RfD ............................................................48
Equation 5-10. Hazard Index Calculation ....................................................................................... ...........49
Equation 6-1. Calculation of Age-Group-Spe cific and Food-Specifi c Ingestion Rates..............................67
Equation 6-2. Calculation of Alternative A ge-Group-Specific Fish Ingestion Rates ..................................69Attachment 2 to Technical Support Document for TR IM-Based Multipathway Screening Scenario for RTR 2-1 

## 1 Introduction

1.1 Purpose and Overview
This document provides a detailed description of the Multimedia Ingestion Risk Calculator (MIRC), an Access-based 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. 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., conservative 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 val ues for many parameters, data permitting. Generally conservative default values were a ssigned to each parameter in the tool and the default configuration is used for initial risk scr eening efforts by EPA’s Office of Air Quality Planning and Standards’ (OAQPS) for Risk and Technology Review (RTR) multimedia risk assessments (the default inputs were used to calculate the de minimis screening thresholds). 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 t he 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 included to estimate exposure and risks to nursing infants.

MIRC was developed to be a flexible, transparent application using Microsoft Access software. 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 approxim ately 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 productsAttachment 2 to Technical Support Document for TR IM-Based Multipathway Screening Scenario for RTR 2-2 

### grown in an area of chemical deposition. Vegetables and fruits in such areas can become contaminated directly by deposition of the airbor ne chemical to foliage, fruits, and vegetables or indirectly by root uptake of the chemical deposit ed to soils. Livestock can be exposed to the PB chemicals via ingestion of contaminated forage and in cidental 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 develop ed 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).

MIRC is an Access-based tool that facilitates calculation of risks associated with the indirect ingestion exposure pathways for persons consuming produce and animal products grown in an air depositional area of concern. The tool uses algorithms described in or adapted from HHRAP to calculate exposures from the produce and animal products. Included with MIRC is a fish ingestion pathway and drinking water ingestion pathway for scenarios in which those pathways may be important. MIRC also includes a breast milk ingestion pathway for nursing infants based primarily on EPA’s Methodology for Assessing Health Risks Associated with Multiple Pathways of Exposure to Combustor Emissions (hereafter MPE; EPA 1998).



### 1.3 Use in EPA’s Air Toxics Program
MIRC was designed to help predict human health risks from PB HAPs for EPA’s RTR assessments. EPA evaluates the fate of HAP re leases to air from source categories after implementation of technology-based Maximu m Achievable Control Technology (MACT) standards. For volatile chemicals that do not partition to other environmental media and for non-persistent chemicals that degrade relatively quickly in the environment, evaluation of health risks from direct inhalation of the chemical in air can provide reasonable estimates of total risk.

For PB-HAPs, however, indirect exposure pathway s, 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 ri sk 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 tr ansport 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 FFCAttachment 2 to Technical Support Document for TR IM-Based Multipathway Screening Scenario for RTR 2-3 

### concentrations, ingestion exposures, and human health risks for OAQPS’ RTR assessments. It uses the same approach that OAQPS intends to impl ement directly in its TRIM system via three modules beyond TRIM.FaTE: TRIM Farm Food Chain, TRIM.Expo Ingestion 

### , and TRIM.Risk

. 1



### 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 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 possi bilities of risk, or the user can select other options or overwrite parameter values to tailor the estimates to a specific scenario or location.

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 latestrecommended values for children issued September 30, 2008, in the Agency’s ChildSpecific Exposure Factors Handbook (CSEFH) (EPA 2008a).



### 1.5 Organization of This Document
Sections 2 through 5 of this document describe the exposure and risk models implemented in MIRC. Section 2 provides an overview of the FFC exposure scenario and indicates options

1 General information about the TRIM system is av ailable at http://www.epa. gov/ttn/fera/trim_gen.html.Attachment 2 to Technical Support Document for TR IM-Based Multipathway Screening Scenario for RTR 2-4 

### 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 conservative risk screening assessments, and Section 8 provides the references. Appendix A provides guidance to users on how to set up and run MIRC for their own applications.

Note that the default parameterization described in Section 7 was used to estimate de minimis releases of PB-HAPs from facilities assumed to pose negligible risk to subsistence communities in the vicinity of a facility emitting the HAPs to air. Users of MIRC can modify the default values for many of the parameters to better represent a specific exposure scenario.Attachment 2 to Technical Support Document for TR IM-Based Multipathway Screening Scenario for RTR 2-5 

## 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 a subsistence farmer/fisher population 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 consumption, incidental soil ingestion, fish ingestion, and ingestion of ten types of farm food chain (FFC) products: exposed fruits, protected fruits, ex posed 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) 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).



### 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 2-1 provides a flowchart displaying the types of inputs required or optional and general flow of calculations carried out by the tool.Attachment 2 to Technical Support Document for TR IM-Based Multipathway Screening Scenario for RTR 2-6 

### Exhibit 2-1. Overview of Access-based MIRC Software Application for Performing Farm-Food-Chain Ingestion Exposure and Risk Calculations

User Selects Receptor Characteristics
From Options or Over-write Body Weight User Specifies Environmental Concentrations for Chemical of Concern

Air Root-zone and Surface Soils Fish Drinking Water

User Option to Add Breast Milk Pathway yes

no User Selects BMP Parameter Values Uptake by foliage / Uptake by roots:
Vegetables, Fruits Grains Hay, Grass Animal Products

Home Grown Food Product Ingestion Rates Fish and Water Ingestion Rates

Duration Breast Feeding; maternal and infant characteristics Average Daily Dose (ADD) for Age Group y; y =1 to 5

Chemical Intake with Food/ Medium Type i; i = 1 to 10

Lifetime Average Daily Dose

Risk Characterization Module Farm Food Chain Biotransfer Calculations

Breast Milk Exposure Module

ADD Maternal
[C] in milk

Infant Dose Adult ADD x absorption efficiency

Exposure Module

Chemical Toxicity Reference

Values:

SF and RfDs Age-specific & Lifetime Exposure Doses

Lifetime Cancer Risks
Age-Specific Hazard Quotients User Selects Chemical(s) of Concern: For Each, MIRC Calculates RisksAttachment 2 to Technical Support Document for TR IM-Based Multipathway Screening Scenario for RTR 2-7 

### An Access 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 s upply values for the following chemical-specific parameters for the scenario being evaluated:

• 

### Air concentration of total chemical,

• 

### Fraction chemical in air in vapor-phase,

• 

### Wet and dry deposition rates for particle-phase chemical,

• 

### Drinking water concentration,

• 

### Chemical concentration in surface soils (two locations; can be tilled and untilled),

• 

### Chemical concentration in root-zone soils (two locations; can be tilled and untilled), and

• 

### Chemical concentrations in pan fish and in game fish.

Users can input measured values or values estimated by TRIM.FaTE or other models for these parameters.

The MIRC application uses the input data and a variety of empirical transfer factor values (included in its database) to estimate chemical concentrations in nine categories of FFC food types (Section 2.2). The FFC algorithms and transfer factor values included in MIRC are based on those presented in Chapter 5 of EPA’s Human Health Risk Assessment Protocol for Hazardous Waste Combustion Facilities, hereafter referred to as 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 PAHs that are carcinogenic by a mutagenic mode of action).

The tool assumes that the same individuals (farming family or household that gardens and raises animals) are exposed via all of the pathways specified (i.e., pathways with non-zero ingestion rates). The tool therefore is useful in estimating risk to the maximally exposed individuals (MEI) in a risk assessment. To evaluate multiple populations, the user must specify the full exposure scenario for each population 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 2-2 summarizes the pathways by which c hemicals are transferred to these food media. Note that for a general screening-level assessment, all of the pathways can be modeled, as is the case for EPA’s Risk and Technology Review (RTR) calculation of de minimis emission rates for persistent and bioaccumulative hazardous air pollutants (PB-HAPs) (EPA 2008b).Attachment 2 to Technical Support Document for TR IM-Based Multipathway Screening Scenario for RTR 2-8 

### Exhibit 2-2. 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 grain a

• Soil ingestion
Pork • Ingestion of silage and grain a

• Soil ingestion Poultry and eggs • Ingestion of grain a

• Soil ingestion

a Chemical concentrations in forage, silage, and gr ain are estimated via intermediate calculations analogous to those used for aboveground produce.



### Produce types included in the FFC can accumulate 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 thro ugh 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.

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 grow n 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.



### 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 2 to Technical Support Document for TR IM-Based Multipathway Screening Scenario for RTR 2-9 

### EPA’s HHRAP (Chapter 4, EPA 2005a) recommends assessing exposures for children and adults separately, but considers all non-infant children in one category. Specifically, HHRAP recommends eight categories of receptor: farmer, child farmer, resident, child resident, fisher, child fisher, acute receptor, and nursing infant. Over time, different EPA programs have used different child age groupings to evaluate body wei ghts, 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 gr oupings 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 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 mu tagenic 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 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 1997a) 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, 1994) remains the most recent survey of ingestion rates for home-grown foods, and EPA’s analysis of those data, published in its 1997 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.Attachment 2 to Technical Support Document for TR IM-Based Multipathway Screening Scenario for RTR 2-10 

### 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).

Exposure and risks to infants under 1 year of age are estimated only for the breast-milkingestion pathway.

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 some of the PAHs.Attachment 2 to Technical Support Document for TR IM-Based Multipathway Screening Scenario for RTR 2-11 

## 3 Exposure Algorithms

The exposure algorithms in MIRC are described below in four sections. Section 3.1 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 betw een 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, 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.F aTE 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 depositi on rates from dispersion model simulations that use unitized 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.

3.1.1 Estimating Chemical Concentrations in Produce Produce (vegetables and fruits) can become cont aminated 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 3-1 lists the pathways by which c hemicals 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 de minimis emission rates for PB-HAPs in its RTR assessments (EPA 2008b), and as described in the “ Technical Support Document for TRIMBased Multipathway Screening Scenario for RTR” 

### . Sections 3.1.1.1 and 3.1.1.1 describe the transfer pathways and algorithms for abovegr ound and belowground produce, respectively.Attachment 2 to Technical Support Document for TR IM-Based Multipathway Screening Scenario for RTR 2-12 

### Exhibit 3-1. Chemical Transfer Pathways for Produce

Farm Food Media Chemical Transfer Pathways Exposed fruits and vegetables • Direct deposition from air of particle-bound chemical

• Air-to-plant transfer of vapor

phase chemical

• Root uptake from soil Aboveground Produce

Protected fruits and vegetables • Root uptake from soil

Belowground Produce Root vegetables • Root uptake from soil



### Aboveground Produce For aboveground exposed produce, chemical mass is assumed to be transferred to plants from the air in three ways, asillustrated in Exhibit 3-2. First, particlebound 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 ( Pr AG-produce 

### ) is estimated using an empirical bioconcentration factor ( Br AG-produce 

### ) that relates the chemical concentration in the plant to the average chemical concentration in the so il at the root-zone depth in the produce-growing area ( Cs root-zone_produce 

### ).

The edible portions of aboveground protected produce are not subject to contamination via particle deposition ( Pd ) or vapor transfer (Pv ). Therefore, root uptake of chemicals is the primary mechanism through which aboveground protected produce becomes contaminated. The chemical concentration in the aboveground plant due to root uptake from soil ( PrAG-produceDW 

### ) is estimated using an empirical bioconcentration factor ( Br AG-produce-DW 

### ) that relates thechemical concentration in the plant to the average chemical concentration in the soil at the rootzone depth in the produce-growing area ( Cs root-zone_produce 

### ). Exhibit 3-2. Estimating Chemical Concentration in Aboveground Produce

Deposition of Particles ( Pd ) Root Uptake from Soil ( Pr AG-produce ) Vapor Transfer

( Pv )

Chemical Concentration in

Aboveground ProduceAttachment 2 to Technical Support Document for TR IM-Based Multipathway Screening Scenario for RTR 2-13 

### Equation 3-1. Chemical Concentration in Aboveground Produce

) ( ) ( ) ( ) ( 

### P i i i DW produce AG i DW produce AG 

### Pv Pd r C + + =

− − − −



### where:

C

AG-produce-DW(i) = Concentration of chemical in edibl e portion of aboveground produce type i

, exposed or protected, on a dry-weig ht (DW) basis (mg/kg produce DW)

Pd (i) = Chemical concentration in edible portion of aboveground produce type i due to deposition of particles (mg/kg produce DW); for protected aboveground produce, Pd equals zero

Pr

AG-produce-DW(i) = Chemical concentration in edible portion of aboveground produce type i

, exposed or protected, due to root uptake from soil at the root-zone depth of the produce growing area (mg/kg produce DW)

Pv (i) = Chemical concentration in edible portion of aboveground produce type i due to air-to-plant transfer ( μ g/g [or mg/kg] produce DW); for protected aboveground produce, Pv equals zero



### Equation 3-2. Chemical Concentration in Aboveground Produce Due to Root Uptake

) ( ce zone_produ

- root ) ( 

### P i DW produce AG i DW produce AG 

### Br Cs r

− − − − 

### × = where:

Pr

AG-produce-DW(i) = Concentration of chemical in edibl e portion of aboveground produce type i

, exposed or protected , due to root uptake from soil at root-zone depth in the produce-growing area, on a dry-weight (DW) basis (mg/kg produce DW)

Cs root-zone_produce = Average chemical concentration in soil at root-zone depth in produce-growing area (mg/kg soil DW)

Br

AG-produce-DW(i) = Chemical-specific plant/soil chemical bioconcentration factor for edible portion of aboveground produce type i

, exposed or protected (g soil DW / g produce DW)Attachment 2 to Technical Support Document for TR IM-Based Multipathway Screening Scenario for RTR 2-14 

### Equation 3-3. Chemical Concentration in Aboveground Produce Due to Deposition of Particle-phase Chemical

) ( ) ( )) ( * ) ( ( ) ( ) ( 

### ) 1 ( )) ( ( 000

, 1

i i i Tp i kp i i 

### kp Yp e Rp Drwp Fw Drdp Pd

× − × × × + ×

= −



### where:

Pd (i) = Chemical concentration in aboveground produce type i on a dry-weight (DW) basis due to particle deposition (mg/kg produce DW); set equal to zero for protected aboveground produce
Drdp = Average annual dry deposition of particle-phase chemical (g/m 2 -yr)

Fw = Fraction of wet deposition that adheres to plant surfaces; 0.2 for anions, 0.6 for cations and most organics (unitless)
Drwp = Average annual wet deposition of particle-phase chemical (g/m 2 -yr)
Rp (i) = Interception fraction of the edible portion of plant type i (unitless)
kp (i) = Plant surface loss coefficient for plant type i (yr -1 )

Tp (i) = Length of exposure to deposition in the fi eld per harvest of the edible portion of plant type i (yr)

Yp (i) = Yield or standing crop biomass of the edible portion of plant type i (kg produce DW/m 2 )



### Note that Equation 3-3 differs from Equation 5- 14 in HHRAP, from which it is derived. In HHRAP, Equation 5-14 includes the term Q x ( 1 – Fv ) to indicate the emissions rate, in g/sec, of chemical from the source and the proportion of the chemical that remains in, or partitions to, the particle-phase in the air. Also in HHRAP, the dry and wet particle phase deposition rates, Dydp and Dywp, respectively, are normalized to the emission rate and are expressed in units of sec/m 2 

### -yr.

With MIRC, the user inputs both the dry and wet particle-phase deposition rates, Drdp and Drwp , respectively, in units of g/m 2 

### -yr for a specific location relative to an emissions source. Those deposition 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 chemic al emissions term used in HHRAP, Q , therefore, is not used in MIRC’s Equation 3-3. In addition, in MIRC, Drdp and Drwp , the average annual dryand wet-particle-phase deposition rates, respectively, are in units of g/m 2 

### -yr. Users of TRIM.FaTE should note that the dry- and wet-particle-deposition rates output from TRIM.FaTE are in units of g/m 2 

### -day; therefore, users must adjust the TRIM.FaTE output values to units of g/m 2 

### -yr (i.e., multiply by 365 days/yr) before inputting values for Drdp and Drwp into MIRC.Attachment 2 to Technical Support Document for TR IM-Based Multipathway Screening Scenario for RTR 2-15 

### Equation 3-4. Chemical Concentration in Aboveground Produce Due to Air-to-Plant Transfer of Vapor-phase Chemical

a i AG i AG i 

### VG Bv Fv Ca Pv ρ ) ( ) ( ) ( 

### × × ×

=

where:

Pv (i) = Concentration of chemical in edibl e portion of aboveground produce type i from air-to-plant transfer of vapor-phase chemical on a dry-weight (DW) basis ( μ g/g produce DW); set equal to zero for protected aboveground produce

Ca = Average annual total chemical concentration in air (g/m 3 )

Fv = Fraction of airborne chemical in vapor phase (unitless)

Bv AG(i) = Air-to-plant biotransfer factor for aboveground produce type i for vapor-phase chemical in air ([mg/g produce DW] / [mg/g air], i.e., g air/ g produce DW)

VG AG(i) = Empirical correction factor for aboveground exposed produce type i to address possible overestimate of the diffusive tran sfer of chemical from the outside to the inside of bulky produce, such as fruit (unitless)

ρ a = Density of air (g/m 3 )



### Note that Equation 3-4 differs from Equation 5- 18 in HHRAP, from which it is derived. In HHRAP, Equation 5-18 includes the term Q x Fv to indicate the emissions rate, in g/sec, of chemical from the source and the fraction of th e chemical in vapor phase in the air. HHRAP also includes the parameter Cyv , or the unitizedyearly average air concentration of vaporphase chemical in units of μ g-sec/g-m 3 

### . For MIRC, the user inputs the average annual total air concentration of the chemical, Ca , for a specific location relative to the source in units of g/m 3 

### ; MIRC includes a chemical-specific default value for Fv for chemicals included in its database. The air concentration might be a value measured near that location or a value estimated by a fate and transport model such as TRIM.FaTE. Users of TRIM.FaTE should note that the average annual concentration of the total chemical in air (i.e., total of both vapor and particulate phases), Ca , output from TRIM.FaTE is in units μg/m 3 

### ; therefore, the user must adjust the value to units of g/m 3 

### (i.e., divide by 1,000 μ g/g) before entering it in MIRC.

The calculations of chemical concentration in aboveground produce, ( C AG-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, C AG-produce-WW 

### , using Equation 3-5 and the moisture content ( MAF ) of the FFC food category.Attachment 2 to Technical Support Document for TR IM-Based Multipathway Screening Scenario for RTR 2-16 

### Equation 3-5. Conversion of Aboveground Produce Chemical Concentration from Dry- to Wet-Weight Basis



### ⎟ ⎟ ⎠ ⎞ ⎜ ⎜ ⎝ ⎛

− × =

− − − − 

### 100 ) 100 ( ) ( ) ( ) ( i i DW produce AG i WW produce AG 

### MAF C C

where:

C

AG-produce-WW(i) = Chemical concentration in edible portion of aboveground produce type i on a wet-weight (WW) basis (mg/kg produce WW)



### C

AG-produce-DW(i) = Chemical concentration in edible portion of aboveground produce type i on a dry-weight (DW) basis (mg/kg produce DW)

MAF (i) = Moisture adjustment factor for aboveground produce type i to convert the chemical concentration estimated for dry- weight produce to the corresponding chemical concentration for full-weight fresh produce (percent water)



### 3.1.1.1 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.

Nonionic Organic Chemicals 

### For belowground produce and for nonionic organic chemicals, the concentration in the tuber or root vegetable derived from exposure to the chemical in soil is estimated using an empirical root concentration factor ( RCF ) and the average chemical concentration in the soil at the root-zone depth in the produce-growing area ( Cs root-zone_produce 

### ), as shown in Equation 3-6. The RCF relates the chemical concentration in the plant on a wet-weight basis to the average chemical concentration in the root-zone soil ( Cs root-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.Attachment 2 to Technical Support Document for TR IM-Based Multipathway Screening Scenario for RTR 2-17 

### Equation 3-6. Chemical Concentration in Belowground Produce: Nonionic Organic Chemicals



### UCF Kds VG RCF Cs rootveg produce zone root



### × × ×

= −

_ WW

- produce

- BG 

### C

where:

C BG-produce-WW = Concentration of chemical in belowgrou nd (BG) produce (i.e., tuber or root vegetable) on a wet-weight (WW) ba sis (mg chemical/kg produce WW) *

Cs root-zone_produce = Average chemical concentration in soil at root-zone depth in produce-growing area, on a dry-weight (DW) basis (mg chemical/kg soil DW)

RCF = Chemical-specific root concentration factor for tubers and root produce (L soil pore water/kg root WW) *

VG rootveg = Empirical correction factor for belowground produce (i.e., tuber or root vegetable) to account for possible overestimate of the diffusive transfer of chemicals from the outside to the insi de of bulky tubers or roots (based on carrots and potatoes) (unitless) *

Kds = Chemical-specific soil/water partition coefficient (L soil pore water/kg soil DW)

UCF = Units conversion factor of 1 kg/L
* Note that there is only one ty pe 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 es timate of the chemical’s Kow from empirically 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).

Inorganic Chemicals 

### For inorganic chemicals and ionized organic chemicals, it is not possible to predict RCF or Kds values from Kow. For inorganic chemicals, one must use empirical values for the root/soil bioconcentration factor measured for specific ch emicals. The root/soil bioconcentration factor, now specified as Br BG-produce-DW 

### , must be obtained from the literature for each inorganic chemical on a DW basis. For inorganic chemicals, therefore, Equation 3-7 is used instead of Equation 3-6.Attachment 2 to Technical Support Document for TR IM-Based Multipathway Screening Scenario for RTR 2-18 

### Equation 3-7. Chemical Concentration in Belowground Produce: Inorganic Chemicals



### 1 _ rootveg DW produce BG produce zone root -DW BG-produce 

### VG Br Cs C × ×

= − − −



### where:

C BG-produce-DW = Concentration of chemical in edible portion of aboveground produce, due to root uptake from soil at root-zone depth in the produce-growing area, on a dryweight (DW) basis (mg/kg produce DW)

Cs root-zone_produce = Average chemical concentration in soil at root-zone depth in produce-growing area (mg/kg soil DW)

Br

BG-produce-DW = Chemical-specific root/soil chemical bioconcentration factor for edible portion of belowground produce (g soil DW / g produce DW)

VG rootveg = Empirical correction factor for belowground produce (as in Equation 3-6) (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 3-8.

Equation 3-8. Conversion of Belowground Produce Chemical Concentration from Dry- to Wet-Weight Basis



### ⎟ ⎠ ⎞ ⎜ ⎝ ⎛

− × =

− − − − 

### 100 ) 100 ( BG DW produce BG WW produce BG 

### MAF C C

where:

C BG-produce-WW = Chemical concentration in edible portion of belowground produce on a weightweight (WW) basis (mg/kg produce WW)

C BG-produce-DW = Concentration of chemical in edible portion of aboveground produce, due to rootuptake from soil at root-zone depth in the produce-growing area, on a dryweight (DW) basis (mg/kg produce DW)

MAF (BG) = Moisture adjustment factor (as in Equation 3-5, but single value for below ground produce) (percent water)



### 3.1.2 Estimating Chemical Concentrations in Animal Products 

### Chemical concentrations in animal products are estimated based on the amount of chemical consumed by each animal group m through each plant feed type i ( Plant Ch-Intake(i,m) 

### ) and incidental ingestion of soil for ground-foraging animals ( Soil Ch-Intake(m) 

### ). Exhibit 3-3 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 de minimis 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 3-3, the algorithms in MIRC for chemical intake with plant feeds ( Plant Ch-Intake(i,m) 

### ) are based on the assumptions that beef and dairy cattle consume all three plant feed products, while pigs consume only silage and grain, and chickens consume only grain.Attachment 2 to Technical Support Document for TR IM-Based Multipathway Screening Scenario for RTR 2-19 

### Exhibit 3-3. Chemical Transfer Pathways for Animal Products

Farm Food Media Chemical Transfer Pathways Beef and total dairy (including milk) • Ingestion of forage, silage, and grain a

• Incidental soil ingestion
Pork • Ingestion of silage and grain a

• Incidental soil ingestion Animal Products

Poultry and eggs • Ingestion of grain a

• Incidental soil ingestion

a Chemical concentrations in plant feed (i.e., forage , silage, and grain) are estimated via intermediate calculations (see Equations 3-13, 3-14, 3-3, and 3-4).



### Forage and silage are exposed to the air and can accumulate chemical via direct deposition of particle-bound chemical and transfer of vapor-phas e 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 anima l feeds using algorithms analogous to those for aboveground farm produce described above. MIRC uses Equation 3-9 to calculate the concentration of chemical in beef, pork, or total dairy and Equation 3-10 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 3-9). MF is set to 1.0 for chemicals that are not metabolized and for chemicals for wh ich the metabolic degradation rate is unknown. Although other vertebrates, including birds, are likely to have similar metabolic pathways for most chemicals, the conservative assumption is that birds do not metabolize any chemicals; therefore, the MF is omitted from Equation 3-10 for poultry and eggs.

Equation 3-9. Chemical Concentration in Beef, Pork, or Total Dairy



### ⎟ ⎟ ⎠ ⎞ ⎜ ⎜ ⎝ ⎛

+ × × = 

# ∑

= − − n

i m i Intake Ch m Intake Ch m m mammal 

### Plant Soil MF Ba C

1 )

, ( ) ( ) ( ) (



### where:

C mammal(m) = Concentration of chemical in mammalian animal product m , where m = beef, pork, or total dairy (mg chemical/kg animal product WW)

Ba (m) = Chemical-specific biotransfer factor for chemical in diet to chemical in animal food product m , where m = beef, pork, or total dairy ([mg chemical/kg animal product WW] / [mg chemical intake/day] or day/kg WW)

MF = Chemical-specific mammalian metabolism factor that accounts for endogenous degradation of the chemical (unitless)

Soil Ch-Intake(m) = Incidental ingestion of chemical in surface soils by livestock type m during grazing or consumption of foods plac ed on the ground (mg/day); see Equation 3-11 below

Plant Ch-Intake(i,m) = For livestock (animal product) type m , ingestion of chemical from plant feed type i (mg chemical/kg livestock WW); see Equation 3-12 below (If m = beef or total dairy, then n = 3 and i = forage, silage, and grain;

m = pork, then n = 2 and i = silage and grain;

m = poultry, then n = 1 and I = grain.)Attachment 2 to Technical Support Document for TR IM-Based Multipathway Screening Scenario for RTR 2-20 

### Equation 3-10. Chemical Concentration in Poultry or Eggs 

# ( ) )

, ( ) ( ) ( ) ( m i Intake Ch m Intake Ch m m poultry 

### Plant Soil Ba C

− − 

### + × = where:

C poultry(m) = Concentration of chemical in food product m , where m = poultry or eggs (mg chemical/kg animal product WW)

Ba (m) = Chemical-specific biotransfer factor for food product m , where m = poultry or eggs (day/kg animal product WW)

Soil Ch-Intake(m) = Incidental ingestion of chemical in surf ace soils by consumption of food on the ground (mg chemical/day) where m = poultry; see Equation 3-11

Plant Ch-Intake(i,m) = For poultry (and eggs), animal m , ingestion of the chemical in plant feed type i (mg chemical/day), which for poultry is limited to grain; see Equation 3-12



### In MIRC, the incidental ingestion of the chemical in soils by livestock during grazing or consumption of feed placed on the ground ( Soil Ch-Intake(m) 

### ) is estimated using empirical soil ingestion rates ( Qs ) and a soil bioavailability factor for livestock ( Bs ), as shown in Equation 3-11. 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, Cs S-livestock 

### , that is distinct from the surface soil concentr ation input for areas where produce may be grown and where humans might incidentally ingest soils (see Section 6.1).

Equation 3-11. Incidental Ingestion of Chemical in Soil by Livestock



### Bs Cs Qs Soil livestock s m Intake Ch 

### × × =

− − ) ( 

### where:

Soil Ch-Intake(m) = Incidental ingestion of the chemical in surface soils by livestock type m during grazing or consumption of foods plac ed on the ground (mg chemical/day)

Qs (m) = Quantity of soil eaten by animal type m each day (kg soil DW/day)

Cs s-livestock = Chemical concentration in surface soil in contaminated area where livestock feed (mg chemical/kg soil DW)

Bs = Soil bioavailability factor for livestock (unitless) (assumed to be the same for birds and mammals)



### Animal ingestion of the chemical in feed is calculated for each type of livestock based on their assumed diets. For m = beef and dairy cattle, chemical intake is estimated for all three feed types: i = forage, silage, and grain. For pork, chemical intake is estimated only for silage and grain. The chemical intake for poultry is based on grain consumption only. The intake of chemical with each feed type, i

, Plant Ch-Intake(i,m) 

### , is calculated separately according to Equation 3-12. Note that the animal feed ingestion rates are on a dry-weight (DW) basis; hence, no DW to wet weight (WW) conversion is needed.Attachment 2 to Technical Support Document for TR IM-Based Multipathway Screening Scenario for RTR 2-21 

### Equation 3-12. Ingestion of Chemical in Feed by Livestock

) ( )

, ( )

, ( )

, ( i feed m i m i m i Intake Ch 

### C Qp F Plant × × =

− 

### where:

Plant Ch-Intake(i,m) = Ingestion of chemical in plant feed type i (mg chemical/day), where i = forage, silage, or grain, for livestock type m

F (i,m) = Fraction of plant feed type i obtained from contaminated area used to grow animal feed, where I = forage, silage, or grain (unitless) for lifestock type m

Qp (i,m) = Quantity of plant feed type i consumed per animal per day (kg plant feed DW/day), where i = forage, silage, or grain, for livestock type m



### C feed(i) = Concentration of chemical in ingested plant feed type i (mg chemical/kg plant feed DW), where i = forage, silage, or grain



### The concentrations of chemical in the three different types of plant feeds for livestock are calculated according to Equation 3-13. The equation is the same as that for aboveground produce in Equation 3-1, 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 3-13. Chemical Concentration in Lifestock Feed (All Aboveground)

(i) (i) ) ( ) ( 

### Pv Pd P + + = i feed i feed 

### r C

where:

C feed(i) = Concentration of chemical in plant feed type i on a dry-weight (DW) basis (mg chemical/kg plant feed DW), where i = forage, silage, or grain

Pr

feed(i) = Concentration of chemical in plant feed type i due to root uptake from soil (mg/kg DW), where i = forage, silage, or grain; see Equation 3-14 below

Pd (i) = Concentration of chemical in plant feed type i due to wet and dry deposition of particle-phase chemical (mg/kg DW), where i = forage, silage, or grain; when i = grain, the Pd term equals zero

Pv (i) = Concentration of chemical in plant feed type i due to air-to-plant transfer of vapor-phase chemical ( μ g/g [or mg/kg] DW) where i = forage, silage, or grain; when i = grain, the Pd term equals zero



### MIRC calculates the chemical concentration in animal feed due to root uptake from the soil using Equation 3-14. The equation is the same as Equation 3-2, except that a Br value appropriate to grasses is used and MIRC allows for different soil concentrations in the area used to grow animal feed than in the area used to grow produce for human consumption (see Section 6.1, user inputs). Note that for feed type i = grains, the Pd and Pv terms do not apply (are set to zero), because the feed products (i.e., corn kernels, soy beans) are protected from the air (i.e., by husks, pods).Attachment 2 to Technical Support Document for TR IM-Based Multipathway Screening Scenario for RTR 2-22 

### Equation 3-14. Chemical Concentration in Livestock Feed Due to Root Uptake

) ( ) (

_ ) ( 

### P i feed i feed zone root i feed 

### Br Cs r × =

− 

### where:

Pr feed(i) = Concentration of chemical in plant feed type i due to root uptake from soil on a dry-weight (DW) basis (mg chem ical/kg plant feed DW), where i = forage, silage, or grain

Cs root-zone_feed(i) = Average chemical concentration in soil at root-zone depth in area used to grow plant feed type i (mg chemical/kg soil DW), where i = forage, silage, or grain

Br

feed(i) = Chemical-specific plant-soil biocon centration factor for plant feed type i (kg soil DW/kg plant feed DW), where i = forage, silage, or grain



### The algorithms used to calculate Pd (i) 

### and Pv (i) 

### when plant feed type i = forage and silage are identical to those used to calculate Pd (i) 

### and Pv (i) 

### for aboveground exposed produce (i.e., Equations 3-3 and 3-4, respectively).

There are no conversions of DW feed to WW feed, because all feed ingestion rates for livestock are based on DW feed.



### 3.2 Chemical Intake Calculations fo r 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 3-15, are expressed in milligrams of chemical per kilogram of receptor body weight per day (mg/kg-day).

Equation 3-15. Average Daily Dose for Specified Age Group and Food Type



### ⎟ ⎟ ⎠ ⎞ ⎜ ⎜ ⎝ ⎛ ⎟ ⎟ ⎠ ⎞ ⎜ ⎜ ⎝ ⎛

× × × ×

= days 365 (y)

(y) (y) (y) (i) (y,i) (i) (y,i) 

### EF

AT BW ED FC IR C ADD

where:

ADD (y,i) = Average daily dose for age group y from food type or ingestion medium i (mg chemical/kg body weight-day)

C (i) 

### = Concentration of chemical in food type i harvested from the contaminated area (mg chemical/kg food or mg food/L water)

IR (y,i) 

### = Ingestion rate for age group y of food type i (kg/day or L/day)

FC (i) 

### = Fraction of food type i that was harvested from contaminated area (unitless)

ED (y) 

### = Exposure duration for age group y (years)

BW (y) 

### = Body weight for age group y (kg)

AT (y) 

### = Averaging time for calculation of daily dose (years) for age group y , set equal to ED in MIRC

EF (y) 

### = Annual exposure frequency for age group y (days)



### Equation 3-15 takes into account the chemical concentration in each food type i (or in water), the quantity of food brought into the home for consumption, the loss of some of the mass of the foods due to preparation and cooking, how much of the food is consumed per year, the amountAttachment 2 to Technical Support Document for TR IM-Based Multipathway Screening Scenario for RTR 2-23 

### of the food obtained from contaminated areas, and the consumer’s body weight (EPA 1997a, 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. An exception is for the breast-milk exposur e pathway, where the dose absorbed by the mother is relevant to calculating the dose available to and absorbed by her nursing infant, as discussed in Section 3.4.

MIRC evaluates only one contaminated area (set of environmental concentrations), or exposure scenario, at a time. For screening level assessments, all components of this equation are assumed to remain constant for consumers in a given age group over time (e.g., seasonal and annual variations in diet are not explicitly taken into account). To calculate an ADD (y,i) 

### from the contaminated area for food group i over an entire lifetime of exposure, age-group-specific ingestion rates and body weights are used for the age groups described in Section 2.3. In MIRC, the averaging time used to calculate the daily dose for an age group ( AT y 

### ) is equal to the exposure duration for that group ( ED y 

### ); therefore these variables drop out of Equation 3-15.

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:

• 

### 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:

o 

### Milk and other dairy products from cows,

o 

### Beef products,

o 

### Pork products, and

o 

### Poultry and eggs;

• 

### Ingestion of drinking water from specified source; and

• 

### Ingestion of breast milk by infants.

Note that the last exposure pathway is limited to infants.

The algorithms for the first six exposure pathw ays listed above are described in Sections 3.2.1 through 3.2.6. The algorithms for the breast-milk ingestion pathway are described in Section 3.4.Attachment 2 to Technical Support Document for TR IM-Based Multipathway Screening Scenario for RTR 2-24 

### 3.2.1 Chemical Intake from Soil Ingestion 

### Equation 3-16 shows the equation used to estimate chemical intake through incidental ingestion of soil.

Equation 3-16. Chemical Intake from Soil Ingestion



### ⎟ ⎟ ⎠ ⎞ ⎜ ⎜ ⎝ ⎛

⎟ ⎟ ⎟ ⎟

⎠ ⎞

⎜ ⎜ ⎜ ⎜

⎝ ⎛

× × ×

= days 365 EF
BW μg mg 0.001 FC IR C
ADD

(y) Soil Soil(y) Soil

(y) Soil



### where:

ADD Soil(y) = Average daily chemical intake from incidental ingestion of soil or ingestion by child in age group y (mg chemical/kg body weight-day)

C Soil 

### = Concentration of chemical in soil from contaminated area on a dry-weight (DW) basis (μg/g soil DW)

IR Soil(y) 

### = Soil ingestion rate for age group y (g DW/day)

FC Soil 

### = Fraction of soil ingested that is from contaminated area (unitless)

BW (y) 

### = Body weight for age group y (kg)

EF 

### = Exposure frequency; number of days per y ear 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 act ual 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 3-17). Two types of fish are included in the exposure algorithm: trophic level 3 (T3) fish, equivalent to small “pan” fish such as bl uegill, and trophic level 4 (T4) fish, equivalent to game fish such as trout and walleye. The chem ical concentration in fish in Equation 3-17 is estimated as the consumption-weighted chemical concentration using Equation 3-18.Attachment 2 to Technical Support Document for TR IM-Based Multipathway Screening Scenario for RTR 2-25 

### Equation 3-17. Chemical Intake from Fish Ingestion



# ( )( ) days 365 g kg 001 0
2 1 1 1 ⎟ ⎟ ⎠ ⎞ ⎜ ⎜ ⎝ ⎛

× ⎟ ⎟ ⎠ ⎞ ⎜ ⎜ ⎝ ⎛

× × ×

×

− ×

− = EF
BW FC

. IR C
L L ADD

(y) Fish Fish(y) Fish

Fish Fish Fish(y)



### Equation 3-18. Consumption-weighted Chemical Concentration in Fish 

# ( ) ( ) 4 4 3 3 T T Fish T T Fish Fish 

### F C F C C × + × = 

### where:

ADD Fish(y) = Average daily chemical intake from ingestion of local fish for age group y (mg/kg-day)

L1 Fish * 

### = Weight of fish brought into home that is discarded during preparation (e.g., head, bones, liver, other viscera, belly fat, skin with fat) (unitless)

L2 Fish * 

### = Loss of weight during cooking, such as evaporation and loss of fluids into pan (unitless)

C FishT3 

### = Chemical concentration in whole fish for trophic level 3 (T3) fish on a wetweight (WW) basis (mg/kg WW)

C FishT4 

### = Chemical concentration in whole fish for trophic level 4 (T4) fish on a wetweight (WW) basis (mg/kg WW)

F T3 

### = Fraction of fish intake that is from T3 (unitless)

F T4 = Fraction of fish intake t hat is from T4 (unitless)

C Fish 

### = Consumption-weighted mean chemical concentration in total fish (i.e., as specified by Equation 3-18) (mg/kg WW)

FC Fish 

### = Fraction of local fish consumed derived from contaminated area (unitless)

BW (y) 

### = Body weight for age y (kg)

IR Fish(y) * 

### = Local fish ingestion rate for age y (g WW/day)

EF 

### = Exposure frequency; number of days per y ear 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), the tables of fish ingestion rate options included in MIRC are from CSFII data (see Section 6.3.4) and, t herefore, 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 al so should enter L1 and L2 values as specified in Section 6.4.3.



