### Risk Assessment for the Aerospace Manufacturing and Rework Facilities Source Category 

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# Residual Risk Assessment for the Aerospace Manufacturing and Rework Facilities Source Category in Support of the January, 2015 Risk and Technology Review Proposal

EPA’s Office of Air Quality Planning and Standards

Office of Air and Radiation January 201 5

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## Table of Contents

1 Introduction

................................ ................................ ................................ .................... 5 2 Methods

................................ ................................ ................................ .......................... 5 2.1 Emissions and source data

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

................................ .......... 6 2.3 Estimating chronic human inhalation exposure

................................ ......................... 9 2.4 Acute Risk Screening and Refined Assessments

................................ ..................... 1 0 2.5 Multipathway human health and environmental risk analysis

................................ .. 11 2.6 Dose

- response assessment

................................ ................................ ...................... 14 2.6.1 Sources of chronic dose

- response information

................................ ................. 14 2.6.2 Sources of acute dose

- response information

................................ .................... 25 2.7 Risk characterization

................................ ................................ .............................. 30 2.7.1 General

................................ ................................ ................................ ............ 30 2.7.2 Mixtures

................................ ................................ ................................ .......... 31 2.7.3 MACT

- All owable Emissions and Risks

................................ .......................... 32 3 Risk results for the Aerospace Manufacturing and Rework facilities

................................ 32 3.1 Source Category Emissions

................................ ................................ .................... 32 3.2 Risk characterization

................................ ................................ .............................. 38 3. 2.1 Baseline Actual Emission Risks

................................ ................................ ........... 38 MACT

- level Inhalation Assessment Results (Actual)

................................ ................ 39 MACT

- level Multipathway Assessment Results (Actual)

................................ .......... 42 MACT

- level Environmental Assessment Results (Actual)

................................ ........ 43 Facility

- wide Inhalation Assessment Results (Actual)

................................ ............... 43 3.2.2 Baseline Allowable Emission Risks

................................ ................................ ..... 44 MACT

- level Inhalation Assessment Results (Allowable)

................................ .......... 44 4 General discussion of uncertainties and how they have been addressed

......................... 45 4.1 Exposure modeling uncertainties

................................ ................................ ............ 45 4.2 Uncertainties in the dose

- response relationships

................................ ..................... 45 5 References

................................ ................................ ................................ .................... 55

Index of Tables

Table 2

- 1 AERMOD version 14134 model options for RTR modeling

................................ .. 7 Table 2.6

- 1 Dose

- Response Values for Chronic Inhalation Exposure to Carcinogens

........... 18 Table 2.6

- 2 (A) Dose

- Response Values for Chronic Inhalation Exposure to Noncarcinogens

................................ ................................ ................................ ................................ ............. 21 Table 2.6

- 2 (B) Dose

- Response Values for Chronic Oral Exposure to Noncarcinogens

........ 25 Table 2.6

- 3 Dose

- Response Values for Acute Exposures

................................ ..................... 27 Table 3.1

- 1 Baseline Actual Emissions from Aerospace and Availability of Dose

- Response Values

................................ ................................ ................................ ................................ .. 33 Table 3.2

- 1. Risk Summary for the Aerospace Manufacturing

................................ ......... 38 Table 3.2

- 2. MACT Level Actual Emissions Chronic Inhalation Risks for

........................... 39 Aerospace Manufacturing and Rework Source Category

................................ ...................... 39 Table 3.2

- 3. MACT Level Actual Emissions Acute Inhalation Risks for

............................... 41 Aerospace Manufacturing and Rework Source Category

................................ ...................... 41

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Table 3.2

- 4. MACT Level Actual Emissions

– Summary of Multipathway Screening for 42 Aerospace Manufacturing and Rework Source Category

................................ ...................... 42 Table 3.2

- 5 MACT Level Actua l Emissions

- Summary of Environmental Risk Screen Results for the Aerospace Manufacturing and Rework Facilities Source Category

................ 43 Table 3.2

- 6 Source Category Contribution to Facility

- Wide

................................ ................. 44 Chronic Cancer Risks (Actual Emissions)

................................ ................................ ............. 44

Appendices Appendix 1 Aerospace Manufacturing and Rework Facilities RTR Modeling File

Preparation Appendix 2 Technical Support Document for HEM 3 Modeling Appendix 3 Meteorological Data for HEM 3 Modeling Appendix 4 Dispersion Model Receptor Revisions and Additions for the Aerospace Source

Category Appendix 5 Technical Support Document for TRIM

- Based Multipathway Tiered Screening

Methodology for RTR Appendix 6 Environmental Risk Screen Appendix 7 Detailed Risk Modeling Results Appendix 8 Acute Impacts Refined Analysis

Index of Acronyms

AERMOD American Meteorological Society/EPA Regu latory Model AEGL Acute exposure guideline level ASTDR US Agency for Toxic Substances and Disease Registry CalEPA California Environmental Agency CTE Central Tendency Estimate ERPG Emergency Response Planning Guideline HAP Hazardous Air Pollutant HEM Human Exposure Model HI Hazard index HQ Hazard quotient IRIS Integrated Risk Information System MACT Maximum Achievable Control Technology MIR Maximum Individual Risk MOA Mode of action NAC National Advisory Committee NAAQS National Ambient Air Qual ity Standards NATA National Air Toxics Assessment NEI National Emissions Inventory NPRM Notice of Proposed Rulemaking PB

- HAP Persistent and Bioaccumulative

- HAP

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POM Polycyclic organic matter REL Reference exposure level RfC Reference concentration R fD Reference dose RME Reasonable Maximum Exposure RTR Risk and Technology TOSHI Target

- organ

- specific hazard index URE Unit risk estimate

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## 1 Introduction

Section 112 of the Clean Air Act (CAA) establishes a two

- stage regulatory process for addressing e missions of hazardous air pollutants (HAP) from stationary sources. In the first stage, section 112(d) requires the Environmental Protection Agency (EPA, or the Agency) to develop technology

- based standards for categories of sources (e.g., petroleum refin eries, pulp and paper mills, etc.) [ 1 ]. EPA has largely completed the initial Maximum Achievable Control Technology (MACT) standards as required under this provision. Under section 112(d)(6), EPA must review each of these technology

- based standards at le ast every eight years and revise a standard, as necessary, “taking into account developments in practices, processes and control technologies.” In the second stage, EPA is required under section 112(f)(2) to assess the health and environmental risks that remain after implementation of the MACT standards. If additional risk reductions are necessary to protect public health with an ample margin of safety or to prevent an adverse environmental effect, EPA must develop standards to address these remaining ris ks. This second stage of the regulatory process is known as the residual risk stage. For each source category for which EPA issued MACT standards, the residual risk stage must be completed within eight years of promulgation of the initial technology

- base d standard.

In December of 2006 we consulted with a panel from the EPA's Science Advisory Board (SAB) on the “Risk and Technology Review (RTR) Assessment Plan,” and in June of 2007, we received a letter with the results of that consultation. Subsequent to the consultation, in June of 2009, a meeting was held with an SAB panel for a formal peer review of the “Risk and Technology Review (RTR) Assessment Methodologies” [ 2 ]. We received the final SAB report on this review in May of 2010 [ 3 ]. Where appropriate, we have responded to the key messages from this review in developing our current risk assessments and we will be continuing our efforts to improve our assessments by incorporating updates based on the SAB recommendations as they are developed and become available. Our responses to the key recommendations of the SAB are outlined in a memo entitled, “EPA’s Actions in Response to Key Recommendations from the SAB Review of RTR risk Assessment Methodologies” [ 4 ].

This document contains the methods and the results of baseline risk assessments (i.e., after the implementation of the MACT standard) performed for the Aerospace Manufacturing and Rework Facilities source category. The methods discussion includes descriptions of the methods used to develo p refined estimates of chronic inhalation exposures and human health risks for cancer and noncancer endpoints, as well as descriptions of the methods used to screen for acute health risks, chronic non

- inhalation health risks, and adverse environmental effe cts.



## 2 Methods

2.1 Emissions and source data The Aerospace Manufacturing and Rework facilities source category includes all facilities that manufacture aerospace vehicles and/or vehicle components and all facilities that rework or repair these items. An aerospac e vehicle or component is any fabricated, processed, or

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assembled set of parts or complete unit of any aircraft including, but not limited to, airplanes, helicopters, missiles, rockets, and space vehicles.

Organic and inorganic HAP emissions in aerospac e facilities originate from cleaning, primer application, topcoat application, paint stripping, and chemical milling maskant application among other secondary emission points. Organic HAP emissions from primer, topcoat, and chemical milling maskant applica tion operations are the result of the evaporation of solvent contained in the coatings. These applications can take place in large open areas, such as hangars, or in partially or fully enclosed spaces, such as within spray booths. Primer and topcoat applic ations can also produce by

- product inorganic HAP emissions. Cleaning and paint stripping operations can result in the release of organic HAP emissions due to the evaporation of the volatile portion of the cleaning solvents and chemical paint strippers,

res pectively. Cleaning emissions are nearly always fugitive in nature and occur at essentially every processing step. A complete description of the aerospace manufacturing and rework facilities source category can be found in the text of the Notice of Propose d Rulemaking ( NPRM )

. Based on 2011 survey data from an information collection request (ICR) as well as emission data updated in 2013 and 2014, we currently estimate that there are 144 Aerospace Manufacturing and Rework facilities operating in the U.S. and 87 of these facilities are identified as major Based on 2011 survey data from an information collection request (ICR) as well as emission data updated in 2013 and 2014, we currently estimate that there are 144 Aerospace Manufacturing and Rework facilit ies operating in the U.S. and 87 of these facilities are identified as major sources.

Details on the development of the emissions and source data for this source categor y can be found in Appendix 1. Section 3 below provides a summary of the emissions.



## 2.2 Dispersion modeling for inhalation exposure assessment Both long

- and short

- term inhalation exposure concentrations and associated health risk s from each facility in the source category were estimated using the Human Exposure Model in combination wi th the American Meteorological Society/EPA Regulatory Model dispersion modeling system (HEM

- AERMOD, or HEM 3 ). The approach used in applying this modeling system is outlined below, and further details are provided in Appendix 2 to this document ( Technical Support Document for HEM3 Modeling )

. The HEM 3 performs three main operations: atmospheric dispersion modeling, estimation of individual human exposures and health risks, and estimation of population risks. This section focuses on the dispersion modeling component. The exposure and risk characterization components are discussed in other subsections of Sections 2 and 3.

The dispersion model in the HEM 3 system, AERMOD version 1 4134 (the latest version of AERMOD that was available at the time of the risk an alysis) , is a state

- of

- the

- science Gaussian plume dispersion model that is preferred by EPA for modeling point, area, and volume sources of continuous air emissions from facility applications [ 5 ]. Further details on AERMOD can be found in the AERMOD Users Guide [ 6 ]. The model is used to develop

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annual average ambient concentrations through the simulation of hour

- by

- hour dispersion from the emission sources into the surrounding atmosphere. Hourly emission rates used for this simulation are generated by ev enly dividing the total annual emission rate from the inventory into the 8,760 hours of the year.

The first step in the application of the HEM 3 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

- 1 AERMOD version 1 413 4 mo del options for RTR modeling

Modeling Option Selected Parameter for chronic exposure
Type of calculations Hourly Ambient Concentration
Source type Poin t, area represented as pseudo point source
Receptor orientation Polar (13 rings and 16 radials) Discrete (census block centroids) and u ser

- supplied receptors
Terrain characterization Actual from USGS 1

- degree DEM data
Building downwash Not Included
Plume deposition/depletion Not Included
Urban source option No
Meteorology 1 year repres entative NWS from nearest site ( 824 stations)



### In HEM3, meteorological data are ordinarily selected from a list of over 800 National Weather Service (NWS) surface observation stations across the continental United States, Alaska, Hawaii, and Puerto Rico. In most cases the nearest station is selected as representative of the conditions at the subject facility. W hen considering off

- site meteorological data most site specific dispersion
, EPA modeling guidelines suggest that modeling efforts employ up to fi ve years of data to capture variability in weather patterns from year to year. However, because dispersion model runtimes using five years of meteorological data would be too long for RTR source categories with many sources, w e modeled only a single year, 2011. While the selection of a single year may result in under

prediction of long

- term ambient levels at some locations, likewise it may result in over

prediction at others. For each facility identified by its characteristic latitude and longitude coord inates, the closest meteorological station was used in the dispersion modeling. The average distance between a modeled facility and the applicab le meteorological station was 18 miles. Appendix 3 to this document ( Meteorological Data Processing Using AERM ET for HEM3 ) provides a complete listing of stations and assumptions along with further details used in pro cessing the data through AERMET, the AERMOD meteorological data processing program

. The document “ Risk and Technology Review (RTR) Risk Assessment Methodologies: For Review by the EPA’s Science Advisory Board with Case Studies

– MACT I Petroleum Refining Sources and Portland Cement Manufacturing ” (US EPA, 2009) provides more details on these modeling approaches.

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Beginning with version 12345, AERMET allows for a minimum wind speed of 0.5 m/s to be utilized when processing monitoring stations that are equipped with sonic anemometers and for an adjustment to the surface friction velocity (u*). The public version of AERMET available at the time we conducted the AERMET processing did not include the surface friction velocity adjustment. Also at the time, an updated version of AERMET (and AERMOD) was under development that incorporated further surface friction velocity adjustments to AERMET version 12345. In anticipation of the update, EPA processed meteorological data using a pre

- public release version of AERMET and incorporated the updated surface friction velocity adjustment in the output. It was EPA’s judgement th at the pre

- public release version of AERMET would generate AERMOD ready meteorological data very close to that generated with the final public release version.

EPA has posted the AERMET meteorological data used in the analysis on the EPA’s FERA (Fate, Exposure, and Risk Analysis) website under the HEM model page. AERMET and AERMOD (version 13350) were released to the public in late December 2013 on the EPA’s Support Center for Regulatory Atmospheric Modeling (SCRAM) website.

The HEM 3 system estimate s ambient concentrations at the geographic centroids of census blocks (using the 20 1 0 Census) and at other receptor locations that can be specified by the

user. See Appendix 4 of this document ( Dispersion Model Receptor Revisions and Addition s for the Petroleum Refining Sector) for a discussion of user receptors and centroid location changes specific to this source category. The model accounts for the effects of multiple facilities when estimating concentration impacts at each block centroid. When eva luating the risks associated with a particular source category we combined the impacts of all facilities within the same source category, and assessed chronic exposure and risk for all census blocks with at least one resident (i.e., locations where people may reasonably be assumed to reside rather than receptor points at the fenceline of a facility). Chronic ambient concentrations were calculated as the annual average of all estimated short

- term (one

- hour) concentrations at each block centroid. Possible f uture residential use of currently uninhabited areas was not considered. Census blocks, the finest resolution available in the census data, are typically comprised of approximately 40 people or about ten households.

In contrast to the development of a mbient concentrations for evaluating long

- term exposures, which was performed only for occupied census blocks, worst

- case short

- term (one

- hour) concentrations were estimated both at the census block centroids and at points nearer the facility that represen t 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).

S hort

- te rm emission rates were needed to screen for the potential for hazard via acute exposures. The average hourly emissions rate used for chronic risk modeling is defined as the total emissions for a year divided by the total number of operating hours in the y ear. If a source operated 8
, 760 hours per year this emissions rate would be appropriate for predicting potential acute impacts. However, many sources in this source sector emit on a more

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intermittent basis and thus specific hourly emissions may be higher than those utilized for annual or chronic risks modeling. While hourly emissions data for each source are not available, in this assessment, we utilized source category specific engineering judgment to determine what the peak hourly emissions for any give n process could be. For the aerospace category this factor was estimated to be 1.2 times the annual emissions level. Further information on this factor is provided in the emissions memo presented in Appendix 1 of this report.

Census block elevations fo r HEM 3 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 short

- and long

- term ambien t concentrations were assumed to be equal to the highest elevation of any census block falling within the polar grid sector corresponding to the grid point. If a sector does not contain any blocks, the model defaults the elevation to that of the nearest b lock. If an elevation is not provided for the emission source, the model uses the average elevation of all sectors within the innermost model ring.

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

.



## 2.3 Estimating chronic human inhalation exposure
We used the estimated annual average ambient air concentration of each HAP
, at each census block centroid
, as a surrogate for the lifetime inhalation exposure concentration of all the people who reside in the census block. That is, the risk analysis did not consider either the short

- term or long

- term behavior (mobility) of the exposed populations and its potential influence on their exposure.

We did not address short

- term human activity , including indoor air concentrations, for two reasons. First, our experience with the NA TA assessments (which modeled daily activity using EPA’s HAPEM model) suggests that, given our current understanding of microenvironment concentrations and daily activities, modeling short

- term activity would, on average, reduce risk estimates about 25 per cent for particulate HAP; it will also reduce risk estimates for gaseous HAP, but typically by much less. Second, basing exposure estimates on average ambient concentrations at census block centroids may underestimate or overestimate actual exposure conce ntrations at some residences. We did not address long

- term migration nor population growth or decrease over 70 years, instead basing the assessment on the assumption that each person’s predicted exposure is constant over the course of their lifetime whic h is assumed to be 70 years. In assessing cancer risk, we generally estimated three metrics; the maximum individual risk (MIR), which is defined as the risk associated with a lifetime of exposure at the highest concentration where people are likely to liv e ; the population risk distribution; and the cancer incidence. The assumption of not considering short or long

- term population mobility does not bias the estimate of the theoretical MIR (assumes a person stays in one location for 70 years) nor does

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it aff ect the estimate of cancer incidence since the total population number remains the same. It does, however, affect the shape of the distribution of individual risks across the affected population, shifting it toward higher estimated individual risks at the upper end and reducing the number of people estimated to be at lower risks, thereby increasing the estimated number of people at specific risk levels.

When screening for potentially significant acute exposures, we used an estimate of the highest hour ly ambient concentration at any off

- site location as the surrogate for the maximum potential acute exposure concentration for any individual. This was generally the facility f enceline.



## 2.4 Acute Risk Screening and Refined Assessments In establishing a scient ifically defensible approach for the assessment of potential health risks due to acute exposures to HAP, we f ollowed the same general approach that has been used for developing chronic health risk assessments under the residual risk program. That is, we d eveloped a tiered, iterative approach. This approach to risk assessment was endorsed by the National Academy of Sciences in its 1993 publication “Science and Judgment in Risk Assessment” and subsequently was adopted in the EPA’s “Residual Risk Report to C ongress” in 1999.

The assessment methodology is designed to eliminate from further consideration those facilities for which we have confidence that no acute adverse health effects of concern will occur. To do so, we use what is called a tiered, iterative approach to the asse ssment. This means that we bega n with a screening assessment, which relies on readily available data and uses conservative assumptions that in combination approximate a worst

- case exposure . The result of this screening proce ss is that either the facility being assessed poses no potential acute health risks (i.e., it “screens out”), or that it requires further , more refined assessment. A more refined acute assessment could u s e industry

- or site

- specific data on the temporal p attern of emissions, the layout of emission points at the facility, the boundaries of the facility, and /or the local meteorology. In some cases, all of these site

- specific data are used to refine the assessment; in others, lesser amount s of site

- specific data can be used to determine that acute exposures are not a concern, and significant additional data collection is not necessary.
Acute health risk screening was performed for each facility as the first step. We used conservative ass umptions for emission rates, meteorology, and exposure location. We used the following worst

- case assumptions in our screening approach:

 Peak 1

- hour emissions were obtained from the ICR and based on the operating characteristics and engineering judgement of aerospace facility emission as described in Appendix 1

.
 We assumed that the peak emissions occur at all emission points at the same time.

 For facilities with multiple emission points, 1

- hour concentrations at each receptor were assumed to be the sum o f the maximum concentrations due to each emission

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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 modeled impact during this same worst

- case 1

- hour period, but no near er to the source than 100
meters.

 The maximum impact was compared to multiple short

- term health benchmarks for the HAP being assessed to determine if a possible acute health risk might exist. These benchmarks are described in section 2.6 of this report.

As mentioned above, when we identify acute impacts which exceed their relevant benchmarks, we pursue refining our acute screening estimates to the extent possible

. In some cases, this may entail determining the actual physical layout and boundaries of a facility to more accurately gauge where people might reasonably be exposed for an hour.

For the aerospace source category, t he refinement included a review of the layout of the emission points at the facilities with the facility boundaries to determine the maximum off

- site acute impact for the facilities that did not screen out during the initial run. Refer to Appendices 5 and 6 for the screening and refined acute results, respectively.



## 2.5 Multipathway human health and environmental risk analysis

The EPA conducted a screening analysis examining the potential for significant human health risks due to exposures via routes other than inhalation (i.e., ingestion). We first determined whether any sources in the source categories emitted any hazardous ai r pollutants known to be persistent and bio

- accumulative in the environment (PB

- HAP) 1 

### . The PB

- HAP compounds or compound classes are identified for the screening from the EPA’s Air Toxics Risk Assessment Library [ 7

]. With respect to PB

- HAP emissions othe r than lead, emissions were evaluated for potential non

- inhalation risks impacts using a Tiered screening approach. We first determined whether the facility

- specific emission rates of each of the emitted PB

– HAP were large enough to create the potential fo r significant non

- inhalation human health risks under reasonable worst

- case conditions. To facilitate this step, we developed emissions rate thresholds for each PB

– HAP using a hypothetical upper

- end screening exposure scenario developed for use in conjunct ion with the EPA’s Total Risk Integrated Methodology. Fate, Transport, and Ecological Exposure (TRIM.FaTE[ 8 ]) model. We conducted a sensitivity analysis on the screening scenario to ensure that its key design parameters would represent the upper end of the range of possible values, such that it would represent a conservative but not impossible scenario. The facility

- specific emissions rates of each of the PB

– HAP were compared to the emission rate threshold values for each of the PB

– HAP identified to assess

1 Although the two

- letter chemical symbol for lead is Pb, in this assessment PB

- HAP refers to the many air pollutants known to be persistent and bioaccumulative in the environment. In instances where the report is specifically referring to lead, it is spelled out (i.e., the two

- letter chemical symbol for lead i s not used in this document).

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the potential for significant human health risks via non

- inhalation pathways. We call this application of the TRIM.FaTE model the Tier 1 TRIM

- Screen. For the purpose of developing emissions rates for our Tier 1 TRIM

- Screen, we derived emission levels for each PB

- HAP (other than lead) at which the maximum excess lifetime cancer risk would be 1

- in

- 1 million or, for HAP that cause non

- cancer health effects, the maximum HQ would be 1. If the emissions rate of any PB

- HAP exceeds the Tier 1 screening emissions r ate for any facility, we conduct a Tier 2 multi

- pathway screen. In the Tier 2 screen, the location of each facility that exceeded the Tier 1 emission rate is used to refine the assumptions associated with the environmental scenario while maintaining the ex posure scenario assumptions. We then adjust the risk

- based Tier 1 screening level for each PB

- HAP for each facility based on an understanding of how exposure concentrations estimated for the screening scenario change with meteorology and environmental assu mptions. PB

- HAP emissions that do not exceed these new Tier 2 screening levels are considered to pose no unacceptable risks. When facilities exceed the Tier 2 screening levels, it does not mean that multi

- pathway impacts are significant, only that we cann ot rule out that possibility based on the results of the screen. These facilities , if needed, may be further evaluated for multi

pathway risks using the TRIM.FaTE model and more site s pecific information. Appendix 5 of this document (Technical Support Doc ument for TRIM

- Based Multipathway Tiered Screening Methodology for RTR) contains a complete discussion of the multipathway Tier 1 and 2 screening approach.

In evaluating the potential multipathway risks from emissions of lead compounds, rather than deve loping a screening emission rate for them , we compared maximum estimated chronic atmospheric concentrations with the current National Ambient Air Quality Standard (NAAQS) for lead. Values below the NAAQS were considered to have a low potential for multipa thway risks of any significance.
The NAAQS value, a public health policy judgment, incorporated the Agency’s most recent health evaluation of air effects of lead exposure for the purposes of setting a national ambient air quality standard. In setting this value, the Administrator promulg ated a standard that was requisite to protect public health with an adequate margin of safety. We consider values below the level of the primary NAAQS to protect against multipathway risks because as mentioned above, the primary NAAQS is set as to protect public health with an adequate margin of safety. However, ambient air lead concentrations above the NAAQS are considered to pose the potential for increased risk to public health. We consider this NAAQS assessment to be a refined analysis given: 1) the numerous health studies, detailed risk and exposure analyses, and level of external peer and public review that went into the development of the primary NAAQS for lead, combined with: 2) the site

- specific dispersion modeling used in this assessment to esti mated ambient lead concentrations due to the source category emissions. It should be noted, however, that this comparison does not account for possible population exposures to lead from sources other than the one being modeled; for example, via consumption of water from untreated local sources or ingestion of local ly grown food. Nevertheless, the Administrator judged that such a standard would protect, with an adequate margin of safety, the health of children and other at

- risk populations against an array of adverse health effects, most notably including neurologic al effects, particularly neurobehavioral and neurocognitive effects, in children (73 FR 67007). The Administrator, in

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setting the standard, also recognized that no evidence

- or risk based bright line indicated a single appropriate level. Instead a collect ion of scientific evidence and other information was used to select the standard from a range of reasonable values (73 FR 67006).

We further note that comparing ambient lead concentrations to the NAAQS for lead, considering the level, averaging time, form and indicator, also informs whether there is the potential for adverse environmental effects. This is because the secondary lead NAAQS, set to protect against adverse welfare effects (including adverse environmental effects), has the same averaging time, form, and level as the primary standard. Thus, ambient lead concentrations above the NAAQS for lead also indicate the potential for adverse environmental effects.

Adverse environmental effects The EPA has developed a screening approach to examine the po tential for adverse environmental effects as required under section 112(f)(2)(A) of the CAA. The environmental screen focuses on the following seven environmental HAP:

 Five persistent bioaccumulative HAP (PB

- HAP)

– cadmium, dioxins/furans, polycyclic org anic matter (POM), mercury (both inorganic mercury and methyl mercury) and lead.

 Two acid gases

- hydrogen chloride (HCl), and hydrogen fluoride (HF).

HAP that persist and bioaccumulate are of particular environmental concern because they accumulate in t he soil, sediment and water. The acid gases, HCl and HF, were included due to their well

- documented potential to cause direct damage to terrestrial plants.

For the environmental risk screening analysis, EPA first determined whether any facilities in the s ource category emitted any of the seven environmental HAP. If one or more of the seven environmental HAP evaluated are emitted by at least one facility in the source category we proceed to the second step of the evaluation.

For cadmium, mercury, POM and dioxins/furans, the environmental screening analysis consists of two tiers. In the first tier, TRIM.FaTE modeling was conducted under worst

- cast environmental conditions to determine whether the facility

- specific off

- site emission rates of each of the emit ted environmental HAP exceeded ecological benchmarks represented as emissions threshold values. If off

- site emissions from a facility do not exceed the Tier 1 thresholds the facility “passes” the screen, and therefore, is not evaluated further under the

s creening approach. If off

- site emissions from a facility exceed the Tier 1 thresholds, we evaluate the facility further in Tier II

.

In Tier 2 of the environmental screening analysis, the screening emission thresholds are adjusted to account for local mete orology and the actual location of lakes in the vicinity of facilities that did not pass the Tier 1 screen. If off

- site emissions from a facility do not exceed the Tier 2 thresholds, the facility passes the screen, and is typically not evaluated further. I f off

- site emissions from a facility exceed the Tier 2 thresholds, the facility does not pass the screen and, therefore, may have the potential to cause adverse environmental effects. Such

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facilities are evaluated further to investigate factors such as the magnitude and characteristics of the area of exceedance.

For acid gases, the environmental screening analysis evaluates the p otential phytotoxicity and reduced productivity of plants due to chronic exposure to acid gases. The environmental risk

scre ening methodology for acid gases is a single

- tier screen that compares the average off

- site ambient air concentration over the modeling domain to ecological benchmarks for each of the acid gases. For purposes of ecological risk screening, EPA identifies a potential for adverse environmental effects to plant communities from exposure to acid gases when the average concentration of the HAP around a facility exceeds the LOAEL ecological benchmark. In such cases, we further investigate factors such as the magn itude and characteristics of the area of exceedance (e.g., land use of exceedance area, size of exceedance area) to determine if there is an adverse environmental effect.

For lead compounds, we currently do not have the ability to calculate media concen trations using the TRIM.FaTE model. However, air concentrations of lead are already calculated as part of the human health exposure and risk analysis using the HEM

- 3 AERMOD air dispersion model. To evaluate the potential for adverse environmental effects from lead, we compare the average modeled air emission concentrations of lead from each facility in the source category emissions to the level of the secondary National Ambient Air Quality Standard (NAAQS) for lead. The secondary lead NAAQS is a reasonable means of evaluating environmental risk because it is set to provide substantial protection against adverse welfare effects which can include “effects on soils, water, crops, vegetation, man

- made materials, animals, wildlife, weather, visibility and climat e, damage to and deterioration of property, and hazards to transportation, as well as effects on economic values and on personal comfort and well

- being.” 2



### Ecological benchmarks included in the e nvironmental r isk s creen are presented in Appendix 6

. 

## 2.6 Dose

- r esponse a ssessment

2.6.1 Sources of chronic dose

- response information
Dose

- response assessment (carcinogenic and non

- carcinogenic) for chronic exposure (either by inhalation or ingestion) for the HAP reported in the emissions inventory for the aerospace

source category were based on the EPA Office of Air Quality Planning and Standards’ (OAQPS) existing recommendations for HAP [ 9 ], which were also used for NATA [ 10 ]. This information has been obtained from various sources and prioritized according to (1) concept ual consistency with EPA risk assessment guidelines and (2) level of peer review

2 A secondary standard, as defined in Section 109(b)(2), must “specify a level of air quality the attainment and maintenance of which, in the judgment of the Administrator, based on criteria, is requisite to protect the publi c welfare from any known or anticipated adverse effects associated with the presence of [the] pollutant in the ambient air.” Welfare effects as defined in section 302(h) (42 U.S.C. 7602(h)) include, but are not limited to, “effects on soils, water, crops, vegetation, man

- made materials, animals, wildlife, weather, visibility and climate, damage to and deterioration of property, and hazards to transportation, as well as effects on economic values and on personal comfort and well

- being.”

### Risk Assessment for the Aerospace Manufacturing and Rework Facilities Source Category 

### 15

received. The prioritization process was aimed at incorporating into our assessments the best available science with respect to dose

- response information. The recommendation s are based on the following sources, in order of priority :
1) US Environmental Protection Agency (EPA). EPA has developed dose

- response assessments for chronic exposure for many of the pollutants in this study. These assessments typically provide a quali tative statement regarding the strength of scientific data and specify a reference concentration (RfC, for inhalation) or reference dose (RfD, for ingestion) to protect against effects other than cancer and/or a unit risk estimate (URE, for inhalation) or slope factor (SF, for ingestion) to estimate the probability of developing

cancer. The RfC is defined as an “ estimate (with uncertainty spanning perhaps an order of magnitude) of a continuous inhalation exposure to the human population (including sensitiv e subgroups) that is likely to be without an appreciable risk of deleterious effects during a lifetime. ” The RfD is “an estimate (with uncertainty spanning perhaps an order of magnitude) of a daily oral exposure to the human population (including sensitiv e subgroups) that is likely to be without an appreciable risk of deleterious effects during a lifetime.” The URE is defined as “ the upper

- bound excess cancer risk estimated to result from continuous lifetime exposure to an agent at a concentration of 1 μg /m 3 

### in air. ” The SF is “a n upper bound, approximating a 95 percent confidence limit, on the increased cancer risk from a lifetime exposure to an agent. This estimate, [is] usually expressed in units of proportion (of a population) affected per mg/kg

- day ...” EPA disseminates dose

response assessment information in several forms, based on the level of review. The Integrated Risk Information System (IRIS) [ 11 ] is an EPA database that contains scientific health assessment information, including dose

- response in formation. All IRIS assessments since 1996 have also undergone independent external peer review. The current IRIS process includes review by EPA scientists, interagency reviewers from other federal agencies, and the public, and peer review by independent scientists external to EPA. New IRIS values are developed and old IRIS values are updated as new health effects data become available. Refer to the “IRIS Track” website 3 

### for detailed information on status and scheduling of current individual IRIS assessm ents and updates. EPA’s science policy approach , under the current carcinogen guidelines, is to use linear low

- dose extrapolation as a default option for carcinogens for which the mode of action (MOA) has not been identified. We expect future EPA dose

- response assessments to identify nonlinear MOAs where appropriate, and we will use those analyses (once they are peer reviewed) in our risk assessments. At this time, however, there are no available carcinogen dose

- response assessments for inhalation exposure that are based on a nonlinear MOA

. 2) US Agency for Toxic Substances and Disease Registry (ATSDR). ATSDR, which is part of the US Department of Health and Human Services, develops and publishes Minimum Risk Levels (MRLs) [ 12 ] for inhalation and ora l exposure to many toxic substances. As stated on the ATSDR web site: “ Following discussions with scientists within the Department of Health and Human Services (HHS) and the EPA, ATSDR chose to adopt a practice similar to that of the EPA's Reference Dose (RfD) and Reference Concentration (RfC) for deriving substance specific health guidance levels for non

- neoplastic endpoints .” The MRL is defined as “an estimate of daily human exposure to a substance

3 http://cfpub.epa.gov/ncea/iristrac/

### Risk Assessment for the Aerospace Manufacturing and Rework Facilities Source Category 

### 16

that is likely to be without an appreciable risk of adv erse effects (other than cancer) over a specified duration of exposure.” ATSDR describes MRLs as substance

- specific estimates to be used by health assessors to select environmental contaminants for further evaluation. Exposures above an MRL do not necess arily represent a threat, and MRLs are therefore not intended for use as predictors of adverse health effects or for setting cleanup levels.

3) California Environmental Protection Agency (CalEPA). The CalEPA Office of Environmental Health Hazard Assessment has developed dose

- response assessments for many substances, based both on carcinogenicity and health effects other than cancer. The process for developing these assessments is similar to that used by EPA to develop IRIS values and incorporates significan t external scientific peer review. As cited in the CalEPA Technical Support Document for developing their chronic assessments 4 

### : “The guidelines for developing chronic inhalation exposure levels incorporate many recommendations of the U.S. EPA [ 13 ] and NAS [ 14 ].” The non

- cancer information includes available inhalation health risk guidance values expressed as chronic inhalation reference exposure levels (RELs) [ 15 ]. CalEPA defines the REL as “the concentration level at or below which no health effects are an ticipated in the general human population.” CalEPA's quantitative dose

- response information on carcinogenicity by inhalation exposure is expressed in terms of the URE [ 16 ], defined similarly to EPA's URE.

In developing chronic risk estimates, we adjust ed dose

- response values for some HAP based on professional judgment, as follows:

1) In the case of manganese (Mn), c onsistent with Agen cy policy supported by the SAB (as mentioned above)
, the EPA considers the ATSDR MRL for manganese the most appropriate va lue to be use d in RTR assessments

. There is an existing IRIS RfC for manganese (Mn) published in 1993. 5 

### Also, ATSDR published an assessment of Mn toxicity (2012) which includes a chronic inhalation reference value (i.e., an ATSDR Minimal Risk Level or MRL). 6 

### Both the 1993 IRIS RfC and the 2012 ATSDR MRL were based on the same stu dy (Roels et al., 1993), however, A TSDR used updated dose

response modeling methodology (benchmark dose approach) and considered recent pharmacokinetic findings to support thei r MRL derivation. Because of the updated methods, EPA has determined that the ATSDR MRL is the appropriate health value to use in RTR risk assessments.

2) In the case of nickel compounds, to provide a conservative estimate of the potential cancer risks, the EPA considers the IRIS URE value for nickel subsulfide (which is considered the most potent carcinogen among all nickel compounds) to be the most appropriate value to be used in RTR assessments. Based on consistent views of major scientific bodies (i.e.,

4 Air Toxics Hot Spots Program, Risk Assessment Guidelines, Part III

- Technical Support Document for the Determination of Non

- cancer Chronic Reference Exposure Lev els. Air Toxicology and Epidemiology Section, Office of Environmental Health Hazard Assessment, California Environmental Protection Agency. February 2000 ( http://www.oehha.ca.gov/a ir/chronic_rels/pdf/relsP32k.pdf )
5 USEPA Integrated Risk Information System Review of Manganese (1993) available at http://www.epa.gov/iris/subst/0373.htm
6 Agency for Toxic Substances & Disease Reg istry Toxicological Profile for Manganese (2012) available at http://www.atsdr.cdc.gov/toxprofiles/tp.asp?id=102&tid=23

### Risk Assessment for the Aerospace Manufacturing and Rework Facilities Source Category 

### 17

N ational Toxicology Program (NTP) in their 12 th 

### Report of the Carcinogens (RoC), 7 

### International Agency for Research on Cancer (IARC), 8 

### and other international agencies 9 

### that consider all nickel compounds to be carcinogenic, we currently consider all nickel compounds to have the potential of being carcinogenic to humans. The 12th RoC states that the “combined results of epidemiological studies, mechanistic studies, and carcinogenic studies in rodents support the concept that nickel compounds generate nickel i ons in target cells at sites critical for carcinogenesis, thus allowing consideration and evaluation of these compounds as a single group.” Although the precise nickel compound (or compounds) responsible for carcinogenic effects in humans is not always cle ar, studies indicate that nickel sulfate and the combinations of nickel sulfides and oxides encountered in industrial emissions of nickel mixtures cause cancer in humans (these studies are summarized in a review by Grimsrud et al., 2010). 10 

### The major scient ific bodies mentioned above have also recognized that there may be differences in the toxicity and/or carcinogenic potential across the different nickel compounds. For this reason, and given that there are two additional URE values 11 

### derived for exposure to mixtures of nickel compounds ( as a group ) that are 2

- 3 fold lower than the IRIS URE for nickel subsulfide
, the EPA conside rs it reasonable , in some instances (e.g., when high quality data is available on the composition of nickel emissions from a specific source category), to use a value that is 50 percent of the IRIS URE for nickel subsulfide for providing an estimate of the lower end of the plausible range of cancer potency values for different mixtures of nickel compounds.

For this specific assessment, based on the lack of information on the composition of nickel mixtures emitted by the Aerospace Manufacturing and Rework source category and to provide a conservative estimate of potential cancer risks, the EPA considers the IRIS URE for nickel (which is considered the most potent carcinogen among all nickel compounds) to be the most appropriate value to be used to estimate the risks associated with nickel emissions from this source category.

3) In the case of emissions of unspecified mixtures of HAP categor ies such as metal compounds and POM, we apply category

- specific chemical speciation profiles appropriate to the source category . For POM emissions that are not speciated into individual compounds, we apply the same simplifying a ssumptions to assessments that are used in

7 National Toxicology Program (NTP), 2011. Report on Carcinogens (RoC). 12 th ed. Research Triangle Park, NC: US Department of Health and Human Services (DHHS), Public Health Service. Available online at http://ntp.niehs.nih.gov/ntp/roc/twelfth/roc12.pdf
8 International Agency for Research on Cancer (IARC), 1990. IARC monographs on the evaluation of carcinogenic risks to humans. Chromium, nickel, and welding. Vol. 49. Lyons, France : International Agency for Research on Cancer, World Health Organization Vol. 49:256.
9 World Health Organization (WHO, 1991) and the Europea n Union’s Scientific Committee o n Health and Environmental Risks (SCHER, 2006)

. 10 Grimsrud TK and Andersen A. Ev idence of carcinogenicity in humans of water

- soluble nickel salts. J Occup Med Toxicol 2010, 5:1

- 7. Available online at http://www.occup

- med.com/content/5/1/7
11 Two UREs (other than the current IRI S values) have been derived for nickel compounds as a group : one developed by the California Department of Health Services ( http://www.arb.ca.gov/toxics/id/summary/nickel_tech_b.pdf ) and the other by the Texas Commission on Environmental Quality ( http://www.epa.gov/ttn/atw/nata1999/99pdfs/healtheffectsinfo.pdf ).

### Risk Assessment for the Aerospace Manufacturing and Rework Facilities Source Category 

### 18

NATA. 12 

### The NATA approach partitions POM into eight different non

- overlapping “groups” that are modeled as separate pollutants. Each POM group includes POM species of similar carcinogenic potency, for which we ca n apply the same URE.

4) In the case of formaldehyde, the EPA determined in 2004 that the Chemical Industry Institute of Toxicology (CIIT) cancer dose

- response value (5.5 x 10

- 9 

### per μg/m 3 

### ) was based on better science than the IRIS cancer dose

- response value (1.3 x 10

- 5 

### per μg/m 3 

### ), and the EPA decided to switch from using the IRIS value to the CIIT value in risk assessments supporting regulatory actions. Subsequent research published by EPA suggested that the CIIT model was not appropriate and in 2010 EPA retu rned to using the 1991 IRIS value. The EPA is currently revising the formaldehyde IRIS assessment and in the interim, we will present findings using the 1991 IRIS value as a primary estimate. EPA may also consider other information as the science evolves.

The emissions inventory for the aerospace source category includes emissions of HAP with availa b le ch

r onic quantitative inhalation dose

- response values. Of these, 42 are classified as known, probably, or possible carcinogens, with quantitative cancer dose

- response values available. These 42 HAP, their quantitative inhalation chronic cancer dose

- response values, and the source of each value are listed in Table 2.6

- 1. One h undred and e leven HAP have quantitative inhalation chronic noncancer threshold va lues available; two of these one hundred and eleven HAP (cadmium and mercury), for which a screening multipathway assessment was performed, also have quantitative oral chronic noncancer threshold values available. These HAP, their threshold values, and th e source of the value are listed in Table 2.6

- 2(A) and Table 2.6

- 2(B).

Table 2.6

- 1 Dose

- Response Values for Chr onic Inhalation Exposure to C arcinogens



### URE (unit risk estimate for cancer) 13 

### = cancer risk per μg/m 3 

### of average lifetime exposure. Sources: IRIS = EPA Integrated Risk Information System, EPA ORD = EPA Office of Research & Development, EPA OAQPS = EPA Office of Air Quality Planning & Standards, CAL = California EPA Office of Environmental Health Hazard A ssessment.

Pollutant CAS Number 14 

### URE (1/μg/m 3 

### ) Source

1,3

- Butadiene 106990 0.00003 IRIS 1,4

- Dioxane 123911 0.00000 5 IRIS 2,4

- Toluene diisocyanate 584849 0.000011 CAL 2

- Nitropropane 79469 0.0000056 EPA OAQPS 3,3'

- Dichlorobenzidine 91941 0.00034 CAL 4,4'

- Methylene bis(2

- chloroaniline) 101144 0.00043 CAL

12 US EPA, 2006. Approach for modeling PO M. Technical support information for the 1999 National Air Toxics Assessment. http://www.epa.gov/ttn/atw/nata1999/99pdfs/pomapproachjan.pdf
13 The URE is the upper

- bound excess cancer risk estimated to result from continuous lifetime exposure to an agent at a concentration of 1 μg/m 3 in air. URE’s are considered upper bound estimates meaning they represent a plausible upper limit to the true value.
14 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.

### Risk Assessment for the Aerospace Manufacturing and Rework Facilities Source Category 

### 19

Table 2.6

- 1 Dose

- Response Values for Chr onic Inhalation Exposure to C arcinogens



### URE (unit risk estimate for cancer) 13 

### = cancer risk per μg/m 3 

### of average lifetime exposure. Sources: IRIS = EPA Integrated Risk Information System, EPA ORD = EPA Office of Research & Development, EPA OAQPS = EPA Office of Air Quality Planning & Standards, CAL = California EPA Office of Environmental Health Hazard A ssessment.

Pollutant CAS Number 14 

### URE (1/μg/m 3 

### ) Source

4,4'

- Methylenedianiline 101779 0.00046 CAL
p

- Dichlorobenzene 106467 0.000011 CAL Acetaldehyde 75070 0.0000022 IRIS Acrylamide 79061 0.00016 IRIS Acrylonitrile 107131 0.000068 IRIS Aniline 62533 0.0000016 

### CAL Arsenic compounds 7440382 0.0043 IRIS Benzene 15 

### 71432 0.0000078 IRIS Beryllium compounds 7440417 0.0024 IRIS Bis(2

- ethylhexyl)phthalate 117817 0.0000024 CAL Cadmium compounds 7440439 0.0018 IRIS Carbon tetrachloride 56235 0.000006 IRIS Chromium & Chromium compounds Barium chromate 10294403 0.012 IRIS Calcium chromate 13765190 0.012 IRIS Chromic acid (VI) 7738945 0.012 IRIS Chromium (VI) as lead chromate 7758976 0.012 IRIS Chromium (VI) compounds 18540299 0.012 IRIS Chromium (VI) trioxide, chromic acid mist 11115745 0.012 IRIS Sodium chromate 7775113 0.012 IRIS Sodium dichromate 10588019 0.012 IRIS Strontium c hromate 7789062 0.012 IRIS Zinc chromate 13530659 0.012 IRIS Zinc potassium chromate 11103869 0.012 IRIS Epichlorohydrin (1

- Chloro

- 2,3

- epoxypropane) 106898 0.0000012 IRIS Ethyl benzene 100414 0.0000025 CAL Ethylene oxide 75218 0.000088 CAL Formaldehyde 16 

### 50000 0.000013 IRIS Hexachlorobenzene 118741 0.00046 IRIS Methylene chloride 75092 0.000000 016 IRIS Naphthalene 91203 0.000034 CAL

15 The EPA IRIS assessment for benzene provides a range of plausible UREs. Thi s assessment used the highest value in that range, 7.8E

- 06 per ug/m 3 . The low end of the range is 2.2E

- 06 per ug/m 3

. 16 The EPA has used the CIIT URE value, 5.5X10

- 9 per mg/m 3 , to characterize formaldehyde cancer risk in some instances.

### Risk Assessment for the Aerospace Manufacturing and Rework Facilities Source Category 

### 20

Table 2.6

- 1 Dose

- Response Values for Chr onic Inhalation Exposure to C arcinogens



### URE (unit risk estimate for cancer) 13 

### = cancer risk per μg/m 3 

### of average lifetime exposure. Sources: IRIS = EPA Integrated Risk Information System, EPA ORD = EPA Office of Research & Development, EPA OAQPS = EPA Office of Air Quality Planning & Standards, CAL = California EPA Office of Environmental Health Hazard A ssessment.

Pollutant CAS Number 14 

### URE (1/μg/m 3 

### ) Source

Nickel & Nickel Compounds 17 

### Nickel compounds 7440020 0.000 48 IRIS Nickel oxide 1313991 0.00048 IRIS Propylene oxide 75569 0.0000037 

### IRIS Tetrachloroethene 127184 0.00000026 IRIS Trichloroethylene 79016 0.00000 48 IRIS Vinyl chloride 75014 0.0000088 IRIS

17 The EPA IRIS assessments for nickel compounds provide a range of plausible UREs. This assessment used the highest value in that range which is equal to the URE for nickel subsulfide, 4.8E

- 04 per ug/m 3 . The low end of the range is equal to 50% of the URE for nickel su bsulfide, 2.4E

- 04 per ug/m 3

.

### Risk Assessment for the Aerospace Manufacturing and Rework Facilities Source Category 

### 21

Table 2.6

- 2 (A) Dose

- Response Values for Chronic Inhalation Exposure to Noncarcinogens
RfC (reference inhalation concentration) = an estimate (with uncertainty spanning perhaps an order of magnitude) of a continuous inhalation exposure to the human population (including sensitive subgroups) that is likely to be without an appreciable risk of deleterious effects during a lifetime. Sources: IRIS = EPA Integrated Risk Information System, CAL = California EPA Office of Environmental Human Health Assessment, ATSDR = US Agency for Toxic Substances Disease Registry, HEAST = EPA Health Effects Assessment Tables, EPA OAQPS = EPA Office of Air Quality Plan ning & Standards, EPA ORD = EPA Office of Research and Development

Pollutant CAS Number 18 

### RfC (mg/m 3 

### ) Source 19



### 1,1,1

- Trichloroethane (methyl chloroform) 71556 5 IRIS

- M 1,2

- Epoxybutane 106887 0.02 IRIS

- M 1,3

- Butadiene 106990 0.002 IRIS

- M 1,4

- Dioxane 123911 0.03 IRIS

- M 2,4

- Toluene diisocyanate 584849 0.00007 IRIS

- M 2

- Nitropropane 79469 0.02 IRIS

- L 4,4'

- Methylenedianiline 101779 0.02 CAL

n

- Hexane 110543 0.7 IRIS

- M p

- Dichlorobenzene 106467 0.8 IRIS

- M Acetaldehyde 75070 0.009 IRIS

- L Acrylamide 79061 0.006 IRIS

- M Acrylic acid 79107 0.001 IRIS

- M Acrylonitrile 107131 0.002 IRIS

- M Aniline 62533 0.001 IRIS

- L Antimony & Antimony Compounds Antimony compounds 7440360 0.0002 IRIS

- L Antimony oxide 1327339 0.0002 IRIS

- L Antimony pentoxide 1314609 0.0002 IRIS

- L Antimony trioxide 1309644 0.0002 IRIS

- L Arsenic compounds 7440382 0.000015 CAL Benzene 71432 0.03 IRIS

- M Beryllium compounds 7440417 0.00002 IRIS

- M Bis(2

- ethylhexyl)phthalate 117817 0.01 CAL Cadmium compounds 7440439 0.00001 ATSDR Carbon tetrachloride 56235 0.1 IRIS

- M Chlorobenzene 108907 1 CAL Chloroform 67663 0.098 ATSDR Chromium & Chromium Compounds

18 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.
19 The descriptors L (low), M (medium), and H (high) have been added for IRIS RfC values to indicate the overall level of confidence in the RfC value, as reported in IRIS.

### Risk Assessment for the Aerospace Manufacturing and Rework Facilities Source Category 

### 22

Table 2.6

- 2 (A) Dose

- Response Values for Chronic Inhalation Exposure to Noncarcinogens
RfC (reference inhalation concentration) = an estimate (with uncertainty spanning perhaps an order of magnitude) of a continuous inhalation exposure to the human population (including sensitive subgroups) that is likely to be without an appreciable risk of deleterious effects during a lifetime. Sources: IRIS = EPA Integrated Risk Information System, CAL = California EPA Office of Environmental Human Health Assessment, ATSDR = US Agency for Toxic Substances Disease Registry, HEAST = EPA Health Effects Assessment Tables, EPA OAQPS = EPA Office of Air Quality Plan ning & Standards, EPA ORD = EPA Office of Research and Development

Pollutant CAS Number 18 

### RfC (mg/m 3 

### ) Source 19



### Barium chromate 10294403 0.0001 IRIS

- M Calcium chromate 13765190 0.0001 IRIS

- M Chromic acid (VI) 7738945 0.0001 IRIS

- M Chromium (VI) as lead chromate 7758976 0.0001 IRIS

- M Chromium (VI) compounds 18540299 0.0001 IRIS

- M Chromium (VI) trioxide, chromic acid mist 11115745 0.000008 IRIS

- L Sodium chromate 7775113 0.0001 IRIS

- M Sodium dichromate 10588019 0.0001 IRIS

- M Strontium chromate 7789062 0.0001 IRIS

- M Zinc chromate 13530659 0.0001 IRIS

- M Zinc potassium chromate 11103869 0.0001 IRIS

- M Cobalt & Cobalt Compounds Cobalt aluminate 1345160 0.0001 ATSDR Cobalt compounds 7440484 0.0001 ATSDR Cobalt naphtha 61789513 0.0001 ATSDR Cobalt oxide 1307966 0.0001 ATSDR Hexanoic acid, 2

- ethyl

- , cobalt(2+) salt 136527 0.0001 ATSDR Cresols (mixed) 1319773 0.6 CAL

m

- Cresol 108394 0.6 CAL

o

- Cresol 95487 0.6 CAL p

- Cresol 106445 0.6 CAL Cumene 98828 0.4 IRIS

- M Cyanide compounds 20 

### 57125 0.0008 IRIS

– L/M Diethanolamine 111422 0.003 CAL Dimethyl formamide 68122 0.03 IRIS

- M Epichlorohydrin (1

- Chloro

- 2,3 epoxypropane) 106898 0.001 IRIS

- M Ethyl benzene 100414 1 IRIS

- L Ethylene glycol 107211 0.4 CAL Ethylene oxide 75218 0.03 CAL Formaldehyde 50000 0.0098 ATSDR Glycol Ethers 21

20 The value for hydrogen cyanide was used as a surrogate for all cyanide compounds without an RfC.
21 The RfC value for ethylene glycol methyl ether was used as a surrogate for all glycol ethers without an RfC.

### Risk Assessment for the Aerospace Manufacturing and Rework Facilities Source Category 

### 23

Table 2.6

- 2 (A) Dose

- Response Values for Chronic Inhalation Exposure to Noncarcinogens
RfC (reference inhalation concentration) = an estimate (with uncertainty spanning perhaps an order of magnitude) of a continuous inhalation exposure to the human population (including sensitive subgroups) that is likely to be without an appreciable risk of deleterious effects during a lifetime. Sources: IRIS = EPA Integrated Risk Information System, CAL = California EPA Office of Environmental Human Health Assessment, ATSDR = US Agency for Toxic Substances Disease Registry, HEAST = EPA Health Effects Assessment Tables, EPA OAQPS = EPA Office of Air Quality Plan ning & Standards, EPA ORD = EPA Office of Research and Development

Pollutant CAS Number 18 

### RfC (mg/m 3 

### ) Source 19



### 1,2

- Dimethoxyethane 110714 0.02 IRIS

- M 2

- (Hexyloxy)ethanol 112254 0.02 IRIS

- M 2

- Butoxyethyl acetate 112072 0.02 IRIS

- M Butyl Carbitol acetate 124174 0.02 IRIS

- M Carbitol acetate 112152 0.02 IRIS

- M Diethylene glycol dimethyl ether 111966 0.02 IRIS

- M Diethylene glycol monobutyl ether 112345 0.02 HEAST Diethylene glycol monoethyl ether 111900 0.02 IRIS

- M Diethylene glycol monomethyl ether 111773 0.02 IRIS

- M Ethylene glycol ethyl ether 110805 0.2 IRIS

- M Ethylene glycol ethyl ether acetate 111159 0.3 CAL Ethylene glycol methyl ether 109864 0.02 IRIS

- M Ethylene glycol methyl ether acetate 110496 0.09 CAL Glycol ethers 181 0.02 IRIS

- M Propyl cellosolve 2807309 0.02 IRIS

- M Hexachlorobenzene 118741 0.003 CAL Hexamethylene

- 1,6

- diisocyanate 822060 0.00001 IRIS

- L Hydrochloric acid (hydrogen chloride) 7647010 0.02 IRIS

- L Hydrofluoric acid (hydrogen fluoride) 7664393 0.014 CAL Isophorone 78591 2 CAL Lead and Lead Compounds Lead (II) oxide 1317368 0.00015 EPA OAQPS Lead as lead chromate 7758976 0.00015 EPA OAQPS Lead compounds 7439921 0.00015 EPA OAQPS Lead dioxide 1309600 0.00015 EPA OAQPS Maleic anhydride 108316 0.0007 CAL Manganese and Manganese Compounds Manganese compounds 7439965 0.0003 ATSDR Manganese dioxide 1313139 0.0003 ATSDR Manganese tetroxide 1317357 0.0003 ATSDR Manganese trioxide 1317346 0.0003 ATSDR Mercury and Mercury Compounds Gaseous divalent mercury 201 0.0003 IRIS

- M Mercury (elemental) 7439976 0.0003 IRIS

- M Particulate divalent mercury 184 0.0003 IRIS

- M Methanol 67561 20 IRIS

– M/H

### Risk Assessment for the Aerospace Manufacturing and Rework Facilities Source Category 

### 24

Table 2.6

- 2 (A) Dose

- Response Values for Chronic Inhalation Exposure to Noncarcinogens
RfC (reference inhalation concentration) = an estimate (with uncertainty spanning perhaps an order of magnitude) of a continuous inhalation exposure to the human population (including sensitive subgroups) that is likely to be without an appreciable risk of deleterious effects during a lifetime. Sources: IRIS = EPA Integrated Risk Information System, CAL = California EPA Office of Environmental Human Health Assessment, ATSDR = US Agency for Toxic Substances Disease Registry, HEAST = EPA Health Effects Assessment Tables, EPA OAQPS = EPA Office of Air Quality Plan ning & Standards, EPA ORD = EPA Office of Research and Development

Pollutant CAS Number 18 

### RfC (mg/m 3 

### ) Source 19



### Methyl isobutyl ketone 108101 3 IRIS

- L/M Methyl methacrylate 80626 0.7 IRIS

- M/H Methylene chloride 75092 0.6 IRIS

– M/H Methylene diphenyl diisocyanate 101688 0.0006 IRIS

- M Naphthalene 91203 0.003 IRIS

- M Nickel and Nickel Compounds Nickel compounds 7440020 0.00009 ATSDR Nickel oxide 1313991 0.00002 CAL Phenol 108952 0.2 CAL Phthalic anhydride 85449 0.02 CAL Propylene oxide 75569 0.03 IRIS

- M Selenium and Selenium Compounds Selenious acid 7783008 0.02 CAL Selenium compounds 7782492 0.02 CAL Styrene 100425 1 IRIS

- M Tetrachloroethene 127184 0.04 IRIS

- M Toluene 108883 5 IRIS

- H Trichloroethylene 79016 0.002 IRIS

- H Triethylamine 121448 0.007 IRIS

- L Vinyl acetate 108054 0.2 IRIS

- H Vinyl chloride 75014 0.1 IRIS

- M Xylenes (mixed) 22 

### 1330207 0.1 IRIS

- M

m

- Xylene 108383 0.1 IRIS

- M

o

- Xylene 95476 0.1 IRIS

- M p

- Xylene 106423 0.1 IRIS

- M

22 

### The RfC for mixed xylene was used as a surrogate for each of the isomers.

### Risk Assessment for the Aerospace Manufacturing and Rework Facilities Source Category 

### 25

Table 2.6

- 2 (B) Dose

- Response Values for Chr onic Oral Exposure to Nonc arcinogens
RfD (reference dose) = an estimate (with uncertainty spanning perhaps an order of magnitude) of a continuous oral exposure to the human population (including sensitive subgr oups) that is likely to be without an appreciable risk of deleterious effects during a lifetime. Sources: IRIS = EPA Integrated Risk Information System, ATSDR = US Agency for Toxic Substances and Disease Registry

.