### 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 propor tion of the weight of whole fish that tends to be lost during preparation and cooking across a variety of fish species (Exposure Factors Handbook Table 13-5, EPA 1997a) and included those losses in its HHRAP algorithms for chemical intake from fish (L1 Fish 

### and L2 Fish 

### in Equation 3-17).Attachment 2 to Technical Support Document for TR IM-Based Multipathway Screening Scenario for RTR 2-26 

### 3.2.3 Chemical Intake from Fruit Ingestion 

### Average daily doses of a chemical from homegr own exposed fruits are calculated separately for exposed and protected fruits (Equations 3-19 and 3-20, respectively).

Equation 3-19. Chemical Intake from Consumption of Exposed Fruits 

# ( )( ) ⎟ ⎟ ⎠ ⎞ ⎜ ⎜ ⎝ ⎛

× ⎟ ⎟ ⎠ ⎞ ⎜ ⎜ ⎝ ⎛

× × × ×

− ×

− = 

### days 365 g kg 001 0 2 1 1 1 EF FC

. IR C L L ADD ExpFruit ) ExpFruit(y ExpFruit ExpFruit ExpFruit ) ExpFruit(y



### Equation 3-20. Chemical Intake from Consumption of Protected Fruits


## ( ) ⎟ ⎟ ⎠ ⎞ ⎜ ⎜ ⎝ ⎛

× ⎟ ⎟ ⎠ ⎞ ⎜ ⎜ ⎝ ⎛

× × × ×

− = days 365 g kg 001 0 1 1 EF FC

. IR C L ADD ProFruit ) ProFruit(y ProFruit ProFruit ) ProFruit(y



### where:

ADD ExpFruit(y) ADD ProFruit(y) = Average daily chemical intake from ingest ion of exposed fruit or protected fruit (depending on subscript) (mg chemical/kg body weight-day)

L1 ExpFruit 

### = Mean reduction in fruit weight resulting from removal of skin or peel, core or pit, stems or caps, seeds and defects, and from draining liquids from canned or frozen forms (unitless)

L1 ProFruit 

### = Mean reduction in fruit weight that re sults from paring or other preparation techniques for protected fruits (unitless)

L2 ExpFruit 

### = Mean reduction in fruit weight that results from draining liquids from cooked forms of the fruit (unitless)

C ExpFruit C ProFruit 

### = Chemical concentration in whole exposed fruits or whole protected fruits (depending on subscript) on a wet-weight (WW) basis (mg chemical/kg exposed fruit WW)

EF 

### = Exposure frequency; number of days per y ear of exposure for family(ies) as specified for scenario ( ≤ 365 days)

FC ExpFruit FC ProFruit 

### = Fraction of exposed fruits or protected fruits (depending on subscript) obtained from contaminated area (unitless)

IR ExpFruit(y) IR ProFruit(y) 

### = Ingestion rate of home-grown exposed fruits or protected fruits (depending on subscript) for age y (g WW/kg body weight-day)



### Fruit ingestion rates in the survey were based on weights of unprepared fruits (e.g., one apple; one pear) or the weight of a can of fruit (e.g., 8 oz can). The weight of the fruit ingested is less than the initial weight owing to common preparation actions ( L1 ExpFruit 

### and L1 ProFruit 

### ; e.g., coring apples and pears; 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 ( L2 ExpFruit 

### ). EPA has assumed that cooking of protected fruit results in no loss of weight for the fruit.

3.2.4 Chemical Intake from Vegetable Ingestion 

### MIRC includes three separate algorithms for homegrown vegetables adapted from EPA’s HHRAP Modeling System (EPA 2005a): one for exposed vegetables such as asparagus, broccoli, lettuce, and tomatoes (although they are actually a fruit); one for protected vegetables such as corn, cabbage, soybeans, and peas; and one for root vegetables such as carrots, beets, and potatoes (see Equations 3-21, 3-22, and 3-23, respectively).Attachment 2 to Technical Support Document for TR IM-Based Multipathway Screening Scenario for RTR 2-27 

### Equation 3-21. Chemical Intake from Exposed Vegetables 

# ( ) ⎟ ⎟ ⎠ ⎞ ⎜ ⎜ ⎝ ⎛

× ⎟ ⎟ ⎠ ⎞ ⎜ ⎜ ⎝ ⎛

× × × ×

− = days 365 g kg 001 0 1 1 EF FC

. IR C L ADD ExpVeg ExpVeg(y) ExpVeg ExpVeg ExpVeg(y)



### Equation 3-22. Chemical Intake from Protected Vegetables 

# ( ) ⎟ ⎟ ⎠ ⎞ ⎜ ⎜ ⎝ ⎛

× ⎟ ⎟ ⎠ ⎞ ⎜ ⎜ ⎝ ⎛

× × × ×

− = days 365 g kg 001 0 1 1 EF FC

. IR C L ADD ProVeg ProVeg(y) ProVeg ProVeg ProVeg(y)



### Equation 3-23. Chemical Intake from Root Vegetables 

# ( )( ) ⎟ ⎟ ⎠ ⎞ ⎜ ⎜ ⎝ ⎛

× ⎟ ⎟ ⎠ ⎞ ⎜ ⎜ ⎝ ⎛

× × × ×

− ×

− = days 365 g kg 001 0 2 1 1 1 EF FC

. IR C L L ADD RootVeg RootVeg(y) RootVeg RootVeg RootVeg RootVeg(y)



### where:

ADD ExpVeg(y) ADD ProVeg(y) ADD RootVeg(y) = Average chemical intake from ingest ion of exposed vegetables, protected vegetables, or root vegetables (depending on subscript) for age group y (mg chemical/kg body weight-day)

L1 ExpVeg 

### = Mean net preparation and cooking we ight loss for exposed vegetables (unitless); includes removing stalks, paring skins, discarding damaged leaves

L1 ProVeg 

### = Mean net cooking weight loss for protected vegetables (unitless); includes removing husks, discarding pods of beans and peas, removal of outer leaves

L1 RootVeg 

### = Mean net cooking weight loss for root vegetables (unitless); includes losses from removal of tops and paring skins

L2 RootVeg = Mean net post cooking weight loss for root vegetables from draining cooking liquids and removal of skin after cooking (unitless)

C ExpVeg C ProVeg C RootVeg 

### = Chemical concentration in exposed veget ables, protected vegetables, or root vegetables (depending on subscript) on a wet-weight (WW) basis (mg chemical/kg vegetable WW)

EF 

### = Exposure frequency; number of days per y ear of exposure for family(ies) as specified for scenario ( ≤ 365 days)
FC ExpVeg FC ProVeg FC RootVeg = Fraction of exposed vegetables, protec ted vegetables, or root vegetables (depending on subscript) obtained from contaminated area (unitless)

IR ExpVeg(y) IR ProVeg(y) IR RootVeg(y) 

### = Ingestion rate of exposed vegetables, prot ected vegetables, or root vegetables (depending on subscript) for age group y(g vegetable WW/kg body weightday)Attachment 2 to Technical Support Document for TR IM-Based Multipathway Screening Scenario for RTR 2-28 

### 3.2.5 Chemical Intake from Animal Product Ingestion


### Calculations of chemical intake from the cons umption of farm animals and related food products are provided below in Equations 3-24 through 3-28 for homegrown beef, dairy (milk), pork, poultry, and eggs, respectively.

Equation 3-24. Chemical Intake from Ingestion of Beef


## ( )( ) ⎟ ⎟ ⎠ ⎞ ⎜ ⎜ ⎝ ⎛

× ⎟ ⎟ ⎠ ⎞ ⎜ ⎜ ⎝ ⎛

× × × ×

− ×

− = days 365 g kg 001 0 2 1 1 1 EF FC

. IR C L L ADD Beef Beef(y) Beef Beef Beef Beef(y)



### where:

ADD Beef(y) = Average daily chemical intake from ingestion of beef for age group y (mg/kgday)

L1 Beef 

### = Mean net cooking loss for beef (unitless)

L2 Beef 

### = Mean net post cooking loss for beef (unitless)

C Beef 

### = Concentration of contaminant in beef (mg/kg WW))

EF 

### = Exposure frequency; number of days per y ear of exposure for family(ies) as specified for scenario ( ≤ 365 days)

IR Beef(y) 

### = Ingestion rate of contaminated beef for age group y (g WW/kg-day)

FC Beef = Fraction of beef consumed raised on co ntaminated area or fed contaminated silage and grains (unitless)



### Equation 3-25. Chemical Intake from Dairy Ingestion

⎟ ⎟ ⎠ ⎞ ⎜ ⎜ ⎝ ⎛

× ⎟ ⎟ ⎠ ⎞ ⎜ ⎜ ⎝ ⎛

× × × = days 365 g kg 001 0 EF FC

. IR C ADD Dairy Dairy(y) Dairy Dairy(y)



### where:

ADD Dairy(y) = Average daily chemical intake from ingestion of total dairy for age group y (mg/kg-day)

C Dairy 

### = Average concentration of contaminant in total dairy (mg/kg WW)

EF 

### = Exposure frequency; number of days per y ear of exposure for family(ies) as specified for scenario ( ≤ 365 days)

IR Dairy(y) 

### = Ingestion rate of contaminated total dairy for age group y (g WW/kg-day)

FC Dairy 

### = Fraction of total dairy products from contaminated area (unitless)Attachment 2 to Technical Support Document for TR IM-Based Multipathway Screening Scenario for RTR 2-29 

### Equation 3-26. Chemical Intake from Pork Ingestion


## ( )( ) ⎟ ⎟ ⎠ ⎞ ⎜ ⎜ ⎝ ⎛

× ⎟ ⎟ ⎠ ⎞ ⎜ ⎜ ⎝ ⎛

× × × ×

− ×

− = days 365 g kg 001 0 2 1 1 1 EF FC

. IR C L L ADD Pork Pork(y) Pork Pork Pork Pork(y)



### where:

ADD Pork(y) = Average daily chemical intake from ingestion of pork for age group y (mg/kgday)

L1 Pork 

### = Mean net cooking loss for pork (unitless); includes dripping and volatile losses during cooking; averaged over various cuts and preparation methods

L2 Pork 

### = Mean net post cooking loss for pork (uni tless); includes losses from cutting, shrinkage, excess fat, bones, scraps, and juices; averaged over various cuts and preparation methods

C Pork 

### = Concentration of contaminant in pork (mg/kg WW)

EF 

### = Exposure frequency; number of days per y ear of exposure for family(ies) as specified for scenario ( ≤ 365 days)

IR Pork(y) 

### = Ingestion rate of contaminated pork for age y (g WW/kg-day)

FC Pork = Fraction of pork obtained from contaminated area (unitless)



### The reduction in the weight of pork during and after cooking may correlate with an increase or decrease in the concentration of the chemical in the pork as consumed depending on the chemical and depending on the cooking method.

Equation 3-27. Chemical Intake from Poultry Ingestion 

# ( )( ) ⎟ ⎟ ⎠ ⎞ ⎜ ⎜ ⎝ ⎛

× ⎟ ⎟ ⎠ ⎞ ⎜ ⎜ ⎝ ⎛

× × × ×

− ×

− = days 365 g kg 001 0 2 1 1 1 EF FC

. IR C L L ADD Poultry Poultry(y) Poultry Poultry Poultry Poultry(y)



### where:

ADD Poultry(y) = Average daily dose (chemical intake) from ingestion of poultry (mg/kg-day)

L1 Poultry 

### = Mean net cooking loss for poultry (unitless)

L2 Poultry 

### = Mean net post cooking loss for poultry (unitless)

C Poultry 

### = Concentration of chemical in poultry (mg/kg WW)

EF 

### = Exposure frequency; number of days per y ear of exposure for family(ies) as specified for scenario ( ≤ 365 days)

IR Poultry(y) 

### = Ingestion rate of poultry for age group y (g WW/kg-day)

FC Poultry = Fraction of poultry from contaminated area or fed contaminated grains (unitless)Attachment 2 to Technical Support Document for TR IM-Based Multipathway Screening Scenario for RTR 2-30 

### Equation 3-28. Chemical Intake from Egg Ingestion

⎟ ⎟ ⎠ ⎞ ⎜ ⎜ ⎝ ⎛

× ⎟ ⎟ ⎠ ⎞ ⎜ ⎜ ⎝ ⎛

× × × = days 365 g kg 001 0 EF FC

. IR C ADD Egg Egg(y) Egg Egg(y)



### where:

ADD Egg(y) = Average daily chemical intake from ingestion of eggs for age group y (mg/kgday)

C Egg 

### = Concentration of contaminant in eggs (mg/kg WW)

EF 

### = Exposure frequency; number of days per y ear of exposure for family(ies) as specified for scenario ( ≤ 365 days)

IR Egg(y) 

### = Ingestion rate of contaminated eggs for age group y (g WW/kg-day)

FC Egg 

### = Fraction of eggs obtained from contaminated area (unitless)



### 3.2.6 Chemical Intake from Drinking Water Ingestion 

### If the user chooses to evaluate chemical ingesti on 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 fr om 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).

Equation 3-29. Chemical Intake from Drinking Water Ingestion

⎟ ⎟ ⎠ ⎞ ⎜ ⎜ ⎝ ⎛

× ⎟ ⎟ ⎠ ⎞ ⎜ ⎜ ⎝ ⎛ × ×

= days 365 EF
BW FC IR C ADD

(y) DW DW(y) DW DW(y)



### where:

ADD DW(y) = Average daily chemical intake from ingestion of drinking water from local residential water source for age group y (mg/kg-day)

C DW 

### = Concentration of contaminant in drinking water (g/L)

IR DW(y) 

### = Drinking water ingestion rate for age group y (mL/day)

FC DW 

### = Fraction of drinking water obtained from contaminated area (unitless)

BW (y) 

### = Body weight of age group y (kg)

EF 

### = Exposure frequency; number of days per y ear 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 chemic al from all of the exposure media that a singleindividual in each age group is expected to contact (e.g., soil, local fish, five types of homegrown produce, and five types of home-raised anima ls 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 3-30 through 3-35 below.Attachment 2 to Technical Support Document for TR IM-Based Multipathway Screening Scenario for RTR 2-31 

### Equations 3-30 to 3-35. Total Average Daily Dose of a Chemical for Different Age Groups

Equation 3-30. breastmilk 

### ADD ADD = < )

1 (



### Equation 3-31. 

# ∑

= − − 

### = n

i i 

### ADD ADD 1 )

, 2 1 ( ) 2 1 (



### Equation 3-32. 

# ∑

= − − 

### = n

i i 

### ADD ADD 1 )

, 5 3 ( ) 5 3 (



### Equation 3-33. 

# ∑

= − − 

### = n

i i 

### ADD ADD 1 )

, 11 6 ( ) 11 6 (



### Equation 3-34. 

# ∑

= − − 

### = n

i i 

### ADD ADD 1 )

, 19 12 ( ) 19 12 (



### Equation 3-35. 

# ∑

= 

### = n

i i adult adult 

### ADD ADD 1 )

, ( ) (



### where i 

### represents the i th 

### food type or ingestion medium and n 

### equals the total number of food types or ingestion media, and ADD 

### parameters are defined below:

ADD (<1) = Total average daily dose of chemical for infants less than one year from ingestion of breast milk (mg/kg-day)

ADD (1-2) = Total average daily dose of chemical from all ingestion sources for children ages 1 through 2 years (mg/kg-day)

ADD (3-5) = Total average daily dose for children ages 3 through 5 years (mg/kg-day)

ADD (6-11) = Total average daily dose for children ages 6 through 11 years (mg/kg-day)

ADD (12-19) = Total average daily dose for children ages 12 through 19 years (mg/kg-day)

ADD (adul t) = 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 3-36).

Equation 3-36. Lifetime Average Daily Dose (LADD)



### ⎟ ⎠ ⎞ ⎜ ⎝ ⎛
+ ⎟ ⎠ ⎞ ⎜ ⎝ ⎛
+ ⎟ ⎠ ⎞ ⎜ ⎝ ⎛
+ ⎟ ⎠ ⎞ ⎜ ⎝ ⎛
+ ⎟ ⎠ ⎞ ⎜ ⎝ ⎛
+ ⎟ ⎠ ⎞ ⎜ ⎝ ⎛

= < 

### 70 50
70 8
70 6
70 3
70 2
70

1
(adult) (12-19) (6-11) (3-5) (1-2) 1) ( 

### ADD ADD ADD ADD ADD ADD LADD



### The time-weighting factors simply equal the duration of exposure for the specified age category in years divided by the total lifespan, assumed to be 70 years. For risk assessments for chemicals with a subchronic RfD or for developmental effects in children, ADD (y) 

### values for the child age groups are compared with the RfD (see Section 5).Attachment 2 to Technical Support Document for TR IM-Based Multipathway Screening Scenario for RTR 2-32 

### 3.4 Chemical Intake Calculat ions for Nursing Infants
The scientific literature indicates that infants can be exposed to some chemicals via their mothers’ breast milk. The magnitude of the expos ure 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 infor mation 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 ( DAI inf



### ) 

### is estimated in MIRC with Equation 3-37. This basic exposure equation relies on the concentration of the chemical in the breast milk, the infant’s breast-milk ingestion rate ( IR milk 

### ), the absorption efficiency of the chemical by the oral route of exposure ( AE inf



### ), the bodyweight of the infant ( BW inf



### ), and the duration of breast feeding ( ED 

### ). Equation 3-37 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 ( C milkfat 

### ) and the concentration of the chemical in the aqueous phase of breast milk ( C aqueous 

### ). The remainder of the DAI inf



### -associated equations assume that most chemicals of concern will partition either 

### to the lipid phase or 

### to the aqueous phase of breast milk, although some c hemicals may partition significantly to both phases of milk. Thus, the remaining equations in MIRC assume that either C milkfat 

### or C aqueous 

### is equal to zero and hence drops out of the equation.

For the parameters in Equation 3-37 (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. The user also can overwrite those parameter values with a different value from the literature as appropriate.Attachment 2 to Technical Support Document for TR IM-Based Multipathway Screening Scenario for RTR 2-33 

### Equation 3-37. Average Daily Dose of Chemical to the Nursing Infant 

# ( ) ( ) 

# ( ) 

# [ ]

AT BW ED AE IR f C f C DAI

inf inf milk mbm aqueous mbm milkfat inf 

### × × × ×

− × + ×

= 

### 1

where:

DAI inf = Average daily dose of chemical absorb ed by infant (mg chemical/kg body weight-day)

C milkfat = Concentration of chemical in lipid phase of maternal milk (mg chemical/kg milk lipid; calculated using Equation 3-38)

f mbm = Fraction of fat in breast milk (unitless)

C aqueous = Concentration of chemical in aqueous phase of maternal milk (mg chemical/kg aqueous phase milk; calculated using Equation 3-42)

IR milk = Infant milk ingestion rate over the duration of nursi ng (kg milk/day)

AE inf = Absorption efficiency of the chemical by the oral route of exposure (i.e., chemical-specific fraction of ingested chem ical that is absorbed by the infant) (unitless)

ED = Exposure duration, i.e., duration of breast feeding (days)

BW inf = Body weight of infant averaged over the duration of nursing (kg)

AT = Averaging time associated with exposure of interest; equal to ED (days)



### As mentioned above, Equation 3-37 includes terms for the chemical in both the lipid- and nonlipid phases of milk. The remaining equations, however, assume that a chemical of concern will partition to the lipid or aqueous phase of breast milk. Different models are used to estimate C milkfat 

### (described in Section 3.4.2) and C aqueous 

### (described in Section 3.4.3).

3.4.2 Chemical Concentration in Breast Milk Fat 

### When developing MPE (EPA 1998), EPA reviewed three first-order kinetics models for estimating chemical concentration in breast milk fat. The model selected for use in MPE is the model used in MIRC. It is a changing-conc entration model that EPA adapted from a model by Sullivan et al. (1991). The model, shown in Equation 3-38, 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. Studi es 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 3-38 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).Attachment 2 to Technical Support Document for TR IM-Based Multipathway Screening Scenario for RTR 2-34 

### Equation 3-38. Chemical Concentration in Breast Milk Fat 

# ( ) ⎥ ⎥ ⎦ ⎤
⎢ ⎢ ⎣ ⎡

− ⎟ ⎟ ⎠ ⎞ ⎜ ⎜ ⎝ ⎛

− − × + × × ×

= − − bf elac fat pn elim t k

elac fat

elim t k

bf elac fat elac fat

elim

fm elim f

mat milkfat 

### e k k e t k k k
f k f DAI C

_ 

### 1 1 1

_ _ _ 

### where:

C milkfat = Concentration of chemical in lipid phase of maternal milk (mg chemical/kg lipid)

DAI mat = Daily absorbed maternal chemical dose (mg chemical/kg maternal body weight-day; calculated using Equation 3-39)

f f = Fraction of total maternal body burden of c hemical that is stored in maternal fat (mg chemical in body fat / mg total chemical in whole body; value from literature or EPA default - see Section 6.5)

k

elim = Chemical-specific total elimination rate c onstant for elimination of the chemical by non-lactating women (per day; e.g., vi a urine, bile to feces, exhalation; value from literature or calculated using Equation 3-40)

f fm = Fraction of maternal body weight that is fat stores (unitless)

k

fat_elac = Chemical-specific rate constant for tota l elimination of chemical in the lipid phase of milk during nursing (per day; value from literature or calculated using Equation 3-41)

t bf = Duration of breast feeding (days)

t pn = Duration of mother’s exposure prior to parturition and initiation of breast feeding (days)



### Equation 3-38 relies on the daily maternal absorbed intake ( DAI mat 

### ) to determine the concentration of the chemical in the breast milk fat. DAI mat 

### is multiplied by the fraction of the chemical that is stored in maternal fat ( f f 

### ) to determine the amount (i.e., mass) of chemical in the fat. This product, divided by the chemical-specific elimination rate constant ( k elim 

### ) for nonlactating adult women and the fraction of the mother’s weight that is fat (f fm 

### ), represents the maximum theoretical steady-state concentration of the chemical in an adult woman. If used alone to estimate the chemical concentration in breast milk fat, the equation as explained thus far is likely to overestimate the chemical conc entration in milk fat because it does not account for losses due to breast feeding. Alone, this term ( DAI mat 

### f f 

### / k elim 

### f fm 

### ) also assumes that the biological half-life of the chemical in the mother’s breast milk fat is small relative to the duration of the mother’s exposure. However, for chemicals with half-lives that are longer than the exposure 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 3-38. This term includes a fraction dependent on two rate constants, k elim 

### and the elimination constant for a lipophilic chemical in lactating women via the lipid phase of breast milk ( k fat



### _ elac 

### ), the duration of the mother’s chemical exposure prior to nursing ( t pn 

### ), and the duration of breast feeding ( t bf



### ). The whole body concentration ( DAI mat 

### f f 

### / k elim 

### f fm 

### ), the maximum 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 3-38, see Appendix B of MPE (EPA 1998).Attachment 2 to Technical Support Document for TR IM-Based Multipathway Screening Scenario for RTR 2-35 

### To estimate an average daily dose absorbed by an infant’s mother, or DAI mat 

### , the average daily dose ( ADD ) (in mg/kg-day) for the chemical from all sources that MIRC calculates for adults ( ADD (adult) 

### , described in Section 3.3, Equation 3-35), is multiplied by an absorption efficiency ( AE mat 

### ) or fraction of the chemical absorbed by the oral route of exposure, as shown in Equation 3-39. The value for AE mat 

### can be estimated from absorption efficiencies for adults in general. Available data for some chemicals, in particular some inorganic compounds, indicate AE values for ingestion exposures of substantially less than 100 percent. For a few of these chemicals, data also indicate lower AEs for the chemical when ingested in food or in soil than when ingested in water (e.g., cadmium). For a screening level assessment, however, it is reasonable to either assume 100 percent for the AE mat 

### or to use the higher AE mat 

### of the food and water AE mat 

### values if available; hence, a single AE mat 

### parameter is included in Equation 3-39.

Equation 3-39. Daily Maternal Absorbed Intake

mat (adult) mat 

### AE ADD DAI × = where:

DAI mat = Daily maternal dose of chemical absorbed from medium i (mg/kg-day)

ADD (adult) 

### = Average daily dose to the mother (mg/ kg-day) (calculated by MIRC – see Section 3.3, Equation 3-35)

AE mat 

### = Absorption efficiency of the chemical by the oral route of exposure (i.e., chemical-specific fraction of ingested chem ical that is absorbed) by the mother (unitless) (value from literature or EPA default – see Section 6.4)



### Equation 3-35, used to calculate ADD (adult), 

### is based on many medium-specific ingestion rates that are normalized to body weight. The adult body weights to which the homegrown food ingestion rates are normalized are the body weights of the consumers in the original USDA survey (see Section 6.3.3), which included both males and females. An assumption in the breast-milk exposure pathway is that those ingestion rates also are applicable to nursing mothers. The original data for ingestion rates for soil, drinking water, and fish are on a per person basis for males and females combined. MIRC divides those chemical intakes by an adult body weight for males and females combined as specified by the user (e.g., 71.4 kg mean value) to estimate the ADD normalized to body weight from those sources. If the user finds that those exposure media contribute the majority of the chemical intake for the risk scenario under consideration, the user may use alternative i ngestion rates for those media and alternative body weights for nursing women, as described in Section 6.5.

Elimination rates for chemicals often are report ed as the half-life of the chemical in the body following a known dose of chemical. Many chem icals 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 3-40 is used to convert a measured half-life for elimination of a chemical for adults or non-lactating women to an eliminatio n rate constant (EPA 1998). The equation can be used to estimate any kind of chemical loss rate constant from a measured chemical half-life.Attachment 2 to Technical Support Document for TR IM-Based Multipathway Screening Scenario for RTR 2-36 

### Equation 3-40. Biological Elimination Rate Constant for Chemicals for Non-lactating Women



### h k elim 

### 2 ln

=

where:

k elim = Chemical-specific elimination rate const ant for elimination of the chemical for non-lactating women (per day; e.g., via urine, bile to feces, exhalation)

ln2 = Natural log of 2 (unitless constant)

h = Chemical-specific biological half-life of chemical for non-lactating women (days)



### For chemicals transferred from the body of lactati ng women to breast milk, the rate of chemical elimination is augmented by the rate of chemical lo ss via the milk. The total elimination rate for lactating women sometimes is measured directly and reported in the literature. Where direct measurements are not available, and for chem icals that partition predominantly to the lipidphase of milk, EPA has used Equation 3-41 to estima te the total chemical elimination rate for lactating women, k fat



### _ elac 

### (EPA 1998).

Equation 3-41. Biological Elimination Constant for Lipophilic Chemicals for Lactating Women

mat fm mbm f milk elim elac fat 

### BW f f f IR k k

× × × + =

_ 

### where:

k fat_elac = Rate constant for total elimination of chemical during nursing (per day); accounts for both elimination by adults in general and the additional chemical elimination via the lipid phase of milk in nursing women

k

elim = Elimination rate constant for chemic al from adults, including non-lactating women (per day; e.g., via urine, bile to feces, exhalation; chemical-specific; value from literature or calculated from half-life using Equation 3-40)

IR milk = Infant milk ingestion rate ov er the duration of nursing (kg/d)

f f = Fraction of total maternal body burden of c hemical that is stored in maternal fat (mg chemical in body fat / mg chemical total in body; value from literature or EPA default)

f mbm = Fraction of fat in breast milk (unitless)

f fm = Fraction of maternal body weight that is fat stores (unitless)

BW mat = Maternal body weight over the entire dura tion of the mother’s exposure to the chemical including during pregnancy and lactation (kg)



### Equation 3-41 is based on a model from Smith (1987) and accounts for the additional elimination pathway for lipophilic chemical s via the breast milk fat. The term 

### K fat



### _ elac 

### is estimated by adding an estimate of the first-order elimination constant for breast feeding losses to 

### k elim 

### , which is the chemical-specific total elimination rate constant for non-lactating women. The breast feeding losses are estimated from the infant’s intake rate of breast milk ( 

### IR milk 

### ), the fraction of the total maternal body burden of the chemical that is stored in maternal body fat ( 

### f f 

### ),Attachment 2 to Technical Support Document for TR IM-Based Multipathway Screening Scenario for RTR 2-37 

### the fraction of the mother’s breast milk that consists of fat (lipids) ( 

### f mbm 

### ), the mother’s body weight ( 

### BW mat 

### ), and the fraction of the mother’s weight that is body fat ( 

### f fm 

### ). In Equation 3-41, 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 3-39. Body weight values for the mother are described in Section 6.5. Smith’s (1987) model assumes that the chemical partitions to the lipid-phase of breast milk to the same degree that it partitions into the mother’s body fat. For highly lipophilic compounds, losses from breast feeding can be larger than losses by all other pathways (EPA 1998).

3.4.3 Chemical Concentration in Aqueous Phase of Breast Milk 

### When developing MPE (EPA 1998), EPA also considered models to estimate chemical concentrations in the aqueous phase of breast milk (C aqueous 

### ). EPA adapted Smith’s (1987) steady state concentration model for estimating C milkfat 

### and developed the C aqueous 

### model shown in Equation 3-42 (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 C aqueous 

### equation assumes that the chemical concentrati on 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 3-42. Chemical Concentration in Aqueous Phase of Breast Milk

pm elac aq bm pl mat
aqueous 

### f k Pc f DAI C

× × ×

=

_ 

### where:

C aqueous = Concentration of chemical in aqueous phase of maternal milk (mg/kg)

DAI mat = Daily absorbed maternal chemical do se (mg/kg-day; calculated by Equation 3-39)

f pl = Fraction of chemical in the body (based on absorbed intake) that is in the blood plasma compartment (unitless; value from literature or calculated by Equation 3-43)

Pc bm = Partition coefficient for chemical betwe en the plasma and breast milk in the aqueous phase (unitless); assumed to equal 1.0

k

aq_elac = Chemical-specific rate constant for tota l elimination of chemical in the aqueous phase of milk during nursing (per day; value from literature or calculated in Equation 3-44)

f pm = Fraction of maternal weight that is blood plasma (unitless)



### Equation 3-42 is a steady-state concentration model that, like the Equation 3-38 for 

### C milkfat 

### , is dependent on the maternal absorbed daily intake ( 

### DAI mat 

### ). In Equation 3-42, 

### DAI mat 

### is multiplied by the fraction of the absorbed chemical that is circulating in the blood plasma compartment ( 

### f pl 

### ) and a partitioning coefficient for the chemical between plasma and the aqueous phase of breast milk ( 

### Pc bm 

### ). For highly water-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 

### Pc bm 

### would equal 1.0. The denominator includes the biological elimination constant for the chemical in the aqueous phase of breast milk in lactatingAttachment 2 to Technical Support Document for TR IM-Based Multipathway Screening Scenario for RTR 2-38 

### women ( 

### k aq_elac 

### ) and the fraction of the mother’s weight that is plasma ( 

### f pl 

### ). Because the model assumes steady-state, it does not account for chemic al 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 wa ter-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 3-42 is based on the relationship between the chemical concentrations in the aqueous phase of breast milk and the blood plasma, a value for the fraction of the chemical in the mother’s blood plasma ( 

### f pl 

### ) is required. Ideally, an empirical value for 

### f pl



### should be used. If empirical values are not available, 

### f pl



### can be estimated from Equation 3-43, provided that an empirical value can be found for the fraction of the chemical in the body that is in the mother’s whole blood compartment ( 

### f bl 

### ; EPA 1998).

Equation 3-43. Fraction of Total Chemical in Body in the Blood Plasma Compartment


# ( ) bp RBC bp bp bl pl 

### f Pc f f f f

− + ×

= 1

where:

f pl = Fraction of chemical in body (based on absorbed intake) that is in the blood plasma compartment (unitl ess); chemical-specific

f bl = Fraction of chemical in body (based on absorbed intake) in the whole blood compartment (unitless); chemical-specific

f bp = Fraction of whole blood that is plasma (unitless)

Pc RBC = Partition coefficient for chemical between red blood cells and plasma (unitless); chemical-specific



### If the fraction of the total chemical in the body that is in the whole blood compartment ( f bl 

### ) is known for a given chemical, then the 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 ( Pc RBC 

### ) and the fraction of whole blood that is plasma ( f bp 

### ).

Another parameter for which a value is needed to solve Equation 3-42 is the total chemical elimination rate for lactating women for hydrophilic chemicals, k aq_elac 

### . As for k fat_elac 

### for lipophilic chemicals, k aq_elac 

### for hydrophilic chemicals would be equal to k elim 

### plus the loss rate for the chemical in the aqueous phase of breast-milk during lactation. In the case of hydrophilic chemicals, EPA has yet to propose a term for t he 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 t ubules 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 k elim 

### , the elimination of the chemical from a non-lactating woman, as shown in EquationAttachment 2 to Technical Support Document for TR IM-Based Multipathway Screening Scenario for RTR 2-39 

### 3-40. The extent to which k elim 

### is an underestimate of k aq_elac 

### for a given chemical will determine the extent of conservative bias in k aq_elac



### . Equation 3-44. Biological Elimination Rate Constant for Hydrophilic Chemicals

elim elac aq 

### k k =

_ 

### where:

k aq_elac = Chemical-specific rate constant for tota l elimination of chemical by lactating women for hydrophilic chemicals (per day)

k

elim = Chemical-specific rate constant for total elimination of chemical by nonlactating women (per day; e.g., via urine, bile to feces, exhalation; value from literature or calculated from half-life using Equation 3-40)



### 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 (f pl 

### , see Exhibit 6-20). This parameter could be estimated using Equation 3-43 if a suitable chemical-specific fraction of chemical in the body that is in the whole blood (f bl 

### ) could be found. However, the value found for f bl 

### is based on a single-dose study and is not considered reliable for use in chronic exposure calculations.

We therefore conducted a literature search to identify existing physiologically based toxicokinetic (PBTK) models of lactational trans fer of methylmercury (MeHg) in humans. Most PBTK models that we identified focused on gestational transfer of mercury between mother and fetus, including a PBTK dynamic compartmental model for gestational transfer of MeHg in humans developed by Gearhart et al. (1995, 199 6), and reparameterized by Clewell et al. (1999).

We did find, however, that Byckowski and Lipscomb (2001) had added a lactational transfer module to the Clewell et al. (1999) model. Byckowski and Lipscomb compared their model’s predictions to epidemiological data from mother-nursing-infant pairs obtained following anaccidental high-dose poisoning in Iraq (Amin-Zaki et al. 1976) and from 34 mother-nursinginfant pairs examined in a low-dose, chroni c exposure environment (Fujita and Takabatake 1977). Using data from the Iraq incident, Byckowski and Lipscomb (2001) found good agreement between their model’s predictions and t he 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 fr om chronic exposure to low doses of MeHg, Byckowski 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 Takabatak’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) μ g/kg-day and the average infant intake of MeHg to be 0.80 ± 0.38 μ g/kg-day. Therefore, for purposes of MIRC, the DAI inf 

### of MeHg is estimated to be the same as the maternal intake per unit body weight (Equation 3-42).Attachment 2 to Technical Support Document for TR IM-Based Multipathway Screening Scenario for RTR 2-40 

### Equation 3-45. Calculation of Infant Average Daily Absorbed Dose of Methyl Mercury

mat_MeHg inf_MeHg 

### DAI DAI = where:

DAI inf_MeHg = Average daily dose ofMeHg absorbed by in fant from breast milk (mg/kg-day)

DAI mat_MeHg = Average daily dose of methyl mercury ab sorbed by the mother, predominantly from fish (mg/kg-day)Attachment 2 to Technical Support Document for TR IM-Based Multipathway Screening Scenario for RTR 2-41 

## 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 cancer slope factors (CSFs) and RfDs for chemicals used to calculate PB-HAP emission thresholds are provided in Exhibit 4-3. Dose-response values in MIRC that are used for EPA’s Risk and Technology Review (RTR) evaluations are c onsistent with dose-response data that the Agency’s Office of Air Quality Planning and Standar ds (OAQPS) uses for risk assessments of hazardous air pollutants (HAPs) (EPA 2007). 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 To xic Substances and Disease Registry (ATSDR); and the California Environmental Protection Agency (CalEPA).