Pollutant CAS Number 23 

### RFD (mg/kg/d) ) Source 24



### Cadmium compounds 7440439 0.0005 IRIS

- H Mercuric chloride 25 

### 7439976 0.0003 IRIS

- H



### 2.6.2 Sources of acute dose

- response information
Hazard identification and dose

- response assessment information for preliminary acute inhalation exposure assessments are based on the existing recommendations of OAQPS for HAP [ 17 ]

. Depending on availability, the results from screening acute assessments are compared to both “no effects” reference levels for the general public, such as the California Reference Exposure Levels (RELs), as well as emergency response levels, such as Acute Exposure Guideline Levels (AEGLs) and Emergency Response Planning Guidelines (ERPGs), with the recognition that the ultimate interpretation of any potential risks associated with an estimated exceedance of a particular reference level depends on the definition of that level and any limitations expressed therein. Comparisons among different available inhalation health effect reference values (both acute and chronic) for selected HA P can be found in an EPA document of graphical arrays [ 18 ].

California Acute Reference Exposure Levels (RELs ). The California Environmental Protection Agency (CalEPA) has developed acute dose

- response reference values for many substances, expressing the r esults as acute inhalation Reference Exposure Levels (RELs).

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

“ t he concentration level at or below which no adverse health effects are anticipated for a specified exposure duration . [ 19 ]

. RELs are based on the most sensitive, relevant, adverse health effect reported in the medical and toxicological literature. RELs are designed to protect the most sensitive ind ividuals in the population by the inclusion of margins of safety. Since margins of safety are incorporated to address data gaps and uncertainties, exceeding the REL does not automatically indicate an adverse health

23 Chemical Abstract Services identification number. For groups of compoun ds 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.
24 The descriptors L (low), M (medium), and H (high) have been added for IRIS RfC values to indicate the overall level of co nfidence in the RfC value, as reported in the IRIS file

. 25 The multipathway exposure assessment for mercury included fate and transport analysis, that included separate oral exposure estimates for divalent mercury and methylmercury.

### Risk Assessment for the Aerospace Manufacturing and Rework Facilities Source Category 

### 26

impact. ” Acute RELs are developed for 1

- hour (and 8

- hour) exposures. The values incorporate uncertainty factors similar to those used in deriving EPA’s Inhalation Reference Concentrations (RfCs) for chronic exposures (and, in fact, California also has developed chronic RELs).

Acute Exposure Guideline Levels (AEGLs ). AEGLs are developed by the National Advisory Committee (NAC) on Acute Exposure Guideline Levels (NAC/AEGL) f or Hazardous Substances , and then reviewed and published by the National Research Council. A s described in the Committee ’s “Standing Operating Procedures (SOP) ” ( http://www.epa.gov/opptintr/aegl/pubs/sop.pdf ), AEGLs “represent threshold exposure limits for the general public and are applicable to emergency exposu res ranging from 10 min to 8 h.” Their intended application is “for conducting risk assessments to aid in the development of emergency preparedness and prevention plans, as well as real time emergency response actions, for accidental chemical releases at fixed facilities and from transport carriers.” The document states that “the primary purpose of the AEGL program and the NAC/AEGL Committee is to develop guideline levels for once

- in

- a

- lifetime , short

- term exposures to airborne concentrations of acutely t oxic, high

- priority chemicals.” In detailing the intended application of AEGL values, the document states that
, “ It is anticipated that the AEGL values will be used for regulatory and nonregulatory purposes by U.S. Federal and State agencies, and possibly the international community in conjunction with chemical emergency response, planning, and prevention programs. More specifically, the AEGL values will be used for conducting various risk assessments to aid in the development of emergency preparedness an d prevention plans, as well as real

- time emergency response actions, for accidental chemical releases at fixed facilities and from transport carriers.”

The NAC/AEGL defines AEGL

- 1 and AEGL

- 2 as:

“ AEGL

- 1 is the airborne concentration (expressed as ppm o r mg/m 3 

### ) of a substance above which it is predicted that the general population, including susceptible individuals, could experience notable discomfort, irritation, or certain asymptomatic nonsensory effects. However, the effects are not disabling and are transient and reversible upon cessation of exposure. ”

“ AEGL

- 2 is the airborne concentration (expressed as ppm or mg/m 3 

### ) of a substance above which it is predicted that the general population, including susceptible individuals, could experience irreversib le or other serious, long

- lasting adverse health effects or an impaired ability to escape. ”

“ Airborne concentrations below AEGL

- 1 represent exposure levels that can produce mild and progressively increasing but transient and nondisabling odor, taste, and sensory irritation or certain asymptomatic, nonsensory effects. With increasing airborne concentrations above each AEGL, there is a progressive increase in the likelihood of occurrence and the severity of effects described for each corresponding AEGL. A lthough the AEGL values represent threshold levels for the general public, including susceptible subpopulations, such as infants, children, the elderly, persons with asthma, and those with other illnesses, it is recognized that individuals, subject to uniq ue or idiosyncratic

### Risk Assessment for the Aerospace Manufacturing and Rework Facilities Source Category 

### 27

responses, could experience the effects described at concentration s below the corresponding AEGL.”

Emergency Response Planning Guidelines (ERPGs). The American Industrial Hygiene Association (AIHA) has developed Emergency Response Pla nning Guidelines (ERPGs) [ 20 ] for acute exposures at three different levels of severity. These guidelines represent concentrations for exposure of the general population (but not particularly sensitive persons) for up to 1 hour associated with effects expe cted to be mild or transient (ERPG

- 1), irreversible or serious (ERPG

- 2), and potentially life

- threatening (ERPG

- 3).

ERPG values https://www.aiha.org/get

involved/aihaguidelinefoundation/emergencyresponseplanningguidelines/Pages/default.aspx

are described in their supporting documentation as follows: “ERPGs are air concentration guidelines for single exposures to agents and are intended for use as tools to assess the adequacy of accident prevention and emergency response plans, including transpor tation emergency planning
, comm unity emergency response plans, and incident prevention and mitigation

. ”

ERPG

- 1 and ERPG

- 2 values are defined by AIHA as follows:

“ ERPG

- 1 is the maximum airborne concentration below which it is believed that nearly all in dividuals could be exposed for up to 1 hour without experiencing other than mild transient adverse health effects or without perceiving a clearly defined, objectionable odor. ”

“ ERPG

- 2 is the maximum airborne concentration below which it is believed that nearly all individuals could be exposed for up to 1 hour without experiencing or developing irreversible or other serious health effects or symptoms which could impair an individual's ability to take protective action. ”

The emissions inventory for the aerospace source category includes emissions of 59 HAP with relevant and available quantitative acute dose

- response threshold values. These HAP, their acute threshold values, and the source of the value are listed below in Table 2.6

- 3

.

Table 2.6

- 3 Dose

- Response Values for Acute Exposures

Pollutant CAS Number 26 

### 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 1,1,1

- Trichloroethane (methyl chloroform) 7155 6 1300 3300 1 900 38 00 68 1,2

- Epoxybutane 10688 7 210 410

26 Chemical Abstract Ser vices 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.

### Risk Assessment for the Aerospace Manufacturing and Rework Facilities Source Category 

### 28

Table 2.6

- 3 Dose

- Response Values for Acute Exposures

Pollutant CAS Number 26 

### 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 1,3

- Butadiene 10699 0 1500 12000 22 440 1,4

- Dioxane 12391 1 61 1200 3 2,4

- Toluene diisocyanate 58484 9 0.14 0.59 0. 071 1.1

n

- Hexane 110543 12000 Acetaldehyde 75070 81 490 18 360 0.47 Acrylic acid 79107 4.4 140 2.9 15 0 6 Acrylonitrile 10713 1 10 12 0 22 77 Aniline 6253 3 30 46 Arsenic compounds 744038 2 0.0002 Benzene 71432 170 2600 16 0 48 0 1.3 Beryllium compounds 7440417 0.025 Carbon tetrachloride 56235 280 1200 130 63 0 1.9 Chlorobenzene 108907 46 690 Chloroform 67663 310 24 0 0.15 Cumene 98828 250 1500 Dimethyl formamide 6812 2 270 6 30 0 Epichlorohydrin (1

- Chloro

- 2,3

- epoxypropane) 106898 22 91 19 76 1.3 Ethyl acrylate 140885 34 150 0.041 12 0 Ethyl benzene 100414 140 4800 Ethylene oxide 75218 81 90 Formaldehyde 50000 1.1 17 1. 2 1 2 0.055 Glycol Ethers 27 

### 1,2

- Dimethoxyethane 11071 4 0.093 2

- (Hexyloxy)ethanol 112 25 4 0.093 2

- Butoxyethyl
acetate 11207 2 0.093 Butyl carbitol acetate 12417 4 0.093 Carbitol acetate 11215 2 0.093 Diethylene glycol dimethyl ether 11196 6 0.093 Diethylene glycol monobutyl ether 112345 0.093 Diethylene glycol monoethyl ether 111900 0.093 Diethylene glycol 11177 3 0.093

27 The acute REL for ethylene glycol methyl ether was used as a surrogate for glycol ether compounds without an acute REL.

### Risk Assessment for the Aerospace Manufacturing and Rework Facilities Source Category 

### 29

Table 2.6

- 3 Dose

- Response Values for Acute Exposures

Pollutant CAS Number 26 

### 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 monomethyl ether Ethylene glycol ethyl ether 11080 5 0.37 Ethylene glycol ethyl ether
acetate 11115 9 0.14 Ethylene glycol methyl ether 10986 4 0.093 Ethylene glycol methyl ether acetate 11049 6 0.093 Glycol ethers 18 1 0.093 Propyl cellosolve 280730 9 0.093 Hydrochloric acid 7647010 2.7 33 4.5 3 0 2.1 Hydrofluoric acid 7664393 0.82 20 1.6 16 0.24 Maleic anhydride 108316 

### 0.8 8 Mercury (elemental) 743997 6 1.7 2 0.0006 Methanol 67561 690 2700 260 13 00 28 Methyl methacrylate 80626 70 490 Methylene chloride 75092 690 1900 100 0 26 00 14 Methylene diphenyl diisocyanate 10168 8 5 Phenol 108952 58 89 3 8 190 5.8 Propylene oxide 7556 9 170 690 12 0 5 90 3.1 Styrene 100425 85 550 210 110 0 21 Tetrachloroethene 127184 240 1600 68 0 14 00 20 Toluene 108883 750 4500 190 110 0 37 Trichloroethylene 79016 700 2400 54 0 27 00 Triethylamine 12144 8 2.8 Vinyl acetate 108054 24 630 18 260 Vinyl chloride 75014 640 3100 130 0 130 00 180 Xylenes (mixed) 28 

### 1330207 560 4000 22

m

- Xylene 108383 22

o

- Xylene 95476 22
p

- Xylene 106423 22

28 The REL for mixed xylenes was used as a surrogate for each isomer.

### Risk Assessment for the Aerospace Manufacturing and Rework Facilities Source Category 

### 30



## 2.7 Risk c haracterization

2.7.1 General The final product of the risk assessment is the risk characterization, in which the information from the previous steps is integrated and an overall conclusion about risk is synthesized that is complete, informative, and useful for decision makers. In general , the nature of this risk characterization depends on the information available, the application of the risk information and the resources available. In all cases, major issues associated with determining the nature and extent of the risk are identified a nd discussed. Further, the EPA Administrator’s March 1995 Policy for Risk Characterization [ 21 ] specifies that a risk characterization “be prepared in a manner that is clear, transparent, reasonable, and consistent with other risk characterizations of simi lar scope prepared across programs in the Agency.” These principles of transparency and consistency have been reinforced by the Agency’s Risk Characterization Handbook [ 22 ], in 2002 by the Agency’s information quality guidelines [ 23 ], and in the OMB/OSTP Se ptember 2007 Memorandum on Updated Principles for Risk Analysis 29 

### , and are incorporated in these assessments.

Estimates of health risk are presented in the context of uncertainties and limitations in the data and methodology. Through our tiered, iterative analytical approach, we have attempted to reduce both uncertainty and bias to the greatest degree possible in these assessments, within the limitations of available time and resources. We provide summaries of risk metrics (including maximum individual ca ncer risks and noncancer hazards, as well as cancer incidence estimates) along with a discussion of the major uncertainties associated with their derivation to provide decision makers with the fullest picture of the assessment and its limitations.

For eac h carcinogenic HAP included in an assessment that has a potency estimate available, individu al and population cancer risks we re calculated by multiplying the corresponding lifetime average exposure estimate by the appropriate URE. This calculated cancer r isk is defined as the upper

- bound probability of developing cancer over a 70

- y r period (i.e., the assumed human lifespan) at that exposure. Because UREs for most HAP are upper

- bound estimates, actual risks at a given exposure level may be lower than predi cted, and could be

zero.
For EPA’s list of carcinogenic HAP that act by a mutagenic mode

- of

- action [ 24 ], we applied EPA’s Supplemental Guidance for Assessing Susceptibility from Early

- Life Exposure to Carcinogens [ 25 ]

. This guidance has the effect of adjus ting the URE by factors of 10 (for children aged 0

- 1), 3 (for children aged 2

- 15), or 1.6 (for 70 years of exposure beginning at birth), as needed in risk assessments . In this case, this has the effect of increasing the estimated life time risks for these pollutants by a factor of 1.6. In addition, although only a small fraction of the total POM emissions may be reported as individual compounds, EPA

29 Memorandum f

or t he Heads o f Executive Departments a nd Agencies

- Updated Principles for Risk Analysis ( September 19, 2007 ), From Susan E. Dudley, Administrator, Office of Information and Regulatory Affairs, Office of Management and Budget; and Sharon L. Hays, Associate Director and Deputy Director for Science, Office of Science and Technology Policy http://www.whitehouse.gov/sites/default/files/omb/memoranda/fy2007/m07

- 24.pdf

### Risk Assessment for the Aerospace Manufacturing and Rework Facilities Source Category 

### 31

expresses carcinogenic potency for compounds in this group in terms of benzo[a]pyrene equivalence, based on evidence that carcinogenic POM have the same mutagenic mechanism of action as does benzo[a]pyrene. For this reason
, EPA implem entation policy [ 26 ] recommends applyi ng the Supplemental Guidance to all carcinogenic PAHs for which risk estimates are based on relative potency. Accordingly, we applied the Supplemental Guidance to all unspeciated POM mixtures.

Increased cancer incidence for the entire receptor population within the area of analysis wa s estimated by multiplying the estimated lifetime cancer ris k for each census block by the number of people residing in that block, then summing the results for the entire modeled domain. This lifetime population incidence estimate wa s divided by 70 years to obtain an estimate of the number of cancer cases per yea r.

Unlike linear dose

- response assessments for cancer, noncancer health hazards generally are not expressed as a probability of an adverse occurrence. Instead, “risk” for noncancer effects is expressed by comparing an exposure to a reference level as a r atio. The “hazard quotient” (HQ) is the estimated exposure divided by a reference level (e.g., the RfC). For a given HAP, exposures at or below the reference level (HQ ≤ 1) are not likely to cause adverse health effects. As exposures increase above the reference level (HQs increasingly greater than 1), the potential for adverse effects increases. For exposures predicted to be above the RfC, the risk characterization includes the degree of confidence ascribed to the RfC values for the compound(s) of conc ern (i.e., high, medium, or low confidence) and discusses the impact of this on possible health interpretations.

The risk characterization for chronic effects other than cancer is expressed in terms of the HQ for inhalation, calculated for each HAP at ea ch census block centroid. As discussed above, RfCs incorporate generally conservative uncertainty factors in the face of uncertain extrapolations, such that an HQ greater than one does not necessarily suggest the onset of adverse effects. The HQ cannot b e translated to a probability that adverse effects will occur, and is unlikely to be proportional to adverse health effect outcomes in a population

. Screening for potentially significant acute inhalation exposures also followed the HQ approach. We divide d the maximum estimated acute exposure by each available short

- term threshold value to develop an array of HQ values relative to the various acute endpoints and thresholds. In general, when none of these HQ values are greater than one, there is no potenti al for acute risk. In those cases where HQ values above one are seen, additional information is used to determine if there is a potential for significant acute risks.

2.7.2 Mixtures Since most or all receptors in these assessments receive exposures to multiple pollutants rather than a single pollutant, we estimated the aggregate health risks associated with all the exposures from a particular source category combined.

### Risk Assessment for the Aerospace Manufacturing and Rework Facilities Source Category 

### 32

To combine risks across multiple carcinogens, our assessments use the mixtures guidelines’ [ 27
, 28 ] default assumption of additivity of effects, and combine risks by summing them using the independence formula in the mixtures guidelines.

In assessing noncancer hazard from chronic exposures, in cases where different pollutants cause adverse health ef

fects via completely different modes of action, it may be inappropriate to aggregate HQs. In consideration of these mode

- of

- action differences, the mixtures guidelines support aggregating effects of different substances in specific and limited ways. To c onform to these guidelines, we aggregated non

- cancer HQs of HAP that act by similar toxic modes of action, or (where this information is absent) that affect the same target organ. This process creates, for each target organ, a target

- organ

- specific hazard index (TOSHI), defined as the sum of hazard quotients for individual HAP that affect the same organ or organ system. A ll TOSHI calculations presented here were based exclusively on effects occurring at the “ critical dose” (i.e., the lowest dose that prod uces adverse health effects). Although HQs associated with some pollutants have been aggregated into more than one TOSHI, this has been done only in cases where the critical dose affects more than one target organ

. Because impacts on organs or systems th at 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 “u nsafe” levels. Rather, the TOSHI is another measure of the potential for adverse health outcomes associated with pollutant exposure, and needs to be interpreted carefully by health scientists and risk managers.

Because of the conservative nature of the a cute inhalation screening and the variable nature of emissions and potential exposures, acute impacts were screened on an individual pollutant basis, not using the TOSHI approach.

2.7.3 MACT

- Allowable Emissions and Risks
The emissions data in the dataset for t he aerospace source category are estimates of actual emissions on an annual basis. The risk results presented in the following sections are based on these actual emissions. For the aerospace source category, estimates of MACT

- allowable emissions were als o estimated, and the risk results based on those emissions are presented below as well. For more detail, please refer to the emissions memo in Appendix 1 to this document

.



## 3 Risk r esults for the Aerospace Manufacturing and Rework facilities

3.1 Source C ategory E missions

### Risk Assessment for the Aerospace Manufacturing and Rework Facilities Source Category 

### 33

Based on 2011 survey data from an information collection request (ICR) as well as emission data updated in 2013 and 2014, we currently estimate that there are 144 Aerospace Manufacturing and Rework facilities operating in the U.S. and 8 7 of these facilities are identified as major sources.

The baseline actual emissions for the Aerospace Manufacturing and Rework facilities source category NPRM data set (of 144 facil ities) are summarized in Table 3. 1

- 1 . The total HAP emissions for the s ource category are approximately 1,130 tons per year. Based on these data, the HAP emitted in the largest quantities across these 144 facilities are: toluene, tetrachloroethene, methyl isobutyl ketone, xylenes (mixed), methylene chloride, phenol, N

hexane, ethylene glycol ethyl ether, ethyl benzene, methanol, diethylene glycol monobutyl ether, diethanolamine, propyl cellosolve, 2

- butoxyethyl acetate, 1,1,1

- trichloroethane, styrene, manganese compounds, ethylene glycol methyl ether, glycol ethers, ethylene g lycol, methylene diphenyl diisocyanate, butyl carbitol acetate and dimethyl formamide. These 23 HAP account for 99 percent of the mass of the total emissions by mass. Persistent and bioaccumulative HAP (PB HAP) 30 

### reported as emissions from these facilitie s include cadmium, mercury, lead compounds and hexachlorobenzene. The following environmental HAP are emitted from the Aero space Manufacturing and Rework F acilities source category and are included in the environmental risk screen: lead, mercury (mercuric chloride and methyl mercury), cadmium, hydrogen chloride and hydrogen fluoride.

Table 3.1

- 1 Baseline Actual Emissions from Aerospace and Availability of Dose

- Response Values

HAP a Emissions (tpy) Number of

Facilities Reporting HAP ( 144 facilities in data set) Prioritized Inhalation Dose

- Response Value

Identified by OAQPS b PB

HAP ? Unit Risk Estimate for Cancer? Reference

Concentration for Noncancer? Health

Benchmark Values for Acute Noncancer? 

### Toluene 348 133 

 

 Tetrachloroethene 139 17 

 

 

 Methyl isobutyl ketone 136 129 

 Xylenes (mixed) 115 131 

 

 Methylene chloride 111 56 

 

 

 Phenol 100 62 

 

 n

- Hexane 36 50 

 

 Ethylene glycol ethyl ether 27 20 

 

 Ethyl benzene 16 129 

 

 

 Methanol 14 90 

 



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

- 453K

- 04

- 001A, as referenced in the NPRM and provided on the EPA’s Technology Transfer Network website for Fate, Exposure, and Risk Assessment at http://www.epa.gov/ttn/fera/risk_atra_vol1.html

.

### Risk Assessment for the Aerospace Manufacturing and Rework Facilities Source Category 

### 34

Table 3.1

- 1 Baseline Actual Emissions from Aerospace and Availability of Dose

- Response Values

HAP a Emissions (tpy) Number of

Facilities Reporting HAP ( 144 facilities in data set) Prioritized Inhalation Dose

- Response Value

Identified by OAQPS b PB

HAP ? Unit Risk Estimate for Cancer? Reference

Concentration for Noncancer? Health

Benchmark Values for Acute Noncancer? 

### Diethylene glycol monobutyl ether 14 28 

 

 Diethanolamine 12 12 

 Propyl cellosolve 11 20 

 

 2

- Butoxyethyl acetate 8 41 

 

 1,1,1

- Trichloroethane 6 8 

 

 Styrene 5 27 

 

 Manganese compounds 3 36 

 Ethylene glycol methyl ether 3 20 

 

 Glycol ethers 3 19 

 

 Ethylene Glycol 3 34 

 Methylene diphenyl diisocyanate 3 42 

 

 Butyl carbitol acetate 2 23 

 

 Dimethyl formamide 1 4 

 

 Ethylene glycol ethyl ether
acetate 1 22 

 

 Cumene 1 66 

 

 Trichloroethylene 0.7 9 

 

 

 Naphthalene 0.7 23 

 

 Strontium chromate 0.6 94 

 

 Manganese dioxide 0.6 13 

 Hexamethylene

- 1,6

- diisocyanate 0.6 83 

 Bis(2

- ethylhexyl)phthalate 0.6 17 

 

 1,2

- Epoxybutane 0.4 6 

 

 Cyanide compounds 0.4 6 

 Dibutylphthalate 0.4 31 Methyl methacrylate 0.4 21 

 

 Acrylic acid 0.3 12 

 

 Nickel compounds 0.3 43 

 

 Triethylamine 0.3 16 

 

 4,4'

- Methylenedianiline 0.2 3 

 

 Formaldehyde 0.2 61 

 

 

 Diethylene glycol monoethyl ether 0.2 7 

 

 o

- Xylene 0.2 4 

 

 Benzene 0.1 33 

 

 

 Diethylene glycol dimethyl ether 0.1 5 

 

 2,4

- Toluene diisocyanate 0.1 25 

 

 



### Risk Assessment for the Aerospace Manufacturing and Rework Facilities Source Category 

### 35

Table 3.1

- 1 Baseline Actual Emissions from Aerospace and Availability of Dose

- Response Values

HAP a Emissions (tpy) Number of

Facilities Reporting HAP ( 144 facilities in data set) Prioritized Inhalation Dose

- Response Value

Identified by OAQPS b PB

HAP ? Unit Risk Estimate for Cancer? Reference

Concentration for Noncancer? Health

Benchmark Values for Acute Noncancer? 

### Chlorobenzene 0.1 7 

 

 Cobalt compounds 0.1 38 

 Lead compounds c 

### 0.1 41 

 

 Chromium (iii) compounds 0.07 49 Diethylene glycol monomethyl ether 0.07 6 

 

 Isophorone 0.06 5 

 Cadmium compounds 0.05 31 

 

 

 Barium chromate 0.05 36 

 

 Chromium (vi) compounds 0.05 60 

 

 2

- Nitropropane 0.05 17 

 

 Cresols (mixed) 0.05 11 

 Hydroquinone 0.04 17 Ethyl acrylate 0.04 13 

 Antimony compounds 0.03 41 

 p

- Xylene 0.03 10 

 

 Antimony trioxide 0.03 26 

 Epichlorohydrin 0.02 11 

 

 

 Zinc chromate 0.02 38 

 

 2,4

- Dinitrophenol 0.02 1 4

- Nitrophenol 0.02 2 N,N

- Dimethylaniline 0.01 9 Selenium compounds 0.009 14 

 Hydrochloric acid 0.007 1 

 

 p

- Cresol (4

- Methy l phenol) 0.006 2 

 1,3

- Butadiene 0.006 4 

 

 

 o

- Cresol 0.005 5 

 Lead (II) oxide c 

### 0.004 2 

 

 Ethylene oxide 0.004 7 

 

 

 Acrylonitrile 0.003 5 

 

 

 m

- Cresol (3

- Methyl phenol) 0.003 3 

 Calcium chromate 0.003 20 

 

 Carbitol acetate 0.003 6 

 

 m

- Xylene 0.002 5 

 

 Chromic oxide 0.002 17 Ethylene glycol methyl ether
acetate 0.002 1 

 

 Carbon tetrachloride 0.002 9 

 

 



### Risk Assessment for the Aerospace Manufacturing and Rework Facilities Source Category 

### 36

Table 3.1

- 1 Baseline Actual Emissions from Aerospace and Availability of Dose

- Response Values

HAP a Emissions (tpy) Number of

Facilities Reporting HAP ( 144 facilities in data set) Prioritized Inhalation Dose

- Response Value

Identified by OAQPS b PB

HAP ? Unit Risk Estimate for Cancer? Reference

Concentration for Noncancer? Health

Benchmark Values for Acute Noncancer? 

### Sodium chromate 0.002 1 

 

 Arsenic compounds 0.002 27 

 

 

 Chromic acid (vi) 0.001 20 

 

 Chromium (vi) trioxide, Chromic acid mist 0.001 13 

 

 p

- Dichlorobenzene 0.001 2 

 

 Acetophenone 0.001 3 2

- (Hexyloxy)ethanol 0.001 2 

 

 Acetaldehyde 0.001 9 

 

 

 1,4

- Dioxane 0.001 6 

 

 

 Aniline 0.0008 5 

 

 

 Hydrofluoric acid 0.0008 11 

 

 Vinyl acetate 0.0007 1 

 

 Propylene oxide 0.0007 8 

 

 

 2,4,5

- Trichlorophenol 0.0005 1 Catechol 0.0004 2 1,2

- Dimethoxyethane 0.0004 1 

 

 Dimethyl phthalate 0.0003 1 Zinc potassium chromate 0.0002 10 

 

 Maleic anhydride 0.0002 1 

 

 Mercury (elemental) 0.0002 6 

 

 

 Cobalt naphtha 0.0001 2 

 p

- Phenylenediamine 0.0001 1 Gaseous divalent mercury 0.0001 6 

 

 4,4'

- Methylene bis(2

- chloroaniline) 0.0001 1 

 Particulate divalent mercury 0.00008 6 

 

 2,2,4

- Trimethylpentane 0.00005 1 Sodium dichromate 0.00003 5 

 

 Antimony oxide 0.00003 1 

 Phthalic anhydride 0.00003 1 

 Antimony pentoxide 0.00003 4 

 Cobalt aluminate 0.00002 5 

 Manganese trioxide 0.00002 1 

 Cobalt oxide 0.00001 1 

 Chromium (vi) As lead chromate 0.000006 6 

 

 Lead As lead chromate c 

### 0.000006 6 

 

 Nickel oxide 0.000003 5 

 



### Risk Assessment for the Aerospace Manufacturing and Rework Facilities Source Category 

### 37

Table 3.1

- 1 Baseline Actual Emissions from Aerospace and Availability of Dose

- Response Values

HAP a Emissions (tpy) Number of

Facilities Reporting HAP ( 144 facilities in data set) Prioritized Inhalation Dose

- Response Value

Identified by OAQPS b PB

HAP ? Unit Risk Estimate for Cancer? Reference

Concentration for Noncancer? Health

Benchmark Values for Acute Noncancer? 

### Chloroform 0.000002 1 

 

 Zinc chromite 0.000002 2 Selenious acid 0.000001 1 

 Chromium zinc oxide 0.000001 1 Vinyl chloride 0.000001 1 

 

 

 Acrylamide 0.0000005 1 

 

 Hexanoic acid, 2

- ethyl

- , Cobalt(2+) salt 0.0000003 2 

 Beryllium compounds 0.0000002 11 

 

 

 3,3'

- Dichlorobenzidine 0.0000001 1 

 Lead dioxide c 

### 0.00000007 1 

 

 Hexachlorobenzene 0.00000005 1 

 

 

 Manganese tetroxide 0.00000002 1 



a Notes for how HAP were speciated for risk assessment:

 For most metals, emissions reported as the elemental metal are combined with metal compound emissions (e.g., “cadmium” emissions modeled as “cadmium & compounds”). In the absence of speciation information, we assume the reported mass is 100 percent metal.

 For emissions reported generically as “chromium” or “chromium & compound,” emissions are speciated “chromium (III) compounds” and “chromium (VI) compounds” according to the individual emitting process speciation profile for this source category. Chromium speciation profiles can be found on the EPA’s Technology Transfer Network website for emissions inventories at http://www.epa .gov/ttn/chief/net/2005inventory.html

 For emissions reported generically as “mercury” or “mercury & compounds,” emissions are speciated for this category as “mercury (elemental)” and “mercuric chloride.” Mercury speciation profile s can be found on the EPA ’s Techn ology Transfer Network website for emissions inventories at http://www.epa.gov/ttn/chief/net/2005inventory.html

.
 For emissions of any chemicals or chemical groups classified as PO M, emissions were grouped into POM subgroups as found on EPA’s Technology Transfer Network website for the 200 5 National

- Scale Air Toxics Assessment at http://www.epa.gov/ttn/atw/nat a2005/methods.html#pom ( Approach to Modeling POM)

.
 For emissions reported generically as “Glycol Ethers” or specific glycol ethers not found on EPA’s Technology Transfer Network for air toxics (see footnote b), emissions were treated as ethylene glycol met hyl ether.

b Specific dose

- response values for each chemical are identified on EPA’s Technology Transfer Network website for air toxi cs at http://www2.epa.gov/fera/dose

- response

- assessment

- assessing

- health

- risks

- associated

- exposure

- hazardous

- air

- pollutants

c There is no reference concentration for lead. In considering noncancer hazards for lead in this assessment, we compared rolling three

- month average exposure estimates to the National Ambient Air Quality Standard (NAAQS) for lead (0.15 μg/m 3 ). These NAAQS for lead were recently reviewed with revisions adopted in October 2008 ( http://www.epa.gov/air/lead/actions.html ). The primary (health

- based) standard is a maximum or not

- to

- be

- exceeded, rolling three

- m onth average, measured as total suspended particles (TSP). The secondary (welfare

- based) standard is identical to the primary standard.

### Risk Assessment for the Aerospace Manufacturing and Rework Facilities Source Category 

### 38



## 3.2 R isk c haracterization This section presents the results of the risk assessment for A erospace M anufactu ring and R ework source category based on the modeling methods described in the previous sections. Table s 3 .2

- 1 presents the summary of the risks from for the Aerospace Manufacturing and Rework Source Category

. The maximum individual lifetime cancer risk (MIR) for both b aseline actual emissions and baseline allowable emissions is 10 in a million. When considering whole facility baseline actual emissions the predicted MIR is 20 in a million. The maximum chronic hazard index is predicted to be below 1 considering all emi ssions scenarios. The a cute inhalation impact screening assessment predicted the maximum hazard quotients is 2 . Both multipathway and ecological screening risks are below their respective screening thresholds. More details on each component of the risks assessment are presented in the sections and tables below.

Table 3.2

- 1

. Risk Summary for the Aerospace Manufacturing

3.2.1 Baseline Actual Emission R isks All baseline risk results are developed using the best estimates of actual HAP emissions summarized in the previous section

. The basic chronic inhalation risk estimates presented here are the maximum individual lifetime cancer risk, the maximum chronic hazard index, and the cancer incidence. We also present results from our acute inhalation impact screening assessment in the form of maximum hazard quotients, as well as the results of our preliminary screen for potential non

- inhalation risks from PB

- HAP. Also presented are the HAP “drivers,” which are the HAP that collectively contribute 90 percent of the maximum cancer risk or maximum hazard at the highest exposure location
, Definitions of these and other Inhalation Cancer Risk Population Cancer Risk Max Chronic Individual Non

Cancer Risk Max Acute Non

- Cancer Risk Multipathway

Analysis

MIR (in 1 million) Risk Driver Cancer Incidence (cases per year) ≥ 1 in 10

in 1 million ≥ 1 in 1

million HI

(TOSHI) Risk Driver HQ Risk Driver Max PB

- HAP

Emissions / Screening Level

Baseline Actual Emissions

Source Category 10 Strontium chromate 0.02 1,500 180,000 0.5 Cadmium compounds 2 Ethylene glycol ethyl ether
acetate (REL) Cadmium

Compounds : 1 (Tier 1)

Divalent Mercury : 1 (Tier 1)

Whole Facility 20 Arsenic, Chromium compounds 0.04 3,200 270,000 0.5 Hexamethylene

- 1,6

- diisocyanate --- --- ---

Baseline Allowable Emissions

Source Category 10 Strontium chromate 0.02 2,000 180,000 0.5 Cadmium Compounds --- --- ---

### Risk Assessment for the Aerospace Manufacturing and Rework Facilities Source Category 

### 39

relevant risk

- related terms can be found on the 2005 National

- Scale Air Toxics Assessment Glossary Web page

. A detailed summary of the facility

- specific inhalation and multipathway risk assessment results is available in Appendix 7 of this document ( Detailed Risk Modeling Results )

.

MACT

- level Inhalation Assessment Results (Actual)

Table s 3 .2

- 2 and 3.2

- 3 summarize the chronic and acute inhalation risk results for this source category based upon baseline actual emissions. T he results of the chronic inhalation cancer risk assessment are that the maximum lifetime individual cancer risk posed by the 14 4 facilities could be 1 0 in a million considering both the maximum facility, and the entire
source sector . Emissions of strontium chromate, from coating operations are the major contributors to this risk. The total estimated cancer incidence from this source category is 0. 02 excess cancer cases per year, or one excess case in every 50 years. A pproximately 1
, 5 00 pe ople were estimated to have cancer risks above 10 in a million and approximatel y 18 0 ,000 peop le were estimated to have cancer risks above 1 in a million considering the 14 4 facilities in this source category . The maximu m chronic noncancer TOSHI value for the source category could be up to 0. 5 (kidney) driven by emissions of cadmium compounds f rom blast depainting.

Worst

- case acute hazard quotients (HQs) were calculated for every HAP shown in Table 3.1

- 1 that has an acute benchmark , and the highest refin ed screening acute HQ value of 2 (based on the acute REL for e thylene glycol ethyl ether acetate ) is shown in Table 3.2

- 2

. This value includes a refinement of determining the highest HQ value that is outside facility boundaries. It’s also important to note that the highest HQ assumes th at the primary source of the e thylene glycol ethyl ether acetate emissions from coating operations , was modeled with an hourly emissions multiplier of 1.2 times the annual emissions rate. Further, t his exceedance was only predicted to occur in a remote, n on

- inhabited area just adjacent t o the facility fence line for 2 hours a year. No facilitie s are estimated to have a n AEGL or an EPRG greater than 1. Acute estimates for each p l ant and pollutant are provided in Appendix 7 of this document, while refined esti mates are provided in Appendix 8 of this document ( Acute Impacts Refined Analysis ).

Table 3.2

- 2

. MACT Level Actual Emissions C hronic Inhalation Risks for Aerospace Manufacturing and Rework Source Category



### Result HAP “Drivers” Facilities in Source Category Number of Facilities Estimated to be i n Source Category 14 4 n/a

Number of Facilities Identified by ICR and Modeled in Risk Assessment 14 4 n/a

Cancer Risks

### Risk Assessment for the Aerospace Manufacturing and Rework Facilities Source Category 

### 40

Table 3.2

- 2

. MACT Level Actual Emissions C hronic Inhalation Risks for Aerospace Manufacturing and Rework Source Category



### Result HAP “Drivers”
Maximum Individual Lifetime Cancer Risk (in 1 million) 1 0 strontium chromate, tetracholoroethene, chromium (VI) compounds, zinc chromate, barium chromate 

### Number of Facilities with Maximum Individual Lifetime Cancer Risk: Greater than or equal to 100 in 1 million 0 n/a
Greater than or equal to 10 in 1 million 2 strontium chromate, tetracholoroethene, chromium (VI) compounds, zinc chromate, barium chromate



### Greater than or equal to 1 in 1 million 27 strontium chromate, tetracholoroethene, chromium (VI) compounds, zinc chromate, barium chromate, cadmium compounds, ethyl benzene, sodium chromate, 4,4'

- methylenedianiline, nickel compounds, chromium (VI) trioxide (chromic acid mist), methylene chloride, zinc potassium chromate, trichloroethylene, calcium chromate, arsenic compounds, naphthalene, chro mic acid (VI), bis(2

- ethylhexyl)phthalate, 2,4

- toluene diisocyanate, formaldehyde, p

dichlorobenzene 

### Chronic Noncancer Risks

Maximum Kidney Hazard Index 0. 5 cadmium compounds 

### Number of Facilities with Maximum Respiratory Hazard Index: Greater than 1 0 n/a Acute Noncancer Screening Results

Maximum Acute Hazard Quotient 31 2 0.03 0.02 0.01 0.005 Ethylene glycol ethyl ether acetate (REL)

Phenol (ERPG

- 1) Phenol (AEGL

- 1) Phenol (AEGL

- 2) Phenol (ERPG

- 2) 

### Number of Facilities With Potential for Acute Effects 1 Ethylene glycol ethyl ether acetate 

### Population Exposure Number of People Living Within 50 Kilometers of Facilities Modeled 130 ,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 1 ,5 00 n/a

31 As shown in Tables 2.6.3 and 3.2

- 2 there are no AEGL or ERPG values for nickel or arsenic compounds. Additionally, there are no short

- term occupational values for these metals to compare to the REL values.

### Risk Assessment for the Aerospace Manufacturing and Rework Facilities Source Category 

### 41

Table 3.2

- 2

. MACT Level Actual Emissions C hronic Inhalation Risks for Aerospace Manufacturing and Rework Source Category



### Result HAP “Drivers” Greater than or equal to 1 in 1

million 180 ,000 n/a Number of People Exposed to Noncancer Respiratory Hazard Index: Greater than 1 0 n/a Estimated Cancer Incidence (excess cancer cases per year) 0. 02 n/a Contribution of HAP to Cancer Incidence

strontium chromate 66 % n/a chromium (VI) compounds 15 % n/a 4,4'

- methylenedianiline 3% n/a nickel compounds 3 % n/a cadmium compounds 3 % n/a 

### Contribution of Process to Cancer Incidence Coatings (CT) 46 % n/a Specialty Coatings (SC) 44 % n/a Blast Depainting (BD) 8% n/a Solvent Cleaning (CL) 2% n/a

Table 3.2

- 3

. MACT Level Actual Emissions Acute Inhalation Risks for Aerospace Manufacturing and Rework Source Category

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 )

Ethylene glycol ethyl ether acetate 0.3 2 0.14

Notes on Refined Process: 1) The initial screening was performed for all emitted HAP with available acute dose

- response values. Only those pollutants whose screening HQs were greater than 1 for at least one acute threshold value are shown in the table. 2) HAP with available acute dose

- response values which are not in the table do not carry any potential for posing acute health risks, based on an analysis of currently available emissions data.

Notes on Acute Dose

- Response Values: R EL

– California EPA reference exposure level for no adverse effects. Most, but not all, RELs are for 1

- hour exposures. AEGL

– Acute exposure guideline levels represent emergency exposure (1

- hour) limits for the general public.

### Risk Assessment for the Aerospace Manufacturing and Rework Facilities Source Category 

### 42

AEGL

- 1 is the exposure level above which it is predicted that the general population, including susceptible individuals, could experience effects t hat are notable discomfort, but which are transient and reversible upon cessation of exposure. AEGL

- 2 is the exposure level above wh ich it is predicted that the general population, including susceptible individuals, could experience irreversible or other serious, long

- lasting adverse health effects of an impaired ability to escape. EPRG

– Emergency Removal Program guidelines represent emergency exposure (1

- hour) limits for the general public. ERPG

- 1 is the maximum level below which it is believed that nearly all individuals could be exposed for up to 1 hour without experiencing other than mild, transient adverse health effects. ERPG

- 2 is the maximum exposure below which it is believed that nearly all individuals could be exposed for up to 1 hour without experiencing or developing irreversible or other serious health effects or symptoms which could impair an individual’s abilit y to take protective action.



### MACT

- level Multipathway Assessment Results (Actual)

To identify potential multipathway health risks from PB

- HAP other than lead, we first performed a screening analysis ( Tier 1 and 2 ) that compared emissions of PB

- HAP emitted from the petroleum refining source sector (based on actual emissions) to screening emission rates (see section 2.5). The PB

- HAP emitted by facilities in th e subject source category include cadmium compounds (31 fac ilities) and mercury compounds ( 6 facilities).

Following the above approach, no facility emit ted cadmium or mercury compounds above the Tier 1 screening level. Table 3.2

- 4 summarizes the results of this screening (Tier 1 ) comparison. Due to the theoret ical construct of the screening model, these factors are not directly translatable into estimates of risk or hazard quotients for these facilities.

Table 3.2

- 4

. MACT Level Actual Emissions

– Summary of Multipathway Screening for

Aerospace Manufacturing and Rework Source Category

Tier 1 Tier 2

PB

- HAP Num

Facilities Emitting PB

HAP (14 4 in source category) Max

PB

- HAP Emissions (TPY) Max PB

- HAP

Emissions / Screening Level Num

Facilities Exceeding PB

- HAP Screening Level Max

PB

- HAP Emissions / Screening Level Num

Facilities Exceeding PB

- HAP Screening Level
Cadmium Compounds 31 0.015 1 0 na na Divalent Mercury 6 0.00019 1 0 na na



### I n evaluating the potential for multipathway effects from emissions of lead, modeled maximum annual lead concentrations were compared to the NAAQS for lead (0.15 μg/m 3 

### )

. Lead emissions were reported from 103 facilities. Results of this analysis estimate that with the exception of one facility, the NAAQS for lead would not be exceeded by any facili ty. At the one facility, the lead NAAQS is exceeded 1.5 times at an on

- site location. Off

- site at this facility, the concentrations are below the NAAQS.

### Risk Assessment for the Aerospace Manufacturing and Rework Facilities Source Category 

### 43

MACT

- level Environmental Assessment Results (Actual)

We conducted a screening

- level evaluation of the potential adverse environmental risks associated with emissions of the following environmental HAP from the Aero space Manufacturing and Rework F acilities source category: lead, mercury, cadmium, hydrogen chloride and hydrogen fluoride. The results of the environmental screening analysis are summarized in Table 3.2

- 5

. In the Tier 1 screening analysis for PB

- HAP (other than lead, which was evaluated d ifferently) the individual modeled Tier 1 concentrations for mercury and cadmium did not exceed any ecological benchmark for any facility in the source category. For lead, we did not estimate any exceedances of the secondary lead NAAQS.

For hydrogen fluoride and hydrogen chloride, the average modeled concentration around each facility (i.e., the av erage concentration of all off

- site data points in the modeling domain) did not exceed the ecological benchmark s

. In addition , each individual modeled concentration of hydrogen chloride and hydrogen fluoride (i.e., each off

- site data point in the modeling domain) was below the ecological benchmarks for all facilities.

Table 3.2

- 5 MACT Level Actual Emissions

- Summary of Environmental Risk Screen Results

for the Aerospace Manufacturing and Rework Facilities Source Category



### Environmental HAP Number of Facilities In Category Exceeding Percent of Modeled Area in Category Exceeding 2 

### Tier 1 Screen Tier 2 Screen 1 

### N O A EL LO AE L N O A EL L O A EL N O AE L LO A EL

PB

HAP Pb None None NA NA 0% 0%

Hg NA None NA NA 0% 0% Cd None None NA NA 0% 0% Acid Gases HF 3 

### N A N one

- - NA 0% HCL 4 

### N A N one

- - NA 0%

NA

– Not Applicable. 1

- Tier 2 screen is performed for PB

- HAP when there are exceedances of the Tier 1 screen. The acid gas screen is a one tier screen. 2

- A value of 0% indicates that none of the modeled data points exceeded the benchmark. For PB

- HAP the percent area is based on the Tier 2 results, if a Tier 2 analysis is performed. Otherwise, the percent area is based on the Tier 1 results. 3

– For HF, we evaluated two benchmarks, one from Canada and th e other from the State of Washington. Although, they are both considered to be LOELs

- the level between a NOAEL and a LOAEL, we have listed the results under the LOAEL column. 4

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



### Facility

- wide Inhalation Assessment Results (Actual)

### Risk Assessment for the Aerospace Manufacturing and Rework Facilities Source Category 

### 44

The facility

- wide chronic MIR and TOSHI, available in Appendix 7 , are based on emissions from all sources at the identified facilities (both MACT and non MACT sources) . The results of the facility

- wide assessment f or cancer ris ks are summarized in Table 3.2

- 6 . The results indicate that 44 facilities with a erospace m anufacturing and r ework processes have a facility

wide cancer MIR greater than or equal to 1 in a million. The maximum facility

- wide cancer MIR is 2 0 in a million
, mainly driven by arsenic
, and chromium (VI) compounds, from internal combustion engine s

. The maximum facility

- wide TOSHI for the source category is estimated to be less than 1 , mainly driven by emissions of h examethylene

- 1,6

- diisocyanate fr om non MACT s pecialty c oatings operations

.

Table 3.2

- 6 Source Category Contribution to Facility

- Wide Chronic Cancer Risks (Actual Emissions)

P etroleum Refining Source

Sector Number of Facilities Binned by Facility

- Wide MIR (in 1 million) Source Sector MIR Contribution to Facility

- Wide MIR <1 1≤ MIR<10 10≤ MIR<100 > 100 Total

> 90% 49 22 1 0 72 

### 50

- 90% 16 1 2 0 19 

### 10

- 50% 14 2 1 0 17 

### < 10% 21 15 0 0 36 

### Total 100 40 4 0 144



### 3.2.2 Baseline Allowable Emission R isks
B aseline risk results were also developed using the best estimates of allowable HAP emissions under the current MACT standards. The basic chronic inhalation risk estimates presented here are the maximum individual lifetime cancer risk, the maximum chronic hazard index, and the cancer incidence. Acute, multipathway or ecological risks are not determined for allowable emissions

.

MACT

- level Inhalation Assessment Results (Allowable)

Analysis of potential differences between actual emissions levels and the maximum emissions allowable under the MACT standards (i.e., MACT

- allowable emissions) were also calculated for the source category. Risk results from the inhalation risk assessment using the MACT

allowable emissions indicate that the maximum lifetime indi vidual cancer risk could as high as 10 in a million, and that the maximum chronic noncancer Hazard Index value is about 0.5. The total estimated cancer incidence from this source category considering allowable emissions is expected to be about 0.02 excess cancer cases per year or one excess case in every 50 years. Based on allowable emission rates approximately 2,000 people were estimated to have cancer risks above 10 in a million and approximately 180,000 people were estimated to have cancer risks above 1

.

### Risk Assessment for the Aerospace Manufacturing and Rework Facilities Source Category 

### 45



## 4 General d iscussion of u ncertainties and h ow t hey h ave b een a ddressed

4.1 E xposure m odeling u ncertainties Although every effort has been made to identify all the relevant facilities and emission points, as well as to develop accurate estimates of the annual emission rates for all relevant HAP, the uncertainties in our emission inventory likely dominate the unc ertainties in the exposure analysis . The chronic ambient modeling uncertainties are considered relatively small in comparison, since we are using EPA’s refined local dispersion model with site

- specific parameters and reasonably representative meteorology. If anything, the population exposure estimates are biased high by not accounting for short

- or long

- term population mobility, and by neglecting processes like deposition, plume depletion, and atmospheric degradation. Additionally, estimates of the MIR c ontain uncertainty , because they are deriv ed at census block centroid locations rather than actual residences. This uncertainty is known to create potential underestimates and overestimates of the actual MIR values for individual facilities, but, overall, it is not thought to have a significant impact on the estimated MIR for a source category. Finally, we did not factor in the possibility of a source closure occurring during the 70

- year chronic exposure period, leading to a potential upward bias in both the MIR and population risk estimates; nor did we factor in the possibility of population growth during the 70

- year chronic exposure period, leading to a potential downward bias in both the MIR and population risk estimates.

A sensitivity analysis perform ed for the 1999 NATA found that the selection of the meteorology dataset location could result in a range of chronic ambient concentrations which varied from as much as 17 percent below the predicted value to as much as 84 percent higher than the predicted value. This variability translates directly to the predicted exposures and risks in our assessment, indicating that the actual risks could vary from 17 percent lower to 84 percent higher than the predicted values.

We have purposely biased the acute scre ening results high, considering that they depend upon the joint occurrence of independent factors, such as hourly emissions rates, meteorology and human activity patterns. Furthermore, i n cases where multiple acute threshold values are considered scientif

ically acceptable we have chosen the most conservative of these threshold values , erring on the side of overestimating potential health risks from acute exposures. In the cases where these results indicated the potential for exceeding short

- term health th resholds, we refined our assessment by developing a better understanding of the geography of the facility relative to potential exposure locations.



## 4.2 Uncertainties in the d ose - r esponse r elationships In the sections that follow, separate discussions are pr ovided 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) , bu t may also be derived for acute (

-

### Risk Assessment for the Aerospace Manufacturing and Rework Facilities Source Category 

### 46

term (>24 hours up to 30 days), and subchronic (>30 days up to 10 percent of lifetime) exposure durations, all of which are derived based on an assumption of continuous exposure throughout the duratio n specified. For the purposes of assessing all potential health risks associated wit h 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.

Alt hough every effort is made to identify peer

- review ed dose

- response values for all HAP emitted by the source category included in an assessment, some HAP have no peer

- reviewed cancer potency values or reference values for chronic non

- cancer or acute effects (inhalation or ingestion) . Since exposures to these pollutants cannot be included in a quantitative risk estimate, an understatement of risk for these pollutants at environmental exposure levels is possible.

Additionally, chronic dose

- response values fo r certain compounds included in the assessment may be under EPA IRIS review and revised assessments may determine that these pollutants are more or less potent than currently thought. We will re

- evaluate risks if, as a result of these reviews, a dose

- resp onse metric changes enough to indicate that the risk assessment may significantly mischaracterize human health risk

Cancer assessment

The discussion of dose

- response uncertainties in the estimation of cancer risk below focuses on the uncertainties associ ated with the specific approach currently used by the EPA to develop cancer potency factors. In general, these same uncertainties attend the development of cancer potency factors by CalEPA, the source of peer

- reviewed cancer potency factors used where EPA

- developed values are not yet available. To place this discussion in context, we provide a quote from the EPA’s Guidelines for Carcinogen Risk Assessment [ 29 ] (herein referred to as Cancer Guidelines ). “The primary goal of EPA actions is protection of hum an health; accordingly, as an Agency policy, risk assessment procedures, including default options that are used in the absence of scientific data to the contrary, should be health protective.” The approach adopted in this document is consistent with this approach as described in the Cancer Guidelines

. For cancer endpoints EPA usually derives an oral slope factor for ingestion and a unit risk value for inhalation exposures. These values allow estimation of a lifetime probability of developing cancer give n long

- term exposures to the pollutant. Depending on the pollutant being evaluated, EPA relies on both animal bioassay and epidemiological studies to characterize cancer risk. As a science policy approach, consistent with the Cancer Guidelines , EPA uses animal cancer bioassays as indicators of potential human health risk when other human cancer risk data are unavailable.

Extrapolation of study data to estimate potential risks to human populations is based upon EPA’s assessment of the scientific databa se for a pollutant using EPA’s guidance documents and other peer

- reviewed methodologies. The EPA Cancer Guidelines describes the Agency’s recommendations for methodologies for cancer risk assessment. EPA believes that cancer risk estimates developed foll owing the procedures described in the Cancer Guidelines and

### Risk Assessment for the Aerospace Manufacturing and Rework Facilities Source Category 

### 47

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 usuall y not a true statistical confidence limit). 32 

### In some circumstances, the true risk could be as low as zero; however, in other circumstances the risk could also be greater. 33 

### 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. 34 

### EPA also uses the upper bound (rather than lower bound or central) estimates in its assessments, although it is noted that this approach can have limitations for some us es (e.g. priority setting, expected benefits analysis).

Such health risk assessments have associated uncertainties, some which may be considered quantitatively, and others which generally are expressed qualitatively. Uncertainties may vary substantially among cancer risk assessments associated with exposures to different pollutants, since the assessments employ different databases with different strengths and limitations and the procedures employed may differ in how well they represent actual biological p rocesses for the assessed substance. EPA’s Risk Characterization Handbook also recommends that risk characterizations present estimates demonstrating the impact on the assessment of alternative choices, data, models and assumptions [ 30 ]. Some of the major sources of uncertainty and variability in deriving cancer risk values are described more fully below.

(1) The qualitative similarities or differences between tumor responses observed in experimental animal bioassays and those which would occur in humans are a source of uncertainty in cancer risk assessment. In general, EPA does not assume that tumor sites observed in an experimental animal bioassay are necessarily predictive of the sites at which tumors would occur in humans. 35 

### However, unless scientific support is available to show otherwise, EPA assumes that tumors in animals are relevant in humans, reg ardless of target organ concordance. For a specific pollutant, qualitative differences in species responses can lead to either under

- estimation or over

- estimation of human cancer risks.

(2) Uncertainties regarding the most appropriate dose metric for a n assessment can also lead to differences in risk predictions. For example, the measure of dose is commonly expressed

32 IRIS glossary (www.epa.gov/NCEA/iris/help_gloss.htm).
33 The exception to this is the URE for benzene, which is considered to cover a range of values, each end of which is considered to be equally plausible, and which is based on maximum likelihood estimates.
34 According to the NRC report Science and Judgment in Risk Assessment (NRC, 1994) “[Default] options are generic approaches, based on general scientific knowledge and policy judgment, that are applied to various elements of the risk

- assessment process when the correct scientific model is unknown or uncertain.” The 1983 NRC report Risk Assessment in the Federal Government: Managing the Process defined default option as “the option chose n 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 t he risks posed by a specific substance when it believes this to be appropriate. In keeping with EPA’s goal of protecting public health and the environment, default assumptions are used to ensure that risk to chemicals is not underestimated (although defau lts are not intended to overtly overestimate risk). See EPA 2004 An Examination of EPA Risk Assessment Principles and Practices, EPA/100/B

- 04/001 available at: http://www.epa.gov/osa/pdfs/ratf

- final.pdf.
35 Per the EPA Cancer Guidelines: “The default optio n 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.”

### Risk Assessment for the Aerospace Manufacturing and Rework Facilities Source Category 

### 48

in units of mg/kg/d ingested or the inhaled concentration of the pollutant. However, data may support development of a pharmacokinetic m odel for the absorption, distribution, metabolism and excretion of an agent, which may result in improved dose metrics (e.g., average blood concentration of the pollutant or the quantity of agent metabolized in the body). Quantitative ncertainties result when the appropriate choice of a dose metric is uncertain or when dose metric estimates are themselves uncertain (e.g., as can occur when alternative pharmacokinetic models are available for a compound). Uncertainty in dose estimates may lead to either ov er or underestimation of risk.

(3) For the quantitative extrapolation of cancer risk estimates from experimental animals to humans, EPA uses scaling methodologies (relating expected response to differences in physical size of the species), which introduce another source of uncertainty. These methodologies are based on both biological data on differences in rates of process according to species size and empirical comparisons of toxicity between experimental animals and humans. For a particular pollutant, the quantitative difference in cancer potency between experimental animals and humans may be either greater than or less than that estimated by baseline scientific scaling predictions due to uncertainties associated with limitations in the test data and th e 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 o f cancer, which is used as the point of departure (or POD) for the remainder of the calculation. Statistical uncertainty in developing a POD using a benchmark dose (BMD) approach is generally addressed though use of the 95 percent lower confidence limit o n the dose at which the specified excess risk occurs (the BMDL), decreasing the likelihood of understating risk. EPA has generally utilized the multistage model for estimation of the BMDL using cancer bioassay data (see further discussion below).