Exhibit 4-1. Oral Dose-response Values for PB-HAP Chemicals Used to Calculate RTR De Minimis Thresholds Cancer Slope Factor Reference Dose Chemical CAS No. 1
day kg mg − ⎟ ⎟ ⎠ ⎞ ⎜ ⎜ ⎝ ⎛

− Source ⎟ ⎟ ⎠ ⎞ ⎜ ⎜ ⎝ ⎛

− day kg mg Source

Inorganics Cadmium compounds in food 7440439 not available 1.0E-03 IRIS Mercury (elemental) 7439976 NA not available
Mercuric chloride 7487947 not available 3.0E-04 IRIS Methyl mercury (MeHg) 22967926 not available 1.0E-04 IRIS Organics Benzo(a)pyrene 50328 1.0E+01 EPA OAQPS a not available 2,3,7,8-TCDD 1746016 1.5E+05 EPA ORD 1.0E-09 ATSDR ATSDR = Agency for Toxic Substances and Disease Regi stry IRIS = Integrated Risk Information System EPA OAQPS = EPA’s Office of Air Quality Pl anning and Standards NA = not applicable EPA ORD = EPA’s Office of Re search and Development

a The method to assign oral cancer slope factors for polycyclic organic matter (POM) is the same as was used in the 1999 National Air Toxics Assessment (EPA 1999b). A complete description of the methodology is available at: http://www.epa.gov/ttn/atw/nata19 99/99pdfs/pomappr oachjan.pdf.



### Cadmium 

### EPA has developed two chronic RfDs for cadmiu m, one for food and one for water, based on data in IRIS indicating a lower absorption efficien cy of cadmium from food than from water. The default RfD set in MIRC is the higher RfD for cadmium compounds in food (no drinking water is assumed to occur when calculating de minimis thresholds). Users of MIRC who assess exposures via drinking water may need to use the RfD for Cd compounds in water (i.e., 5.0E-4 mg/kg-day).

Mercury 

### EPA’s RfD for MeHg of 1E-04 mg/kg-day is based on a Benchmark Dose Lower Limit (BMDL) to dose-response data from an epidemiological study of neurobehavioral effects in children for which mercury concentrations had been measured in cord blood at birth. The island populations included in the study had been exposed for many years to MeHg in their seafoods. The RfD applies to the pregnant mother as well as young children. EPA has not specified theAttachment 2 to Technical Support Document for TR IM-Based Multipathway Screening Scenario for RTR 2-42 

### minimum exposure duration at the RfD level of exposure that is appropriate to use in characterizing risk; we assume ten years for women of child-bearing age and 1 year for infants.

We note that human exposures to MeHg are pr imarily through the consumption of fish and shellfish (EPA 2001b). EPA found that on average, approximately 76 percent of the exposure to MeHg for women of childbearing age could be attributed to ingestion of mercury in freshwater and estuarine fish and shellfish, with the remaining 24 percent derived from marine fish and shellfish. Other sources accounted for less than 0.06 percent of total exposures (EPA 2001b).

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 exposur e duration and timing of exposure for comparison with the RfD (or comparable value) require spec ial 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 im portant 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 avera ge 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.

For 2,3,7,8-TCDD, although exposures may start in utero, a period of special concern is the nursing stage, because the highly lipophilic chemical is effectively transferred to the infant in the lipid phase of its mother’s milk. ATSDR has established a minimal risk level (MRL) of 1E-09 mg/kg-day for exposures of the mother or inf ant of 365 days or longer to 2,3,7,8-TCDD. The MRL is based on a behavioral study of offspring of female rhesus macaques that were exposed prior to conception, during gestation, and while nursing (ATSDR 1998). In the critical study, all mothers were exposed for seven months prior to opportunities for mating; however, dates of conception ranged over a five month period (some fe males did not conceive for several cycles). When the offspring were born, their mothers had been exposed for an average of 16.2 months. Exposure continued for the 4-month lactation period, after which the offspring were weaned and tested for non-social and social behavioral deficits (ATSDR 1998, Bowman et al. 1989, Schantz and Bowman 1989, Schantz et al. 1992). It is not known whether the behavior deficits resulted from pre- or post-natal exposures or both. In th is case, it is appropriate to compare the 365-day MRL to the ADD for women of child-bearing age and to the ADD for a nursing infant less than 1 year of age during risk characterization (see Section 5).

The convention for assessing risk from mixtures of dioxins is by application of a toxic equivalency factor (TEF) 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 two most toxic congeners. It is therefore assigned a TEF of one, with the other dioxin congener TEQ concentrations scaled relative to 2,3,7,8-TCDD concentrations on the basis of toxicity. The World Health Organization (WHO) 2005 TEFs presented in Exhibit 4-2 are used for risk assessment of dioxins for RTR.Attachment 2 to Technical Support Document for TR IM-Based Multipathway Screening Scenario for RTR 2-43 

### Exhibit 4-2. WHO 2005 Toxic Equivalency Factors (TEFs) for Dioxins

Dioxin Congener CAS No. WHO 2005 Toxic Equivalency Factor

1,2,3,4,6,7,8-Heptachl orodibenzofuran 67562394 0.01 1,2,3,4,7,8,9-Heptac hlorodibenzofuran 5567389 7 0.01 1,2,3,4,7,8-Hexachloro d-benzofuran 70648269 0.1 1,2,3,6,7,8-Hexachloro dibenzofuran 57117449 0.1 1,2,3,7,8,9-Hexachloro dibenzofuran 72918219 0.1 2,3,4,6,7,8-Hexachloro dibenzofuran 60851345 0.1 1,2,3,4,7,8-Hexachlorodi benzo-p-dioxin 39227286 0.1 1,2,3,6,7,8-Hexachlorodi benzo-p-dioxin 57653857 0.1 1,2,3,7,8,9-Hexachlorodi benzo-p-dioxin 19408743 0.1 1,2,3,4,6,7,8-Heptachlor odibenzo-p-dioxin 35822469 0.01 1,2,3,4,6,7,8,9-Octachlo rodibenzofuran 39001020 0.0003 1,2,3,4,6,7,8,9-Octachloro dibenzo-p-dioxin 3268879 0.0003 1,2,3,7,8-Pentachlorodibenzofuran 57117416 0.03 2,3,4,7,8-Pentachlorodibenzofuran 57117314 0.3 1,2,3,7,8-Pentachlorodibenzo-p-dioxin 40321764 1 2,3,7,8-Tetrachlorodibenzofuran 51207319 0.1 2,3,7,8-Tetrachlorodibenzo-p-dioxin 1746016 1 Source: van den Berg et al. 2006



### Polycyclic Organic Matter 

### Previously, for risk assessment of inhalation expos ures to polycyclic organic matter (POM) for EPA’s National 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 NEI. Individual PAHs 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 group. The same approach was used to derive oral CSFs for POMs for use in multipathway risk assessment for RTR. POM groups (with their member POM species reported in NEI) and the corresponding CSFs used for RTR risk assessment are presented in Exhibit 4-3. As noted in the main TSD, a de minimis threshold for non-inhalation risk was derived onl y for benzo[a]pyrene, and facility emissions were then screened by comparing the total POM emi ssions to this threshold (where a toxicityweighted sum of POM emissions is calc ulated for each evaluated source).Attachment 2 to Technical Support Document for TR IM-Based Multipathway Screening Scenario for RTR 2-44 

### Exhibit 4-3. Oral Dose-response Values for Polycyclic Organic Matter (POM) Groups

Individual POM or POM group CAS No. Cancer Slope Factor 1

1
day kg mg − ⎟ ⎟ ⎠ ⎞ ⎜ ⎜ ⎝ ⎛

− POM Group 71002 Benz(a)Anthracene/Chrysene (7PAH) 103
Total PAH 234 Polycyclic Organic Matter 246
16-PAH 40
16PAH-7PAH 75040 0.5

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 Methylbenzopyrenes 247 Methylchrysene 248 Methylanthracene 26914181
Benzofluoranthenes 56832736 9-Methylbenz(a)Anthracene 779022 1-Methylphenanthrene 832699 Acenaphthene 83329
Phenanthrene 85018
Fluorene 86737 2-Methylnaphthalene 91576 2-Chloronaphthalene 91587 0.5

POM Group 73002 7,12-Dimethylbenz[a]anthracene 57976 1000 POM Group 74002 Dibenzo[a,i]pyrene 189559 D[a,h]pyrene 189640 100Attachment 2 to Technical Support Document for TR IM-Based Multipathway Screening Scenario for RTR 2-45 

### Exhibit 4-3, continued. Oral Dose-response Values for Polycyclic Organic Matter (POM) Groups

Individual PAH or PAH group CAS No. Cancer Slope Factor a

1
day kg mg − ⎟ ⎟ ⎠ ⎞ ⎜ ⎜ ⎝ ⎛

− POM Group 75002 3-Methylcholanthrene 56495 D[a,e]pyrene 192654 5-Methylchrysene 3697243 Benzo[a]pyrene 50328 Dibenzo[a,h]anthracene 53703 10

POM Group 76002 Benzo[b+k]fluoranthene 102 Indeno[1,2,3-c,d]pyrene 193395 B[j]fluoranthen 205823 Benzo[b]fluoranthene 205992 Benzo[k]fluoranthene 207089 D[a,j]acridine 224420 Benz[a]anthracene 56553 1

POM Group 77002 Chrysene 218019 0.1 POM Group 77002
7-PAH 75 0.5

a The method to assign oral cancer slope factors fo r polycyclic organic matter (POM) is the same as was used in the 1999 National Air Toxics Assessment (EPA 1999b). A complete description of the methodology is available at: http://www.epa.gov/ttn/atw/nata19 99/99pdfs/pomappr oachjan.pdf.Attachment 2 to Technical Support Document for TR IM-Based Multipathway Screening Scenario for RTR 2-46 

## 5 Risk Characterization

For persistent and bioaccumulative hazardous air pollutants (BP-HAPs), risks from inhalation of a chemical directly from air generally will be neglig ible compared with risks from ingestion of the chemical with foodstuffs grown in an area subjec t to air deposition of the chemical. For other (non-PB) HAPs, inhalation risks can be estimated separately and compared with risks associated with ingestion exposure to determine the focus of subsequent tiers of the risk assessment. Risk characterization for carcinogens with a linear mode of action at low doses is described in Section 5.1. 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 expos ure 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 (SF):

Equation 5-1. Calculation of Excess Lifetime Cancer Risk



### ELCR = LADD × 

### CSF where:

ELCR = Estimated excess lifetime cancer risk from a chemical summed across all exposure pathways and media (unitless)

LADD = Lifetime average total daily dose from all exposure pathways and media (mg/kg-day)

CSF = Oral carcinogenic potency slope factor for chemical (per mg/kg-day)



### As described in Section 3.3, the LADD (in mg/kg-day) for a chemical is calculated to reflect agerelated differences in exposure rates that are experienced by a hypothetical individual throughout his or her lifetime of exposure. The tota l 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 lifest age 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 acti on (MOA) for carcinogenesis for which EPA has adequate data to develop a reasonable quantit ative default approach is mutagenesis (EPA 2005b,c). For carcinogens with a mutageni c MOA for cancer, EPA concluded that the carcinogenic potency of a chemical may be approximat ely 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 ar e represented by agedependent adjustment factors (ADAFs) of 10, 3, and 1 for the first two lifestages and for adults, respectively.Attachment 2 to Technical Support Document for TR IM-Based Multipathway Screening Scenario for RTR 2-47 

### 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:



# ( )( )

3 5 6 2 yr 1 3 yr 1 10 10

5 3 2 1



### = = × + ×

= = <

)

- ( )

- ( 1) (



### ADAF . ADAF ADAF 

# ( )( )

1 2 8 yrs 4 1 yrs 4 3 3

19 12 11 6



### = = × + ×

= =

(adult) )

- ( )

- (



### ADAF ADAF ADAF

To estimate total lifetime risk from a lifetime of exposure to such a chemical, EPA recommends estimating the cancer risk for each of the three lifestages separately and then adding the risks for i = 1 to 6 age groups.

Equations 5-2 to 5-8. Lifetime Cancer Risk: Chemicals with a Mutagenic MOA for Cancer

Equation 5-2. 

### Risk (<1) 

### = ADD (0-<1) 

### x 10 x C SF x (1 yr/70 yr)

Equation 5-3



### . Risk (1-2) 

### = ADD (1-2) 

### x 6.5 x C SF x (2 yr/70 yr)

Equation 5-4. 

### Risk (3-5) 

### = ADD (3-5) 

### x 3 x CSF x (3 yr/70 yr)

Equation 5-5. 

### Risk (6-11) 

### = ADD (6-11) 

### x 3 x CSF x (6 yr/70 yr)

Equation 5-6. 

### Risk (12-19) 

### = ADD (12-19) 

### x 2 x CSF x (8 yr/70 yr)

Equation 5-7. 

### Risk (adult) 

### = ADD (adult) 

### x 1 x CSF x (50 yr/70 yr)

Equation 5-8. 

### ELCR 

# ∑

= 

### = n

i i 

### Risk 1 ) (



### In other words, Equation 5-8 indicates that the total extra lifetime cancer risk (ELCR) equals the sum of the age-group-specific risks estimated by Equations 5-2 through 5-7, where:

Risk (<1) = Risk from chemical ingestion in first year of life Risk (1-2) = Risk from chemical ingestion from first birthday through age 2 years Risk (3-5) = Risk from chemical ingestion from age 3 through 5 years of age Risk (6-11) = Risk from chemical ingestion from age 6 through 11 years of age Risk (12-19) = Risk from chemical ingestion from age 12 through 19 years of age
Risk (adult) = Risk from chemical ingestion from age 20 to 70 years age

ADD (<1) = Average daily dose for infants under one year of age (mg/kg-day) ADD (1-2) = Average daily dose from first birthday through age 2 years of age (mg/kg-day) ADD (3-5) = Average daily dose from age 3 th rough 5 years of age (mg/kg-day) ADD (6-11) = Average daily dose from age 6 thr ough 11 years of age (mg/kg-day) ADD (12-19) = Average daily dose from age 12 th rough 19 years of age (mg/kg-day)
ADD (adult) = Average daily dose for adults age 20 to 70 years of age (mg/kg-day)

CSF = Oral carcinogenic potency slope factor for chemical (per mg/kg-day)

Risk (i) = Risk from chemical ingestion for the i th age group

ELCR = Total extra lifetime cancer risk (incremental or extra risk)

n = Number of age groups (i.e., 6)Attachment 2 to Technical Support Document for TR IM-Based Multipathway Screening Scenario for RTR 2-48 

### 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, however, EPA cautions that adverse health effects are possible, although an HQ above 1 does not indicate an effect will definitely occur. This is because of the margin of safety inherent in the derivation of all RfD values. The larger the HQ value, the more likely it is that an adverse effect may occur.

5.2.1 Hazard Quotients for Chemicals with a Chronic RfD 

### For chemicals with a chronic RfD, MIRC calc ulates an HQ for each age group separately using Equation 5-9 to indicate the potential for adverse health effects associated with chronic exposure via ingestion pathways. The HQ is t he ratio of a long-term, daily average exposure normalized to the receptor's body weight (i.e., ADD) to the RfD for that chemical.

Equation 5-9. Hazard Quotient for Chemicals with a Chronic RfD

RfD ADD HQ =



### where:

HQ = Hazard quotient for chemical (unitless)

ADD = Average daily ingested dose of chemical (mg/kg-day) from all food types and ingested media for the age group

RfD = Chronic oral reference dose for chemical (mg/kg-day)



### 5.2.2 Hazard Quotients for Chemicals with RfD Based on Developmental Effects 

### For chemicals for which the toxicity reference value is an RfD based on developmental effects in infants, children, or young animals, 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 developm ental 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-leve l risk assessments, however, a conservative approach is to compare the highest ADD from among the child age categories provided in MIRC to the RfD.

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 as shown in Equation 5-12. 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 is advised to 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 c hemicals with the highest HQs to develop an appropriate approach to assessing their potential joint action.Attachment 2 to Technical Support Document for TR IM-Based Multipathway Screening Scenario for RTR 2-49 

### Equation 5-10. Hazard Index Calculation



### HI = HQ 1 

### + HQ 2 

### ... HQ n 

### where:

HI = Hazard index (unitless)

HQ 1 = Hazard Quotient for chemical 1 (unitless)

HQ 2 = Hazard Quotient for chemical 2 (unitless)

HQ n = Hazard Quotient for chemical n (unitless)



### The HI approach can be appropriate for chemicals with the same MOA and same target organ; however, MOA often is difficult to determine. An HI usually is “developed for each exposure route of interest, and for a single toxic effect or for toxicity to a single target organ” (EPA 2000; p 79). If a receptor is exposed to multiple chemicals that affect different target organs or that operate by different MOAs, and if more than one HQ is close to 1, the risk assessor is advised to perform a follow-on evaluation of assumptions and to consider whether chemical interactions may 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 (t oxicologically 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.Attachment 2 to Technical Support Document for TR IM-Based Multipathway Screening Scenario for RTR 2-50 

## 6 Model Input Options

This section describes the input options currently included in MIRC. Required user inputs for environmental media concentrations and air deposit ion rates, such as those predicted by (output of) TRIM.FaTE, are described in Section 6.1. Values for farm-food-chain (FFC) parameters for specific types of produce and anima l products are discussed in Section 6.2.Options for parameterizing receptor characteristics are described in Section 6.3, including agegroup-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-specif ic parameter values for a large number of chemicals, because all of the chemical-specif ic 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, and the HHRAP input s provided for other chemicals have not been evaluated. The data presented in this chapter were reviewed and used to develop the set of modeling defaults used to calculate de minimis screening thresholds 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.



### 6.1 Environmental Concentrations
As noted in Section 2, MIRC is intended to estimate exposures and risks to self-sufficient farming families from airborne chemicals. The tool analyzes one exposure scenario at a time; therefore, it is best used to evaluate a maxima lly 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/m 3 

### );

• 

### the fraction of chemical in the air that is in the vapor phase;

• 

### air-to-surface deposition rates for both vapor- and particle-phase chemical in the air (in g/m 2 

### -yr);

• 

### two fish tissue concentrations, one each for forage and 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 μ g/g dry weight), one each for: 1. surface soil in produce growing area, 2. surface soil where livestock feed, 3. root-zone soil in produce growing area, and 4. 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).

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. For RTR evaluations, a tool to facilitate this process was developed using a Microsoft Ex cel routine written in Visual Basic.Attachment 2 to Technical Support Document for TR IM-Based Multipathway Screening Scenario for RTR 2-51 

### 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 co mmonly 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. Parameter values required for these HHRAP algorithms, including chemical-specific media transfer factors (e.g., soil-to-plant transfer coefficients) and plant- and animal-specific properties (e.g., plant interception fraction, quantity of forage consumed by cattle), are included in tables in MIRC. As described in Section 7, the HHRAP-recommended parameter values are the def ault values in MIRC; however, these and other inputs in MIRC can be edited as needed. Exhibit 6-1 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 6-1. MIRC Parameters Used to Estimate Chemical Concentrations in Farm Foods

Parameter Description Units

Plants/Produce

Br

AG-produce-DW(i) Chemical-specific plant/soil chemical bioconcentration factor for edible portion of aboveground produce type i, exposed or protected Unitless (g soil DW / g produce DW)

Bv AG(i) Chemical-specific air-to-plant biotransfer factor for aboveground produce type i for vapor-phase chemical in air Unitless ([mg chemical / g DW plant] / [mg chemical / g air])

Fw Fraction of wet deposition that adheres to plant surfaces; 0.2 for anions, 0.6 for cations and most organics Unitless

Kds Chemical-specific soil/water partition coefficient L soil pore water / kg soil DW

kp (i) Plant-specific surface loss coefficient for aboveground exposed produce and animal forage and silage yr -1

MAF (i) Moisture adjustment factor for aboveground produce type ito convert the chemical concentration estimated for dryweight 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

VG AG(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 UnitlessAttachment 2 to Technical Support Document for TR IM-Based Multipathway Screening Scenario for RTR 2-52 

### Exhibit 6-1, continued. MIRC Parameters Used to Estimate Chemical Concentrations in Farm Foods

Parameter Description Units

VG rootveg Empirical correction factor for belowground produce (i.e., tuber or root vegetable) to account for possible overestimate of the diffusive transfer of chemicals from 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/m 2

Animal Products
Bs Soil bioavailability factor for livestock Unitless

MF Chemical-specific mammalian metabolism factor that accounts for endogenous degradation of the chemical Unitless

Ba (beef) Chemical-specific biotransfer factor for chemical in diet of cow to chemical in beef on a fresh-wet (FW; equivalent to WW) basis mg chemical/kg FW tissue/mg chemical/day

or

day/kg FW tissue
Ba (dairy) Biotransfer factor in dairy day/kg FW tissue

Ba (pork) Biotransfer factor in pork day/kg FW tissue
Ba (poultry) Biotransfer factor in poultry day/kg FW tissue
Ba (eggs) Biotransfer factor in eggs day/kg FW tissue
Qs (m) Quantity of soil eaten by animal type m each day kg/day

Qp (i,m) Quantity of plant feed type i consumed per animal type m each day kg/day

Source: EPA Source: EPA 2005a DW = dry weight; FW = fresh weight; WW = wet weight



### 6.2.2 Produce Parameter Values


### Exhibit 6-2 and Error! Reference source not found. 

### provide the chemical-specific input values that are the current defaults for produce FFC food types in MIRC. Exhibit 6-4 presents additional non-chemical-specific input values for parameters used in the algorithms that calculate chemical concentrations in produce. Unless otherwise noted, the default parameter values were obtained from HHRAP. Options for other parameter values are not included in MIRC at this time; however, the user can overwr ite values if appropriate. Refer to HHRAP (EPA 2005a, Chapter 5 and associated appendices) for detailed descriptions of these parameters and documentation of input values.Attachment 2 to Technical Support Document for TR IM-Based Multipathway Screening Scenario for RTR 2-53 

### Exhibit 6-2. Chemical-Specific Inputs for Produce Parameters for Chemicals Included in MIRC

Chemical Fraction of Wet Deposition ( Fw ) (unitless) a Root Concentration Factor ( RCF ) (belowground) (L/kg) b Soil-Water Partition Coefficient ( Kds ) (L/kg) c Chemical Air-toPlant Biotransfer Factor ( Bv AG(i) ) (unitless) d

Inorganics Cadmium compounds 0.6 NA 7.5E+01 NA e Mercury (elemental) 0.6 NA 1.0E+03 0 f

Mercuric chloride 0.6 NA 5.8E+04 1.8E+03 Methyl mercury 0.6 NA 7.0E+03 0 f

Organics Benzo(a)anthracene 0.6 5. 7E+03 6.0E+04 1.9E+04 Benzo(a)pyrene 0.6 9.7E +03 1.6E+05 1.2E+05 Benzo(b)fluoranthene 0.6 1.2E+04 1.0E+04 1.7E+03 Benzo(k)fluoranthene 0.6 1.2E+04 1.9E+05 2.1E+05 Chrysene 0.6 5.7E+03 6.0E+04 6.9E+02 Dibenz(a,h)anthracene 0.6 2.3E+04 5.8E+05 3.1E+07 Indeno(1,2,3-cd)
pyrene 0.6 2.8E+04 5.3E+05 3.7E+05 2,3,7,8-TCDD 0.6 4.0E +04 3.9E+04 6.6E+04 Source: EPA 2005a. NA = not applicable.

a 6E-01 is the value for cations and most organic chemicals. As described in HHRAP (EPA 2005a), Appendix B (available at http://www.epa.gov/os w/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 radionuc lides 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 va lues based on the behavior of insoluble particles tagged with radionuclides may not accurately represent the behavio r of organic compounds under site-specific conditions.

b For nonionic organic chemicals, as described in HHRAP (EPA 2005a), Appendix A (available at http://www.epa.gov/osw/hazar d/tsd/td/combust/finalmac t/ssra/05hhra papa.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 3-6. 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 K ow . For metals and mercuric compounds, empirical values for soil to root vegetable transfer on a dr y-weight basis are available from EPA or other sources.

c As discussed in HHRAP (EPA 2005a), Appendix A, Kds describes the partitioning of a compound between soil pore-water and soil particles and strongly influences the release and movement of a compound into the subsurface soils and underlying aquifer. Kds values for mercuric compounds were obtained from EPA (1997b). For all PAHs except for benzo(b)fluoranthene, Kds values were obtained from EPA 2004a. For benzo(b)fluoranthene and 2,3,7,8-TCDD, Kds values were calculated using the correlation equation provided in EPA 1993.

d As discussed in HHRAP (EPA 2005a), Appendix A, the value for mercuric chloride was obtained from EPA 1997b. Bv AG(i) values for PAHs were calculated using the correlati on 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 2,3,7,8- TCDD value was obtained from Lorber and Pinsky (2000).

e It is assumed that metals, with the exception of vapor-p hase elemental mercury, do not transfer significantly from air into leaves.

f Speciation and fate and transport of mercury from emissions suggest that Bv AG(i) values for elemental and methyl mercury are likely to be zero (EPA 2005a).Attachment 2 to Technical Support Document for TR IM-Based Multipathway Screening Scenario for RTR 2-54 

### Exhibit 6-3. Chemical-Specific Inputs by Plant Type for Chemicals in MIRC

Compound Name Plant Part Plant-Soil BioConcentration Factor ( Br AG-produce-DW(i) ) (unitless) a Empirical Correction Factor- Belowground Produce ( VG rootveg ) (unitless) b Empirical Correction Factor- Aboveground Produce ( VG AG(i) ) (unitless) c

Inorganics Exp. Fruit 1.3E-01 NA 1.0E+00 Exp. Veg. 1.3E-01 NA 1.0E+00 Forage 3.6E-01 NA 1.0E+00
Grain 6.2E-02 NA NA

Prot. Fruit 1.3E-01 NA NA Prot. Veg. 1.3E-01 NA NA

Root 6.4E-02 1.0E+00 NA Cadmium compounds

Silage 3.6E-01 NA 5.0E-01 Exp. Fruit NA NA 1.0E+00 Exp. Veg. NA NA 1.0E+00 Forage NA NA 1.0E+00
Grain NA NA NA

Prot. Fruit NA NA NA Prot. Veg. NA NA NA

Root NA 1.0E+00 NA Mercury (elemental)

Silage NA NA 5.0E-01 Exp. Fruit 1.5E-02 NA 1.0E+00 Exp. Veg. 1.5E-02 NA 1.0E+00 Forage 0 NA 1.0E+00
Grain 9.3E-03 NA NA

Prot. Fruit 1.5E-02 NA NA Prot. Veg. 1.5E-02 NA NA

Root 3.6E-02 1.0E+00 NA Mercuric chloride

Silage 0 NA 5.0E-01 Exp. Fruit 2.9E-02 NA 1.0E-02 Exp. Veg. 2.9E-02 NA 1.0E-02 Forage 0 NA 1.0E+00
Grain 1.9E-02 NA NA

Prot. Fruit 2.9E-02 NA NA Prot. Veg. 2.9E-02 NA NA

Root 9.9E-02 1.0E-02 NA Methyl mercury

Silage 0 NA 5.0E-01Attachment 2 to Technical Support Document for TR IM-Based Multipathway Screening Scenario for RTR 2-55 

### Exhibit 6-3, continued. Chemical-Specific Inputs by Plant Type for Chemicals in MIRC

Compound Name Plant Part Plant-Soil BioConcentration Factor ( Br AG-produce-DW(i) ) (unitless) a Empirical Correction Factor- Belowground Produce ( VG rootveg ) (unitless) b Empirical Correction Factor- Aboveground Produce ( VG AG(i) ) (unitless) c

Organics Exp. Fruit 2.0E-02 NA 1.0E-02 Exp. Veg. 2.0E-02 NA 1.0E-02 Forage 2.0E-02 NA 1.0E+00
Grain 2.0E-02 NA NA

Prot. Fruit 2.0E-02 NA NA Prot. Veg. 2.0E-02 NA NA

Root 9.5E-02 1.0E-02 NA Benzo(a)anthracene

Silage 2.0E-02 NA 5.0E-01 Exp. Fruit 1.3E-02 NA 1.0E-02 Exp. Veg. 1.3E-02 NA 1.0E-02 Forage 1.3E-02 NA 1.0E+00
Grain 1.3E-02 NA NA

Prot. Fruit 1.3E-02 NA NA Prot. Veg. 1.3E-02 NA NA

Root 6.1E-02 1.0E-02 NA Benzo(a)pyrene

Silage 1.3E-02 NA 5.0E-01 Exp. Fruit 1.1E-02 NA 1.0E-02 Exp. Veg. 1.1E-02 NA 1.0E-02 Forage 1.1E-02 NA 1.0E+00
Grain 1.1E-02 NA NA

Prot. Fruit 1.1E-02 NA NA Prot. Veg. 1.1E-02 NA NA

Root 1.2E+00 1.0E-02 NA Benzo(b)fluoranthene

Silage 1.1E-02 NA 5.0E-01 Exp. Fruit 1.2E-02 NA 1.0E-02 Exp. Veg. 1.2E-02 NA 1.0E-02 Forage 1.2E-02 NA 1.0E+00
Grain 1.2E-02 NA NA

Prot. Fruit 1.2E-02 NA NA Prot. Veg. 1.2E-02 NA NA

Root 6.1E-02 1.0E-02 NA Benzo(k)fluoranthene

Silage 1.2E-02 NA 5.0E-01 Exp. Fruit 2.0E-02 NA 1.0E-02 Exp. Veg. 2.0E-02 NA 1.0E-02 Forage 2.0E-02 NA 1.0E+00
Grain 2.0E-02 NA NA

Prot. Fruit 2.0E-02 NA NA Prot. Veg. 2.0E-02 NA NA

Root 9.5E-02 1.0E-02 NA Chrysene

Silage 2.0E-02 NA 5.0E-01Attachment 2 to Technical Support Document for TR IM-Based Multipathway Screening Scenario for RTR 2-56 

### Exhibit 6-3, continued. Chemical-Specific Inputs by Plant Type for Chemicals in MIRC

Compound Name Plant Part Plant-Soil BioConcentration Factor ( Br AG-produce-DW(i) ) (unitless) a Empirical Correction Factor- Belowground Produce ( VG rootveg ) (unitless) b Empirical Correction Factor- Aboveground Produce ( VG AG(i) ) (unitless) c

Exp. Fruit 6.8E-03 NA 1.0E-02 Exp. Veg. 6.8E-03 NA 1.0E-02 Forage 6.8E-03 NA 1.0E+00
Grain 6.8E-03 NA NA

Prot. Fruit 6.8E-03 NA NA Prot. Veg. 6.8E-03 NA NA

Root 4.1E-02 1.0E-02 NA Dibenz(a,h)anthracene

Silage 6.8E-03 NA 5.0E-01 Exp. Fruit 5.9E-03 NA 1.0E-02 Exp. Veg. 5.9E-03 NA 1.0E-02 Forage 5.9E-03 NA 1.0E+00
Grain 5.9E-03 NA NA

Prot. Fruit 5.9E-03 NA NA Prot. Veg. 5.9E-03 NA NA

Root 5.3E-02 1.0E-02 NA Indeno(1,2,3-cd)pyrene

Silage 5.9E-03 NA 5.0E-01 Exp. Fruit 4.6E-03 NA 1.0E-02 Exp. Veg. 4.6E-03 NA 1.0E-02 Forage 4.6E-03 NA 1.0E+00
Grain 4.6E-03 NA NA

Prot. Fruit 4.6E-03 NA NA Prot. Veg. 4.6E-03 NA NA

Root 1.0E+00 1.0E-02 NA 2,3,7,8-TCDD

Silage 4.6E-03 NA 5.0E-01 Source: EPA 2005a. NA = not applicable.

a As discussed in HHRAP (EPA 2005a), the Br AG-produce-DW(i) for aboveground produce and forage accounts for the uptake from soil and the subsequent tr ansport of contaminants through the r oots 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 slo pe factors provided in EPA 1992. Uptake slope is the ratio of contaminant concentration in dry weight plant tissue to the mass of contaminant applied per hectare soil. Br aboveground values for mercuric chloride and methyl mercury were calculated using methodology and data from Baes, et al. (1984). Br forage values for mercuric chloride and methyl mercury (on a dry weight basis) were obtained from EPA 1997b. The HHRAP met hodology assumes that elemental mercury doesn’t deposit onto soils. Therefore, it’s assumed t hat there is no plant upt ake through the soil.

b As discussed in HHRAP (EPA 2005a), Appendix B, VG rootveg represents an empirical correction factor that reduces produce concentration. Becaus e of the protective outer skin, size, and shape of bulky produce, transfer of lipophilic chemicals (i.e., log Kow greater than 4) to the c enter of the produce is not likely. In addition, typical preparation techniques, such as washing, peeling, and cooki ng, further reduce the concen tration of the chemical in the vegetable as consumed by removing the high concentrat ion 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 VGr ootveg value of 0.01 for PB-HAP with a log Kow greater than 4 and a value of 1.0 for PB-HAP with a log Kow less than 4 based on information provided in EPA 1994b. In developing these values, EPA (1994b) assu med that the density of the skin and the whole vegetable are equal (potentially overestimating the conc entration of PB-HAP in belowground produce due to root uptake).Attachment 2 to Technical Support Document for TR IM-Based Multipathway Screening Scenario for RTR 2-57 

### Exhibit 6-3, continued. Chemical-Specific Inputs by Plant Type for Chemicals in MIRC

c As discussed in HHRAP (EPA 2005a), Appendix B, VG ag represents an empirical correction factor that reduces aboveground produce concentration and was developed to es timate 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 VG ag 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 l og Kow less than 4 based on information provided in EPA 1994b. In developing these values, EPA (1994b) assu med the following: (1) translocation of compounds deposited on the surface of abovegrou nd vegetation to inner parts of abov eground produce would be insignificant (potentially underestimating the concentration of PB-HAP in aboveground pr oduce due to air-to-plant transfer); (2) the density of the skin and the whole vegetable are equal (potentially overestimating t he 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-H AP in aboveground produce due to air-toplant transfer unknown). For forage, HHRAP recommends a VG ag value of 1.0, also based on info rmation provided in EPA 1994b. A VG ag 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 specif ic rationale for this recommendation. Depending on the composition of the site-specific silage, this value may under- or overestimate the actual value.Attachment 2 to Technical Support Document for TR IM-Based Multipathway Screening Scenario for RTR 2-58 

### Exhibit 6-4. Non-Chemical-Specific Produce Inputs

Plant Part Interception Fraction

( Rp (i) ) (unitless) a Plant Surface

Loss Coefficient ( kp (i) ) (1/year) b Length of Plant Exposure to Deposition ( Tp (i) ) (year) c Yield or Standing

Crop Biomass

( Yp (i) ) (kg/m 2 ) d Plant TissueSpecific Moisture Adjustment Factor ( MAF (i)

) (percent) e
Exposed Vegetable 0.982 18 0.16 5.66 92
Protected Fruit NA NA NA NA 90
Protected Vegetable NA NA NA NA 80

Forage (animal feed) 0.5 18 0.12 0.24 92

Exposed Fruit 0.053 18 0.16 0.25 85 Root Vegetables NA NA NA NA 87 Silage (animal feed) 0.46 18 0.16 0.8 92 Grain (animal feed) NA NA NA NA 90 Source: EPA 2005a. NA = not applicable.

a Baes et al. (1984) used an empirical relationship developed by Chamberlain (1970) to identify a correlation between initial Rp values and pasture grass productivity (standing crop biomass [ Yp ]) to calculate Rp values for exposed vegetables, exposed fruits, forage, and silage. Tw o 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 consta nts developed by Baes et al. (1984) for use in the empirical relationship developed by Chamberlain (1970) ma y not accurately r epresent the site-specific mixes of aboveground produce consumed by humans or the site-specifi c mixes of forage or silage consumed by livestock.

b The term kp is a measure of the amount of chem ical that is lost to natural physical processes (e.g., wind, water) over time. The HHRAP-recommended value of 18 yr -1 (also recommended by EPA 1994a and 1998) represents the midpoint of a range of values reported by Miller and Hoffman (1983). There are two key uncertainties associated with using these values for kp : (1) The recommended equation for calculating kp includes a conservative bias in that it does not consider chemical degrad ation processes. (2) Gi ven the reported range of kp values from 7.44 to 90.36 yr -1 , plant concentrations could range from about 1. 8 times higher to about 5 times lower than the plant concentrations estimated in FFC media using the midpoint kp value of 18.

c HHRAP (EPA 2005a) recommends using a Tp value of 0.16 years for aboveground produce and cattle silage. This is consistent with earlier reports by EPA (1994 a, 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/y ear = 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 ar e associated with use of these values for Tp : (1) The average period between successive hay harvests (60 days) may not reflect the length of the growing season or the length between successive harvests for site-specific aboveground produce crops. The concentration of chemical in aboveground produce due to direct (wet and dry) deposition ( Pd ) will be underestimated if the site-specific value of Tp is less than 60 days, or overestimated if the site-specific value of Tp is more than 60 days.

d Yp values for aboveground produce and forage were calculat ed using an equation presented in Baes et al. (1984) and Shor et al. (1982): Yp = Y hi / A hi , where Y hi = Harvest yield of i th crop (kg DW) and A hi = Area planted to i th crop (m2), and using values for Y h and A h from USDA (1994b and 1994c). A production-weighted U.S. average Yp of 0.8 kg DW/m2 for silage was obtained from Shor et al. 1982.

e MAF represents the plant tissue-specific moisture adjustment factor to convert dry-weight concentrations into wetweight concentrations (which are lower owing to the diluti on by water compared with dry-weight concentrations). Values obtained from Chapter 10 of EPA's 2003 SAB Review materials for 3M RA Modeling System, Volume II, “Farm Food Chain and Terrestrial Food Web Data” (EPA 20 03a), which references EPA 1997c. Note that the value for grain used as animal feed is based on corn and soybea ns, not seed grains such as barley, oats, or wheat.Attachment 2 to Technical Support Document for TR IM-Based Multipathway Screening Scenario for RTR 2-59 

### 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 6-5 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 6-6.

Exhibit 6-5. Animal Product Chemical-specific Inputs for Chemicals Included in MIRC Biotransfer Factors (Ba m ) (day/kg FW tissue) a and Metabolism Factors (MF) (unitless) b

Mammal Non-mammal Compound Name Soil BioAvailability Factor ( Bs ) (unitless) Beef ( Ba beef ) Dairy ( Ba dairy ) Pork ( Ba pork ) MF Eggs ( Ba eggs ) Poultry ( Ba poultry ) MF