(5) Ext rapolation from high to low doses is an important, and potentially large, source of uncertainty in cancer risk assessment. EPA uses different approaches to low dose risk assessment (i.e., developing estimates of risk for exposures to environmental doses o f an agent from observations in experimental or epidemiological studies at higher dose) depending on the available data and understanding of a pollutant’s mode of action (i.e., the manner in which a pollutant causes cancer). EPA’s Cancer Guidelines expres s a preference for the use of reliable, compound

- specific, biologically

- based risk models when feasible; however, such models are rarely available. The mode of action for a pollutant (i.e., the manner in which a pollutant causes cancer) is a key considera tion in determining how risks should be estimated for low

- dose exposure. A reference value is calculated when the available mode of action data show the response to be nonlinear (e.g., as in a threshold response). A linear low

- dose (straight line from PO D) approach is used when available mode of action data support a linear (e.g., nonthreshold response) or as the most common default approach when a compound’s mode of action is unknown. Linear extrapolation can be supported by both pollutant

- specific data and broader scientific considerations. For example, EPA’s Cancer Guidelines generally consider a linear dose

- response to be appropriate for pollutants that interact with DNA and induce mutations. Pollutants whose effects are additive to background biolo gical processes in cancer development can also be predicted to have low

- dose linear responses, although the

### Risk Assessment for the Aerospace Manufacturing and Rework Facilities Source Category 

### 49

slope of this relationship may not be the same as the slope estimated by the straight line approach.

EPA most frequently utilizes a linear low

- do se extrapolation approach as a baseline science

policy choice (a “default”) when available data do not allow a compound

- specific determination. This approach is designed to not underestimate risk in the face of uncertainty and variability. EPA believes t hat linear dose

- response models, when appropriately applied as part of EPA’s cancer risk assessment process, provide an upper bound estimate of risk and generally provide a health protective approach. Note that another source of uncertainty is the charact erization of low

- dose nonlinear, non

- threshold relationships. The National Academy of Sciences has encouraged the exploration of sigmoidal type functions (e.g., log

- probit models) in representing dose response relationships due to the variability in respo nse within human populations. Another National Research Council (NRC) report [ 31 ] 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 sourc es of variability in the human population: “One might expect these individual tolerances to vary extensively in humans depending on genetics, coincident exposures, nutritional status, and various other susceptibility factors...” Thus, if a distribution of thresholds approach is considered for a carcinogen risk assessment, application would depend on ability of modeling to reflect the degree of variability in response in human populations (as opposed to responses in bioassays with genetically more uniform rodents). Note also that low dose linearity in risk can arise for reasons separate from population variability: due to the nature of a mode of action and additivity of a chemical’s effect on top of background chemical exposures and biological processes.
As noted above, EPA’s current approach to cancer risk assessment typically utilizes a straight line approach from the BMDL. This is equivalent to using an upper confidence limit on the slope of the straight line extrapolation. The impact of the choice o f the BMDL on bottom line risk estimates can be quantified by comparing risk estimates using the BMDL value to central estimate BMD values, although these differences are generally not a large contributor to uncertainty in risk assessment (Subramaniam et. al., 2006) [ 32 ]. 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 invol ve the use of a POD. Subramaniam et. al. (2006) also provide comparisons indicating that slopes based on straight line extrapolation from a POD do not show large differences from those based on the upper confidence limit of the multistage model.

(6) Canc er risk estimates do not generally make specific adjustments to reflect the variability in response within the human population

— resulting in another source of uncertainty in assessments. In the diverse human population, some individuals are likely to be more sensitive to the action of a carcinogen than the typical individual, although compound

- specific data to evaluate this variability are generally not available. There may also be important life stage differences in the quantitative potency of carcinog ens 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.

### Risk Assessment for the Aerospace Manufacturing and Rework Facilities Source Category 

### 50

However, one approach used commonly in EPA assessments that may help address variability in response is to extrapolate human response from results observed in the most sensitive species and sex tested, resulting typically in the highest URE which can be supported by reliable data, thus suppo rting estimates that are designed not to underestimate risk in the face of uncertainty and variability.

Chronic noncancer assessment

Chronic noncancer reference values represent chronic exposure levels that are intended to be health

- protective. That is, EPA and other organizations which develop noncancer reference values (e.g., the Agency for Toxic Substances and Disease Registry

– ATSD R) utilize an approach that is intended not to underestimate risk in the face of uncertainty and variability. When there are gaps in the available information, uncertainty factors (UFs) are applied to derive reference values that are intended to be protec tive against appreciable risk of deleterious effects . Uncertainty factors are commonly default values 36 

### e.g., factors of 10 or 3, used in the absence of compound

- specific data; where data are available, uncertainty factors may also be developed using compo und

- specific information. When data are limited, more assumptions are needed and more default factors are used. Thus there may be a greater tendency to overestimate risk

— in the sense that further study might support development of reference values that a re higher (i.e., less potent) because fewer default assumptions are needed. However, for some pollutants it is possible that risks may be underestimated.

For non

- cancer endpoints related to chronic exposures, EPA derives a Reference Dose (RfD) for exposu res via ingestion, and a Reference Concentration (RfC) for inhalation exposures. These values provide an estimate (with uncertainty spanning perhaps an order of magnitude) of daily oral exposure (RfD) or of a continuous inhalation exposure (RfC) to the hu man population (including sensitive subgroups) that is likely to be without an appreciable risk of deleterious effects during a lifetime. 37 

### To derive values that are intended to be “without appreciable risk,” EPA’s methodology relies upon an uncertainty fa ctor (UF) approach [ 33 ], [ 34 ] which includes consideration of both uncertainty and variability.

EPA begins by evaluating all of the available peer

- reviewed literature to determine non

cancer endpoints of concern, evaluating the quality, strengths and lim itations of the available studies. EPA typically chooses the relevant endpoint that occurs at the lowest dose, often

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

- assessment process when t he 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 choic e 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 appropri ate. In keeping with EPA’s goal of protecting public health and the environment, default assumptions are used to ensure that risk to chemicals is not underestimated (although defaults are not intended to overtly overestimate risk). See EPA 2004 An examina tion of EPA Risk Assessment Principles and Practices, EPA/100/B

- 04/001 available at: http://www.epa.gov/osa/pdfs/ratf

- final.pdf.

37 See IRIS glossary

### Risk Assessment for the Aerospace Manufacturing and Rework Facilities Source Category 

### 51

using statistical modeling of the available data, and then determines the appropriate point of departure (POD) for derivation of the refer ence value. A POD is determined by (in order of preference): (1) a statistical estimation using the benchmark dose (BMD) approach; (2) use of the dose or concentration at which the toxic response was not significantly elevated (no observed adverse effect level

— NOAEL); or (3) use of the lowest observed adverse effect level (LOAEL).

A series of downward adjustments using default UFs is then applied to the POD to estimate the reference value [ 35 ] . While collectively termed “UFs”, these factors account for a number of different quantitative considerations when utilizing observed animal (usually rodent) or human toxicity data in a risk assessment. The UFs are intended to account for: (1) variation in susceptibility among the members of the human population (i .e., inter

- individual variability); (2) uncertainty in extrapolating from experimental animal data to humans (i.e., interspecies differences); (3) uncertainty in extrapolating from data obtained in a study with less

- than

- lifetime exposure (i.e., extrapolat ing from subchronic to chronic exposure); (4) uncertainty in extrapolating from a LOAEL in the absence of a NOAEL; and (5) uncertainty when the database is incomplete or there are problems with applicability of available studies

. When scientifically sound , peer

- reviewed assessment

- specific data are not available, default adjustment values are selected for the individual UFs. For each type of uncertainty (when relevant to the assessment) , EPA typically applies an UF value of 10 or 3 with the cumulative UF v alue leading to a downward adjustment of 10

- 3000 fold from the selected POD. An UF of 3 is used when the data do not support the use of a 10

- fold factor. If an extrapolation step or adjustment is not relevant to an assessment (e.g., if applying human
tox icity data and an interspecies extrapolation is not required) the associated UF is not used. The major adjustment steps are described more fully below.

1) Heterogeneity among humans is a key source of variability as well as uncertainty. Uncertainty related to human variation is considered in extrapolating doses from a subset or smaller

- sized population, often of one sex or of a narrow range of life stages (typical of occupational epidemiologic studies), to a larger, more diverse population. In the a bsence of pollutant

- specific data on human variation, a 10

- fold UF is used to account for uncertainty associated with human variation. Human variation may be larger or smaller; however, data to examine the potential magnitude of human variability are ofte n unavailable. In some situations, a smaller UF of 3 may be applied to reflect a known lack of significant variability among humans.

2) Extrapolation from results of studies in experimental animals to humans is a necessary step for the majority of chemi cal risk assessments . When interpreting animal data, the concentration at the POD (e.g. NOAEL, BMDL) in an animal model (e.g. rodents) is extrapolated to estimate the human response. While there is long

- standing scientific support for the use of animal s tudies as indicators of potential toxicity to humans , there are uncertainties in such extrapolations. In the absence of data to the contrary, the typical approach is to use the most relevant endpoint from the most sensitive species and the most sensitive sex in assessing risks to the average human. Typically
, compound specific data to evaluate relative sensitivity in humans versus rodents are lacking, thus leading to uncertainty in this extrapolation. Size

- related differences (allometric relationships) indicate that typically

### Risk Assessment for the Aerospace Manufacturing and Rework Facilities Source Category 

### 52

humans are more sensitive than rodents when compared on a mg/kg/day basis. The default choice of 10 for the interspecies UF is consistent with these differences. For a s pecific chemic al, 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 p ossible. Information may not be available to quantitatively assess toxicokinetic or toxicodynamic differences between animals and humans, and in many cases a 10

- fold UF (with separate factors of 3 for toxicokinetic and toxicodynamic components) is used to account for expected species differences and associated uncertainty in extrapolating from laboratory animals to humans in the derivation of a reference value. If information on one or the other of these components is available and accounted for in the cr oss

- species extrapolation, a UF of 3 may be used for the remaining component.

3) In the case of reference values for chronic exposures where only data from shorter durations are available (e.g., 90

- day subchronic studies in rodents) or when such data are judged more appropriate for development of an RfC, an additional UF of 3 or 10

- fold is typically applied unless the available scientific information supports use of a different value.

4) Toxicity data are typically limited as to the dose or exposure leve ls that have been tested in individual studies; in an animal study, for example, treatment groups may differ in exposure by up to an order of magnitude. The preferred approach to arrive at a POD is to use BMD analysis; however, this approach requires adeq uate 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 ex posure level at which an adverse effect is not observed (i.e., a NOAEL is not identified). When using data limited to a LOAEL, a UF of 10 or 3

- fold is often applied.

5) The database UF is intended to account for the potential for deriving an underprotec tive RfD/RfC due to a data gap preventing complete characterization of the chemical’s toxicity. In the absence of studies for a known or suspected endpoint of concern, a UF of 10 or 3

- fold is typically applied.

There is no RfD or other comparable chronic health benchmark value for lead compounds. Thus, to address multipathway human health and environmental risks associated with emissions of lead from this facility, ambient lead concentrations were compared to the NAAQS for lead. In developing the NAAQS for lead, EPA considered human health evidence reporting adverse health effects associated with lead exposure, as well as an EPA conducted multipathway risk assessment that applied models to estimate human exposures to air

- related lead and the associated r isk (73FR at 66979). EPA also explicitly considered the uncertainties associated with both the human health evidence and the exposure and risk analyses when developing the NAAQS for lead. For example, EPA considered uncertainties in the relationship betwe en ambient air lead and blood lead levels (73FR at 66974), as well as uncertainties between blood lead levels and loss of IQ points in children (73FR at 66981). In considering the evidence and risk analyses and their associated uncertainties, the EPA

### Risk Assessment for the Aerospace Manufacturing and Rework Facilities Source Category 

### 53

Admi nistrator noted his view that there is no evidence

- or risk

- based bright line that indicates a single appropriate level. Instead, he noted, there is a collection of scientific evidence and judgments and other information, including information about the un certainties inherent in many relevant factors, which needs to be considered together in making this public health policy judgment and in selecting a standard level from a range of reasonable values (73FR at 66998). In so doing, the Administrator decided t hat, a level for the primary lead standard of 0.15 μg/m 3 

### , in combination with the specified choice of indicator, averaging time, and form, is requisite to protect public health, including the health of sensitive groups, with an adequate margin of safety (7 3FR at 67006). A thorough discussion of the health evidence, risk and exposure analyses, and their associated uncertainties can be found in EPA’s final rule revising the lead NAAQS (73 FR 66970

- 66981, November 12, 2008).

We also note the uncertainties associated with the health

- based (i.e., primary) NAAQS are likely less than the uncertainties associated with dose

- response values developed for many of the other HAP, particularly those HAP for which no human health data exist. In 1988, EPA’s IRIS progr am reviewed the health effects data regarding lead and its inorganic compounds and determined that it would be inappropriate to develop an RfD for these compounds, saying, “A great deal of information on the health effects of lead has been obtained through decades of medical observation and scientific research. This information has been assessed in the development of air and water quality criteria by the Agency's Office of Health and Environmental Assessment (OHEA) in support of regulatory decision

- making b y the Office of Air Quality Planning and Standards (OAQPS) and by the Office of Drinking Water (ODW). By comparison to most other environmental toxicants, the degree of uncertainty about the health effects of lead is quite low. It appears that some of thes e effects, particularly changes in the levels of certain blood enzymes and in aspects of children's neurobehavioral development, may occur at blood lead levels so low as to be essentially without a threshold. The Agency's RfD Work Group discussed inorganic lead (and lead compounds) at two meetings (07/08/1985 and 07/22/1985) and considered it inappropriate to develop an RfD for inorganic lead.” EPA’s IRIS assessment for Lead and compounds (inorganic) (CASRN 7439

- 92

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

.

We note further that because of the multi

- pathway, multi

- media impacts of lead, the risk assessment supporting the NAAQS considered direct inhalation exposures and indirect air

related multi

- pathw ay exposures from industrial sources like primary and secondary lead smelting operations. It also considered background lead exposures from other sources (like contaminated drinking water and exposure to lead

- based paints). In revising the NAAQS for lead , we note that the Administrator placed more weight on the evidence

- based framework and less weight on the results from the risk assessment, although he did find the risk estimates to be roughly consistent with and generally supportive of the evidence

- base d framework applied in the NAAQS determination (73FR at 67004). Thus, when revising the NAAQS for lead to protect public health with an adequate margin of safety, EPA considered both the evidence

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

### Risk Assessment for the Aerospace Manufacturing and Rework Facilities Source Category 

### 54

Acute noncancer assessment

Many of the UFs used to account for variability and uncertainty in the development of acute reference values are quite similar to those developed for chronic durations, but more often using individual UF values that m ay be less than 10. UFs are applied based on chemical

specific or health effect

- specific information (e.g., simple irritation effects do not vary appreciably between human individuals, hence a value of 3 is typically used), or based on the purpose for the reference value (see the following paragraph). The UFs applied in acute reference value derivation include: 1) heterogeneity among humans; 2) uncertainty in extrapolating from animals to humans; 3) uncertainty in LOAEL to NOAEL adjustments; and 4) uncer tainty in accounting for an incomplete database on toxic effects of potential concern. Additional adjustments are often applied to account for uncertainty in extrapolation from observations at one exposure duration (e.g., 4 hours) to arrive at a POD for d erivation of an acute reference value at another exposure duration (e.g., 1 hour).

Not all acute reference values are developed for the same purpose and care must be taken when interpreting the results of an acute assessment of human health effects relative to the reference value or values being exceeded. W here relevant to the estimated exposures, the lack of threshold values at different levels of severity should be factored into the risk characterization as potential uncertainties.

### Risk Assessment for the Aerospace Manufacturing and Rework Facilities Source Category 

### 55



## 5 References

1 . US EPA. National Emission Standards for Hazardous Air Pollutants

. http://www.epa.gov/ttn/atw/mactfnlalph.html

. 2 . US EPA, 2009. Risk and Technology Review (RTR) Risk Assessment Methodologies: For Review by the EPA’s Science Advisory Board with Case Studies

– MA CT I Petroleum Refining Sources and Portland Cement Manufacturing

. EPA

- 452/R

- 09

- 006. http://www.epa.gov/ttn/atw/rrisk/rtrpg.html.

3 . US EPA, 2010. SAB’s Response to EPA’s RTR Risk Assessment Methodologies. http://yosemite.epa.gov/sab/sabproduct.nsf/4AB3966E263D943A8525771F00668381/$File/E PA

- SAB

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4 . US EPA, 2010. Memorandum from Dave Guinnup to Docket EPA

- HQ

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- 0600, entitled, “EPA’s Actions in Response to Key Recommendations of the SAB Review of RTR Risk Assessment Methodologies”.

5 . US EPA, 2005. Revision to the Guideline on Air Quality Models: Adoption of a Preferred General Purpose (Flat and Com plex 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/B

03

- 001

. http://www.epa.gov/ttn/scram/models/aermod/aermod_userguide.zip

7 . US EPA, 2004. Air Toxics Risk Assessment Reference Library, Volume 1. EPA

- 453

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

8

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

9

. US EPA, 2014. Table 1. Prioritized Chronic Dose

- Response Values (5 / 9 / 14 ). Office of Air Quality Planning and Standards. http://www2.ep a.gov/fera/dose

- response

- assessment

assessing

- health

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- air

- pollutants

10 . US EPA, 2005. 1999 National Air Toxics Risk Assessment. http://www.epa.gov/ttn/atw/nata1999

11

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

. 12

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

. 13

. US EPA, 1994

. US Environmental Protection Agency. Methods for Derivation of Inhalation Reference Concentrations and Application of Inhalation Dosimetry. EPA/600/8

- 90/066F. Office of Research and Development. Washington, DC: U.S.EPA.

14

. NRC, 1994. National Research Council. Science and Judgment in Risk Assessment. Washington, DC: National Academy Press.

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15

. CA Office of Environmental Health Hazard Assessment, 2014. Chronic Reference Exposure Levels Adopted by OEHHA as of January 2014. http://oehha.ca.gov/air/allrels.html

16

. CA Office of Environmental H ealth Hazard Assessment, 2005. Technical Support Document for Describing Available Cancer Potency Factors, May 2005

. http://www.oehha.ca.gov/air/hot_spots/pdf/may2005Hotspots.p df

17 . US EPA, 2005. Table 2. Acute Dose

- Response Values for Screening Risk Assessments (6/02/2005). Office of Air Quality Planning and Standards. http://www2.epa.gov/fera/dose

response

- assessment

- assessing

- health

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- associated

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- air

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18

. U .S. EPA , 2009 . Graphical Arrays of Chemical

- Specific Health Effect Reference Values for Inhalation Exposures [Final Report]. EPA/600/R

- 09/061, 2009. http://cfpub.epa.gov/ncea/cfm/ recordisplay.cfm?deid=211003

19 . CA Office of Environmental Health Hazard Assessment, 2014. All Acute Reference Exposure Levels developed by OEHHA as of January 2014. http://oehha.ca.gov/air/allrels.h tml

20 . American Industrial Hygiene Association, 20 13 . Current AIHA ERPG Values. https://www.aiha.org/get

involved/aihaguide linefoundation/emergencyresponseplanningguidelines/Pages/default.aspx

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

. 22 . US EPA, 2000. Risk Characterization Handbook. EPA 100

- B

- 00

- 002.

23 . US EPA, 2002. EPA’s Guidelines for Ensuring and Maximizing the Quality, Objectivity, Utility, and Integrity of Information Disseminated by the Environmental Protection Agency. EPA Off ice of Environmental Information. EPA/260R

- 02

- 008

. http://www.epa.gov/quality/informationguidelines/documents/EPA_InfoQualityGuidelines.pdf

24

. US EPA , 2006. Performing risk assessments that include carcinogens described in the Supplemental Guidance as having a mutagenic mode of action. Science Policy Council Cancer Guidelines Implementation Workgroup Communication II: Memo from W.H. Farland dated 14 June 2006. http://www.epa.gov/osa/spc/pdfs/CGIWGCommunication_II.pdf

25

. US EPA, 2005. Supplemental guidance for assessing early

- life exposure to carcinogens. EPA/630/R

- 03003F. http://www.epa.gov/ttn/atw/childrens_supplement_final.pdf

.
26

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

27

. US EPA, 1986. Guidelines for the Health Risk Assessment of Chemical Mixtures. EPA

- 630

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- 002. http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=22567

28

. US EPA, 2000. Supplementary Guidance for Conducting Health Risk As sessment of

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Chemical Mixtures. EPA

- 630/R

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- 002. http://www.epa.gov/raf/publications/pdfs/CHEM_MIX_08_2001.PDF

29

. US EPA, 2005. Guidelines for Carcinogen Risk Assessment (2005). U.S. Environmental Protection Agency, Washington, DC, EPA/630/P

- 03/001F, 2 005. http://cfpub.epa.gov/ncea/raf/recordisplay.cfm?deid=116283

30

. US EPA

. 2000

. Risk Characterization Handbook. EPA 100

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31

. NRC (National Research Council) 2006. Assessing the Human Health Risks of Trichloroethylene. National Academies Press, Washington DC

. 32

. R.P. Subramaniam, P. White and V.J. Cogliano. 2006. Comparison of cancer slope factors using different statistical approaches, Risk Anal. Vol 26, p. 825

– 83 0.

33 . US EPA. 1993. Reference Dose (RfC): Description and Use in Health Risk Assessments. http://www.epa.gov/iris/rfd.htm

. 34 . US EPA. 1994. Methods for Derivation of Inhalation Reference Concentration s and Application of Inhalation Dosimetry. http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=71993

. 35 . US EPA. 2002. A Review of the Reference Dose and Reference Concentration Processes. http://www.epa.gov/ncea/iris/RFC_FINAL1.pdf

### 1 Appendix 1

Aerospace Manufacturing and Rework Facilities RTR Modeling File Preparation

### 2

MEMORANDUM

TO: Anne Pope and Kim Teal , U.S. EPA

FROM: Stacie Enoch, Jason Huckaby, Ashley Penrod, and Darcy Wilson , E RG

DATE: December 17 , 2014

SUBJECT: Aerospace Manufacturing and Rework Facilities RTR Modeling File Preparation

I. Introduction

The purpose of this memorandum is to provide information on the U.S. Environmental Protection Agency (EPA) Risk and Technology Review (RTR) database used in estimating residual risk for the Aerospace Manufacturing and Rework Facilities Nation al Emission Standards For Hazardous Air Pollutants (NESHAP) source category. This NESHAP covers only major sources.
Section 112 of the Clean Air Act (CAA) establishes a two

- stage regulatory process to address emissions of hazardous air pollutants (HAPs) fr om stationary sources. In the first stage, section 112(d) requires the EPA to develop technology

- based standards for categories of industrial sources. In the second stage, known as the residual risk stage, section 112(f)(2) requires EPA to assess the healt h and environmental risks that remain after sources come into compliance with standards based on the Maximum Achievable Control Technology (MACT) . If additional risk reductions are necessary to protect public health with an ample margin of safety or to pre vent adverse environmental 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 Aerospace Manufacturing
NESHAP source category.

The 2002 National Emissi ons Inventory (NEI) data served as the starting point for the initial phase of this assessment. Using the process regulatory code, 1 

### a subset of the NEI was developed that contained facility, process, and emissions data for each facility potentially subject to the Aerospace Manufacturing and Rework Facilities NESHAP (40 CFR 63, subpart GG) and in the Aerospace Manufacturing source category ( regulatory code 63GG ) . The 2002 NEI data for Aerospace Manufacturing facilities were released throug h an Advanced Notice of Proposed Rulemaking (ANPR) in March of 2007, where 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

wer e incorporated into the 2005 NEI.

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

### www.erg.com

### 3

The 2005 NEI data then served as the starting point for the next phase of the RTR assessment.
On February 4, 2011, EPA sent out an information collection request ( ICR ) requesting data on
aerospace

- related processes and ge neral information about each facility

. Information was requested on operations subject to 40 CFR 63, subpart GG (coatings, blast depainting operations
, solvent depainting operations, and solvent cleaning operations) as well as specialty coatings, chemical milling and metal finishing operations, composite processing, storage tanks, and wastewater
treatment. Information was also requested on booth characteristics and control devices and locational coordinates.

On October 2, 2012, EPA sent out an ICR requesting stack emissions testing data for select coating operations and spray booths and blast depainting, composite processing and metal finishing operations.

On May 23, 2013, EPA provided the estimated facility

- wide emissions rates for major sources a nd synthetic minor sources based on the results of the ICR data collection effort for industry review

. On September 18, 2013, EPA issued a request for supplemental coatings analyses for select facilities.

On May 15, 2014, EPA provided i ndustry the opportunity for a final review of the estimated emissions ( and emission estimation methods ) for processes subject to 40 CFR 63, subpart GG and other processes included in the February 4, 2011 ICR request, as well as other facility operations included in the 2011 NEI. Review comments were also solicited on stack parameters (including fugitive area dimensions) and locational coordinates.
The following sections detail how EPA calculat ed emissions and conducted quality assurance/quality control ( QA/QC ) of the data.

II. Category and Specialty Coatings Actual Emissions Development

a. Regulated processes include d coatings, blast depainting, solvent depainting, and solvent cleaning

. Non

- regulated processes include d specialty coatings, chemical milling and metal finishi ng, composite processing, storage tanks, and wastewater
treatment.

b. February 4, 2011 ICR Data Compilation The ICR request ed data on coating usage and physical property data, emissions, booth characteristics, and locational coordinates. Forms A

- 1 and A

- 2 requested facility address and contact information. Form B

- 1 requested paint spray

- booth

- specific information. Form C

- 1 (and C

- 2 in metric units) requested coating usage and physical property data that EPA then used in calculating HAP emission estimates

. Forms D

- 1, E

- 1 through E

- 3, G

- 1, and H

- 1 requested HAP emission estimates from tanks used for chemical milling or metals finis hing operations, blast depainting/cleaning operations, solvent depainting operations, solvent cleaning operations, storage tanks, and
wastewater treatment. Form F

- 1 requested resins usage and physical property data that EPA then used in calculating HAP emi ssion estimates from composites processing

### 4 operations . Forms J

- 1 through J

- 10 requested information specific to each control device used.

c. Development of Actual Emissions Estimates The following equations and assumptions were used to develop actual emissio ns estimates from the ICR data

.
Coating Operations (C1)

Emissions were calculated for each HAP within a coating and for each booth in which the coating was used. The following equations were used to calculate coating emissions in tons per year

.

 Organic HAP stack emissions from coating usage inside a booth :

Volume ∗ Density ∗ HAP Mass Fraction ∗ ( Coating Usage Pct inside boot h / 100 ) ∗ ( Capture Efficiency / 100 ) ∗ ( 1

− ( Control Efficie ncy / 100 ) ) ∗ ( 1 / 2000 )

 Organic HAP fugitive emissions from coating usage inside a booth :

Volume ∗ Density ∗ HAP Mass Fraction ∗ ( Coating Usage Pct inside boot h / 100 ) ∗ ( 1

− Capture Efficiency / 100 ) ∗ ( 1 / 2000 )

 Organic HAP fugitive emissions from coating usage out side of a booth:

Volume ∗ Density ∗ HAP Mass Fraction

∗ ( Coating Usage Pct outside boot h / 100 ) ∗ ( 1 / 2000 )

 Inorganic HAP stack emissions:

Volume ∗ Density ∗ HAP Mass Fraction ∗ ( Coating Usage Pct inside boot h 100 ⁄ ) ∗ Adjustment Factor ∗ ( Capture Efficiency / 100 ) ∗ ( 1

− ( Filter Control Efficiency / 100 ) ) ∗ ( 1 / 2000 )

 Inorganic HAP fugitive emissions:

Volume ∗ Density ∗ HAP Mass Fraction ∗ ( Coating Usage Pct inside boot h 100 ⁄ ) ∗ Adjustment Factor ∗ ( 1

− Capture Efficiency / 100 ) ∗ ( 1 / 2000 )

 Inorganic HAP fugitive emissions (out of booth usage):

Volume ∗ Density ∗ HAP Mass Fraction ∗ ( Coating Usage Pct outside boot h 100 ⁄ ) ∗ Adjustment Factor ∗ (1/2000)

### 5 Where:

Volume = total coating volume used (gal/yr); Density = density of the coating (lb coating/gal coating); HAP Mass Fraction = the fraction of a specific HAP in the coating (lb HAP/lb coating); Coating Usage Pct = the portion of the annual coating usage at the spec ified location (i.e., in the identified booth, or outside of booth usage); Adjustment Factor = factor to account for the transfer of coating solids to the part being coated (assumed 65%) and fallout for coatings applied within a booth (assumed 50%); Captur e Efficiency = the percent of emissions captured by the booth; Control Efficiency = the percent reduction achieved by the control device (organic); Filter control efficiency = the percent reduction achieved by the booth filter (inorganic HAP ).

Assumptions

 Due to the overall lack of control efficiency data, all control efficiency values were assumed based on the type of control device (95% emission control for carbon adsorbers and scrubbers and 98% control for thermal incinerators). If no control device wa s listed, the control efficiency was assumed to be zero.

 All materials applied inside a booth were assumed to be spray applied.

 If no filter control efficiency was provided, a value of 98% was assumed.

 If no capture efficiency was provided, the capture efficiency was assumed based on the booth type and pollutant type.

o For organic pollutants, the capture efficiencies were assumed to be as follows: (open/open face/other : 90%, permanent total enclosure (PTE) : 100%; enclosed but not a PTE : 98%); if the facility did not provide a capture efficiency or booth type, a value of 90% was assumed.

o For inorganic pollutants, the capture efficiencies we re assumed to be as follows: (o pen/open face/other : 99%, permanent total enclosure (PTE) : 100%; enclosed but not a PTE : 99.5%); if the facility did not provide a capture efficiency or booth type, a value of 99% was assumed.

 It was assumed that inorganic fugitive emissions from applicati on of coatings outside of the booth are zero if the coatings we re not spray applied. If the coatings we re spray applied, the transfer efficiency was assumed to be 65%. If the application method for coatings applied outside of the booth was not specified, i t was assumed to be spray applied.

Chemical Milling and Metal Finishing Operations (D1)

Facilities conducting these types of operations were required to report emissions directly instead of providing the usage/composition data required to calculate emis sions (such as in C1)

. Therefore, the only calculation required was to convert emissions from pounds per year to tons per year.

### 6 Blast Depainting/Cleaning Operations (E1), Solvent Depainting Operations (E2), and Storage Tanks (G1)

Facilities conducting th ese types of operations were required to report emissions directly instead of providing the usage/composition data required to calculate emissions (such as in C1). However; it was assumed that the emission values reported ( Emislbyr in equations below) were stack emissions after capture and control, and it was therefore necessary to back

- calculate the fugitive emissions from the stack emissions. The following equations were used to calculate emissions for E1, E2, and G1 operations in tons per year

.

 Stack em issions:

Emislbyr ∗ ( 1 / 2000 )

 Fugitive emissions for units not equipped with a control device:

( Emislbyr / ( Capture Efficiency / 100 ) ) ∗ ( 1

− ( Capture Efficiency / 100 ) ) ∗ ( 1 / 2000 )

 Fugitive emissions for units equipped with a control device:

( Emislbyr / ( ( Capture Efficiency / 100 ) ∗ ( 1

− ( Control Efficiency / 100 ) ) ) ) ∗ ( 1

− ( Capture Efficiency / 100 ) ) ∗ ( 1 / 2000 )

Assumptions

 As stated above, it was assumed that the emi ssion values reported in the survey are stack emissions after capture and control.

 If no capture efficiency was provided, a value of 70% was used for E1 and E2 operations, and zero was used for G1 operations.

 All control efficiency values were assumed bas ed on the type of control device, as outlined for C1 operations.

 If a facility indicated that total mass balance emissions were reported instead of stack emissions after control, then the emissions were adjusted to take into account the capture and contro l efficiencies as requested.

Solvent Cleaning Operations

– Point and Fugitive Sources (E3)

Facilities conducting solvent cleaning operations were required to report emissions directly as either stack or fugitive emissions. Therefore, the only calculation required was to convert emissions from pounds per year to tons per year.

Composite Processing Operations (F1 Resins)

Emissions were calculated for each HAP within the resin and for each booth in which the resin was used. The following equations were used to calculate emissions from composite processing operations in tons per year

.

### 7  Stack emissions:

Volume ∗ Density ∗ HAP Mass Fraction ∗ ( Pct HAP emitted / 100 ) ∗ ( Resin Usage Pct inside boot h / 100 ) ∗ ( Capture Efficiency / 100 ) ∗ ( 1

− ( Control Efficiency / 100 ) ) ∗ ( 1 / 2000 )

 Fugitive emissions:

Volume ∗ Density ∗ HAP Mass Fraction ∗ ( Pct HAP emitted / 100 ) ∗ ( Resin Usage Pct inside boot h / 100 ) ∗ ( 1

− ( Capture Efficiency / 100 ) ) ∗ ( 1 / 2000 )

 Fugitive emissions (out of booth usage):

Volume ∗ Density ∗ HAP Mass Fraction ∗ ( Pct HAP emitted / 100 ) ∗ ( Resin Usage P ct outside boot h / 100 ) ∗ ( 1 / 2000 )

Assumptions

 All control efficiency values were assumed based on the type of control device, as outlined for C1 operations.

 All materials applied inside a booth were assumed to be spray applied.

 If no capture efficiency was provided, the capture efficiency was assumed based on the booth type and pollutant type.

o For organic pollutants, the capture efficiencies are assumed to be as follows: (open/open face/other : 90%, permanent total enclosure (PTE ) : 100%; enclosed but not a PTE : 98%); if the facility did not provide a capture efficiency or booth type, a value of 90% was assumed.

o For inorganic pollutants, the capture efficiencies are assumed to be as follows: (open/open face/other : 99%, permanent t otal enclosure (PTE) : 100%; enclosed but not a PTE : 99.5%); if the facility did not provide a capture efficiency or booth type, a value of 99% was assumed.

 For outside the booth application, if the percent HAP emitted was not provided, a value of 100 perc ent was assumed for organic pollutants. For inorganic pollutants from resins that are applied outside of a booth that are not spray applied, it was assumed that 0% was emitted. If the out of booth resins are spray applied, the transfer efficiency was assum ed to be 65% (or 35% emitted). If the application method for resins applied outside of the booth was not specified, it was assumed to be spray applied.

 For inorganic pollutants from resins applied inside a booth, the percent HAP emitted was adjusted to acc ount for an assumed transfer efficiency of 65% and 50% fallout within the booth (see adjustment factor used in equations for C1).

Wastewater Treatment Operations (H1)

Facilities conducting wastewater treatment operations were required to report emissions directly as either stack or fugitive emissions. Therefore, the only calculation required was to convert emissions from pounds per year to tons per year.

### 8 Assumptions

I t was assumed that the reported stack emission values are after capture and control

. T herefore, no capture or control efficiencies were applied. If a facility did not report whether an emission value was fugitive or stack, it was assumed to be stack.

d. Reg ulatory Code and Emission Process Group A ssignment s i. The regulatory code “63GG” was assigned for processes that are currently subject to 40 CFR 63, Subpart GG: coatings, blast depainting, solvent depainting, and solvent cleaning. ii. Emission process group and source classification code ( SCC ) were assigned as shown below based on information provided in the ICR response:

Table 1. Aerospace Manufacturing and Rework Emission Process Groups and SCCs

EMISSION_PROCESS_GROUP SCC SCC Description

Blast Depainting 30900201 Industrial Processes; Fabricated Metal Products; Abrasive Blasting of Metal Parts; General

Blast Depainting 68241001 MACT Source Categories; Miscellaneous Processes; Paint Stripper Users

- Non

- chemical Strippers; Media Blasting

Coatings 40202401 Petroleum and Solvent Evaporation; Surface Coating Operations; Large Aircraft; Prime Coating Operation

Coatings 40202406 Petroleum and Solvent Evaporation; Surface Coating Operations; Large Aircraft; Topcoat Operation

Coatings 40202499 Petroleum and Solvent Evaporation; Surface Coating Operations; Large Aircraft; Other Not Classified

Solvent Cleaning 40202402 Petroleum and Solvent Evaporation; Surface Coating Operations; Large Aircraft; Cleaning/Pretreatment

Solvent Depainting 68240030 MACT Source C ategories; Miscellaneous Processes; Paint Stripper Users

- Chemical Strippers; Application, Degradation, and Coating Removal Steps

Specialty Coatings 40200801 Petroleum and Solvent Evaporation; Surface Coating Operations; Coating Oven

- General; General

Specialty Coatings 40202401 Petroleum and Solvent Evaporation; Surface Coating Operations; Large Aircraft; Prime Coating Operation

Specialty Coatings 40202406 Petroleum and Solvent Evaporation; Surface Coating Operations; Large Aircraft; Topcoat Operation

Specialty Coatings 40202499 Petroleum and Solvent Evaporation; Surface Coating Operations; Large Aircraft; Other Not Classified

Chemical Milling and Metal Finishing 30901018 Industrial Processes; Fabricated Metal Products; Electroplating Operations; Hard Chromium

- Electroplating Tank

### 9 Table 1. Aerospace Manufacturing and Rework Emission Process Groups and SCCs

EMISSION_PROCESS_GROUP SCC SCC Description

Chemical Milling and Metal Finishing 30901038 Industrial Processes; Fabricated Metal Products; Electroplating Operations; Chromic Acid Anodizing

- Anodizing Tank

Chemical Milling and Metal Finishing 30901042 Industrial Processes; Fabricated Metal Products; Electroplating Operations; Copper (cyanide, including strike)

- Electroplating Tank

Chemical Milling and Metal Finishing 30901048 Industrial Processes; Fabricated Metal Products; Electroplating Operations; Copper (general)

- Electroplating Tank 1000 amp

- hr current applied

Chemical Milling and Metal Finishing 30901052 Industrial Processes; Fabricated Metal Products; Electroplating Operations; Cadmium (cyanide)

- Electroplating Tank 1000 amp

- hr current applied

Chemical Milling and Metal Finishing 30901058 Industrial Processes; Fabricated Metal Products; Electroplating Operations; Cadmium (general)

- Electroplating Tank 1000 amp

- hr current applied

Chemical Milling and Metal Finishing 30901065 Industrial Processe s; Fabricated Metal Products; Electroplating Operations; Nickel (sulfamate or watts)

- Electroplating Tank 1000 amp

- hr current

Chemical Milling and Metal Finishing 30901068 Industrial Processes; Fabricated Metal Products; Electroplating Operations; Nickel (general)

- Electroplating Tank Chemical Milling and Metal Finishing 30901098 Industrial Processes; Fabricated Metal Products; Electroplating Operations; Other Not Classified Chemical Milling and Metal Finishing 30901101 Industrial Processes; Fabricated Metal Products; Conversion Coating of Metal Products; Alkaline Cleaning Bath Chemical Milling and Metal Finishing 30901102 Industrial Processes; Fabricated Metal Products; Conversion Coating of Metal Products; Acid Cleaning Bath (Pickling)
Chemical Mill ing and Metal Finishing 30901103 Industrial Processes; Fabricated Metal Products; Conversion Coating of Metal Products; Anodizing Kettle Chemical Milling and Metal Finishing 30901199 Industrial Processes; Fabricated Metal Products; Conversion Coating of Metal Products; Other Not Classified Chemical Milling and Metal Finishing 30901501 Industrial Processes; Fabricated Metal Products; Chemical Milling of Metal Products; Milling Tank

Chemical Milling and Metal Finishing 30904001 Industrial Processes; Fabri cated Metal Products; Metal Deposition Processes; Metallizing: Wire Atomization and Spraying Chemical Milling and Metal Finishing 30904010 Industrial Processes; Fabricated Metal Products; Metal Deposition Processes; Thermal Spraying of Powdered Metal Chemical Milling and Metal Finishing 30999999 Industrial Processes; Fabricated Metal Products; Other Not Classified; Other Not Classified

### 10 Table 1. Aerospace Manufacturing and Rework Emission Process Groups and SCCs

EMISSION_PROCESS_GROUP SCC SCC Description

Composite Processing 30800720 Industrial Processes; Rubber and Miscellaneous Plastics Products; Fiberglass Resin Prod ucts; General

Storage Tanks 40100550 Petroleum and Solvent Evaporation; Organic Solvent Evaporation; Solvent Storage; General Processes: Drum Storage

- Pure Organic Chemicals

Wastewater Treatment 40282001 Petroleum and Solvent Evaporation; Surface Coating Operations; Wastewater, Aggregate; Process Area Drains

III. Testing ICR

a. The test data were reviewed to confirm there we re no additional source ID or pollutant matches that were missed when incorporating test data into the modeling file

. b. T he methodology that was used to include (or not include) emissions test data in the modeling file was reviewed . Findings were as follows :

 Test data were compiled for 15 facilities.

 The compiled test data for one facility were not included for modeling file updates , because the units of measure could not be converted to a pound per hour or other comparable emissions rate.

 One test point for one facility was excluded from the modeling file updates because it overlapped with a different test point that was included in the modeling file updates.

 With the exception of the test point noted above, test data for the remaining
14 facilities were added to the modeling file if the data met three criteria:

 There was at least one test run where emissions were not below detection limits. For the purpose of risk analysis for the aerospace source category, test points where all three runs were below detection limits were assumed to not emit that HAP from that process type and these emissions were not included in the risk modeling file. This assumption was made for this source category because of the variability in processes and input materials used at aerospace facilities (for example, some coatings do not contain certain HAP and, therefore, are not expected to be so urces of those HAP).

 The data that could be mapped to source IDs were already included in the modeling file.

 The test pollutants were not reported in the original ICR data. If the test pollutants were already reported in the original ICR data for the

### 11 corr esponding source ID, then the stack test data were not added to the modeling file.

 If the test data met the three criteria, then the average emissions from the test runs were added to the modeling file. If at least one run was above the detection limit, o ne

- half of the detection limit was used for any runs that were below the detection limit . If all three runs were below the detection limit, then
emissions of that HAP were assumed to not be present for th at process in the aerospace source category. Stack t est data were added to the modeling file for 11 out of 14 facilities.

c. I ndustry

- supplied materials and prior EC/R analysis of emissions test data and capture efficiencies were reviewed to determine whether any calculations or assumptions currently used sho uld be revised

. d. E missions test data amendments and results of coatings test data were reviewed to: 1) identify which pollutants in the emissions test data were not being reported under material balance

- based coatings records already (i.e., should be added to the coatings category records); 2) which pollutants were measured in the emission tests, but were not found in coatings tests, and therefore should not be allocated to the coatings process group; 3) confirm that those pollutants in the emissions test da ta that were not flagged for appending were reflected in material balance

- based coatings records ; and 4) provide analytical test data instead of MSDS

- supplied metals content values where the tested coating could be matched up to coatings reportedly being u sed

.

IV. Speciation

a. Chromium (Cr) emissions from coatings and specialty coatings were speciated into its two most prevalent oxidation states: trivalent chromium (Cr III) and hexavalent chromium (Cr VI)

. Chromium from ICR processes were speciated using a profile of 20.6% Cr VI and 79.4% Cr III

. The coatings test results showed that strontium chromate is the predominant compound

. Therefore, as a first step, a 25.5% weight fraction was applied to estimate how much of the compound is chromium

. The 25.5% weigh t fraction was then multiplied by 0.807 to adjust this factor downward, because the test results indicated that not all of the Cr in the coatings is Cr VI

. The average ratio of Cr VI to Cr in the coatings test data is 0.807 , as can be seen in the Cr VI to Cr ratio calculation from coatings test data found in Appendix A

. Records that were previously speciated using a 25% Cr VI/75% Cr III were re

- speciated using the profile described above for ICR processes.

b. Mercury ( Hg) emissions were speciated into three forms (elemental gaseous, particulate divalent, and gaseous divalent) using SCC

- based NEI defaults

. If a SCC from the modeling file did not match the NEI defaults, the generic 20% particulate divalent, 30% gaseous divalent, and 50% elemental gaseous profil e was used

.

V. Industry Reviews

### 12 a. Draft modeling file records were provided to all parent companies ( in the EPA modeling file format) on May 15, 2014
, for review. The file included the coating emission calculations in addition to modeling data

. EPA provided review files to 171 facilities

. Responses were received for 84 facilities

. Nine facilities were not included in the draft modeling file as of May 2014 and were added after the industry review period

.

b. Revisions to the modeling file records included change s to emission values, control device types, stack parameters, locational coordinates, fugitive area dimension s, pollutant information, and removal of units or processes that are shutdown or otherwise not part of the facility

. Revisions to the coating calcu lations included adding missing coatings and pollutants, removing pollutants no longer emitted, and revisi ng the coating calculation inputs (volume, density, HAP mass fraction, coating usage percent, booth type and capture percent, control efficiency, filt er control efficiency, and the adjustment factor).

VI. Development of Allowable Emissions for Category

Facilities were asked to provide a multiplier in the ICR survey to scale up average hourly emissions to maximum hourly emissions for air dispersion modelin g

. As there are typically a large number of emission points at a facility
, determining the maximum hourly emissions from each and every emission point can be difficult . Therefore, EPA chose to use a single multiplier of 1.02 to scale average annual emissions to allowable annual emissions

. The allowable emissions multiplier is based on the difference between 2008 production utilization rate of 83.1 and the 20

- year historical max imum production utilization rate of 85.0 ( i.e., 85 ÷ 83.1 = 1.02 )

.

VII. Develo pment of Acute Emissions for Category

EPA determined that an acute factor of 1.2 was appropriate for the aerospace manufacturing industry . This multiplier is based on the ratio of the 2008 production utilization rate (83.1) and the maximum production rate of 100 ( i.e., 100 ÷ 83.1 = 1.2 )

.

VIII. Non

- Category Aerospace Manufacturing Facility Emissions

a. Non

- category emissions for aerospace manufacturing facilities were obtained from the 2011 NEI

. N on

- category emissions were not available in the 2011 NEI or 2008 NEI for three facilities; emissions were pulled from the 2005 National

- Scale Air Toxics Assessment ( NATA ) inventory for these three facilities

.

b. Appendix B shows a list of SCCs that were included in the non

- category records

. Appendix C shows a list of additional SCCs that were included in the non

- category records only if the record had the specific unit description shown

. Individual chromium records were excluded if they were found to overlap with a regulated
process.

c. Speciat ion

### 13 i. Chromium SCC

- based NEI Cr speciation defaults were used for non

- ICR processes

. If a SCC from the modeling file did not match the NEI default, the Cr profile for ICR processes was used

.

ii. Mercury Mercury (Hg) emissions were speciated into three forms (elemental gaseous, particulate divalent, and gaseous divalent) using SCC

- based NEI defaults

. If a SCC from the modeling file did not match the NEI defaults, the generic 20% particulate divalent, 30% gaseous divalent, and 50% elemental gaseous profile

was used.

d. Industry R eview

The 2014 industry review responses described in S ection V above included changes for non

- category records from NEI.

IX. Q uality Assurance/Quality Control (QA/QC)

All of the QA/QC procedures discussed in the revised Quality Assurance P roject Plan (QAPP) dated June 6, 2014 were implemented for the aerospace manufacturing and rework emissions estimates, locational coordinates, stack parameters, fugitive parameters, and

non

- category emissions records

. Manual and computerized verification by a team member who did not perform the original data entry or calculations was conducted to ensure that all ICR data were correctly incorporated, and that all automated emission calculations were implemented correctly. Additional c omputerized QA/QC check s were then implemented, thereby conducting checks on close to 100% of the records.

a. Aerospace Manufacturing and Rework Facilities Emission Estimates

The Aerospace Manufacturing and Rework Facilities emission records included in the modeling file were reviewed to verify that no emissions were double counted, and that the emitted pollutants were consistent with the reported SCCs/unit or process descriptions. HAPs not typically emitted from the processes were removed. For example, EPA removed homopolymers from coating emissions where facilities reported both the homopolymer and hexamethylene diisocyanate (HMDI) components of a coating material and adjusted HMDI estimates that were overestimated using an average ratio from the study “Determination of 1,6

- He xamethylene Diisocyanate (HDI) Emissions from Spray Booth Operations 2 

### .” Outliers (e.g., HAPs emitted in very large quantities relative to similar processes at other facilities) were flagged for investigation. For example, one facility reported specialty

co ating emissions in pounds per year instead of reporting coating inputs; EPA adjusted these emissions to tons per year. EPA contacted four facilities and made revisions for two facilities as needed based on responses. For example, specialty coating emission s were reassigned to non

- category processes and duplicate emission values were

2 Ontario Ministry of t he Environment, April 2006.

### 14 removed for one facility, and fugitive chromium emissions from surface coating were removed for the other facility.

b. Locational Coordinat es Location coordinates (X_COORDINATE and Y_COORDINATE) were reviewed using Arc GIS ®

. Coordinates that placed stack emission release points over facility buildings, stacks, hangers, etc., were considered suitable

. If coordinates placed an emission release point over parking lots, vegetation, resid ential housing, or other non

- facility building, new coordinates were assigned

. Fugitive emission release points are represented in modeling files as rectangles or squares

. Coordinates of these release points should be located in the southwest corner of the fugitive area to be modeled

. Fugitive coordinates were also QA’ed based on the fugitive length (y) , width (x) , and angle associated with the fugitive area or building

. These parameters are discussed below and an example is displayed in Figure 1

. If a fugi tive area was shown to be over vegetation, residential housing, etc., the coordinates were reassigned to the southwest corner of the facility building based on the fugitive length, width, and angle.

Figure 1

. Depiction of Fugitive Area Source Parameters 3



### c. Stack Parameters

Stack parameters included in the ICR submittals and the modeling file include stack diameter, stack height, exhaust stream temperature, and stream flow rate

. Stream velocity is provided in the modeling file and was calculated based on stack diameter and flow rate

. If one or more of the ICR stack parameters was not provided, aerospace stack parameter defaults were assigned using the average of stack parameters

3 Figure created by ICF International.

### 15 reported in the ICR for other aerospace release points

. The average aerospace stack parameter s assigned as defaults are:
Stack height: 48.43091684 ft
Stack temperature: 76.27270788 ° F

Stack diameter: 4.846738822 ft
Stack velocity: 50.7294305266868 ft/sec

Stack flow rate: 978.086679026503 ft 3 

### /sec
The QA ranges for stack parameters for the aerospace modeling f ile are listed below. One exception to the QA ranges is for engine testing and internal combustion engines where the temperature can exceed the normal QA maximum

. If either diameter or velocity was out of range for ca tegory records, diameter, velocity and flow rate were all set to the aerospace stack parameter defaults.

Table 2. Aerospace Stack Parameter QA Ranges

Parameter QA Range for Category Records QA Range for Non

Category Records Units Min Max Min Max Stack Height 0.1 1 , 200 0.1 1 , 200 ft Exhaust Gas Temperature 50 1 , 800 50 1 , 800 ° F Stack Diameter 0.1 40 0.1 50 ft Exhaust Gas Velocity 0.1 200 0.1 200 ft/sec Exhaust Gas Flow Rate ft 3 

### /sec

d. Fugitive Parameters

Fugitive parameters in the modeling file include the same stack parameters as well as fugitive length, width, and angle

. The fugitive area is comprised of the southwest corner of the representative building, or latitude and longitude, the length and width, or x and y depicted in Figure 1 , and the angle at which the area is rotated in relation to the southwest corner

. The fugitive angle must be between 0 and 9 0 degrees

. The national defaults for fugitive parameters are listed below

.

Table 3. Fugitive Stack Parameter Defaults Applied

National Defaults Units Stack Height 10 ft Exhaust Gas Temperature 72 ° F Stack Diameter 0.003 ft Exhaust Gas Velocity 0.0003 ft/sec

### 16 Exhaust Gas Flow Rate 0 ft 3 

### /sec

e. Addition of new HAPs to RTR file

There were several new HAPs provided in the facility ICRs that were not yet included in the RTR (or NEI) pollutant look

- up tables

. The HAP s added to the RTR look

- up table

are provided in the following table.
Table 4. Additional Aerospace HAPs

Pollutant Po llutant Code Cadmium Selenide Sulfide 12626367

Cobalt Neodecanoate 27253312 Cobalt(II) Nitrate 10141056
Cobalt(II) Phosphate Octahydrate 10294505

Coronene 191071 Dibasic Lead Phosphate 15845520
Iron Chromite Brown Spinel (C.I. Pigment Brown 35) 68187097
Lead Chromate Molybdate Sulfate (C.I. Pigment Red 104) 12656858
Manganese Ferrite Black Spinel (C.I. Pigment Black 26) 68186947 Magnesium Dichromate 14104859 Phosphoric Acid, Reaction Products with Aluminum Hydroxide and Chromium Oxide (CrO3) 92203026

Zinc Potassium Chromate 37224570

f. Non

- category Emission Estimates

The NEI non

- aerospace emission records included in the modeling file were reviewed to verify that no emissions were double counte d , and that the emitted pollutants were consistent with the r eported SCCs/unit or process descriptions. HAPs not typically emit ted from the processes were removed. Outliers (e. g. , HAPs emitted in very large quantities relative to similar processes at other facilities) were flagged for investigation. For example, the NEI emissions from a wastewater treatment process at one facility w ere adjusted. EPA c ontact ed three facilities and made revisions as needed based on responses

. For example, combustion emissions were corrected at two facilities , and tetrachloroethylene emissions were removed from the third facility

.