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 Benzo(a)anthracene 1 4.0E-02 8.4E-03 4.8E-02 0.01 1.7E -02 2.9E-02 NA Benzo(a)pyrene 1 3.8E-02 7.9E-03 4.5E-02 0.01 1.6E -02 2.8E-02 NA Benzo(b)fluoranthene 1 3.6E-02 7.6E-03 4.4E-02 0.01 1.5E -02 2.7E-02 NA Benzo(k)fluoranthene 1 3.6E-02 7.7E-03 4.4E-02 0.01 1.5E -02 2.7E-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)anthracene 1 3.1E-02 6.5E-03 3.7E-02 0.01 1.3E -02 2.3E-02 NA Indeno(1,2,3-cd)
pyrene 1 2.9E-02 6.2E-03 3.6E-02 0.01 1.2E -02 2.2E-02 NA

2,3,7,8-TCDD 1 2.6E-02 5.5E-03 3.2E-02 1 1.1E- 02 1.9E-02 NA Source: EPA 2005a, unless otherwise indicated. NA = not applicable.

a As discussed in HHRAP (EPA 2005a), Appendix A, biot ransfer factors for mercury compounds were obtained from EPA 1997b. Considering speciation, fate, and trans port 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 el emental mercury into animal tissues. Biotransfer factors for cadmium compounds were obtained from EPA 1995b. Biotransfer factors for 2,3,7,8-TCDD and PAHs were calculated from chemical octanol-water partitioning coeffici ents (Kow values) using the correlation equation from RTI (2005) and assuming the following fat contents: m ilk - 4%; beef - 19%; pork - 23%; poultry -14%; and eggs - 8%.

b As discussed in HHRAP (EPA 2005a), EPA (1995c) recommends using a metabolism factor ( MF ) to account for metabolism of PAHs by mammals to offset the amount of bioaccumulation suggested by biotransfer factors. EPA has recommended an MF of 0.01 for bis(2-ethylhexyl)phthalate ( BEHP) and 1.0 for all other chemicals (EPA 1995d). For MIRC, an MF of 0.01 is also used to calculate concen trations of PAHs in food products from mammalian species based on the work of Hofelt et al. ( 2001). This factor takes in to 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 2 to Technical Support Document for TR IM-Based Multipathway Screening Scenario for RTR 2-60 

### Exhibit 6-6. Soil and Plant Ingestion Rates for Animals

Animal Soil Ingestion Rate – Qs (m) (kg/day) a Plant Part Consumed by Animal Plant Ingestion Rate – Qp (I,m) (kg/day)
Silage 2.5 Forage 8.8 Beef cattle b 0.5

Grain 0.47 Silage 4.1 Forage 13.2 Dairy cattle c 0.4

Grain 3.0 Silage 1.4 Swine d 0.37 Grain 3.3 Chicken (eggs) e 0.022 Grain 0.2
Source: EPA 2005a HHRAP (Chapter 5).

a Beef cattle : NC DEHNR (1997) and EPA (1994b) recommended a soil ingestion rate for subsistence beef cattle of 0.5 kg/day based on Fries (1994) and NAS (1987). As discussed in HHRAP, Fries (1994) reported soil ingestion to be 4 percent of the total dry matter intake. NAS (1987) cited an average beef cattle weight of 590 kg, and a daily dry matter intake rate (non-lactating cows) of 2 percent of body weight. This results in a daily dry matter intake rate of 11.8 kg DW/day and a dai ly soil ingestion rate of about 0.5 kg/day. Dairy cattle : NC DEHNR (1997) and EPA (1994b) recommended a soil ingestion rate for dairy cattle of 0.4 kg/day based on Fries (1994) and NAS (1987). As discussed in HHRAP, Fries (1994) reported soil ingestion to be 2 percent of the total dry matter intake. NAS (1987) cited an average beef cattle weight of 630 kg and a daily dry matter intake rate (non-lactating cows) of 3.2 percent of b ody weight. This resulted in a daily dry matter intake rate of 20 kg/day DW, and a daily soil i ngestion rate of approximately 0.4 kg/day

. Uncertainties associated with Qs include the lack of current empirical data to suppor t soil ingestion rates for dairy cattle and the assumption of uniform contamination of soil ingested by cattle. Swine : NC DEHNR (1997) recommended a soil ingestion rate for swine of 0.37, estimated by assuming a soil intake that is 8% of the plant ingestion rate of 4.3 kg DW/day. Uncertainties include the lack of current empirical data to support soil ingestion rates and the assumption of uniform contamination of the soil ingested by swine. Chicken : HHRAP (EPA 2005a) assumes that chickens consume 10 percent of their total diet (which is approximately 0.2 kg/day grain) as soil, a percentage that is consistent with the study from Stephens et al. (1995). Uncertainties include the lack of current empirical da ta to support soil ingestion rates for chicken and the assumption of uniform contamination of soil ingested by chicken.

b The beef cattle ingestion rates of forage, silage, and grai n are based on the total daily intake rate of about 12 kg DW/day (based on NAS [1987] reporting a daily dry matter in take that is 2 percent of an average beef cattle body weight of 590 kg) and are supported by NC DEHNR (199 7), EPA (1994b and 1990), and Boone et al. (1981). The principal uncertainty associated with these Qp values is the variability between forage, silage, and grain ingestion rates for cattle.

c The dairy cattle ingestion rates of forage, silage, and grai n are based on the total daily intake rate of about 20 kg DW/day (NAS 1987; EPA 1992) as recommended by NC DEHNR (1 997). Uncertainties include the proportion of each food type in the diet, which varies from location to location. Assuming uniform contamination of plant materials consumed by cattle also introduces uncertainty.

d Swine are not grazing animals and are assumed not to eat forage (EPA 1998). EPA (1994b and 1998) and NC DEHNR (1997) recommended including only silage and gr ains in the diet of swine. EPA (1995c) recommended an ingestion rate of 4.7 kg DW/day for a swine, referenc ing NAS (1987). Assuming a diet of 70 percent grain and 30 percent silage (EPA 1990), HHRAP estimated ingestion ra tes of 3.3 kg DW/day (grain) and 1.4 kg DW/day (silage). Uncertainties associated with Qp include variability of the proportion of grain and silage in the diet, which varies from locati on to location.

e Chickens consume grain provided by the farmer. The daily quantity of grain feed consumed by chicken is assumed to be 0.2 kg/day (Ensminger [1980], Fries [1982 ], and NAS [1987]). Uncertainties associated with this variable include the variability of actual grain ingestion rates from site to site. In addition, assuming uniform contamination of plant materials consumed by chicken introduces some uncertainty.Attachment 2 to Technical Support Document for TR IM-Based Multipathway Screening Scenario for RTR 2-61 

### 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 conservative screening scenario. Parameter value options were primarily obtained or estimated from EPA’s Exposure Factors Handbook (EFH; EPA 1997a) and Child-Specific Exposure Factors Handbook (CSEFH; EPA 2008a). Where values were reported for age groupings other than those used in MIRC (see Section 2.3 above for MIRC age groups), time-weighted average values were estimated for the MIRC age groups from the available data.

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., g wet
weight 

### /kg-day), as presented in the original data analysis (EPA 1997a). The body weight parameter values presented in Exhibit 6-7, ther efore, 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 6-7, 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, 5 th 

### , 10 th 

### , 50 th 

### , 90 th 

### , and 95 th 

### percentiles for adults and the five children’s age groups. 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 6-7. For adults, BW represents the weighted average of male and female mean body weights for all races, ages 18-74 years, from EPA’s 1997 EFH (EPA 1997a; Tables 7-4 and 7-5). 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 6-7, 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 differed by approximately 10 percent or less.Attachment 2 to Technical Support Document for TR IM-Based Multipathway Screening Scenario for RTR 2-62 

### Exhibit 6-7. Mean and Percentile Body Weight Estimates for Adults and Children Body Weight (kg) Lifestage

(years) Duration (years) Mean 5 th 10 th 50 th 90 th 95 th

Adult a (20-70) 50 71.4 52.9 56.0 69.3 89.7 97.6
Child < 1 b 1 7.83 6.03 6.38 7.76 9.24 9.66
Child 1-2 c 2 12.6 9.9 10.4 12.5 14.9 15.6
Child 3-5 d 3 18.6 13.5 14.4 17.8 23.6 26.2
Child 6-11 e 6 31.8 19.7 21.3 29.3 45.6 52.5
Child 12-19 f 8 64.2 39.5 45.0 64.2 83.5 89.0 [Child 12-19 g 8 64.3 41.1 44.6 60.9 88.5 98.4]

a BW represents the sample-size weighted average of male and female mean body weights (all races, 18-74 years) from EPA’s 1997 EFH (Tables 7-4 for males and 7-5 for females). Note that these weights include the weight of clothing, estimated to rang e from 0.09 to 0.28 kg. Although the 18 to 74 year age category in EPA’s EFH does not match exactly the age 20 to 70 year categorization of adults in MIRC, the magn itude of error in the mean and percentile body weights is likely to be very small (i.e., less than 1%).

b Each BW represents a time-weighted average of body we ights 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 t he 2008 CSEFH. Original sample sizes for each of these age groups can also be found in Table 8-3.

c Each BW represents a time-weighted average of body weights for age groups 1 to <2 years and 2 to <3 years from Table 8-3 of the 2008 CSEFH. Original sample sizes for each of these age groups can also be found in Table 8-3.

d BWs obtained directly from Table 8-3 of the 2008 CSEFH (age group 3 to <6 years).

e BWs obtained directly from Table 8-3 of the 2008 CSEF H (age group 6 to <11 years). This value represents a conservative (i.e., slightly low) estimate of BW for ages 6 through 11 years since 11-year olds are not included in this CSEFH age group.

f Mean BW estimated using Table 8-22 of the 2008 CSEFH, which is based on NHANES IV data as presented in Portier et al. (2007). This estimate was calculated as the average of the 8 single-y ear age groups from 12 to 13 years through 19 to 20 years. Values for the other per centiles were estimated using Portier et al., 2007.

g Each BW represents a time-weighted average of body weights for age groups 11 to <16 years and 16 to <21 years from Table 8-3 of the 2008 CSEF H. Note that estimated values include 11-year-olds and individuals through age 20, which contributes to uncertainty in the estimate s for 12 to 19 years. Those values are provided for comparison purposes only and are not included in MIRC.



### 6.3.2 Water Ingestion Rates 

### MIRC also includes the option of calculating ch emical 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 reflec t 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 6-8 includes the drinking water ingestion rates for children that are included in MIRC.

Mean and percentile adult drinking water i ngestion rates were obtained from EPA (2004b), which presents estimated per capita water ingestion rates for various age categories based onAttachment 2 to Technical Support Document for TR IM-Based Multipathway Screening Scenario for RTR 2-63 

### data collected by the USDA’s 1994–1996 and 1998 CSFII (USDA 2000). Adult ingestion rates, presented in Exhibit 6-8, represent community water ingestion, both direct and indirect as defined above, for males and females combined, ages 20 years and older.

Exhibit 6-8. Estimated Daily Per Capita Mean and Percentile Water Ingestion Rates for Children and Adults a Ingestion Rates, Community Water (mL/day) Lifestage (years) Mean 50 th 90 th 95 th 99 th

Child <1 b 324 146 866 * 1,011 * 1,377 * Child 1-2 c 294 217 654 857 1,290 * Child 3-5 d 380 291 834 1,078 1,654 Child 6-11 e 447 350 980 1,235 1,870 * Child 12-19 f 697 516 1,537 2,022 * 3,195 * Adult g 1,098 920 2,224 2,801 4,488 Sources: EPA 2004b, 2008a * The sample size does not meet minimum reporting requi rements as described in EPA 2008a. For some of these MIRC age groupings, the values are based on the time-wei ghted average value for 2 or more age ranges from CSEFH Table 3-4. One or more age ranges within t he group may not meet the minimum reporting requirements, but not necessarily all of them fall within this category.

a Source is Kahn and Stralka 2008, also presented in the CSEFH (EPA 2008a).
b Each IR represents a time-weighted average of ingestion rates for age groups birth to <1 month, 1 to <3 months, 3 to <6 months, and 6 to <12 months fr om Table 3-4 of the 2008 CSEFH.

c Each IR represents a time-weighted average of ingesti on rates for age groups 1 to <2 years and 2 to <3 years from Table 3-4 of the 2008 CSEFH.
d Each IR represents the ingestion rate for age group 3 to <6 years from Table 3-4 of the 2008 CSEFH.

e Each IR represents the ingestion rate for age group 6 to <11 years from Table 3-4 of the 2008 CSEFH. This value represents a conservative (i.e., slightly low) esti mate of IR for ages 6 through 11 years since 11-year olds are not included in this CSEFH age group.
f Each IR represents a time-weighted average of ingesti on 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 t hat estimated values include 11-year-olds and individuals through age 20, which contributes to uncerta inty in the estimates for 12 to 19 years.

g Adult drinking water ingestion rates were obtained from EPA (2004b), Appendix E, Part I, Table A1 for community water, both sexes (ages 20+), direct plus indirect water ingestion.



### 6.3.3 Local Food Ingestion Rates 

### MIRC includes mean, median, 90 th 

### , 95 th 

### , and 99 th 

### percentile food-specific ingestion rates (IRs) for consumers-only of farm food chain (FFC) media for adults and children. The mean 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 1997a). 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. 2



### For the adult age group in MIRC, we compiled data on food-specific IRs separately for two types of households as indicated in the “Response to Questionnaire” (EPA 1997a, Chapter 13): (1) households that farm (F) and (2) households that garden or raise animals (HG for homegrown).

2 Note that EPA’s 2008 CSEFH does not distinguish be tween exposed and protected fruits and vegetables when recommending food ingestion rates based on the same dat a set for the same age categories. EPA’s 1997 analysis for its EFH therefore remains the most appr opriate data source for use in MIRC.Attachment 2 to Technical Support Document for TR IM-Based Multipathway Screening Scenario for RTR 2-64 

### This division reflects EPA’s data analysis. EPA tabulated IRs for fruits and vegetables for households that that farm and for households t hat garden. 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 home grown/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 perso ns consuming the food. EPA averaged the actual consumption rate for home-grown f oods over the 1-week survey period.

The default food-specific ingestion rates in MIRC for adults are those for farming households (F) in Exhibit 6-9. The user can specify use of the generally less conservative, non-farming household (HG) ingestion rates if they are more appropriate for the user’s exposure scenario (second column of IR values under Adults in Exhibit 6-9).

Exhibit 6-9. Summary of Age-Group-Specific Food Ingestion Rates for Farm Food Items Ingestion Rate by Age Group (g/kg-day) Children (Farm and Homegrown) e Adults Product Child <1 Child 1-2 Child 3-5 Child 6-11 Child 12-19 Farm f Homegrown g

Mean
Beef a NA 1.49 2.21 3. 77 1.72 2.63 2.66 Dairy d NA 67 37 24.79 10.90 17.1 15.9 Eggs d NA 2.5 1.4 0. 86 0.61 0.90 0.75 Exposed Fruit b NA 1.8 2.6 2. 52 1.33 2.32 1.55 Exposed Vegetable NA 3. 5 1.7 1.39 1.07 2.17 1.57

Pork d NA 2.2 2.1 1. 49 1.17 1.30 1.34 Poultry d NA 3.6 3.4 2. 13 1.59 1.54 1.58

Protected Fruit d NA 19 13 8.13 5.44 5.19 c 5.9 Protected Vegetable NA 2.5 1.3 1.1 0.776 1.3 1.01 Root Vegetable NA 2. 5 1.3 1.32 0. 937 1.39 1.15Attachment 2 to Technical Support Document for TR IM-Based Multipathway Screening Scenario for RTR 2-65 

### Exhibit 6-9, continued. Summary of Age-Group-Specific Food Ingestion Rates for Farm Food Items Ingestion Rate by Age Group (g/kg-day) Children (Farm and Homegrown) e Adults Product Child <1 Child 1-2 Child 3-5 Child 6-11 Child 12-19 Farm f Homegrown g

50 th Percentile
Beef a NA 0.84 1.23 2. 11 1.51 1.64 1.83 Dairy d NA 102 60 39 14 12.1 10.8 Eggs d NA 1.5 0.79 0. 56 0.49 0.67 0.48 Exposed Fruit b NA 1.2 1.82 1. 11 0.609 1.3 0.88 Exposed Vegetable NA 1. 9 1.2 0.643 0. 656 1.38 0.89

Pork d NA 1.8 1.4 1. 02 1.02 0.924 0.97 Poultry d NA 2.9 2.7 1. 4 1.4 1.06 1.37

Protected Fruit d NA 10.2 7.6 4.2 2.3 2.08 c 2.42 Protected Vegetable NA 1.94 1.04 0.791 0.583 0.599 0.64 Root Vegetable NA 0. 92 0.46 0.523 0. 565 0.88 0.67

90 th Percentile
Beef a NA 4.5 6.7 11. 4 3.53 5.39 5.39 Dairy d NA 148 82 54.67 26.98 34.9 34.9 Eggs d NA 5.1 2.8 1. 8 1.34 1.65 1.36 Exposed Fruit b NA 3.7 5.4 6.98 3.41 5 3.41 Exposed Vegetable NA 10. 7 3.47 3.22 2. 35 6.01 3.63

Pork d NA 4.5 4.4 3. 04 2.65 3.08 2.9 Poultry d NA 7.4 6.8 4. 58 3.28 3.47 2.93

Protected Fruit d NA 53 36 24.14 16.19 15.14 c 16 Protected Vegetable NA 3.9 2.5 2.14 1.85 3.55 2.32 Root Vegetable NA 7. 3 4.3 3.83 2.26 3.11 2.81

95 th Percentile
Beef a NA 5.0 7.3 12. 5 3.57 7.51 7.51 Dairy d NA 139 75 52 27 44 44 Eggs d NA 5.5 3.7 2. 4 1.5 1.85 1.85 Exposed Fruit b NA 4.1 6.1 12 4.8 6.12 5.0 Exposed Vegetable NA 11. 9 6.29 5.5 3.8 6.83 5.45

Pork d NA 6.2 6.0 4. 7 3.3 3.7 3.4 Poultry d NA 8.2 7.2 5. 3 3.7 4.8 3.3

Protected Fruit d NA 59 42 28 20 19.16 c 19.1 Protected Vegetable NA 9.4 5.1 3.12 2.2 5.4 3.05 Root Vegetable NA 10. 4 4.73 5.6 3.3 4.6 3.64Attachment 2 to Technical Support Document for TR IM-Based Multipathway Screening Scenario for RTR 2-66 

### Exhibit 6-9, continued. Summary of Age-Group-Specific Food Ingestion Rates for Farm Food Items Ingestion Rate by Age Group (g/kg-day) Children (Farm and Homegrown) e Adults Product Child <1 Child 1-2 Child 3-5 Child 6-11 Child 12-19 Farm f Homegrown g

99 th Percentile
Beef a NA 5.3 7.8 13. 3 4.3 11 12.5 Dairy d NA 113 56 37 24 80 80 Eggs d NA 16 12 8.6 5.0 6.6 6.6 Exposed Fruit b NA 22 32.5 16 5.9 16 12.9 Exposed Vegetable NA 12 7.4 13 5.7 10 10

Pork d NA 9.1 9.9 6. 3 4.2 4.9 4.3 Poultry d NA 10 10 6.4 4.8 6.2 5.3

Protected Fruit d NA 113 81 57 45 34.42 c 47.3 Protected Vegetable NA 9.4 5.3 5.4 2.69 9.2 6.49 Root Vegetable NA 10. 4 4.7 7.5 5.1 7.5 7.5 Source: EPA 1997a (Chapter 13), unless otherwise noted. NA = not applicable

a No data are available for Child 1-2 or Child 3-5. The value for Child 6-11 was used, scaled down by the ratio of the mean body weight for Child 1-2 or Child 3-5, as appropriate, to the mean body weight of Child 6-11.
b No data are available for Child 1-2. The value for this ag e group is the IR for Child 3-5, scaled down by the ratio of the mean body weight for Child 1-2 to the mean body weight for Child 3-5.

c These values represent a time-weighted average IR for two age groups, using exposure duration (ED) for the 20- 39 (ED=20 years) and 40-69 year age groups (ED=30 years).
d In many cases, intake ra tes for children were not available in EPA’ s 1997 EFH. Intakes for these receptor groups were calculated using the methodology recomme nded in HHRAP (EPA 2005a), Section 6.2.2.2. Sources to develop these values included EPA 1997a and EPA 2003b.

e In Chapter 13 of the 1997 EPA EFH, age group-specific IRs are provided fo r home produced items as a whole; separate IRs are not presented for children from househ olds that raise animals and households that farm.
f These values represent the IRs for “households who farm.”

g These values represent the IRs for “households who raise animals.”



### For children, EPA estimated food-specific IRs for four age categories (EPA 1997a): 1 to 2 years, 3 to 5 years, 6 to 11 years, and 12 to 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 6-9). For some food types and age categories, t here 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. Foodspecific intake rates (IRs) for those child age groups and food items not included in Chapter 13 of the 1997 EFH, that is IR age_group_x 

### , were derived using the following information:

• 

### Mean or percentile-specific consumer-only intake of the farm food item, as brought into the home, for the total NFCS survey population (from EFH Chapter 13) – IR CO_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) – IR PC, age_group_x 

### ; and

• 

### Mean or percentile-specific per capita intake of the farm food item for the total CSFII survey population (from Chapter 3 of EPA 2003c) – IR PC_total



### .Attachment 2 to Technical Support Document for TR IM-Based Multipathway Screening Scenario for RTR 2-67 

### The ratio of IR PC, age_group_x 

### to IR PC_total 

### from the CSFII data shows the consumption rate of a particular food type by a specific age group relative to the consumption rate for that food type for the population as a whole. The ratio of IR CO, age_group_x 

### to IR CO_total 

### , that is the consumption rate of a particular food type by a specific age group (consumers only) relative to the consumption rate for that food type for the NFCS survey population as a whole (consumers only), should be approximately the same. Given the assumption that the two ratios are equal, Equation 6-2 was used to calculate the “missing” age-specific consumer-only IRs :
Equation 6-1. Calculation of Age-Group-Specific and Food-Specific Ingestion Rates



### IR IR IR IR

PC_total oup_x PC, age_gr CO_total
oup_x CO, age_gr 

### ×

=

where:

IR CO, age_group_x = Mean or percentile-specific consumer-only intake of the food type from all sources, as consumed, for the specific child age group X

IR CO_total = Mean or percentile-specific consumer-only intake of the farm food item, as brought into the home, for the total NFCS survey population

IR PC, age_group_x = Mean or percentile-specific per capita intake of the food type from all sources, as consumed, for the specific child age group X from the CSFII

IR PC_total = Mean or percentile-specific per capita intake of the farm food item for the total CSFII survey 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 becaus e 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 90 th 

### percentile ingestion estimates, which are likely more reliable than the 95 th 

### or 99 th 

### percentile estimates in this particular calculation.

6.3.4 Local Fish Ingestion Rates


### The USDA’s 1987 to 1988 Nationwide Food Cons umption 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 1997a), includes family-caught fish ingestion rates by age category. There are several disadvantages, however, to using that data source toAttachment 2 to Technical Support Document for TR IM-Based Multipathway Screening Scenario for RTR 2-68 

### estimate fish ingestion rates. First, due to i nadequate sample sizes, EPA did not report fish IRs for children less than 6 years of age. Second, the NFCS data were collected approximately two decades ago. Third, the reported fish IRs are for ages 6 to 11 and 12 to 19 and are based on 29 and 21 individuals in each age category, respectively (EPA 1997a, Table 13-23). Finally, the IRs from NFCS data are based on total weight of fish as brought into the home, and do not include losses from preparation of the fish (i.e., removal of inedible parts and, possibly, the skin). Estimates of preparation losses for fish intended to apply to the NFCS fish IR data are very uncertain and are based on a wide variety of freshwater, estuarine, and marine fish, and squid (EPA 1997a, Table 13-5). Therefore, a more recent survey was sought that included larger sample sizes, data for children younger than six years, and IRs for the parts of fish actually consumed.

EPA’s (2002) analysis freshwater and estuarine fi sh consumption from the USDA’s Continuing Survey of Food Intake by Individuals (CSFII) for 1994-96 and 1998 was chosen to provide per capita fish IR options by age category in MIRC. Although the fish consumption rates reported in the CSFII include all sources, commercial and self-caught, for purposes of screening level risk assessments, it was assumed that all freshwater and estuarine fish consumed are self-caught. The inclusion of commercially obtained and estuarine fish will overestimate locally caught freshwater fish IRs for many rural populations in the United States; however, it also may underestimate locally caught fish IRs for some populations (e.g., Native Americans, Asian and Pacific Island communities, rural African Amer ican communities). Because consumption of locally caught fish varies substantially from region to region in the United States and from one population or ethnic group to the next, users of MIRC are encouraged to use more locally relevant data whenever available.

For children ages 3 to 17 years and for adults, MIRC includes values for the mean and the 90th, 95th, and 99th percentile fish ingestion rates (freshwater and estuarine fish only) based on EPA’s analysis of 1994-96 and 1998 CSFII data (EPA 2002, 2008a). As shown in EPA’s 2008(a) CSEFH, Table 10-7, the 90th percentile per capita ingestion rates estimated from the two-day CSFII recall period are zero for some child age groups. Although not presented in CSEFH Table 10-7, median ingestion rates for all child age groups would be zero (considering the “consumer only” sample sizes [CSEFH Table 10-9] relative to the “ per capita ” sample sizes in Table 10-7).

The high percentile fish IRs that are zero result from the short duration of the CSFII survey (two days) compared with the averaging time of interest (a year) and the relatively infrequent consumption of fish (e.g., on the order of once a week to once a month or less) compared with the near daily ingestion of other types of food products (e.g., dairy, produce, meat). Use of zero for fish IRs, however, is not useful in MIRC. As a result, an alternative method was used to estimate fish ingestion rates for children and adults that could provide reasonable, non-zero values for all age groups and percentiles.

The alternative, age-group-specific fish ingestion rates were derived using values for each age
group, y :

• 

### Mean or other appropriate percentile consumer-only fish ingestion rates for age group y , IR CO,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. 3

3 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 per centiles are slightly different due to rounding.Attachment 2 to Technical Support Document for TR IM-Based Multipathway Screening Scenario for RTR 2-69 • 

### Fraction of the population consuming freshwater/estuarine fish, F PC,y 

### , calculated as consumer-only sample size / U.S. population sample for age group y . The data to calculate these fractions are ava ilable in the 2008 CSEFH and EPA 2002.

Equation 6-2 was used to calculate the alternative, per capita fish ingestion rates by age group 

### ( IR PC,y 

### ):

Equation 6-2. Calculation of Alternative Age-Group-Specific Fish Ingestion Rates

y PC, y CO, y PC, 

### F IR IR × = where:

IR PC,y = Per capita fish ingestion rate for age group y (g/day)

IR CO,y = Consumer-only fish ingestion rates for age group y (g/day) (EPA 2002, Section 5.2.1.1, Table 5, for freshwater/estuarine habitat)

F

PC,y = Fraction of the population consuming freshwater/estuarine fish, calculated as consumer-only sample size / total U.S. population sample size for age group y (unitless) (2008 CSEFH, EPA 2002)



### In the above, per capita (as opposed to consumer-only ) indicates the intake rates are based on the entire population rather than the subset of the population 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. However, over 90% of the respondents consumed milk products.

The mean and percentile consumer-only fish ingestion rates for children and adults and the fraction of the population consuming freshwater/e stuarine fish used in calculating long-term per capita fish ingestion rates by age group are presented in Error! Reference source not found. 

### and Exhibit 6-11. The mean and percentile per capita fish ingestion rates estimated using this methodology are summarized in Exhibit 6-12 and are available in MIRC.

The fish ingestion rates provided in Exhibit 6-12 and included in MIRC are intended to represent the harvest and consumption of fish in surface waters in a hypothetical depositional area. For site-specific application of this tool, users should consider using more localized survey data to estimate more appropriate fish ingestion rates. The fishing season varies substantially across the United States by latitude, and fish consumpt ion 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.

As noted in Section 6.4.3, if the user overwrites the fish IRs shown in Exhibit 6-12 with freshweight 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 3-15. Suggested values are presented in Section 6.4.3.Attachment 2 to Technical Support Document for TR IM-Based Multipathway Screening Scenario for RTR 2-70 

### Exhibit 6-11. Fraction of Population Consuming Freshwater/Estuarine Fish on a Single Day ( F PC,y 

### )

Lifestage (years) Fraction Consuming Fish

Child 3-5 0.0503 a

Child 6-11 0.0440 b

Child 12-19 0.0493 c

Adult 0.08509 d
Sources: EPA 2002, 2008a

a This value was calculated using the ages 3 to 5 sample size for consumers only divided by the sample size for the U.S. population divided by 2 to represent the proportion consuming fish on a single day (the consumers-only group includes individuals who consumed fish on at least one of two survey days) to match the one-day ingestion rate.
b As in footnote a, the value was calculated using t he ages 6 to 10 sample size for consumers only divided by the sample size for U.S. population divided by 2.

c The value was calculated by summing the ages 11 to 15 and 16 to 17 sample sizes for consumers only and dividing by both by the sum of the sample si zes for U.S. population and by a factor of 2.
d The value was calculated using the ages 18 and older sample size for consumers only divided by the sample size for U.S. population from Section 5.1.1.1 Table 4. The resu lt was divided by 2 to represent a one-day sampling period in order to match the one-day ingestion rate.



### Exhibit 6-10. Daily Mean and Percentile Consumer-Only Fish Ingestion Rates for Children and Adults ( IR CO,y 

### ) a Ingestion Rates, All Fish (g/day) Lifestage (years) Mean 50th 90 th 95 th 99 th

Child <1 NA NA NA NA NA
Child 1-2 b 27.31 15.61 64.46 87.60 138.76 *
Child 3-5 c 40.31 23.04 95.16 129.31 204.84 *
Child 6-11 d 61.49 28.46 156.86 * 247.69 * 385.64 *
Child 12-19 e 79.07 43.18 181.40 * 211.15 * 423.38 *
Adult f 81.08 47.39 199.62 * 278.91 505.65 * Sources: EPA 2002, 2008a NA = not applicable; it is assumed that ch ildren < 1 year of age do not consume fish. * Indicates that the sample size does not meet minimum reporting requirements as described in EPA 2002. Owing to the small sample sizes, these upper percentiles values are highly uncertain.

a Per capita fish ingestion (FI) rates for children by age group are available from Chapter 10 of the CSEFH (EPA 2008a); however, all 50th and some 90th percentile ingestion rates are zero. Per capita FI rates were therefore estimated as described in Equation 6-2 to provide reasonab le, non-zero values for all age groups and percentiles.
b A fish IR for ages 1-2 years was not available. The va lue represents the consumer-only fish ingestion rate for ages 3 to 5 from EPA (2002) (Section 5.2.1.1 Table 5 [freshw ater/estuarine habitat]), scaled down by the ratio of the mean Child 1-2 body weight to the mean Child 3-5 body weight.

c These values represent the consumer-only fish ingesti on rate for ages 3 to 5 from EPA (2002), Section 5.2.1.1 Table 5 (freshwater/estuarine hab itat). Sample size = 442.
d These values represent the consumer-only fish ingesti on rate for ages 6 to 10 from EPA (2002), Section 5.2.1.1 Table 5 (freshwater/estuarine hab itat). Sample size = 147.

e These values represent the time-weighted average per capita fish ingestion rate for ages 11 to 15 and 16 to 17 years from EPA (2002), Section 5.1.1.1 Table 5 (freshwater/estuarine habi tat); the value may underestimate ingestion rate for ages 12 to 19 years. Sample size = 135.
f These values represent the consumer-only fish ingestion rate for individuals 18 years and older from EPA (2002), Section 5.2.1.1 Table 4 ( freshwater/estuarine habitat ) . Sam p le size = 1

, 633.Attachment 2 to Technical Support Document for TR IM-Based Multipathway Screening Scenario for RTR 2-71 

### Exhibit 6-12. Calculated Long-term Mean and Percentile Per capita Fish Ingestion Rates for Children and Adults ( IR PC,y 

### ) Ingestion Rates, All Fish (g/day) Lifestage (years) Mean 50th 90 th 95 th 99 th

Child <1 NA NA NA NA NA
Child 1-2 a 1.37 0.79 3.24 4.41 6.98
Child 3-5 b 2.03 1.16 4.79 6.51 10.3
Child 6-11 c 2.71 1.25 6.90 10.9 17.0
Child 12-19 d 3.90 2.13 8.95 10.4 20.9
Adult e 6.90 4.03 16.99 23.73 43.02

Sources: EPA 2002, 2008a NA = not applicable; it is assumed that ch ildren < 1 year of age do not consume fish.

a Values were calculated as (consumer-only IR for Child 1- 2) x (fraction of population consuming fish for Child 3-5).
b Values were calculated as (consumer-only IR for Child 3- 5) x (fraction of population consuming fish for Child 3-5).

c Values were calculated as (consumer-only IR for Child 6-11) x (fraction of population consuming fish for Child 6- 11).
d Values were calculated as (consumer-only IR estimated for Child 12-19) x (fraction of population estimated to consume fish for Child 12-19).

e Values were calculated as (consumer-only IR for Adults ) x (fraction of population consuming fish for Adults).



### Applications to date of MIRC have used whole fi sh concentrations estimated by TRIM.FaTE. The proportion 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).

For lipophilic chemicals (e.g., log Kow greater than 4), which partition primarily into the fatty tissues of fish, much of the higher concentration tissues might be stripped from the fish during preparation (e.g., belly fat, viscera which includes fat in liver, etc, fat under skin). The degree to which the concentration of chemical in a fillet is less than the average total concentration in the whole fish is chemical specific. Assuming that the chemical concentration in the fillet is the same as in the whole fish may result in a conservative 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 is protective.

6.3.5 Soil Ingestion Rates 

### Adult gardeners 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 6-13 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 andAttachment 2 to Technical Support Document for TR IM-Based Multipathway Screening Scenario for RTR 2-72 

### 3 to 6 years and were not specific to families that garden or farm. The default ingestion rates in MIRC are the 90 th 

### percentile values, as for other ingestion rate parameters.

Exhibit 6-13. Daily Mean and Percentile Soil Ingestion Rates for Children and Adults Soil Ingestion Rate (mg/day) Age Group (years) Mean a 50 th a 90 th 95 th 99 th

Child < 1 NA
Child 1-2 50 50 400 b 400 b 400 b

Child 3-5 50 50 400 b 400 b 400 b

Child 6-11 50 50 201 c 331 d 331 d

Child 12-19 50 50 201 c 331 d 331 d

Adult 20-70 50 50 201 c 331 d 331 d

Sources: EPA 1997a, 2008a

a For the mean and 50 th percentile soil ingestion rates for children, value represents a “central tendency” estimate from EPA’s 2008 CSEFH, Table 5-1. For adults, val ue is the recommended mean value for adults from EPA’s 1997 EFH, Chapter 4, Table 4-23.
b These values are the recommended “upper percentile” value for children from EPA’s 1997 EFH, Chapter 4, Table 4-23. The 2008 CSEFH included a high-end value associated with pica only.

c These values are 90th percentile adult ingestion rates calculated in Stanek et al. 1997, and they are used to represent older children and adults.
d These values are 95th percentile adult ingestion rates calculated in Stanek et al. 1997, and they are 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 6-14 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 2 to Technical Support Document for TR IM-Based Multipathway Screening Scenario for RTR 2-73 

### Exhibit 6-14. Daily Mean and Percentile Per Capita Total Food Intake for Children and Adults
Lifestage (years) Percent of Group Consuming Food Mean 50th 90 th 95 th 99 th

Total Food Intake (g/day, as consumed)

Child < 1 a 67.0% - 99.7% h 322 270 599 779 1152

Child 1-2 b 100% 1,032 996 1537 1703 2143

Child 3-5 c 100% 1,066 1,020 1,548 1,746 2,168

Child 6-11 d 100% 1,118 1,052 1,642 1,825 2,218

Child 12-19 e 100% 1,197 1,093 1,872 2,231 2,975
Adult f 100% 1,100 1,034 1,738 2,002 2,736 Total Food Intake (g/kg-day, as consumed)

Child < 1 a 67.0% - 99.7% h 39 34 72 95 147

Child 1-2 b 100% 82 79 125 144 177

Child 3-5 c 100% 61 57 91 102 132

Child 6-11 d 100% 40 38 61 70 88

Child 12-19 e 100% 21 19 34 40 51
Adult g 100% 14.8 13.9 23.7 27.6 35.5 Sources: EPA 2005e, 2008a

a These values represent a time-weighted average for age groups birth to <1 month (N=88), 1 to <3 months (N=245), 3 to <6 months (N=411), and 6 to <12 months (N=678) from Table 14-3 of the 2008 CSEFH.
b These values represent a time-weighted average for age groups 1 to <2 years (N=1,002) and 2 to <3 years (N=994) from Table 14-3 of the 2008 CSEFH.

c These values were obtained from Table 14-3 of the 2008 CSEFH (age group 3 to <6 years, N=4,112).
d These values were obtained from Table 14-3 of the 2008 CSEFH (age group 6 to <11 years, N=1,553). These values represents a conservative (i.e., slightly low) esti mate for ages 6 through 11 years since 11-year olds are not included in this CSEFH age group.

e These values represent a time-weighted average for age groups 11 to <16 years (N=975) and 16 to <21 (N=743) years from Table 14-3 of the 2008 CSEFH. Note that estimated values include 11-year-olds and individuals through age 20, which contributes to uncertainty in the estimates.
f These values represent a time-weighted average for age groups 20 to 39 years (N=2,950) and 40 to 69 years (N=4,818) from Table 5B of th e 2005 EPA analysis of CSFII.

g These values represent a time-weighted average for age groups 20 to 39 years (N=2,950) and 40 to 69 years (N=4,818) from Table 5A of th e 2005 EPA analysis of CSFII.
h Percents consuming foods from Table 14-3 of the 2008 CSEFH include: 67.0% (birth to <1 month); 74.7% (1 to <3 months); 93.7% (3 to <6 months); and 99.7% (6 to <12 months). Infants under the age of 1 that consume breast milk are classified as “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 potentialAttachment 2 to Technical Support Document for TR IM-Based Multipathway Screening Scenario for RTR 2-74 

### 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 pr oducts every day of the year.

If the user wishes to evaluate daily intake rates based on shorter averaging times, the user can overwrite both the food-specific ingestion rates and the EF for each home-grown food product. Users of MIRC might want to specify a lower EF values for various food types where residents obtain some of their diet from commercial sources and where consumption of home grown produce is seasonal.