### A

- 1 Appendix A. Coatings Test Data where Chromium and Chromium (VI) Data are Available Facility MSDSCoatingID AvgCrVIContent_mgPerkg AvgChromiumContent_mgPerkg FractionChromeVI Adjusted Fraction ChromeVI

1 Black Super Koropon base #528x310 31.1 48.6 0.639917695 0.639917695

1 Eco Prime Yellow

- Green # EWDEO72 A Base 19590 21005 0.932635087 0.932635087 2 515X349 BASE COMPONENT 25033.33333 30015.33333 0.834018168 0.834018168

2 BR 127 Corrosion Inhibiting Primer 2080.666667 2699.333333 0.770807607 0.770807607

5 10P20

- 44_High Solids Epoxy Primer 36912 49047.96411 0.752569463 0.752569463

6 10P8

- 10NF; Fluid Resistant Epoxy Primer 14814.66667 21178.15975 0.699525683 0.699525683

6 515X346 Room Temperature Bonding Primer 15944.5 37971.39558 0.41990819 0.41990819

8 463

- 6

- 4; Aluminized Epoxy Primer Base 639.5 2801 0.228311317 0.228311317

8 BMS10

- 11AA

- TYPE I

- CLASS A GRADE E BASE 64922.5 65226 0.995346948 0.995346948 9 10P8

- 11LC Primer 18874.5 22901 0.824177983 0.824177983

10 MIL

- PRF

- 85582D, TYPE I, CLASS C2 BASE 34477 40365 0.854131054 0.854131054

10 MIL

- PRF

- 85582D

- TYPE I

- CLASS C2

- CATALYST 2.53 1.92 1.317708333 a 

### 1

11 MIL

- PRF

- 23377K TYPE 1 CLASS C2 PART A BASE 35750 36691 0.974353384 0.974353384

11 MIL

- PRF

- 23377K TYPE 1 CLASS C2 PART B CATALYST 3.07 3.03 1.01320132 a 

### 1

11 MIL

- PRF

- 85582D, TYPE I, CLASS C2, 44GN072 BASE 66158 84002 0.787576486 0.787576486 13 10P8

- 10NMF Base 19021.5 21092.5 0.901813441 0.901813441 13 BMS10

- 11A Type I Class A Cat 7.54 3.8 1.984210526 a 

### 1

### A

- 2 Appendix A. Coatings Test Data where Chromium and Chromium (VI) Data are Available Facility MSDSCoatingID AvgCrVIContent_mgPerkg AvgChromiumContent_mgPerkg FractionChromeVI Adjusted Fraction ChromeVI

13 BMS10

- 11A Type I Class A Primer 64112.5 67715.5 0.946792093 0.946792093

14 AQUAPRIME PRIMER, MIL

- PRF

85582D, TYPE I, CLASS C2, PART A 30680 39440 0.777890467 0.777890467 Average: 0.807

a 

### M easured Cr VI wa s greater than Cr ; all reported Cr is assumed to be Cr VI.

### B

- 1

Appendix B. Non

- category SCCs
SCC SCC Description
10100224 External Combustion Boilers; Electric Generation; Subbituminous Coal; Boiler, Spreader Stoker
10100501 External Combustion Boilers; Electric Generation; Distillate Oil

- Grades 1 and 2; Boiler, Normal firing
10100602 External Combustion Boilers; Electric Generation; Natural Gas; Boiler < 100 Million BTU, except tangential
10102101 External Combustion Boilers; Electric Generation; Other Oil; A ll
10200204 External Combustion Boilers; Industrial; Bituminous Coal; Spreader Stoker
10200206 External Combustion Boilers; Industrial; Bituminous Coal; Underfeed Stoker
10200212 External Combustion Boilers; Industrial; Bituminous Coal; Pulverized Coal: Dry Bottom (Tangential)
10200401 External Combustion Boilers; Industrial; Residual Oil

- Grade 6; Boiler
10200404 External Combustion Boilers; Industrial; Residual Oil; Grade 5 Oil
10200501 External Combustion Boilers; Industrial; Distillate Oil

- Grad es 1 and 2; Boiler
10200502 External Combustion Boilers; Industrial; Distillate Oil; 10

- 100 Million BTU/hr **
10200503 External Combustion Boilers; Industrial; Distillate Oil; < 10 Million BTU/hr **
10200601 External Combustion Boilers; Industrial; Natural Gas; > 100 Million BTU/hr
10200602 External Combustion Boilers; Industrial; Natural Gas; 10

- 100 Million BTU/hr
10200603 External Combustion Boilers; Industrial; Natural Gas; < 10 Million BTU/hr
10200799 External Combustion Boilers; Industrial; P rocess Gas; Other: Specify in Comments
10300203 External Combustion Boilers; Commercial/Institutional; Bituminous Coal; Cyclone Furnace
10300209 External Combustion Boilers; Commercial/Institutional; Bituminous Coal; Spreader Stoker
10300402 External Combustion Boilers; Commercial/Institutional; Residual Oil; 10

- 100 Million BTU/hr **
10300403 External Combustion Boilers; Commercial/Institutional; Residual Oil; < 10 Million BTU/hr **
10300404 External Combustion Boilers; Commercial/Institutional; Resi dual Oil; Grade 5 Oil
10300501 External Combustion Boilers; Commercial/Institutional; Distillate Oil

- Grades 1 and 2; Boiler
10300502 External Combustion Boilers; Commercial/Institutional; Distillate Oil; 10

- 100 Million BTU/hr **
10300503 External Combustion Boilers; Commercial/Institutional; Distillate Oil; < 10 Million BTU/hr **
10300504 External Combustion Boilers; Commercial/Institutional; Distillate Oil; Grade 4 Oil
10300601 External Combustion Boilers; Commercial/Institutional; Natural Gas; > 100 Million BTU/hr
10300602 External Combustion Boilers; Commercial/Institutional; Natural Gas; 10

- 100 Million BTU/hr
10300603 External Combustion Boilers; Commercial/Institutional; Natural Gas; < 10 Million BTU/hr

### B

- 2

Appendix B. Non

- category SCCs
SCC SCC Description
10301002 External Combustion Boilers; Commercial/Institutional; Liquified Petroleum Gas (LPG); Propane
10500105 External Combustion Boilers; Space Heaters; Industrial; Distillate Oil
10500106 External Combustion Boilers; Space Heaters; Industrial; Natural Gas
10500206 External Comb ustion Boilers; Space Heaters; Commercial/Institutional; Natural Gas
20100102 Internal Combustion Engines; Electric Generation; Distillate Oil (Diesel); Reciprocating
20100107 Internal Combustion Engines; Electric Generation; Distillate Oil (Diesel); Reciprocating: Exhaust
20100201 Internal Combustion Engines; Electric Generation; Natural Gas; Turbine
20100202 Internal Combustion Engines; Electric Generation; Natural Gas; Reciprocating
20100901 Internal Combustion Engines; Electric Generation; Keros ene/Naphtha (Jet Fuel); Turbine
20200102 Internal Combustion Engines; Industrial; Distillate Oil (Diesel); Reciprocating
20200103 Internal Combustion Engines; Industrial; Distillate Oil (Diesel); Turbine: Cogeneration
20200104 Internal Combustion Engines; Industrial; Distillate Oil (Diesel); Reciprocating: Cogeneration
20200201 Internal Combustion Engines; Industrial; Natural Gas; Turbine
20200202 Internal Combustion Engines; Industrial; Natural Gas; Reciprocating
20200203 Internal Combustion En gines; Industrial; Natural Gas; Turbine: Cogeneration
20200252 Internal Combustion Engines; Industrial; Natural Gas; 2

- cycle Lean Burn
20200253 Internal Combustion Engines; Industrial; Natural Gas; 4

- cycle Rich Burn
20200254 Internal Combustion Engines; Industrial; Natural Gas; 4

- cycle Lean Burn
20200401 Internal Combustion Engines; Industrial; Large Bore Engine; Diesel
20200402 Internal Combustion Engines; Industrial; Large Bore Engine; Dual Fuel (Oil/Gas)
20200901 Internal Combustion Engines; Indust rial; Kerosene/Naphtha (Jet Fuel); Turbine
20201001 Internal Combustion Engines; Industrial; Liquified Petroleum Gas (LPG); Propane: Reciprocating
20201012 Internal Combustion Engines; Industrial; Liquified Petroleum Gas (LPG); Reciprocating Engine
20300101 Internal Combustion Engines; Commercial/Institutional; Distillate Oil (Diesel); Reciprocating
20300102 Internal Combustion Engines; Commercial/Institutional; Distillate Oil (Diesel); Turbine
20300201 Internal Combustion Engines; Commercial/Insti tutional; Natural Gas; Reciprocating
20300203 Internal Combustion Engines; Commercial/Institutional; Natural Gas; Turbine: Cogeneration
20300301 Internal Combustion Engines; Commercial/Institutional; Gasoline; Reciprocating

### B

- 3

Appendix B. Non

- category SCCs
SCC SCC Description
20301001 Internal Combustion Engines; Commercial/Institutional; Liquified Petroleum Gas (LPG); Propane: Reciprocating
20400101 Internal Combustion Engines; Engine Testing; Aircraft Engine Testing; Turbojet
20400102 Internal Combustion Engines; Engine Testing; Aircraft Engine Testing ; Turboshaft
20400110 Internal Combustion Engines; Engine Testing; Aircraft Engine Testing; Jet A Fuel
20400199 Internal Combustion Engines; Engine Testing; Aircraft Engine Testing; Other Not Classified
20400201 Internal Combustion Engines; Engine Testing; Rocket Engine Testing; Rocket Motor: Solid Propellant
20400302 Internal Combustion Engines; Engine Testing; Turbine; Diesel/Kerosene
20400305 Internal Combustion Engines; Engine Testing; Turbine; Kerosene/Naphtha
20400402 Internal Combustion En gines; Engine Testing; Reciprocating Engine; Diesel/Kerosene
20400406 Internal Combustion Engines; Engine Testing; Reciprocating Engine; Kerosene/Naphtha (Jet Fuel)
30101602 Industrial Processes; Chemical Manufacturing; Phosphoric Acid: Wet Process; Gypsum Pond
30188801 Industrial Processes; Chemical Manufacturing; Fugitive Emissions; Specify in Comments Field
30400360 Industrial Processes; Secondary Metal Production; Grey Iron Foundries; Castings Finishing
30400401 Industrial Processes; Secondary Metal Production; Lead; Pot Furnace
30400409 Industrial Processes; Secondary Metal Production; Lead; Casting
30400867 Industrial Processes; Secondary Metal Production; Zinc; Kettle (Pot) Melting Furnace
30400873 Industrial Processes; Secondary Metal Production; Zinc; Casting
30402201 Industrial Processes; Secondary Metal Production; Metal Heat Treating; Furnace: General
30499999 Industrial Processes; Secondary Metal Production; Other Not Classified; Specify in Comments Field
30501050 Industrial Processes; Mineral Products; Coal Mining, Cleaning, and Material Handling; Vehicle Traffic: Light/Medium Vehicles
30600101 Industrial Processes; Petroleum Industry; Process Heaters; Oil

- fired **
30601301 Industrial Processes; Petroleum Industry; Coke Han dling System; Storage and Transfer
30622003 Industrial Processes; Petroleum Industry; Underground Storage Remediation & Other Remediation; Soil: Natural Gas

30700752 Industrial Processes; Pulp and Paper and Wood Products; Plywood Operations; Softwood Ply wood, Veneer Dryer, Direct

Natural Gas

- Fired, Heated Zones
30900500 Industrial Processes; Fabricated Metal Products; Welding; General
30901003 Industrial Processes; Fabricated Metal Products; Electroplating Operations; Entire Process: Nickel
30901006 Industrial Processes; Fabricated Metal Products; Electroplating Operations; Entire Process: Chrome

### B

- 4

Appendix B. Non

- category SCCs
SCC SCC Description
30901018 Industrial Processes; Fabricated Metal Products; Electroplating Operations; Hard Chromium

- Electroplating Tank
30901028 Industrial Processes; Fab ricated Metal Products; Electroplating Operations; Decorative Chromium

- Electroplating Tank
30901098 Industrial Processes; Fabricated Metal Products; Electroplating Operations; Other Not Classified
30901102 Industrial Processes; Fabricated Metal Products; Conversion Coating of Metal Products; Acid Cleaning Bath (Pickling)
30901103 Industrial Processes; Fabricated Metal Products; Conversion Coating of Metal Products; Anodizing Kettle
30901104 Industrial Processes; Fabricated Metal Products; Conve rsion Coating of Metal Products; Rinsing/Finishing
30901199 Industrial Processes; Fabricated Metal Products; Conversion Coating of Metal Products; Other Not Classified
30905100 Industrial Processes; Fabricated Metal Products; Shielded Metal Arc Welding (SMAW); General
30905200 Industrial Processes; Fabricated Metal Products; Gas Metal Arc Welding (GMAW); General
30990003 Industrial Processes; Fabricated Metal Products; Fuel Fired Equipment; Natural Gas: Process Heaters
30999999 Industrial Processes; Fabricated Metal Products; Other Not Classified; Other Not Classified
31299999 Industrial Processes; Machinery, Miscellaneous; Miscellaneous Machinery; Other Not Classified
31399999 Industrial Processes; Electrical Equipment; Other Not Classified; Other Not Classified
31499999 Industrial Processes; Transportation Equipment; Other Not Classified; Other Not Classified
39000699 Industrial Processes; In

- process Fuel Use; Natural Gas; General
39001299 Industrial Processes; In

- process Fuel Use; Solid Waste; General
39090001 Industrial Processes; In

- process Fuel Use; Fuel Storage

- Fixed Roof Tanks; Residual Oil: Breathing Loss
39090003 Industrial Processes; In

- process Fuel Use; Fuel Storage

- Fixed Roof Tanks; Distillate Oil (No. 2): Breathing Loss
3909000 4 Industrial Processes; In

- process Fuel Use; Fuel Storage

- Fixed Roof Tanks; Distillate Oil (No. 2): Working Loss
39090011 Industrial Processes; In

- process Fuel Use; Fuel Storage

- Fixed Roof Tanks; Dual Fuel (Gas/Oil): Breathing Loss
39091004 Industrial Processes; In

- process Fuel Use; Fuel Storage

- Floating Roof Tanks; Distillate Oil (No. 2): Withdrawal Loss
39091011 Industrial Processes; In

- process Fuel Use; Fuel Storage

- Floating Roof Tanks; Dual Fuel (Gas/Oil): Standing Loss
39900601 Ind ustrial Processes; Miscellaneous Manufacturing Industries; Process Heater/Furnace; Natural Gas

39990003 Industrial Processes; Miscellaneous Manufacturing Industries; Miscellaneous Manufacturing Industries; Natural Gas: Process

Heaters
39999992 Industrial Processes; Miscellaneous Manufacturing Industries; Miscellaneous Industrial Processes; Other Not Classified
39999993 Industrial Processes; Miscellaneous Manufacturing Industries; Miscellaneous Industrial Processes; Other Not Classified
39999999 Industri al Processes; Miscellaneous Manufacturing Industries; Miscellaneous Industrial Processes; Other Not Classified

### B

- 5

Appendix B. Non

- category SCCs
SCC SCC Description
40100198 Petroleum and Solvent Evaporation; Organic Solvent Evaporation; Dry Cleaning; Other Not Classified
40200801 Petroleum and Solvent Evap oration; Surface Coating Operations; Coating Oven

- General; General

40301001 Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Fixed Roof Tanks (Varying Sizes); Gasoline RVP

13: Breathing Loss (67000 Bbl. Tank Size)

40301005 Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Fixed Roof Tanks (Varying Sizes); Gasoline RVP

10: Breathing Loss (250000 Bbl. Tank Size)

40301007 Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Fixe d Roof Tanks (Varying Sizes); Gasoline RVP

13: Working Loss (Tank Diameter Independent)

40301016 Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Fixed Roof Tanks (Varying Sizes); Jet Kerosene:

Breathing Loss (67000 Bbl. Tank Si ze)

40301017 Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Fixed Roof Tanks (Varying Sizes); Jet Kerosene:

Breathing Loss (250000 Bbl. Tank Size)

40301018 Petroleum and Solvent Evaporation; Petroleum Product Storage at Refin eries; Fixed Roof Tanks (Varying Sizes); Jet Kerosene:

Working Loss (Tank Diameter Independent)

40301019 Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Fixed Roof Tanks (Varying Sizes); Distillate Fuel

#2: Breathing Loss (67000 Bbl. Tank Size)

40301020 Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Fixed Roof Tanks (Varying Sizes); Distillate Fuel

#2: Breathing Loss (250000 Bbl. Tank Size)

40301021 Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Fixed Roof Tanks (Varying Sizes); Distillate Fuel

#2: Working Loss (Tank Diameter Independent)

40301098 Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Fixed Roof Tanks (Varying Sizes); Specify L iquid:

Breathing Loss (250000 Bbl. Tank Size)

40301099 Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Fixed Roof Tanks (Varying Sizes); Specify Liquid:

Working Loss (Tank Diameter Independent)

40301114 Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Floating Roof Tanks (Varying Sizes); Jet Kerosene:

Standing Loss (250000 Bbl. Tank Size)

40301119 Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Floating Roof Tanks (Varyin g Sizes); Jet Kerosene:

Withdrawal Loss

40400102 Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non

- Refinery); Bulk Terminals; Gasoline RVP 10: Breathing Loss

(67000 Bbl Capacity)

- Fixed Roof Tank
40400108 Petroleum and Solvent Evaporatio n; Petroleum Liquids Storage (non

- Refinery); Bulk Terminals; Gasoline RVP 10: Working Loss

### B

- 6

Appendix B. Non

- category SCCs
SCC SCC Description

(Diameter Independent)

- Fixed Roof Tank

40400131 Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non

- Refinery); Bulk Terminals; Gasoline RVP 13: Stan ding Loss

-

Ext. Floating Roof w/ Primary Seal
40400250 Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non

- Refinery); Bulk Plants; Loading Racks
40400254 Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non

- Refinery); Bulk Plants; Tank Truck Vapor Losses

40400270 Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non

- Refinery); Bulk Plants; Specify Liquid: Standing Loss

- Int.

Floating Roof w/ Secondary Seal

40400316 Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non

- Refinery); Oil and Gas Field Storage and Working Tanks;

Fixed Roof Tank, Diesel, working+breathing+flashing losses

40400402 Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non

- Refinery); Petroleum Products

- Underground Tanks;

Gasoline RVP 13: Working Loss

40400411 Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non

- Refinery); Petroleum Products

- Underground Tanks; Jet

Kerosene: Breathing Loss

40400412 Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non

- Refinery); Petroleum Products

- Underground Tanks; Jet

Kerosene: Working Loss

40400497 Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non

- Refinery); Petroleum Products

- Underground Tanks;

Specify Liquid: Breathing Loss

40400498 Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non

- Refinery); Petroleum Products

- Underground Tanks;

Specify Liquid: Working Loss

40600131 Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Tank Cars and Trucks; Gasoline:

Submerged Loading (Normal Service)

40600133 Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Tank Cars and Trucks; Jet Naphtha:

Submerged Loading (Normal Service)

40600136 Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Tank Cars and Trucks; Gasoline:

Splash Loading (Normal Service)

40600301 Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Gasoline Retail Operat ions

- Stage I;

Splash Filling

40600302 Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Gasoline Retail Operations

- Stage I;

Submerged Filling w/o Controls

40600306 Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Gasoline Retail Operations

- Stage I;

Balanced Submerged Filling

### B

- 7

Appendix B. Non

- category SCCs
SCC SCC Description

40600307 Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Gasoline Retail Operations

- Stage I;

Underground Tank B reathing and Emptying

40600401 Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Filling Vehicle Gas Tanks

- Stage II;

Vapor Loss w/o Controls

40600402 Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Filling Vehicle Gas Tanks

- Stage II;

Liquid Spill Loss w/o Controls

40600503 Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Pipeline Petroleum Transport

-

General

- All Products; Pump Station

40600707 Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Consumer (Corporate) Fleet

Refueling

- Stage I; Underground Tank Breathing and Emptying
40799997 Petroleum and Solvent Evaporation; Organic Chemical Storage; Miscellaneous; Specify in Comments
41082001 Petroleum and Solvent Evaporation; Dry Cleaning; Petroleum Solvent

- Wastewater, Aggregate; Process Area Drains
42500101 Petroleum and Solvent Evaporation; Fixed Roof Tanks (210 Bbl Size) Breathing Loss
42500102 Petroleum and Solvent Evaporation; Fixed Roof Tanks (210 Bbl Size) Working Loss
50100402 Waste Disposal; Solid Waste Disposal

- Government; Landfill Dump; Fugitive Emissions
50100404 Waste Disposal; Solid Waste Disposal

- Government; Landfill Du mp; Trench Method
50100406 Waste Disposal; Solid Waste Disposal

- Government; Landfill Dump; Gas Collection System: Other
50100601 Waste Disposal; Solid Waste Disposal

- Government; Fire Fighting; Structure: Jet Fuel
50200103 Waste Disposal; Solid Waste Disposal

- Commercial/Institutional; Incineration; Controlled Air

50200602 Waste Disposal; Solid Waste Disposal

- Commercial/Institutional; Landfill Dump; Municipal: Fugitive Emissions ** (Use 5

- 01

-

004

- 02)
50300205 Waste Disposal; Solid Waste Disposal

- Industrial; Open Burning; Rocket Propellant
50300702 Waste Disposal; Solid Waste Disposal

- Industrial; Liquid Waste; Waste Treatment: General

### C

- 1

Appendix C. Additional Non

- C ategory SCCs with Unit Descriptions Emission Unit Description SCC SCC Description Building 1665

- open top chemical tank 39999995 Industrial Processes; Miscellaneous Manufacturing Industries; Miscellaneous Industrial Processes; Other Not Classified

Explosives Manufacturing 49099998 Petroleum and Solvent Evaporation; Organic Solvent Evaporation; Miscellaneous Volatile Organic Compound Evaporation; Identify the Process and Solvent in Comments FIRING RANGE TRAINING OPERATIONS 40200101 Petroleum and Solvent Evapor ation; Surface Coating Operations; Surface Coating Application

- General; Paint: Solvent

- base

Solids Handling 39999994 Industrial Processes; Miscellaneous Manufacturing Industries; Miscellaneous Industrial Processes; Other Not Classified Autoclave (from facility ID 5555) 40201001 Petroleum and Solvent Evaporation; Surface Coating Operations; Coating Oven Heater; Natural Gas Autoclave (NTR) 40201001 Petroleum and Solvent Evaporation; Surface Coating Operations; Coating Oven Heater; Natural Gas Boilers and Humidifiers (I and S) 40201001 Petroleum and Solvent Evaporation; Surface Coating Operations; Coating Oven Heater; Natural Gas Clean Line

- Heating Units (I and S) 40201001 Petroleum and Solvent Evaporation; Surface Coating Operations; Coating Oven He ater; Natural Gas Coating Ovens (NTR) 40201001 Petroleum and Solvent Evaporation; Surface Coating Operations; Coating Oven Heater; Natural Gas GAS OVENS

- PAINT CURING 40201001 Petroleum and Solvent Evaporation; Surface Coating Operations; Coating Oven Heater; Natural Gas HEATERS (I and S and NTR) 40201001 Petroleum and Solvent Evaporation; Surface Coating Operations; Coating Oven Heater; Natural Gas Ovens 1

- 10 (from facility ID 5555)(I and S) 40201001 Petroleum and Solvent Evaporation; Surface Coating Operations; Coating Oven Heater; Natural Gas

Fire training pit 39999994 Industrial Processes; Miscellaneous Manufacturing Industries; Miscellaneous Industrial Processes; Other Not Classified Building No. 4035

- hard chrome plating tank [MACT, Subpart N] 39999994 Industrial Processes; Miscellaneous Manufacturing Industries; Miscellaneous Industrial Processes; Other Not Classified CADMIUM ELECTROPLATING LINE 39999995 Industrial Processes; Miscellaneous Manufacturing Industries; Miscellaneous Industrial Pr ocesses; Other Not Classified

Wastewater Treatment 39999995 Industrial Processes; Miscellaneous Manufacturing Industries; Miscellaneous Industrial Processes; Other Not Classified

### C

- 2

Appendix C. Additional Non

- C ategory SCCs with Unit Descriptions Emission Unit Description SCC SCC Description Wastewater treatment facility 40188898 Petroleum and Solvent Evaporation; Organic Solvent Evaporation; Fugitive Emissions; Specify in Comments Field

Welding Sources 39999995 Industrial Processes; Miscellaneous Manufacturing Industries; Miscellaneous Industrial Processes; Other Not Classified

### Appendix 2 Technical Support Document for HEM

- AERMOD Modeling

# Modeling for the Residual Risk and Technology Review

Using the Human Exposure Model 3

– AERMOD Version

Updated 01/08/2014

Technical Support Document

Prepared for:

U.S. Environmental Protection Agency Office of Air and Radiation Office of Air Quality Planning and Standards Health and Environmental Impacts Division Air Toxics Assessment Group Research Triangle Park, NC 27711

Prepared by:

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

### iii 

# Disclaimer

The research described in this document has been funded by the United States Environmental Protection Agency contracts 68

- D

- 01

- 076, EP

- D

- 06

- 119, and currently EP

- W

- 12

- 011 to EC/R Incorporated. Although it has been subject to the Agency's review, it does not necessarily reflect the views of the Agency, and no official endorsement should be inferred.

### iv 

# Contents
1. Introduction

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

2. Overview of the HEM

- 3

– AERMOD System

................................ ................................ ......... 2 2.1 Preparation of Dispersion Modeling Inputs

................................ ................................ ........ 3 2.1.1 Compiling Emission Source Data

................................ ................................ ................ 3 2.1.2 Defining the Modeling Domain and Receptors

................................ ............................ 4 Treatment of Nearby Census Blocks and Screening for Overlapping Blocks

.................... 4 Polar receptor network

................................ ................................ ................................ ..... 5 Elevations and hill heights for model receptors

................................ ................................ . 5

2.1.3 Selection of Meteorological Data

................................ ................................ ................. 6 2.2 Running of AERMOD

................................ ................................ ................................ ....... 6 2.2.1 AERMOD Dispersion Options Used by HEM

- 3

................................ .......................... 6
2.2.2 Use of Dilution Factors

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

- 3

................................ ................................ 7 2.3.1 Calculation of Impacts at Modeled Receptors

................................ .............................. 8 2.3.2 Interpolation of Impacts at Outer Census Blocks

................................ ......................... 9 2.3.3 Calculation of Population Exposures and Incidence

................................ ................... 10 2.3.4 Model Outputs

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

2.4 Data Libraries Used in HEM

- 3

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

2.4.1 Chemical Health Effects Information

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

2.4.2 Census Block Locations and Elevation Data

................................ .............................. 13 2.4.3 Meteorological Data

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

................................ ................................ ................. 14

3. Modeling for the Residual Risk Technology Review

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

................................ ................................ ................................ .... 15

3. 2 Pollutant Cross

- Referencing

................................ ................................ ............................ 15 3.3 Meteorological Data

................................ ................................ ................................ ........ 16 3.4 Model Options Selected

................................ ................................ ................................ ... 19 3.4.1 Urban or Rural Dispersion Characteristics

................................ ................................ . 19

### v

3.4.2 Deposition and Plume Depletion

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

................................ ................... 20 3.4.4 Facility Boundary Assumptions

................................ ................................ ................. 20 3.5 Modeling of Multiple Facilities

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

4. Quality Assurance

................................ ................................ ................................ ................. 23 4.1 Engineering Review

................................ ................................ ................................ ......... 23

4.2 Ge ographic Pre

- Modeling Checks

................................ ................................ .................... 23 4.3 Geographic Post

- Modeling Checks

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

5. Uncertainties

................................ ................................ ................................ ......................... 26

6. References

................................ ................................ ................................ ............................. 32



# Figure s

Figure 3

- 1. AERMOD Meteorological Stations

................................ ................................ ......... 18



# Tables

Table 2

- 1. Parameters Used to Delineate the Modeling Domain in HEM

- 3

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

Table 3

- 1. HEM

- 3 Domain and Set

- Up Options As Used in the Residual Risk and Technology Review Assessments

................................ ................................ ................................ ................. 21

Table 5

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

................................ ................................ ................................ ...................... 28

### 1 

# 1. Introduction

This document describes the general modeling approach used to estimate the risks to human populations in support of the Residual Risk and Technology Review (RTR) currently being carried out by the U.S. Environmental Protection Agency (EPA). It is important to note that risk characterizations of individual source categories under the RTR program may not follow every item/approach noted in this document. The reader is referred to the main body of the risk assessment document for more details on source category specific approaches that may have been included in the analysis.

The model used in these risk assessments is the Human Exposure Model, Version 3 (HEM

- 3). HEM

- 3 incorporates AERMOD, a state of the art air dispersion model developed under the direction of the American Meteorological Soci ety / Environmental Protection Agency Regulatory Model Improvement Committee (AERMIC).

Section 2 of this report provides an overview of the HEM

- 3

- AERMOD system; and Section 3 describes inputs and choices made in implementing the model for the RTR progra m. Quality assurance efforts undertaken in the modeling effort are discussed in Section 4, and uncertainties associated with the modeling effort are discussed in Section 5.

### 2



# 2. Overview of the HEM

- 3

– AERMOD System

HEM

- 3 performs three main operations: dispersion modeling, estimation of population exposure, and estimation of human health risks. The state

- of

- the

- art American Meteorological Society (AMS) EPA Regulatory Model (AERMOD) 1,2 

### is used for dispersion modeling. AERMOD can handle a wide range of dif ferent source types which may be associated with an industrial source complex, including stack (point) sources, area and polygon sources, and volume

sources.
To prepare dispersion modeling inputs and carry out risk calculations, HEM

- 3 draws on four data libraries, which are provided with the model. The first is a library of meteorological data for over 824 stations, which are used for dispersion calculations. A second library of Census block (“centroid”) internal point locations and populations provides the basis of human exposure calculations. The Census library also includes the elevations of every Census block, which are used in the dispersion calculations fo r the RTR assessments. A third library of pollutant unit risk estimates and reference concentrations is used to calculate population risks. These unit risk estimates and reference concentrations are based on the latest values recommended by EPA for hazardo us air pollutants (HAP) and other toxic air pollutants. The fourth data library, which provides deposition parameters for gaseous pollutants, is used only when the user opts to compute deposition and plume depletion (not computed for the RTR assessments to date).

HEM

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

- 3”). Multi HEM

- 3 is used in the RTR risk assessment modeling. Both versions operate under the same general principles. In essence, Multi HEM

- 3 provides a platform for running the single facility version multiple times. In both versions, source location and emissions data are input through a set of ExcelTM spreadsheets. The main difference is in the user interface for other model input s. Single HEM

- 3 includes a graphical user interface (GUI) for the selection of various dispersion modeling options. In Multi HEM

- 3, a control file replaces many of these GUI inputs.

The model estimates cancer risks and noncancer adverse health effects d ue to inhalation exposure at Census block internal point locations (or “centroids”), at concentric rings surrounding the facility center, and at other receptor locations that can be specified by the user. Cancer risks are computed using EPA’s recommended u nit risk estimates for Hazardous Air Pollutants (HAP) and other toxic air pollutants. The resulting estimates reflect the excess cancer risk for an individual breathing the ambient air at a given receptor site 24

- hours per day over a 70

- year lifetime. The model estimates the numbers of people exposed to various cancer risk levels. In addition, HEM

- 3 estimates the total incremental cancer risks for people living within different distances of the modeled emission sources.

Potential noncancer health effects due to chronic exposures are quantified using hazard quotients and hazard indices for various target organs. The “hazard quotient” (HQ) for a given chemical and receptor site is the ratio of the ambient concentration of the chemical to the reference conce ntration. The “hazard index” (HI) for a given organ is the sum of hazard quotients for substances that affect that organ. HEM

- 3 computes target

- organ

- specific hazard indices (TOSHI) for HAPs and other toxic air pollutants, and estimates the numbers of peop le

### 3

exposed to different hazard index levels. In addition, maximum short term (“acute”) concentrations are computed for all pollutants, and concentrations are compared with threshold levels for acute health effects.

The following sections outline the met hodologies used in the HEM

- 3

– AERMOD system. Section 2.1 describes the preparation of dispersion modeling inputs, Section 2.2 describes the running of AERMOD, Section 2.3 describes calculations performed by HEM

- 3 to calculate risks and exposures, and Sectio n 2.4 details the sources and methods used to produce HEM

- 3’s data libraries. The HEM

- 3 User’s Manuals

– for single HEM

- 3 and Multi HEM

- 3

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

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

## 2.1 Preparation of Dispersion Modeling Inputs

HEM

- 3 compiles data that will be needed for dispersion modeling, and prepares an input file suitable for running AERMOD. The dispersion modeling inputs can be divi ded into three main components: emission source data, information on the modeling domain and receptors for which impacts will be computed, and meteorological data.
2.1.1 Compiling Emission Source Data



### A series of ExcelTM spreadsheet files are used to spe cify the emissions and configuration of the facility to be modeled. At a minimum, two files are needed: a pollutant emission file, and an emission location file. The emission file includes an emission source identification code for each emission source at the facility, the names of pollutants emitted by each source, and the emission rate for each pollutant. In addition, if the model run is to incorporate deposition or plume depletion, the emission file must also specify the percentage of each pollutant that is in the form of particulate matter. The balance is assumed to be in vapor form.

The emission location file includes the coordinates of each source, as well as information on the configuration and other characteristics of the source. HEM

- 3 can analyze point sources, area and polygon sources, and volume sources

- configurations that are described in AERMOD's documentation. 1, 2 

### For stack (point) sources, the location file must provide the stack height, stack diameter, emission velocity, and emission temp erature. The file must also provide dimensions for each area or volume source, as well as the height of the source above the ground. For area sources, the angle of rotation from north can also be specified. The user can also provide the terrain elevation a t the base of each source. (The controlling hill height is also used in AERMOD’s flow calculations. Calculation of the controlling hill height by HEM

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

- 3 will calcu late elevations and controlling hill heights based on elevations and hill heights provided by the Census database for the Census blocks nearest to the facility.

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

- 3 requires a th ird input file to specify the particle size distribution. This input file must include the average particle diameter, the mass fraction percentage, and the average particle density for each size range emitted. Another optional file can be used to specify b uilding dimensions if building wake effects are to be modeled.

### 4

2.1.2 Defining the Modeling Domain and Receptors



### HEM

- 3 defines a modeling domain for each facility that is analyzed based on parameters specified by the model user or calculated by the model. These parameters are summarized in Table 2

- 1. The modeling domain is circular, and is centered on the facility, w ith a radius specified by the user. For the RTR analysis, the radius of the modeling domain is 50 kilometers (km). HEM

- 3 identifies all of the Census block locations in the modeling domain from its Census database, and divides the blocks into two groups ba sed on their distance from the facility. For the inner group of Census blocks (closest to the facility), each block location is modeled as a separate receptor in AERMOD. The cutoff distance for modeling individual Census blocks is generally set to 3 km for the RTR assessments, although it can be set differently by the model user. The model user can also provide an ExcelTM spreadsheet specifying additional locations to be included as model receptors in AERMOD. These additional discrete “user receptors” may in clude facility boundary locations, monitoring sites, individual residences, schools, or other locations of interest.

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

pping Blocks



### Census block locations near the facility are modeled as separate receptors within AERMOD. The cutoff distance for modeling of individual Census blocks may be chosen by the user, but is typically 3000 meters for the RTR assessments. This dis tance is not measured from the center of the facility, but is the minimum distance from any source at the facility. Therefore, any Census block location that is within the cutoff distance from any emission source is treated as a discrete AERMOD receptor.

HEM

- 3 checks Census blocks that are very close to the facility in order to assess whether they overlap any point, area or volume emission sources. In addition, the user can specify an overlap distance, within which receptors will be considered to be on fa cility property. The default value for the overlap distance is 30 meters, or approximately equal to the width of a narrow buffer and a roadway. HEM

- 3 tests each nearby receptor to determine whether it is within this distance from any stack or from the peri meter of any area or volume source. If a Table 2

- 1 . Parameters Used to Delineate the Modeling Domain in HEM

- 3

Parameter Typical value Modeling domain size

– maximum radial distance to be modeled from

facility center 50 km

Cutoff distance for modeling of individual blocks a 

### 3,000 m Overlap distance

– where receptors are considered on facility property a 

### 30 m

Polar receptor network: Distance to the innermost ring b 

### ≥100 m Number of concentric rings 13 Number of radial directions 16

a Measured from each stack at the facility, and from the edges of each area or volume source
b Generally model

- calculated to encompass all emission sources but not less than 100 meters from the facility
center

### 5

receptor falls within this distance, HEM

- 3 will not calculate risks based on the location of that receptor, but will instead assume that the risks associated with the receptor are the same as the highest predicted v alue for any receptor that is not overlapping. The location for calculating the default impact may be either another Census block, one of the polar grid receptors, or one of the additional discrete user

- specified receptor locations.
Polar receptor network



### The polar receptor network used in HEM

- 3 serves three functions. First, it is used to estimate default impacts if one or more Census locations are inside the overlap cutoff distance used to represent the facility boundary. Second, it is used to evaluat e potential acute effects that may occur due to short

- term exposures in locations outside the facility boundary. Third, the polar receptor network is used to interpolate long

- term and short

- term impacts at Census block locations that are outside the cutoff distance for modeling of individual blocks.

Generally, the model calculates the inner radius (or first ring distance) for the polar receptor network to be just outside the emission source locations, but not less than 100 meters from the facility center. However, the user can override the default dis tance calculated by the model to fit the size and shape of the facility properties to be modeled. Likewise, the model will also use default values for the number of concentric rings to be analyzed (13 rings by default), and the number of radial directions (16 radials by default), although these default values can also be changed by the user to meet the needs of a specific modeling study. The inner radius of the polar network should be the minimum distance from the facility center that is generally outside

o f facility property. (For complex facility shapes, it is sometimes useful to specify an inner ring that encroaches on facility property in some directions.) HEM

- 3 will distribute the radial directions evenly around the facility. For the concentric rings, t he model will generate a logarithmic progression of distances starting at the inner ring radium and ending at the outer radium of the modeling domain.
Elevations and hill heights for model receptors



### HEM

- 3 includes terrain elevations by default for the RTR assessments, but the user can choose to exclude terrain effects when running AERMOD. If the default terrain option is used, HEM

- 3 obtains elevations and controlling hill heights for Census block receptors from its internal Census location library. Sect ion 2.4.2 describes the derivation of these elevations and hill heights.

Elevations and controlling hill heights for the polar grid receptors are also estimated based on values from the Census library. HEM

- 3 divides the modeling domain into sectors base d on the polar receptor network, with each Census block assigned to the sector corresponding to the closest polar grid receptor. Each polar grid receptor is then assigned an elevation based on the highest elevation for any Census block in its sector. The c ontrolling hill height is also set to the maximum hill height within the sector. If a sector does not contain any blocks, the model defaults to the elevation and controlling hill height of the nearest block outside the sector.

### 6

2.1.3 Selection of Meteorological Data



### In addition to source and receptor information, AERMOD requires surface and upper air meteorological observations in a prescribed format. The model user can select a meteorological station from the HEM

- 3 meteorological data library, or add new files to the library if site

- specific data are available. If the user does not specify a meteorological station, HEM

- 3 will select the closest station to the center of the modeling domain, as is generally done for the RTR

assessmen ts. 

## 2.2 Running of AERMOD

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

- 3 produces an input file suitable for AERMOD. HEM

- 3 then runs AERMOD as a compiled executable program. No changes have been made from the versio n of AERMOD released to the public by EPA. The following sections give additional information on how AERMOD is used within HEM

- 3.
2.2.1 AERMOD Dispersion Options Used by HEM

- 3



### AERMOD provides a wide array of options for controlling dispersion modeling calculations. In general, HEM

- 3 uses the regulatory default options when running AERMOD. 1 

### These options include the following:

 Use stack

- tip downwash (except for Schulman

- Scire downwash);

 Use buoyancy

- induced dispersion (except for Schulman

- Scire downwas h);

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

 Use the “calms processing” routines;

 Use upper

- bound concentration estimates for sources influenced by building downwash from super

- squat buildings;

 Use default wind profile exponents;

 Use l ow wind speed threshold;

 Use default vertical potential temperature gradients;

 Use of missing

- data processing routines; and

 Consider terrain effects.

The following additional AERMOD options are available to the HEM

- 3 user:

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

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

 Consideration of building wake effects;

 Calculation of short term (acute) impacts; and

 Use of the FASTALL option, which conserves model runtime by simplifying the AERMOD algorithms used to represent meander of the pollutant plume.

### 7

As noted in Section 2.1, the calculation of deposition or depletion and the consideration of building wake effects require additional use r inputs.

The user can opt to analyze short term impacts on a number of different time scales (i.e., 1 hour, 6 hours, 8 hours, or 24 hours) however only one short term time scale can be selected per run. If the user chooses to analyze short term impacts , a multiplier must be specified to reflect the ratio between the maximum short term emission rate and the long term average emission rate. The default multiplier for short term emissions is a factor of 10. This means that in the default case the maximum s hort term emission rate is assumed to be 10 times the long term average emission rate. The multiplier can be set to 1.0 if emissions from the facility are known to be constant. For RTR assessments, acute impacts are generally included in the modeling and t he default multiplier of 10 is used, unless more source

- specific information is available upon which to base the acute factor for the source category being modeled.
2.2.2 Use of Dilution Factors



### To save computer run time when analyzing the impacts of mu ltiple pollutants, HEM

- 3 does not model each pollutant separately. Instead, AERMOD is used to compute a series of dilution factors, specific to each emission source and receptor. The dilution factor for a particular emission source and receptor is defined as the predicted ambient impact from the given source and at the given receptor, divided by the emission rate from the given source.

If the user chooses not to analyze deposition and plume depletion, the dilution factor does not vary from pollutant to pollutant. If deposition and depletion is chosen as a model option, separate dilution coefficients must be computed for each gaseous pollutant. In addition, separate dilution factors must be computed for different components of particulate matter if the

co mponents do not have the same particle size distribution. In the current version of HEM

- 3, this can be done by creating a separate emission record for each pollutant emitted by from each source. (Common location data and source configurations can be used f or different pollutant records representing the same emission source.) 

## 2.3 Postprocessing of AERMOD Results in HEM

- 3

HEM

- 3 estimates total excess cancer risks and potential chronic noncancer health effects for all Census block locations in the modeling domain, all user

- defined receptors, and all points in the polar receptor network. Potential chronic noncancer health effects are expressed in terms of TOSHI. Based on the results for Census blocks and other receptors, HEM

- 3 estimates the maximum individual risk (MIR) and maximum TOSHI for populated receptors, and determines the locations of these maximum impacts. The model also determines the concentrations of different pollutants at the sites of the maximum risks, and the contributions of different emissio n sources to these maximum estimated risks. It should be noted that the locations may differ for the maximum individual cancer risk and for the hazard indic es for different target organs.

For acute impacts, HEM

- 3 calculates the maximum short term concen trations for all pollutants emitted by the facility. These maximum short term concentrations are compared with various threshold levels for acute health effects (e.g., the California EPA reference exposure level [REL] for no adverse effects).

### 8

At the opt ion of the model user, HEM

- 3 will also compute the long term and short term predicted ambient concentrations of all pollutants emitted by the facility at all of the receptors in the modeling domain. In addition, pollutant contributions from each emission s ource at the facility are computed under this option.

Section 2.3.1. describes methods used to calculate cancer risks and hazard indices for receptors that are explicitly modeled using AERMOD. Section 2.3.2 describes the interpolation approach used to estimate cancer risks and hazard indices at Census blocks that are not explicitly modeled.
2.3.1 Calculation of Impacts at Modeled Receptors



### As noted in Section 2.2.2, HEM

- 3 does not model each pollutant separately unless deposition or depletion is being analyzed. Instead, AERMOD is used to compute a series of dilution factors, speci fic to each emission source and receptor. HEM

- 3 also conserves computer memory by computing cancer risks and hazard indices directly, without recording the concentration of each pollutant at each receptor. The following algorithms are used to compute

cance r risks and TOSHI for chronic noncancer health effects.

For cancer risk: CR T 

### = ∑ i,j 

### CR i,j



### CR i,j 

### = DF i,j 

### × CF × ∑ k 

### [E i,k 

### × URE k 

### ]

For TOSHI: TOSHI T 

### = ∑ i,j 

### TOSHI i,j



### TOSHI i,j 

### = DF i,j 

### × CF × ∑ k 

### [E i,k 

### / RfC k 

### ]

where: CR T 

### = total cancer risk at a given receptor (probability for one person) ∑ i,j 

### = the sum over all sources i and pollutant types j (particulate or gas) CR i,j 

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

### = dilution factor [(μg/m 3 

### ) / (g/sec)] at the given receptor for source i and pollutant type j CF = conversion factor, 0.02877 [(g/sec) / (ton/year)] ∑ k 

### = sum over all pollutants k within pollutant type j (particulate or gas) E i,k 

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

### = cancer unit risk factor for pollutant k TOSHI T 

### = total target

- organ

- specific hazard index at a given receptor TOSHI i,j 

### = target

- organ

- specific hazard index at the given receptor for source i and pollutant

type j RfC k 

### = non

- cancer health effect reference concentration for pollutant k

### 9 The above equations are equivalent to the following simpler equations:

CR T 

### = ∑ i, k 

### AC i,k 

### × URE k



### HI T 

### = ∑ i, k 

### AC i,k 

### / RC k 

### where: AC i,k 

### = ambient concentration (μg/m 3 

### ) for pollutant k at the given receptor. This is the same as [E i,k 

### × DF i.j 

### × CF]

However, use of these simpler equations would require modeling all pollutants individually in AERMOD, and performing separate risk calculations for each pollutant.

If the cancer unit risk estimate is not available for a given chemical, then that chemica l is not included in the calculation of cancer risk. Likewise, if the noncancer reference concentration is not available for a given chemical, that chemical is not included in the calculation of hazard indices. Note also that separate reference concentrati ons are used for acute and chronic hazard indices.

HEM

- 3 computes short term concentrations and records the highest short term concentration for each pollutant. In addition, the user can opt to compute and record the short term and long concentrations a t each receptor. Concentrations are computed as follows.

Long term concentrations: AC T ,k 

### = ∑ i 

### AC i, k



### AC i, k 

### = E i, k x 

### DF i, j 

### × CF

Short term concentrations: AC T 

### = ∑ i 

### AC i, k



### AC i, k 

### = E i, k x 

### DF i, j 

### × CF × M where: AC T ,k 

### = total estimated ambient concentration for pollutant k at a given receptor ∑ i 

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

### ) AC i, k 

### = estimated ambient concentration of pollutant k at the given receptor as a result of

emissions from source i (μg/m 3 

### ) M = ratio between the estimated maximum short term emission rate and the long term average emission rate (dimensionless)
2.3.2 Interpolation of Impacts at Outer Census Blocks



### For Census blocks outside of the cutoff distance for individual block modeling, HEM

- 3 estimates cancer risks and hazard indices by interpolation from the polar receptor network. Impacts at the polar grid receptors are estimated using AERMOD modeling result s and the algorithms described in Section 2.3.1. If terrain elevation is part of the modeling, then an elevation is estimated for each polar receptor. HEM

- 3 estimates elevations and controlling hill heights for the polar grid receptors based on values from the census library. HEM

- 3 divides the

### 10

modeling domain into sectors based on the polar grid receptor network, with each census block assigned to the sector corresponding to the closest polar grid receptor.

HEM

- 3 then assigns each polar grid receptor an elevation based on the highest elevation for any census block in its sector. The controlling hill height is also set to the maximum hill height within the sector. If a sector does not contain any blocks, the model defaults to the elevation and controlling hill height of the nearest block outside the sector.

The impacts at each outer Census block are interpolated from the four nearest polar grid receptors. The interpolation is linear in the angular direction, and logarithmic in the radial direction, as su mmarized in the following equations:

I a,r 

### = I A1, r 

### + (I A2, r 

### – I A1, r 

### ) × (a

– A1) / (A2

– A1)

I A1, r 

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

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

### )

– ln (I A1,R1 

### )] × [(ln r)

– ln(R1)] / [ln(R2)

– ln(R1)]} I A2, r 

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

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

### )

– ln (I A2,R1 

### )] × [(ln r)

– ln(R1)] / [ln(R2)

– ln(R1)]}

where: I a,r 

### = the impact (cancer risk, hazard index, or concentration) at an angle, a, from north
,

and radius, r, from the center of the modeling domain

a = the angle of the target receptor, from north

r = the radius of the target receptor, from the center of the modeling domain A1 = the angle of the polar network receptors immediately counterclockwise from the

target receptor A2 = the angle of the polar network receptors immediately clockwise from the tar get

receptor R1 = the radius of the polar network receptors immediately inside the target receptor R2 = the radius of the polar network receptors immediately outside the target receptor
2.3.3 Calculation of Population Exposures and Incidence



### Using the predicted impacts for Census blocks, HEM

- 3 estimates the numbers of people exposed to various cancer risk levels and TOSHI levels. This is done by adding up the populations for receptors that have predicted cancer risks or TOSHI above the given t hreshold.

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

TCR = ∑ m 

### [CR m 

### × P m 

### ] / LT where: TCR = the estimated total annual cancer risk, or incidence, (cancers/year) to the

population living within the modeling domain ∑ m 

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

### = the total lifetime cancer risk (from all modeled pollutants and emis sion sources)

at Census block m P m 

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

### 11

Furthermore, HEM

- 3 estimates the contributions of different chemicals and emission sources to total annual cancer incidence for the ov erall modeling domain using the following equations:

TCR i, j 

### = ∑ m 

### [( ∑ k 

### E i, k 

### × URE k 

### ) × DF i,j, m 

### × CF  LT]

TCR i, k 

### = TCR i j 

### × E i, k 

### × URE k 

###  ( ∑ k 

### E i, k 

### × URE k 

### )

where: TCR i,j 

### = the estimated total annual cancer incidence (cancers/year) to the population in the

modeling domain due to emissions from pollutant type j (1 = particulate, 2 = gas) and emission source i ∑ m 

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

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

### E i,k 

### = emissions of pollutant k from source i (tons/year) URE k 

### = unit risk factor for pollutant k DF i,j,m 

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

pollutant k), from source i CF = conversion factor, 0.02877 [(g/sec) / (ton/year)] 

### TCR i, k 

### = the estimated annual cancer incidence (cancers/year) of the population in the

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


### 2.3.4 Model Outputs

The following is a summary of the outputs produced by HEM

- 3. These are written to a collection of files in ExcelTM and dBaseTM format (dbf).

 Maximum long term impacts at populated locations

o maximum lifetime individual cancer risk (MIR)

o maximum TOSHI for the following health effects

- respiratory system effects

- liver effects

- neurological system effects

- developmental effects

- repr oductive system effects

- kidney effects

- ocular system effects

- endocrine system effects

- hematological system effects

- immunological system effects

- skeletal system effects

- spleen effects

- thyroid effects

- whole body effects

o locations of the maximum cancer risk and TOSHI

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

### 12 o contributions of different chemicals and emission sources to the maximum risk and TOSHI



###  Max imum acute impacts

o maximum short term ambient concentration for each chemical

o threshold levels for acute health effects of each chemical (compared with the maximum short term concentrations)

o locations of the maximum impacts for different chemicals (often p olar receptors)

o Census block identification codes at the locations of maximum concentration, and number of people in the block

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

 Outputs for all receptors

o maximum individual cancer risk and TOSHI (all target organs) for each Census block and each user

- specified discrete receptor (monitoring sites, etc.)

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

o estima ted deposition (optional)

o predicted ambient concentration resulting from each emission source at each Census block and polar grid receptor (optional)

 Population exposures and total cancer risk, or incidence

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

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

o total cancer risk, or incidence, in estimated cancer deaths per year, over the entire modeling domain, and for each pollutant and source combination



## 2.4 Data Libraries Used in HEM

- 3

2.4.1 Chemical Health Effects Information



### HEM

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

 unit risk estimate (URE) for cancer

 reference concentration (RfC) for chronic noncancer health effects

 reference concentrations for acute health effects

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

Unit risk estimates and reference concentrations included in the HEM

- 3 chemical library have been taken from EPA’s database of recommended dose

- response factors for HAPs, which is updated periodically, consistent with continued research on these p arameters. 4 

### The URE represents the upper

- bound excess lifetime cancer risk estimated to result from continuous exposure to an agent at a concentration of 1 microgram per cubic meter (μg/m 3 

### ) in air (e.g., if the

### 13

URE = 1.5 x 10

- 6 

### per μg/m 3 

### , 1.5 excess tumors are expected to develop per 1 million people if all 1 million people were exposed daily for a lifetime to 1 microgram of the chemical in 1 cubic meter of air). 5



### The RfC is a concentration estimate of a continuous inhalation exposure to the human populat ion that is likely to be without an appreciable “risk” of deleterious non

- cancer health effects during a lifetime. No adverse effects are expected as a result of exposure if the ratio of the potential exposure concentration to the RfC, defined as the hazar d quotient (HQ), is less than 1. 5



### The reference benchmark concentration for acute health effects, similar to the chronic RfC, is the concentration below which no adverse health effects are anticipated when an individual is exposed to the benchmark concen tration for 1 hour (or 8 hours, depending on the specific acute benchmark used and the formulation of that benchmark). Target organs are those organs (e.g., kidney) or organ systems (e.g., respiratory) which may be impacted with chronic

non

- cancer health e ffects by exposure to the chemical in question. A more in

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

- cancer hazard may be found in the EPA’s Air Toxics Risk Assessment Library. 6



### The model user can a dd pollutants and associated health effects to HEM

- 3’s chemical health effects (dose

- response) library, as needed.

2.4.2 Census Block Locations and Elevation Data



### The HEM

- 3 Census library includes Census block identification codes, locations, populations, elevations, and controlling hill heights for all of the over 6 million Census blocks identified in the 2010 Census and the over 5 million Census blocks identified in the 2000 Census. The model user may choose to use either Census database according to their modeling needs. The location coordinates reflect the internal “centroid” of the block, which is a point selected by the Census to be roughly in the center of the block. For complex shapes, the internal point may not be in the geographic center of the block. Locations and population data for Census blocks in the 50 states and Puerto Rico were extracted from the LandView® database For the 2000 Census 7 

### and from the U .S. Census Bureau website for the 2010 Census. 8 

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

U.S. Geological Survey data was used to estimate the elevation of each census block in the cont inental U.S. and Hawaii. The data used for the 2000 Census elevations have a resolution of 3 arc seconds, or about 90 meters. 9 

### The data used for the 2010 Census elevations have a resolution of 1/3 of an arc second, or about 10 meters. 10 

### Using analysis tool s (ArcGIS® 9.1 software application for the 2000 Census, and ArcGIS® 10 for the 2010 Census), elevation was estimated for each census block in Alaska and the U.S. Virgin Islands. The point locations of the census blocks in Alaska and the U.S. Virgin Island s were overlaid with a raster layer of North American Digital Elevation Model (DEM) elevations (in meters). 9 

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

An algorithm used in AERMAP, the AERMOD terrain processor, is used to det ermine controlling hill heights. 11,12 

### These values are used for flow calculations within AERMOD.

### 14

To save run time and resources, the HEM

- 3 census block elevation database is substituted for the DE M data generally used in AERMAP. As noted above, the census block elevations were originally derived from the DEM database. To determine the controlling hill height for each census block, a cone is projected away from the block centroid location, represent ing a 10% elevation grade. The controlling hill height is selected based on the highest elevation above that 10% grade (in accordance with the AERMAP methodology). The distance cutoff for this calculation is 100 km. (This corresponds to an elevation differ ence at a 10% grade of 10,000 m, which considerably exceeds the maximum elevation difference in North America.)
2.4.3 Meteorological Data



### HEM

- 3 includes an extensive library of meteorological data to support the AERMOD dispersion model. Currently 824 met eorological stations have been preprocessed for AERMOD as part of the RTR effort. Section 3.3 includes a depiction of these meteorological stations and Appendix 2 discusses the preparation of meteorological data for the RTR in more detail.

2.4.4 Gaseous Deposition Parameters



### HEM

- 3 provides options to compute the deposition of air pollutants, and to take into account the impacts of plume depletion due to deposition of gaseous and particulate pollutants. If the deposition and depletion option is selected by the model user for gaseous pollutants, a number of pollutant properties are required by AERMOD. (These include the diffusivity of the pollutant in air, the diffusivity of the pollutant in water, the Henry’s Law constant, and a parameter reflecting the c uticular resistance to uptake of the pollutant by leaves r CL 

### ). 13 

### HEM

- 3 includes a library of these parameters for most gaseous HAPs. This library is based on a compendium of gaseous deposition parameters developed by Argonne National Laboratories. 14 

### It sho uld be noted, however, that the deposition and depletion option of HEM

- 3 and AERMOD have not been used to date for the RTR assessments.

### 15



# 3. Modeling for the Residual Risk Technology Review

This section discusses the general approach used to implement the HEM

- 3 AERMOD system for the RTR modeling analyses. Separate rep orts have been prepared for each of the emission source categories analyzed to date. These reports provide information on the emissions inputs and results for specific emission categories. 

## 3.1 Emission Source Inputs

HEM

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

 pollutants emitted;

 emission rate for each pollutant;

 emission source coordinates;

 stack hei ght (or emission height for fugitive and other area sources);

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

 emission velocity; and

 emission temperature.

E missions data for the RTR assessments are compiled form a variety of data so urces (i.e., the 2005 National Emissions Inventory (NEI) 15 

### ,information data requests). Each source category under the RTR program, in most cases, utilizes the latest best available data. These data include HAP emission rates, emission source coordinate s, stack heights, stack diameters, flow rates, and exit temperatures. EPA performs an engineering review of the NEI data. In cases where new or better data were known to exist for a particular source category, that information is integrated into the data u sed in modeling for that category. For each source category, the emissions are summarized in the source category specific report. Detailed computer files containing all emission and release characteristics are available in the docket prepared for the speci fic RTR source category proposed or final rule.

As noted in the previous section, industrial emission sources can be characterized in AERMOD as point, area, polygon, or volume sources. Fugitive emissions are generally characterized as low point sources with minimal exit velocities. For some categories, additional information was available on the configuration of fugitive emission sources. This information was incorporated into the emissions database as part of the engineering review. Thus, fugitive emiss ion sources were characterized as area or volume sources when sufficient configuration information was available.



## 3.2 Pollutant Cross

- Referencing

Because the NEI is developed from a number of different data sources, a single chemical may be listed in th e inventory under different names (i.e. a “common name” and one or more
structure

- based names). In addition, pollutant groupings such as polycyclic organic matter (POM), can be listed in the NEI under the names of individual member compounds, and under dif ferent synonyms (e.g. polynuclear aromatic hydrocarbons). HEM

- 3 requires an exact match in

### 16

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 th e chemical library. Therefore, all of the HAP names used in the NEI were linked to the appropriate chemical names in the HEM

- 3 reference file.

Pollutant

- specific dose response values are used in the HEM

- 3 modeling whenever available, including when mode ling POM pollutants. Pollutant groupings, such as POM groupings, are used for POMs without a chemical

- specific unit URE’s. These POMs are assigned a URE associated with various POM compounds having similar characteristics. “An Overview of Methods for EPA’s National

- Scale Air Toxics Assessment” 2011 document 16 

### provides more details regarding POM modeling, including:

[S]ome emissions of POM were reported in [the] NEI as “7

- PAH” or “16

- PAH,” representing subsets of certain POM, or simply as “total PAH” or “polycyclic organic matter.” In other cases, individual POM compounds are reported for which no quantitative cancer dose

- response value has been published in the sources used for NATA. As a result, simplifying assumptions that characterize emissions reported as POM are applied so that cancer risk can be quantitatively evaluated for these chemicals without substantially under

- or overestimating risk (which can occur if all reported emissions of POM are assigned the same URE). To accomplish this, POM emissions as reported in NEI are grouped into categories. EPA assigns dose

- response values based on the known or estimated t oxicity for POM within each group and on information for the POM speciation of emission sources, such as wood fires and industrial processes involving combustion.

Emissions of metal compounds are also adjusted using algorithms developed for the Emission s Modeling System for Hazardous Air Pollutants (EMS

- HAP) under the National

- scale Air Toxics Assessment (NATA). A mass adjustment factor was applied to the emissions of metal compounds to account for a particular portion (e.g., the lead portion of lead sul fate) or to partition them among multiple pollutant categories (e.g. chromium arsenate into chromium VI compounds and arsenic compounds). In addition, where no specific compound information was available, metals were speciated into appropriate oxidation st ates (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 w ere taken from the HAP Table module of EMS

HAP. 17, 18



## 3. 3 Meteorological Data

Nationwide meteorological data files are accessed by HEM

- 3 and used for the RTR modeling. The current HEM

- 3 AERMOD Meteorological Library includes over 800 nationwide locations, depicted in Figure 3

- 1. This library contains surface and upper air meteorological data from National Weather Service (NWS) observation stations, which are named beginning with the state abbreviation for the state in which the station is located. AERMOD requires surface and upper air meteorological data that meet specific format requirements. 19,20 

### Appendix 2 discusses

### 17

the preprocessing performed on the meteorological data used by AERMOD and includes a detailed listing of the 8 24 meteorological station pairs

.

### 18 Figure 3

- 1 . AERMOD Meteorological Stations

### 19



## 3.4 Model Options Selected

HEM

- 3 presents a number of options for characterizing the modeling domain and data sources. As many sources are generally modeled in RTR assessments, established defaults and common practices are relied on to make these choices. The choices available to a HEM

- 3 user and the selections that are made in most RTR assessments are presented in Table 3

- 1. Some of the key selections are discussed in more detail in the paragraphs below.