6.4.2 Fraction Contaminated 

### The fraction contaminated (FC) represents the fraction of each food product consumed that is contaminated by the chemical at a level consis tent with environmental concentrations in the area of concern (e.g., area with maximum depositi on rates). Obviously, the most conservative assumption is that all food products consum ed (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 6-15. 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 EFH.Attachment 2 to Technical Support Document for TR IM-Based Multipathway Screening Scenario for RTR 2-75 

### Exhibit 6-15. 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 Fruit c 0.244 0.305 Exposed Vegetable 0.162 d NA
Protected Fruit 0.29 e NA Protected Vegetable 0.088 f NA Root Vegetable g 0.075 0.22 Beef 0.27 0.24
Pork 0.28 0.36 Poultry 0.32 0.295 h

Fish i 0.0 0.0 Source: EPA 1997a (Chapter 13; Tables 13- 5 [meats], 13-6 [fruits], and 13-7 [vegetables]) NA = Not Available

a For fruits , includes losses from draining cooked forms. For vegetables , includes losses due to paring, trimming, flowering the stalk, thawing, draining, scraping, shelling, slicing, husking, chopping, and dicing and gains from the addition of water, fat, or other ingredients. For meats , includes dripping and volatile losses during cooking.
b For fruits , includes losses from removal of skin or peel, core or pit, stems or caps, seeds and defects; may also include losses from removal of drained liquids from canned or frozen forms. For vegetables , includes losses from draining or removal of skin. For meats , includes losses from cutting, shrinkage, excess fat, bones, scraps, and juices.

c These values represent averages of means for all frui ts with available data (except oranges) (Table 13-6).
d This value represents an average of means for all expos ed vegetables with available data (Table 13-7). Exposed vegetables include asparagus, broccoli, cabbage, cucumber , lettuce, okra, peppers, snap beans, and tomatoes.

e This value was set equal to the value for oranges (Table 13-6).
f This value represents an average of means for all prot ected vegetables with available data (Table 13-7). Protected vegetables include pumpkin, corn, peas, and lima beans.
g These values represent averages of means for all root vegetables with available data (Table 13-7). Root vegetables include beets, carr ots, onions, and potatoes.
h This value represents an average of means for chicken and turkey (Table 13-5).
i If the user changes fish ingestion rates to match a survey of the whole weight of fish brought into the home from the field (divided by the consumers of the fish), an appropr iate value for L1 would be 0.3 (EPA 1997a, Table 13-5). For volatile or water soluble chemicals, a non-zero va lue for L2 also may be appropriate. Although EPA (1997a) recommended 0.11 for L2, it varies substantially by chemical.



### There are substantial uncertainties associated with the L1 and L2 parameters, including the wide variation in values across produce types that were averaged together to recommend a central tendency value for each. For example, the L2 factor does not distinguish between weight loss during cooking by water evaporation, which might leave the chemical in the fruit, 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 eval uate L1 included negative losses (i.e., weight gains) due to hydration of dried vegetables (e.g., peas and lima beans), which increases the range of L1 values across different vegetables.

Note that the default L1 and L2 values for fish are set to zero. That is because the data source for the fish ingestion rates is not the USDA’s 1987 to 1988 NFCS (USDA 1993, 1994) as reported in EPA’s EFH, which reported food as brought into the home, as is the case for the other food categories. Instead, the fish IR data included in MIRC are from a more recent and larger survey, EPA’s (2002) analysis of freshw ater and estuarine fish consumption from the USDA’s 1994-96 and 1998 CSFII. That survey repor ts ingestion rates of fish parts actuallyAttachment 2 to Technical Support Document for TR IM-Based Multipathway Screening Scenario for RTR 2-76 

### consumed, and so no loss processes for preparation are needed. The zero value for L2 assumes that no chemical is lost by volatilization and that pan juices are consumed. The user may reset that value where chemical-specific data are available.

If the user manually changes fish ingestion rates to match a local survey of the whole weight of fish brought into the home from the field (divided by number of persons consuming the fish), the user should also set the L1 and L2 parameter to non-zero values. An appropriate value for L1 would be 0.3 (EPA 1997a, Table 13-5). For volat ile or water soluble chemicals, a non-zero value for L2 also may be appropriate. Although EPA recommended 0.11 for L2 (EPA 1997a, Table 13-5), it varies substantially by chemical.



### 6.5 Breast-Milk Infant Exposure Pathway Parameter Values
Values used for parameters in the breast-milk exposure pathway algorithms (Section 3.4) 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 6-16 lists the parameters and their default values. The text that follows describes the input value or value options for each exposure parameter required by MIRC to calculate the infant absorbed chemical intake rate, or DAI inf 

### . For parameter values that can be estimated when empirical values are not available, see the equation description in Section 3.4.

Exhibit 6-16. 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

BW inf Body weight of infant (kg) averag ed over duration of nursing exposure 7.8

BW mat Body weight of mother (kg) averaged over duration of mother’s
exposure 66

DAI mat Daily absorbed intake of chemical by mother (mg/kg-day) Equation 3-36

ED Exposure duration for infant, i.e., dur ation of breast feeding (days) =AT
AT/ED Averaging time divided by exposure duration 1.0
f bp Fraction of mother’s whole blood that is plasma (unitless) 0.65
f fm Fraction of mother’s body weight that is fat (unitless) 0.30
f mbm Fraction of fat in mother’s breast milk (unitless) 0.04
f pm Fraction of mother’s body weight that is plasma (unitless) 0.046

IR milk Mean infant milk ingestion rate ov er duration of nursing (kg/day) 0.709

t bf Duration of breast feeding (days) 365

t pn Duration of maternal chemical exposure prior to nursing (days) 3285Attachment 2 to Technical Support Document for TR IM-Based Multipathway Screening Scenario for RTR 2-77 

### 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 ( BW inf



### ). The user selects a value for BW inf 

### , the time-weighted average body weight of the infant over the entire duration of breast feeding, based on the age at which the infant stops breast feeding. For example, if the infant breast feeds for one year, the user should select the body weight for an infant that is aver aged from birth to the first birthday. Similarly, if an infant breast feeds for 6 months, the user should select the body weight for an infant that is averaged from birth to six months. Because the default breast feeding duration ( t bf



### ) is one year (i.e., 365 days), the default infant body weight is 7.8 kg, which is the time-weighted average for the mean infant body weight between birth and its first birthday from EPA’s (2008) Child Specific Exposure Factors Handbook (CSEFH; EPA 2008a). Exhibit 6-17 presents additional values for the infant body weight parameter that the user can select instead of the MIRC default.

Exhibit 6-17. 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 7.8 a 9.0 9.6

5 th percentile 5.0 6.0 7.0 7.5
10 th percentile 5.3 6.4 7.4 7.8
15 th percentile 5.5 6.7 7.7 8.2
25 th percentile 5.8 7.0 8.1 8.7
50 th percentile 6.4 7.8 8.9 9.5

75 th percentile 7.1 8.6 9.9 10.5
85 th percentile 7.4 9.0 10.3 11.0
90 th percentile 7.7 9.2 10.6 11.3
95 th percentile 8.0 9.7 11.1 11.8
Source: EPA 2008a; each value is the time-weighted av erage from the data summaries presented in the CSEFH, Table 8-3.

a MIRC default



### Maternal body weight ( BW mat 

### ). This parameter represents the body weight of the mother averaged over the entire duration of the mother’s exposure to the chemical of concern. The maternal body weight is needed to calculate the biological elimination constant for the lipophilic chemical in lactating women ( k fat_elac 

### ). MIRC assumes that the mother will be pregnant for 9 months (i.e., 0.75 year) and will be lactating for 1 year. The MIRC default maternal body weight also assumes that the mother has been exposed for 10 years total. For 8.25 years, she is not pregnant or lactating, for 0.75 year she is pregnant, and for 1 year she is lactating. The MIRC default BW mat 

### of 66 kg is based on CSFII data compiled by EPA for non-lactating and nonpregnant women between the ages of 15 and 44 (i.e., women of child-bearing age), lactating women, and pregnant women (EPA 2004b). Exhibit 6-18 presents additional values for the maternal body weight parameter which the user may choose to use instead of the MIRC default. The BW mat 

### value is not the value that MIRC uses to estimate the mother’s absorbed daily intake ( DAI mat 

### ). The daily ingestion rates for home-grown/raised food products in MIRC are for men and women combined, with the rates normalized to body weight. The ingestion rates for soil,Attachment 2 to Technical Support Document for TR IM-Based Multipathway Screening Scenario for RTR 2-78 

### 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 6-18. Time-weighted Average Body Weight for Mothers

Statistic Weight (kg)

Mean 66.0 a

5 th 47.1 10 th 50.2 25 th 54.3 50 th 62.0 75 th 72.0 90 th 85.7 95 th 97.0
Source: EPA 2004b

a MIRC default value



### Exposure duration ( ED ). See discussion of AT and ED above.

Fraction of mother’s whole blood that is plasma ( f bp 

### ). Steinbeck (1954) reported that plasma volume accounts for 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) is the value recommended by EPA in its Methodology for Assessing Health Risks Associated with Multiple Pathways of Exposure to Combustor Emissions (MPE, EPA 1998).

Fraction of mother’s body weight that is fat ( f fm 

### ). 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 3-38, used to estimate the concentration of a lipophilic chemical in breast mi lk fat, assumes that the mother’s body fat will remain constant over the entire duration of breast feeding (t bf 

### ), which is unlikely to be true (EPA 2001a). Another limitation of the single analytic m odel 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 tothe blood (EPA 2001a). Studies cited in ATSDR’s toxicological profile for chlorinated dibenzo-pdioxins show a correlation between percent body fat and the elimination rate of dioxins, with longer half-lives for dioxins in individuals with a higher proportion of fat in their bodies (ATSDR 1998). In the context of a screening model, how ever, EPA recommends a default value for the fraction of a mother’s body comprised of fat of 0.3 based on data and discussions presented by Smith (1987) and Sullivan et al. (1991) (EPA 1998). A fraction of 0.3 indicates that 30 percent of the mother’s body weight is fat, which is a conservative value (EPA 2001a). To establish a conservative screening scenario, the MIRC default value for f fm 

### is 0.30

. 

### Fraction of fat in mother’s breast milk ( f mbm 

### ). The C milkfat 

### model (Equation 3-38) assumes that a constant fraction of breast milk is fat, even though there is evidence that indicates variation in the fat content of breast milk throughout lactation (Sim and McNeil 1992). Different studies suggest a fat content of breast milk in humans of between 1 and 5 percent (Jensen 1987, Schecter et al. 1994, Hong et al. 1994, McLachlan 1993, Bates et al. 1994, NAS 1991, Butte et al. 1984, Maxwell and Burmaster 1993, EPA 1997a, Smith 1987, Sullivan et al. 1991). TheAttachment 2 to Technical Support Document for TR IM-Based Multipathway Screening Scenario for RTR 2-79 

### MIRC default value for f mbm 

### of 0.04 (i.e., 4 percent) is the value EPA recommended for MPE (EPA 1998).

Fraction of maternal weight that is plasma ( f pm 

### ) . Altmann and Dittmer (1964) estimated that plasma volume for adult women ranged from 37 to 60 mL/kg of body weight and averaged about 45 mL/kg. Ueland (1976) observed that the average plasma 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 f pm 

### therefore is 0.046.

Infant breast milk ingestion rate ( IR milk 

### ) . Milk ingestion rates vary with several factors, including the age and size of the infant and use of other foods such as formula. Based on its review of a several studies, EPA recommended time-weighted average and upper percentile milk ingestion rates for infants that nurse for six and for twelve months (EPA 1997a, Chapter 14, Table 14-15). To estimate an “average” value, EPA first estimated study-sample-size weighted average values for 1, 3, 6, 9, and 12 months of age and then developed time-weighted average milk ingestion rates from those (EPA 1997a). 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 estima tes (kg/day) assuming the density of human milk to be 1.03 g/mL (reported by NAS 1991 and recommended by EPA 1997a). The resulting values are shown in the first two rows of Exhibit 6-19. The MIRC screening-level default of 980 mL/day is an upper-bound estimate based on a one-year nursing period.

Exhibit 6-19 also includes the recommended values for four non-overlapping age categories from the CSEFH (EPA 2008, Table 15-1). The values demonstrate that although infants grow substantially from birth to one year of age, t he “upper bound” estimates of their milk ingestion rates are very close to 1 liter per day at all stages of development in the first year.

Exhibit 6-19. 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 1997a †

0 to < 12 months 688 0.709 980 a 1.01 a EPA 1997a †

0 to < 1 month 510 0.525 950 0.979 EPA 2008 ††

1 to < 3 months 690 0.711 980 1.01 EPA 2008 †
3 to < 6 months 770 0.793 1,000 1.03 EPA 2008 †

6 to < 12 months 620 0.639 1,000 1.03 EPA 2008 †

a MIRC default; † Based on review of multiple studies; †† Based on a single study



### Duration of breast feeding ( t bf



### ). This parameter is equal to the infant’s exposure duration ( ED ) and the infant’s averaging time ( AT ). In its MPE Methodology, EPA asserts a conservative 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 conservative, given than 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 conservative screening scenario, the MIRC default for t bf



### is 365 days.Attachment 2 to Technical Support Document for TR IM-Based Multipathway Screening Scenario for RTR 2-80 

### Duration of the mother’s exposure to the chemical of concern prior to nursing ( t pn 

### ). The model shown as Equation 3-38 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-l ife of 8 years and a nursing period of 1 year. If exposure of the mother starts at the beginning of nursing, using Equation 3-38 without the t pn 

### term results in an over-estimate of the concentration of the chemical in breast milk by a factor of 28.1 compared with the prediction using Equation 3-38 with the t pn 

### term (EPA 1998, Table 9-6). However, at longer pre-natal exposures of the mother, the magnitude of the over-estimate is reduced: for a 10-year exposure, the magnitude of the overestimate without the t pn 

### term is 2.28, and for a 30-year exposure, the overestimate is reduced to 1.39.

For purposes of the screening-level of assessment for dioxins, we assume an exposure duration equal to the half-life of the chemical, or 10 years. Only 3285 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 conservative 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 6-20. 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).

Absorption efficiency of the chemical by the oral route of exposure for the infant ( AE inf



### ). The models included in MIRC assume that the AE inf



### from the lipid phase of breast milk is equal to the AE inf



### from the aqueous phase of the milk. Reviewers of the model stated that this assumption may not be valid and that ideally, the equation DAI inf



### would include variables for the AE inf



### from the breast milk fat and the AE inf



### from the aqueous phase of 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 AE inf



### 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 AE inf 

### . Reviewers also recommended that chemic al-specific values come from studies that account for absorption of the chemical fr om 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 conservative default value for AE inf



### for a screening level assessment is 1.0, which assumes 100 percent absorption (EPA 1998).

The default value for AE inf



### in MIRC for both MeHg and dioxin is 1.0. For ingested lipophilic chemicals, it is reasonable to assume that absorption will be high (EPA 2004c). ATSDR (1998) reported that dioxins are well absorbed by the oral route 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).Attachment 2 to Technical Support Document for TR IM-Based Multipathway Screening Scenario for RTR 2-81 

### Exhibit 6-20. Chemical-specific Input Parameter Values for Breast Milk Exposure Pathway
Parameter and Description 2,3,7,8- TCDD MeHg

AE inf Infant absorption efficiency of the chemical by the oral route of exposure (i.e., fraction of ingested chemical that is absorbed by the infant; unitless) 1.0 (default) 1.0 (default)

AE mat Maternal 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)

f bl Fraction of steady-state total body burden of hydrophilic chemical in mother that is in the mother’s whole blood compartment (unitless) NA 0.059 (Kershaw et al. 1980) a

f f Fraction of steady-state lipophilic chemical body burden in mother that is stored in body fat (unitless) ≥ 0.90 (ATSDR 1992) NA

f pl Fraction of steady-state total hydrophilic chemical body burden in mother that is in the blood plasma compartment (unitless) NA Not yet identified b

h Biological half-life for chemical in non-lactating women (days) 3650 (EPA 1994c) 50 (Sherlock et al. 1984)



### k

aq_elac Rate constant for total elimination of hydrophilic chemicals by lactating women (per day) NA = 

### k elim

k

elim Rate constant for elimination of chemical for nonlactating women (per day; related to chemical halflife) 1.9E-04 b 1.4E-02 c

k

fat_elac Rate constant for total elimination of lipophilic chemicals by lactating women (per day) Est. using Equation 3-41 NA

Pc bm 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)

Pc RBC Partition coefficient for hydrophilic or proteinbound chemical between red blood cells (RBC) and plasma in maternal blood (mL whole blood/mL RBC) NA 40 (Hollins et al. 1975)

NA = not applicable. ND = not yet determined from literature.

a This value is based on a single-dose study and may not be appropriate for a chronic exposure model.
b An empirical value for this variable is currently missing for application of model.
d This value was calculated from biological half-life ( h ) using Equation 3-40.



### Note that AE values for some inorganic compounds are substantially less than 1.0. For cadmium, for example, AEs for adults of 0.025 to 0.05 have been reported (EPA 2004c, Exhibit B-4).

Absorption efficiency of the chemical by the oral route of exposure for the mother ( AE mat 

### ) . 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 ( f bl 

### ). The default value for MeHg in MIRC, 0.059, is from Kershaw et al. (1980), which reported kinetics of blood deposition and clearance of MeHg in humans. Individuals consumed one meal of fish that contained between 18 and 22 μg Hg/kg body weight. The fraction of the dose deposited in the blood volume after mercury was fully distributed in tissues was 5.9 percent or 0.059. This study used a single-dose and thus may not be appropriate for a chronic exposure analysis.Attachment 2 to Technical Support Document for TR IM-Based Multipathway Screening Scenario for RTR 2-82 

### Fraction of total maternal chemical body burden that is in body fat ( f f 

### ). Based on ATSDR’s Toxicological Profile for Selected PCBs (ATSDR 1992) and Sullivan et al. (1991), EPA concluded that the “fraction of ingested contam inant 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 ( f pl 

### ). For hydrophilic chemicals, this parameter represents the steady-sta te fraction of the total chemical in the body that is circulating in the blood plasma. Values for f pl



### may be available for some chemicals in the scientific literature. No value for this parameter for methyl mercury has been identified from the literature at this time. A value can be calculated using Equation 3-43. However, this equation requires a reliable value for f bl 

### , and the value found for mercury may not be appropriate for a chronic exposure analysis (see above).

Chemical half-life in non-lactating women ( h 

### ) . In general, highly lipophilic chemicals tend to have relatively long biological half-lives. EPA esti mates 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 conservative screening scenario, the MIRC default half-life for dioxins is set to 10 years or 3650 days.

The half-life for methylmercury is on the order of weeks, not years. Greenwood et al. (1978) measured blood clearance rates for MeHg in lactating Iraqi women exposed accidentally to MeHg via bread prepared from wheat treated with a fungicide that contained MeHg. The data indicated a mean half-life for MeHg of approximately 42 days. Sherlock et al. (1984) reported an average measured half-life for MeHg of 50 days with a range of 42-70 days. The MIRC default for MeHg is set to the longer average half life of 50 days.

Chemical elimination rate constant for lactating women – aqueous ( k aq_elac 

### ). The parameter k

aq_elac 

### is equal to k elim 

### plus the loss rate for the chemical in the aqueous phase of breast-milk during lactation. EPA has yet to propose a term for the additional elimination of a chemical in the aqueous phase of milk from breast feeding. In the absence of empirical values, a reasonable assumption for water soluble chemicals is that k aq_elac 

### is equal to k elim 

### as discussed for Equation 3-43. The extent to which k elim 

### is an underestimate of k aq_elac 

### for a given chemical will determine the extent of conservative bias in k aq_elac



### . Chemical elimination rate constant for non-lactating women ( k elim 

### ). Although values for this parameter often are reported directly in the literature, MIRC estimates k elim 

### from chemical halflife assuming first-order kinetics as shown in E quation 3-40. For example, for a biological halflife of 3,650 days for dioxins, k elim 

### is estimated to be 1.9E-04 per day. Assuming a biological half-life of 50 days for MeHg, the value for k elim 

### is estimated to be 0.014 per day.

Rate constant for total elimination of lipophilic chemicals by lactating women ( k fat_elac 

### ). Although values for this parameter might be found in the scientific literature for some chemicals, in MIRC, k

fat_elac 

### for dioxins is calculated from Equation 3-41. When the parameters in that equation are set to the default values in MIRC for dioxins, MIRC estimates a value of 0.0015 per day for k

fat_elac



### . Partition coefficient for chemical between maternal blood plasma and aqueous phase of breast milk ( Pc bm 

### ). The aqueous model, presented in Equation 3-42, 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.Attachment 2 to Technical Support Document for TR IM-Based Multipathway Screening Scenario for RTR 2-83 

### Partition coefficient for chemical between red blood cells and plasma in maternal blood ( Pc RBC 

### ). Chemical-specific values for this parameter should be located in the scientific literature. If chemical-specific values are unavailable and it is assumed that there is equal distribution of the chemical in the plasma and red blood cells, EPA suggests a default value of 1.0 (EPA 1998). 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).Attachment 2 to Technical Support Document for TR IM-Based Multipathway Screening Scenario for RTR 2-84 

## 7 Summary of MIRC Default Exposure Parameter Settings

The default settings included in MIRC are intended to be characteristic of a conservative (but plausible) exposure scenario that results in a negligible or extremely low chance of underestimating risk to farming households in an area with chemical concentrations and air deposition rates as specified by the user. These default parameter values were used to derive the de minimis 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. This section summarizes the default parameter values used to calculate screening thresholds.

This chapter is organized to present the chemic al- and scenario-specific inputs to MIRC by data type. The screening-level analysis uses 90 th 

### percentile ingestion rates, presented in Section 7.1, and population-specific characteristic assumptions, presented in Section 7.2, that are generally conservative in nature. De minimis 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.



### 7.1 Default Ingestion Rates
The screening-level (or default) values for ingestion rates for soil, fish, breast milk, and for each farm food type are equal to the 90th percentile of the distribution of national data for that ingestion medium (Exhibit 7-1). The default settings also 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 de minimis emission rates for PB-HAPS, environmental concentrations and air deposition rates were estimated using TRIM.FaTE for the area of maximal deposition in the vicinity of a hypothetical facility, and thus represent risks estimated for a maximally exposed individual/farm/family.

Exhibit 7-1 also includes a sum of the 90 th 

### percentile ingestion rates for homegrown food categories and fish ingestion (preceding rows) to show the implied total food ingestion rate associated with setting multiple food-type-specific ingestion rates at a 90 th 

### percentile. Because the 90 th 

### percentiles for each farm food category are likely to reflect different individuals, it is likely that addition of multiple 90 th 

### percentile intake values will exceed the total food ingestion rates likely for the general population.

The final row in Exhibit 7-1 lists the likely magnitude of the overestimates by age category. The preceding row includes the 90 th 

### percentile of the distribution of individual total food ingestion rates from the USDA’s 1994-96 and 1998 Continuing Survey of Food Intakes by Individuals (CSFII) (USDA 2000) data sets, as analyzed by EPA (EPA 2005e). The total ingestion rate for the farming households takes into account the cooking losses typical of each food category to provide a better comparison with the 90 th 

### percentile individual total food ingestion rates (which are based on consumption of prepared foods).Attachment 2 to Technical Support Document for TR IM-Based Multipathway Screening Scenario for RTR 2-85 

### Exhibit 7-1. Farm Food Category Ingestion Rates for Conservative Screening Scenario for Farming Households
90 th Percentile Consumer Ingestion Rate

Product Infants < 1 yr Child 1-2 yrs Child 3-5 yrs Child 6-11 yrs Child 12-19

yrs Adult Units a

Farm Food Item Beef NA 4.5 6.7 11.4 3.53 5.39 g/kg-day Dairy c NA 148 82 54.7 27.0 34.9 g/kg-day Eggs c NA 5.1 2.8 1.80 1.34 1.65 g/kg-day Exposed Fruit NA 3.7 5. 4 6.98 3.41 5 g/kg-day Exposed Vegetable NA 10.7 3. 5 3.22 2.35 6.01 g/kg-day Pork c NA 4.5 4.4 3.04 2.65 3.08 g/kg-day Poultry c NA 7.4 6.8 4.58 3.28 3.47 g/kg-day Protected Fruit c NA 53 36 24.1 16.2 15.1 b g/kg-day Protected Vegetable NA 3.9 2.5 2.14 1.85 3.55 g/kg-day Root Vegetable NA 7.3 4.3 3.83 2.26 3.11 g/kg-day

Other Breast milk d 1.01 NA NA NA NA NA kg/day Soil (dry) NA 400 e 400 e 201 f 201 f 201 f mg/day Water NA 654 834 980 1537 2224 mL/day Fish (per individual) g NA 3.24 4.79 6.9 8.95 17 g/day Fish (per kg BW) h NA 0.26 0.26 0.22 0.14 0.24 g/kg-day Total Food Ingestion Rates for Comparison Only (not in MIRC; excludes soil and water)
Total Food: Homegrown only i NA 219 131 95 52 67 g/kg-day

Total Food: All Sources j NA 125 91 61 34 23.7 g/kg-day
Overestimate (ratio of Homegrown/Total) k NA 1.8 1.4 1.6 1.5 2.8 (unitless)

Sources: EPA 1997a (Chapter 13), unless otherwise noted. NA = not applicable

a As indicated by the units, the ingestion rates for produce and animal products are already normalized to consumer body weight . Ingestion rates for soil (mg/day) and water (mL/ day) are not normalized to body weight. Soil is reported as dry weight, water as volume, and the remaining values on a wet-weight basis.
b This value represents a weighted average for the 20-39 and 40-69 age groups.

c For several farm food categories, ingestion rates we re not available in EPA’s 1997 EFH or 2008 CSEFH (EPA 1997a, 2008a). Ingestion rates for these child ag e categories were calculated using the methodology recommended in HHRAP (EPA 2005a), Section 6.2.2.2, as described in Section 6.3.3 of this document. Sources to develop these values included EPA 1997a and EPA 2003c.
d Infants are assumed to consume on ly breast milk for one year.

e This value represents an estimated “u pper percentile” for children (EPA 1997a).
f These values represent soil ingestion rates for individuals who consume homegrown food products from Stanek et al. 1997.

g 90 th percentile adult fish ingestion rates are based on data from 1995-1996 and 1998 CSFII as summarized in EPA 2002; child fish ingestion rates are based on the same survey data, but estimated by multiplying average two-day consumption rate for childr en who consumed fish on one or both days of the survey by the frequency of fish consumption (i.e., propor tion of children that reported consuming fish out of all children sampled).
h Fish ingestion rates, original data in g/day, have be en normalized to body weight in this table to allow addition into total food estimate using t he mean body weight for each age category.
i Sum of 90 th percentile post-cooking food food ingestion rates. Th is estimate is calculated by multiplying the food ingestion rates on previous rows (excluding soil and water) by (1-L 1 )x(1-L 2 ), where L 1 and L 2 are the loss rates from Exhibit 6-15. The rows are then su mmed to get the total post-cooking ingestion rate. j 90 th percentile total food intake rates from EPA 2008 a and 2005e based on CSFII data 1994-96 and 1998; see Exhibit 6-14 of this document.Attachment 2 to Technical Support Document for TR IM-Based Multipathway Screening Scenario for RTR 2-86 

### The final row of Exhibit 7-1 is the ratio of the two preceding rows. The magnitude of the overestimate that results from adding 90 th 

### percentile values for 10 different categories of homegrown foods for children is a factor of 1.8. The overestimate of the 90 th 

### percentile total food ingestion rate for adults is larger, a factor of 2.8. This bias may be considered when evaluating the cancer risks and noncancer hazard quotients estimated by MIRC.



### 7.2 Default Screening-Level Popul ation-Specific Parameter Values
The screening-level values for body weights (BWs) for the RTR de minimis analysis, which serve as the default values in MIRC, are mean values and are presented in Exhibit 7-2. As stated in Section 6, EPA recommends using the mean BW for each age group when using upper (90 th 

### ) percentile values for medium ingestion rates. Use of the mean body weights introduces no bias toward over- or underestimating risk.

Exhibit 7-2. Mean Body Weight Estimates for Adults and Children a
Lifestage (years) Duration (years) Mean Body Weight (kg)

Adult b (20-70) 50 71.4
Child < 1 c 1 7.83
Child 1-2 c 2 12.6
Child 3-5 d 3 18.6
Child 6-11 e 6 31.8
Child 12-19 f 8 64.2

a Sources: EPA 1997, 2008a

b These values were calculated from data presented in EPA’s (1997a) Exposure Factors Handbook

.
c These values were calculated as time-weighted average body weight (BW) from data presented in T able 8-3 of EPA’s (2008a) Child-Specific Exposure Factors Handbook (CSEFH).

d These values were obtained directly from Table 8-3 of the 2008 CSEFH.

e These values were obtained directly from Table 8-3 of the 2008 CSEFH for age group 6 to <11 years. The values represents a slight underestimate of BW for ages 6 through 11 years, since 11-year olds are not included in this CSEFH
age group.

f These values were calculated as time-weighted average BW for age groups 11 to <16 years and 16 to <21 years from Table 8-3 of the 2008 CSEFH. The direction of the possible bias is unknown. The values match the estimate based on Table 8-22 of the NHANES IV data as presented by Portier et al. (2007).


### 7.3 Default Chemical-Specific Parameter Values for Screening Analysis
Exhibit 7-3 presents chemical-specific parameter values for input to MIRC for the screeninglevel analysis. Values for reference dose (RfD), cancer slope factor (CSF), bioavailability when ingested in soil ( Bs ), mammalian metabolism factors ( MF ), correction factors for belowground produce ( VG rootveg 

### ), wet deposition fractions ( Fw ), air to plant transfer factors ( Bv AG 

### ), root concentration factors ( RCF ), and soil-water partition coefficient ( Kds ) are presented in Exhibit 7-3.Attachment 2 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR2-87Exhibit 7-3. Chemical-Specific Parameter Values for Input to MIRCaParameterDescriptionBenzo(a)-pyreneCadmiumMercuricchlorideMethylmercury2,3,7,8-TCDDUnitsRfDReference Dose for Chemical00.0005 0.0003 0.0001 1E-09mg/kg-dayCSFOral carcinogenic potency slope factorfor chemical100 0 0 150,000mg/kg-dayBsSoil bioavailability factor for livestock11 1 1 1unitlessMFMammalian metabolism factor0.011 1 1 1unitlessVGrootvegEmpirical correction factor forbelowground produce, i.e., tuber orroot vegetable, to account for possibleoverestimate of the transfer ofchemicals from the outside to theinside of bulky tubers or roots (basedon carrots and potatoes)0.011 1 0.01 0.01unitlessFwFraction of wet deposition that adheresto plant surfaces; 0.2 for anions, 0.6 forcations and most organics0.60.6 0.6 0.6 0.6unitlessBvAGAir-to-plant biotransfer factor foraboveground produce for vapor-phasechemical in air124,7420 1,800 0 65,500[mg/g produce DW]/ [mg/g air]RCFChemical-specific root concentrationfactor for tubers and root produce9,6840 0 0 39,999L soil pore water/kgroot WWKdsChemical-specific soil/water partitioncoefficient160,00075 58,000 7,000 38,904.51L soil pore water/kgsoil DWaValues presented in this Exhibit are previously presented in Exhibit 4-1, Exhibit 6-2,Error! Reference source not found., and Exhibit 6-5. However,exact values used in the analysis are presented here,rather than values restricted by significant figures.Attachment 2 to Technical Support Document for TR IM-Based Multipathway Screening Scenario for RTR 2-88 

### Only single estimates were developed for eac h 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 Error! Reference source not found. 

### , 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 7-4 presents biotransfer factors for each of the chemicals and animal types assessed in the screening level assessment.

Exhibit 7-4. Chemical and Animal-Type Specific Biotransfer Factor (Ba) values for Input to MIRC ([mg chemical/kg WW tissue or dairy] / [mg ch emical intake/day] = day/kg WW tissue or dairy)

Chemical Beef Dairy Pork Poultry Eggs

Benzo(a)pyrene 0.03756242 0.00790788 0.0454703 0.02767758 0.01581576
Cadmium 0.00012 0.0000065 0.00019149 0.10625 0.0025
Mercuric chloride 0.000105 0.00000143 0.00003393 0.023925 0.023925 Methyl mercury 0.00124 0.0000169 0.00000507 0.003575 0.003575 2,3,7,8-TCDD 0.02612123 0.0054992 1 0.03162044 0.01924722 0.01099841
Note: Exact values used in the analysis are presented her e, rather than values restricted by significant figures.



### Sensitivity analyses were conducted to identify which, if any, of these parameter values significantly influence risk estimates from MI RC; results are presented elsewhere. Further evaluation of the possible range and distribution of values can be conducted for any parameter that appears important to the model outputs.


### 7.4 Screening-Level Parameter Values for Nursing Infant Exposure

EPA also included an assessment of 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 va lues were summarized in Section 6.5.

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 1998a). The assumption that lactational transfer of dioxins to the infant occurs via the lipid-phase of milk appears reasonable. The following screening-level assumptions used in that model should bias the results toward some overestimate 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.

• 

### 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 90 th 

### percentile ingestion rates for the different homegrown foods (see discussion for Exhibit 7-1); this assumption might overestimate total ingestion of homegrown foods by a factor of more than 3.Attachment 2 to Technical Support Document for TR IM-Based Multipathway Screening Scenario for RTR 2-89 • 

### 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 pharmacoki netic (PBPK) model of lactational transfer of MeHg developed and partially validated by Byckowski and Lipscomb (2001) to support a very simple predictive model. Both the human data and the PBPK model indicated that for relatively low MeHg exposures, the concentration of MeHg in the nursing infant’s blood is similar to its concentration in the mother’s blood. The PBPK model suggested in addition that the average daily dose of MeHg absorbed from milk by the nursing infant ( DAI inf



### ) is indistinguishable from the dose of MeHg absorbed by its mother from her food ( DAI mat 

### ). The data are limited, and the model includes various assumptions; however, there is no known directional bias in the estimates.Attachment 2 to Technical Support Document for TR IM-Based Multipathway Screening Scenario for RTR 2-90 

## 8 REFERENCES

Altman, P.L., and D.S. Dittmer, Eds. 1964. Biology Data Book. Volume 1. Bethesda, MD. 263- 264 (As cited in U.S. Environmental Protection Agency (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 poisoing in Iraq. Am. J. Diseases in Children 130: 1070-1076 (As cited in Byckowski and Lipscomb 2001).

Agency for Toxic Substances and Disease Regist ry (ATSDR). 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, P ublic Health Service (As cited in U.S. Environmental Protection Agency (EPA) 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.

Agency for Toxic Substances and Disease Registry (ATSDR). 1999. Toxicological profile for Mercury. Atlanta, GA: U.S. Department of He alth and Human Services, Public Health Service.

Agency for Toxic Substances and Disease Regist ry (ATSDR). 2000. Toxicological profile for selected PCBs. 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 parametersand assessing transport of environmentally released radionuclides through agriculture. ORNL5786. 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.

Bowman, R.E., S.L. Schantz, M.L. Gross, et al . 1989. Behavioral effects in monkeys exposed to 2,3,7,8-TCDD transmitted maternally during gestation and for four months of nursing. Chemosphere 18: 235-242 (As cited in ATSDR 1998).

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 U.S. Environmental Protection Agency (EPA) 2005a, Appendix A-2).Attachment 2 to Technical Support Document for TR IM-Based Multipathway Screening Scenario for RTR 2-91 

### 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 retent ion of radioactive aerosols by vegetation. Atmospheric Environment 4: 57-78.

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 Byckowski 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 U.S. Environmental Protection Agency (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. Qual. 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 Byckowski 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 Byckowski 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.Attachment 2 to Technical Support Document for TR IM-Based Multipathway Screening Scenario for RTR 2-92 

### 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 bipheny ls in human milk from Mohawk and control women: Effects of maternal factors and previous lactation. Arch. Environ. Contam. Toxicol. 27(3): 431-437.

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 absorpt ion 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 ex amination of the environmental half-time for radionuclides deposited on vegetation. Health Physics 45(3): 731-744.

National Academy of Sciences (NAS). 1987. Predicting Feed intake of Food-Producing Animals. National Research Council, Committee on Animal Nutrition, Washington, D.C.

National Academy of Sciences (NAS). 1991. Nu trition During Lactation. National Academies Press. Washington, DC.

North Carolina Department of Health, Environment, and Natural Resources (NC DEHNR). 1997. North Carolina Protocol for Performing Indirect Exposure Risk Assessments for Hazardous Waste Combustion Units. January.Attachment 2 to Technical Support Document for TR IM-Based Multipathway Screening Scenario for RTR 2-93 

### Portier K., J. Tolson, and S. Roberts. 2007. B ody weight distributions for risk assessment. Risk Anal. 27(1): 11-26.

Prentice, A.M., G.R. Goldberg, and A. Prentice. 1994. Body mass index and lactation performance. European Journal of Clinical Nutrition 48(Supplement 3): S78-S86.

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.

Schantz, S., and R.E. Bowman. 1989. Learning in monkeys exposed perinatally to 2,3,7,8- tetrachlorodibenzo-p-dioxin (TCDD). Neurotox. Teratol. 11: 13-19.

Schantz, S.L., S.A. Ferguson, and R.E. Bowman. 1992. Effects of 2,3,7,8-tetrachlorodibenzop-dioxin on behavior of monkeys in peer groups. Neurotox. Teratol. 14: 433-446.

Schecter, A., P. Fürst, C. Fürst, O. Päpke, M. Ba ll, 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 methylme rcury in fish. Human Toxicology 3: 117-131 (As cited in USFDA 2009).

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. Monito ring 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. The Science of the 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 foodchains. Environ. Sci. Technol. 23(6): 699-707.Attachment 2 to Technical Support Document for TR IM-Based Multipathway Screening Scenario for RTR 2-94 

### Travis, C.C., and A.D. Arms. 1988. Bioconcentration of organics in beef, milk, and vegetation. Environ. Sci. Technol. 22:271-274.

Ueland, K. 1976. Maternal cardiovascular dynamics. VII. Maternal cardiovascular dynamics. Intrapartum blood volume changes. Am. J. Obstetrics Gynecol. 126(6): 671-677.

U.S. Department of Agriculture (USDA). 1980. Food and Nutrient Intakes of Individuals in One Day in the United States, Spring 1977. Nationwide Food Consumption Survey 1977-1978. Preliminary Report No. 2.

U.S. Department of Agriculture (USDA). 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 U.S. EN VIRONMENTAL PROTECTION AGENCY (EPA) 1997)

U.S. Department of Agriculture (USDA). 1993. Food and Nutrient Intakes by Individuals in the United States, 1 Day, 1987-88. Nationwide Food Consumption Survey 1987-88, NFCS Report No. 87-I-1. (As cited in U.S. ENVIRONMENTAL PROTECTION AGENCY (EPA) 1997)

U.S. Department of Agriculture (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 U.S. ENVIRONMENTAL PROTECTION AGENCY (EPA) 1997)

U.S. Department of Agriculture (USDA). 1994b. Vegetables 1993 Summary. National Agricultural Statistics Service, Agricultural Statistics Board. Washington, D.C. Vg 1-2 (94). Jan.

U.S. Department of Agriculture (USDA). 1994c. Noncitrus Fruits and Nuts 1993 Summary. National Agricultural Statistics Service, Agricultural Statistics Board, Washington, D.C. Fr Nt 1-3 (94).

U.S. Department of Agriculture (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. Ava ilable from the National Technical Information Service, Springfield, VA, Accession Number PB-2000500027. (As cited in U.S. ENVIRONMENTAL PROTECTION AGENCY (EPA) 2008a, Chapter 14)

U.S. Environmental Protection Agency (EPA). 1989. Risk Assessment Guidance for Superfund. Volume I. Human Health Evaluati on Manual (Part A). Office of Emergency and Remedial Response (OERR), Washington, D.C. December. OERR 9200 6-303-894. Available at: http://www.epa.gov/oswer/riskassessment/ragsa/index.htm.

U.S. Environmental Protection Agency (EPA). 1990. Interim Final Methodology for Assessing Health Risks Associated with Indirect Expos ure to Combustor Emissions. Environmental Criteria and Assessment Office. ORD.EPA-600-90-003. January.

U.S. Environmental Protection Agency (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.

U.S. Environmental Protection Agency (EPA). 1993. Review Draft Addendum to the Methodology for Assessing Health Risks Associ ated with Indirect Exposure to Combustor Emissions. Office of Health and Environment al Assessment. Office of Research and Development. EPA-600-AP-93-003. November 10.Attachment 2 to Technical Support Document for TR IM-Based Multipathway Screening Scenario for RTR 2-95 

### U.S. Environmental Protection Agency (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.

U.S. Environmental Protection Agency (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.

U.S. Environmental Protection Agency (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.

U.S. Environmental Protection Agency (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.

U.S. Environmental Protection Agency ( EPA). 1995b. Memorandum Regarding Further Studies for Modeling the Indirect Exposure Impacts from Combustor Emissions. From Mathew Lorber, Exposure Assessment Group, and Glenn Ri ce, Indirect Exposure Team, Environmental Criteria and Assessment Office. Washington, D.C. January 20.

U.S. Environmental Protection Agency (EPA). 1 995c. Further Issues for Modeling the Indirect Exposure Impacts from Combustor Emissions. Office of Research and Development. Washington, D.C. January 20.

U.S. Environmental Protection Agency (EPA) . 1995d. Waste Te chnologies Industries Screening Human Health Risk Assessment (SH HRA): Evaluation of Potential Risk from Exposure to Routine Operating Emissions. Volu me V. External Review Draft. U.S. EPA Region 5, Chicago, Illinois.

U.S. Environmental Protection Agency (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/.

U.S. Environmental Protection Agency (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.

U.S. Environmental Protection Agency (EPA). 1997c. Parameter Guidance Document. National Center for Environmental Assessment, NCEA-0238.

U.S. Environmental Protection Agency (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.

U.S. Environmental Protection Agency (EPA). 1999a. Background Document for the Human Risk Module for HWIR99 Multimedia, Multipathw ay, and Multireceptor Risk Assessment (3MRA)Attachment 2 to Technical Support Document for TR IM-Based Multipathway Screening Scenario for RTR 2-96 

### Model. Office of Solid Waste, Washington, D.C. October. Available at: http://epa.gov/epawaste/hazard/wastetypes/wasteid/hwirwste/pdf/risk/modules/s0054.pdf.

U.S. Environmental Protection Agency (EPA). 1999b. 1999 National-Scale Air Toxics Assessment Results; Approach for Modeling POM. Available at: http://www.epa.gov/ttn/atw/nata1999/nsata99.html.

U.S. Environmental Protection Agency (EPA). 2000. Supplementary Guidance for Conducting Health Risk Assessment of Chemical Mixtures . Risk Assessment Forum, Washington D.C. EPA/630/R-00/002. August. Available at: http://www.epa.gov/ncea/raf/pdfs/chem_mix/chem_mix_08_2001.pdf.

U.S. Environmental Protection Agency (EPA). 2 001a. Peer Review of EPA’s Hazardous Waste Identification Rule Risk Assessment Model: Breas t milk exposure model for the HWIR 3MRA Model. Prepared by Eastern Research Group for EPA Office of Solid Wastes. 68-W5-0057.

U.S. Environmental Protection Agency (EPA). 2 001b. 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.

U.S. Environmental Protection Agency (EPA). 2002. Estimated Per capita Fish Consumption in the United States. Office of Water, Office of Science and Technology, Washington, D.C. EPA821- C- 02-003. August. Available at: http://www.epa.gov/waterscience/fish /files/consumption_report.pdf.

U.S. Environmental Protection Agency (EPA). 2 003a. 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/wastety pes/wasteid/hwirwste/risk03.htm.

U.S. Environmental Protection Agency (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/crit eria/humanhealth/method/tsdvol2.pdf.

U.S. Environmental Protection Agency (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.

U.S. Environmental Protection Agency (EPA). 2004a. Superfund Chemical Data Matrix. OERR. Washington, D.C. January.

U.S. Environmental Protection Agency (EPA). 2004b. Estimated Per capita Water Ingestion and Body Weight in the United States – An Update. Office of Water, Office of Science and Technology, Washington, D.C. EPA-822-R-00-001. October. Available at: http://www.epa.gov/waterscience/crit eria/drinking/percapita/2004.pdfAttachment 2 to Technical Support Document for TR IM-Based Multipathway Screening Scenario for RTR 2-97 

### U.S. Environmental Protection Agency ( EPA). 2004c. Risk Assessment Guidance for Superfund. Volume I: Human Health Evaluati on 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.

U.S. Environmental Protection Agency (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.

U.S. Environmental Protection Agency (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.

U.S. Environmental Protection Agency (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.

U.S. Environmental Protection Agency (EPA). 2005d. Supplemental Guidance for Assessing Susceptibility from Early-Life Exposure to Ca rcinogens. Risk Assessment Forum: Washington, D.C. EPA-630/R-03-003F. March. Available at: http://www.epa.gov/ttn/atw/childrens_supplement_final.pdf.

U.S. Environmental Protection Agency (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 andDevelopment, National Center for Environmental Assessment, Washington, D.C. EPA/600/R05/062F. Available at: http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=132173.

U.S. Environmental Protection Agency (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 .

U.S. Environmental Protection Agency (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

U.S. Environmental Protection Agency (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 .

U.S. Environmental Protection Agency (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, Po rtland Cement Manufacturing. Office of Air Quality Planning and Standards, Office of Air and Radiation, Research Triangle Park, NC. July 14, 2008.

U.S. Environmental Protection Agency (EPA). 2009. Guidance for Implementing the January 2001 Methylmercury Water Quality Criterion. Office of Water, Office of Science andAttachment 2 to Technical Support Document for TR IM-Based Multipathway Screening Scenario for RTR 2-98 

### Technology, Washington, D.C. EPA 823-R-09-002. January. Available at: http://www.epa.gov/waterscience/criteria/m ethylmercury/pdf/guidance-final.pdf.

U.S. Food and Drug Administration (FDA). 2009. Draft Risk and Benefit Assessment Report and Draft Summary of Published Research. Center for Food Safety and Applied Nutrition. January 15. Available at: http://www.cfsan.fda.gov/seafood1.html

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.

Whitehead, R.G., and A.A. Paul. 1981. Infant growth and human milk requirements: A fresh approach. Lancet 2(8239): 161-163.

### Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR: Summary of Approach and Evaluation



# ATTACHMENT 3: Systematic Sensitivity Analysis Variables and ResultsAttachment 3 to Technical Support Document for TR IM-Based Multipathway Screening Scenario for RTR 3-i [This page intentionally left blank.]Attachment 3 to Technical Support Document for TR IM-Based Multipathway Screening Scenario for RTR 3-ii 

### TABLE OF CONTENTS

1 Systematic Sensitivity Analysis Va riables and Results.....................................................1

Exhibit 1-1. Variables Included in the Syst ematic Sensitivity Analysis ..................................2

Exhibit 1-2. Elasticities and Rankings for t he Variables with the Highest Elasticities for Benzo[a]Pyrene .................... ...............................................................18

Exhibit 1-3. Elasticities and Rankings for t he Variables with the Highest Elasticities for 2,3,7,8 - TCDD.....................................................................................11

Exhibit 1-4. Elasticities and Rankings for t he Variables with the Highest Elasticities for
Cadmium............................................................................................13

Exhibit 1-5. Elasticities and Rankings for t he Variables with the Highest Elasticities for Divalent Mercury..................................................................................15

Exhibit 1-6. Elasticities and Rankings for t he Variables with the Highest Elasticities for Methyl Mercury....................................................................................18Attachment 3 to Technical Support Document for TR IM-Based Multipathway Screening Scenario for RTR 3-1 

## 1 Systematic Sensitivity Anal ysis Variables and Results

This attachment provides the tables of the variables included in the systematic sensitivity analysis in Exhibit 1. The variables are organized into three categories: TRIM.FaTE variables, MIRC farm food chain variables, and MIRC ingestion and bodyweight variables.

This attachment also provides detailed elasticities and rankings for the variables with the highest elasticities for Benzo[a]Pyrene (Exhibit 2), 2,3,7,8 – TCDD (Exhibit 3), cadmium (Exhibit 4), divalent mercury (Exhibit 5), and methyl mercury (Exhibit 6) . In each case, the elasticities and rankings are provided for the local systematic sensitivity anal ysis (variables perturbed up and down by 5%) and the range systematic sensitivity analysis (var iables perturbed up and down by 50%).Attachment 3 to Technical Support Document for TR IM-Based Multipathway Screening Scenario for RTR 3-2 

### Exhibit 1-1. Variables Included in the Systematic Sensitivity Analysis

Variable Name Variable Description

TRIM.FaTE Variables AirTemp Ambient Air Temperature AlgaeCarbonCont Carbon C ontent of Algae in Pond AlgaeDensity Density of Algae in Pond AlgaeGrowthRate Growth Rate of Algae in Pond AlgaeRadius Radius of Single Algae in Pond Biomass Biomass in Pond for all Aquatic Species ChlorideConc Water- Chloride Concentration ChlorophyllConc Water- Chlorophyll Concentration EmissionRate Emission Rate of all PBHAPs EroRate Erosion Rate for all Parcels FishMass Fish Body Weight for all Aquatic Species HorizWindSpeed Horizontal Wind Speed MixHeight Mixing Height Rain Annual Rainfall RootSoilAir Root Soil- Fraction Air RootSoilOCC Root Soil- Organic Carbon Fraction RootSoilpH Root Soil- pH RootSoilSand Root Soil- Fraction Sand RootSoilVertVel Root Soil- Average vert ical velocity of water (percolation) RootSoilWat Root Soil- Fraction Water RunoffRate Total Water Runoff Rate SedDepVel, SedRusVel Sediment Deposition Velocity and Resuspension Velocity
SedOCC Sediment- Organic Carbon Fraction SedpH Sediment- pH SedPorosity Sediment Porosity SedSand Sediment- Fraction Sand StackHeight Stack Height SurfSoilAir Surface Soil- Fraction Air SurfSoilOCC Surface Soil- Organic Carbon Fraction SurfSoilpH Surface Soil- pH SurfSoilSand Surface Soil- Fraction Sand SurfSoilVertVel Surface Soil- Average vert ical velocity of water (percolation) SurfSoilWater Surface Soil- Fraction Water SurfWatTemp Water Temperature WatFractSand Water- Fraction Sand WatOCC Water- Organic Carbon Fraction WatpH Water- pH
WatRetTime Rentention Time in Pond WatSuspSed Water- Suspended Sediment ConcentrationAttachment 3 to Technical Support Document for TR IM-Based Multipathway Screening Scenario for RTR 3-3 

### Exhibit 1-1. Variables Included in the Systematic Sensitivity Analysis

Variable Name Variable Description

MIRC Farm Food Chain Variables 100-MAF_ef 100 - Moisture adjustment factor, Exposed Fruit 100-MAF_ev 100 - Moisture adjustment factor, Exposed Vegetable 100-MAF_pf 100 - Moisture adjustment factor, Protected Fruit 100-MAF_pv 100 - Moisture adjustment factor, Protected Vegetable 100-MAF_rv 100 - Moisture adjus tment factor, Root Vegetable Ba_beef PBHAP-specific biotransfer factor, Beef Ba_dairy PBHAP-specific biotransfer factor, Dairy Ba_egg PBHAP-specific biotransfer factor, Eggs Ba_pork PBHAP-specific bi otransfer factor, Pork Ba_poultry PBHAP-specific biotransfer factor, Poultry Br_AG_produce_ef Plant-soil PBHAP bioconcentration factor, Exposed Fruit Br_AG_produce_ev Plant-soil PBHAP biocon centration factor, Exposed Vegetables Br_AG_produce_fo Plant-soil PBHAP bi oconcentration factor, Forage Feed Br_AG_produce_gr Plant-soil PBHAP bioconcentration factor, Grain Feed Br_AG_produce_pf Plant-soil PBHAP bioc oncentration factor, Protected Fruit Br_AG_produce_pv Plant-soil PBHAP biocon centration factor, Protected Vegetable Br_AG_produce_si Plant-soil PBHAP bioconcentration factor, Silage Feed Br_AG_rootveg Plant-soil PBHAP bioconc entration factor, Root Vegetables
Bs a,b Soil bioavailability factor for livestock

Bv_AG Air-to-plant biotransfer factor for aboveground produce for vapor-phase PBHAP in air C_FishT3 Concentration of PBHAP in whole fish for T3 fish C_FishT4 Concentration of PBHAP in whole fish for T4 fish C_Soil Concentration of PBHAP in soil from contaminated area Ca Average annual total PBHAP concentration in air Cs_root_zone_feed Average soil concentration in contaminated area used to grow animal feed Cs_root_zone_ produce Average PBHAP concentration in soil at r oot-zone depth in produce-growing

area Cs_S_animal_ ingest PBHAP concentration in surface soil in c ontaminated area where livestock feed
Drdp Average annual dry deposition of particle-phase PBHAP Drwp Average annual wet deposition of particle-phase PBHAP
Fv b,c Fraction of airborne PBHAP in vapor phase Fw Fraction of wet deposition that adheres to plant surfaces Kds PBHAP-specific soil/water partition coefficient Kp_ef Plant surface loss coefficient, Exposed Fruit Kp_ev Plant surface loss coefficient, Exposed Vegetable Kp_fo Plant surface loss coefficient, Forage Kp_si Plant surface loss coefficient, Silage L1_beef Loss type 1, Beef L1_ExpFruit Loss type 1, Exposed Fruit L1_ExpVeg Loss type 1, Exposed Vegetable L1_Fish Loss type 1, Fish L1_pork Loss type 1, PorkAttachment 3 to Technical Support Document for TR IM-Based Multipathway Screening Scenario for RTR 3-4 

### Exhibit 1-1. Variables Included in the Systematic Sensitivity Analysis

Variable Name Variable Description L1_poultry Loss type 1, Poultry L1_ProFruit Loss type 1, Protected Fruit L1_ProVeg Loss type 1, Protected Vegetable L1_RootVeg Loss type 1, Root Vegetable L2_beef Loss type 2, Beef L2_ExpFruit Loss type 2, Exposed Fruit L2_Fish Loss type 2, Fish L2_pork Loss type 2, Pork L2_poultry Loss type 2, Poultry L2_RootVeg Loss type 2, Root Vegetable
MF d,e Mammalian metabolism factor

ρ _a Density of air Qp_fo_beef Quantity of forage plant type eaten per animal per day, Beef Qp_fo_dairy Quantity of forage plant type eaten per animal per day, Dairy Qp_gr_beef Quantity of grain plant ty pe eaten per animal per day, Beef Qp_gr_dairy Quantity of grain plant type eaten per animal per day, Dairy Qp_gr_egg Quantity of grain plant ty pe eaten per animal per day, Eggs Qp_gr_pork Quantity of grain plant type eaten per animal per day, Pork Qp_gr_poultry Quantity of grain plant ty pe eaten per animal per day, Poultry Qp_si_beef Quantity of silage plant type eaten per animal per day, Beef Qp_si_dairy Quantity of silage plant type eaten per animal per day, Dairy Qp_si_pork Quantity of silage plant type eaten per animal per day, Pork Qs_beef Quantity of soil eaten by the animal each day, Beef Qs_dairy Quantity of soil eaten by the animal each day, Dairy Qs_egg Quantity of soil eaten by the animal each day, Eggs Qs_pork Quantity of soil eaten by the animal each day, Pork Qs_poultry Quantity of soil eaten by the animal each day, Poultry RCF PBHAP-specific root concentration factor for tubers and root produce Rp_ef Interception fraction of the edible portion of plant, Exposed Fruit Rp_ev Interception fraction of the edibl e portion of plant, Exposed Vegetable Rp_fo Interception fraction of the edible portion of plant, Forage Rp_si Interception fraction of the edible portion of plant, Silage Tp_ef Length of plant exposure to deposition per harvest, Exposed Fruit Tp_ev Length of plant exposure to depo sition per harvest, Exposed Vegetable Tp_fo Length of plant exposure to deposition per harvest, Forage Tp_si Length of plant exposure to deposition per harvest, Silage VG_AG_ef f,g Empirical correction factor, Exposed Fruit VG_AG_ev f,g Empirical correction factor, Exposed Vegetable VG_AG_fo a,b Empirical correction factor, Forage VG_AG_si Empirical correction factor, Silage VG_rootveg f,g Empirical correction factor, Root Vegetable Yp_ef Yield or standing crop biomass, Exposed Fruit Yp_ev Yield or standing crop biomass, Exposed Vegetable Yp_fo Yield or standing crop biomass, Forage Yp_si Yield or standing crop biomass, SilageAttachment 3 to Technical Support Document for TR IM-Based Multipathway Screening Scenario for RTR 3-5 

### Exhibit 1-1. Variables Included in the Systematic Sensitivity Analysis

Variable Name Variable Description
MIRC Ingestion and Body Weight Variables BW_adlt Body weight, Adult 20-70 BW_ch1 Body weight, Child 1-2 BW_ch2 Body weight, Child 3-5 BW_ch3 Body Weight, Child 6-11 BW_ch4 Body Weight, Child 12-19 EF_beef a,b Exposure factor, Beef EF_dairy a,b Exposure factor, Dairy EF_egg a,b Exposure factor, Eggs EF_ExpFruit a,b Exposure factor, Exposed Fruit EF_ExpVeg a,b Exposure factor, Exposed Vegetables EF_Fish a,b Exposure factor, Fish EF_pork a,b Exposure factor, Pork EF_poultry a,b Exposure factor, Poultry EF_ProFruit a,b Exposure factor, Protected Fruit EF_ProVeg a,b Exposure factor, Protected Vegetables EF_RootVeg a,b Exposure factor, Root Vegetables EF_Soil a,b Exposure factor, Soil EF_water a,b Exposure factor, Water F_fo_beef a,b Fraction of forage plant type from cont aminated area used to grow feed, Beef F_fo_dairy a,b Fraction of forage plant type from contaminated area used to grow feed, Dairy F_gr_beef a,b Fraction of grain plant type from c ontaminated area used to grow feed, Beef F_gr_dairy a,b Fraction of grain plant type from c ontaminated area used to grow feed, Dairy F_gr_egg a,b Fraction of grain plant type from c ontaminated area used to grow feed, Eggs F_gr_pork a,b Fraction of grain plant type from c ontaminated area used to grow feed, Pork F_gr_poultry a,b Fraction of grain plant type from cont aminated area used to grow feed, Poultry F_si_beef a,b Fraction of silage plant type from cont aminated area used to grow feed, Beef F_si_dairy a,b Fraction of silage plant type from contaminated area used to grow feed, Dairy F_si_pork a,b Fraction of silage plant type from contaminated area used to grow feed, Pork F_T3 Fraction of fish in take that is from T3 F_T4 Fraction of fish in take that is from T4 FC_beef a,b Fraction contaminated, Beef FC_dairy a,b Fraction contaminated, Dairy FC_egg a,b Fraction contaminated, Eggs FC_ExpFruit a,b Fraction contaminated, Exposed Fruit FC_ExpVeg a,b Fraction contaminated, Exposed Vegetable FC_Fish a,b Fraction contaminated, Fish FC_pork a,b Fraction contaminated, Pork FC_poultry a,b Fraction contaminated, Poultry FC_ProFruit a,b Fraction contaminated, Protected Fruit FC_ProVeg a,b Fraction contaminated, Protected Vegetable FC_RootVeg a,b Fraction contaminated, Root Vegetable FC_Soil a,b Fraction contaminated, Soil IR_beef_adlt Ingestion rate, Beef, Adult 20-70 IR_beef_ch1 Ingestion rate, Beef, Child 1-2Attachment 3 to Technical Support Document for TR IM-Based Multipathway Screening Scenario for RTR 3-6 

### Exhibit 1-1. Variables Included in the Systematic Sensitivity Analysis

Variable Name Variable Description IR_beef_ch2 Ingestion rate, Beef, Child 3-5 IR_beef_ch3 Ingestion rate, Beef, Child 6-11 IR_beef_ch4 Ingestion rate, Beef, Child 12-19 IR_dairy_adlt Ingestion rate, Dairy, Adult 20-70 IR_dairy_ch1 Ingestion rate, Dairy, Child 1-2 IR_dairy_ch2 Ingestion rate, Dairy, Child 3-5 IR_dairy_ch3 Ingestion rate, Dairy, Child 6-11 IR_dairy_ch4 Ingestion rate, Dairy, Child 12-19 IR_egg_adlt Ingestion rate, Eggs, Adult 20-70 IR_egg_ch1 Ingestion rate, Eggs, Child 1-2 IR_egg_ch2 Ingestion rate, Eggs, Child 3-5 IR_egg_ch3 Ingestion rate, Eggs, Child 6-11 IR_egg_ch4 Ingestion rate, Eggs, Child 12-19 IR_ExpFruit_adlt Ingestion rate, Exposed Fruit, Adult 20-70 IR_ExpFruit_ch1 Ingestion ra te, Exposed Fruit, Child 1-2 IR_ExpFruit_ch2 Ingestion ra te, Exposed Fruit, Child 3-5 IR_ExpFruit_ch3 Ingestion ra te, Exposed Fruit, Child 6-11 IR_ExpFruit_ch4 Ingestion ra te, Exposed Fruit, Child 12-19 IR_ExpVeg_adlt Ingestion rate, Exposed Vegetable, Adult 20-70 IR_ExpVeg_ch1 Ingestion rate, Exposed Vegetable, Child 1-2 IR_ExpVeg_ch2 Ingestion rate, Exposed Vegetable, Child 3-5 IR_ExpVeg_ch3 Ingestion rate, Exposed Vegetable, Child 6-11 IR_ExpVeg_ch4 Ingestion rate, Exposed Vegetable, Child 12-19 IR_Fish_adlt Ingestion rate, Fish, Adult 20-70 IR_Fish_ch1 Ingestion rate, Fish, Child 1-2 IR_Fish_ch2 Ingestion rate, Fish, Child 3-5 IR_Fish_ch3 Ingestion rate, Fish, Child 6-11 IR_Fish_ch4 Ingestion rate, Fish, Child 12-19 IR_pork_adlt Ingestion rate, Pork, Adult 20-70 IR_pork_ch1 Ingestion rate, Pork, Child 1-2 IR_pork_ch2 Ingestion rate, Pork, Child 3-5 IR_pork_ch3 Ingestion rate, Pork, Child 6-11 IR_pork_ch4 Ingestion rate, Pork, Child 12-19 IR_poultry_adlt Ingestion rate, Poultry, Adult 20-70 IR_poultry_ch1 Ingestion rate, Poultry, Child 1-2 IR_poultry_ch2 Ingestion rate, Poultry, Child 3-5 IR_poultry_ch3 Ingestion rate, Poultry, Child 6-11 IR_poultry_ch4 Ingestion rate, Poultry, Child 12-19 IR_ProFruit_adlt Ingestion rate, Protected Fruit, Adult 20-70 IR_ProFruit_ch1 Ingestion rate , Protected Fruit, Child 1-2 IR_ProFruit_ch2 Ingestion rate , Protected Fruit, Child 3-5 IR_ProFruit_ch3 Ingestion rate , Protected Fruit, Child 6-11 IR_ProFruit_ch4 Ingestion rate, Protected Fruit, Child 12-19 IR_ProVeg_adlt Ingestion rate, Protected Vegetable, Adult 20-70 IR_ProVeg_ch1 Ingestion rate, Protected Vegetable, Child 1-2Attachment 3 to Technical Support Document for TR IM-Based Multipathway Screening Scenario for RTR 3-7 

### Exhibit 1-1. Variables Included in the Systematic Sensitivity Analysis

Variable Name Variable Description IR_ProVeg_ch2 Ingestion rate, Protected Vegetable, Child 3-5 IR_ProVeg_ch3 Ingestion rate, Protected Vegetable, Child 6-11 IR_ProVeg_ch4 Ingestion rate, Protected Vegetable, Child 12-19 IR_RootVeg_adlt Ingestion rate, Root Vegetable, Adult 20-70 IR_RootVeg_ch1 Ingestion rate, Root Vegetable, Child 1-2 IR_RootVeg_ch2 Ingestion rate, Root Vegetable, Child 3-5 IR_RootVeg_ch3 Ingestion rate, Root Vegetable, Child 6-11 IR_RootVeg_ch4 Ingestion rate, Root Vegetable, Child 12-19 IR_Soil_adlt Ingestion rate, Soil, Adult 20-70 IR_Soil_ch1 Ingestion rate, Soil, Child 1-2 IR_Soil_ch2 Ingestion rate, Soil, Child 3-5 IR_Soil_ch3 Ingestion rate, Soil, Child 6-11 IR_Soil_ch4 Ingestion rate, Soil, Child 12-19 IR_water_adlt Ingestion rate, Water, Adult 20-70 IR_water_ch1 Ingestion rate, Water, Child 1-2 IR_water_ch2 Ingestion rate, Water, Child 3-5 IR_water_ch3 Ingestion rate, Water, Child 6-11 IR_water_ch4 Ingestion rate, Water, Child 12-19