It should be noted that although routine emissions are not expected to vary significantly with time, nonroutine (upset) emissions can be significant relative to routine emissions. Upset emissions occur during periods of startup, shutdown, and malfunction. Upset emissions are not likely for equipment or storage tanks, but do result from malfunctioning control devices and leaks in cooling tower heat exchangers. There is some limited data on upset emissions available, 21 

### but no facility

- specific analyses of these data were performed to characterize short

term emissions from these emiss ion sources, and upset emissions are generally not modeled for the RTR risk assessments.
3.4.1 Urban or Rural Dispersion Characteristics



### Current RTR source category assessments which use the 2010 Census are based on either urban or rural dispersion characteristics, depending on the land characteristics surrounding each modeled facility. The EPA provides guidance on whether to select urban or rural dispersion coefficients in its Guideline on Air Quality Models. 22 

### In general, the urban option is used i f (1) the land use is classified as urban for more than 50% of the land within a 3

- kilometer radius of the emission source, or (2) the population density within a 3

- kilometer radius is greater than 750 people per square kilometer. Of these two criteria, th e land use criterion is more definitive.

Using the 2010 Census, the HEM

- 3 model determines, by default, whether to use rural or urban dispersion characteristics. HEM

- 3 will find the nearest census block to the facility center and determine whether that c ensus block is in an urban area, as designated by the 2010 Census. 23 

### The population of the designated urban area will be used to specify the population input for AERMOD's urban mode. (Alternatively, a user may select the rural or urban option to override d etermination by the model. If a user selects an urban dispersion environment, then the user must provide the urban population as well.)

For the 2008 and prior screening

- level RTR assessments of 51 source categories, the rural option was chosen to be mo st conservative (i.e., more likely to overestimate risk results). The rural option is also chosen by default by the HEM

- 3 model whenever the 2000 Census is selected by the user.
3.4.2 Deposition and Plume Depletion



### The RTR modeling analysis to date has not taken into account the depletion of pollutant concentrations in the plume due to wet or dry deposition, although HEM

- 3 can model deposition and depletion using AERMOD. In addition, reactivity and decay have not been considered. It is possible that thi s approach may overestimate air concentrations and therefore risk. However, one of the main metrics used by EPA in the residual risk program is the risk to the individual most

### 20

exposed (the maximum individual risk, or MIR). Because the maximum risk usually occurs at a receptor very close to the emission source, it is unlikely to be influenced by altered plume dispersion characteristics of this type. For more refined, multipathway assessments, EPA may consider deposition and depletion.
3.4.3 Cutoff Distance for Modeling of Individual Blocks



### The cutoff distance for modeling individual Census blocks is initially set to 3 km by default. This distance generally ensures that the maximum individual cancer risk and the maximum TOSHI are modeled explicitly and not interpolated. Following a modeling run, the results for each facility are checked to determine whether the maximum impacts are located inside the modeling cutoff distance. If the maximum impacts are outside the cutoff distance, and if any of the impacts a re significant, then HEM

- 3 is rerun for the facility with a cutoff distance greater than 3 km. In general, this is done if the cancer risk exceeds 1 in 1 million or any TOSHI exceeds 1. However, the risks for such facilities are generally very low, since t he maximum impacts are in most cases only interpolated when the nearest Census block is more than 3 km from the facility (i.e., in sparsely populated areas).
3.4.4 Facility Boundary Assumptions



### The main input mechanisms for incorporating facility bound ary information in HEM

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

- specified receptor locations. The NEI does not provide information on facility boundaries. However, satellite/aerial images are used to locate re sidential populations that are closer to a facility than the Census block centroid. User

- specified receptor locations are used in such assessments to avoid underestimating risk. Conservative default assumptions are used for the overlap distance and the inn ermost polar receptor ring. However, these are adjusted for some categories where facility sites are known to be large. In addition, satellite imagery is used to check the facility boundary assumptions for facilities with large projected impacts. These che cks are discussed further in the section on Quality Assurance (Section 4).

### 21

Table 3

- 1 . HEM

- 3 Domain and Set

- Up Options As Used in the Residual Risk and Technology Review Assessments

Option Selection Dispersion model AERMOD Census database: 2010 or 2000 Based on date of analysis Type of analysis: chronic, acute, or both Both Averaging time for short term impacts 1

- hour Multiplier for short term emissions 10 generally, although multiple source type

specific factors are also used if available Dispersion characteristics: urban or rural, as determined by model, based on closest 2010 Census block to each facility (when using 2010 Census). Rural by default, wh en using the 2000 Census. Urban or Rural base d on facility location; (Rural for 2000 Census) Include terrain impacts Yes Include building wake effects No Calculate deposition (wet, dry, or both) & include impacts of plume depletion No

User

- specified re ceptor locations (for residential population locations, facility boundary sites, or other sites of interest) Yes, for some facilities Modeling domain size

– maximum distance to be modeled 50 km Cutoff distance for modeling of individual blocks 3 km a 

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

– measured from each source measured from each source 30 m b



### Polar receptor network specifications: Distance from the facility center to the innermost ring ≥ 100 m c 

### Number of rings 13 Number of directions 16 Meteorology data Closest site

a The individual block modeling cutoff is increased for categories and for som e facilities to ensure that the maximum individual risk values are not interpolated.
b The overlap distance is adjusted for some facilities to avoid modeling locations that are on facility property (see section 4.2).

c HEM

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

### 22



## 3.5 Modeling of Multiple Facilities

HEM

- 3 models one facility at a time. However, clusters of nearby facilities may impact the same people, resulting in higher risk to those people. To account for this situation, risks are summed at each Census block for all facilit ies affecting the Census block.

As described earlier (Section 2.3.4), HEM

- 3 produces detailed output tables containing the risk and population for every Census block in the modeling domain. These detailed tables are combined for all facilities in a sour ce category and the risk for each Census block is summed, using the RTR Summary Program add

- on module to the Multi HEM

- 3 model. 3 

### Thus, the effect of multiple facilities in the same source category on the same receptor are estimated. The resulting “combined facility” or “cluster

- effect” Census block risks are used to calculate population exposure to different cancer risk levels, noncancer hazard indices, and source category incidence.

### 23 

# 4. Quality Assurance

The National Emissions Inventory (NEI) is subject to an extensive program of quality assurance (QA) and quality control (QC). The QA/QC program for the point source component of the NEI is documented in a separate report, available from the NEI website. 24 

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

## 4.1 Engineering Review

In addition to the standardized QA steps taken for the entire NEI, EPA performs an engineering review of NEI data for the emission source categories included in the RTR analysis. This engineering review includes two main components. The first component addresses the list of facilities included in each source category. EPA engineers review independent sources of information to identify all sources in the category that are included in the NEI. In add ition, EPA reviews the list of sources represented as part of each category in the NEI to make sure that the facilities actually manufacture products characteristic of the source category.

The second component of the engineering review focuses on the ap propriateness of facility emissions. EPA reviews the list of HAPs reportedly emitted by each facility to make sure that the pollutants are appropriate to the source category. In addition, EPA engineers review the magnitude of those HAP emissions. In cases where new or better data are known to exist for a particular source category, that information is integrated into the data used in modeling for that category. In these cases, the source category specific documents provide additional details on the emission s inputs used. 

## 4.2 Geographic Pre

- Modeling Checks

The NEI QA process includes some basic checks on location data for point sources. The coordinates for each source are checked to ensure that they are in the county that has been specified for the source. If this is not the case, or if no geographic coordinates are available for the emission source, then the coordinates are set to a default location based on the nature of the emission source category. 25 

### In addition, coordinates for all emission sour ces at a given facility

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

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

- 3 input files.

Another pre

- modeling geographic QA check regard s the location of the census block receptors. As noted above, to estimate ambient concentrations for evaluating long

- term exposures, the HEM

- 3 model uses the census block centroids as dispersion model receptors. The census block centroids are often good su rrogates for where people live within a census block. A census block generally encompasses about 40 people or 10

- 15 households. However, in cases where a block centroid is located on industrial facility property, or where a census block is large and the ce ntroid less likely to be representative of the block’s residential locations, the block centroid may not be an appropriate surrogate.

Census block centroids that are on facility property can sometimes be identified by their proximity to emission sources. In cases where a census block centroid is within 300 meters of

### 24

any emission source, aerial images of the facility are reviewed to determine whether the block centroid is likely located on facility property. The selection of the 300

- meter distance reflects a compromise between too few and too many blocks identified as being potentially on facility property. Distances smaller than 300 meters would identify only block centroids very near the emission sources and could exclude some block centroids that are sti ll within facility boundaries, particularly for large facilities. Distances significantly larger than 300 meters would identify many block centroids that are outside facility boundaries, particularly for small facilities. Block centroids confirmed to be lo cated on facility property are moved to a location that best represents the residential locations in the block.

In addition, census block centroids for blocks with large areas may not be representative of residential locations. Risk estimates based on su ch centroids can be understated if there are residences nearer to a facility than the centroid, and overstated if the residences are farther from the facility than the centroid. To avoid understating the maximum individual risk associated with a facility, block centroids are relocated in some cases, or additional user

- specified receptors are added to a block. Aerial images of all large census blocks within one kilometer of any emission source are examined. Experience from previous risks characterizations sh ow that in most cases the MIR is generally located within 1 km of the facility boundary. If the block centroid does not represent the residential locations, it is relocated in the HEM

- 3 input files to better represent them. If residential locations cannot be represented by a single receptor (that is, the residences are spread out over the block), additional user

- specified receptors are included in the HEM

- 3 input files to represent residences nearer to t he facility than the centroid. 

## 4.3 Geographic Post

- Mo deling Checks

As part of the RTR modeling analysis, additional geographical QA checks are made for some facilities, after initial HEM

- 3 modeling results are reviewed. Facilities subjected to these additional checks include:

 cases where the initial est imates of maximum risks are particularly high

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

o any maximum TOSHI above 10

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

HEM

- 3 produces a detailed Google EarthTM m ap of the modeled point and area emission sources and surrounding receptors (including Census block centroids, polar receptors and user

specified receptors) overlaying Google EarthTM’s satellite imagery. This map allows a QA check of the specific source loc ations, as well as an approximate check of the facility boundaries. The emission source coordinates are reviewed for each of these facilities and compared with the address reported for the facility. If the address and the coordinates represent the same loc ation, then the coordinates are taken to be correct. For more recent modeling of source categories, the emission coordinates initially modeled by HEM

- 3 tend to be correct, as they undergo pre

modeling scrutiny and QA checks (as discussed in Section 4.2).

More rarely, the modeled emission coordinates will be determined post initial modeling not to be located on facility property. If the facility and emission coordinate locations are

### 25

different, then the satellite imagery for the address and the coordinate l ocation are reviewed to determine whether either photograph includes an industrial facility. Generally for the 2008 and prior screening

- level RTR assessments of 51 source categories, where the two locations were different, the facility address was found to be correct (and the emission source coordinates required correction). In some cases, this comparison could not be made because the reported address was a Post Office box or a headquarters address. Where this occurred, the aerial photograph for the coordin ate location was reviewed to determine whether an industrial facility was located at or near the location. If emission source coordinates are found to be incorrect, HEM

- 3 is rerun using corrected coordinates. These changes are described in the source categ ory documents.

For the high

- risk facilities, the coordinates used to represent the most impacted Census blocks are also reviewed. This review draws on detailed Census block boundary maps and satellite imagery. Large industrial facilities will frequently occupy one or mor e entire Census blocks. However, these blocks may also include one or more residences on the periphery of the industrial land. Generally, the centroid coordinates listed for a Census block are near the center of the block. In these cases of mixed industria l and residential blocks, the coordinates may be on facility property.

In general, block coordinates are considered to be on facility property if they are located between the different emission source locations listed for the facility. In these situatio ns, HEM

- 3 is rerun with an expanded overlap distance, in order to exclude the Census block coordinates that appear to be located on facility property. The distance to the innermost polar receptor ring is also adjusted to ensure that this ring is not on fac ility property, but as close to the apparent facility boundaries as possible.

### 26 

# 5. Uncertainties

The RTR risk assessments using HEM

- 3 and AERMOD are subject to a number of uncertainties. For instance, model verification studies for AERMOD show predicted maximum annual concentrations ranging from 0.3 to 1.6 times measured values, with an average of 0.9. Predicted maximum short term (1 to 24 hours) concentrations were 0.25 to 2.5 times measured values, with an average of 1.0. 25



### In addition, a number of si mplifying assumptions are made in these modeling analyses. First, the coordinates reported by the Department of Census for Census block internal points (“centroids”) have been used as a surrogate for long

- term population exposures. Locations of actual resi dences have not been modeled. In addition, the current version of HEM

- 3 does not take into account the movement of people from one Census block to another during the course of their lives, or commuting patterns during a given day. Nor does the model take i nto account the attenuation of pollutant from outside emission sources in indoor air. Ideally, risks to individuals would be modeled as they move through their communities and undertake different activities. However, such modeling is time

- and resource

- inte nsive and can only capture a portion of the uncertainty associated with the full range of human activities. In general, it is expected that long

term exposures will be overstated for high

- end estimates (as most individuals will not spend all their time at their highly affected residences), but may understate the total population exposed (as some individuals living outside the modeled area may regularly commute into the area for work or school).

When considering long

- term or lifetime exposures, it should be noted that relatively few people in the United States reside in one place for their entire lives. For the purposes of this assessment, cancer risk estimates are based on a lifetime exposure at the Census

- identified place of residence. While it is imposs ible to know how this assumption affects the risk experiences by a particular individual (as people can move into higher

- or lower

- risk areas), it is likely that this assumption will overstate the exposure to those most exposed (i.e., people already living in high exposure areas are unlikely to move to yet higher exposure areas). However, this assumption will also tend to underestimate the total number of people exposed and population risk (i.e., incidence) because population levels are generally increasing

. In the current analyses, only direct inhalation is modeled. Other pathways such as the deposition of pollutants to drinking water, and to bioaccumulation of deposited pollutants in the food supply may be a significant source of exposure for persistent and bioaccumulative hazardous air pollutants (PB HAP). Screening level evaluations of the potential human health risks associated with emissions of PB HAP from the modeled facilities are used to determine if additional analyses are needed, but these analy ses are outside the scope of this document. Because the HEM

- 3 AERMOD analyses are restricted to the inhalation pathway, the impacts of plume depletion due to deposition are not taken into account. Thus, inhalation impacts may be overestimated for some poll utants, but exposures through other pathways would be underestimated.

A number of other simplifications are made in the dispersion modeling analyses, as noted in Table 3

- 1. For instance, building wake effects are not considered. In addition, meteorologic al

### 27

observations are based on the closest station in the HEM

- 3 meteorological library (see Figure 3

- 1). Alternative meteorological stations may be more appropriate for some facilities. Ideally, facility

- specific meteorological observations would be used. A single year of meteorological data (2011) is currently used for AERMOD’s dispersion modeling. (The 2008 and prior screening

level RTR assessments of 51 source categories used meteorological data based on the year 1991.) When considering off

- site meteoro logical data most site specific dispersion modeling efforts will employ up to five years of data to capture variability in weather patterns from year to year. However, because of the large number of facilities in the analyses and the extent of the dispersi on modeling analysis (national scale), it is not practical to model five years of data. Other national studies such as NATA also consider only a single year of meteorological data. A sensitivity analyses performed by the NATA assessment found that variabil ity attributable to the selection of the meteorology location/time (both temporal and spatial) resulted in a 17

- 84% variation in predicted concentrations at a given station. 26



### Finally, risk and exposure factors are also subject to uncertainty. Not all in dividuals experience the same degree of exposure or internal dose of a given pollutant due to individual

specific parameters such as weight, age, and gender. While the health benchmarks used in the analyses crudely account for sensitive populations, a prot otypical human (e.g., body weight, ventilation rate) is used to define the benchmark. Because of the variability of these parameters in the population, this factor will result in a degree of uncertainty in the resulting risk estimate.

Table 5

- 1 summariz es the general sources of uncertainty for the RTR modeling analyses. The table also gives a qualitative indication of the potential direction of bias on risk estimates. The sources of uncertainty in Table 5

- 1 are divided into four categories, based on the major components of the analyses:

 emissions inventory;

 fate and transport modeling;

 exposure assessment; and

 toxicity assessment.

It must also be noted that individual source categories may be subject to additional uncertainties. These are discussed in separate reports which are prepared for each emission source category included in the RTR assessments.

### 28

Table 5

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

Parameter Assumption Uncertainty/Variability Discussion Potential Direction of Bias on Risk Estimates Emissions Inventory

Individual HAP emissions rates and facility characteristics (stack parameters, property boundaries) Emissions and facility characteristics fro m the NEI provide an accurate characterization of actual source emissions. Our current emissions inventory is based on the 2005 NEI, our internal review, and public comments received. The degree to which the data in our inventory represents actual emissions is likely to vary across sources. For the 2008 screening level assessments, nearly half of the sources in a given source category submitted a review of their emissions and facility characteristics data. Some detailed data, such as property bounda ry information is not available for most facilities. This is an important consideration in determining acute impacts. Unbiased overall, magnitude variable

Multiplier for short

- term emission rates Generally, maximum short term emission rates are estimated by applying a simple multiplier (a factor of 10) to average annual emissions. The ratio between short

- term and long

- term average emission rates may vary among the different emission sources at a facility. In addition, the use of a simple multiplier means t hat impacts of maximum short term emissions are modeled for all meteorological conditions, including the worst

- case conditions for population exposure. Potential overestimate due to the fact that worst

- case emissions are assumed to occasionally coincide wi th worst

- case meteorology.

Overestimate due to lack of actual information on short

term emission rates. Fate and Transport Modeling

Atmospheric dispersion model choice AERMOD is EPA's recommended dispersion model for assessing pollutant concentrations f rom industrial facilities Field testing of dispersion models, including AERMOD, have shown results to generally be within a factor of 2 of measured concentrations. Unbiased overall

Building downwash Not included in assessments Use of this algorithm in AERMOD could improve the dispersion calculations at individual facilities. However, data are not readily available to utilize this option. Potential underestimate of maximum risks near facility. No effect on risks further out.

Plume deposition and deplet ion Not included in assessments Ignoring these impacts for pollutants that deposit minimally, and whose risks derive predominantly from inhalation, should have minimal effect on risk estimates. Unbiased or minimal overestimate.

### 29

Table 5

- 1. Summary of General Uncertainties Associated with Risk and Technology Review Risk Assessments (continued)

Parameter Assumption Uncertainty/Variability Discussion Potential Direction of Bias on Risk Estimates

Meteorology One year of meteorological data from the nearest weather station (selected from 824 nationwide) is representative of long

- term weather conditions at the facility. The use of one year of data rather than the five or more adds uncertainty based on whether that year is representative of each locatio n’s climatology. Use of weather station data rather than on

- site data can add to uncertainty. Additionally, the use of default surface parameters in the generation of the meteorological datasets imparts uncertainty to the results from any individual facil ity. Minimal underestimate or overestimate.

Reactivity Not included in the assessments Chemical reactions and transformations of individual HAP into other compounds due to solar radiation and reactions with other chemicals happens in the atmosphere. Howev er, in general, the HAP in this assessment do not react quickly enough for these transformations to be important near the sources, where the highest individual risks are estimated. Further, most of the HAP do not react quickly enough for these transformati ons to be important to risk estimates in the entire modeled domain (i.e., within 50 km of the source). No impact on maximum risk estimates. Minimal impact on population risks and incidence.

Maximum modeling distance 50 kilometers from center of facility This distance is considered to be the maximum downwind distance for a Gaussian plume model such as AERMOD. This is because, in general, winds cannot be considered to follow straight line trajectories beyond this distance. No effect on maximum individual ri sks. Minimal underestimation of incidence.

Exposure Assessment

Locations and short

- term movements of individuals Ambient concentration at centroid of each off

- site census block is equal to the exposure concentration for all people living in that census block.

Effect of human activity patterns on exposures is not included in the
assessment. People live at different areas within block that may have higher or lower exposures than at the centroid. Individuals also move from outdoors to indoors and from home to school/work to recreation, etc., and this can affect their total exposure from these sources. Unbiased across population for most pollutants and individuals, likely overestimate for most exposed and underestimate for least exposed persons.

### 30

Table 5

- 1 . Summary of General Uncertainties Associated with Risk and Technology Review Risk Assessments (continued)

Parameter Assumption Uncertainty/Variability Discussion Potential Direction of Bias on Risk Estimates

Long

- term movements of individuals MIR individual is exposed continuously to the highest exposure concentration for a 70

- year lifetime. Maximum individual risk (MIR) is defined in this way to be a maximum theoretical risk at a point where a person can actually reside. Unbiased for most individu als, likely overestimate for the actual individual most exposed and likely underestimate for the least exposed. Incidence remains unbiased unless population around facilities increases or decreases over 70 years. Toxicity Assessment

Reference concentrations (RfC) Consistent with EPA guidance, RfCs are developed including uncertainty factors to be protective of sensitive subpopulations. Additionally, RfCs are developed based on the level producing an effect in the most sensitive target organ or system. While other organ systems may be impacted at concentrations above the RfC, these are not included in the calculation of target organ

- specific hazard indices. In general, EPA derives RfCs using procedures whose goal is to avoid underestimating risk s in light of uncertainty and variability. The greater the uncertainties, the greater the potential for overestimating risks.

Unit Risk Estimate (URE) Use of unit risk estimates developed from dose

- response models such as linear low

- dose extrapolation. Uncertainty in extrapolating the impacts from short

duration, high

- dose animal or work

- related exposures to longer duration, lower

- dose environmental impacts. Overestimate of risks for nonlinear carcinogens and for linear carcinogens with sparse health eff ects data. In general, EPA derives URE values using procedures aimed at overestimating risks in light of uncertainty and variability.

Toxicity of mixtures Cancer risks and noncancer hazard quotients were calculated for each HAP individually and then summe d into a total risk or hazard index (assumption of additivity). Concurrent exposures to multiple chemicals may result in either increased or decreased toxicity due to chemical interactions but the data needed to quantify these effects are generally not ava ilable. Unbiased overall. Some mixtures may have underestimated risks, some overestimated, and some correctly estimated.

### 31

Table 5

- 1. Summary of General Uncertainties Associated with Risk and Technology Review Risk Assessments (continued)

Parameter Assumption Uncertainty/Variability Discussion Potential Direction of Bias on Risk Estimates

Surrogate dose

- response values for HAPs without values In the case of groups of HAPs such as glycol ethers, the most conservative dose

- response value of the chemical group was used as a surrogate for missing dose

- response values in the group. For others, such as unspeciated metals, we have applied speciation p rofiles appropriate to the source category to develop a composite dose

response value for the group.

For HAP which are not in a group and for which no URE’s or RfC’s are available from credible sources, no assessment of risk is made. Rather than neglecti ng the assessment of risks from some HAPs lacking dose response values, conservative assumptions allow the examination of whether these HAPs may pose an unacceptable risk and require further examination, or whether the conservative level examination with s urrogates screens out the HAPs from further assessment. Overestimate where most conservative values used. Unbiased where category

specific profiles applied.

There is the potential to underestimate risks for pollutants which are not included in the as sessment.

### 32 

# 6. References

1. EPA. 2004. AERMOD: Description of Model Formulation . EPA

- 454/R

- 03

- 004, U.S. Environmental Protection Agency, Research Triangle Park, NC. http://www.epa.gov/ttn/scram/7thconf/aermod/aermod_mfd.pdf

2. EPA. 2004. User's Guide for the AMS/EPA Regulatory Model

- AERMOD . EPA

- 454/B

03

- 001, U.S. Environmental Protection Agency, Research Triangle Park, NC. www.epa.gov/scram001/dispersion_prefrec.htm#aermod

3. EPA. 2013. The Human Exposure Model (HEM): Single

- facility HEM

- 3 User’s Guide; and Multi

- facility HEM

- 3 and RTR Summary Programs User’s Guide. Prepared for the U.S. Environmental Protection Agency, Research Triangle Park, NC, by EC/R Inc., Chapel Hill, NC. February 2013. http://www.epa.gov/ttn/fera/human_hem.html

4. EPA. 2012. Dose

- Response Assessment for Assessing Health Risks Associated With Exposure to Hazardous Air Pollutants. U.S. Environment al Protection Agency, Research Triangle Park, NC. Available at: http://www.epa.gov/ttn/atw/toxsource/summary.html

5. EPA. 2011. Glossary of Key Terms . Technology Transfer Network Air Toxics, 20 05 National

- Scale Air Toxics Assessment. U.S. Environmental Protection Agency. http://www.epa.gov/ttn/atw/natamain/gloss1.html

.
6. EPA. 2004. Air Toxics Risk Assessment Reference Library, EPA

- 453

- K

- 04

- 001A, U.S. Environmental Protection Agency, Research Triangle Park, NC. April 2004. www.epa.gov/ttn/fera/risk_atra_vol1.ht ml

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

8. Census. 2010. Census Summary File 1

– United States: http://www2.census.gov/census_2010/04

- Summary_File_1/ p repared by the U.S. Census Bureau, Washington, DC, 2011. See also Technical Documentation for the 2010 Census Summary File 1.

9. USGS. 2006. US GeoData Digital Elevation Models

– Fact Sheet 040

- 00 (April 2000)

. U.S. Department of the Interior

- U.S. Geologica l Survey, Washington, DC. http://data.geocomm.com/sdts/fs04000.pdf

10. USGS. 2010. USGS Seamless Data Warehouse. U.S. Department of the Interior

- U.S. Geological Survey, Washington, DC. http://nationalmap.gov/viewer.html

### 33

11. EPA. 2004. User's Guide for the AERMOD Terrain Preprocessor (AERMAP). EPA

454/B

- 03

- 003, U.S. Environmental Protection Agency, Research Triangle Park, NC. http://www.epa.gov/scram001/dispersion_related.htm#aermap

12. EPA. 2011. Addendum to the User’s Guide for the AERMOD Terrain Preprocessor (AERMAP) (EPA

- 454/B

- 03

- 003, October 2004), U.S. Environmental Protection Agency, Research Triangle Park, NC. March 2011. http://www.epa.gov/scram001/dispersion_related.htm#aermap

13. EPA. 2012. Addendum: User's Guide for the AMS/EPA Regulatory Model

– AERMOD (EPA

- 454/B

- 03

- 001, September 2004) (Section 2.2 related to deposition). U.S. Environmental Protection Agency, Research Triangle Park, NC. December 2012. http://www.epa .gov/scram001/dispersion_prefrec.htm#aermod

. See also the AERMOD Deposition Algorithms

– Scien ce Document (Revised Draft)” at http://www.epa.gov/ttn/scram/7thconf/aermod/aer_scid.pdf

14. Wesely, M.L., P.V. Doskey, and J.D. Shannon. 2002. Deposition Parameterizations for the Industrial Source Complex (ISC3) Model . ANL/ER/TR

– 01/003, Argonne National Laboratory, Argonne, Illinois 60439. See “AERMOD Deposition Parameterizations Document” pdf link under “Model Supporting Documents” section of TTN’s Preferred/Recommended Models webpage at http://www.epa.gov/scram001/dispersion_prefrec.htm#aermod

15. EPA. 2012. 2005 National Emissions Inventory Data & Documentation . U.S. Environmental Protection Agency, Research Triangle Park, NC. http://www.epa.gov/ttn/chief/net/2005inventory.ht ml#inventorydata

16. EPA. 2011. An Overview of Methods for EPA’s National

- Scale Air Toxics Assessment. Prepared for EPA’s Office of Air Quality, Planning, and Standards, Research Triangle Park, NC, by ICF International , Durham, NC. January 2011, p 55. http://www.epa.gov/ttn/atw/nata2005/05pdf/nata_tmd.pdf

.
17. EPA. 2004. EMS

- HAP Modeling System

. Environmental Protection Agency, Research Triangle Park, NC. http://www.epa.gov/scram001/dispersion_related.htm#ems

- hap

18. Strum, Madeleine, Richard Mason, and James Thurman. 2004. User's Guide for the Emissions Modeling System for Hazardous Air Pollutants (EMS

- HAP) Version 3.0

. EPA

- 454/B

- 03

- 006, Environmental Protection Agency, Research Triangle Park, NC. August 2004. http://www.epa.gov/ttn/scram/userg/other/emshapv3ug.pdf

### 34

19. EPA. 2004. User’s Guide for the AERMOD Meteorological Preprocessor (AERMET), EPA

- 454/B

- 03

- 002, U.S. Environmental Protection Agency, Research Triangle Park, NC. http://www.epa.gov/scr am001/7thconf/aermod/aermetugb.pdf

20. EPA. 2012. Addendum to the User’s Guide for the AERMOD Meteorological Preprocessor (AERMET) (EPA

- 454/B

- 03

- 002, November 2004), U.S. Environmental Protection Agency, Research Triangle Park, NC. December 2012. http://www.epa.gov/ttn/scram/metobsdata_procaccprogs.htm#aermet

21. Texas Commission on Environmental Quality. 2006. Reports on Air Emission Events. http://www.tceq.texas.gov/field/eventreporting

22. EPA. 2005. Revision to the Guideline on Air Quality Models: Adoption of a Preferred General Purpose (Flat and Complex Terrain) Dispersion Model and Other Revisions. Appendix W of 40 CFR Part 51. http://www.epa.gov/ttn/scram/guidance/guide/appw_05.pdf

23. Federal Register, 2012. Qualifying Urban Areas for the 2010 Census. FR 77:59 (27 March 2012). p.18652. http://www.gpo.gov/fdsys/pkg/FR

- 2012

- 03

- 27/pdf/2012

- 6903.pdf

24. EPA. 2006. NEI Quality Assurance and Data Augmentation for Point Sources . U.S. Environmental Protection Agency, Emission Inventor y Group, RTP, NC. http://www.epa.gov/ttn/atw/nata2005/05pdf/2002nei_qa_augmentation_report0206.pdf

25. EPA. 2003. AERMOD: Latest Features and Evaluation Re sults. EPA

- 454/R

- 03

- 003, U.S. Environmental Protection Agency, Research Triangle Park, NC. http://www.epa.gov/scram001/7thconf/aermod/eval.pdf

26. EPA, 2001, National

- Scale Air Toxics Assessment for 1996; EPA

- 453/R

- 01

- 003; EPA 454/B

- 03

- 003, U.S. Environmental Protection Agency, Research Triangle Park, NC. http://www.epa.gov/ttn/atw/nata/peer. html

## DRAFT

METEOROLOGICAL DATA PROCESSING

USING AERMET FOR HEM3

US EPA Air Toxic Assessment Group RTP NC 27711

September , 2014Meteorological Data Processing using AERMET December 201 3

For HEM3



### 1 BACKGROUND

The AERMOD meteorological processor, AERMET, and its supporting modeling system (AERSURFACE and AERMINUTE) w ere used to process one year of meteorological data for over 800 observation stations across the continental United States, Alaska, Hawaii, and Puerto Rico.

METEOROLOGICAL DATA

To estimate the boundary layer parameters required by AERMOD, AERMET requires hourly surface weather observations ( which may includ e hourly values calculated from 1

- minute data) and the full (i .e., meteorological variables reported at all levels) twice

- daily upper air soundings. The surface and upper air stations are paired to produce the required input data for AERMOD.

USEPA meteorologists obtained calendar year 2011 Integrated Surface Hour ly Data (ISHD) for over 800 Automated Surface Observation System (ASOS) stations spanning the entire US
, as well as Puerto Rico and the US Virgin Islands
, from the Natio nal Climatic Data Center (NCDC). To support AERMET, ASOS 1

- m inute d ata for each surface station w ere also obtained from NCDC in a DSI 6405 format.

Further, upper air sounding data for the same time period for over 80 observation sites were obtained from the “ NOAA/ESRL ” online Radiosonde Database

. These datasets were produced b y ESRL in Forecast Systems Laboratory (FSL) format. Appendix 1 lists the surface stations
, as well as the location, ground elevation, and anemometer height for each station. Figure 1 is a map that shows the locations of all the surface stations. Appendi x 2 lists the upper air stations and their locations

.

AERMET PROCESSING

Utilizing the AERMET meteorological data preprocessor , and the ASOS surface and FSL upper air stations, surface and profile files for input in to AERMOD were generated nationwide. The surface stations were paired with representative upper air stations by taking the upper air station closest to each surface station. The AERSURFACE tool was used to estimate the surface characteristics for input in to AERMET utilizing land cover data s urrounding the surface station

. In addition, the AERMINUTE preprocessor was

u sed to process 1

- minute ASOS wind data for input in to AERMET. Table 1 outlines the approach/inputs each of the data preprocessors and tools used to generate the AERMOD meteorolo gical data.Meteorological Data Processing using AERMET December 201 3

For HEM3



### 2 Figure 1 . Surface StationsMeteorological Data Processing using AERMET December 201 3

For HEM3



### 3 Table 1

. AERMET Processing Options AERMET Options Version 13350 1 

### ASOS Site Yes Surface Data Format NCDC TD

- 3505 (ISHD) Upper Air Data Format FSL, all levels, tenths m/s Wind Speed Threshold 0.5 m/s Beta Option (U*) Y es AERMINUTE Options Version 1132 5 Include 1 minute ASOS Data Yes, where available TD

- 6405 format AERSURFACE Options Version 13016 Landcover data USGS NLCD92 GeoTIFF format (except Alaska and Hawaii which used 20 0 1 landcover data) Radius for Surface Roughness Calculations 1 km

Site Characteristics Airport Site (where applicable) Arid Regions for all sites in AZ, NM, UT only Temporal resolution Monthly, 12 sectors Site Surface Moisture Average Snow Cover Late Winter/Winter without continuous snow cover

– all states (except Alaska where we used continuous snow) 1 : generated with the pre

- public release version of AERMET 13350.

RESULTS

Beginning with version 12345, AERMET allows for a minimum wind speed of 0.5 m/s to be utilized when processing monitoring stations that are equipped with sonic anemometers and for a n adjustment to the surface friction velocity (u*). The public version of AERMET available at the time we conducted the processing of this data did not include the surface friction velocity adjustment. Also at the time, an updated version of AERMET (and AERMOD) was under development that incorporated further surface friction velocity adjustments to AERMET version 12345. In anticipation of the update, EPA processed meteorological data using a pre

- public release version of AERMET and incorporated the updat ed surface friction velocity adjustment in the output. It was EPA’s judgement that the pre

- public release version of AERMET would generate AERMOD ready meteorological data very close to that generated with the 13350 version of AERMET. The AERMET meteorol ogical data posted on the EPA’s FERA (Fate, Exposure, and Risk Analysis) website under the HEM model page was generated with the pre

- public release version of AERMET 13350.Meteorological Data Processing using AERMET December 201 3

For HEM3



### 4 ly, USEPA meteorologists ran AERMOD using a model plant. Further, the surface file s were examined for completeness. If more than 10 percent of the data were missing, the station was not considered suitable for the HEM

- 3 meteorological database. In all, 824 met station pairs ran successfully and will be included in the HEM

- 3 meteorologi cal library. A summary of these station pairs are presented in Appendix 3 of this document.

### Appendix 4 Dispersion Model R eceptor Revisions and Additions for the Aerospace Source Category

### Dispersion Model Receptor Revisions and Additions

To estimate ambient concentrations for evaluating long

- term exposures , t he HEM

- 3 model uses the geographic centroids of census blocks ( currently utilizing the 201 0 Census) as dispersion model receptors . The census block centroids are generally good surrogate s for where people live within a census block

. A census block generally encompasses about 40 people or 10

- 15 households. However, in cases where a block centroid is located on industrial property , or where a census block is large and the centroid less likely to be representative of the block’s resident ial locations , the block centroid may not be an appropriate sur rogate.
Census block centroids that are on facility property can sometimes be identified by their proximity to emission sources. In cases where a census block centroid was within 300 meters of

an y emission source, we viewed aerial images of the facility to determine whether the block centroid was likely located on facility property. The selection of the 300

- meter distance reflects a compromise between too few and too many blocks identified as b eing potentially on facility property. Distances smaller than 300 meters would identify only block centroids near the emission sources and could exclude some block centroids that are still within facility boundaries, particularly for large facilities. Di stances significantly larger than 300 meters would identify many block centroids that are outside facility boundaries, particularly for small facilities. Where we confirmed a block centroid on facility property, we moved the block centroid to a location
t hat best represents the residential locations in the block

.
In addition , census block centroids for blocks with large areas may not be representative of residential locations. Risk estimates based on such centroids can be understated if there are residenc es nearer to a facility than the centroid, and overstated if the residences are farther from the facility than the centroid. To avoid understating the maximum individual risk associated with a facility, in some cases we relocate d block centroid s , or add ed dispersion model receptors other than the block centroid . We examined aerial images of all large census blocks within one kilometer of any emission source

. Experience f r om previous risks characterizations show that i n most cases the MIR is generally loc ated within 1 km of the facility boundary. If the block centroid did not represent the residen tial locations , we relocated it to better represent the m . If residen tial locations could not be represented by a single receptor (that is, the residences were

s pread out over the block), we added additional receptors for residences nearer to the facility than the centroid

.
For th ese source categories , t he table below contains each census block for which we changed the centroid location because it was on facility property or was otherwise not representative of the residential locations in the block. The table also contains the locations of additional receptors that were included to represent residential locations nearer to the facility than the block centroid.Block ID NEI ID New Latitude New Longitude Comment

010730003003000 33.551805 -86.759025 On plant property 020900018002007 64.674932 -147.069107 Centroid doesn't represent population 051139501003052 34.549923 -94.20052 Centroid doesn't represent population 060375742022013 33.797825 -118.148638 On plant property 090076802002008 41.559581 -72.606323 Centroid doesn't represent population 090076802002030 41.530871 -72.568172 Centroid doesn't represent population 090035106001020 41.743352 -72.631209 On plant property 120090671001045 28.218591 -80.604812 On plant property 120310138002003 30.389219 -81.398452 Centroid doesn't represent population 130099705003027 33.083926 -83.306343 Centroid doesn't represent population 150030087012030 21.392368 -157.979934 Centroid doesn't represent population 191751903001185 41.050938 -94.330568 Centroid doesn't represent population 191751903001184 41.057247 -94.336577 Centroid doesn't represent population 200354933001017 37.183071 -97.024316 Centroid doesn't represent population 201259507002092 37.164787 -95.764402 Centroid doesn't represent population 201730073022000 37.683842 -97.235703 Centroid doesn't represent population 201730092002069 37.670818 -97.435695 Centroid doesn't represent population 201730096031008 37.628536 -97.425269 Centroid doesn't represent population 201730058001012 37.616576 -97.29574 Centroid doesn't represent population 240054515003002 39.326719 -76.438174 Centroid doesn't represent population 240010020003030 39.565437 -78.855722 Centroid doesn't represent population 240010020001059 39.567312 -78.853521 Centroid doesn't represent population 240150305034001 39.608694 -75.862987 Centroid doesn't represent population 250092544023011 42.641297 -71.193259 On plant property 270530251003004 44.848346 -93.232734 Centroid doesn't represent population 280890302041010 32.513103 -90.115196 Centroid doesn't represent population 291833102022024 Remove - no apparent population 291833104002011 38.798652 -90.472628 Centroid doesn't represent population 330110026003011 42.947922 -71.445989 Centroid doesn't represent population 330110026002029 42.941096 -71.420333 Centroid doesn't represent population 361119505001033 42.023843 -74.103 Centroid doesn't represent population 371330010001078 Remove - no apparent population 390210104003056 40.113107 -83.756062 Centroid doesn't represent population 390210106003038 40.095165 -83.758549 Centroid doesn't represent population 390210115045026 40.090555 -83.760095 Centroid doesn't represent population 390610232011004 39.231316 -84.441067 Centroid doesn't represent population 391093653021008 40.022496 -84.222607 On plant property 391336009012020 41.163415 -81.25601 Centroid doesn't represent population 401091074032002 35.417778 -97.364281 Centroid doesn't represent population 401430059001112 36.173684 -95.850039 Centroid doesn't represent population 401430059001102 36.169252 -95.852922 Centroid doesn't represent population 420034511052004 40.507363 -80.22897 Centroid doesn't represent population 420550104002034 Remove - no apparent population 470370157001012 36.112564 -86.694519 Centroid doesn't represent population 481210203083090 32.997107 -97.326708 Centroid doesn't represent population 482013413012014 29.56424 -95.106548 Centroid doesn't represent population 482319611003051 33.072415 -96.086581 Centroid doesn't represent population 482319611003064 33.057274 -96.077758 Centroid doesn't represent population 490111252002238 41.114114 -112.003497 On plant property 490111255022016 Remove - no apparent population 490351139071081 Remove - no apparent population



### Dispersion Modeling Receptor Revisions and Addiitons

page 1 of 3Block ID NEI ID New Latitude New Longitude Comment 

### Dispersion Modeling Receptor Revisions and Addiitons

530330253021004 47.502702 -122.203415 On plant property 530330309011001 47.281439 -122.246982 On plant property 530530714063015 47.075226 -122.345873 Centroid doesn't represent population 530530731082001 47.085115 -122.335884 Centroid doesn't represent population 530610419032005 47.910351 -122.259637 Centroid doesn't represent population 530610419011004 47.888713 -122.280334 On plant property 530610413042008 47.92782 -122.286347 Centroid doesn't represent population 530610419031003 47.922014 -122.258728 Centroid doesn't represent population 550870126013002 44.246606 -88.500529 On plant property

050272001 33.279137 -93.24732 Additional receptor 01015223 33.636798 -85.917779 Additional receptor 01015223 33.64429 -85.922 Additional receptor 010515017 32.483183 -85.891947 Additional receptor 05113377 34.553318 -94.201272 Additional receptor 05143331 36.163941 -94.129957 Additional receptor 05143331 36.164968 -94.134175 Additional receptor 05143331 36.162945 -94.133167 Additional receptor 06037429 33.799406 -118.155953 Additional receptor 06037490 33.921879 -118.378028 Additional receptor 06037490 33.921247 -118.378083 Additional receptor 06065520 33.946768 -117.464655 Additional receptor 06065520 33.943259 -117.468022 Additional receptor 06065520 33.942524 -117.462057 Additional receptor 06065520 33.943152 -117.460244 Additional receptor 06065520 33.946694 -117.45886 Additional receptor 09001688 41.230118 -73.188762 Additional receptor 09001688 41.229658 -73.181106 Additional receptor 09001693 41.250318 -73.102572 Additional receptor 09001693 41.254451 -73.100727 Additional receptor 09003626 41.859277 -72.699027 Additional receptor 09003626 41.860102 -72.696426 Additional receptor 090032025 41.702315 -72.861 Additional receptor 090075021 41.583854 -72.709395 Additional receptor 090075021 41.584044 -72.712449 Additional receptor 13067506 33.93187 -84.521904 Additional receptor 13067506 33.928956 -84.541948 Additional receptor 13153431 32.63233 -83.606056 Additional receptor 132332017 34.000155 -85.037232 Additional receptor 19163356 41.545325 -90.610764 Additional receptor 20035486 37.174104 -97.022622 Additional receptor 20173193 37.665652 -97.376544 Additional receptor 24015282 39.611523 -75.873373 Additional receptor 24015282 39.60952 -75.870575 Additional receptor 25009450 42.641822 -71.190422 Additional receptor 25009450 42.639385 -71.195099 Additional receptor 25009450 42.634538 -71.187894 Additional receptor 25009450 42.635331 -71.179925 Additional receptor 25009450 42.636161 -71.19115 Additional receptor 25009450 42.631334 -71.184271 Additional receptor 25009450 42.631605 -71.178394 Additional receptor 25009477 42.456095 -70.970509 Additional receptor



### page 2 of 3Block ID NEI ID New Latitude New Longitude Comment 

### Dispersion Modeling Receptor Revisions and Addiitons

29183329 38.802311 -90.477323 Additional receptor 29183329 38.80376 -90.479095 Additional receptor 33011652 42.945817 -71.445529 Additional receptor 36103521 40.70906 -73.39568 Additional receptor 36103521 40.710546 -73.396134 Additional receptor 36111630 42.025358 -74.104536 Additional receptor 39017343 39.300248 -84.445037 Additional receptor 391335027 41.162827 -81.25431 Additional receptor 40143460 36.170512 -95.849741 Additional receptor 401432003 36.204015 -95.90583 Additional receptor 401432013 36.2485 -95.92349 Additional receptor 42003603 40.509554 -80.23638 Additional receptor 4703758 36.121575 -86.697288 Additional receptor 4703758 36.123731 -86.697024 Additional receptor 482015020 29.564051 -95.07879 Additional receptor 48231376 33.063178 -96.079797 Additional receptor 483672020 32.774411 -97.80383 Additional receptor 484392009 32.807578 -97.154854 Additional receptor 53033532 47.271577 -122.237263 Additional receptor 54067418 38.226333 -80.582851 Additional receptor 54067418 38.229495 -80.584709 Additional receptor 550876001 44.258407 -88.506004 Additional receptor 2017365 37.68934 -97.207254 Additional receptor 2017365 37.687577 -97.207454 Additional receptor 15003346 21.334582 -157.95132 Additional receptor



### page 3 of 3

## APPENDIX 5: Technical Support Document for the T RIM

- Based Multip athway Tiered Screening Methodology for RTR

## Technical Support Document for the TRIM

- Based Multipathway Tiered Screening Methodology for RTR

October 201 4

Prepared For:

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

Prepared By:

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

- Based Tiered Screening Methodology for RTR



## C ONTENTS

1. Introduction and Background

................................ ............................................................. 1
1.1 Tier 1

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

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

......................................................... 4
1.2 Tier 2

................................ ......................................................................................... 7
1.3 Tier 3

................................ ....................................................................................... 10
1.4 Site

- Specific Assessment

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

2. Tier 1 Methodology

........................................................................................................... 12
2.1 Introduction

................................ .............................................................................. 12
2.2 Summary of Approach

............................................................................................. 12 2.2.1 Overview

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

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

................................ ................................ . 16 2.2 .4 Approach to Development of the Tier 1 Scenario

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

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

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

- based Emission Scaling Factors for POM and Dioxin Emissions

................................ ....................................... 22
2.3 De scription of Environmental Modeling Scenario

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

.................................................................................. 26 2.3.2 Spatial Layout

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

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

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

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

.................................................. 33
2.4 Description of Exposure and Risk Modeling Scenario

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

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

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

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

................................ ................................ 39
2. 5 E valuation of Screening Scenario

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

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

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

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

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

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

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

3. Tier 2 Methodology

................................ ........................................................................... 52
3.1 Overview of Approach

................................ ............................................................. 52
3.2 Selection of Site

- Specific Characteristics for the Tier 2 Assessment

........................ 55
3.3 Estimation of Adjustment Factors for Selected Site

- Specific Parameters

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

- Based Tiered Screening Methodology for RTR



### 3.3.1 Selection Values for Variables of Interest

.................................................. 56 3.3.2 Estimation of Adjustment Factors

.............................................................. 59
3.4 Preparing National Databases o f Lake and Meteorological Data

............................. 64 3.4.1 Processing Lake Data for Tier 2 Assessment

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

............................. 68
3.5 Implementation of Tier 2 Assessment

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

4. Tier 3 Methodology

................................ ........................................................................... 74
4.1 Overview of Approach

................................ ............................................................. 74
4.2 Lake Assessment

................................ .................................................................... 74
4.3 Plume

- rise Assessment

................................ ........................................................... 76
4.4 Assessment of Time

- series Meteorology and Effective Release Heights

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

5. References

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

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

................................................. B-1
Dermal Risk Screening

................................................................................. C-1
Summary of TRIM.FaTE Parameters Considered for Inclusion in Tier 2 Assessment

................................ ........................................................ D-1
Analysis of Lake and Sustainable Fish Population

............................... E-1TRIM

- Based Tiered Screening Methodology for RTR



## 1. Introduction and Background

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

— with MACT regulations in place

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

- HAPs) via non

- inhalation pathways , namely ingestion and dermal exposure

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

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

- HAPs is discussed in Section 2.2.2 ). These non

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

- HAPs, exposures via ingestion are anticipated to be significantly higher than any dermal exposures that might occur as a result of the same emissions (see Attachment C ). Consequently, a methodology has been developed to evaluate ingestion ex posure and risk for PB

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

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

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

- HAP
em issions unlikely to pose health risks above levels of concern (i.e., a cancer risk of 1 in 1 million or a noncancer hazard of 1.0 ) and to focus additional resources on sources of greater
concern within the category

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

• 

### Tier 1 of the approach begins by identifying the facility -l evel emissions of PB

- HAPs within a source category and comparing them to risk

- based emi ssion thresholds. The risk

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

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

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

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

. If, however, the emission rate of any PB

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

• 

### In Tier 2, the actual location of the facility is used to refine some assumptions associated with the environmental scenario while maintaining the Tie r 1 ingestion exposure scenario assumptions . Th e environmental scenario assumptions are refined by incorporating binned site

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

. T he risk

- based threshold for each PB

- HAP is then adjusted for that

Introduction and Background 1 October 2014TRIM

- Based Tiered Screening Methodology for RTR



### facility based on an understanding of how exposure concentrations estimated for the screening scenario change with meteorology and lake location. PB

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

.

• 

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

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

- rise and time

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

.

• 

### For facilities emitting PB

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

, site

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

- specific c haracteristics regarding the environment to which PB

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

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

- specific assessment, resulting in a range of risk estimates.
The key processes and decisions that make up t his approach are summarized in Exhibit 1

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

. If a site

- specific assessment is conducted

, a separate report detailing that assessment will be prepa red.



### 1.1 Tier 1

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

- HAP emissions from multiple facilities in a source category quickly and efficiently and to remove from consideration those that are unlikely to pose risks above levels of conc ern , while also minimizing the possibility of EPA’s failing to identify risks that exceed levels of concern . The hypothetical scenario used to estimate Tier 1 thresholds is designed to be health

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

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

1.1.1 Chemicals of Concern

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

- HAPs are reported:

• 

### Cadmium compounds

,

• 

### Chlorinated dibenzodioxins and furans (dioxins)

,

• 

### Mercury compounds

, and

• 

### Polycyclic organic matter (POM).

Introduction and Background 2 October 2014TRIM

- Based Tiered Screening Methodology for RTR



### Exhibit 1

. Conceptual Decision Tree for Evaluati ng Non

- Inhalation Exposures for PB

- HAPs

Introduction and Background 3 October 2014TRIM

- Based Tiered Screening Methodology for RTR



### B ased on current emissions, bioaccumulation potential, and toxicity considerations, emissions of these four PB

- HAPs are expected to pose the majority of the non

- inhalation risks to humans from air emissions at sources subject to residual risk provisions of the CAA 1 

### Thus, although EPA has identified nine other PB

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

. If emissions of any of the four P B- HAPs listed above are reported for a facility, the emission rate for that PB

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

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

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

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

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

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

.
1.1.2 Develo pment of Emission Thresholds
Generally, the approach used to assess ingestion exposures and resulting risk s for RTR has four components ( Exhibit 2 ) :

1. F ate and transport modeling of PB

- HAPs emitted to air that partition into soil, water, and other environmental media (including fish uptake );
2. M odeling of uptake of PB

- HAPs by farm food chain media f rom soil and air;
3. E stimating ingestion exposures in terms of average daily dose for consumption of farm food items by a hypothetical ex posed human; and
4. C alculating lifetime cancer risk estimates or chronic non

- cancer HQs for each HAP and corresponding screening threshold emission rates

. The TRIM.FaTE model is used in the first component, and the MIRC model is used to conduct calculations for the other three components. 2 

### To derive the emission thresholds used in Tier 1, these models are used to estimate the emission rate corresponding to a cancer risk of 1 in

1 Potential impacts on human health from non

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

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

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

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

Introduction and Background 4 October 2014TRIM

- Based Tiered Screening Methodology for RTR



### 1 million or a noncancer HQ of 1 (depending on the PB

- HAP) for each PB

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

Exhibit 2

. Overview of Ingest ion Exposure and Risk Screening Evaluation Method

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

. 3 

### The two main components of the fate and transport modeling are (1) the modeled domain, including the meteorological d ata and (2) the environmental and chemical

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

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

- specific properties governing fate and transport of PB

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

tendency values. The mix of health protective and central

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

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

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

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

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

.

3 http://www.epa.gov/ttn/fera/trim_fate.html 

### Chemical Emissions to Air

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

Produce and livestock Human Ingestion Exposure Risk and Hazard Estimation

Introduction and Background 5 October 2014TRIM

- Based Tiered Screening Methodology for RTR



### 1.1.2.2 Modeling Transfer and Uptake
MIRC was developed to conduct the required calculations involving farm food chain transfer, ingestion exposure, and risk. TRIM.FaTE outputs that are us ed as inputs to MIRC include :

• 

### PB

- HAP concentrations in air

,

• 

### A ir

- to

- surface deposition rates for PB

- HAPs in both particle and vapor phases

,

• 

### PB

- HAP concentrations in fish tissue for fish consumed

, and

• 

### PB

- HAP concentrations in surface soil and root zone soil

. From these inputs

, MIRC calculates the transfer and uptake of PB

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

- to

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

- HAP concentrations in contaminated food items.

1.1.2.3 Estimating Ingestion Exposure

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

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

• 

### Incidental ingestion of soil

• 

### Ingestion of homegrown produce

• 

### Ingestion of homegrown beef

• 

### Ingestion of milk from homegrown
cows • 

### Ingestion of homeg rown poultry/eggs

• 

### Ingestion of homegrown pork

• 

### Ingestion of fish

• 

### Ingestion of breast milk (children <1 year old; dioxins only ) 4



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

- HAP summed a cross all ingestion exposure pathways

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

. The ingestion rate for each exposure pathway listed abov e was set (as feasible) equal to an upper percentile value (99 th 

### percentile for fish and 90 th 

### percentile for all other food types) based on EPA’s Exposure Factors Handbook (EPA 2011 a ) or other sources as appropriate

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

. T his approach results in an overestimate of total chemical exposure for a hypothetical exposure scenario

. F or example, the resulting total food ingestion rate is extremely high for a hypothetical consumer, with ingestion rates at the 99 th 

### percentile for fish and the 90 th 

### percentile for every other farm food type

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

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

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

.

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

Introduction and Background 6 October 2014TRIM

- Based Tiered Screening Methodology for RTR



### 1.1.2.4 Calculating Lifetime Cancer Risk and Non

- Cancer HQs
Lifetime cancer risks and the potential for chronic non

- cancer effects are estimated using chemical

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

- HAP. As provided in Section 5.1 of Attachment B

, age

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

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

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

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

1.1.2.5 Determining Threshold Emission Rates

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

- cancer HQ of 1

. Given the generally health protect ive nature of the scenario inputs, these thresholds are assumed to be appropriate for screening facilities e mitting these PB

- HAPs.



### 1.2 Tier 2
The Tier 1 screening approach is, by design, generic and health protective

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

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

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

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

. S ite

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

. The use of some site

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

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

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

- specific approach while also maintaining health p rotectiveness.
In selecting the scenario characteristics to modify in Tier 2, a balance was struck between the degree of impact on the risk estimate, the ease of implementation, and the ease of obtaining relatively certain site

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

- HAP concentrations in environmental media estimated by TRIM.FaTE

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

.

Introduction and Background 7 October 2014TRIM

- Based Tiered Screening Methodology for RTR



### 1.2.1.1 Site Specific Considerations

F or Tier 2, location -specific data on two types of variables are taken into account :

• 

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

• 

### Location s of fishable lake(s) relative to the facility 5 

### (including the absence of a fishable lake)

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

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

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

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

- HAP. These adjustment factors represent the ratio between the risk metric (i.e., cancer risk or HQ) obtained using the Tier 1 screening scenario and the risk metric obtained fro m the Tier 2 run. For a given facility, for each PB

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

. The contribution of each exposure media toward each scenario’s Tier 2 risk metric (i.e., the individual contributions of fish ingestion, soil ingestion, beef ingestion, etc. toward the total Tier 2 risk metric) was included in the matrix because the Tier 2 assessment separates chemical exposure from fish ingestion from exposure from farm food cha in ingestion.
To facilitate the implementation of this approach without requiring facility

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

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

, paired with their closest upper

- air station with available data

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

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

Introduction and Background 8 October 2014TRIM

- Based Tiered Screening Methodology for RTR



### their lake dynamics are too complex to realistically model in the TRIM.FaTE system. Lakes and bays larger than 100,000 acres include the Great Lakes, the Great Salt Lake, Lake Okeechobee, Lake Pont chartrain, Lake Champlain, Green Bay, and Galveston Bay.
When the Tier 2 screening is conducted, three additional processing steps are completed for each facility and PB

- HAP that will be analyzed in Tier 2 before looking up the appropriate adjustment fac tors. First, a proximity analysis for each lake is conducted, whereby each relevant lake within 50 km of the facility is identified and matched to its respective directional “octant” relative to the facility. For the purposes of Tier 2, a “relevant” lake meets the size and designation criteria discussed in the previous paragraph. Second, the lakes are manually evaluated to remove lakes whose names suggest uses related to disposal, evaporation, or treatment (sometimes the name indicates one of these uses while the USGS designations do not ; f or example, the Gavin Fly Ash Impooundment would not be included in the screening process ). Third, the lakes around the facility that remain after the first two processing steps are ranked in order of highest to lowest P B- HAP concentrations in fish . These rankings are then used to refine the Tier 2 risk metric for the fisher , as discussed in the next section

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

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

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

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

.
1.2.1.2 Refined Tier 2 Fisher A ssessment

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

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

, 373 g/d for adults )

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

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

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

- HAP concentration in fish ). I f the highest

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

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

Introduction and Background 9 October 2014TRIM

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### 1.3 Tier 3

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

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

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

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

- specific assessment ( discussed in Section 1.4 ). One of the Tier 3 assessments (i.e., the lake assessment) potentially results in the rescreening of the facility’s emissions using the Tier 2 methods described in Section 1.2 and using a revised lake dataset

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

- rise and time

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

- rise and time

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

- series assessment, if conducted, supplants the plume

- rise assessment already conducted)

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

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

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

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

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

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

. If

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

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

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

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

- HAP exposure and reduce the scr eening result. The plume

- rise adjustment factor

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

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

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

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

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

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

. Using hourly meteorology data adds additional site

- specificity compared to the summarized, binned meteorology statistics used in Tier 2 . U sing th e se data in

Introduction and Background 10 October 2014TRIM

- Based Tiered Screening Methodology for RTR



### combination with hourly effective release heights is a more complete evaluation of hourl y chemical losses due to plume rise compared with the Tier 3 plume

- rise assessment described above. These time

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

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

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

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

- series assessment is converted to a time

- series adjustment factor

— the revised screening result divided by the screening result after the Tier 3 lake asses sment . T his ratio can then be multiplied by the screening result after the Tier 3 lake assessment, yielding the revised screening result accounting for the time -series assessment.



### 1.4 Site

- Specific Assessment

If , based on results of the screening assessments

, a risk assessor concludes that there is a reasonable probability that individuals could be adversely affected by the facility emissions, a refined site

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

, petroleum re finery facility (EPA 2014b ), two secondary lead smelting facilities (EPA 2011b), assessments of two coal

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

Whereas Tier 2 and Tier 3 assessments incorporate some site

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

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

- spec ific properties are incorporated into model scenarios configured in TRIM.FaTE and MIRC. Important site -specific data likely would include emission release height and plume buoyancy, hourly meteorology (e.g., wind flow, temperatu re, mixing height, and precipitation)

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

, surface water and sediment properties , water transfer rates , and aquatic eco system information

. The outputs from the site

- specific run of TRIM.FaTE (i.e., chemical concentrations in environmental media and fish) are used in MIRC to produce estimates of exposure and health risk (i.e., risk and/or HQ values). Additional analyses of t he media concentrations, exposure estimates, and risk estimates for the various ingested media using a range of ingestion rates for each modeled PB

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

- HAPs.

Introduction and Background 11 October 2014TRIM

- Based T iered Screening Methodology f or RTR



## 2. Tier 1 Methodology

2.1 Introduction

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

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

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

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

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

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

- specific multipathway assessments, can be conducted as appropriate.

This section describes the technical basis for the first screening

- level tie r of EPA’s multipathway human health evaluation of PB

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

• 

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

• 

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

• 

### Section 2.5 provides a brief discussion of the screening threshold emissions for each of the chemicals assessed.

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

, respectively, and references cited are provided in Section 5.



### 2.2 Summary of Approach

2.2.1 Overview
The Tier 1 approach for evaluating non

- inhalation, multipathway exposures to PB

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

- HAPs of concern for non

inhalation pathways. If such emissions are reported, the emission rates are compared to Tier 1 threshold -sc reening emission rates that have been derived using the TRIM

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

.

Tier 1 Methodology 12 October 2014TRIM

- Based T iered Screening Methodology f or RTR



### Exhibit 3

. Conceptual Decision Tree for Tier 1 Evaluation of Non

- Inhalation Exposures of PB

- HAPs

Facility screens out

Non-inhalation risks not likely, but evaluate on a case-by-case basis

Facility screens out; no concern for multipathway risk (check emissions of PB-HAPs other thanCd, dioxins, Hg, POM, Pb on a case-bycase basis) Are any PB-HAPs emitted?

Are PB-HAPs of primary concern for RTR emitted? (Cd, dioxins, Hg, POM)? Evaluate HAP emissions by facility

YES YES

NO NO NO

Does the emission

rate for any PBHAP exceed Tier

1 threshold? Diagram Key

Outcome Process Decision

PointTIER 1 EVALUATION

Proceed to Tier 2 YES

Tier 1 Methodology 13 October 2014TRIM

- Based T iered Screening Methodology f or RTR



### The TRIM

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

, Tier 1 scenario

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

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

- HAPs of concern

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

• 

### Non

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

• 

### Risks above the levels of concern from n on

- inhalation exposure s cannot be ruled out

. An ideal screening approach strikes a balance between being health

- protective

— to ensure that risks above levels of concern are identified

, and being accurate

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

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

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

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

- inhalation exposures are projected to be minimal

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

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

- specific characteristics of the facility being modeled.
The Tier 1 screening evaluation for RTR compares reported air emission rates of PB

- HAPs (summed by PB

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

- cancer hazard quotient ( HQ ) that, for the purposes of the evaluation being conducted, is assumed to be below a level of concern. Tier 1 t hreshold emission rates were calculated for a cancer risk of 1 in 1 million or an HQ of 1.0 and are presented in Exhibit 4

. 6 

### Conceptually, a threshold le vel for the RTR multipathway screening evaluation could be obtained by back

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

6 For chemicals known to cause both cancer and chronic non

- cancer impacts, and for which acceptable quantitative dose

- response values are available for both cancer and non

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

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

- dioxins and

- furans meet both criteria. Because the cancer dose

- response value is lower than that for
non

- cancer effects, the screening threshold value is based on the can cer endpoint.
Tier 1 Methodology 14 October 2014TRIM

- Based T iered Screening Methodology f or RTR



### range of emission rates for each chemical. The estimated screening

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

Exhibit 4

. Emission Thresholds for Screening of Multipathway Exposures

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

POM (as b enzo(a)pyrene equivalents) a 

### 2. 58 E

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

- TCDD equi valents) a 

### 2. 81 E

- 09 Cancer
Cadmium 1.18E

- 02 Non

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

- 04 Non

- c ancer

T PY = U.S. short tons per year

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


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

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

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

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

- end risk estimates

. I n the real world, the probability of such risk

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

. Tier 1 emission thresholds w ere developed individually for elemental and divalent mercury. Both were based on the lower of the thresholds associated with multipathway exposures to divalent mercury and methyl mercury. 7 

### Only speciated emissions of divalent mercury are screened becaus e the sum of elemental mercury emissions across all National Emission Inventory (NEI) facilities is less than the elemental mercury screening threshold level. See Section 2.5.3 for a detailed discussion of mercury.

2.2.2 Chemicals of Potential Concern

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

- HAPs 8 

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

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

- HAPs based on a two

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

7 Note that TRIM.FaTE models the transformation of mercury within the environment; thus, emissions of both divalent and elemental mercury will result in multipathway exposures to elemental mercury, methyl mercury, and divalent mercury.
8 Although POM (polycyclic organic matter) is the name of the HAP listed in the Clean Air Act, the term “polycyclic aromatic hydrocarbons” or PAHs is used in many cases. Much of the literature regarding toxicity and fate and transport of this chemical group refers to PAHs rather than POM. In addition, the individual POM species that are of concern with respect to health risk for RTR evaluations are all PAHs (i.e., there are no POM species explicitly evaluated for RTR that do not include an aromatic ring). The terms are used interchangeably throughout this text.
Tier 1 Methodology 15 October 2014TRIM

- Based T iered Screening Methodology f or RTR

• 

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

• 

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

The list’s development and utility in hazard identification for multipathway risk assessment are explained further in Chapter 14 and Appendix D of Volume I of EPA’s Air Toxics Risk Assessmen t (ATRA) Reference Library ( EPA 2004a )

. Exhibit 5 presents the 14 chemicals and groups that are PB

- HAPs

. Exhibit 5 . OAQPS PB

- HAP Compounds

PB

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

- dichloro

- 2,2

- bis(p

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

a Source of list: EPA (2004a).


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

- HAPs on the list

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

- HAPs ( as indicated in Exhibit 5 )

. These four PB

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

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

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

. 9



### 2.2.3 Conceptu al Exposure Scenario
A conceptual exposure scenario was developed that encompasses the specific exposure routes and pathways of interest for the four PB

- HAPs that are assessed in the Tier 1 assessment

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

• 

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

9 Potential impacts on human health from non

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

- inhalation exposures to the other nine PB

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

. Tier 1 Methodology 16 October 2014TRIM

- Based T iered Screening Methodology f or RTR

• 

### A retention medium, or a transport mechanism and subsequent retention medium in cases involving media transfer of chemicals;

• 

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

• 

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

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

PB

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

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

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

• 

### Incidental ingestion of soil,

• 

### Ingestion of homegrown fruits and vegetables,

• 

### Ingestion of homegrown beef,

• 

### Ingestion of dairy products from homegrown cows,

• 

### Ingestion of homegrown poultry and eggs,

• 

### Ingestion of homegrown pork,

• 

### Ingestion of locally caught fish, and

• 

### Ingestion of breast milk (for children less than 1 year old and for dioxins only ). 10



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

10 Breast milk ingestion is an important exposure pathway for lipophilic compounds like dioxins and has been shown not to contribute meaningfully for exposures to mercury, cadmium, and POM. See Section 2.4.2.3 below and Section 3.4 of Attachment B for full discussions of infant exposures via breast milk ingestion.
Tier 1 Methodology 17 October 2014TRIM

- Based T iered Screening Methodology f or RTR



### Other n on

- inhalation exposure routes of possible concern for PB

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

. The drinking water exposure pathway is not likely for the modeling scenario developed for th is assessment because the likelihood that humans would use a lake as a drinking water source was assumed to be low. 11 

### D ermal absorption of chemicals that are originally airborne has been shown to a relatively minor pathway of exposure compared to other exposure pathways ( EPA 2006

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

- HAP s , presented in Attachment C

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

2.2.4 Approach to Development of the Tier 1 Scenario

The TRIM

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

- inhalation risks above the levels of concern for PB

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

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

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

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

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

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

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

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

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

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

, respectively

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

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

11 An exception to this generality would be reservoirs used for drinking water supplies. This situation might be worthy of additional analysis, if warranted by the characteristics of a given assessment (e.g., to estimate PB -HAP concentrations in treated drinking water derived from reservoirs).
Tier 1 Methodology 18 October 2014TRIM

- Based T iered Screening Methodology f or RTR



### 1. Fate and transport modeling of PB

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

### );
2. Modeling of transfer and uptake of PB

- HAPs into farm food chain media (e.g., produce, livestock, dairy products) from soil and air, and calculation of FFC media concentrations ; 3. Estimating exposures from ingestion of selected media and estimating average d aily ingestion doses for a hypothetical human receptor; and
4. Calculating lifetime cancer risk estimates or chronic non

- cancer HQs

, as appropriate

, for each PB

- HAP and comparing these to selected evaluation criteria

.
The relationship among these four process es is shown in Exhibit 6

.

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

### Currently , ho wever, only one component corresponding to the first step included in Exhibit 6

— the fate and transport module

— is available for application in an ingestion risk assessment

. EPA has completed some development activities for TRIM.Ex po

- Ingestion and TRIM.Risk

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

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

### Excel TM- based comput ing framework

, was constructed to complete the calculations required for estimating PB

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

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

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

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

### Exhibit 6 . Overview of Ingestion Exposure and Risk Screening Evaluation Method

Chemical fate and transport: Physical environment and aquatic ecosystem



### TRIM.FaTE Uptake & transfer into produce and livestock



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

## Chemical Emissions to Air

Cancer Risk Hazard Quotient

Tier 1 Methodology 19 October 2014TRIM

- Based T iered Screening Methodology f or RTR



### cancer risks and chronic non

- cancer HQs

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

2.2.4.2 Model Configuration and Parameterization

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

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

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

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

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

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

- specific and non

- chemical

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

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

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

The mix of health protective and central

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

- HAPs in media of interest fo r this evaluation

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

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

. 2.2.5 Fate and Transport Modeling (TRIM.FaTE)
The fate and transport modeling step depicted in the first box in Exhibit 6 is implemented for RTR using t he Fate, Transport, and Ecological Ex posure module of the TRIM modeling system (TRIM.FaTE ). 14 

### In developing the Tier 1 scenario, Version 3.6.2 of TRIM.FaTE was used to

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

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

. Outputs include pollutant concentrations in multiple environmental media and biota

. Tier 1 Methodology 20 October 2014TRIM

- Based T iered Screening Methodology f or RTR



### model the fate and transport of emitted PB

- HAPs and to estimate concentrations in relevant media

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

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

- dioxin and

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

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

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

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

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

. 2.2.6 Exposure Modeling and Risk Characterization (MIRC)
The algorithms included in MIRC that calculate chemical concentrations in farm food chain media and ingestion exposure s for hypothetical individuals were obtained from EPA’s Human Health Risk Assessment Protocol for Hazardous Waste Combustion Facilities, or HHRAP ( EPA 2005a). 15 

### These algorithms, and the required exposure factors and other parameter values, were compiled into a database. An overview of the computational processes this tool carrie s out and the types of input data it require s is presented in Exhibit 7. This exhibit demonstrates the general relationship s between the relevant TRIM.Fa TE outputs (i.e., chemical concentrations in environmental media and fish) and the ingestion exposure and risk calculations carried out using MIRC. Additional discussion of exposure and risk calculations for the Tier 1 scenario is presented in Section 2.4 and Attachment B , and all inputs required by these calculations are documented in Attachment B

.

15 The farm food chain calcula tions and ingestion exposure equations to be included in the TRIM.Expo software are expected to be very similar to those included in HHRAP.
Tier 1 Methodology 21 October 2014TRIM

- Based T iered Screening Methodology f or RTR



### Exhibit 7

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

2.2.7 Implementation of Risk

- based Emission Scaling Factors for POM and Dioxin Emissions

Two of the four PB

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

— POM and dioxins

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

- methylnaphthalene, and chrysene, as well as
non

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

- HAP categories are grouped together not only because they are types of the “same” HAP, but also because members of these groups are assumed to have similar characteristics with respect to toxicity and behavior in the environment.
To facilitate a practical application of the multipathway screening methods for RTR, reported emissions of POM and dioxins are normalized or scaled to a single reference chemical for each group. The reference chemicals used in RTR for POM and dioxins are benzo[a]pyrene and 2,3,7,8

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

studied among the members of the two groups and are also among the most toxic species within each group.
Derivation of appropriate scaling factors begins with an evaluation of the basic relationship used to characterize health risk:

Risk 

# ∝ (

Exposure Concentration) × (Toxicity)

For a given air pollutant, the incremental exposure concentration is directly proportional to the emissions of that substance. That is, as the emissions increase, so too does the exposure to Farm Food Chain

Concentration

Calculator Ingestion Exposure Calculator Risk and Hazard Calculator TRIM.FaTE

outputs

Human activity/ exposure factors Plant- and animal-specific

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:

Tier 1 Methodology 22 October 2014TRIM

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

Emiss PAHi:BaP 

### = Emiss PAHi 

### × EEF PAHi:BaP 

### × TEF PAHi :BaP



### w here :

Emis s PAHi : BaP = Risk

- weighted emissions of PAH i (weighted according to cancer risk relative to BaP for oral exposures)
Emiss PAHi = Emission rate of PAH i

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

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


### In combination, the product of the EEF and TEF for a given substance is considered to be a “r isk equivalency factor” for the purposes of RTR evaluations that enables scaling of emissions of a given substance for a given exposure scenario.
The TEF for each PAH and dioxin species can be calculated on the basis of relative toxicities. Toxicities wer e not evaluated separately for RTR but are based on analyses conducted by EPA elsewhere. For PAHs, oral toxicity values for individual species have been derived following the same approach used to develop inhalation toxicity values. For dioxins, TEFs are based on the relative toxicities developed by EPA recently and are ultimately based on the values developed by the World Health Organization (van de n Berg et al. 2006). Refer to Section 4 of Attach ment B for more information on these values.

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

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

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

2.2.7.1 Calculation of Scaling Factors for POM C ongeners

The calculated EEFs , TEFs, and total REFs for the 14 POM congeners that are configured in TRIM.FaTE , plus 15 others not configured in TRIM.FaTE, are shown in Exhibit 8. To determine appropriate exposure surrogates for chemicals not parameterized in TRIM.FaTE, EPA evaluated the relationships between chemical

- specific properties (e.g., K ow 

### and Henry’s law constant, k h 

### ) and intermediate modeled values (e.g., de position , soil concentration ) and exposure in terms of lifetime average daily dose (LADDs) where the average daily doses (ADDs) for the youngest two age groups were adjusted by the age

- dependent adjustment factors (ADAFs) to account for the mutagenic mode of action of PAHs. The correlation between K ow 

### and exposure is stronger than for any other chemical

- specific property. Exposure Tier 1 Methodology 23 October 2014TRIM

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### surrogates were thus identified for each congener by calculating Total Lifetime Average Daily Dose (Age Adjusted) for each bas ed on the congener’s K ow 

### and the power regression of the modeled PAHs. Exhibit 9 shows that as K ow 

### increases, so too does exposure.

Exhibit 8 . Exposure, Toxicity, and Risk Equivalency Factors Relative t o BaP for POM Congeners Currently Evaluated in Tiers 1 and 2 Assessments

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

- Dimethylbenz(a)anthracene Y 5.4 34.2 186 3

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

- cd)pyrene Y 4.5 0.2 0.7 PAH, total n 5.1 0.07 0.3 Polycyclic Organic Matter n 5.1 0.07 0.3 Benzo(g,h,i)perylene Y 4.3 0.07 0.3 Benzo(e)pyrene n 4.5 0.07 0.3 Retene n 3.7 0.07 0.3 Dibenzo(a,j)acridine n 0.78 0.2 0.1 Perylene n 1.2 0.07 0.08 Benzo(a)anthracene Y 0.0 9 0.16 0.01 Chrysene Y 0.25 0.02 0.004 2

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

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

- Methylna phthalene n 0.02 0.07 0.001 2

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

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

### 4 of Attachment B

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


### For POMs reported as unspeciated groups (i.e., “PAH, total” and “Pol ycyclic Organic Matter”) EPA assigned surrogates with K ow 

### values near the upper end of the range of all of the K ow 

### values, corresponding to an exposure near the upper end of the range (log K ow 

### = 6.5). This assignment is assumed to be health protective and likely will not under predict exposure

.
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### Exhibit 9 . Relationship between Exposure and K ow 

### for POM Congeners

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

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

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

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

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

.
2.2.7.2 Calculation of Scaling Factors for Dioxin Congeners
The calculated EEFs, TEFs, and REFs for the 17 dioxin congeners that are configured in TRIM.FaTE are presented in Exhibit 10

. Although there are many dioxins reported in the NEI other than the 17 configured for TRIM.FaTE, to date, none of them have been included in emissions datasets that have been screened. Therefore, no surrogate EEF’s have been developed for dioxins. In future screening assessments, if surrogate EEFs are needed, an approach similar to that used for POM will be used to develop surrogate EEFs for dioxins.
Many facilities report dioxins as “ Dioxins, Total, without Indiv idual Isomers R ep or ted ,” “Dioxins,” or as “2,3,7,8

- TCDD TEQ,” and in these cases, we do not adjust or scale the emissions. That is, we assume that they behave like and possess the toxic characteristics of TCDD. This approach could be improved by obtaining information on the speciation of dioxin emissions for each facility or an average speciation profile that could be assumed to apply to all facilities in a source category. Tier 1 Methodology 25 October 2014TRIM

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### Exhibit 10 . Exposure and Toxicity Equivalency Factors Relative to TCDD for Modeled Dioxin Congeners

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

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

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

Dioxins 1 1 1
2,3,7,8

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

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

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

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

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

- 0.5 0.1 0.05 HexaCDD, 1,2,3,7,8,9

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

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

- 0.2 0.1 0 .02 PentaCDF, 1,2,3,7,8

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

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

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

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

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

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

- 0.2 0.0003 0.0001

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

- hexaCDD, which is calculated based on the ratio of the IRIS

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

- hexaCDD to the IRIS

- based CSF for 2,3,7,8

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

. 

### 2.3 Description of Environmental Modeling Scenario

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

- term PB

- HAP exposures, and the environmental and chemical

- specific properties were parameterized with either healt h protective or central

- tendency values

. Information regarding the scenario configuration and important aspects of the parameterization process, justifications for selecting particular property values, and model uncertainties is presented in the sections that follow. Comprehensive documentation of TRIM.FaTE property values for this scenario is provided in Attachment A

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

- reviewed and standard reference sources . N umerous other chemical

- specific properties are related more specifically to Tier 1 Methodology 26 October 2014TRIM

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### a particular abiotic or biotic compartment type; these properties are discussed generally in the sections that follow and are documented in Attachment A

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

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

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

- to

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

- west line and is wedge

- shaped to refl ect Gaussian dispersion of the emission plume.
A lateral, downwind distance of 10 km was established for the watershed included in the scenario. Based on the results of dispersion modeling

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

- km radius. Additionally, deposition rates for the PB

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

- km radius. Extending the modeling layout beyond a 10

- km downwind distance would increase the amount of deposi tion “captured” by the modeled watershed, but the incremental chemical mass expected to accumulate in the watershed diminishes rapidly with distance. In addition, the impact of this additional deposited mass on ingestion exposures is expected to be neglig ible. 16 

### Given these conditions, a downwind length of 10 km was determined to be appropriate for the screening scenario.

Exhibit 11

. TRIM.FaTE Surface Parcel Layout

16 M ass deposited at the outer edge of the watershed is expected to result in only a very small increase in estimated exposure via fish consumption by increasing the chemical mass transported to the lake through erosion and runoff. The distance from these more distant locations to the lake would attenuate transport of chemical mass by erosion and runoff, dampening the effect of including additional deposition beyond 10 km. (Other exposure pathways largely would be unaffected; the soil concentrations used to calculate exposures for the farm food scenario are derived from soil parcels located close to the source and unaffe cted by deposition to the far reaches of the watershed.)
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### 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 Industrial Source Complex 3 Dispersion Model Manual (among other sources). 17 

### For a relatively neutral atmosphere (stability class D), σ at 10km i s about 550 m using this estimation. In a Gaussian distribution, about 99.6 percent of the plume spread area is contained within 3σ of the median line. Therefore, the plume σ was set at 3 times 550 m, or approximately 1.75 km from the centerline at a distance of 10 km. The plume width for these conditions is expected to be about twice this distance, or 3.5 km. These dimensions were used to define the dimensions of the overall air and surface parcel layouts for the screening scenario.
The surface (land a nd surface water) modeling area was initially divided into five pairs of parcels the areas of which increase with distance from the source, which approximately corresponds to the spatial gradient that is expected in the downwind direction from the source

. The second north parcel from the source was divided further into two parts; one of them tilled soil (Parcel N6) to represent agricultural conditions and the other (Parcel N7) untilled to represent pasture.
The depth of the surface soil compartments was set to 1 cm , except for Parcel N6, for which the depth was set to 20 cm to simulate the effect of tillage

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

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

2.3.3 Watershed and Water Body Parameterization
Properties associated with the watershed soil and lake determine how pollutants in the system are transported throug h and accumulate in various media compartments

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

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

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

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

2.3.3.1 Water Balance

Water

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

. Although TRIM.FaTE maintains a chemical mass balance

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

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

17 http://www.epa.gov/scram001/userg/regmod/isc3v2.pdf
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### [ total precipitation] = [evapotranspiration] + [total runoff]
In this equation, total runoff is equal to the sum of overland runoff to the lake and seepage to the lake via groundwater

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

[total runoff] + [direct precipitation to the lake] = [evaporation from the lake surface] + [outflow from the lake]
Note that TRIM.FaTE actually uses only some of these properties (e.g., precipitation rate and surface runoff, but not evapotranspiration )

. The water characteristics assumed for the Tier 1 scenario are meant to represent a relatively wet and moderately warm location in the United States (USGS 198 7) . F ollowing are the assumptions for this scenario:

• 

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

• 

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

• 

### 40 percent of total precipitation contributes to overland runoff.
For these calculations, the source parcel was considered to be outside the watershed and therefore was not included in the water balance

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

. The runoff rate was defined to be spatially constant and temporally constant (i.e., it is not linked to precipitation events) throughout the modeled domain. Based on these assumptions, the outfl ow of water from the lake is about 18 million m 3 

### /yr, which translates to a volumetric turnover rate of about 12.2 lake volumes per year.
Other quantitative water body and watershed characteristics TRIM.FaTE uses are listed in Attachment A

. 2.3.3.2 Sediment Balance

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

- related properties

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

[total surface soil transfers to the lake via erosion] = [removal of sediment from the water column via outflow] + [sediment burial]
where the second term (removal of sediment from the water column via outflow) is accounted for in TRIM.FaTE by lake flushing rate and the third term (sediment burial) is the transfer of sediment from the unconsolidated benthic sediment compartment to the consolidated sediment layer.
To maintain the sediment balance, erosion rates were calculated for each surface soil compartment using the univer sal soil loss equation (USLE, Wischmeier and Smith 1978)

, assuming a relatively high rate of erosion

. The total suspended sediment concentration is Tier 1 Methodology 29 October 2014TRIM

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### assumed to remain constant in TRIM.FaTE

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

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

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

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

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

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

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

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



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

. For the Tier 1 scenario, however, this approach was considered to be appropriate in that health protective assumptions are a goal of the sc reening scenario. 18 

### S urface soil compartments adjacent to the lake are linked directly to the lake for the purposes of estimating erosion and runoff transfers (see layout in Exhibit 11 )

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

. Note that erosion from parcels not directly adjacent to the lake is assumed to be somewhat attenuated, effected by using a lower sediment delivery ratio in the USLE.
Using the calculated surface soil erosion rates for the scenario, the total average daily sediment load to the lake from the watershed is about 16,60 0 kg/day

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

2.3.4 Meteorology
Meteorological p roperties used in TRIM.FaTE algorithms include air temperature, mixing height, wind speed and direction, and precipitation rate

. These properties, which can vary significantly among geographic locations

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

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

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

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

- grown farm foodstuffs and self

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

18 Based on sensitivity analysis, a higher erosion rate will both increase surface water concentrations and decrease surface soil concentrations; the relative impact on resulting concentrations, however, will be proportionally greater in the waterbody.
Tier 1 Methodology 30 October 2014TRIM

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### source). Ensuring t hat the meteorological parameters were not overly protective of health , such
as always having the wind blow toward the location of interest, however, was also important to avoid too many false positives

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

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

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

. The sensitivity of modeled PB

- HAPs to changes in these meteorological variables was tested. Lower wind speeds and mixing heights affected concentra tions the most. This sensitivity is not unexpected because lower wind speeds should increase pollutant deposition onto the soil and lower mixing heights should reduce the volume through which pollutants disperse. The wind speed used for the screening sce nario was 2.8 m/s, the 5 th 

### percentile of the annual averages among 239 stations; by comparison, the mean annual average wind speed is approximately 4 m/s in the contiguous United States). The mixing height (mean heights from 4 states) used was 710 m (the 5 th 

### percentile of annual averages among all 40 states in the SCRAM database).

Exhibit 12 . Summary of Key Meteorological Inputs
Meteorological Property Selected Value Justification
Air temperature Constant at 298 Kelvin Recommend ed default value listed in HHRAP ( EPA 2005a). Value is similar to the mean daily June temperature in much of the U.S. Southern Plains and Southeast. a Mixing height Constant at 710 m Value is 5 th percentile of annual average mixing heights for 75 U.S. l ocations, 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 val ue (Stull 1988). Wind direction Blows from source parcel into scenario domain (west to east) 3 days per week; during other times does not blow into domain A wind direction that favors the location of interest (for example, a watershed downwind of a source of concern) will tend to result in more emitted mass accumulating in the location of interest. For much of the U.S. mid

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

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

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

. Horizontal wind speed Constant at 2.8 m/sec Set to 5 th percentile of annual average speed for 239 stations across the contiguous United States (about 50 years of data per station). Value is similar to the annual average wind speeds of Tier 1 Methodology 31 October 2014TRIM

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Meteorological Property Selected Value Justification

ma ny areas of the U.S. east coast and west coast. a Precipitation frequency Precipitation occurs 3 days per week; wind direction blows into domain 2 of these days This value was selected so that two

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

– 40% of the time (Holzworth 1972). These locations include parts of the U.S. Northeast and Northwest. a Total Precipitation 1.5 m/yr As sumed to represent rainy conditions for the United States. This annual precipitation amount is experienced in parts of the U.S. Deep South and parts of the U.S. northwest coast. a Conditional precipitation rate (rainfall rate when precipitation is occurring) is 9.59 mm/d, which is similar to conditions in many areas along the U.S. east coast and in the Midwest and Plains. a

a National Climatic Data Center CliMaps (NCDC

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

- bin/climaps/climaps.pl

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

.
c National Climatic Data Center 1981

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



### 2.3.5 Aquatic Food Web
The aquatic food web is an important part of the screening scenario because the chemical concentrations modeled in fish are used to calculate human ingestion exposure and risks associated with eating contaminated local fish . A biokinetic approach to modeling bioaccumulation in fish is used in the RTR screening scenario. The primary producers (first trophic level) in the TRIM.FaTE aquatic ecosystems are algae and macrophytes in the water column and detritus in the sediments (the latter simulated as sediment particles). Zooplankton feed on algae in the water column, while benthic invertebrates, represented as a single compartment , consume detritus that settles to the sediment compartment. In the water column, small young

- of

- the

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

- feeding fish (young

- of

- the

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

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

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

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

. The RTR Tier 1 scenario includes a generic aquatic ecosystem with a 47 -h ectare (116

- acre) lake

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

, a 47

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

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### Also, t his size was compatible with the overall size of the defined watershed in the screening scenario. The fish types, biomass, diet fractions, and body weights recommended for fish compar tments for the Tier 1 scenario are listed in Exhibit 13

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

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

### /m 2



### , ICF 2005). That assumption yields health protective (i.e., higher) estimates of chemical concentrations in fish than would the assumption of higher standing biomass and fish productivity.
In general, the food web implemented in the Tier 1 scenario is consistent with aquatic food webs tha t support trophic level 4 fish (to maximize bioaccumulation), and is intended to be generally health protective.

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

TRIM.FaTE Compartment Type Organisms Rep resented by

Compartment Biomass

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

Algae green algae, diatoms, blue

green algae 7.95 – Autotrophic –

Zooplankton crustaceans, rotifers, protozoans 6.36 – 100% algae a 5.7 E−8

Macrophyte hydrilla, milfoil 500

– – – Water column planktivore young

- of

- the

year, minnows 2.0 35.1% 100% zooplankton 0.025

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

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

Benthic invertebrate aquatic insect larvae, crustaceans, mollusks b 20 – detritus in sediments 0.000255

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

Benthic carnivor e large catfish, sculpins 1.0 17.5% 50% benthic invert. 50% benthic omniv. 2.0

Total Fish Biomass c 5.7

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

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

c Total fish biomass does not include algae, macrophytes, zooplankton, or benthic invertebrat es.



### 2.3.6 Using TRIM.FaTE Media Concentrations

The Tier 1 scenario outputs include average PB

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

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

. For each air parcel, air concentrations are provided

. For the lake, surface water concentrations and concentrations in the va rious levels of the aquatic food chain are provided. For the ingestion exposure
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### calculations, some concentrations are used to calculate direct exposure (e.g., soil ingestion)

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

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

.

Exhibit 14 . Spatial Considerations – TRIM.FaTE Results Selected for Calculating Farm Food Chain Media Concentrations and Receptor Exposures
TRIM.FaTE Output Used in Exposure Calculations Representative Compartment
Concentration in air, for uptake by plants via vapor transfer Air compartment in air Parcel N2 (air over tilled soil)

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



### TRIM.FaTE can output instantaneous chemical concentrations for a user

- specified time step and also can be configured to calculate temporal averages (e.g., annual averages). For the Tier 1 scenario, the model is set up to output results on a daily basis, largely because daily is the smallest time st ep 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

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

. 

### 2.4 Description of Exposure and Risk Modeling Scenario

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

, partitioning of PB

- HAPs into FFC media is modeled with MIRC

, not as a part of the TRIM.FaTE modeling

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

.

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### 2.4.1 Calculating Concentrations in Farm Food Chain Media

As was shown in Exhibit 7

, MIRC was compiled to calculate concentrations of PB

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

• 

### exposed and protected fruit,

• 

### exposed and protected vegetables,

• 

### root vegetables,

• 

### beef,

• 

### dairy products,

• 

### pork, and

• 

### poultry and eggs.
The algorithms used in M IRC were obtained from EPA’s Human Health Risk Assessment Protocol for Hazardous Waste Combustion Facilities (HHRAP; EPA 2005a ). These algorithms model the transfer of concentrations of PB

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

• 

### PB

- HAP concentrations in air;

• 

### air

- to

- surface deposition rates for PB

- HAPs in both particle and vapor phases;

• 

### PB

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

• 

### PB

- HAP concentrations in surface soil and root zone soil

. In gener al, p lant - and animal

- specific parameter values, including chemical

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

. Attachment B provides parame ter values used in MIRC for the Tier 1 assessment.

2.4.2 Ingestion Exposure

MIRC was used to estimate ingestion rates as ADDs

, normalized to body weight for a range of exposure pathways

. Exposure pathways included are incidental ingestion of soil

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

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

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

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

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### Exhibit 15 . 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 Untilled surface soil N/A Surface soil Deposition; transfer via erosion and runof f c

Consumption of fish Fish from local water body N/A Fish tissue Direct uptake from water and consumption of food compartments modeled in TRIM.FaTE c
Consumption of breast milk d Breast milk N/A Breast milk Contaminant i ngested by mother partition s to b reast 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 produ ce 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 Ing estion 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 Ingestio n from surface

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

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

b Process by which HAP enters medium ingested by humans.

c Modeled in TRIM.FaTE.

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

. 

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

- end values (equal to the 90 th 

### percentile values for all food types except for fish, which was set at 99 th 

### percentile value s)

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

. Although this approach could result in an overestimate of Tier 1 Methodology 36 October 2014TRIM

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

. The exposure chara cteristics selected for the Tier 1 scenario are summarized in Exhibit 16

. Exhibit 16 . Overview of Exposure Factors Used for RTR Multipathway Screening a,b
Exposure Factor Selection for Screening Assessme nt Age group evaluated Infants under 1 year (breast milk only) Children 1 –2 years of age Children 3

– 5 years of age Children 6

– 11 years of age Children 12

– 19 years of age Adult (20

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

- prep ared ingestion rate representative of subsistence fisher woman

. For children, based on 99 th percentile

, as

prepared

, consumer

- only

, national ingestion rates

– adjusted. Exposure frequency (EF) 365 days/year Exposure duration Lifetime, for estimating 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 B ) . Cooking losses were not considered for fish consumption because intake rates represent “as prepared” values. Food preparation/cooking adjustment factor for fish e Mercury = 1.5 Cadmium = 1.5 Dioxin = 0.7 PAH = 1.0

a Data for exposure characteristics are presented in Attachment B

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

. See Attachment B for d etails.

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

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

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

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

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

e Because “as consumed,” fish consumptio n rates are used with whole

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

. 

### 2.4.2.2 Calculating Average Daily Doses

MIRC calculates chemical

- specific ADDs normalized to body weight (mg PB

- HAP per kg of body weight per day)

. Equations used to calculate ADDs were adapted from the algorit hms 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
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### from the Exposure Factors Handbook ( EPA 2011 )

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

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

. 2.4.2.3 Infan t Ingestion of Breast Milk
A nursing mother exposed to contaminants by any ingestion pathway described above can pass the contaminants to her infant through breast milk (ATSDR 1998). The nursing infant’s exposure can be estimated from the levels of chemical concentrations in the breast milk, which in turn can be estimated based on the mother’s chemical intake.
Reports of bioaccumulation of lipophilic compounds such as polychlorinated biphenyls (PCBs), polychlorinated dibenzofurans (PCDFs), and dioxins ar e prevalent in the scientific literature. Due to their high lipophilicity, these compounds partition almost exclusively to the milk fat of breast milk rather than the aqueous phase ( EPA 1998). PCBs, PCDFs, and PCDDs are the most documented groups of contaminants found in breast milk. Other compounds with lower octanol

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

- HAP included in the breast milk exposure pathway for RTR at this time. This approach is consistent with the risk assessment procedures included in EPA’s Human Health Risk Assessment Protocol ( EPA 2005 a ).
Exposure via the breast milk consumption pathway is estimated in MIRC for dioxins only. This pathway is included in computing total exposure for developing the screening threshold for dioxins. In the absence of congener

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

- TC DD

. 2.4.3 Calculating Risk

MIRC was used to calculate excess lifetime cancer risk and non

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

- response values

. Chemical dose

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

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

- HAPs included in the Tier 1 scenario are presented in Exhibit 17 and are discussed in more detail i n Attachment B . Eq uations used to estimate cancer risk and non

- cancer hazard also are provided in Attachment B

.

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### Exhibit 17 . Dose

- response Values for PB

- HAPs Addressed by the Screening Scenario

PB

- HAP CSF ([mg/kg

- da y] -1 ) Source RfD (mg/kg

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

- TCDD 1

. 5 E+5 EPA ORD 7E−10 IRIS

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

a For consistency with the overall approach for dose

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

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

. 

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

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

.
2.4.4 Summary of Tier 1 Assumptions
As emphasized previously, the scre ening scenario created for evaluating PB

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

. The scenario also is intended to avoid grossly overestimating risk to the point where no emissions
screen. The overall degree to which the scenario is health protective is the sum of the multiple assumptions that affect the outputs of the fate and transport, exposure, and risk modeling. Exhibit 18 summarizes important characteristics that influence exposure and risk estimates for this scenario and indicates the general degree of health protectiveness associated with the values for each assumption. Although this summary does not provide a quantitative estimate of the output uncertainty or the degree to which exposures and risks estimated using the scenario would be overestimated, it does demonstrate qualitatively that the scenario generally overestimates exposure and thus favors a health

- protective risk output.

Exhibit 18 . Summary of RTR Tier 1 Screening Scenario Assumptions

Characteristic Value Neutral or Health Protective? Comments on Assumptions

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

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Characteristic Value Neutral or Health Protective? Comments on Assumptions

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

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

- to

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

. location; set to highest state

- wide average for the contiguous United States

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

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

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

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

. Mixing height 710 m Health Protective Relatively low long

- term average mixing height (5 th percentile of long

- term averages for contiguous United States ) ; increases estimated air concentration. **Watershed and Water Body Characteristics** Evaporation of lake surface water 700 mm/yr Neutral Based on sensitivity analyses, value is not expected to under - or overestimate concentration in surface water. Surfac e runoff into lake Equal to 40% of total precipitation Health Protective Based on typical water flow in wetter U.S

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

. Note that after evapotranspiration, remaining water volume added via precip itation is assumed to flow into or through lake.

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Characteristic Value Neutral or Health Protective? Comments on Assumptions

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

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

., higher erosion rates were favored)

. Might underestimate erosion for locations susceptible to high erosion rates

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

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

- level fish Health Protective Inclusion of upper trophic

- level fish and absence of large

- bodied herbivore/detr itivore fish favor higher concentrations of bioaccumulative chemicals and result in higher concentrations in consumed fish

. Linear food

- chain maximizes concentration of bioaccumulative chemicals in higher trophic

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

Soil

- and air

- to

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

- reviewed and standard EPA reference sources.

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

- reviewed and standard EPA reference sources.

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

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

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

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

Fish ingestion rate Adult: 373 g/day

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

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

- only fish ingestion rates from EPA 2002

. R ates were a djusted Tier 1 Methodology 41 October 2014TRIM

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Characteristic Value Neutral or Health Protective? Comments on Assumptions

to be represent ative of the age groups used in the screening scenario. See Attachment B for a detailed discussion. Exposure frequency Consumption of contaminated food items occurs 365 days/yr Health Protective All meal s 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 fa te 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 Health Protective Values used are those determined to be appropriate for risk assessment by OAQPS; values are developed to be health protective.



### 2.5 Evaluation of Screening Scenario

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

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

2.5.2 Cadmium Compounds

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

- zinc ores or cadmium

- zinc

- lead ores

, coal - and oil

- fir ed boilers

, other urban and industrial facilities

, phosphate fertilizer manufacturing facilities

, road dust

, and municipal sewage sludge incinerators (ATSDR 2008).

2.5.2.1 Behavior in the Environment
Once emitted into the environment, airborne cadmium particles can be transported over long distances before being d eposited

. Cadmium has been observed to partition primarily to soil when released to the environment (ATSDR 2008). The mobility of cadmium in soil depends Tier 1 Methodology 42 October 2014TRIM

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### strongly on soil pH, clay content, and availabi lity of organic matter

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

. In general, c admium adsorbs to soil particles in the surface layers of the soil profile, but to a lesser degree than many other heavy metals (HSDB 20 05). C admium also bi nds strongly to organic matter, rendering the metal relatively immobile in some soils. Nonetheless, some plants still can take up cadmium efficiently , thus providing an entry point for cadmium into the food chain (ATSDR 2008).

Cadmi um also enters surface waters , which can occur via atmospheric deposition, runoff and erosion, or wastewater streams. M ost cadmium compounds entering the water column are quickly removed through adsorption to organic matter in sediment or to other suspend ed compounds. Cadmium that remains in the water column is expected to exist primarily in the dissolved state where it is available for uptake by aquatic organisms.
Freshwater fish accumulate cadmium primarily through direct uptake of the dissolved form th rough the gills and secondarily through the diet, which plays a variable role in total cadmium uptake (Reinfelder et al. 1998; Chen et al. 200 0; Saiki et al. 1995). Although some biomagnification of cadmium has been reported for aquatic food chains in sal twater systems, biomagnification in freshwater systems appears to be present only at lower trophic levels (Chen et al. 2000) and in narrowly defined niches (e.g., plankton/macroinvertebrate food chains; Croteau et al. 2005). Biomagnification factors (BMFs ) of less than 1 generally have been reported for fish at higher trophic levels, indicating that cadmium concentrations generally biodiminish from lower to higher trophic levels (Chen et al. 2000 ; Mason et al. 2000).

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

2.5.2.2 Concentrations in Ingestible Products

Most non

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

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

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

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

2.5.2.3 Average Daily Dose (ADD)
To determine the media most relevant to exposure and risk, the ingestion exposure factors must be considered in addition to the estimated media concentrations (i.e., a higher concentration for a particular medium does not necessarily mean higher risk). In Exhibit 19 , the contribution s of ingestion exposure pathways to the average daily dose ( ADD ) (and thus the HQ) for the different age categories are presented. As shown in the ex hibit , fish ingestion is the dominant exposure pathway across all age categories, accounting for nearly 100 percent of the ADD for all groups. The combined contribution from all other exposure pathways accounts for less than 0 .7 percent of the total ADD f or all age groups. Most of the additional exposure was from Tier 1 Methodology 43 October 2014TRIM

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### ingestion of fruits and vegetables. The highest ADD corresponds to children aged 1 –2 years ; thus, the exposure corresponding to this group was used to determine the emission threshold for cadmiu m. In other words, the threshold emissions rate for cadmium is set at the level where the HQ for this age category is equal to 1.0.

Exhibit 19

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

2.5.3 Mercury Compounds

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

- bound pha ses ( EPA 1997 )

. 2.5.3.1 Behavior in the Environment
Once emitted into the environment, mercury undergoes changes in form and species as it moves through environmental media. Elemental mercury is the most prevalent species of mercury in the atmosphere. Due to the long residence time of elemental mercury in the atmosphere, this compound is relatively well distributed, even on a global scale.
Divalent mercury is removed from the atmosphere at a faster rate than elemental mercury, and it can be transferred to the s urface near the emission source via wet or dry deposition where it appears to adsorb tightly to soil particles ( EPA 1997) or dissolved organic carbon. Divalent mercury in soil also can be methylated by microbes or reduced to elemental mercury and revolati lized back into the atmosphere. Most divalent mercury from atmospheric deposition will
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### remain in the soil profile, however, in the form of inorganic compounds bound to soil organic matter. Although this complexing behavior with organic matter significant ly limits mercury transport, the ability of mercury to form these complexes greatly depends on soil conditions such as pH, temperature, and soil humic content. For example, mercury strongly adsorbs to humic materials and sesquioxides in soil at pH > 4 and in soils with high iron and aluminum content (ATSDR 1999). Small amounts of mercury in soil can be transported to surface water via runoff or leaching.
Mercury could also enter the water column through atmospheric fallout

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

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

. The solubility of mercury in water depends on the species and form of mercury present as well as properties of the water such as water pH and chloride ion concentration (ATSDR 1 999). Low pH favors the methylation of mercury in the water column, typically performed by sulfur

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

. A considerable amount (2 5– 60 percent) of both divalent mercury compounds and methyl mercury is strongly bound to particulates in the water column ( EPA 1997). The remaining mercury is dissolved

. Most of the e lemental mercury produced as a result of reduction of divalent mercury volatilizes back into the atmosphere.
For the screening scenario, the partitioning behavior modeled in TRIM.FaTE generally was consistent with trends noted in the literature. Divalent mercury was the most prevalent species in modeled surface soil, surface water, and sediment compartments, while methyl mercury was the dominant species in fish

. 2.5.3.2 Concentrations in Ingestible Products
Available data indicate that mercury bioaccumulates in plants, aquatic organisms, and terrestrial animals, providing multiple ingestion exposure pathways ( EPA 1997; ATSDR 1999). Low levels of mercury are found in plants, with leafy vegetables containing higher concentrations than potatoes, grains, legumes, and other vegetables and f ruits (ATSDR 1999; EPA 1997). Cattle demethylate mercury in the rumen and, therefore, store very little of the mercury they ingest by foraging or consuming silage or grain. Thus, mercury content in meat and cow’s milk is low (ATSDR 1999). Concentrations of methyl mercury in fish are generally highest in larger, older specimens at the higher trophic levels ( EPA 1997).
Although data on mercury in foods other than fish are not abundant in the literature, total mercury concentrations output by MIRC were generally consistent with the reported values that were available. The exposure pathways that most influenced the mercury HQs in the model are presented in Exhibit 20 . As shown, the dominant exposure pathway for all age groups is ingestion of fish. Relative to divalent mercury

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

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### Exhibit 20

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

2.5.3.3 Average Daily Dose

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

. The combined contribution of all other exposure pathways accounts for less than 1 percent of the total ADD for all age groups. The high degree of exposure to methyl mercury through fish ingestion is attributed to the ease with which this compound bioaccumulate s and biomagnifies in fish and to the health protective ingestion assumptions used in the screening scenario. The highest ADD corresponds to children aged 1 –2 years ; thus, the exposure corresponding to this group was used to determin e the emission threshold for mercury.

2.5.4 Dioxins

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

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

- TCDD emissions in the United States are municipal waste incineration, medical waste incineration, hazardous waste kilns from Portland cement manufacturing, secondary a luminum smelting, and biological incineration

.

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### 2.5.4.1 Behavior in the Environment
Dioxins emitted to the atmosphere can be transported long distances in vapor form or bound to particulates, depositing in soils and water bodies in otherwise pristine locations fa r from the source. Although airborne dioxins are susceptible to wet and dry deposition, most dioxins emitted to the atmosphere through incineration/combustion processes are not deposited close to the source (ATSDR 1998).
In soil, dioxins strongly adsorb to organic matter and show very little vertical movement, particularly in soils with a high organic carbon content (ATSDR 1998). Most dioxins deposited in soil are expected to remain buried in the soil profile, with erosion of contaminated soil particles the only significant mechanism for transport to water bodies.
The dry deposition of dioxins from the atmosphere to water bodies is another important transport process. Because of the hydrophobic nature of dioxins, most dioxins entering the water column are expected to adsorb to suspended organic particles or partition to bed sediment, which appears to be the primary environmental sink for this chemical group ( EPA 2004c). Although dioxins bound to aquatic sediment primarily become buried in the sediment
com partment, some resuspension and remobilization of congeners can occur if sediments are disturbed by benthic organisms (ATSDR 1998).
Bioaccumulation factors (BAFs) in fish are high as a result of the lipophilic nature of chlorinated dioxins. Alt hough the processes by which freshwater fish accumulate dioxins are not well understood, both fish and invertebrates bioaccumulate congeners that have partitioned to sediment or have become suspended in water ( EPA 2004c ). B ecause most dioxins in the aquatic enviro nment are adsorbed to suspended particles, h owever, direct uptake from the water is unlikely to be the primary route of exposure for most aquatic organisms at higher trophic levels ( ATSDR 1998). At lower trophic levels, the primary route of exposure appea rs to be through uptake of water in contaminated sediment pores, and the primary route of exposure in the higher trophic levels appears to be through food chain transfer. Following ingestion, some fish can slowly metaboliz e certain congeners, such as 2,3, 7,8

- TCDD, and releas e the polar metabolites in bile. This process ultimately might limit bioaccumulation at higher trophic levels ( ATSDR 1998).
For the RTR screening scenario, the partitioning behavior modeled in TRIM.FaTE was consistent with the behavio r of 2,3,7,8

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

2.5.4.2 Concentrations in Ingestible Products
The primary source of non

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

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

. Another major source of exposure to dioxins is through ingestion of cont aminated soil by animals

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

- TCDD in the fish compartment for the screening Tier 1 Methodology 47 October 2014TRIM

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

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

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

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

. 2.5.4.3 Lifetime Average Daily Dose (LADD)

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

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

### , molecular weight). The differences ar e also likely attributed to differences in the congener

- specific half

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

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

.

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### Exhibit 21

. Estimate d Contributions of Modeled Food Type s to Dioxin Ingestion Exposures

2.5.5.1 Behavior in the Environment
PAHs emitted to the atmosphere can travel long distances in vapor form or attached to particl es, 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 (ATSD R 1995).
As a result of sustained input from anthropogenic sources, PAHs are ubiquitous in soil. High molecular weight PAHs, such as benzo( a) pyrene , strongly adsorb to organic carbon in soil, indicating that adsorption to soil particles will limit the mobility of these compounds following deposition to soil (ATSDR 1995).
Most PAHs enter the water column directly through atmospheric deposition (ATSDR 1995). Following deposition onto surface waters, approximately two

- thirds of PAHs adsorb strongly to sedi ment and suspended particles, while only small amounts revolatilize back to the atmosphere (ATSDR 1995). Aquatic organisms can accumulate PAHs via uptake from water, sediment, or food. Although fish and other organisms readily take up PAHs from contamina ted food (e.g., aquatic insects, other benthic invertegrates, smaller fish)

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

- dwelling organisms can experience increased Tier 1 Methodology 49 October 2014TRIM

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### exposure to PAHs through association (e.g., direct uptake, consumption) with contaminated sediment (ATSDR 1995).
For the screening scenario, the partitioning behavior of benzo(a)pyrene is generally consistent with trends reported in the literature.

2.5.5.2 Concentrations in Ingestible Products
The primary source of n on

- inhalation exposure to benzo(a) pyrene outside of occupational settings is through dietary intake. Exposure can depend on the origin of the food (higher values
are often recorded at contaminated sites) and the method of food preparation (higher values have been reported for food that is smoked or grilled). PAHs have been observed to bioaccumulate in aquatic organisms and terrestrial animals through uptake of contaminated water, soil, and food. T hese compounds are readily metabolized by higher trophic level organisms, including humans, however, so biomagnification is not considered to be significant ( ATSDR 1995). Plants accumulate PAHs primarily through atmospheric deposition, but chemical concentrations tend to be below detection levels. PAHs in meat have been observed at concentrations below detection levels up to higher concentrations when the meat is smoked. Similar concentrations have been reported for fish, with smoked fish concentrations sometimes quadruple those found in terrestrial animals. Because PAH concentrations are highest in products that are smoked or grilled, most of the available data for benzo (a) pyrene in food is for products that have been prepared using these processes. As a result, reported values might be significantly higher t han those output by MIRC.
For the RTR screening scenario, concentrations output by MIRC were generally lower tha n the reported ranges for benzo(a) pyrene in ingestible products. This trend is likely the result of background exposure in reported measuremen ts and available data that are skewed toward concentrations in highly contaminated products. Considering these mitigating factors, the RTR screening scenario output concentrations are within the range of plausible values for PAHs in ingestible products.

2.5.5.3 L ifetime Average Daily Dose

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

. As shown

, the variability in the driving exposure pathways acro ss PAHs is significant , with fish, beef, dairy, fruits, and vegetables comprising between 90 and 99 percent of exposure for different PAHs.
This variability can be accounted for in part by differences in the physiochemical properties that drive the environ mental fate and transport processes of these PAH s ( e.g., K ow 

### , molecular
we ight, chemical structure), differences in the PAH

- specific half

- life in abiotic media

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

2.5.6 Summary

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

- HAP categories of interest to EPA’s RTR Program

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

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

- food

- chain also is a major route of exposure, with beef and dairy contributing significantly to the L ADD. Tier 1 Methodology 50 October 2014TRIM

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### Exhibit 22

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

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## 3. Tier 2 Method ology

3.1 Overview of Approach
The Tier 1 screening scenario is, by design, generic and health

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

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

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

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

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

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

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

- protective assumptions in a Tier 1 screen with more site

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

• 

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

• 

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

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

- concentration) lake is unable to provide him an adequate catch to satisfy the assumed ingestion rate (i.e., 373 g/d for adults). This assessment uses the assumption that the biological productivity limitation of each lake is 1 gram of fish per acre of water: meaning that in order to fulfill the adult ingestion rate, the fisher will need to fish from 373 total acres of lakes.
In selecting the fate and transport variab les listed above to include in Tier 2 adjustments , a balance was struck between: 1) the degree of impact on the potential risk estimate; 2) the ease of implementation in TRIM.FaTE; and 3) the ease of obtaining site

- specific values on a facility

by

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

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

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

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

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

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

- HAP . Next, t he facility

- specific es timates of the Tier 2 meteorological and lake

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

- calculated databases (discussed
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### below) rather than gathering the input data and performing TRIM.FaTE and MIRC modeling speartely for each facility.

Exh ibit 23

. Basic Process for Implementing Tier 2

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

average summary statistics on wind direction, wind speed, and precipitation for more than 800 surface stations paired with t heir closest upper

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

- ready data used by EPA OAQPS for RTR inhalation modeling

. As discussed below , in most cases the 2011 precipitation data were n ot used, and instead

, the 30

- year average annual precipitation data for each station
were used. The database of lakes, available from ESRI ® and based on U.S. Geological Survey data, includes information on the location, size, use or type designation, and name (if available) of all lakes in the United States. To focus on lakes that can support angling of upper trophic level fish, lakes used for disposal, evaporation, or treatment were excluded, and only lakes greater than 25 acres in area are included. Lakes larger than 100,000 acres in area are not included because the sizes of their watersheds and the complexity of their lake dynamics are not feasible to model with the TRIM.FaTE modeling system.
In parallel to the meterological and lake data collection, a series of TRIM.FaTE simulations was performed that systematically varied the values for four of the five selected fate and transport variables (shown in orange in the figure, consisting of lake location, wind speed, precipitation rate, and mixing height). Wind direction affects only whether the chemical mass advects toward the farm and lake, so the effect of site

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

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

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



### Run TRIM.FaTE/MIRC Test Cases to Create Matrix of Tier 2 Adjustment Factors (Reflecting all combinations of meteorology statistics and lake distance values) 

### Facility’s Tier 1 Screening Results

Identify Facility’s Closest

Meteorology Station and All Qualifying Lakes 

## One Time For Each Facility Assessment

Identify and Apply Tier 2 Adjustment Factors

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### (and the subsequent exposure and risk characterization, conducted using MIRC), a matrix of Tier 2 threshold adjustment factors was calculated, with each element of the matrix corresponding to a unique combination of values from each of the selected variables. The contribution of each exposure media toward each scenario’s Tier 2 risk metric (i.e., the individual contributions of fish ingestion, soil ingestion, beef ingestion, etc. toward the total Tier 2 risk metric) was included in the matrix because the Tier 2 assessment separates chemical exposure from fish ingestion from exposure from farm food chain ingestion.
These TRIM.FaTE and MIRC simulations are used to estimate part of the Tier 2 threshold adjustment factors. Wind direction is the other part of the Tier 2 threshold adjustment factors. The wind direction adjustment for a given facility is the ratio of the frequency that winds blow toward the Tier 1 farm and lake (43 percent of the time) and the frequency that winds blow toward the facility

- specific farm and lake in Tier 2. These Tier 2 threshold adjustment factors are multiplied by each other and represent the ratio between the risk metric (i.e., cancer risk or HQ) obtained using the Tier 1 screening scenario and the risk metric obtained from the Tier 2 TRIM.FaTE runs. For a given facility, an adjusted Tier 2 ratio (emissions compared to the emission threshold) can be estimated by dividing the Tier 1 emission ratio (the output of the Tier 1 screen) by the adjustment factor that best corresponds to the meteorological conditions present at the site and the presence and location of lakes at the site:

Tier 2 Ratio = Tier 1 Ratio ÷ Tier 2 Adjustment Factor
Matrices of threshold adjustment factors from the TRIM.FaTE and MIRC simulations were separately developed for the four PB

- HAPs that curren tly have screening emission thresholds in the Tier 1 process: benzo(a)pyrene (BaP, representative of PAHs), cadmium, divalent mercury and 2,3,7,8

- Tetrachlorodibenzo -p- Dioxin (TCDD, representative of dioxins). The wind direction threshold adjustment factors were irrespective of chemical (specific only to the facility). In implementing the Tier 2 process, a risk equivalency approach was used to convert speciated emissions of PAHs and dioxins into BaP and TCDD equivalents, respectively, similar to the Tier 1 s creening approach.
Finally, to implement the Tier 2 screen, a Microsoft® AccessTM tool was created that includes the database of meteorological data and lake data as well as the Tier 2 adjustment factors for the combinations of variables simulated in the Tier 2 TRIM.FaTE simulations. In the tool, each facility is matched with the same surface meteorological station used in the RTR inhalation risk assessment , and the values for the four relevant parameters at that station are recorded (wind speed, wind direc tion, precipitation rate, and mixing height). The tool also facilitates the identification of all qualifying lakes in the area surrounding the facility and their distances and directions relative to the facility. These five values become the set of facili ty

- specific parameters. The threshold adjustment factor corresponding to this set of site

- specific data is then estimated using the matrix of adjustment factors and the wind direction adjustment

. Wind direction values are used as

- is with no rounding or bi nning. If one of the four facility matrix variables (wind speed, precipitation rate, mixing height, or lake location) is between two of the computed levels available for that variable in the simulation matrix, the more health

- protective of the two levels is selected (i.e., the level that resulted in the smaller adjustment to the Tier 1 exposure). The Tier 1 screening emission threshold for a PB

- HAP is then divided by the appropriate adjustment factors to obtain an updated Tier 2 emission threshold for that PB

- HAP at that facility. A facility then screens out in Tier 2 if the emission s are below the Tier 2 threshold for the fisher scenario and for the farmer scenario

.