a Values cannot be increased by 5% because the variable has an upper bound.
b Values cannot be increased by 50% because the variable has an upper bound. c Values can only be increased by 5% for 2,3,7,8 – TCDD and Benzo[a]Pyrene because the variable has an upper bound.
d Values can only be increased by 5% for Benzo[a]Pyrene because the variable has an upper bound.
e Values can only be increased by 50% for Benzo[a]Pyrene because the variable has an upper bound.
f Values can only be increased by 5% for Benzo[a]Pyrene, 2,3,7, 8-TCDD, and methyl mercury because the variable has an upper bou nd.
g Values can only be increased by 5% for Benzo[a]Pyrene, 2,3,7,8-TCDD, and methyl me rcury because the variable has an upper bou nd.Attachment 3 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR3-8Exhibit 1-2. Elasticities and Rankings for the Variables with the Highest Elasticities for Benzo[a]PyreneElasticity of Risk for InputVariable PerturbationRanking of Elasticity forInput VariablePerturbationName of VariableChangedVariable DefinitionVariableCategorya-50% -5% 5% 50% -50% -5% 5% 50%MixHeight Mixing Height TRIM -1.97 -1.05 -0.95 -0.66 1 1 2 2EmissionRate Emission Rate of all Chemicals TRIM 1.00 1.00 1.00 1.00 3 2 1 1HorizWindSpeed Horizontal Wind Speed TRIM -1.74 -0.97 -0.88 -0.63 2 3 3 3Rain Annual Rainfall TRIM 0.63 0.63 0.63 0.63 5 4 4 4DrwpAverage annual wet deposition ofparticle-phase chemicalFFC 0.45 0.45 0.45 0.45 6 5 5 5FwFraction of wet deposition thatadheres to plant surfacesFFC 0.45 0.45 0.45 0.45 7 6 6 6MF Mammalian metabolism factor FFC 0.41 0.41 0.41 0.41 9 7 7 7Yp_foYield or standing crop biomass,ForageFFC -0.69 -0.36 -0.33 -0.23 4 8 10 12Rp_foInterception fraction of the edibleportion of plant, ForageFFC 0.34 0.34 0.34 0.34 10 9 8 8Ba_dairyChemical-specific biotransferfactor, DairyFFC 0.34 0.34 0.34 0.34 12 10 9 9EF_dairy Exposure factor, Dairy Ing./BW 0.34 0.34 N/A N/A 13 11FC_dairy Fraction contaminated, Dairy Ing./BW 0.34 0.34 N/A N/A 14 12DrdpAverage annual dry deposition ofparticle-phase chemicalFFC 0.30 0.30 0.30 0.30 16 13 11 10Qp_fo_dairyQuantity of forage plant typeeaten per animal per day, DairyFFC 0.29 0.29 0.29 0.29 18 14 12 11F_fo_dairyFraction of forage plant typeobtained from contaminated areaused to grow feed, DairyIng./BW 0.29 0.29 N/A N/A 17 15Kp_foPlant surface loss coefficient,ForageFFC -0.34 -0.25 -0.24 -0.19 11 16 13 15Yp_efYield or standing crop biomass,Exposed FruitFFC -0.43 -0.23 -0.20 -0.14 8 17 16 17100-MAF_ef100 - Moisture adjustment factor,Exposed FruitFFC 0.22 0.22 0.22 0.22 22 18 14 13EF_ExpFruit Exposure factor, Exposed Fruit Ing./BW 0.22 0.22 N/A N/A 20 19Attachment 3 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR3-9Exhibit 1-2. Elasticities and Rankings for the Variables with the Highest Elasticities for Benzo[a]PyreneElasticity of Risk for InputVariable PerturbationRanking of Elasticity forInput VariablePerturbationName of VariableChangedVariable DefinitionVariableCategorya-50% -5% 5% 50% -50% -5% 5% 50%FC_ExpFruitFraction contaminated, ExposedFruitIng./BW 0.22 0.22 N/A N/A 21 20Rp_efInterception fraction of the edibleportion of plant, Exposed FruitFFC 0.21 0.21 0.21 0.21 23 21 15 14Kp_efPlant surface loss coefficient,Exposed FruitFFC -0.27 -0.19 -0.17 -0.13 19 22 17 18Yp_evYield or standing crop biomass,Exposed VegetableFFC -0.33 -0.17 -0.16 -0.11 15 23 19 21100-MAF_ev100 - Moisture adjustment factor,Exposed VegetableFFC 0.17 0.17 0.17 0.17 26 24 18 16EF_ExpVegExposure factor, ExposedVegetablesIng./BW 0.17 0.17 N/A N/A 27 25FC_ExpVegFraction contaminated, ExposedVegetableIng./BW 0.17 0.17 N/A N/A 28 26Rp_evInterception fraction of the edibleportion of plant, ExposedVegetableFFC 0.16 0.16 N/A N/A 29 27Kp_evPlant surface loss coefficient,Exposed VegetableFFC -0.21 -0.14 -0.13 -0.10 24 28 20 25C_SoilConcentration of chemical in soilfrom contaminated areaFFC 0.12 0.12 0.12 0.12 31 29 21 19EF_Soil Exposure factor, Soil Ing./BW 0.12 0.12 N/A N/A 32 30FC_Soil Fraction contaminated, SoilIng./BW 0.12 0.12 N/A N/A 33 31IR_dairy_ch1 Ingestion rate, Dairy, Child1-2 Ing./BW 0.11 0.11 0.11 0.11 34 32 22 20IR_ExpFruit_adltIngestion rate, Exposed Fruit,Adult 20-70Ing./BW 0.10 0.10 0.10 0.10 35 33 23 23Tp_foLength of plant exposure todeposition per harvest, ForageFFC 0.17 0.10 0.09 0.06 25 34 26 31IR_dairy_adlt Ingestion rate, Dairy, Adult20-70 Ing./BW 0.10 0.10 0.10 0.10 36 35 24 24L2_ExpFruit Loss type 2, Exposed Fruit FFC-0.10 -0.10 -0.10 -0.10 37 36 25 26aFFC refers to farm food chain variables and Ing./BW refers to ingestion and body weight variables.Attachment 3 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR3-10Exhibit 1-3. Elasticities and Rankings for the Variables with the Highest Elasticities for 2,3,7,8 - TCDDElasticity of Risk for InputVariable PerturbationRanking of Elasticity forInput VariablePerturbationName of VariableChangedVariable DefinitionVariableCategorya-50% -5% 5% 50% -50% -5% 5% 50%MixHeight Mixing Height TRIM -1.99 -1.05 -0.95 -0.67 1 1 2 2EmissionRate Emission Rate of all Chemicals TRIM 1.00 1.00 1.00 1.00 3 2 1 1HorizWindSpeed Horizontal Wind Speed TRIM -1.80 -0.96 -0.87 -0.61 2 3 3 3FC_Fish Fraction contaminated, FishIng./BW 0.56 0.56 N/A N/A 8 4EF_Fish Exposure factor, Fish Ing./BW 0.56 0.56 N/A N/A 7 5BW_adlt Body weight, Adult 20-70 Ing./BW -0.86 -0.45 -0.41 -0.29 4 6 6 11C_FishT4Concentration of chemical inwhole fish for T4 fishFFC 0.43 0.43 0.43 0.43 9 7 4 5IR_Fish_adlt Ingestion rate, Fish, Adult 20-70 Ing./BW 0.43 0.43 0.43 0.43 10 8 5 6MF Mammalian metabolism factor FFC 0.43 0.43 N/A N/A 11 9SurfWatTemp Water TemperatureTRIM 0.32 0.39 0.41 0.53 16 10 7 4WatOCC Water- Organic Carbon Fraction TRIM -0.62 -0.38 -0.35 -0.25 5 11 9 14SedDepVelSediment Deposition Velocity andResuspension VelocityTRIM 0.28 0.36 0.39 0.30 20 12 8 8Ba_dairyChemical-specific biotransferfactor, DairyFFC 0.32 0.32 0.32 0.32 13 13 10 7EF_dairy Exposure factor, Dairy Ing./BW 0.32 0.32 N/A N/A 14 14FC_dairy Fraction contaminated, Dairy Ing./BW 0.32 0.32 N/A N/A 15 15F_T3Fraction of fish intake that is fromT3Ing./BW -0.30 -0.30 -0.30 -0.30 17 16 12 10F_T4Fraction of fish intake that is fromT4Ing./BW 0.30 0.30 0.30 0.30 18 17 11 9Yp_foYield or standing crop biomass,ForageFFC -0.56 -0.30 -0.27 -0.19 6 18 15 19Rp_foInterception fraction of the edibleportion of plant, ForageFFC 0.28 0.28 0.28 0.28 19 19 13 12DrdpAverage annual dry deposition ofparticle-phase chemicalFFC 0.28 0.28 0.28 0.28 22 20 14 13WatSuspSedWater- Suspended SedimentConcentrationTRIM -0.37 -0.25 -0.24 -0.19 12 21 16 18Attachment 3 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR3-11Exhibit 1-3. Elasticities and Rankings for the Variables with the Highest Elasticities for 2,3,7,8 - TCDDElasticity of Risk for InputVariable PerturbationRanking of Elasticity forInput VariablePerturbationName of VariableChangedVariable DefinitionVariableCategorya-50% -5% 5% 50% -50% -5% 5% 50%Qp_fo_dairyQuantity of forage plant typeeaten per animal per day, DairyFFC 0.23 0.23 0.23 0.23 24 22 17 15F_fo_dairyFraction of forage plant typeobtained from contaminated areaused to grow feed, DairyIng./BW 0.23 0.23 N/A N/A 23 23Kp_foPlant surface loss coefficient,ForageFFC -0.28 -0.21 -0.20 -0.16 21 24 19 21IR_dairy_adlt Ingestion rate, Dairy, Adult20-70 Ing./BW 0.20 0.20 0.20 0.20 25 25 18 16Rain Annual Rainfall TRIM 0.20 0.20 0.20 0.19 26 26 20 17SurfSoilAir Surface Soil- FractionAir TRIM 0.13 0.14 0.15 0.17 29 27 21 20C_FishT3Concentration of chemical inwhole fish for T3 fishFFC 0.13 0.13 0.13 0.13 28 28 22 22Cs_S_animal_ingestChemical concentration insurface soil in contaminated areawhere livestock feedFFC 0.12 0.12 0.12 0.12 31 29 23 23BsSoil bioavailability factor forlivestockFFC 0.12 0.12 N/A N/A 30 30Ba_beefChemical-specific biotransferfactor, BeefFFC 0.09 0.09 0.09 0.09 33 31 24 24EF_beef Exposure factor, Beef Ing./BW 0.09 0.09 N/A N/A 34 32FC_beef Fraction contaminated, BeefIng./BW 0.09 0.09 N/A N/A 35 33Tp_foLength of plant exposure todeposition per harvest, ForageFFC 0.14 0.08 0.08 0.05 27 34 25 29Qs_dairyQuantity of soil eaten by theanimal each day, DairyFFC 0.07 0.07 0.07 0.07 38 35 26 25aFFC refers to farm food chain variables and Ing./BW refers to ingestion and body weight variables.Attachment 3 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR3-12Exhibit 1-4. Elasticities and Rankings for the Variables with the Highest Elasticities for CadmiumElasticity of Risk for InputVariable PerturbationRanking of Elasticity forInput VariablePerturbationName of VariableChangedVariable DefinitionVariableCategorya-50% -5% 5% 50% -50% -5% 5% 50%MixHeight Mixing Height TRIM -1.92 -1.03 -0.93 -0.66 1 1 2 3HorizWindSpeed Horizontal Wind Speed TRIM -1.92 -1.03 -0.93 -0.66 2 2 3 4EmissionRate Emission Rate of all Chemicals TRIM 1.00 1.00 1.00 1.00 3 3 1 1Rain Annual Rainfall TRIM 0.78 0.75 0.74 0.71 4 4 4 2Cs_root_zone_produceAverage chemical concentrationin soil at root-zone depth inproduce-growing areaFFC 0.63 0.63 0.63 0.63 5 5 5 5Br_AG_produce_pfPlant-soil chemicalbioconcentration factor, ProtectedFruitFFC 0.39 0.39 0.39 0.39 7 6 6 8IR_ProFruit_ch1Ingestion rate, Protected Fruit,Child 1-2Ing./BW 0.39 0.39 0.39 0.39 10 7 7 9EF_ProFruit Exposure factor, ProtectedFruit Ing./BW 0.39 0.39 N/A N/A 8 8FC_ProFruitFraction contaminated, ProtectedFruitIng./BW 0.39 0.39 N/A N/A 9 9100-MAF_pf100 - Moisture adjustment factor,Protected FruitFFC 0.39 0.39 0.39 0.39 11 10 8 7BW_ch1 Body weight, Child 1-2 Ing./BW -0.54 -0.28 -0.26 -0.18 6 11 11 13EroRate Erosion Rate for all ParcelsTRIM -0.31 -0.27 -0.26 -0.24 12 12 10 10SurfWatTemp Water TemperatureTRIM 0.17 0.27 0.30 0.54 16 13 9 6IR_Fish_ch1 Ingestion rate, Fish, Child1-2 Ing./BW 0.19 0.19 0.19 0.19 15 14 12 12EF_Fish Exposure factor, Fish Ing./BW 0.19 0.19 N/A N/A 13 15FC_Fish Fraction contaminated, FishIng./BW 0.19 0.19 N/A N/A 14 16SurfSoilAir Surface Soil- FractionAir TRIM 0.15 0.17 0.17 0.19 19 17 13 11L1_ProFruit Loss type 1, Protected FruitFFC -0.16 -0.16 -0.16 -0.16 18 18 14 14C_FishT3Concentration of chemical inwhole fish for T3 fishFFC 0.15 0.15 0.15 0.15 21 19 15 15WatRetTime Rentention Time in Pond TRIM 0.16 0.15 0.14 0.13 17 20 17 16SedDepVelSediment Deposition Velocity andResuspension VelocityTRIM 0.10 0.13 0.14 0.07 30 21 16 32Attachment 3 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR3-13Exhibit 1-4. Elasticities and Rankings for the Variables with the Highest Elasticities for CadmiumElasticity of Risk for InputVariable PerturbationRanking of Elasticity forInput VariablePerturbationName of VariableChangedVariable DefinitionVariableCategorya-50% -5% 5% 50% -50% -5% 5% 50%100-MAF_ev100 - Moisture adjustment factor,Exposed VegetableFFC 0.11 0.11 0.11 0.11 23 22 18 18IR_ExpVeg_ch1Ingestion rate, ExposedVegetable, Child 1-2Ing./BW 0.11 0.11 0.11 0.11 26 23 19 19EF_ExpVegExposure factor, ExposedVegetablesIng./BW 0.11 0.11 N/A N/A 24 24FC_ExpVegFraction contaminated, ExposedVegetableIng./BW 0.11 0.11 N/A N/A 25 25F_T3Fraction of fish intake that is fromT3Ing./BW 0.11 0.11 0.11 0.11 28 26 20 20F_T4Fraction of fish intake that is fromT4Ing./BW -0.11 -0.11 -0.11 -0.11 29 27 21 21SurfSoilOCCSurface Soil- Organic CarbonFractionTRIM 0.15 0.10 0.10 0.08 20 28 22 25SurfSoilVertVelSurface Soil- Average verticalvelocity of water (percolation)TRIM -0.11 -0.10 -0.10 -0.09 27 29 23 22SurfSoilpH Surface Soil- pH TRIM 0.13 0.09 0.09 0.07 22 30 25 31aFFC refers to farm food chain variables and Ing./BW refers to ingestion and body weight variables.Attachment 3 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR3-14Exhibit 1-5. Elasticities and Rankings for the Variables with the Highest Elasticities for Divalent MercuryElasticity of Risk for InputVariable PerturbationRanking of Elasticity forInput VariablePerturbationName of VariableChangedVariable DefinitionVariableCategorya-50% -5% 5% 50% -50% -5% 5% 50%EmissionRate Emission Rate of all Chemicals TRIM 1.00 1.00 1.00 1.00 5 1 1 1MixHeight Mixing Height TRIM -1.73 -0.97 -0.89 -0.63 1 2 2 3HorizWindSpeed Horizontal Wind Speed TRIM -1.73 -0.97 -0.89 -0.63 2 3 3 4EroRate Erosion Rate for all ParcelsTRIM -1.27 -0.77 -0.71 -0.52 3 4 5 7Rain Annual Rainfall TRIM 0.76 0.74 0.74 0.71 6 5 4 2BW_ch1 Body weight, Child 1-2 Ing./BW -1.17 -0.61 -0.56 -0.39 4 6 8 8C_SoilConcentration of chemical in soilfrom contaminated areaFFC 0.56 0.56 0.56 0.56 7 7 6 5EF_Soil Exposure factor, Soil Ing./BW 0.56 0.56 N/A N/A 8 8FC_Soil Fraction contaminated, SoilIng./BW 0.56 0.56 N/A N/A 9 9IR_Soil_ch1 Ingestion rate, Soil, Child1-2 Ing./BW 0.56 0.56 0.56 0.56 10 10 7 6Cs_root_zone_produceAverage chemical concentrationin soil at root-zone depth inproduce-growing areaFFC 0.33 0.33 0.33 0.33 11 11 9 9Br_AG_produce_pfPlant-soil chemicalbioconcentration factor, ProtectedFruitFFC 0.15 0.15 0.15 0.15 12 12 10 10IR_ProFruit_ch1Ingestion rate, Protected Fruit,Child 1-2Ing./BW 0.15 0.15 0.15 0.15 15 13 11 11100-MAF_pf100 - Moisture adjustment factor,Protected FruitFFC 0.15 0.15 0.15 0.15 16 14 12 12EF_ProFruit Exposure factor, ProtectedFruit Ing./BW 0.15 0.15 N/A N/A 13 15FC_ProFruitFraction contaminated, ProtectedFruitIng./BW 0.15 0.15 N/A N/A 14 16SurfSoilAir Surface Soil- FractionAir TRIM 0.11 0.13 0.13 0.15 17 17 13 13Br_AG_rootvegPlant-soil chemicalbioconcentration factor, RootVegetablesFFC 0.11 0.11 0.11 0.11 18 18 14 15IR_RootVeg_ch1Ingestion rate, Root Vegetable,Child 1-2Ing./BW 0.11 0.11 0.11 0.11 21 19 15 16Attachment 3 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR3-15Exhibit 1-5. Elasticities and Rankings for the Variables with the Highest Elasticities for Divalent MercuryElasticity of Risk for InputVariable PerturbationRanking of Elasticity forInput VariablePerturbationName of VariableChangedVariable DefinitionVariableCategorya-50% -5% 5% 50% -50% -5% 5% 50%100-MAF_rv100 - Moisture adjustment factor,Root VegetableFFC 0.11 0.11 0.11 0.11 23 20 16 17EF_RootVeg Exposure factor, Root Vegetables Ing./BW 0.11 0.11 N/A N/A 19 21FC_RootVegFraction contaminated, RootVegetableIng./BW 0.11 0.11 N/A N/A 20 22VG_rootvegEmpirical correction factor, RootVegetableFFC 0.11 0.11 N/A N/A 22 23Cs_S_animal_ingestChemical concentration insurface soil in contaminated areawhere livestock feedFFC 0.08 0.08 0.08 0.08 25 24 17 18BsSoil bioavailability factor forlivestockFFC 0.08 0.08 N/A N/A 24 25L1_ProFruit Loss type 1, Protected FruitFFC -0.06 -0.06 -0.06 -0.06 26 26 19 19SedDepVelSediment Deposition Velocity andResuspension VelocityTRIM 0.02 0.05 0.06 0.11 48 27 18 14Ba_eggChemical-specific biotransferfactor, EggsFFC 0.05 0.05 0.05 0.05 27 28 20 20IR_egg_ch1 Ingestion rate, Eggs, Child1-2 Ing./BW 0.05 0.05 0.05 0.05 30 29 21 21EF_egg Exposure factor, Eggs Ing./BW 0.05 0.05 N/A N/A 28 30FC_egg Fraction contaminated, EggsIng./BW 0.05 0.05 N/A N/A 29 31Qs_eggQuantity of soil eaten by theanimal each day, EggsFFC 0.05 0.05 0.05 0.05 31 32 22 22Ba_poultryChemical-specific biotransferfactor, PoultryFFC 0.03 0.03 0.03 0.03 32 33 23 23IR_poultry_ch1 Ingestion rate, Poultry, Child 1-2 Ing./BW 0.03 0.03 0.03 0.03 35 34 24 24EF_poultry Exposure factor, Poultry Ing./BW 0.03 0.03 N/A N/A 33 35FC_poultry Fraction contaminated, Poultry Ing./BW 0.03 0.03 N/A N/A 34 36Qs_poultryQuantity of soil eaten by theanimal each day, PoultryFFC 0.03 0.03 0.03 0.03 36 37 25 25aFFC refers to farm food chain variables and Ing./BW refers to ingestion and body weight variables.Attachment 3 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR3-16Exhibit 1-6. Elasticities and Rankings for the Variables with the Highest Elasticities for Methyl MercuryElasticity of Risk for InputVariable PerturbationRanking of Elasticity forInput VariablePerturbationName of VariableChangedVariable DefinitionVariableCategorya-50% -5% 5% 50% -50% -5% 5% 50%SedDepVelSediment Deposition Velocity andResuspension VelocityTRIM 0.78 1.56 2.01 3.67 9 1 1 1BW_ch1 Body weight, Child 1-2 Ing./BW -1.91 -1.00 -0.91 -0.64 1 2 3 4EmissionRate Emission Rate of all Chemicals TRIM 1.00 1.00 1.00 1.00 5 3 2 2IR_Fish_ch1 Ingestion rate, Fish, Child 1-2 Ing./BW 0.90 0.90 0.90 0.90 8 4 4 3FC_Fish Fraction contaminated, FishIng./BW 0.90 0.90 N/A N/A 7 5EF_Fish Exposure factor, Fish Ing./BW 0.90 0.90 N/A N/A 6 6MixHeight Mixing Height TRIM -1.02 -0.70 -0.66 -0.51 3 7 5 8HorizWindSpeed Horizontal Wind Speed TRIM -1.01 -0.70 -0.65 -0.51 4 8 6 9Rain Annual Rainfall TRIM 0.70 0.64 0.63 0.60 10 9 7 6C_FishT4Concentration of chemical inwhole fish for T4 fishFFC 0.55 0.55 0.55 0.55 12 10 8 7EroRate Erosion Rate for all ParcelsTRIM -0.57 -0.45 -0.43 -0.35 11 11 10 11SedPorosity Sediment Porosity TRIM-0.30 -0.40 -0.43 -0.60 15 12 9 5C_FishT3Concentration of chemical inwhole fish for T3 fishFFC 0.36 0.36 0.36 0.36 13 13 11 10SurfWatTemp Water Temperature TRIM-0.22 -0.22 -0.22 -0.21 18 14 12 12F_T3Fraction of fish intake that is fromT3Ing./BW -0.19 -0.19 -0.19 -0.19 19 15 13 13F_T4Fraction of fish intake that is fromT4Ing./BW 0.19 0.19 0.19 0.19 20 16 14 14WatSuspSedWater- Suspended SedimentConcentrationTRIM -0.32 -0.18 -0.16 -0.12 14 17 15 16SurfSoilAir Surface Soil- FractionAir TRIM 0.14 0.14 0.15 0.15 21 18 16 15WatRetTime Rentention Time in Pond TRIM 0.23 0.14 0.13 0.09 17 19 17 17FishMassFish Body Weight for all AquaticSpeciesTRIM 0.10 0.07 0.07 0.06 22 20 19 19Runoff Total Water Runoff Rate TRIM 0.07 0.07 0.07 0.07 23 21 18 18C_SoilConcentration of chemical in soilfrom contaminated areaFFC 0.05 0.05 0.05 0.05 24 22 20 20Attachment 3 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR3-17Exhibit 1-6. Elasticities and Rankings for the Variables with the Highest Elasticities for Methyl MercuryElasticity of Risk for InputVariable PerturbationRanking of Elasticity forInput VariablePerturbationName of VariableChangedVariable DefinitionVariableCategorya-50% -5% 5% 50% -50% -5% 5% 50%IR_Soil_ch1 Ingestion rate, Soil, Child1-2 Ing./BW 0.05 0.05 0.05 0.05 27 23 21 21EF_Soil Exposure factor, Soil Ing./BW 0.05 0.05 N/A N/A 25 24FC_Soil Fraction contaminated, SoilIng./BW 0.05 0.05 N/A N/A 26 25Cs_root_zone_produceAverage chemical concentrationin soil at root-zone depth inproduce-growing areaFFC 0.04 0.04 0.04 0.04 28 26 22 22100-MAF_pf100 - Moisture adjustment factor,Protected FruitFFC 0.03 0.03 0.03 0.03 33 27 25 23Br_AG_produce_pfPlant-soil chemicalbioconcentration factor, ProtectedFruitFFC 0.03 0.03 0.03 0.03 29 28 23 24IR_ProFruit_ch1Ingestion rate, Protected Fruit,Child 1-2Ing./BW 0.03 0.03 0.03 0.03 32 29 24 25aFFC refers to farm food chain variables and Ing./BW refers to ingestion and body weight variables.

### Appendix 6 Detailed Risk Modeling ResultsSource CategoryFacility NEI IDFacility NameAddressCity StateSHIP BUILDING AND SHIPREPAIR OPERATIONSNEI11169NORTHROP GRUMMAN SHIP SYSTEMSINC1000 JERRY SAINTPETER HIGHWAYPASCAGOULA MSSHIP BUILDING AND SHIPREPAIR OPERATIONSNEI12086TAMTampa Ship LLC811 WynkookRoad/Maritime CenterTampa FLSHIP BUILDING AND SHIPREPAIR OPERATIONSNEI13187BARGE CLEANING AND REPAIR 18310 MARKET STREETCHANNELVIEW TXSHIP BUILDING AND SHIPREPAIR OPERATIONSNEI16611GUNDERSON LLC 4350 NW FRONT AVEPORTLAND ORSHIP BUILDING AND SHIPREPAIR OPERATIONSNEI22045CENCentral Gulf Shipyard5310 Cornelius P VoorhiesRdNew Iberia LASHIP BUILDING AND SHIPREPAIR OPERATIONSNEI23077NATL STEEL & SHIPBUILDING AGENERAL DYNAMICS CO 28TH ST & HARBOR DRSAN DIEGO CASHIP BUILDING AND SHIPREPAIR OPERATIONSNEI23453SAN FRANCISCO DRY DOCK 20TH STREET, FOOT OFSAN FRANCISCO CASHIP BUILDING AND SHIPREPAIR OPERATIONSNEI28047GULGulf Ship LLC Seaway RoadGulfport MSSHIP BUILDING AND SHIPREPAIR OPERATIONSNEI2PA101569AKER PHILADELPHIA SHIPYARDPHILA. NAVAL BUS.CENTERPHILADELPHIA PASHIP BUILDING AND SHIPREPAIR OPERATIONSNEI2TX06138AMFELS, INC.20000 HWY. 48,BROWNSVILLE, TEXAS78520BROWNSVILLE TXSHIP BUILDING AND SHIPREPAIR OPERATIONSNEI310NEWPORT NEWS SHIPBUILDING & DRYDOCK CO 4101 WASHINGTON AVENEWPORT NEWS VASHIP BUILDING AND SHIPREPAIR OPERATIONSNEI31325JEFFBOATPO BOX 610 1030 EMARKET STREEJEFFERSONVILLE INSHIP BUILDING AND SHIPREPAIR OPERATIONSNEI314BAE Systems Norfolk Ship Repair Inc. 750 W BERKLEY AVENORFOLK VASHIP BUILDING AND SHIPREPAIR OPERATIONSNEI33201U.S. COAST GUARD YARD - CURTIS BAY2401 HAWKINS POINTROADCURTIS BAY MDSHIP BUILDING AND SHIPREPAIR OPERATIONSNEI33221ISG SPARROWS POINT INC5111 NORTH POINTBOULEVARDSPARROWS POINT MDSHIP BUILDING AND SHIPREPAIR OPERATIONSNEI34481TRINITY MARINE PRODUCTS INC PLANT75 265 COUNTY HWY 346CARUTHERSVILLE MOSHIP BUILDING AND SHIPREPAIR OPERATIONSNEI40623Cascade General, Inc.5555 N CHANNEL AVEBLDG 71PORTLAND ORSHIP BUILDING AND SHIPREPAIR OPERATIONSNEI42049ERIErie Shipbuilding 220 East Bayfront ParkwayErie PASHIP BUILDING AND SHIPREPAIR OPERATIONSNEI42051BROBrownsville Marine Products 1800 Paul Thomas BlvdBrownsville PASHIP BUILDING AND SHIPREPAIR OPERATIONSNEI42101ATLAtlantic Marine Philadelphia LLC 5120 South 17th StreetPhiladelphia PATable 1 - Facility Identification Information1 of 5Source CategoryFacility NEI IDFacility NameAddressCity StateTable 1 - Facility Identification InformationSHIP BUILDING AND SHIPREPAIR OPERATIONSNEI44260GENERAL DYNAMICS ELECTRIC BOATCORP165 DILLABUR AVEQUONSET POINTFACILITYNORTH KINGSTOWRISHIP BUILDING AND SHIPREPAIR OPERATIONSNEI51710LYOLYON SHIPYARD - SEALIFT DRYDOCK 500 Rose AveNorfolk VASHIP BUILDING AND SHIPREPAIR OPERATIONSNEI51710MARMarine Hydraulics 543 East Indian River RoadNORFOLK VASHIP BUILDING AND SHIPREPAIR OPERATIONSNEI53033FOSFoss Maritime Co 660 W Ewing StSeattle WASHIP BUILDING AND SHIPREPAIR OPERATIONSNEI6097J. RAY MCDERMOTT,INC/EASTFABRICATION YARD 2317 HWY 662 S.AMELIA LASHIP BUILDING AND SHIPREPAIR OPERATIONSNEI6105TRINITY MARINE PRODUCTS/PLANT NO.38 150 HWY 21MADISONVILLE LASHIP BUILDING AND SHIPREPAIR OPERATIONSNEI6184NORTHROP GRUMMAN SHIPSYSTEMS/AVONDALE OPERATIONSMAIN YARD 5100 RIVER RDAVONDALE LASHIP BUILDING AND SHIPREPAIR OPERATIONSNEI6243BATH IRON WORKS - HARDING FACILITY RT 1 BATH RDBRUNSWICK MESHIP BUILDING AND SHIPREPAIR OPERATIONSNEI6292BATH IRON WORKS CORP 700 WASHINGTON STBATH MESHIP BUILDING AND SHIPREPAIR OPERATIONSNEI6951Platzer Ship Yards, Inc. 13600 Industrial RoadHouston TXSHIP BUILDING AND SHIPREPAIR OPERATIONSNEI7371BROWN & ROOT INC GREENS BAYOUFAB 14035 INDUSTRIAL ROADHOUSTON TXSHIP BUILDING AND SHIPREPAIR OPERATIONSNEI779US NAVY - NORFOLK NAVAL SHIPYARD 2601 EFFINGHAM STPORTSMOUTH VASHIP BUILDING AND SHIPREPAIR OPERATIONSNEI8274ATLANTIC DRY DOCK, LLC 8500 HECKSCHER DRIVEJACKSONVILLE FLSHIP BUILDING AND SHIPREPAIR OPERATIONSNEI8609Atlantic Marine Inc Pinto IslandMobile ALSHIP BUILDING AND SHIPREPAIR OPERATIONSNEIAL09736603B$1BENDER SHIPBUILDING & REPAIR COINC 265 S WATER STMOBILE ALSHIP BUILDING AND SHIPREPAIR OPERATIONSNEICA0732740USN NORTH ISLAND 937 North Harbor DriveSan Diego CASHIP BUILDING AND SHIPREPAIR OPERATIONSNEICA0733199SOUTHWEST MARINE INC. 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Highway 308 P.O. Box 278Lockport LASHIP BUILDING AND SHIPREPAIR OPERATIONSNEISHP4Caddell Dry Dock FOOT OF BROADWAYSTATEN ISLAND NYSHIP BUILDING AND SHIPREPAIR OPERATIONSNEISHP6Colonna Marine LLC - Colonna Yachts 2401 Kimball TerraceNorfolk VASHIP BUILDING AND SHIPREPAIR OPERATIONSNEITNT$4754TRINITY MARINE PRODUCTS INC PLANTNO 296 1050 TRINITY RDASHLAND CITY TNSHIP BUILDING AND SHIPREPAIR OPERATIONSNEITNT$5074LMS SHIPMANAGEMENTINCORPORATED 2725 RIVERPORT RD.MEMPHIS TNSHIP BUILDING AND SHIPREPAIR OPERATIONSNEITX40978362K$3KIEWIT OFFSHORE SERVICES LTD 2440 KIEWIT DRINGLESIDE TXSHIP BUILDING AND SHIPREPAIR OPERATIONSNEITXRGB0463PELICAN ISLAND INC 2920 NEWPARK RDGALVESTON TXSHIP BUILDING AND SHIPREPAIR OPERATIONSNEITXT$11635NEWPARK SHIPBUILDING-PASADENAINC 1600 N. WITTERPASADENA TXSHIP BUILDING AND SHIPREPAIR OPERATIONSNEIVA7100003Metro Machine of VA 200 Ligon Street.Norfolk VASHIP BUILDING AND SHIPREPAIR OPERATIONSNEIVA71000249Lyon Shipyard Foot of Brown Avenue.Norfolk VA4 of 5Source CategoryFacility NEI IDFacility NameAddressCity StateTable 1 - Facility Identification InformationSHIP BUILDING AND SHIPREPAIR OPERATIONSNEIVAT$2469Colonnas Shipyard Pescara Creek ToRailway 4400 EAST INDIAN RIVERRDNorfolk VASHIP BUILDING AND SHIPREPAIR OPERATIONSNEIWA10654DUWAMISH SHIPYARD INC5658 W. MARGINAL WAYS.W.SEATTLE WASHIP BUILDING AND SHIPREPAIR OPERATIONSNEIWA14082US Navy Puget Sound Naval Shipyard PSNS 1400 Farragut AveBremerton WASHIP BUILDING AND SHIPREPAIR OPERATIONSNEIWI0755480MARINETTE MARINE CORP 1600 ELY STMARINETTE WISHIP BUILDING AND SHIPREPAIR OPERATIONSNEIWIT$8533BAY SHIPBUILDING COMPANY 605 N 3RD AVESTURGEON BAY WISHIP BUILDING AND SHIPREPAIR OPERATIONSNEI8020850 Pine Street Inc 850 B PINE STREETBEAUMONT TXSHIP BUILDING AND SHIPREPAIR OPERATIONSSHP_NEIMS1698NORTHROP GRUMMAN SHIP SYSTEMSINC13303 INDUSTRIALSEAWAY ROADGULFPORT MS5 of 5Source Category Chronic Risk1Facility-Wide Chronic Risk1,2CancerMIRCancerIncidenceNoncancerMax HITargetOrganCancerMIRNoncancerMax HITargetOrganNEI111690.00E+00 0.00E+00 6.77E-03 neurological1.59E-062.85E-01 neurological 0%NEI12086TAM3.98E-07 9.42E-05 8.29E-03 neurological 3.98E-07 8.29E-03 neurological 100%NEI131870.00E+00 0.00E+00 4.10E-03 neurological9.07E-063.60E-02 respiratory 0%NEI166116.07E-08 3.01E-05 1.34E-03 neurological 6.07E-08 1.34E-03 neurological 100%NEI22045CEN4.88E-07 4.43E-06 3.52E-03 neurological 4.88E-07 3.52E-03 neurological 100%NEI230770.00E+00 0.00E+00 3.26E-02 reproductive3.53E-063.46E-01 neurological 0%NEI234532.87E-07 4.52E-05 5.31E-03 neurological 6.76E-07 2.75E-01 neurological 42%NEI28047GUL0.00E+00 0.00E+00 2.97E-03 neurological1.22E-04 2.98E+00neurological 0%NEI2PA1015691.73E-08 2.62E-05 9.81E-05 neurological 1.77E-08 1.75E-04 neurological 98%NEI2TX061381.05E-08 4.12E-06 2.56E-04 neurological 5.27E-08 1.28E-03 neurological 20%NEI3100.00E+00 0.00E+00 2.15E-07 neurological1.84E-061.43E-01 neurological 0%NEI313251.08E-06 2.13E-05 6.41E-02 neurological9.56E-054.21E-01 neurological 1%NEI3142.35E-061.06E-04 9.41E-04 developmental9.80E-061.32E-01 neurological 24%NEI332018.04E-08 6.99E-06 3.66E-03 neurological 8.60E-08 3.75E-03 neurological 94%NEI332212.41E-07 1.50E-05 5.30E-03 neurological1.03E-05 3.52E+00neurological 2%NEI344817.28E-07 6.21E-06 1.52E-02 neurological4.67E-05 1.04E+00neurological 2%NEI406230.00E+00 0.00E+00 7.37E-03 neurological 6.21E-07 1.48E-02 respiratory 0%NEI42049ERI8.22E-08 4.47E-06 1.71E-03 neurological 8.22E-08 1.71E-03 neurological 100%NEI42051BRO9.35E-08 8.99E-06 1.63E-04 neurological 9.35E-08 1.63E-04 neurological 100%NEI42101ATL4.03E-08 6.78E-05 8.41E-04 neurological 4.03E-08 8.41E-04 neurological 100%NEI442601.21E-07 7.61E-06 2.67E-03 neurological4.08E-063.07E-02 respiratory 3%NEI51710LYO7.41E-07 2.11E-05 1.63E-02 neurological 7.41E-07 1.63E-02 neurological 100%NEI51710MAR8.01E-08 2.97E-06 1.67E-03 neurological 8.01E-08 1.67E-03 neurological 100%NEI53033FOS1.60E-064.74E-05 2.52E-02 neurological1.60E-062.52E-02 neurological 100%NEI60971.00E-06 5.03E-06 2.09E-02 neurological 1.40E-06 7.89E-02 neurological 72%NEI61051.34E-08 3.38E-07 1.41E-03 neurological1.89E-04 1.12E+00neurological 0%NEI61844.77E-062.55E-04 1.00E-01 neurological6.65E-05 1.38E+01neurological 7%NEI62433.09E-07 4.70E-06 6.81E-03 neurological6.88E-061.19E-02 respiratory 4%NEI62920.00E+00 0.00E+00 3.57E-03 neurological 4.89E-07 3.75E-02 neurological 0%NEI69511.95E-08 2.41E-05 4.29E-04 neurological 1.95E-08 4.29E-04 neurological 100%NEI73712.07E-08 2.42E-05 4.56E-04 neurological 2.84E-08 4.72E-04 neurological 73%NEI7791.75E-07 6.51E-06 6.63E-03 neurological 8.71E-07 2.92E-02 respiratory 20%NEI80203.28E-062.48E-05 4.92E-01 neurological3.28E-064.92E-01 neurological 100%NEI82740.00E+00 0.00E+00 1.57E-02 neurological 1.06E-08 1.58E-02 neurological 0%NEI86092.29E-07 4.83E-05 1.74E-03 neurological 8.31E-07 3.01E-02 neurological 28%NEIAL09736603B$15.26E-07 1.48E-05 1.16E-02 neurological 5.26E-07 2.29E-02 neurological 100%Table 2 – Maximum Predicted HEM-3 Chronic RisksSC % ofFacility-wideCancer RiskFacility NEI ID1 of 3Source Category Chronic Risk1Facility-Wide Chronic Risk1,2CancerMIRCancerIncidenceNoncancerMax HITargetOrganCancerMIRNoncancerMax HITargetOrganTable 2 – Maximum Predicted HEM-3 Chronic RisksSC % ofFacility-wideCancer RiskFacility NEI IDNEICA07327402.40E-061.57E-04 5.00E-02 neurological3.30E-05 1.15E+00respiratory 7%NEICA07331992.93E-07 1.20E-04 6.11E-03 neurological3.28E-062.59E-02 neurological 9%NEICT06052.26E-068.19E-06 4.70E-02 neurological2.09E-064.25E-02 neurological 108%NEIFL03102315.32E-08 1.26E-05 1.11E-03 neurological 5.32E-08 1.11E-03 neurological 100%NEIFL05700211.01E-06 1.37E-04 1.08E-02 neurological 1.01E-06 1.08E-02 neurological 100%NEIFL05702861.21E-06 1.29E-04 2.53E-02 neurological3.82E-063.04E-01 neurological 32%NEIFL05704421.10E-06 3.47E-05 2.42E-02 neurological4.90E-067.68E-02 neurological 22%NEIKY01900166.81E-07 7.91E-06 1.50E-02 neurological 6.95E-07 1.50E-02 neurological 98%NEILA00514.40E-07 7.50E-06 4.96E-03 neurological2.25E-05 2.27E+00neurological 2%NEILA00595.98E-08 3.37E-06 1.32E-03 neurological 2.56E-07 1.34E-01 neurological 23%NEILA10170381C$22.94E-07 1.52E-06 6.48E-03 neurological 2.94E-07 1.85E-02 neurological 100%NEILAT$105451.48E-06 4.33E-06 3.27E-02 neurological 1.48E-06 3.27E-02 neurological 100%NEILAT$106102.73E-08 3.36E-06 6.02E-04 neurological 1.56E-07 9.76E-02 neurological 18%NEIMA02514351.04E-07 2.91E-05 2.29E-03 neurological 1.04E-07 2.29E-03 neurological 100%NEIMD03321.58E-066.66E-05 3.49E-02 neurological2.09E-051.37E-01 neurological 8%NEIME03100535.05E-07 7.19E-06 1.11E-02 neurological 6.89E-07 1.22E-02 neurological 73%NEIMS16900.00E+00 0.00E+00 6.62E-04 neurological 0.00E+00 6.62E-04 neurologicalNEIMS16963.51E-07 3.15E-05 7.32E-03 neurological 4.26E-07 7.62E-03 neurological 83%NEIMS16981.71E-09 1.19E-06 3.76E-05 neurological 1.79E-07 6.64E-03 neurological 1%NEIMS17201.03E-06 1.92E-05 2.14E-02 neurological 1.95E-06 2.51E-02 neurological 53%NEIMS17213.34E-07 1.83E-05 6.95E-03 neurological 3.64E-07 7.07E-03 neurological 92%NEIMS17269.77E-07 2.62E-05 2.04E-02 neurological 1.71E-06 2.32E-02 neurological 57%NEIMS18432.76E-07 8.68E-06 5.75E-03 neurological 3.09E-07 6.41E-03 neurological 89%NEINJ017125171.82E-08 1.46E-04 3.94E-04 neurological 1.82E-08 3.94E-04 neurological 100%NEINJ017125249.87E-10 3.76E-07 2.06E-05 neurological 9.87E-10 2.06E-05 neurological 100%NEIRSC0-00741.11E-07 5.52E-06 1.29E-03 neurological 1.03E-06 3.95E-02 neurological 11%NEIRSC0-02361.58E-068.57E-05 1.87E-02 neurological 7.83E-06 2.07E-01 neurological 20%NEISC23143.16E-08 1.49E-06 6.40E-04 neurological 1.57E-05 8.90E-01 neurological 0%NEISC23220.00E+00 0.00E+00 7.44E-04 neurological 0.00E+00 7.44E-04 neurologicalNEISHP102.22E-07 3.48E-04 4.89E-03 neurological 2.22E-07 4.89E-03 neurological 100%NEISHP142.25E-061.39E-04 4.96E-02 neurological 2.25E-06 4.96E-02 neurological 100%NEISHP159.53E-07 2.74E-04 2.10E-02 neurological 4.75E-06 1.05E-01 neurological 20%NEISHP171.84E-07 3.58E-06 4.06E-03 neurological 1.84E-07 4.06E-03 neurological 100%NEISHP185.25E-07 1.18E-05 1.16E-02 neurological 5.25E-07 1.16E-02 neurological 100%NEISHP193.43E-069.25E-06 7.56E-02 neurological 5.18E-06 1.14E-01 neurological 66%NEISHP49.40E-07 1.50E-04 2.07E-02 neurological 9.40E-07 2.07E-02 neurological 100%2 of 3Source Category Chronic Risk1Facility-Wide Chronic Risk1,2CancerMIRCancerIncidenceNoncancerMax HITargetOrganCancerMIRNoncancerMax HITargetOrganTable 2 – Maximum Predicted HEM-3 Chronic RisksSC % ofFacility-wideCancer RiskFacility NEI IDNEISHP61.02E-06 2.31E-05 2.24E-02 neurological 1.02E-06 2.24E-02 neurological 100%NEITNT$47547.06E-07 5.33E-06 1.56E-02 neurological 2.12E-042.57E+00neurological 0%NEITNT$50747.41E-08 1.30E-05 1.63E-03 neurological 7.41E-08 1.63E-03 neurological 100%NEITX40978362K$32.19E-08 1.62E-06 4.83E-04 neurological 1.03E-07 4.83E-04 neurological 21%NEITXRGB04633.12E-08 7.48E-06 6.88E-04 neurological 3.12E-08 1.05E-03 neurological 100%NEITXT$116351.01E-07 1.54E-05 1.95E-03 neurological 1.01E-07 6.72E-03 neurological 100%NEIVA71000031.22E-06 1.97E-05 2.70E-02 neurological 1.26E-06 2.70E-02 neurological 97%NEIVA710002492.89E-062.60E-05 6.37E-02 neurological 2.91E-06 2.29E-01 neurological 99%NEIVAT$24691.01E-059.12E-05 2.10E-01 neurological 1.01E-05 2.70E-01 neurological 100%NEIWA106548.34E-08 2.81E-05 1.84E-03 neurological 8.34E-08 3.66E-03 neurological 100%NEIWA140821.51E-066.25E-05 1.01E-02 respiratory 5.86E-052.48E+00neurological 3%NEIWI07554809.50E-07 2.95E-05 6.08E-03 neurological 2.05E-05 7.07E-01 respiratory 5%NEIWIT$85337.58E-061.79E-05 1.67E-01 neurological 7.61E-06 2.70E-01 neurological 100%1 BOLD indicates a cancer risk greater than 1 in a million or a noncancer risk greater than 12 Facility cancer risk represent MIR at census block locations using Draft 2005 NATA3 of 3REL AEGL1 AEGL2 ERPG1 ERPG2 NEI11169 Xylenes (mixed) 7.60E-01 2.98E-02 4.18E-03 0.00E+00 0.00E+00 NEI12086TAM Xylenes (mixed) 5.19E-01 2.04E-02 2.85E-03 0.00E+00 0.00E+00 NEI12086TAM Toluene 2.77E-02 1.37E-03 2.28E-04 5.40E-03 9.06E-04 NEI12086TAM Ethyl benzene 0.00E+00 1.57E-02 4.57E-04 0.00E+00 0.00E+00 NEI13187 Xylenes (mixed) 4.93E-02 1.94E-03 2.71E-04 0.00E+00 0.00E+00 NEI16611 Xylenes (mixed) 1.48E-01 5.82E-03 8.15E-04 0.00E+00 0.00E+00 NEI16611 Toluene 8.01E-03 3.95E-04 6.58E-05 1.56E-03 2.62E-04 NEI16611 Ethyl benzene 0.00E+00 4.23E-03 1.23E-04 0.00E+00 0.00E+00 NEI22045CEN Toluene 8.82E-02 4.35E-03 7.25E-04 1.72E-02 2.89E-03 NEI22045CEN Xylenes (mixed) 6.03E-02 2.37E-03 3.31E-04 0.00E+00 0.00E+00 NEI22045CEN Ethyl benzene 0.00E+00 2.65E-03 7.73E-05 0.00E+00 0.00E+00 NEI23077 Glycol Ethers 4.40E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 NEI23077 Xylenes (mixed) 4.22E-03 1.66E-04 2.32E-05 0.00E+00 0.00E+00 NEI23077 Toluene 5.69E-04 2.81E-05 4.68E-06 1.11E-04 1.86E-05 NEI23453 Xylenes (mixed) 2.35E-01 9.24E-03 1.29E-03 0.00E+00 0.00E+00 NEI23453 Toluene 5.68E-04 2.80E-05 4.67E-06 1.11E-04 1.86E-05 NEI23453 Ethyl benzene 0.00E+00 9.08E-03 2.65E-04 0.00E+00 0.00E+00 NEI28047GUL Xylenes (mixed) 1.32E-01 5.17E-03 7.24E-04 0.00E+00 0.00E+00 NEI2PA101569 Xylenes (mixed) 3.10E-02 1.22E-03 1.70E-04 0.00E+00 0.00E+00 NEI2PA101569 Ethyl benzene 0.00E+00 3.43E-03 1.00E-04 0.00E+00 0.00E+00 NEI2TX06138 Xylenes (mixed) 2.69E-01 1.05E-02 1.48E-03 0.00E+00 0.00E+00 NEI2TX06138 Toluene 3.38E-02 1.67E-03 2.78E-04 6.60E-03 1.11E-03 NEI2TX06138 Ethyl benzene 0.00E+00 6.96E-03 2.03E-04 0.00E+00 0.00E+00 NEI310 1,1,1-Trichloroethane 2.26E-04 1.18E-05 4.66E-06 1.18E-05 4.66E-06 NEI31325 Xylenes (mixed) 1.51E-01 5.95E-03 8.33E-04 0.00E+00 0.00E+00 NEI31325 Methanol 1.18E-02 4.79E-04 1.22E-04 4.79E-04 1.22E-04 NEI31325 Toluene 6.68E-06 3.30E-07 5.50E-08 1.30E-06 2.19E-07 NEI31325 Ethyl benzene 0.00E+00 1.60E-03 4.67E-05 0.00E+00 0.00E+00 NEI314 Ethyl benzene 0.00E+00 2.05E-02 5.98E-04 0.00E+00 0.00E+00 NEI33201 Xylenes (mixed) 1.04E-01 4.09E-03 5.73E-04 0.00E+00 0.00E+00 NEI33201 Toluene 2.13E-02 1.05E-03 1.75E-04 4.16E-03 6.98E-04 NEI33201 Benzene 1.20E-02 9.17E-05 5.99E-06 9.17E-05 5.99E-06 NEI33201 n-Hexane 0.00E+00 0.00E+00 1.12E-04 0.00E+00 0.00E+00 NEI33221 Xylenes (mixed) 1.30E-01 5.10E-03 7.14E-04 0.00E+00 0.00E+00 NEI33221 Toluene 7.02E-03 3.46E-04 5.77E-05 1.37E-03 2.30E-04 NEI33221 Ethyl benzene 0.00E+00 3.71E-03 1.08E-04 0.00E+00 0.00E+00 NEI34481 Xylenes (mixed) 5.32E-01 2.09E-02 2.92E-03 0.00E+00 0.00E+00 NEI34481 Toluene 2.84E-02 1.40E-03 2.33E-04 5.53E-03 9.28E-04 NEI34481 Ethyl benzene 0.00E+00 1.61E-02 4.69E-04 0.00E+00 0.00E+00 NEI40623 Xylenes (mixed) 1.41E-01 5.53E-03 7.75E-04 0.00E+00 0.00E+00 NEI42049ERI Xylenes (mixed) 8.16E-02 3.21E-03 4.49E-04 0.00E+00 0.00E+00 NEI42049ERI Toluene 4.35E-03 2.15E-04 3.58E-05 8.49E-04 1.42E-04 NEI42049ERI Ethyl benzene 0.00E+00 2.47E-03 7.19E-05 0.00E+00 0.00E+00 NEI42051BRO Xylenes (mixed) 3.04E-02 1.19E-03 1.67E-04 0.00E+00 0.00E+00 NEI42051BRO Toluene 1.98E-02 9.74E-04 1.62E-04 3.85E-03 6.46E-04 NEI42051BRO Ethyl benzene 0.00E+00 1.12E-02 3.26E-04 0.00E+00 0.00E+00 NEI42101ATL Xylenes (mixed) 3.10E-01 1.22E-02 1.70E-03 0.00E+00 0.00E+00 NEI42101ATL Toluene 1.67E-02 8.26E-04 1.38E-04 3.26E-03 5.48E-04 NEI42101ATL Ethyl benzene 0.00E+00 9.34E-03 2.72E-04 0.00E+00 0.00E+00 NEI44260 Xylenes (mixed) 1.93E-01 7.56E-03 1.06E-03 0.00E+00 0.00E+00 NEI44260 Toluene 1.04E-02 5.13E-04 8.56E-05 2.03E-03 3.40E-04 NEI44260 Ethyl benzene 0.00E+00 5.50E-03 1.60E-04 0.00E+00 0.00E+00 NEI51710LYO Xylenes (mixed) 1.28E-01 5.03E-03 7.05E-04 0.00E+00 0.00E+00 Pollutant Maximum Hazard Quotient 1 Facility NEI ID Table 3 – Maximum Predicted Acute Risks (HEM-3)