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### 3.2 Selection of Site

- Specific Characteristics for the Tier 2 Assessment
The screening scenar io used to derive Tier 1 thresholds incorporates assumptions regarding meteorological conditions, the spatial configuration of the hypothetical exposure setting, physical parameters of the environment, and chemical

- specific parameters that result in genera lly health

- protective results. In Tier 2, selected assumptions used in the fate and transport modeling conducted using TRIM.FaTE are modified to reflect more site

- specific information for the facilities being evaluated. 19 

### To determine which scenario chara cteristics should be incorporated into the Tier 2 assessment , the following issues were considered for TRIM.FaTE model parameters:

• 

### How sensitive are the modeled risks to a specific user

- input model parameter (e.g., wind direction, wind speed) ?

• 

### Do the plaus ible values for a given parameter span a large range when comparing different RTR facilty locations ?

• 

### Which site

- specific characteristics can be found easily and reliably for facilities with emissions exceeding Tier 1 thresholds?

• 

### In general, is the uncert ainty associated with the parameter high or low ?

• 

### How complicated or time

- consuming is the incorporation of a given parameter (e.g., wind speed) into the screening scenario set

- up ?

Attachment D provides an exhibit showing all th e TRIM.FaTE variables considered for the Tier 2 assessment . These variables were evaluated qualitatively using the criteria above to determine whether the variable was of high, medium, or low priority. The following five “high priority” variables were selected for implementation in the current Tier 2 assessment :

• 

### Wind direction (the percent of time the wind blows toward the lake and farm),

• 

### Wind speed,

• 

### Precipitation,

• 

### Mixing height, and

• 

### Location of fishable lake s relative to the facility

. These parameters were judged to represent a balance between range of potential variability, ease of implementation within the modeling scheme used for RTR, and ease of obtaining site

specific values with a relatively high level of confidence.



### 3.3 Estimation of Adjustment Factors for Selected Site

- Specific Parameters
The purpose of including site

- specific detail for a facility evaluated in Tier 2 is to develop a more realistic estimate of risk associated with facility emissions. This purpose is achieved within the assessment by g enerating revised emission thresholds of potential concern specific to a given PB

- HAP on a facility

- by

- facility basis. However, instead of performing full

- fledged, site

- specific model runs for each facility that does not “screen out” in Tier 1, a set of g enerally applicable threshold adjustment factors for each PB

- HAP was developed based on a set of model runs.

19 Only TRIM.FaTE parameters were considered for inclusion in Tier 2 adjustments because of the difficulty in identifying substantial **location**

**- related**  differences in values for exposure factors (and other inputs to MIRC). The exposure characteristics used in MIRC are considered to be generally consistent across different locations and facilities.

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### For each PB

- HAP, these runs corresponded to unique combinations of values for wind speed, precipitation rate, mixing height, and lake location (wind direction is assessed separately). These adjustment factors were based on a set of runs in which the values for these parameters were varied systematically. The wind direction adjustment factor is the ratio of the frequency that winds blow toward the lake and farm in the Tier 1 screening scenario (43 percent) and the frequency that winds blow toward the facility’s farm and lake in Tier 2 , with a separate wind direction adjustment factor for each directional octant (i.e., separate factors for the farm a nd lakes in the northern octant, the farm and lakes in the northeastern octant, etc.). The appropriate adjustment factors are then applied to the Tier 1 threshold for that PB

- HAP when evaluating a facility in Tier 2.
The analyses conducted to select the parameters to derive the threshold adjustment factors are described in this section. Section 3.3.2 also describes the development of appropriate “bins” for the selected parameters. These bins correspond to the subset of parameter values for which adjustment factors were calculated, based on the anticipated range of plausible values for facilities evaluated in RTR.

3.3.1 Selection Values for Variables of Interest

For each site

- specific parameter that is assessed in the Tier 2 assessment

, other than wind direction, adjustment factors were estimated that correspond to a set of four to six particular values for the parameter. The adjustment factor for wind direction directly relates Tier 1 and site

- specific Tier 2 wind direction frequencies, with respect to the directions of the lake and farm. These individual adjustment factors can the be multiplied for a particular variable combination to get an overall adjustment factor. To facilitate this, bins were created for each parameter of int erest and a relevant range of values, with the exception of wind direction (as described below, representative bins were not necessary for this parameter). The rationale for selecting the range for each bin for each parameter of interest is described belo w.

Wind Direction : Within the context of the hypothetical exposure scenario used in Tier 1 (and under actual conditions), when t he frequency with which the wind blow s towards the modeled domain (i.e., where the hypothetical farm and lake are located) in creases , greater pollutant deposition will occur over and around the farm and lake . T he percentage of time the wind blows toward the farm and lake is therefore positively correlated with ingestion exposure and risk

. In the screening scenario used to esti mate Tier 1 thresholds , the wind is assumed to blow toward the modeled domain 3 days a week, or 43 percent of the time. This assumption is intended to approximate an unusually consistent long

- term wind pattern and is representative of wind direction patterns in Yakima, Washington ( where the wind blows eastward approximately 40 percent of the time

, based on a review of wind direction data compiled by the Nati onal Weather Service)

. I n the model runs to develop the Tier 2 bins , this factor was changed to 1, 2, and 4 days per week, corresponding to 14 percent , 29 percent , and 57 percent of the time. This range of values was chosen to obtain a good understanding of the impact of wind direction on risk for the range of wind direction patterns likely to be present at real facilities. Review of these results indicated that, within this modeling scenario, estimated ingestion exposure varies directly with percentage of time the wind blows toward the farm and lake. Given the exactly linear nature of the relationship observed in model results obtained from these runs, the adjustment of the threshold for wind direction in Tier 2 is a direct, linear adjustment using the actual site value rather than an incremental, bin

- based approach. In other words, the Tier 1 threshold is adjusted for wind direction in direct proportion to the difference between conditions for the actual facility location and the wind direction parameters included in the Tier 1 screening scenario (i.e., blowing toward the lake/farm 43 percent of the time on average).

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### Wind Speed : Although the impact of wind speed on non

- inhalation risks also is likely to depend on configurational parameters such as the location of farms and lakes, in general it is reasonable to assume that higher wind speeds lead to more rapid chemical transfer out of the modeled domain

, allowing less time for chemical deposition and

, therefore

, less total near

- field deposition and a lower exposure and risk

. The Tier 1 screening assessment assumed a wind speed of 2.8 m/s, corresponding to the 5 th 

### percentile (i.e., slower) of annual average speed for the contiguous United States (distribution was based on data from a climate publication from the National Oceanic and Atmospheric Administration [NOAA], which used data from over 200 stati ons nationwide). 20 

### This value is similar to the annual average wind speeds of the U.S. Deep S outh. 21 

### In the model runs to develop the Tier 2 threshold adjustment factors

, we calculated the change to exposure resulting from increasing this value to 3.5 m/s, 4 m/s, and 5 m/s ( values also shown in Exhibit 24 ; 5 m/s is the 88 th 

### percentile in the NOAA data). Based on these values, the bins used to classify wind speed are: 2.8

– 3.5 m/s, 3.5

– 4 m/s, 4

– 5 m/s and above 5 m/s. In all modeled cases, increasing the wind speed while maintaining constant lake location, wind direction, mixing height, and precipitation led to Tier 2 estimations of high

- end risk or hazard that were smaller than that of Tier 1. To ensure that the approach is health

- pr otective, a facility was assigned the lower end value of the bin into which it was placed. Facilities with wind speeds less than 2.8 m/s were assumed to have a wind speed of 2.8 m/s.
Precipitation : Higher levels of precipitation over the modeled domain are expected to increase
non

- inhalation risks by increasing particulate and gaseous wet deposition near

- field to the
source. The screening scenario used in Tier 1 assessment assumed an annual precipitation rate of 1,500 mm/year. This level of annual precip itation was estimated to represent rainy conditions in the U.S., such as parts of the U.S. Deep South and parts of the U.S. Northwest Coast 22 

### . Though the rate was an estimate, it does correspond to approximately the 95 th 

### percentile (i.e., higher rate) precipitation in t he N ational Climatic Data Center’s (NCDC) 30

- year ( 1981

– 2010 ) data from U.S. stations

. 23 

### To estimate adjustment factors in the Tier 2 assessment

, model simulations were conducted with the parameter value set to three lower values (i.e., 1,187 mm/year, 924 mm/year and 512 mm/year; values also shown in Exhibit 24 ), corresponding to the 75 th 

### , 50 th 

### and 25 th 

### percentiles, respectively, of the NCDC data

. Locations with lower precipitation levels were assumed to have a minimum precipitation of 512 mm/year. Based on these levels, the bins used to classify precipitation were: 0

- 512 mm/year, 512

- 924 mm/year, 924

- 1,187 mm/year, 1,187

- 1,500 mm/year and above 1,500 mm/year. In nearly all modeled cases, decreasing the precipitation rate while maintaining constant lake location, wind speed, wind direction, and mixing height led to Tier 2 estimations of high

- end risk or hazard that were smaller than that of Tier 1. To be health

- protective, a facility was assigned the upper end value of the bin in which it was placed. Facilities with precipitation levels above 1,500 mm/year were assumed to experience precipitation of 1,500 mm/year.

20 http://ols.nndc.noaa.gov/plolstore/plsql/olstore.prodspecific?prodnum=C00095

- PUB

- A0001#TABLES – this website is updated yearly, so its current data may not match the data used to develop wind speeds for screening analyses.

21 National Climatic Data Center CliMaps (NCDC

- CliMaps) (2007).

22 National Climatic Data Center Historical Climate Series (NCDC

- HCS) (2007).

23 http://www.ncdc .noaa.gov/oa/climate/normals/usnormals.html

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### Exhibit 24

. Lake Distance and Meteorological Parameter Values for which Adjustm ent Factors were Developed in Tier 2 a ,b

Parameter Value

Wind Speed (m/s) 2.8 3.5 4 5

Precipitation (mm/yr) 512 924 1,187 1,500

Mixing Height (m) 710 865 1,097 1,537

Lake Distance (km) No lake
2 5 10 20 40

a Bold indicates th e value is equal to the value used in Tier 1.

b Wind direction is not shown here because its effect on modeled exposure and risk in TRIM.FaTE is linear.



### Mixing Height: Greater mixing heights can dilute the concentration of pollutants in air, resulting in lower deposition and other transfer rates from air to surfaces and consequently also lower ingestion exposures. The Tier 1 screening assessment assumed a mixing height of 710 meters. This value is the 5 th 

### percentile (i.e., lower) of annual average mixing heights for 463 U.S. locations, based on data obtained from EPA’s SCRAM Web site. 24 

### To estimate adjustment factors in the Tier 2 assessment , mdoel simulations were conducted with the parameter value set to three larger values (i.e., 865 m, 1,079 m, and 1,537 m; values also shown in Exhibit 24 ). These values correspond to North Little Rock, Arkansas ; Boise, Idaho ; and Tucson, Arizona ; and they are intended to encompass the range of annual average mixing heights experienced in diff erent parts of the United States. Based on these levels, the following bins were selected for categorization of mixing height: 710

– 865 m, 865

– 1,079 m, 1,079

– 1,537 m, and above 1,537. In all modeled cases, increasing the mixing heights while maintaining constant lake location, wind direction, wind speed, and precipitation rate led to Tier 2 estimations of high

- end risk or hazard that were smaller than that of Tier 1. To be health

- protective, a facility was assigned the lower end value of the bin into whic h it was placed. Facilities with mixing heights above 1,537 m and those below 710 m were assumed to have mixing heights of 1,537 m and 710 m, respectively.

Lake Location : Moving the lake included in the hypothetical Tier 1 scenario to a location farther from the actual source in the modeled domain will reduce modeled (TRIM.FaTE) deposition to the lake and its watershed and consequently reduce exposures associated with

24 Support Center for Regulatory Atmospheric Modeling; http://www.epa.gov/scram001/tt24.htm

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### the fish consumption pathway

. For the scenario modeled in Tier 1, the center of the lake was situated approximately 2 km from the source. To estimate lake location adjustment factors f or use in Tier 2, we completed a series of model runs in which the lake was located 5 km, 10 km, 20 km, and 40 km from the source, as well as runs with no lake (values also shown in Exhibit 24 ). Accordingly, the bins used to classify lake location relative to the facility are: no lake, 2

– 5 km, 5

– 10 km, 10

– 20 km, 20

– 40 km, and 40

– 50 km (lakes farther than 50 km are not considered). To be health

- protective, a facility was assigned the lower end value of the bin into which it was p laced. A facility with a lake closer than 2 km was assumed to have a lake at 2 km.
In resituating the lake at these alternative locations, we maintained ratios consistent with those included in the Tier 1 screening scenario for (1) lake area to total land area in the modeled domain, (2) runoff watershed area to lake area, and (3) erosion watershed area to lake area. Exhibit 25 provides a diagram of the TRIM.FaTE layout in each of the alternate lake location simulations except no -l ake. We used a “thin” lake shape (i.e., downwind width much smaller than the cross

- wind length) that minimized the potential effect of declining deposition with distance from stack that might affect lakes that are long in the downwind direction. By contr olling for these potentially confounding effects, we could isolate the effect of lake location on risk appropriately . Moving the lakes to increasing distances from the stack required expansion of the modeled domain. Maintaining the same overall ratio of land area to lake area in each domain resulted in scenarios with increasingly large lake s, with surface area increasing with distance from the source. This approach also was taken for reasons of modeling convenience (i.e., taking into account resource requirements associated with reconfiguring the TRIM.FaTE spatial layout). The changes in lake size between these four runs are not expected to have a substantial independent effect on exposure and risk because the effect of increased lake size is offset by g reater total deposition and runoff . Furthermore, the lake depth was not changed, which might be as important a parameter as lake surface area in determining the chemical concentrations in the water column and sediment

. As noted above

, we set up the confi gurations to ensure that the lakes in the different scenarios received runoff and erosion from equivalent watersheds on a per surface area basis.

3.3.2 Estimation of Adjustment Factors
Adjustment factors were estimated for each variable described above and app lied as divisors to the Tier 1 emissions thresholds. The resulting Tier 2 emissions thresholds are used to assess whether facilities with corresponding configurations carry some potential for significant multipathway health risks by comparing their actual emissions to the Tier 2 thresholds

. 3.3.2.1 General Approach
The core principle in the estimation of adjustment factors is the assumption of direct proportionality of risk and emissions in the modeling approach used for RTR involving TRIM.FaTE and MIRC. Although not strictly present across all variations due to feedback mechanisms and other processes encompassed by the TRIM.FaTE model, a generally linear relationship between risk and emissions has been observed across model simulations conducted for RTR. This suggests that the ratio of total estimated exposures (and consequently risks) obtained for the screening scenario and any alternative configuration could be used as an adjustment factor to scale emissions for that specific alternative configuration. The risk in the alternative configuration following such a scaling of emissions would be equal to the risk in the screening scenario (which in Tier 1 was s et at a risk level of 1E−6 incremental lifetime cancer risk or an incremental HQ of 1, depending on the toxic effe ct of the chemical in question)

.

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### Exhibit 25 . Layouts for Tier 2 TRIM.FaTE Simulations Using Alternate Distances B etween the Facility and the Fishable Lake a

a The no

- lake scenario is not shown. Tier 2 Methodology 60 October 2014TRIM

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### To account for potential interactions between the chosen Tier 2 variables, matrix adjustment factors were estimated by performing TRIM.FaTE runs for each unique combination of the specified variable values (that is, all permutations of the selected values for the wind speed, precipitation, mixing height, and lake location). Adjustment factors for each configuration were then estimated as the ratio of risks in the Tier 1 assessment to the estimated risk for the particular TRIM.FaTE configuration. This approach results in a large matrix of adjustment factors and has the advantage of accounting for all possible interaction effects between the variables. The contribution of each exposure media toward each scenario’s Tier 2 risk metric (i.e., the individual contributions of fish ingestion, soil ingestion, beef ingestion, etc. toward the total Tier 2 risk metric) was included in the matrix because the Tier 2 assessment separ ates chemical exposure from fish ingestion from exposure from farm food chain ingestion.
The adjustment factor for wind direction is handled separately. Based on TRIM.FaTE test runs, the fraction of the time the wind blows in the direction of the farm and lake was observed to have a direct, linear effect on the resulting risk estimates. For this reason, it was not necessary to include this variable in the TRIM runs conducted to create the matrix of adjustment factors, as described above. Instead, the adjustment factor is calculated with a linear factor that divides the Tier 1 value (0.43, or 43% of the time in the direction of the farm and lake) by the site

- specific facility value, as follows:

Adjustment Factor Wind Direction 

### = 0.43 / (fraction of time wind blows towards domain)
The adjustment factor for wind direction is then multiplied by the matrix adjustment factor discussed above to obtain a consolidated threshold adjustment factor that accounts for all the five variables considered in Tier 2.

3.3.2.2 Inco rporation of the Risk Equivalency Approach
The adoption of a risk

- equivalency approach to convert speciated emissions of PAHs and dioxins to BaP and TCDD equivalents, respectively, in Tier 1 required the development of risk equivalency factors (REFs) for e ach reported species in these groups. The REFs for PAHs and dioxins represent the ratio of the risk posed by a particular species to the risk posed by BaP and TCDD, respectively, at equivalent emissions rates in a given scenario.
The REFs can be represented as the product of exposure equivalency factors (EEFs) and toxicity equivalency factors (TEFs). For the PAHs, this can be expressed as:

REF PAH 

### = EEF PAH 

### × TEF PAH 

### The EEFs for PAHs represent the ratio of the exposure to a particular species to the exposure to BaP (and similarly for dioxins and TCDD) at equivalent emission rates. These ratios are thus specific to the TRIM.FaTE layout and input assumptions. The TEFs for PAHs and dioxins represent the ratio of the oral cancer slope factor (CSF) for a particular species to the CSF for BaP and TCDD, respectively, and are the same in Tier 1 and in Tier 2.
The EEFs depend on the TRIM.FaTE configuration, including layout and meteorlogical input values. For example, the exposure profile (i.e., how the diffe rent ingestion pathways contribute to total exposure and risk) is different for each PAH. For a PAH where fish is a dominant driver of risk, moving the lake will have a large effect on the overall risk; however, if produce is the dominant driver of risk, moving the lake will have a much smaller effect on overall risk. Thus, for Tier 2, EEFs were recomputed for each of the representative scenarios modeled in Tier 2 separately for the PAH species currently evaluated (some based on direct TRIM.FaTE modeling, o thers 15 based on K ow 

### - based regression estimates) and the dioxin species currently Ti er 2 Methodology 61 October 2014TRIM

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### evaluated (most based on direct TRIM.FaTE modeling, and a small number assumed to behave like TCDD).
The following mathematical formulas demonstrate how the Tier 2 adjustment factors are estimated for the PAH and dioxin species. The formulas presented below are for a representative PAH species, but they are also applied to dioxin species.
For a given PAH congener emitted at a rate E PAH 

### at a facility, the risk

- equivalent BaP emission level can be expressed as:

E

BAP EQIV 

### = E PAH SPECIES 

### × EEF PAH SPECIES 

### × TEF PAH SPECIES


### Then, the Tier 1 screening result is estimated by comparing the risk

- equivalent BaP emissions to the BaP emission threshold:

Ratio TIER 1_PAH SPEC IES 

### = E TIER 1_BAP EQUIV 

### Threshold TIER 1_BAP 

### If the ratio is less than 1, the facility “screens out” of the Tier 1 assessment . Similarly, for Tier 2, the ratio of risk

- equivalent BaP emissions to the Tier 2 BaP threshold may be expressed as:

Ratio TIER 2_PAH SPECIES 

### = E TIER 2_BAP EQUIV 

### Threshold TIER 2_BAP 

### Using the definition of the risk

- equivalent BaP emissions, this can be re

- expressed for a given PAH species as:

Ratio TIER 2_PAH SPECIES 

### = E PAH SPECIES 

### × EEF TIER 2_PAH SPECIES 

### × TEF PAH SPECIES 

### Threshold TIER 2_BAP 

### This expression may be further reconfigured, after some algebraic rearrangement, in terms of the Tier 1 ratio as:

Ratio TIER 2_PAH SPECIES 

### = Ratio TIER 1_PAH SPECIES 

### × Threshold TIER 1_BAP 

### Threshold TIER 2_ BAP 

### × EEF TIER 2_PAH SPECIES 

### EEF TIER 1_PAH SPECIES



### TTTTTTTT 2 AAAAAAAAAAAAAA AAAA FF FFFF FF PPPPPP SS SSSSSSSSSS 

### = Threshold TIER 1_BAP 

### Threshold TIER 2_BAP 

### × EEF TIER 2_PAH SPECIES 

### EEF TIER 1_PAH SPECIES 

### These equations simply state that to adjust the Tier 1 threshold to a Tier 2 threshold for a particular PAH species, the adjustment factor must include the ratio of the BaP Tier 1 and 2 thresholds (as is true for cadmium, mercury, and TCDD, as discussed above) and the ratio of the EEFs for the particular PAH species in Tier 2 and Tier 1. This additional EEF factor is needed to account for the fact that the EEFs are different for each Tier 2 TRIM.FaTE configuration.

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### Finally, the ratio of total BaP equivalents contributed by all PAH species at a given facility to the BaP Tier 2 threshold ma y be expressed, by summing the above expression, as:

Ratio TIER 2_ALL PAHs = � Ratio TIER 1_PAH SPECIES × Threshold TIER 1_BAP Threshold TIER 2_BAP × EEF TIER 2_PAH SPECIES EEF TIER 1_PAH SPECIES ALL PAHs



### If the ratio of total BaP equivalents contributed by all PAH species to the BaP Tier 2 threshold is greater than 1, the facility would not have screened out of the Tier 2 assessment for the PAH group.

3.3.2.3 Refined Fisher Scenario

The refined fisher scenario is based on the idea that an adult f isher might fish from multiple lakes if the first (i.e., highest

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

. Under the refined fisher scenario, a fisher fishes from each surrounding lake in order of highest chemical concentration in fish to lowest concentration and catches fish up to the biological productivity limit. A maximum travel radius is used to maintain a realistic scenario. The total screening result to the fisher can be expressed as the sum of all screening results for each lake (which is based on the ingestion rate and fish concentration for each respective lake)

. A 50 km radius of lakes is used to maintain a realistic scenario. Depending on the model configuration, very large modeling configurations put a strain on computing resources, and TRIM.FaTE can crash if the domain is very large or if the domain is divided into too many comp artments. In most scenarios, the vast majority of chemical deposition occurs across distances smaller than 50 km, so the added computing resources needed to model a domain larger than 50 km will usually not be offset by additional, substantial chemical dep osition and exposure. Indeed, wind speeds must exceed 13 m/s (approximately 29 mph) sustained across an hour in order for the chemical plume to travel farther than 50 km; those wind speeds are unlikely to occur across the many days or weeks needed to substantially affect chronic exposure. In addition , a 50

- km limit also puts a reasonable constraint on the domain of lakes for the fisher scenario

. In order for the Tier 2 screening to remain health protective, it is assumed that the fisher visits lakes in order of highest to lowest with respect to risk. If the first lake is 373 acres or greater, the fisher will only travel to that lake. If the acreage is smaller than 373 acres, then the fishe r will travel to the next highest risk lake, and continues to travel to subsequent lakes until a total acreage of 373 acres is achieved, or until all qualifying lakes have been visited.
The associated risk for each lake is then scaled by the total adult ingestion rate (equations 1 and 2).

( 1 ) SS SS TT 2 FF h rr = TT 2 FF h rr × � LL SS ( )
37 3 �

Equation 1: Scaled fish risk for each lake 1 to n -1

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### ( 2 ) SS SS TT 2 rr = TT 2 FF h rr × � 37 3

− ∑ ( SS oo PP PP PP VV LL ( ))
37 3 acs �

Equation 2: Scaled fish risk for nth lake



### The risk associated with each lake visited is then summed together for a final risk (equation 3).

( 3 ) TT SS SS TT 2 FF h rr = ( Eq . 2 ) + � ( Eq

. 1) Equat ion 3: Total fish risk for scenario



### If

the total acreage of fishable lakes surrounding the facility fails to exceed 373 acres, then the risk for each lake is found using equation 1, and the total risk is the sum of all scaled risks (equation 4)

. ( 4 ) TT SS SS TT 2 FF h rr = � ( Eq

. 1) Equation 4: Total fish risk if total acreage of lakes does not reach 373 acres



### 3.4 Preparing National Databases of Lake and Meteorological Data
To facilitate the effective application of the Tier 2 screening procedures, databases were prepared containing national

- scale information about lakes (locations and sizes) and meteorological data at available surface stations (including wind direction, wind speed, precipitation, and estimated mixing height). The development and content of these two databases are discussed in the following sections.

3.4.1 Processing Lake Data for Tier 2 Assessment
The lake database was built using a geospatial file (U.S. Water Bodies) provided by ESRI ® for their ArcGIS TM products. 25 

### Because t his geospatial file excluded water bodies in Alaska, Puerto Rico, and the U.S. Virgin Islands, it was augmented with water body information (directly from the USGS National Hydrography Dataset) for those other locations. The data generally have an estimate d horizontal accuracy of 50 m. For the Tier 2 assessment , we focused on the hundreds of thousands of water bodies classified as “Lake/Pond” or “Reservoir” but not designated for disposal, evaporation, or treatment. We refer to these water bodies simply as “lakes” in the remainder of this document. The approximately 100,000 other water bodies (classified as canal/ditch, ice mass, inundation area, playa, stream/river, swamp marsh, or unclassified) were not included. In a more subjective step during the processing of the lake database for a specific source category (when Tier 2 is run “operationally”), the lake names (when provided) are scrutinized manually, and lakes are removed from the assessment when their names suggest industrial or treatment use (e.g., wastewater treatment ponds, sludge ponds, fly ash ponds, paper mill ponds, sewage pools, etc.). In this respect, the lake dataset is never truly final; lake names are scrutinized each time a new set of facilities is assessed using the Tier 2 methods and t ools , which can lead to the permanent removal of some lakes from the dataset.
Early in the process of compiling this database, we encountered the question: “What size water body qualifies as a ‘lake’ for the purposes of this assessment?” The Tier 2 assessment must focus on lakes large enough to support relatively intensive angling pressure to be compatible

25 Specifically , the geospatial file in the ESRI ® Data & Maps 2009 Data Update for ArcGIS TM version 9.3.1. It was derived by the United States Geological Survey (USGS), EPA, and ESRI ® from the USGS National Hydrography Dataset (USGS 2012).

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### with the assumed exposure scenario. To estimate the relationship between high fish consumption rates, harvest rates

, and lake size, the following fi ve key assumptions were made. Information and citations to peer

- reviewed literature that support these assumptions are provided in Attachment E . Note that in the TRIM.FaTE model screening scenario, water

- column carnivores (WCCs) are modeled as trophic level 4 (TL4) fish (e.g., pickerel, largemouth bass), with all of their diet consisting of smaller “prey” or “pan” fish in the water column that are simulated as trophic level 3 (TL3). The benthic carnivores (BCs) in TRIM.FaTE are modeled to represent an intermediate trophic level between 3 and 4, i.e., TL3.5. The BCs (e.g., catfish) obtain half of their diet from TL2 (benthic invertebrates that feed on detritus at the sediment surface) and half from TL3 fish in the benthic environment. For the screening scenario, we assume that fisher s consume fish biomass in a ratio of 50:50 from the BC and WCC compartments, respectively. Together, these two fish compartments are referred to as piscivorous fish.

1. Piscivorous fish , when present, c omprise approximately 21 percent of the standing biomass of fish. BC fish represent 17 .5 percent of the standing fish biomass in natural lakes ; WCC fish account for 3.5 percent of this total fish biomass. Thus, WCC (or TL4) fish represent the limiting co mpartment for fish harvesting and consumption.
2. Humans can harvest 10 percent of any single fish compartment without threatening the population due to overharvesting.
3. The minimum viable effective population size for a single fish species is at least 50 adul t fish for a local population to survive over the short term ( e.g., more than a decade)

. 4. Only 33 percent of the fish caught for consumption is edible fillet muscle

. A 0.33 edible fraction is used to estimate total fish biomass associated with human consum ption.
5. A total fish standing biomass of 40 g wet weight/m 2 

### represents an approximate upper bound for natural ponds and lakes in the United States.
Using the above assumptions and a series of equations (see Attachment E ), the maximum fish ingestion rates as a function of standing biomass and lake size were estimated. Exhibit 26 presents these findings, where the grey shading indicat ing when WCC fish would probably not be present, the white (unshaded) cel ls represent combinations of lake size and productivity that could sustain the listed fish ingestion rates over some time, and the yellow cells represent likely long

- term sustainability associated with more than 500 adult WCC fish in the lake (see Exhibit notes).

The Tier 2 assessment is intended to retain most of the health

- protective attributes of the screening scenario used in Tier 1 so that no facilities of potential concern erroneously “pass” the screen. For a given facility, the smaller the lake size threshold, the greater the number of lakes and the greater the probability that a lake is closer to the facility. Lakes closer to a facility will result in higher chemical concentrations in fish compared with lakes farther from the facility. Thus, Exhibit 26 was used to determine the smallest lake size that would support a TL4 population. At the assumed upper

- limit standing fish biomass of 40 g ww/m 2 

### , this corresponds to 25 acres (the first “white” box when moving from small er to larger lakes)

. The fish ingestion rate supported by a 25

- acre lake is not as high as the adult ingestion rate used in the Tier 1 and Tier 2 assessments (i.e., 373 g ww fillet per day ). Exhibit 26 indicates that at a total fish biomass productivity of 40 g ww/m 2 

### , the maximum sustainable fish harvest from the WCC compartment would correspond to an ingestion rate of only 26 g/day for those fish, with an additional 26 g/day from the benthic carnivores (which are not the limiti ng population) , for a total of 52 g per day

. However, to be health protective and t o ensure that small lakes that might be more highly contaminated than estimated by the TRIM.FaTE screens were

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### not eliminated, we selected 25 acres as the “cutoff” for the minimum size for an actual lake near a facility to be included in the Tier 2 assessment

. In add ition, l arger lakes (larger than 100,000 acres) were not considered since they cannot be readily modeled in TRIM.FaTE. For the purposes of proximity matching lakes to emitting facilities, the location of each lake is identified as the geographic centroid i nside the lake.

It should be noted that an individual l ake as small as 25 acres could be stocked annually at a rate adequate to support the assumed fish ingestion rate. For stocked fish, however, we would have to assume that when introduced to the lake, the fish were uncontaminated by the chemicals of interest. Moreover, the period over which accumulation of chemical from the lake could occur would be roughly three to six months (fishing season) for the majority of the fish stocked as adults (i.e., at approximately 2 kg), instead of several years for fish hatched in the lake.
To place an upper bound on the radial distance from the source up to which lake

- derived risks need to be assessed, we took into account the limitations of TRIM.FaTE. We limit ed the lake analysis to an outward radial bound of 50 km from the stack. For facilities with no lakes within 50 km, lake

- derived risk is assumed 0.

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### Exhibit 26

. Maximum Fish I ngestion R ate (g/day) A ssociated with S ustainable F i shing a
Standing Biomass (g ww/m 2 ) b Size of Pond or L ake (acres )
1 2 3 4 5 7.5 10 15 25 35 50 75 100 150 200 400
2 0 0 0 0 0 0 1 1 1 2 3 4 5 8 10 20 3 0 0 0 0 0 1 1 1 2 3 4 6 8 12 15 31 4 0 0 0 0 1 1 1 2 3 4 5 8 10 15 20 41 5.7 0 0 0 1 1 1 1 2 4 5 7 11 15 22 29 58 10 0 1 1 1 1 2 3 4 6 9 13 19 26 38 51 102 15 0 1 1 2 2 3 4 6 10 13 19 29 38 58 77 154 20 1 1 2 2 3 4 5 8 13 18 26 38 51 77 102 205 30 1 2 2 3 4 6 8 12 19 27 38 58 77 115 154 307 35 1 2 3 4 4 7 9 13 22 31 45 67 90 134 179 359 40 1 2 3 4 5 8 10 15 26 36 51 77 102 154 205 410 50 1 3 4 5 6 10 13 19 32 45 64 96 128 192 256 512 60 2 3 5 6 8 12 15 23 38 54 77 115 154 231 307 615 70 2 4 5 7 9 13 18 27 45 63 90 134 179 269 359 717 80 2 4 6 8 10 15 20 31 51 72 102 154 205 307 410 820 90 2 5 7 9 12 17 23 35 58 81 115 173 231 346 461 922 100 3 5 8 10 13 19 26 38 64 90 128 192 256 384 512 1025 110 3 6 8 11 14 21 28 42 70 99 141 211 282 423 563 1127 120 3 6 9 12 15 23 31 46 77 108 154 231 307 461 615 1229 130 3 7 10 13 17 25 33 50 83 117 16 6 250 333 499 666 1332

a Dark g ray shading indicates insufficient population size for T L 4 (WCC) fish (<50 adults) to be sustainable for more than a decade ; y ellow

- shaded cells indicate the likel ihood to provide long

- term sustainable fish population s with a t least 500 TL4 adult fish present; white area indicates medium

- term sustainability

. b Represents the standing biomass of TL4 fish. At the upper

- limit standing biomass of 40 g ww/ m 2 estimated for natural lakes, 25 acres could support a water

- column TL4 fish population, but would provide for no more than 26 grams of fillet per day for a single fisher over a full year.

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### 3.4.2 Processing Meteorological Data for Tier 2 Assessment
In addition to the lake database, a database of U.S. surface stations with complete data was created, so that each source category facility can be paired with the closest meteorological station data. This process of pairing dozens or hundreds of facilities with meteorological data is not unprecedented

. In their report to the Science Ad visory Board (SAB) on the 1996 NATA, EPA described pairing each facility with the closest meteorological station in an inventory of over 350 meteorological stations nationwide, creating an average facility

- to

- station distance of less than 50 km in the 1996 NATA (EPA 2001b) . In a separate 2009 report to the SAB on the RTR program, EPA described using 158 meteorological stations to choose from nationwide, with a standard practice of selecting the station nearest to each facility unless the facility provides onsite meteorological data (EPA 2009) . Using 156 petroleum refineries as a sample data set, the average facility

- to

- station distance was 72 km. In both instances, t he SAB accepted this matching as standard practice when modeling large numbers of sources, al though they recommended providing high

- level siting maps (e.g., meteorological stations overlaid with terrain gradients or regional climate regimes) to qualify some of the uncertainties related to meteorological data in air dispersion modeling (EPA 2001a ; EPA 2010 b)

. The current effort builds on this practice but increases the number of available meteorological stations as described below.

3.4.2.1 Sources of Meteorological Data
To construct a database of meteorological statistics for all available U.S. surface stations for use in multipathway screening assessments, EPA started with the same U.S. meteorology dataset used in RTR inhalation assessments. RTR inhalation assessments use data from 824 ASOS (Automated Surface Observing System) stations that record hourly and sub

- hourly measurements. These data represent the year 2011, and the surface stations are paired with their closest, regularly

- reporting upper

- air stations. This number of stations is far greater than the 350 and 158 stations

, respectively

, used in th e 1996 NATA report (EPA 2001b) and the 2009 RTR report (EPA 2009)

. The 2011 precipitation measurements reflect 2011 weather conditions, and, like any other year, some areas of the country experienced rainfall that was significantly less than normal, and so me areas received much more rainfall than normal. To reduce this bias in precipitation data

, we used average annual precipitation data from the 1981

− 2010 National Climatic Data Center 30

- year normal dataset wherever possible. If 30

- year normal precipitation data were not available for a station, as was the case for a few stations in the dataset, we used the ASOS precipitation data as

- is

. 3.4.2.2 Coverage of Meteorological Stations Compared with Facility Locations

Exhibit 27 shows the prox imity of the evaluated meteorology sites to the locations of U.S. point source facilities from the 2005 NATA. Generally, the spatial density of the surface meteorological stations in this dataset was similar to the spatial density of the 2005 NATA faciliti es. That is, the density tends to be greatest in the Great Lakes region, along the East and West Coasts, and in the Southern Plains, and tends to be lowest in the Rockies (except Colorado) and Northern Plains

.

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### 3.4.2.3 Data Processing
To facilitate application of the meteorological data to the Tier 2 assessment , EPA gathered wind information in directional octants that could be linked to the direction of the relevant lake s (see Introduction and Section 3.5 ). EPA divided the periphery a round a meteorological station into eight octants representing the direction toward which the wind was blowing:

N: >337.5 to 22.5 degrees NE: >22.5 to 67.5 degrees E: >67.5 to 112.5 degrees SE: >112.5 to 157.5 degrees S: >157.5 to 202.5 degrees SW: >202.5 to 247.5 degrees W: >247.5 to 292.5 degrees NW: >292.5 to 337.5 degrees

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### Exhibit 27

. The Locations of Meteorological Stations and Point Source Facilities a

a The 2005 NATA was the most recent, comprehensive, finalized dataset of nationwide point source emitters of hazardous air pollutants, and it is used here only for illustrati ve purposes. The 2005 NATA used a meteorological dataset different from the one used in this report.

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### A software program was developed to calculate the following statistics for each of these stations:

• 

### Number of hourly observations,

• 

### Number of hours with calm winds or missing winds,

• 

### Percentage of time the wind blows into each octant (after excluding missing wind hours),

• 

### Median wind s peed blowing into each octant

,

• 

### Median mixing h eight used (if heat flux > 0, convective mixing height was used, otherwise mechanical mixing height was used), and

• 

### Average annual precipitation (irrespective of wind octant and preferring 30

- year normal data if available).
Total annual precipitation data in the 2011 meteorological data included anomalies (relative to normal conditions) in areas that experienced extreme drought conditions or large rainfall surplusses. To address this, the 30

- year (1981

- 2010) average annual precipitation was used instead. 26


### The choice of using median values for wind speed and mixing height was based on a comparison of median and mean values. For nearly all stations, the median value was smaller than the mean value; because smaller values are more health protective, we selected the median statistic for all stations.



### 3.5 Implementation of Tier 2 Assessment
The Tier 2 screening assessment is conducted using a Microsoft® AccessTM tool that was developed specifically for this purpose. Simil ar to the Tier 1 screening assessment tool, the Tier 2 tool was created so that facilities that do not screen out during the Tier 1 assessment can be screened concurrently, if desired.
The Tier 2 tool requires three inputs to run: the results of the Tier 1 screening assessment, a list of facilities to model, associated meteorology stations that can be matched to using a proximity analysis, and the matrix of adjustments factors that are used to adjust the Tier 1 screening thresholds to the Tier 2 screening levels. The matrix is used to standardize the scenario based on the chemical and on the lake distance and meteorology characteristics of the assessed facility ( refer to Section 3.3 for a discussion of these adjustment factors ).
The tool contains procedures that match each facility to all lakes within a user

- defined radius (currently 50 km) that are within user

- defined size limitations (currently 25 acres to 100,000 acres), and based on water body type (e.g., they are not waste d isposal or treatment facilities, retention ponds, etc). In addition, each facility is matched to the same meteorology station used in RTR inhalation assessments, which is usually the nearest meteorology station ; when the meteorology station match is not known from the RTR inhalation assessment, the tool conducts a proximity analysis. The tool allows the user to review matching lakes and exclude ones that are not considered suitable for modeling (e.g., based on names indicating industrial, waste, or treatmen t purposes, as discussed in Section 3.4.1 .) Any excluded lakes are recorded by the tool so that they are omitted from subsequent screening assessments.

26 30

- year average annual precipitation was obtained from the National Climatic Data Center (NCDC). http://www.ncdc.noaa.gov/oa/climate/normals/usnormals.html

.
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### Per facility and PB

- HAP, the tool evaluates the screening result at each lake and hypothetical farm. For each lake, the initial calculations produce screening results consistent with the full screening scenario ingestion rate (i.e., 373 g/day for adults, regardless of assumed lake fish productivity), and then additional calculati ons adjust the screening results for the lakes based on the initial screening results and areas of the lakes (see Section 3.3.2.3 ). Each modeling scenario consists of a farm and a lake (or, in some cases, no lake )

. Alt hough the distance from the facility to the modeled farm remains fixed for each octant, the location of the lake affects chemical concentrations in some farm food chain media due to modeled runoff and erosion patterns that are directed mostly toward the lake. Therefore, there can be multiple different farm screening results in the same octant for the same facility, each corresponding to a different lake

distance scenario. The tool identifies the largest farm screening result from all scenarios evaluated for a facili ty. T he tool presents the final Tier 2 screening results for the fisher and farmer separately, including results specific to individual farm food chain media, for each chemical, and then summed together for each PB

- HAP group.

The tool also can incorporate the results of Tier 3 assessments. Since the tool allows the user to manually exclude lakes, if any lakes are removed from the assessment in Tier 3, those lakes can be removed in the tool and the Tier 2 screening can be repeated with the revised lake datab ase. If Tier 3 plume

- rise or time

- series assessments are conducted, the resulting Tier 3 adjustment factors can be entered into the tool and it will update the Tier 2 screening results to produce Tier 3 screening results.
The tool outputs several results tables, including an intermediate table that provides information on each lake and farm, including the fish ingestion and screening

- level risk associated with each lake . The final screening results for each facility and PB

- HAP group are also output. A summ ary table identifies the facilities exceeding the Tier 2 emission screening threshold of each PB

- HAP group (separately for the fisher and farmer) and the largest screening results. All intermediate and final results tables present the Tier 1 and Tier 2 scr eening results side

- by

- side for comparison. Exhibit 28 through Exhibit 30 provide screen shots of tool output tables.

Exhibit 28

. Example of the Summary Output Table from Tier 2 Tool a

a The screen shot examples shown in this section are for actual facilities in actual source categories ; however their source categories, NEI IDs, and coordinates have been altered or masked so the data are not linked to specific facilities. “SV” means “screening value”, which is the ratio of emissions to emission screening threshold.

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### Exhibit 29

. Example of the Facility Output Table from Tier 2 Tool a, b

a The screen shot examples shown in this section are for actual facilities in actual source categories ; however their source categories, NEI IDs, and coordinates have been altered or masked so the data are not linked to specific facilities. “SV” means “screening value”, which is the ratio of emissions to emission screening threshold.

b Only a portion of the table is shown

. Individual farm ingestion media are in the table but only “soil” is displayed for space reasons

.



### Exhibit 30

. Example of the Lake Assessment Output Table from Tier 2 Tool a ,b

a The screen shot examples shown in this section are for actual facilities ; however their source categories, NEI IDs, and coordinates have been altered or masked so the data are not linked to specific facilities. Only a portion of the table might be shown. The headers “Stationary Fisher” and “Traveling Fisher” are shorthand references to the initial fisher scenario (i.e., catches and consumes 373 g/d per lake, regardless of lake area) and the refined fisher scenario (i.e., assumes a 1 g/acre/d lake productivity), respectively. “SV” means “screening value”, which is the ratio of emissions to emission screening threshold.

b The table displayed reflect s a Tier 3 Screening Assesment having been conducted. In a Tier 2 Screening Assesssment, the Add’l Lake Assessment, Plume Rise Adj Factors, and Time Series Adj Factors would not be shown

.

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## 4. Tier 3 Methodology

4.1 Overview of Approach
The Tier 3 screening assessment is an additional assessment that can be conducted , at the discretion of the risk assessor, on facili ties that do not screen out with the Tier 2 assessment. The Tier 3 screening approach consists of three individual assessments that further refine the screening scenario (beyond the refinements in Tier 2) based on additional site

- specific data and evaluati ons. The assessments introduce additional site

- specificity to the screening scenario, requiring a potentially higher level of effort than the Tier 2 assessment, but still a much lower level of effort than the full site

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

- rise and time

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

- rise and time

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

- series assessment, if conducted, supplants the plume

- rise a ssessment already conducted).

The remainder of this section discusses each of these assessments.



### 4.2 Lake Assessment
Other than verifying a facility’s emission rates, one of the simplest assessments that can be conducted (beyond the Tier 2 methods) is to asse ss the existence, the potential purpose, the accessibility and fishability, and the suitability of lakes for the models and methods used in Tier 2. The full USGS dataset of lakes and reservoirs nationwide that is used in Tier 2 does not contain information on lake accessibility or fishability. In addition, it occasionally identifies a lake that no longer exists (e.g., has evaporated or been drained) or it uses a classification that might not accurately reflect the lake’s purpose or type. A erial and street -v iew imagery and internet searches can be used to quickly ascertain if an assessed lake actually exists, if it is likely not fished (e.g., appears swampy or covered in algae), if it is likely used for industrial or waste disposal/treatment purposes, and/or if it is adjacent to or connected to a river or saltwater body (estuaries, rivers, and saltwater areas are not ideal for the assessment models and methods).
For example, the blue outline in Exhibit 31 identifies a lake from the USGS dataset that originally qualified for Tier 2 based on the information provided in that dataset, but aerial imagery shows it is likely evaporated or drained. Similarly, the outline in Exhibit 32 identifies a lake that originally qualified, but aerial imagery shows that it is directly adjacent to an industrial facility and likely used only for on

- site industrial purposes.
For facilities undergoing Tier 3 screening, all lakes are assessed from which the fish er catches and consumes fish according to the refined Tier 2 fisher methods discussed in Section 3

. A ll lakes meeting the criteria discussed in the previous paragraphs are permantly removed from the screening assessment so that they are no longer used in any screening assessment. If a lake is removed from the facility’s assessment, it might be necessary for the hypothetical fisher to catch and consume fish from additional lake(s) in order to fish a total of 373 acres. In this case, additional lakes th at were not fished previously are evaluated , using the methods discussed in Section 3 , in descending order of initial Tier 2 fisher screening result and lake area.
If any lakes are removed from the screening, the screening asse ssment is conducted again (using the Tier 2 methods) with the revised lake dataset. Although this additional lake assessment is conducted based on the Tier 2 screening results of a particular set of facilities, lakes removed during that assessment could af fect the screening results of other facilities in the

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### source category beyond that original set. This can happen if an assessed facility is within 50 km of another assessed facility, whereby removing a lake could affect the screening results of both facilit ies. It should be noted, however, that screening results across different facilities are not summed

. Exhibit 31 . Example of Lake Removed from Screening

— Likely Evaporated or Drained

Note: Aerial imagery from ESRI World Imagery ( 2014).



### Exhibit 32 . Example of Lake Removed from Screening

— Likely an Industrial Lake

Note: Aerial imagery from ESRI World Imagery (2014). This map was created during the assessment of a specific source category, thus the reference to “the assessment in this report” in the text box.

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### 4.3 Plume

- rise Assessment
In Tiers 1 and 2, all chemicals are emitted inside the mixing layer and are available for ground

level exposure. In reality, the physical height of an emission source, in combination with the temperature and velocity of the chemical plume as it leaves the source, can cause some of the chemical plume to reach areas above the mixing layer. In TRIM.FaTE modeling, the chemical mass deposited above the mixing layer (i.e., the model’s upper

- air layer) is unavailable for ground

- level exposure (i.e., the upper

- air layer functions as a chemical sink). Many emission sources in RTR are physically taller than the mixing height during some hours, and hot exit gas temperatures (i.e., buoyancy) and/or high exit gas velocities (i.e., momentum) can further elevate the chemical plume.
The Tier 3 methodology utilizes methods summarized by Seinfeld and Pandis (1998) to estimate how often a facility’s emissions reach the upper

- air sink, which decrease s availability for ground

- level exposure. The methods to estimate the amount of chemical lost to the upper

- air sink require use of the facility’s corresponding meteorology data (e.g., air temperature and vertical temperature gradient, wind speed, and atmospheric stability), the mass of the PB

- HAP emitted from each source, the physical characteristics of the sources (i.e., release height, inside diameter at the release point, and exit gas temperature and velocity), and an estimate of the size of the facility

. For each relevant emission source, estimates of the hourly effective release height (i.e., sum of actual release height and plume rise) are compared to the hourly mixing height to determine the mass of chemical remaining in the mixing layer. T he mass of chemical remaining in the mixing layer, summed across the sources, is compared to the total emitted mass of the chemical. This results in an adjustment factor expressing the fraction of emissions remaining in the mixing layer after accounting for release height and plume rise. T his final plume

- rise adjustment factor is then applied to the Tier 2 screening results.



### 4.4 Assessment of Time

- series Meteorology and Effective Release Heights

As discussed in Section 3 , the Tier 2 screenin g results are based on the average meteorological conditions prevailing at the facility being assessed. The use of time

- series meteorology data, which captures hour

- by

- hour changes in each of the assessed meteorological parameters instead of using constant average values, increases the accuracy of the estimates of potential risk by accounting for potential statistical interactions between the meteorology parameters and by improving the method of accounting for mass advected into the modeling domain.

Tier 2 screening result s also are based on a constant emission release height that is always within the mixing layer. Including hourly effective release heights along with hourly meteorology data further increases the accuracy of the screening results

. For a fac ility undergoing a time

- series assessment, a time series of hourly effective release heights is developed and the Tier 2 spatial scenario that best matches each lake from which the hypothetical fisher fishes is identified . Using these data along with the f acility’s matching hourly meteorology data, the facility’s emission rates, and the fish ingestion rates, chemical fate and transport is modeled in TRIM.FaTE and corresponding screening

- level risks are calculated using MIRC. This results in a screening

- leve l risk value for each lake. If the farmer scenario is specifically being assessed, the modeling will also result in a screening

- level risk value for each farm. T he screening

- level risk associated with each lake is multiplied by the percent of daily ingeste d fish caught from the lake; summing those products across all modeled lakes results in the screening

- level fisher risk (i.e., fisher screening result ) associated with the evaluated PB

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### HAP emitted by the facility. The time

- series

- adjusted Tier 3 screening result s are compared to the Tier 2 screening results calculated previously.

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## 5. References

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### 02

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. EPA 2007. Prioritized Chronic Dose

- Response Values for Screening Risk Assessments (Table 1). Office of Air Quality Plann ing and Standards; June 12, 2007. Available at: http://www.epa.gov/ttn/atw/toxsource/summary.html

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### EPA 2010a Development of a Relative Potency Factor (RPF) Approach for Polycyclic Aromatic Hydrocarbon (PAH) Mixtures (External Review Draft). Washington, DC, EPA/635/R

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## TRIM .FaTE Inputs

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### Exhibits, Attachment A

Exhibit A

- 1. TRIM.FaTE Simulation Parameters for the TRIM.FaTE Screening Scenario

........................................................................................................... A-1
Exhibit A

- 2. Meteorological Inputs for the TRIM.FaTE Screening Scenario

............................ A-2
Exhibit A

- 3. Air Parameters for the TRIM.FaTE Screening Scenario

...................................... A-3
Exhibit A

- 4. Soil and Groundwater Parameters for the TRIM.FaTE Screening Scenario

........................................................................................................... A-4
Exhibit A

- 5. Runoff Assumptions for the TRIM.FaTE Screening Scenario

.............................. A-6
Exhibit A

- 6. USLE Erosion Parameters for the TRIM.FaTE Screening Scenario

.................... A-7
Exhibit A

- 7. Terrestrial Plant Placement for the TRIM.FaTE Screening Scenario

.................... A-8
Exhibit A

- 8. Terrestrial Plant Parameters for the TRIM.FaTE Screening Scenario

................. A-9
Exhibit A

- 9. Surface Water Parameters for the TRIM.FaTE Screening Scenario

.................. A- 11
Exhibit A

- 10. Sediment Parameters for the TRIM.FaTE S creening Scenario

....................... A- 12
Exhibit A

- 11. Aquatic Animals Food Chain, Density, and Mass for the TRIM.FaTE Screening Scenario

................................ ..................................... A- 13
Exhibit A- 12. Cadmium Chemical

- Specific Parameters for the TRIM.FaTE Screening Scenario

......................................................................................... A- 14
Exhibit A

- 13. Mercury Chemical

- Specific Parameters for the TRIM.FaTE Screening Scenario

......................................................................................... A- 15
Exhibit A

- 14. PAH Chemical

- Specific Parameters for the TRIM.FaTE Screening Scenario

................................ ......................................................................... A- 16
Exhibit A

- 15. Dioxin Chemical

- Specific Parameters for the TRIM.FaTE Screening Scenario

......................................................................................... A- 18
Exhibit A

-- 16. Cadmium Chemical

- Specific Parameters for Abiotic Compartments in the TRIM.FaTE Screening Scenario

................................... A- 20
Exhibit A

- 17. Mercury Chemical

- Specific Parameters for Abiotic Compartments in the TRIM.FaTE Screening Scenario

............................................................ A- 21
Exhibit A

- 18. PAH Chemical

- Specific Parameters for Abiotic Compartments in the TRIM.FaTE Screening Scenario

............................................................... A- 25
Exhibit A

- 19. Dioxin Chemical

- Specific Parameters for Abiotic Compartments in the TRIM.FaTE Screening Scenario

............................................................... A- 28
Exhibit A

- 20. Cadmium Chemical

- Specific Parameters for Plant Compartments in the TRIM.FaTE Screening Scenario

............................................................ A- 32
Exhibit A

- 21. Mercury Chemical

- Specifi c Parameters for Plant Compartments in the TRIM.FaTE Screening Scenario

............................................................... A- 33
Exhibit A

- 22. PAH Chemical

- Specific Parameters for Plant Compartments in the TRIM.FaTE Screening Scenario

..................................................................... A- 34
Exhibit A

- 23. PAH Chemical

- Specific Parameters for Plant Compartments in the TRIM.FaTE Screening Scenario

..................................................................... A- 35

Attachment A iii October 2014 TRIM.FaTE InputsTRIM

- Based Tiered Screening Methodology for RTR



### Exhibit A

- 24. Cadmium Chemi cal

- Specific Parameters for Aquatic Species in the TRIM.FaTE Screening Scenario

................................ ..................................... A- 37
Exhibit A

- 25. Mercury Chemical

- Specific Parameters for Aquatic Species in the TRIM.FaTE Screening Scenario

................................ ..................................... A- 39
Exhibit A

- 26. PAH Chemical

- Specific Parameters for Aquatic Species in the TRIM.FaTE Screening Scenario

................................ ..................................... A- 40
Exhibit A

- 27. Dioxin C hemical

- Specific Parameters for Aquatic Species in the TRIM.FaTE Screening Scenario

................................ ..................................... A- 43

Attachment A iv October 2014 TRIM.FaTE InputsTRIM

- Based Tiered Screening Methodology for RTR



### This attachment provides tables of the modeling inputs for the TRIM.FaTE screening scenario. Exhibit A-1 pre sents runtime settings for TRIM.FaTE. Exhibit A-2 and Exhibit A-3 present meteorological and air parameters, respectively, entered into the model. Exhibit A-4

. , Exhibit A-5 , and Exhibit A-6 present the parameters for soil and groundwater, runoff assumptions, and the USLE (universal soil loss equation) erosion parameters, respectively, for the screening scenario. Exhibit A-7 and Exhibit A-8 present terrestrial parameters. Exhibit A-9 through Exhibit A- 11 present lake parameters, and Exhibit A- 12 through Exhibit A-27 present par ameters specific to the chemical s modeled in the scenario.

Exhibit A-1 . TRIM.FaTE Simulation Parameters for the TRIM.FaTE Screening Scenario

Parameter Name Units Value Used Reference Start of simulation date/time 1/1/1990, midnight Consistent with met data. End of simulation date/time 1/1/2040, midnight Consistent with met data set; selected to provide a 50

- year modeling period. Simulation time step hr 1 Selected value. Output time step a hr 4 Selected value.

a Output time step is set in TRIM.FaTE using the scenario properties "simulationStepsPerOutputStep" and "simulationTimeStep."