1 of 6REL AEGL1 AEGL2 ERPG1 ERPG2 Pollutant Maximum Hazard Quotient 1 Facility NEI ID Table 3 – Maximum Predicted Acute Risks (HEM-3)

NEI51710LYO Toluene 6.93E-03 3.42E-04 5.70E-05 1.35E-03 2.27E-04 NEI51710LYO Ethyl benzene 0.00E+00 3.66E-03 1.07E-04 0.00E+00 0.00E+00 NEI51710MAR Xylenes (mixed) 1.64E-02 6.43E-04 9.00E-05 0.00E+00 0.00E+00 NEI51710MAR Toluene 8.73E-04 4.31E-05 7.18E-06 1.70E-04 2.86E-05 NEI51710MAR Ethyl benzene 0.00E+00 4.94E-04 1.44E-05 0.00E+00 0.00E+00 NEI53033FOS Xylenes (mixed) 1.09E-01 4.30E-03 6.02E-04 0.00E+00 0.00E+00 NEI53033FOS Ethyl benzene 0.00E+00 4.38E-03 1.28E-04 0.00E+00 0.00E+00 NEI6097 Xylenes (mixed) 6.68E-01 2.62E-02 3.67E-03 0.00E+00 0.00E+00 NEI6097 Toluene 3.57E-02 1.76E-03 2.93E-04 6.95E-03 1.17E-03 NEI6097 Ethyl benzene 0.00E+00 2.02E-02 5.89E-04 0.00E+00 0.00E+00 NEI6105 Xylenes (mixed) 5.31E-02 2.08E-03 2.92E-04 0.00E+00 0.00E+00 NEI6105 Ethyl benzene 0.00E+00 3.18E-04 9.27E-06 0.00E+00 0.00E+00 NEI6184 Xylenes (mixed) 6.93E-01 2.72E-02 3.81E-03 0.00E+00 0.00E+00 NEI6184 Methoxytriglycol 3.05E-01 0.00E+00 0.00E+00 0.00E+00 0.00E+00 NEI6184 Methanol 1.48E-01 5.99E-03 1.53E-03 5.99E-03 1.53E-03 NEI6184 Toluene 9.67E-04 4.77E-05 7.95E-06 1.88E-04 3.16E-05 NEI6184 Ethyl benzene 0.00E+00 2.16E-02 6.31E-04 0.00E+00 0.00E+00 NEI6243 Xylenes (mixed) 1.62E-01 6.37E-03 8.92E-04 0.00E+00 0.00E+00 NEI6243 Toluene 8.76E-03 4.32E-04 7.21E-05 1.71E-03 2.87E-04 NEI6243 Ethyl benzene 0.00E+00 4.63E-03 1.35E-04 0.00E+00 0.00E+00 NEI6292 Xylenes (mixed) 8.78E-02 3.45E-03 4.83E-04 0.00E+00 0.00E+00 NEI6951 Xylenes (mixed) 1.30E-01 5.11E-03 7.15E-04 0.00E+00 0.00E+00 NEI6951 Toluene 7.03E-03 3.47E-04 5.78E-05 1.37E-03 2.30E-04 NEI6951 Ethyl benzene 0.00E+00 3.72E-03 1.08E-04 0.00E+00 0.00E+00 NEI7371 Xylenes (mixed) 1.30E-01 5.11E-03 7.15E-04 0.00E+00 0.00E+00 NEI7371 Toluene 7.03E-03 3.47E-04 5.78E-05 1.37E-03 2.30E-04 NEI7371 Ethyl benzene 0.00E+00 3.72E-03 1.08E-04 0.00E+00 0.00E+00 NEI779 Xylenes (mixed) 5.18E-02 2.04E-03 2.85E-04 0.00E+00 0.00E+00 NEI779 Benzene 1.15E-02 8.76E-05 5.73E-06 8.76E-05 5.73E-06 NEI779 Acetaldehyde 7.32E-03 4.25E-05 7.02E-06 4.25E-05 7.02E-06 NEI779 Hydrochloric acid 8.69E-04 6.76E-04 5.53E-05 6.76E-04 5.53E-05 NEI779 Hydrofluoric acid 8.08E-04 2.37E-04 9.70E-06 2.37E-04 9.70E-06 NEI8020 Xylenes (mixed) 1.33E+00 5.23E-02 7.33E-03 0.00E+00 0.00E+00 NEI8020 Toluene 1.82E-02 9.00E-04 1.50E-04 3.56E-03 5.97E-04 NEI8020 Phenol 3.06E-03 3.06E-04 1.99E-04 3.06E-04 1.99E-04 NEI8020 Methanol 1.05E-03 4.27E-05 1.09E-05 4.27E-05 1.09E-05 NEI8020 Cumene 0.00E+00 3.13E-05 5.21E-06 0.00E+00 0.00E+00 NEI8020 Ethyl benzene 0.00E+00 4.23E-03 1.23E-04 0.00E+00 0.00E+00 NEI8274 Xylenes (mixed) 1.92E-01 7.56E-03 1.06E-03 0.00E+00 0.00E+00 NEI8274 Toluene 3.33E-03 1.64E-04 2.74E-05 6.49E-04 1.09E-04 NEI8274 Methanol 2.30E-03 9.33E-05 2.39E-05 9.33E-05 2.39E-05 NEI8609 Xylenes (mixed) 2.01E-01 7.90E-03 1.11E-03 0.00E+00 0.00E+00 NEI8609 Formaldehyde 2.69E-02 1.34E-03 8.70E-05 1.34E-03 8.70E-05 NEI8609 Toluene 6.94E-04 3.42E-05 5.71E-06 1.35E-04 2.27E-05 NEI8609 n-Hexane 0.00E+00 0.00E+00 5.19E-08 0.00E+00 0.00E+00 NEI8609 Ethyl benzene 0.00E+00 1.53E-02 4.47E-04 0.00E+00 0.00E+00 NEIAL09736603B$1 Xylenes (mixed) 1.32E-01 5.20E-03 7.28E-04 0.00E+00 0.00E+00 NEIAL09736603B$1 Toluene 7.16E-03 3.53E-04 5.89E-05 1.40E-03 2.34E-04 NEIAL09736603B$1 Ethyl benzene 0.00E+00 3.78E-03 1.10E-04 0.00E+00 0.00E+00 NEICA0732740 Xylenes (mixed) 4.60E-01 1.81E-02 2.53E-03 0.00E+00 0.00E+00 NEICA0732740 Toluene 2.45E-02 1.21E-03 2.02E-04 4.78E-03 8.03E-04 NEICA0732740 Ethyl benzene 0.00E+00 1.39E-02 4.05E-04 0.00E+00 0.00E+00 NEICA0733199 Xylenes (mixed) 4.60E-01 1.81E-02 2.53E-03 0.00E+00 0.00E+00

2 of 6REL AEGL1 AEGL2 ERPG1 ERPG2 Pollutant Maximum Hazard Quotient 1 Facility NEI ID Table 3 – Maximum Predicted Acute Risks (HEM-3)

NEICA0733199 Toluene 2.45E-02 1.21E-03 2.02E-04 4.78E-03 8.03E-04 NEICA0733199 Ethyl benzene 0.00E+00 1.39E-02 4.05E-04 0.00E+00 0.00E+00 NEICT0605 Xylenes (mixed) 9.01E-02 3.54E-03 4.96E-04 0.00E+00 0.00E+00 NEICT0605 Xylenes (mixed) 9.01E-02 3.54E-03 4.96E-04 0.00E+00 0.00E+00 NEICT0605 Toluene 4.81E-03 2.37E-04 3.95E-05 9.37E-04 1.57E-04 NEICT0605 Toluene 4.81E-03 2.37E-04 3.95E-05 9.37E-04 1.57E-04 NEICT0605 Ethyl benzene 0.00E+00 2.72E-03 7.94E-05 0.00E+00 0.00E+00 NEICT0605 Ethyl benzene 0.00E+00 2.72E-03 7.94E-05 0.00E+00 0.00E+00 NEIFL0310231 Xylenes (mixed) 6.93E-02 2.72E-03 3.81E-04 0.00E+00 0.00E+00 NEIFL0310231 Toluene 3.70E-03 1.83E-04 3.04E-05 7.21E-04 1.21E-04 NEIFL0310231 Ethyl benzene 0.00E+00 2.10E-03 6.11E-05 0.00E+00 0.00E+00 NEIFL0570021 Glycol Ethers 4.08E-01 0.00E+00 0.00E+00 0.00E+00 0.00E+00 NEIFL0570021 Xylenes (mixed) 2.25E-01 8.84E-03 1.24E-03 0.00E+00 0.00E+00 NEIFL0570021 Toluene 1.47E-04 7.23E-06 1.21E-06 2.86E-05 4.80E-06 NEIFL0570021 Ethyl benzene 0.00E+00 1.32E-02 3.84E-04 0.00E+00 0.00E+00 NEIFL0570286 Xylenes (mixed) 4.81E-01 1.89E-02 2.64E-03 0.00E+00 0.00E+00 NEIFL0570286 Toluene 2.57E-02 1.27E-03 2.11E-04 5.00E-03 8.39E-04 NEIFL0570286 Ethyl benzene 0.00E+00 1.45E-02 4.24E-04 0.00E+00 0.00E+00 NEIFL0570442 Xylenes (mixed) 1.36E-01 5.33E-03 7.46E-04 0.00E+00 0.00E+00 NEIFL0570442 Toluene 7.33E-03 3.62E-04 6.03E-05 1.43E-03 2.40E-04 NEIFL0570442 Ethyl benzene 0.00E+00 3.87E-03 1.13E-04 0.00E+00 0.00E+00 NEIKY0190016 Xylenes (mixed) 1.94E-01 7.61E-03 1.07E-03 0.00E+00 0.00E+00 NEIKY0190016 Toluene 1.05E-02 5.17E-04 8.61E-05 2.04E-03 3.43E-04 NEIKY0190016 Ethyl benzene 0.00E+00 5.53E-03 1.61E-04 0.00E+00 0.00E+00 NEILA0051 Xylenes (mixed) 3.22E-01 1.26E-02 1.77E-03 0.00E+00 0.00E+00 NEILA0051 Ethylene glycol methyl ether 2.67E-01 0.00E+00 0.00E+00 0.00E+00 0.00E+00 NEILA0051 Methanol 6.36E-02 2.58E-03 6.60E-04 2.58E-03 6.60E-04 NEILA0051 Toluene 4.02E-02 1.98E-03 3.31E-04 7.84E-03 1.32E-03 NEILA0051 Styrene 3.37E-03 8.34E-04 1.29E-04 8.34E-04 1.29E-04 NEILA0051 Phenol 1.95E-04 1.95E-05 1.27E-05 1.95E-05 1.27E-05 NEILA0051 Cumene 0.00E+00 3.18E-04 5.30E-05 0.00E+00 0.00E+00 NEILA0051 Ethyl benzene 0.00E+00 1.51E-02 4.40E-04 0.00E+00 0.00E+00 NEILA0059 Xylenes (mixed) 1.88E-01 7.40E-03 1.04E-03 0.00E+00 0.00E+00 NEILA0059 Toluene 1.02E-02 5.03E-04 8.38E-05 1.99E-03 3.33E-04 NEILA0059 Ethyl benzene 0.00E+00 5.38E-03 1.57E-04 0.00E+00 0.00E+00 NEILA10170381C$2 Xylenes (mixed) 1.88E-01 7.40E-03 1.04E-03 0.00E+00 0.00E+00 NEILA10170381C$2 Toluene 1.02E-02 5.03E-04 8.38E-05 1.99E-03 3.33E-04 NEILA10170381C$2 Ethyl benzene 0.00E+00 5.38E-03 1.57E-04 0.00E+00 0.00E+00 NEILAT$10545 Xylenes (mixed) 1.88E-01 7.40E-03 1.04E-03 0.00E+00 0.00E+00 NEILAT$10545 Toluene 1.02E-02 5.03E-04 8.38E-05 1.99E-03 3.33E-04 NEILAT$10545 Ethyl benzene 0.00E+00 5.38E-03 1.57E-04 0.00E+00 0.00E+00 NEILAT$10610 Xylenes (mixed) 1.50E-01 5.90E-03 8.26E-04 0.00E+00 0.00E+00 NEILAT$10610 Toluene 8.11E-03 4.00E-04 6.67E-05 1.58E-03 2.65E-04 NEILAT$10610 Ethyl benzene 0.00E+00 4.29E-03 1.25E-04 0.00E+00 0.00E+00 NEIMA0251435 Xylenes (mixed) 1.76E-01 6.92E-03 9.69E-04 0.00E+00 0.00E+00 NEIMA0251435 Toluene 9.52E-03 4.70E-04 7.83E-05 1.85E-03 3.11E-04 NEIMA0251435 Ethyl benzene 0.00E+00 5.03E-03 1.47E-04 0.00E+00 0.00E+00 NEIMD0332 Xylenes (mixed) 1.30E-01 5.10E-03 7.14E-04 0.00E+00 0.00E+00 NEIMD0332 Toluene 7.02E-03 3.46E-04 5.77E-05 1.37E-03 2.30E-04 NEIMD0332 Ethyl benzene 0.00E+00 3.71E-03 1.08E-04 0.00E+00 0.00E+00 NEIME0310053 Xylenes (mixed) 1.62E-01 6.37E-03 8.92E-04 0.00E+00 0.00E+00 NEIME0310053 Toluene 8.76E-03 4.32E-04 7.21E-05 1.71E-03 2.87E-04 NEIME0310053 Ethyl benzene 0.00E+00 4.63E-03 1.35E-04 0.00E+00 0.00E+00

3 of 6REL AEGL1 AEGL2 ERPG1 ERPG2 Pollutant Maximum Hazard Quotient 1 Facility NEI ID Table 3 – Maximum Predicted Acute Risks (HEM-3)

NEIMS1690 Xylenes (mixed) 1.74E-01 6.82E-03 9.55E-04 0.00E+00 0.00E+00 NEIMS1696 Xylenes (mixed) 4.70E-01 1.84E-02 2.58E-03 0.00E+00 0.00E+00 NEIMS1696 Toluene 2.51E-02 1.24E-03 2.06E-04 4.88E-03 8.20E-04 NEIMS1696 Ethyl benzene 0.00E+00 1.42E-02 4.14E-04 0.00E+00 0.00E+00 NEIMS1698 Xylenes (mixed) 1.88E-01 7.40E-03 1.04E-03 0.00E+00 0.00E+00 NEIMS1698 Toluene 1.02E-02 5.03E-04 8.38E-05 1.98E-03 3.33E-04 NEIMS1698 Ethyl benzene 0.00E+00 5.38E-03 1.57E-04 0.00E+00 0.00E+00 NEIMS1720 Xylenes (mixed) 4.70E-01 1.84E-02 2.58E-03 0.00E+00 0.00E+00 NEIMS1720 Toluene 2.51E-02 1.24E-03 2.06E-04 4.88E-03 8.20E-04 NEIMS1720 Ethyl benzene 0.00E+00 1.42E-02 4.14E-04 0.00E+00 0.00E+00 NEIMS1721 Xylenes (mixed) 4.70E-01 1.84E-02 2.58E-03 0.00E+00 0.00E+00 NEIMS1721 Toluene 2.51E-02 1.24E-03 2.06E-04 4.88E-03 8.20E-04 NEIMS1721 Ethyl benzene 0.00E+00 1.42E-02 4.14E-04 0.00E+00 0.00E+00 NEIMS1726 Xylenes (mixed) 4.70E-01 1.84E-02 2.58E-03 0.00E+00 0.00E+00 NEIMS1726 Toluene 2.51E-02 1.24E-03 2.06E-04 4.88E-03 8.20E-04 NEIMS1726 Ethyl benzene 0.00E+00 1.42E-02 4.14E-04 0.00E+00 0.00E+00 NEIMS1843 Xylenes (mixed) 5.26E-01 2.06E-02 2.89E-03 0.00E+00 0.00E+00 NEIMS1843 Toluene 2.80E-02 1.38E-03 2.31E-04 5.47E-03 9.18E-04 NEIMS1843 Ethyl benzene 0.00E+00 1.59E-02 4.63E-04 0.00E+00 0.00E+00 NEINJ01712517 Xylenes (mixed) 1.30E-01 5.10E-03 7.14E-04 0.00E+00 0.00E+00 NEINJ01712517 Acrolein 6.44E-02 2.33E-03 7.00E-04 2.33E-03 7.00E-04 NEINJ01712517 Toluene 7.02E-03 3.46E-04 5.77E-05 1.37E-03 2.30E-04 NEINJ01712517 Ethyl benzene 0.00E+00 3.71E-03 1.08E-04 0.00E+00 0.00E+00 NEINJ01712524 Xylenes (mixed) 1.10E-04 4.34E-06 6.08E-07 0.00E+00 0.00E+00 NEINJ01712524 Toluene 5.89E-06 2.91E-07 4.85E-08 1.15E-06 1.93E-07 NEINJ01712524 Ethyl benzene 0.00E+00 3.34E-06 9.74E-08 0.00E+00 0.00E+00 NEIRSC0-0074 Xylenes (mixed) 1.26E-01 4.96E-03 6.94E-04 0.00E+00 0.00E+00 NEIRSC0-0074 Cumene 0.00E+00 9.03E-05 1.50E-05 0.00E+00 0.00E+00 NEIRSC0-0074 Ethyl benzene 0.00E+00 6.81E-03 1.99E-04 0.00E+00 0.00E+00 NEIRSC0-0236 Xylenes (mixed) 5.42E-01 2.13E-02 2.98E-03 0.00E+00 0.00E+00 NEIRSC0-0236 Methanol 3.51E-03 1.42E-04 3.64E-05 1.42E-04 3.64E-05 NEIRSC0-0236 Toluene 1.33E-03 6.58E-05 1.10E-05 2.60E-04 4.36E-05 NEIRSC0-0236 Styrene 7.12E-04 1.76E-04 2.72E-05 1.76E-04 2.72E-05 NEIRSC0-0236 Cumene 0.00E+00 2.67E-04 4.46E-05 0.00E+00 0.00E+00 NEIRSC0-0236 Ethyl benzene 0.00E+00 2.89E-02 8.42E-04 0.00E+00 0.00E+00 NEISC2314 Xylenes (mixed) 1.72E-02 6.74E-04 9.43E-05 0.00E+00 0.00E+00 NEISC2314 Ethyl benzene 0.00E+00 5.32E-04 1.55E-05 0.00E+00 0.00E+00 NEISC2322 Xylenes (mixed) 8.23E-03 3.23E-04 4.53E-05 0.00E+00 0.00E+00 NEISHP10 Xylenes (mixed) 1.30E-01 5.10E-03 7.14E-04 0.00E+00 0.00E+00 NEISHP10 Toluene 7.02E-03 3.46E-04 5.77E-05 1.37E-03 2.30E-04 NEISHP10 Ethyl benzene 0.00E+00 3.71E-03 1.08E-04 0.00E+00 0.00E+00 NEISHP14 Xylenes (mixed) 1.30E-01 5.10E-03 7.14E-04 0.00E+00 0.00E+00 NEISHP14 Toluene 7.02E-03 3.46E-04 5.77E-05 1.37E-03 2.30E-04 NEISHP14 Ethyl benzene 0.00E+00 3.71E-03 1.08E-04 0.00E+00 0.00E+00 NEISHP15 Xylenes (mixed) 7.63E-02 3.00E-03 4.19E-04 0.00E+00 0.00E+00 NEISHP15 Xylenes (mixed) 7.63E-02 3.00E-03 4.19E-04 0.00E+00 0.00E+00 NEISHP15 Toluene 4.12E-03 2.03E-04 3.39E-05 8.03E-04 1.35E-04 NEISHP15 Toluene 4.12E-03 2.03E-04 3.39E-05 8.03E-04 1.35E-04 NEISHP15 Ethyl benzene 0.00E+00 2.18E-03 6.36E-05 0.00E+00 0.00E+00 NEISHP15 Ethyl benzene 0.00E+00 2.18E-03 6.36E-05 0.00E+00 0.00E+00 NEISHP17 Xylenes (mixed) 1.50E-01 5.90E-03 8.26E-04 0.00E+00 0.00E+00 NEISHP17 Toluene 8.11E-03 4.00E-04 6.67E-05 1.58E-03 2.65E-04 NEISHP17 Ethyl benzene 0.00E+00 4.29E-03 1.25E-04 0.00E+00 0.00E+00

4 of 6REL AEGL1 AEGL2 ERPG1 ERPG2 Pollutant Maximum Hazard Quotient 1 Facility NEI ID Table 3 – Maximum Predicted Acute Risks (HEM-3)

NEISHP18 Xylenes (mixed) 1.88E-01 7.40E-03 1.04E-03 0.00E+00 0.00E+00 NEISHP18 Toluene 1.02E-02 5.03E-04 8.38E-05 1.99E-03 3.33E-04 NEISHP18 Ethyl benzene 0.00E+00 5.38E-03 1.57E-04 0.00E+00 0.00E+00 NEISHP19 Xylenes (mixed) 1.33E-01 5.24E-03 7.34E-04 0.00E+00 0.00E+00 NEISHP19 Xylenes (mixed) 1.33E-01 5.24E-03 7.34E-04 0.00E+00 0.00E+00 NEISHP19 Toluene 7.21E-03 3.56E-04 5.93E-05 1.40E-03 2.36E-04 NEISHP19 Toluene 7.21E-03 3.56E-04 5.93E-05 1.40E-03 2.36E-04 NEISHP19 Ethyl benzene 0.00E+00 3.81E-03 1.11E-04 0.00E+00 0.00E+00 NEISHP19 Ethyl benzene 0.00E+00 3.81E-03 1.11E-04 0.00E+00 0.00E+00 NEISHP4 Xylenes (mixed) 1.30E-01 5.10E-03 7.14E-04 0.00E+00 0.00E+00 NEISHP4 Toluene 7.02E-03 3.46E-04 5.77E-05 1.37E-03 2.30E-04 NEISHP4 Ethyl benzene 0.00E+00 3.71E-03 1.08E-04 0.00E+00 0.00E+00 NEISHP6 Xylenes (mixed) 1.28E-01 5.03E-03 7.05E-04 0.00E+00 0.00E+00 NEISHP6 Toluene 6.93E-03 3.42E-04 5.70E-05 1.35E-03 2.27E-04 NEISHP6 Ethyl benzene 0.00E+00 3.66E-03 1.07E-04 0.00E+00 0.00E+00 NEITNT$4754 Xylenes (mixed) 1.84E-01 7.23E-03 1.01E-03 0.00E+00 0.00E+00 NEITNT$4754 Toluene 9.95E-03 4.91E-04 8.18E-05 1.94E-03 3.26E-04 NEITNT$4754 Ethyl benzene 0.00E+00 5.26E-03 1.53E-04 0.00E+00 0.00E+00 NEITNT$5074 Xylenes (mixed) 1.73E-01 6.80E-03 9.52E-04 0.00E+00 0.00E+00 NEITNT$5074 Toluene 9.36E-03 4.62E-04 7.69E-05 1.82E-03 3.06E-04 NEITNT$5074 Ethyl benzene 0.00E+00 4.95E-03 1.44E-04 0.00E+00 0.00E+00 NEITX40978362K$3 Xylenes (mixed) 1.34E-01 5.27E-03 7.38E-04 0.00E+00 0.00E+00 NEITX40978362K$3 Toluene 7.25E-03 3.58E-04 5.96E-05 1.41E-03 2.37E-04 NEITX40978362K$3 Ethyl benzene 0.00E+00 3.83E-03 1.12E-04 0.00E+00 0.00E+00 NEITXRGB0463 Xylenes (mixed) 1.30E-01 5.11E-03 7.15E-04 0.00E+00 0.00E+00 NEITXRGB0463 Toluene 7.03E-03 3.47E-04 5.78E-05 1.37E-03 2.30E-04 NEITXRGB0463 Ethyl benzene 0.00E+00 3.72E-03 1.08E-04 0.00E+00 0.00E+00 NEITXT$11635 Xylenes (mixed) 6.38E-02 2.51E-03 3.51E-04 0.00E+00 0.00E+00 NEITXT$11635 Toluene 1.43E-02 7.04E-04 1.17E-04 2.78E-03 4.67E-04 NEITXT$11635 Phenol 6.19E-03 6.19E-04 4.03E-04 6.19E-04 4.03E-04 NEITXT$11635 Cumene 0.00E+00 6.04E-05 1.01E-05 0.00E+00 0.00E+00 NEITXT$11635 Ethyl benzene 0.00E+00 2.10E-03 6.11E-05 0.00E+00 0.00E+00 NEIVA7100003 Xylenes (mixed) 1.28E-01 5.03E-03 7.05E-04 0.00E+00 0.00E+00 NEIVA7100003 Toluene 6.93E-03 3.42E-04 5.70E-05 1.35E-03 2.27E-04 NEIVA7100003 Ethyl benzene 0.00E+00 3.66E-03 1.07E-04 0.00E+00 0.00E+00 NEIVA71000249 Xylenes (mixed) 1.28E-01 5.03E-03 7.05E-04 0.00E+00 0.00E+00 NEIVA71000249 Toluene 6.93E-03 3.42E-04 5.70E-05 1.35E-03 2.27E-04 NEIVA71000249 Ethyl benzene 0.00E+00 3.66E-03 1.07E-04 0.00E+00 0.00E+00 NEIVAT$2469 Xylenes (mixed) 4.54E-01 1.78E-02 2.50E-03 0.00E+00 0.00E+00 NEIVAT$2469 Toluene 2.42E-02 1.20E-03 1.99E-04 4.72E-03 7.93E-04 NEIVAT$2469 Ethyl benzene 0.00E+00 1.37E-02 4.00E-04 0.00E+00 0.00E+00 NEIWA10654 Xylenes (mixed) 1.29E-01 5.07E-03 7.10E-04 0.00E+00 0.00E+00 NEIWA10654 Toluene 6.97E-03 3.44E-04 5.73E-05 1.36E-03 2.28E-04 NEIWA10654 Ethyl benzene 0.00E+00 3.69E-03 1.08E-04 0.00E+00 0.00E+00 NEIWA14082 2-Butoxyethyl Acetate 1.58E-01 0.00E+00 0.00E+00 0.00E+00 0.00E+00 NEIWA14082 Xylenes (mixed) 1.17E-01 4.61E-03 6.45E-04 0.00E+00 0.00E+00 NEIWA14082 Ethylene glycol ethyl ether acetate 7.74E-02 0.00E+00 0.00E+00 0.00E+00 0.00E+00 NEIWA14082 Methylene chloride 7.57E-02 1.54E-03 5.58E-04 1.54E-03 5.58E-04 NEIWA14082 Formaldehyde 2.82E-02 1.41E-03 9.11E-05 1.41E-03 9.11E-05 NEIWA14082 Toluene 9.75E-03 4.81E-04 8.02E-05 1.90E-03 3.19E-04 NEIWA14082 Phenol 8.32E-03 8.32E-04 5.42E-04 8.32E-04 5.42E-04 NEIWA14082 Styrene 4.57E-03 1.13E-03 1.75E-04 1.13E-03 1.75E-04 NEIWA14082 Methanol 4.14E-03 1.68E-04 4.29E-05 1.68E-04 4.29E-05

5 of 6REL AEGL1 AEGL2 ERPG1 ERPG2 Pollutant Maximum Hazard Quotient 1 Facility NEI ID Table 3 – Maximum Predicted Acute Risks (HEM-3)

NEIWA14082 Benzene 3.97E-04 3.04E-06 1.99E-07 3.04E-06 1.99E-07 NEIWA14082 Epichlorohydrin 1.99E-04 1.17E-05 2.84E-06 1.17E-05 2.84E-06 NEIWA14082 1,1,1-Trichloroethane 3.04E-05 1.59E-06 6.26E-07 1.59E-06 6.26E-07 NEIWA14082 n-Hexane 0.00E+00 0.00E+00 2.23E-05 0.00E+00 0.00E+00 NEIWA14082 Trichloroethylene 0.00E+00 3.91E-05 1.14E-05 3.91E-05 1.14E-05 NEIWA14082 Cumene 0.00E+00 2.21E-04 3.68E-05 0.00E+00 0.00E+00 NEIWA14082 Chlorobenzene 0.00E+00 4.21E-04 2.80E-05 0.00E+00 0.00E+00 NEIWA14082 Vinyl acetate 0.00E+00 1.72E-03 6.55E-05 2.29E-03 1.59E-04 NEIWA14082 Ethyl benzene 0.00E+00 8.85E-03 2.58E-04 0.00E+00 0.00E+00 NEIWI0755480 Xylenes (mixed) 8.33E-02 3.27E-03 4.58E-04 0.00E+00 0.00E+00 NEIWI0755480 Cumene 0.00E+00 9.87E-04 1.64E-04 0.00E+00 0.00E+00 NEIWI0755480 Ethyl benzene 0.00E+00 8.18E-03 2.39E-04 0.00E+00 0.00E+00 NEIWIT$8533 Xylenes (mixed) 1.58E-01 6.22E-03 8.70E-04 0.00E+00 0.00E+00 NEIWIT$8533 Toluene 8.55E-03 4.22E-04 7.03E-05 1.67E-03 2.80E-04 NEIWIT$8533 Ethyl benzene 0.00E+00 4.52E-03 1.32E-04 0.00E+00 0.00E+00

1 Some maximum acute impacts may be at onsite locations. Note: BOLD indicates acute risks greater than 1

6 of 6NEI23077 Glycol Ethers REL 4.40E+00 1.10E-01 Refined AERMOD Modeling; examined mapped results for maximum off-site impact Facility NEI ID

1 Facilites with a HEM-3 screening acute value greater than 1 were remodeled with a more refined approach Shibuilding and Ship Repair Operations: Table 4 – Maximum Predicted Acute Risks Greater than 1 (Refined Approach)

Refined Modeling Approach 2 Pollutant Criteria HEM-3 (Screening) Refined
Results 1

1 of 1

### Appendix 7 Acute Impacts Refined Analysis Figures

### Refined Acute Modeling Approach

Initial acute screening risk calculation s were performed with the HEM-3 model. HEM-3 estimates acute (1-hour) impacts at both polar and census block receptors. It is assumed for this short period of time that an exposed individual coul d be located at any off-site location. The lack of readily av ailable 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 info rmation, the first ring of polar receptors was placed 100 meters from the plant center for ma ny facilities. However, these polar rings often transected on-site locati ons, restricting public access to exposures at these levels and thereby overestimating exposures.

The screening approach used by HEM-3 to estimate maximum 1-hour exposures also likely overestimates. To estimate ma ximum 1-hour concentrati ons at each receptor, HEM-3 sums the maximum concentrations at tributed to each source, regardless of whether those maximum concentrations occurr ed during the same hour. In other words, HEM-3 assumes that the maximum impact from each source at each receptor occurs at the same time. In actuality, maximum imp acts from different sources may occur at different times.

This appendix includes the results of refined acute assessments performed for facilities that exceeded short-term health benchmarks using the HEM-3 approach. The refinements can address both areas of cons ervatism described above. The simplest refinement is to plot the HE M-3 polar grid results on aerial photographs of the facilities; This allows the assessment of off-site loca tions that may be accessible to the public ( e.g., roadways and public buildings.). In cases wh ere this refinement results in lower acute risks no further refinement was conducted. Howeve r, if acute risk sti ll remain at or near levels of concern a further refinement is conducted. This second refinement includes remodeling with the AERMOD model directly (instead of using HEM) to estimate maximum 1-hour concentrations at each recepto r by pairing individual source impacts in time and space. The AERMOD results are then plotted on aerial photographs as noted above to determine off-site impacts. Modeling with AERMOD also allows the estimation of the frequency of higher impacts at or above the health levels of concern. The modeled 1-hour concentration of a pollu tant divided by its short-term health benchmark) at the polar receptors near the f acility was below a HQ of 1. Refer to below for a brief summary;

• NEI23077; Acute Risk Screening HQ was equal to 4.2 for glycol ethers, initial evaluation of the map indicated the HQ was on-site, with an off-site HQ of 2. This site required a refined assessment with AERMOD, in which the off-site HQ was less than 1. The acute REL value for ethylene glycol methyl ether was used as a surrogate for all glycol ethers.Shipbuilding and Ship Repair Facilities:(Refined Acute Risk Assessment)• Figure 1 – NEI23077; maximum offsiteacute HQ(REL)for glycol ethers less than 1,North of the facilityFigure 1 – NEI23077 Acute Glycol Ethers HQ (REL)