### .

Attachment A A-1 October 2014 TRIM.FaTE InputsTRIM

- Based Tiered Screening Methodology for RTR



### Exhibit A-2

. Meteorological Inputs for the TRIM.FaTE Screening Scenario

Parameter Name Units Value Used Reference **Meteorological Inputs** Air temperature degrees K 298 USEPA 2005. Horizontal wind speed m/sec 2.8 5th percentile annual average value for contig uous U nited S tates , calculated from 30 yrs of annual normal temperature values. Vertical wind speed m/sec 0.0 Assumption; vertical wind speed not used by any of the algorithms in the version of the TRIM.FaTE library used for screening. Wind direction deg rees 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 r ate m 3 [rain]/m 2 [surface area]

- day varies daily 1.5 m/yr is the maximum statewide 30

- year (1971

- 2000) average for the contiguous United States, excluding Rhode 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 Ho lzworth, 1972. Mixing height (used to set air VE property named “top”) m 710 5th percentile annual average mixing heights (calculated from daily morning and afternoon values), for all stations on SCRM (40 state, 70 stations). isDay_SteadyState_forAir uni tless

-- Value not used in current dynamic runs (would need to be reevaluated if steady

state runs are needed). isDay_SteadyState_forOther unitless

--

Attachment A A-2 October 2014 TRIM.FaTE InputsTRIM

- Based Tiered Screening Methodology for RTR



### Exhibit A-3

. Air Parameters for the TRIM.FaTE Screening Scenario

Parameter Name Units Value Used Reference Atmospheric dust load kg[dust]/m 3 [air] 6.15E

- 08 Bidleman 1988 Density of air g/cm 3 0.0012 US EPA 1997 b Dust density kg[dust]/m 3 [dust] 1,400 Bidleman 1988 Fraction organic matter on particulates unitless 0.2 Harner and Bidleman 1998

Attachment A A-3 October 2014 TRIM.FaTE InputsTRIM

- Based Tiered Screening Methodology for RTR



### Exhibit A-4

. Soil and Groundwater Parameters for the TRIM.FaTE Screening Scenario

Parameter Name Uni ts Value Used Reference **Surface Soil Compar t**  **ment Type** Air content volume[air]/volume[compartment] 0.28 McKone et al. 2001. Average vertical velocity of water (percolation) m/day 8.22E

- 04 Assumed to be 0.2 times average precipitation for site. Boundary 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). Thickn ess

- tilled a m 0.20 USEPA 2005. Erosion fraction unitless varies b See E xhibit 5

. Fraction of area available for erosion m 2 [area available]/m 2 [total] 1 Assumption ; area assumed rural.

Fraction of area available for runoff m 2 [area available]/m 2 [total] 1 Assumption ; area assumed rural. Fraction of area available for vertical diffusion m 2 [area available]/m 2 [total] 1 Assumption ; area assumed rural.

Fraction s and unitless 0.25 Assumption

. Organic carbon fraction unitless 0.008 U.S. average in McKone et al. 2001 (Table 16 and A

- 3). pH unitless 6.8 Assumption

. Runoff fraction unitless varies b See Exhibit A

- 5

. Total erosion rate kg [soil]/m 2 /day varies b See Exhibit A

- 6

. Total runoff rate m 3 [water]/m 2 / day 1.64E

- 03 Calculated using scenario

- specific precipitation rate and assumptions associated with water balance. Water content volume[water]/volume[compartment] 0.15 McKone et al. 2001 **Root Zone Soil Compartment Type** Air content volume[air]/volume[comp artment] 0.25 McKone et al 2001 (Table 16). Average vertical velocity of water (percolation) m/day 8.22E

- 04 Assumed as 0.2 times average precipitation for New England in McKone et al. 2001. Density of soil solids (dry weight) kg[soil]/m 3 [soil] 2,600 McKo ne et al. 2001 (Table 3). Fraction s and unitless 0.25 Assumption

.

Attachment A A-4 October 2014 TRIM.FaTE InputsTRIM

- Based Tiered Screening Methodology for RTR



### Exhibit A-4

. Soil and Groundwater Parameters for the TRIM.FaTE Screening Scenario

Parameter Name Units Value Used Reference **Root Zone Soil Compartment Type**  **, continued** Thickness

- untilled a m 0.79 McKone et al. 2001 (Table 16

- U.S. a verage). Thickness

- tilled a m 0.6 Adjusted from McKone et al. 2001 (Table 16). Organic carbon fraction unitless 0.008 McKone et al. 2001 (Table s 16 and A

- 3, U .S.
a verage). pH unitless 6.8 Assumption

. Water content volume[water]/volume[compartment] 0.15 McKone et al. 2001 **Vadose Zone Soil Compartment Type** Air content volume[air]/volume[compartment] 0.22 McKone et al. 2001 (Table 17). Average vertical veloci ty of water (percolation) m/day 8.22E

- 04 Assumed as 0.2 times average precipitation for New England in McKone et al. 2001. Density of soil solids (dry weight) kg[soil]/m 3 [soil] 2,600 Default in McKone et al. 2001 (Table 3). Fraction s and unitless 0.35 As sumption

. Thickness a m 1.4 McKone et al. 2001 (Table 17). Organic carbon fraction unitless 0.003 McKone et al. 2001 (Table s 16 and A

- 3, U.S.
a verage). pH unitless 6.8 Assumption

. Water content volume[water]/volume[compartment] 0.21 McKone et al. 2001 ( Table 17

- national average). **Ground w**  **ater Compartment Type** Thickness a m 3 McKone et al. 2001 (Table 3). Fraction s and unitless 0.4 Assumption

. Organic carbon fraction unitless 0.004 Assumption

. pH unitless 6.8 Assumption

. Porosity volume[total pore
s pace]/volume[compartment] 0.2 Default in McKone et al. 2001 (Table 3).

Density of s olid material in aquifer kg[soil]/m 3 [soil] 2,600 Default in McKone et al. 2001 (Table 3).

a Set using the volume element properties file

. b See separate tables ( Exhibit A

- 5 and Exhibit A

- 6 ) for erosion/runoff fractions and total erosion rates.

Attachment A A-5 October 2014 TRIM.FaTE InputsTRIM

- Based Tiered Screening Methodology for RTR



### Exhibit A-5

. Runoff Assumptions for the TRIM.Fa TE Screening Scenario Originating Compartment Destination Compartment Runoff/Erosion F raction SurfSoil_Source SurfSoil_N1 0.0 SurfSoil_S1 0.0 sink 1.0 SurfSoil_N1 SW_Pond 1.0 SurfSoil_Source 0.0

SurfSoil_N6 0.0 SurfSoil_S1 0.0 sink 0.0 SurfS oil_S1 SW_Pond 1.0 SurfSoil_Source 0.0

SurfSoil_N1 0.0 sink 0.0 SurfSoil_N6 SW_Pond 1.0 SurfSoil_N1 0.0 SurfSoil_N7 0.0 sink 0.0 SurfSoil_N7 SW_Pond 1.0 SurfSoil_N6 0.0 SurfSoil_N3 0.0 sink 0.0 SurfSoil_N3 SW_Pond 1.0 SurfSoil_N7 0.0 SurfSoil_N4 0.0 sink 0.0 SurfSoil_N4 SW_Pond 1.0 SurfSoil_N3 0.0 SurfSoil_N5 0.0 SurfSoil_S4 0.0 sink 0.0 SurfSoil_S4 SW_Pond 1.0 SurfSoil_N4 0.0 SurfSoil_S5 0.0 sink 0.0 SurfSoil_N5 a SW_Pond 0.0 SurfSoil_N4 0.5 SurfSoil_S5 0.5 sink 0.0 SurfSoil_S5 a SW_Pond 0.0 SurfSoil_N5 0.0 SurfSoil_S4 1.0 sink 0.0

a Assumes that N5 is higher ground tha n S5, and half of the runoff flows into N4, and the other half in to S5. Assumes all runoff from S5 flows into S4.

Attachment A A-6 October 2014 TRIM.FaTE InputsTRIM

- Based Tiered Screening Methodology for RTR



### Exhibit A-6

. USLE Erosion Parameters for the TRIM.FaTE Screening Scenario

Soil Parcel Area Rainfall/Erosivity Index Soil Erodibility Index Length

Slope Factor Land Use Cover Mgmt Factor Supporting Practices

Factor Unit Soil Loss Sediment Delivery

Ratio a Calculated (Adjusted)

Erosion

Rate

Code m 2 R (100 ft

- ton/ac) K (ton/ac/(100 ft

- ton/acre)) LS (USCS) type C (USCS) P A (ton/ac/yr) A (kg/m 2 /d) SDR a kg/m 2 /d

N1 5.8E+04 300 0.39 1.5 grass 0.1 1 17.55 0.010779 0.533 0.005740 N6 4.1E+04 300 0.39 1.5 crops 0.2 1 35.1 0.021557 0.557 0.012014 N7 7.3E+04 300 0.39 1.5 grass 0.1 1 17.55 0.010779 0.518 0.005580 N3 3.5E+05 300 0.39 1.5 grass 0.1 1 17.55 0.010779 0.385 0.004151 N4 2.0E+06 300 0.39 1.5 f orest 0.1 1 17.55 0.010779 0.309 0.003331 N5 6.7E+06 300 0.39 1.5 forest 0.1 1 17.55 0.010779 0.196 0.002116 S1 5.8E+04 300 0.39 1.5 grass 0.1 1 17.55 0.010779 0.533 0.005740 S4 2.0E+06 300 0.39 1.5 forest 0.1 1 17.55 0.010779 0.309 0.003331 S5 6.7E+06 300 0.39 1.5 forest 0.1 1 17.55 0.010779 0.196 0.002116

a Calculated using SD R = a * (AL)

- b ; where a is the empirical intercept coefficient (based on the size of the watershed), AL is the total watershed area receiving deposit ion (m 2 ), and b is the empiri cal slope coefficient (always 0.125).

Attachment A A-7 October 20 14 TRIM.FaTE InputsTRIM

- Based Tiered Screening Methodolo gy for RTR



### Exhibit A-7

. Terrestrial Plant Placement for the TRIM.FaTE Screening Scenario

Surface Soil Volume Element Surface Soil Depth (m) Coniferous Forest Grasses/ Herbs None

Source 0.01 x

N1 0.01 x N6 0.20 (tilled) x N7 0.01 x N3 0.01 x N4 0.01 x N5 0.01 x S1 0.01 x S4 0.01 x S5 0.01 x

Attachment A A-8 October 2014 TRIM.FaTE InputsTRIM

- Based Tiered Screening Methodolo gy for RTR



### Exhibit A-8

. Terrestrial Plant Parameters for the TRIM.FaTE Screening Scenario

Parameter Name Units Coniferous a Grass/Herb a Value Used Reference Value Used Reference **Leaf Compartment Type** Allow exchange 1=yes, 0=no 1

- S easonal b

- Average leaf area index m 2 [leaf]/

m 2 [area] 5.0 Harvard Forest, dom. red oak and red maple, CDIAC website 5.0 Mid

- range of 4

- 6 for old fields, R.J. Luxmoore, ORNL. Calculate wet deposition interception fraction ( Boolean ) 1=yes, 0=no 0 Assumption

. 0 Assumption

.

C orrection exponent, octan o l to lipid unitless 0.76 From roots, Trapp 1995. 0.76 From roots, Trapp 1995.

Degree stomatal opening unitless 1 Assumed value of 1 for daytime (stomatal diffusion is turned off at night using a different property, IsDay). 1 Assu med value of 1 for daytime (stomatal diffusion is turned off at night using a different property, IsDay).

Density of wet leaf kg/m 3 820 Paterson et al. 1991. 820 Paterson et al. 1991. Leaf wetting factor m 3.00E

- 04 1E

- 04 to 6E

- 04 for different crops and elements, Muller and Prohl 1993. 3.00E

- 04 1E

- 04 to 6E

- 04 for different crops and elements, Muller and Prohl 1993.

Length of leaf m 0.01 Assumption

. 0.05 Assumption

. Lipid content kg/kg wet weight 0.00224 European beech, Riederer 1995. 0.00224 European b eech, Riederer 1995. Litter fall rate 1/day 0.0021 value assumes 1st

- order relationship and that 99% of leaves fall over 6 years S easonal c

-

Stomatal area normalized effective diffusion path length 1/m 200 Wilmer and Fricker 1996. 200 Wilmer and Fricker 1996.

Vegetation attenuation factor m 2 /kg 2.9 Grass/hay, Baes et al. 1984. 2.9 Grass/hay, Baes et al. 1984. Water content unitless 0.8 Paterson et al. 1991. 0.8 Paterson et al. 1991. Wet deposition interception fraction unitless 0.2 Calculated based on 5 years of local met data, 1987

- 1991. 0.2 Calculated based on 5 years of local met data, 1987

- 1991. Wet mass of leaf per soil area kg[fresh leaf]/m 2 [area] 2.0 Calculated from leaf area index, leaf thickness (Simonich and Hites, 1994), density of wet folia ge. 0.6 Calculated from leaf area index and Leith 1975.

**Particle on Leaf Compartment Type** Allow exchange 1=yes, 0=no 1

- S easonal b

-

Attachment A A-9 October 2014 TRIM.F aTE InputsTRIM

- Based Tiered Screening Methodolo gy for RTR



### Exhibit A-8

. Terrestrial Plant Parameters for the TRIM.FaTE Screening Sc enario

Parameter Name Units Coniferous a Grass/Herb a Value Used Reference Value Used Reference **Particle on Leaf Compartment Type , continued**  Volume particle per area leaf m 3 [leaf particles]/m 2 [leaf] 1.00E

- 09 Based on particle density and size distribut ion for atmospheric particles measured on an adhesive surface, Coe and Lindberg 1987. 1.00E

- 09 Based on particle density and size distribution for atmospheric particles measured on an adhesive surface, Coe and Lindberg 1987. **Root Compartment Type**

**– Nonwoo**  **dy Only** Allow exchange 1=yes, 0=no S easonal b

- Correction exponent, octanol to lipid unitless 0.76 Trapp 1995.

Lipid content of root kg/kg wet weight 0.011 Calculated. Water content of root kg/kg wet weight 0.8 Assumption

. Wet density of root kg/m 3 820 Soybean, Paterson et al. 1991. Wet mass per soil area kg/m 2 1.4 Temperate grassland, Jackson et al. 1996. **Stem Compartment Type**

**– Nonwoody Only**  Allow exchange 1=yes, 0=no S easonal b

- Correction exponent, octanol to lipid u nitless 0.76 Trapp 1995

. Density of phloem fluid kg/m 3 1,000 Assumption

. Density of xylem fluid kg/cm 3 900 Assumption

. Flow rate of transpired water per leaf area m 3 [water]/m 2 [leaf ] 0.0048 Crank et al. 1981.

Fraction of transpiration fl ow rate that is phloem rate unitless 0.05 Paterson et al. 1991.

Lipid content of stem kg/kg wet weight 0.00224 Leaves of European beech, Riederer 1995. Water content of stem unitless 0.8 Paterson et al. 1991 Wet density of stem kg/m 3 830 Assumption

. Wet mass per soil area kg/m 2 0.24 Calculated from leaf and root biomass density.

a See Exhibit A

- 7 for assignment of plant types to surface soil compartments.

b Begins March 9 (set to 1), ends November 7 (set to 0). Nationwide 80th percentile.

c Begins November 7, ends December 6; rate = 0.15/day during this time (value assumes 99 percent of leaves fall in 30 days).

Attachment A A- 10 October 2014 TRIM.F aTE InputsTRIM

- Based Tiered Screening Methodolo gy for RTR



### Exhibit A-9

. S urface Water Parameters for the TRIM.FaTE Screening Scenario

Parameter Name Units Value Used Reference Algae carbon content (fraction) unitless 0.465 APHA 1995.

Algae density in water column g[algae]/L[water] 0.0025 Millard et al. 1996 as cited in ICF 20 05. Algae growth rate 1/day 0.7 Hudson et al. 1994 as cited in Mason et al. 1995b

. Algae radius um 2.5 Mason et al. 1995b. Algae water content (fraction) unitless 0.9 APHA 1995. Average algae cell density (per vol ume cell, not water) g[algae]/m 3 [algae] 1,000,000 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 200 7

- 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 po nd dimensions and flow calculations. Fraction Sand unitless 0.25 Assumption

. Organic carbon fraction in suspended sediments unitless 0.02 Assumption

.
pH unitless 7.3 Assumption

. Suspended sediment deposition velocity m/day 2 USEPA 1997 b

.
Total suspen ded sediment concentration kg[sediment]/m 3 [water column] 0.05 USEPA 2005.

Water temperature degrees K 298 USEPA 2005.

a Set using the volume element properties named "top" and "bottom."

Attachment A A- 11 October 2014 TRIM.FaTE InputsTRIM

- Based Tiered Screening Methodolo gy for RTR



### Exhibit A- 10

. Sediment Parameters for the TRIM.FaTE Screening Scenario

Parameter Name Units Value Used Reference Depth a m 0.05 McKone et al. 2001 (Table 3). Fraction s and unitless 0.25 Assumption

. Organic carbon fraction unitless 0.02 McKone et al. 2001 (Table 3). Porosity of the sediment
zone volume[total pore space]/volume[sediment compartment] 0.6 USEPA 2005

.

Solid material density in sediment kg[sediment]/m 3 [sediment] 2,600 McKone et al. 2001 (Table 3). pH unitless 7.3 Assumption

. Sediment resuspension velocity m/day 6.69E

- 05 Calculated from water balance model.

a Set using the volume element properties named "top" and "bottom."

Attachment A A- 12 October 2014 TRIM.FaTE InputsTRIM

- Based Tiered Screening Methodolo gy for RTR



### Exhibit A- 11

. Aq uatic Animals Food Chain, Density, and Mass for the TRIM.FaTE Screening Scenario

Aquatic Biota (Consuming

Organism) Fraction Diet

Biomass (kg/m 2 ) Body Weight

(kg) ReferenceAlgaeZooplanktonBenthicInvertebrateWaterColumnHerbivoreBenthicOmnivoreWaterColumnOmnivoreBenthicCarnivoreWaterColumnCarnivore

Benthic Invertebrate 0% 0% 0% 0% 0% 0% 0% 0% 0.020 2.55E

- 04 Assumption

.

Water Column Herbivore 0% 100% 0% 0% 0% 0% 0% 0% 0.002 0.025 Assumption

.

Benthic Omnivore 0% 0% 100% 0% 0% 0% 0% 0% 0 .002 2.50E

- 01 Assumption

. Water Column Omnivore 0% 0% 0% 100% 0% 0% 0% 0% 0.001 0.25 Assumption

.

Benthic Carnivore 0% 0% 50% 0% 50% 0% 0% 0% 0.001 2.0 Assumption

. Water Column Carnivore 0% 0% 0% 0% 0% 100% 0% 0% 0.0002 2.0 Assumption

.
Zooplankton 100% 0% 0% 0% 0% 0% 0% 0% 0.0064 5.70E

- 08 Assumption

.

Attachment A A- 13 October 2014 TRIM.FaTE Inpu tsTRIM

- Based Tiered Screening Methodolo gy for RTR



### Exhibit A- 12

. Cadmium Chemical

- Specific Parameters for the TRIM.FaTE Screening Scenario

Parameter Name a Units Value Reference

CAS number b unitless 7440

- 43

- 9

- Diffusion coefficient in pure air m 2 [air]/day 0.71 USEPA 1999 (Table A

- 2

- 35). Diffusion coefficient in pure water m 2 [water]/day 8.16E

- 05 USEPA 1999 (Table A

- 2

- 35). Henry's Law c onstant Pa

- m 3 /mol 1.00E

- 37 USEPA 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 (K oa ) m 3 [air]/m 3 [octanol]

- -

Octanol

- carbon partition coefficient (K oc ) - -

Octanol

- water partition coefficient (K ow ) L[water]/kg[octanol]

- -

a All parameters in this table are TRIM.FaTE chemical properties.

b Th is CAS numbers appl ies to elemental Cd; however, the cations of cadmium are being modeled.

Attachment A A- 14 October 2014 TRIM.FaTE InputsTRIM

- Based Tiered Screening Methodolo gy for RTR



### Exhibit A- 13

. Mercury Chemical

- Specific Parameters for the TRIM.FaTE Screening Scenario

Parameter Name Units Value Reference Hg(0) b Hg(2) b MHg b
CAS number unitless 7439

- 97

- 6 14302

- 87

- 5 22967

- 92

- 6

- Diffusion coefficient in pure air m 2 [air]/day 0.478 0.478 0.456 USEPA 1997 b

.

Diffusion coefficient in pure water m 2 [water]/day 5.54E

- 05 5.54E

- 05 5.28E

- 05 USEPA 1997 b

.
Henry's Law constant Pa

- m 3 /mol 719 7.19E

- 05 0.0477 USEPA 1997 b

.
Melting p oint degrees K 234 5.5 0E+02 443 CARB 1994. Molecular weight g/mol 201 201 216 USEPA 1997 b

. Octanol

- water partition coefficient (K ow ) L[water]/kg[octanol] 4.15 3.33 1.7 Mason et al. 1996.

Vapor washout ratio m 3 [air]/m 3 [rain] 1,200 1.6E+06 0 USEPA 1997 b

, based on Petersen et a l. 1995.

a All parameters in this table are TRIM.FaTE chemical properties.

b On this and all following tables, Hg(0) = elemental mercury, Hg(2) = divalent mercury, and MHg = methyl mercury.

Attachment A A- 15 October 2014 TRIM.FaTE InputsTRIM

- Based Tiered Screening Methodolo gy for RTR



### Exhibit A- 14

. PAH Chemical

- Specific Parameters for the TRIM.FaTE Screening Scenario

Parameter Name Units Value 2Methyl 712DMB Acenaphthene Acenaphthylene BaA BaP BbF BghiP CAS number unitless 91

- 57

- 6 57

- 97

- 6 83

- 32

- 9 208

- 96

- 8 56

- 55

- 3 50

- 32

- 8 205

- 99

- 2 191

- 24

- 2 Diffusion coefficient in pure air m 2 /day 0.451 0.691 0.009 0.388 0.441 0.372 0.009 0.190

Diffusion coefficient in pure water m 2 /day 6.70E

- 05 6.91E

- 05 8.64E

- 05 6.03E

- 05 7.78E

- 05 7.78E

- 05 8.64E

- 05 4.54E

- 05

Henry's Law constant P a

- m 3 /mol 50.56 0.20 18.50 12.70 1.22 0.07 0.05 0.03

Melting point degrees K 307.75 396.65 366.15 365.65 433 452 441 550.15 Molecular weight g/mol 142.20 256.35 154.21 152.20 228.29 252.32 252.32 276.34 Octanol

- water partition coefficient (K ow ) L[water]/ L[octanol] 7.24E+03 6.31E+05 8.32E+03 1.00E+04 6.17E+05 9.33E+05 6.03E+05 4.27E+06

Parameter Name Units Value BkF Chr DahA Fluoranthene Fluorene IcdP CAS number unitless 207

- 08

- 9 218

- 01

- 9 53

- 70

- 3 206

- 44

- 0 86

- 73

- 7 193

- 39

- 5 Diffusion coefficient in pure air m 2 /day 0.009 0.009 0.009 0.009 0.009 0.009

Diffusion coefficient in pure water m 2 /day 8.64E

- 05 8.64E

- 05 8.64E

- 05 8.64E

- 05 8.64E

- 05 8.64E

- 05

Henry's Law constant Pa

- m 3 /mol 0.04 0.53 0.0 076 1.96 9.81 0.03

Melting point degrees K 490 531 539 383.15 383.15 437 Molecular weight g/mol 252.32 228.29 278.33 202.26 166.20 276.34 Octanol

- water partition coefficient (K ow ) L[water]/L[octanol] 8.71E+05 5.37E+05 3.16E+06 1.45E+05 1.51E+04 5.25E+0 6

Attachment A A- 16 October 2014 TRIM.FaTE InputsTRIM

- Based Tiered Screening Methodolo gy for RTR



### Exhibit A-1 4. PAH Chemical

- Specific Parameters for the TRIM.FaTE Screening Scenario

Parameter Name Units Reference

CAS number unitless

- Diffusion coefficient in pure air m 2 /day USEPA 2005. Exceptions include USEPA 1997 a (7,12

- Dimethylbenz(a)anthracene), and USEPA 2007 (2

- Methylnaphthalene, Acenaphthylene, and Benzo(g,h,i)perylene) Diffusion coefficient in pure water m 2 /day USEPA 2005. Exceptions include USEPA 1997 a (7,12

- Dimethylbenz(a)anthracene), and USEPA 2007 (2

- Methylnaphthalene, Acenaphthylene, and Benzo(g,h,i)perylene) Henry's Law constant Pa

- m 3 /mol USEPA 2005. Exceptions include USEPA 2003 (2

- Methylnaphthalene) HSDB 2001a (7,12

- Dimethylbenz(a)anthracene), HSDB 2001b (Acenaphthylene), and HSDB 2001c (Benzo(g,h,i)perylene) Melting point degrees K Budavari 1996. Exceptions include US EPA 2003 (2

- Methylnaphthalene), HSDB 2001a (7,12

- Dimethylbenz(a)anthracene), HSDB 2001b (Acenaphthylene), HSDB 2001c (Benzo(g,h,i)perylene), and USEPA 2005 (Acenaphthene, Fluoranthene, and Fluorene) Molecular weight g/mol Budavari 1996. Exceptions include USEPA 2003 (2

- Methylnaphthalene), HSDB 2001a (7,12

- Dimethylbenz(a)anthracene), HSDB 2001b (Acenaphthylene), HSDB 2001c (Benzo(g,h,i)perylene), and USEPA 2005 (Acenaphthene, Fluoranthene, and Fluorene) Octanol

- water partition coefficient (K ow ) L[water]/L[ octanol] Hansch et al. 1995. Exceptions include Passivirta et al. 1999 (Acenaphthylene, Benzo(k)fluoranthene, and Indeno(1,2,3

- cd)pyrene), and Sangster 1993 (Benzo(b)fluoranthene)

Attachment A A- 17 October 2014 TRIM.FaTE InputsTRIM

- Based Tiered Screening Methodolo gy for RTR



### Exhibit A- 15

. Dioxin Chemical

- Specific Parameters for the TRIM.FaTE Screening Scenario

Parameter Name Units Value1,2,3,4,6,7,8,9-OCDD1,2,3,4,6,7,8,9-OCDF1,2,3,4,6,7,8-HpCDD1,2,3,4,6,7,8-HpCDF1,2,3,4,7,8,9-HpCDF1,2,3,4,7,8-HxCDD1,2,3,4,7,8-HxCDF

CAS number unitless 3268

- 87

- 9 39001

- 02

- 0 35822

- 46

- 9 67562

- 39

- 4 55673

- 89

- 7 39227

- 28

- 6 70648

- 26

- 9 Diffusion coefficient in pure air m 2 /day 0.751 0.168 0.782 0.176 0.176 0.816 0.183

Diffusion coefficient in pure water m 2 /day 6.91E

- 05 6.91E

- 0 5 6.91E

- 05 6.91E

- 05 6.91E

- 05 6.91E

- 05 6.91E

- 05

Henry's Law constant Pa

- m 3 /mol 0.684 0.19 1.22 1.43 1.42 1.08 1.45 Melting p oint degrees K 603.0 259.0 538.0 236.5 222.0 546.0 499.0 Molecular weight g/mol 460 443.76 425.2 409.31 409.31 391 374.87 Octanol

- water partition coefficient (K ow ) L[water]/L[octanol] 1.58E+08 1.00E+08 1.00E+08 2.51E+07 7.94E+06 6.31E+07 1.00E+07

Parameter Name Units Value1,2,3,6,7,8-HxCDD1,2,3,6,7,8-HxCDF1,2,3,7,8,9-HxCDD1,2,3,7,8,9-HxCDF1,2,3,7,8-PeCDD1,2,3,7,8-PeCDF2,3,4,6,7,8-HxCDF

CAS number unitless 57653

- 85

- 7 57117

- 44

- 9 19408

- 74

- 3 72918

- 21

- 9 40321

- 76

- 4 57117

- 41

- 6 60851

- 34

- 5 Diffusion coefficient in pure air m 2 /day 0.816 0.183 0.816 0.183 0.854 0.192 0.183

Diffusion coefficient in pure water m 2 /day 6.91E

- 05 6.91E

- 05 6.91E

- 05 6.91E

- 05 6.91E

- 05 6.91E

- 05 6.91E

- 05

Henry's Law constant Pa

- m 3 /mol 1.11 0.741 1.11 1.11 0.263 0.507 1.11 Melting point degrees K 558.0 506.0 517.0 509.0 513.0 499.0 512.5 Molecular weight g/mol 390.84 374.87 390.84 374.87 356.4 340.42 374.87 Octanol

- water partition coefficient (K ow ) L[water]/L[octanol] 1.62E+08 8.24E+07 1.62E+08 3.80E+07 1.86E+07 6.17E+06 8.31E+07

Attachment A A- 18 October 2014 TRIM.FaTE InputsTRIM

- Based Tiered Screening Methodolo gy for RTR



### Exhibit A-1 5. Dioxin Chemical

- Specific Parameters for the TRIM.FaTE Screening Scenario

Parameter Name Units Value

Reference2,3,4,7,8-PeCDF2,3,7,8-TCDD2,3,7,8-TCDF

CAS number unitless 57117

- 31

- 4 1746

- 01

- 6 51207

- 31

- 9

- Diffusion coefficient in pure air m 2 /day 0.192 0.899 0.203 US EPA 2005

Diffusion coefficient in pure water m 2 /day 6.91E

- 05 4.84E

- 05 5.19E

- 05 US E PA 2005

Henry's Law constant Pa

- m 3 /mol 0.505 3.33 1.46 US EPA 2005 Melting point degrees K 469.3 578.0 500.0 Mackay et al. 2000. Exceptions include USEPA 2000a (1,2,3,6,7,8

- HxCDD, 1,2,3,7,8,9

- HxCDF, and 1,2,3,7,8

- PeCDD), ATSDR 1998 (1,2,3,6,7,8

- HxCDF, 1, 2,3,7,8

- PeCDF, and 2,3,4,6,7,8

- HxCDF), and NLM 2002 (1,2,3,7,8,9

- HxCDD) Molecular weight g/mol 340.42 322 306 Mackay et al. 2000. Exceptions include ATSDR 1998 (1,2,3,6,7,8

- HxCDF, 1,2,3,7,8,9

- HxCDF, 1,2,3,7,8

- PeCDD, 1,2,3,7,8

- PeCDF, and 2,3,4,6,7,8

- HxCDF) and NLM 2002 (1,2,3,6,7,8

- HxCDD and 1,2,3,7,8,9

- HxCDD) Octanol

- water partition coefficient (K ow ) L[water]/L[octanol] 3.16E+06 6.31E+06 1.26E+06 Mackay et al. 1992 as cited in USEPA 2000b. Exceptions include Mackay et al. 2000 (1,2,3,4,7,8,9

- HpCDF), USEPA 2000a (1,2,3,6,7,8

- HxCDD, 1,2,3,6,7,8

- HxCDF, 1,2,3,7,8,9

- HxCDD, 1,2,3,7,8,9

- HxCDF, and 2,3,4,6,7,8

- HxCDF), and Sijm et al. 1989 as cited in USEPA 2000b (1,2,3,7,8

- PeCDD)

Attachment A A- 19 October 2014 TRIM.FaTE InputsTRIM

- Based Tiered Screening Methodolo gy for RTR



### Exhibit A- 16

. Cadmium Chemical

- Specific Parameters for Abiotic Compartments in the TRIM.FaTE Screening Scenario

Parameter Name Units Value Reference

**Air Compartment Type** Particle dry deposition velocity m/day 260 Calculated from Mu h lbaier and Tis s ue 1980. Washout r atio m 3 [air]/m 3 [rain] 200,000 MacKay et al. 1986. **Surface Soil Compartment Type** Use input characteristic depth ( Boolean ) 0 = no, Else = yes 0 Assumption

. **Root Zone Soil Compartment Type** Use input characteristic depth ( Boolean ) 0 = n o, Else = yes 0 Assumption

.
**Vadose Zone Soil Compartment Type** Use input characteristic depth ( Boolean ) 0 = no, Else = yes 0 Assumption

.
**Surface Water Compartment Type** Ratio of concentration in water to concentration in algae to concentration dissolved in water L[water]/g[algae wet wt] 1.87 McGeer et al. 2003.

Attachment A A- 20 October 2014 TRIM.FaTE InputsTRIM

- Based Tiered Screening Methodolo gy for RTR



### Exhibit A-17

. Mercury Chemical

- Specific Parameters for Abiotic Compartments in the TRIM.FaTE Screening Scenario

Parameter Name Units Value Reference Hg(0) Hg(2) MHg **Air Compartment Type** Particle dry deposition velocity m/day 500 500 500 CalTOX value cited in McKone et al. 2001. Demethylation rate 1/day N/A N/A 0 Assumption

. Methylation rate 1/day 0 0 0 Assu mption

. Oxidation r ate 1/day 0.00385 0 0 Low end of half

- life range (6 months to 2 years) in USEPA 1997 b

. Reduction rate 1/day 0 0 0 Assumption

. Washout r atio m 3 [air]/m 3 [rain] 200,000 200,000 200,000 Assumption

. **Surface Soil Compartment Type** Use input characteristic depth ( Boolean ) 0 = no, Else = yes 0 0 0 Assumption

.

Soil

- water partition coefficient L[water]/kg[soil wet wt] 1,000 58,000 7,000 USEPA 1997 b

.
Vapor dry deposition velocity m/day 50 2500 0 Hg(0)

- from Lindberg et al. 1992 ; Hg(2)

- estim ate by USEPA using the Industrial Source Complex (ISC) Model - [See Vol. III, App. A of the Mercury Study Report (USEPA 1997 b )]. Demethylation rate 1/day N/A N/A 0.06 Range reported in Porvari and Verta 1995 is 3E

- 2 to 6E

- 2 /day; value is average maximum potential demethylation rate constant under anaerobic conditions. Methylation rate 1/day 0 0.001 0 Range reported in Porvari and Verta 1995 is 2E

- 4 to 1E

- 3 /day; value is average maximum potential methylation rate constant under anaerobic conditions. Oxi dation rate 1/day 0 0 0 Value assumed in USEPA 1997 b

. Reduction rate 1/day 0 1.25E

- 05 0 Value used for untilled surface soil (2cm), 10% moisture content, in USEPA 1997 b ; general range is (0.0013/day)*moisture content to (0.0001/day)*moisture content for forested region (Lindberg 1996; Carpi and Lindberg 1997).

Attachment A A- 21 October 2014 TRIM.FaTE InputsTRIM

- Based Tiered Screening Methodolo gy for RTR



### Exhibit A- 17 . Mercury Chemical

- Specific Parameters for Abiotic Compartments in the TRIM.FaTE Screening Scenario

Parameter Name Units Value Reference Hg(0) Hg(2) MHg Root Zone Soil Compartmen t Type Use input characteristic depth ( Boolean ) 0 = no, Else = yes 0 0 0 Assumption

. Soil

- water partition coefficient L[water]/kg[soil wet wt] 1,000 58,000 7,000 USEPA 1997 b.

Demethylation rate 1/day N/A N/A 0.06 Range reported in Porvari and Verta 199 5 is 3E

- 2 to 6E

- 2 /day; value is average maximum potential demethylation rate constant under anaerobic conditions. Methylation rate 1/day 0 0.001 0 Range reported in Porvari and Verta 1995 is 2E

- 4 to 1E

- 3 /day; value is average maximum potential methylati on rate constant under anaerobic conditions. Oxidation rate 1/day 0 0 0 Value assumed in USEPA 1997 b

. Reduction rate 1/day 0 3.25E

- 06 0 Value used for tilled surface soil (20cm), 10% moisture content, in USEPA 1997 b (Lindberg 1996; Carpi and Lindberg 199 7). **Vadose Zone Soil Compartment Type** Use input characteristic depth ( Boolean ) 0 = no, Else = yes 0 0 0 Assumption

. Soil

- water partition coefficient L[water]/kg[soil wet wt] 1,000 58,000 7,000 USEPA 1997 b

.
Demethylation rate 1/day N/A N/A 0.06 Range r eported in Porvari and Verta 1995 is 3E

- 2 to 6E

- 2 /day; value is average maximum potential demethylation rate constant under anaerobic conditions. Methylation rate 1/day 0 0.001 0 Range reported in Porvari and Verta 1995 is 2E

- 4 to 1E

- 3 /day; value is average maximum potential methylation rate constant under anaerobic conditions. Oxidation rate 1/day 0 0 0 Value assumed in USEPA 1997 b

. Reduction rate 1/day 0 3.25E

- 06 0 Value used for tilled surface soil (20cm), 10% moisture content, in USEPA 1997 b (Lindberg 1996; Carpi and Lindberg 1997).

Attachment A A- 22 October 2014 TRIM.FaTE InputsTRIM

- Based Tiered Screening Methodolo gy for RTR



### Exhibit A- 17 . Mercury Chemical

- Specific Parameters for Abiotic Compartments in the TRIM.FaTE Screening Scenario

Parameter Name Units Value Reference Hg(0) Hg(2) MHg **Groundwater Compartment Type** Soil

- water partit ion coefficient L[water]/kg[soil wet wt] 1,000 58,000 7,000 USEPA 1997 b

.
Demethylation rate 1/day N/A N/A 0.06 Range reported in Porvari and Verta 1995 is 3E

- 2 to 6E

- 2 /day; value is average maximum potential demethylation rate constant under anaerobic
c onditions. Methylation rate 1/day 0 0.001 0 Range reported in Porvari and Verta 1995 is 2E

- 4 to 1E

- 3 /day; value is average maximum potential methylation rate constant under anaerobic conditions. Oxidation rate 1/day 1.00E

- 08 0 0 Small default nonzero va lue (0 assumed in USEPA 1997 b ). Reduction rate 1/day 0 3.25E

- 06 0 Value used for tilled surface soil (20cm), 10% moisture content, in USEPA 1997 b (Lindberg 1996; Carpi and Lindberg 1997). **Surface Water Compartment Type** Algal surface area

- specific uptake rate constant nmol/[μm2

- day

- nmol] 0 2.04E

- 10 3.60E

- 10 Assumes radius = 2.5mm, Mason et al. 1995b, Mason et al. 1996; Hg(0) assumed same as Hg(2). D ow ("overall K ow ") L[water]/kg[octanol] 0

- a

- b Mason et al. 1996. Solids

- water partition coefficient L[wa ter]/kg[solids wet wt] 1,000 100,000 100,000 USEPA 1997 b

.
Vapor dry deposition velocity m/day N/A 2500 USEPA 1997 b (Vol. III, App. A). Demethylation rate 1/day N/A N/A 0.013 Average range of 1E

- 3 to 2.5E

- 2/day from Gilmour and Henry 1991. Methylation rate 1/day 0 0.001 0 Value used in EPA 1997; range is 1E

- 4 to 3E

4/day (Gilmour and Henry 1991). Oxidation rate 1/day 0 0 0 Assumption

. Reduction rate 1/day 0 0.0075 0 Value used in USEPA 1997 b ; reported values range from less than 5E

- 3/day for depths g reater than 17m, up to 3.5/day (Xiao et al. 1995; Vandal et al. 1995; Mason et al. 1995a; Amyot et al. 1997). Attachment A A- 23 October 2014 TRIM.FaTE InputsTRIM

- Based Tiered Screening Methodolo gy for RTR



### Exhibit A- 17 . Mercury Chemical

- Specific Parameters for Abiotic Compartments in the TRIM.FaTE Screening Scenario

Parameter Name Units Value Reference Hg(0) Hg(2) MHg **Sediment Compartment Type** Solids

- water partition coefficient L[water]/kg[solids wet wt] 3,000 50,000 3,000 USEPA 1997 b

.
Demethylation rate 1/day N/A N/A 0.0501 Average range of 2E

- 4 to 1E

- 1/day from Gilmour and Henry 1991. Me thylation rate 1/day 0 1.00E

- 04 0 Value used in EPA 1997 b ; range is 1E

- 5 to 1E

- 3/day, Gilmour and Henry 1991. Oxidation rate 1/day 0 0 0 Assumption

. Reduction rate 1/day 0 1.00E

- 06 0 Inferred value based on presence of Hg(0) in sediment porewater (USEPA 1997 b ; Vandal et al. 1995).

a TRIM.FaTE Formula Property, which varies from 0.025 to 1.625 depending on pH and chloride concentration.

b TRIM.FaTE Formula Property, which varies from 0.075 to 1.7 depending on pH and chloride concentration.

Attachment A A- 24 October 2014 TRIM.FaTE InputsTRIM

- Based Tiered Screening Methodolo gy for RTR



### Exhibit A- 18

. PAH Chemical

- Specific Parameters for Abiotic Compartments in the TRIM.FaTE Screening Scenari o

Parameter Name Units Value
2Methyl 712DMB Acenaph

thene Acenaph

thylene BaA BaP BbF BghiP BkF
**Air Compartment Type** Particle dry deposition velocity m/day 500 500 500 500 500 500 500 500 500 Half

- life day 0.154 0.092 0.3 0.208 0.125 0.046 0.596 0.215 0.458 Washout r atio 200000 200000 200000 200000 200000 200000 200000 200000 200000 **Surface Soil Compartment Type** User input characteristic depth ( Boolean ) 0 = No, Else = Yes 0 0 0 0 0 0 0 0 0

Half

- life day 18 24 56 66.5 680 530 610 415 2140 **Root Zone Soil Compartment Type** User input characteristic depth ( Boolean ) 0 = No, Els e = Yes 0 0 0 0 0 0 0 0 0

Half

- life day 18 24 56 66.5 680 530 610 415 2140 **Vadose Zone Soil Compartment Type** User input characteristic depth ( Boolean ) 0 = No, Else = Yes 0 0 0 0 0 0 0 0 0

Half

- life day 36 48 112 133 1360 1060 1220 830 4280 **Groundwater Compartment Type**  Half

- life day 36 48 112 133 1360 1060 1220 830 4280 **Surface Water Compartment Type** Ratio Of Conc In Algae To Conc Dissolved In Water (g[chem]/kg[algae]) / (g[chem]/L[water]) 2.6 333.4 3 3.7 325 510 317 1539 473

Half

- life day 78 216 25 184 0.375 0.138 90 1670 62.4 **Sediment Compartment Type** Half

- life day 2290 2290 2290 2290 2290 2290 2290 2290 2290

Attachment A A- 25 October 2014 TRIM.FaTE InputsTRIM

- Based Tiered Screening Methodolo gy for RTR



### Exhibit A-1 8. PAH Chemical

- Specific Parameters for Abiotic Compartments in the TRIM.FaTE Screening Scenario

Parameter Name Units Value R eference Chr DahA Fluoran

thene Fluor

ene IcdP
**Air Compartment Type** Particle dry deposition velocity m/day 500 500 500 500 500 McKone et al. 2001

. Half

- life day 0.334 0.178 0.46 0.46 0.262 Howard et al. 1991 / upper bound measured or estimated value. Exceptions include ATSDR 2005 (2

- Methylnaphthalene), USEPA 1998 (7,12

- Dimethylbenz(a)anthracene, Benzo(g,h,i)perylene, and Fluoranthene) / average of range, HSDB 2001d (Acenaphthene), HSDB 2001b (Acenaphthylene), and Spero et al. 2000 (Fluorene)

. Washou t r atio 200000 200000 200000 200000 200000 Mackay et al. 1986

. **Surface Soil Compartment Type** User input characteristic depth ( Boolean ) 0 = No, Else = Yes 0 0 0 0 0 Assumption.

Half

- life day 1000 940 275 33 730 MacKay et al. 2000 / average of range. Exceptions include ATSDR 2005 (2

- Methylnaphthalene), USEPA 1998 (7,12

- Dimethylbenz(a)anthracene, Benzo(g,h,i)perylene, and Fluoranthene) / average of range, HSDB 2001d (Acenaphthene), HSDB 2001b (Acenaphthylene), and HSDB 2001e (Fluorene)

. **Root Zone Soil Compartment Type**  User input characteristic depth ( Boolean ) 0 = No, Else = Yes 0 0 0 0 0 Assumption.

Half

- life day 1000 940 275 33 730 Howard et al. 1991 / upper bound measured or estimated value. Exceptions include ATSDR 2005 (2

- Methylnaphthalene), USEP A 1998 (7,12

- Dimethylbenz(a)anthracene, Benzo(g,h,i)perylene, and Fluoranthene) / average of range, HSDB 2001d (Acenaphthene), HSDB 2001b (Acenaphthylene), and HSDB 2001e (Fluorene)

. **Vadose Zone Soil Compartment Type** User input characteristic depth ( Bool ean ) 0 = No, Else = Yes 0 0 0 0 0 Assumption.

Attachment A A- 26 October 2014 TRIM.FaTE InputsTRIM

- Based Tiered Screening Methodolo gy for RTR



### Exhibit A-1 8. PAH Chemical

- Specific Parameters for Abiotic Compartments in the TRIM.FaTE Screening Scenario

Parameter Name Units Value Reference Chr DahA Fluora
n - thene Fluor

ene IcdP
**Vadose Zone Soil Comp artment Type**  **, continued** Half

- life day 2000 1880 550 66 1460 Howard et al. 1991 / upper bound measured or estimated value. Exceptions include ATSDR 2005 (2

- Methylnaphthalene), USEPA 1998 (7,12

- Dimethylbenz(a)anthracene, Benzo(g,h,i)perylene, and Fluoranthe ne) / twice average of range, HSDB 2001d (Acenaphthene) / multiplied by 2, HSDB 2001b (Acenaphthylene) / multiplied by 2, and HSDB 2001e (Fluorene) / multiplied by 2

. **Groundwater Compartment Type** Half

- life day 2000 1880 550 66 1460 Howard et al. 1991 / upper bound measured or estimated value. Exceptions include ATSDR 2005 (2

- Methylnaphthalene), USEPA 1998 (7,12

- Dimethylbenz(a)anthracene, Benzo(g,h,i)perylene, and Fluoranthene) / twice average of range, HSDB 2001d (Acenaphthene) / multiplied by 2, HSDB 2001b (Acenaphthylene) / multiplied by 2, and HSDB 2001e (Fluorene) / multiplied by 2

. **Surface Water Compartment Type** RatioOfConcInAlgaeToConcD issolvedInWater (g[chem]/kg[algae])/ (g[chem]/L[water]) 280 1388 67.4 5.8 1653 K ow from Del Vento and Dachs 2002

.

Half

- life day 1.626 97.8 160 8.5 750 Howard et al. 1991 / upper bound measured or estimated value. Exceptions include HSDB 2005 (2

- Methylnaphthalene), HSDB 2001a (7

- 12 Dimethylbenz(a)anthracene), HSDB 2001d (Acenaphthene), HSDB 2001b (Acenaphthylene), a nd HSDB 2001c (Benzo(g,h,i)perylene), Montgomery 2000 (Fluoranthene), and Boyle 1985 (Fluorene)

. **Sediment Compartment Type** Half

- life day 2290 2290 2290 2290 2290 Mackay et al. 1992 / PAH values are the mean half

- life of the log class that Mackay et al. assigned for sediment, except for BbF and IcdP, which were not i n Table 2.3 of Mackay et. al.

Attachment A A- 27 October 2014 TRIM.FaTE InputsTRIM

- Based T iered Screening Methodolo gy for RTR



### Exhibit A- 19

. Dioxin Chemical

- Specific Parameters for Abiotic Compartments in the TRIM.FaTE Screening Scenario

Parameter Name Units Value1,2,3,4,6,7,8,9-OCDD1,2,3,4,6,7,8,9-OCDF1,2,3,4,6,7,8-HpCDD1,2,3,4,6,7,8-HpCDF1,2,3,4,7,8,9-HpCDF1,2,3,4,7,8-HxCDD

**Air Compartment Type** Deposition Velocity m/day 500 500 500 500 500 50 0

Half

- life day 162 321 64 137 122 42 Washout Ratio m 3 [air]/m 3 [rain] 91000 22000 64000 32000 32000 9000 **Surface Soil Compartment Type** Input characteristic depth m 0.08 0.08 0.08 0.08 0.08 0.08 Use input characteristic depth ( Boolean ) 0 = No, Else = Ye s 0 0 0 0 0 0

Halflife day 3650 3650 3650 3650 3650 3650 **Root Zone Soil Compartment Type** Input characteristic depth m 0.08 0.08 0.08 0.08 0.08 0.08 Use input characteristic depth 0 = No, Else = Yes 0 0 0 0 0 0

Half

- life day 3650 3650 3650 3650 3650 3 650 **Vadose Zone Soil Compartment Type** Input characteristic depth m 0.08 0.08 0.08 0.08 0.08 0.08 Use input characteristic depth ( Boolean ) 0 = No, Else = Yes 0 0 0 0 0 0

Half

- life day 1008 1008 1008 1008 1008 1008 **Groundwater Compartment Type** Half

- lif e day 1008 1008 1008 1008 1008 1008 **Surface Water Compartment Type** Ratio Of Conc In Algae To Conc Dissolved In Water (g[chem]/g[algae])/ (g[chem]/L[water]) 5.31 4.54 4.54 2.83 1.9 3.88

Half

- life day 0.67 0.58 47 0.58 0.58 6.3 **Sediment Compartment Type** Half

- life day 1095 1095 1095 1095 1095 1095

Attachment A A- 28 October 2014 TRIM.FaTE InputsTRIM

- Based T iered Screening Methodolo gy for RTR



### Exhibit A-1 9. Dioxin Chemical

- Specific Parameters for Abiotic Compartments in the TRIM.FaTE Screening Scenario

Parameter Name Units Value1,2,3,4,7,8-HxCDF1,2,3,6,7,8-HxCDD1,2,3,6,7,8-HxCDF1,2,3,7,8,9-HxCDD1,2,3,7,8,9-HxCDF1,2,3,7,8-PeCDD

**Air Compartment Type** Deposition v elocity m/day 500 500 500 500 500 500 Half

- life day 78 28 55 28 51 18 Washout r atio m 3 [air]/m 3 [rain] 10000 9000 10000 9000 10000 18000 **Surface Soil Compartment Type** Input charac teristic depth m 0.08 0.08 0.08 0.08 0.08 0.08 Use input characteristic depth ( Boolean ) 0 = No, Else = Yes 0 0 0 0 0 0

Half

- life day 3650 3650 3650 3650 3650 3650 **Root Zone Soil Compartment Type** Input characteristic depth m 0.08 0.08 0.08 0.08 0.08 0.0 8 Use input characteristic depth 0 = No, Else = Yes 0 0 0 0 0 0

Half

- life day 3650 3650 3650 3650 3650 3650 **Vadose Zone Soil Compartment Type** Input characteristic depth m 0.08 0.08 0.08 0.08 0.08 0.08 Use input characteristic depth ( Boolean ) 0 = No, E lse = Yes 0 0 0 0 0 0

Half

- life day 1008 1008 1008 1008 1008 1008 **Groundwater Compartment Type** Half

- life day 1008 1008 1008 1008 1008 1008 **Surface Water Compartment Type** Ratio Of Conc In Algae To Conc Dissolved In Water (g[chem]/g[algae])/ (g[chem]/L[ water]) 2.06 5.36 4.25 5.36 3.26 1.55

Half

- life day 0.58 6.3 0.58 6.3 0.58 2.7 **Sediment Compartment Type** Half

- life day 1095 1095 1095 1095 1095 1095

Attachment A A- 29 October 2014 TRIM.FaTE InputsTRIM

- Based T iered Screening Methodolo gy for RTR



### Exhibit A-1 9. Dioxin Chemical

- Specific Parameters for Abiotic Compartments in the TRIM.FaTE Screening Scenario

Parameter Name Units Value1,2,3,7,8-PeCDF2,3,4,6,7,8-HxCDF2,3,4,7,8-PeCDF2,3,7,8-TCDD2,3,7,8-TCDF

**Air Compartment Type** Deposition v elocity m/day 500 500 500 500 500 Half

- life day 31 59 33 12 19 Washout r atio m 3 [air]/m 3 [rain] 13000 10 000 14000 18000 19000 **Surface Soil Compartment Type** Input characteristic depth m 0.08 0.08 0.08 0.08 0.08 Use input characteristic depth ( Boolean ) 0 = No, Else = Yes 0 0 0 0 0

Half

- life day 3650 3650 3650 3650 3650 **Root Zone Soil Compartment Type** Inp ut characteristic depth m 0.08 0.08 0.08 0.08 0.08 Use input characteristic depth 0 = No, Else = Yes 0 0 0 0 0 Half

- life day 3650 3650 3650 3650 3650 **Vadose Zone Soil Compartment Type** Input characteristic depth m 0.08 0.08 0.08 0.08 0.08 Use input cha racteristic depth ( Boolean ) 0 = No, Else = Yes 0 0 0 0 0

Half

- life day 1008 1008 1008 1008 1008 **Groundwater Compartment Type** Half

- life day 1008 1008 1008 1008 1008 **Surface Water Compartment Type** Ratio Of Conc In Algae To Conc Dissolved In Water (g[che m]/g[algae])/ (g[chem]/L[water]) 1.75 4.26 1.39 1.76 0.71

Half

- life day 0.19 0.58 0.19 2.7 0.18 **Sediment Compartment Type** Half

- life day 1095 1095 1095 1095 1095

Attachment A A- 30 October 2014 TRIM.FaTE InputsTRIM

- Based T iered Screening Methodolo gy for RTR



### Exhibit A-1 9. Dioxin Chemical

- Specific Parameters for Abiotic Compartments in the TRIM.FaTE Screening Scenario

Parameter Name Reference

**Air Compartment Type** Deposition v elocity McKone et al. 2001

. Half

- life Atkinson 1996 as cited in USEPA 2000 b ; vapor phase reaction with hydroxyl radical

. Washout r atio Vulykh et al. 2001

. **Surface Soil Compartment Type**  Input characteristic depth Not used (model set to calculate value)

. Use input characteristic depth ( Boolean ) Assumption. Half

- life Mackay et al. 2000; the degradation rate was cited by multiple authors, value is for 2,3,7,8

- TCDD

. **Root Zone Soil Compartment Type**  Input characteristic depth N ot used (model set to calculate value)

. Use input characteristic depth Assumption. Half

- life Mackay et al. 2000; the degradation rate was cited by multiple authors, value is for 2,3,7,8

- TCDD

. **Vado se Zone Soil Compartment Type**  Input characteristic depth Not used (model set to calculate value). Use input characteristic depth ( Boolean ) Assumption

. Half

- life Average value of the range presented in Mackay et al. 2000; based on estimated unacclimated aerobic biodegradation half

- life, value is for 2,3,7,8

- TCDD. **Groundwater Compartment Type** Half

- life Average value of the range presented in Mackay et al. 2000; based on estimated unacclimated aerobic biodegradation half

- life, value is for 2,3,7,8

- TCDD. **Surface Water Compartment Type** Ratio Of Conc In Algae To Conc Dissolved In Water Estimated from K ow value using model from DelVento and Dachs 2002 Half

- life Kim and O’Keefe 1998, as cited in USEPA 2000 b

. **Sediment Compartment Type** Half

- life Estimation based on Adriaens and Grbic

- Galic 1992,1993 and Adriaens et al. 1995 as cited in USEPA 2000 b

.

Attachment A A- 31 October 2014 TRIM.FaTE InputsTRIM

- Based T iered Screening Methodolo gy for RTR



### Exhibit A- 20

. Cadmium C hemical

- Specific Parameters for Plant Compartments in the TRIM.FaTE Screening Scenario

Parameter Name Units Value Reference

**Leaf Compartment Type** Transfer factor to leaf particle 1/day 0.002 Assumption

. **Particle on Leaf Compartment Type** Transfer factor to leaf 1/day 0.200 Assumption

. **Root Compart ment Type** 

**- Grasses and Herbs**  **a** Root to Root Soil Partition

Alpha of Steady State unitless 0.95 Henning et al. 2001.

Root to Root Soil Partition

Partitioning Coefficient m 3 [bulk root soil]/m 3 [root] 0.23 Nriagu 1980; based on average value calculated from various agricultural plant species. Root to Root Soil Partition

Time to Reach Alpha day 28 Henning et al. 2001.

**Stem Compartment Type**

**- Grasses and Herbs**  **a** Transpiration stream concentration factor (TSCF) m 3 [soil pore water]/m 3 [xylem fluid] 0.45 Tsiro s et al. 1999.

a Roots and stems are not modeled for deciduous or coniferous forest in the current version of TRIM.FaTE.

Attachment A A- 32 October 2014 TRIM.FaTE InputsTRIM

- Based Tiered Screening Methodolo gy for RTR



### Exhibit A- 21

. Mercury Chemical

- Specific Parameters for Plant Compartments in the TRIM.FaTE Screening Scenario

Parameter Name Units Value Reference Hg(0) Hg(2) MHg **Leaf Compartment Type** Transfer factor to leaf particle 1/day 0.002 0.002 0.002 A ssumed based on 1% of transfer factor from leaf particl e to leaf. Demethylation rate 1/day N/A N/A 0.03 Calculated from Bache et al. 1973. Methylation rate 1/day 0 0 0 Assumed from Gay 1975, Bache et al. 1973. Oxidation rate 1/day 1.0E+06 0 0 A ssumed to be nearly instantaneous Reduction rate 1/day 0 0 0 As sumption

. **Particle on Leaf Compartment Type** Transfer factor to leaf 1/day 0.2 0.2 0.2 Assumption

. Demethylation rate 1/day N/A N/A 0 Assumption

. Methylation rate 1/day 0 0 0 Assumption

. Oxidation rate 1/day 0 0 0 Assumption

. Reduction rate 1/day 0 0 0 Assumption

. **Root Compartment Type**

**- Grasses and Herbs**  **a** Alpha for root

- root zone bulk soil unitless 0.95 0.95 0.95 Selected value. Root/root

- zone

- soil

- water partition coefficient m 3 [bulk root soil]/ m 3 [root] 0 0.18 1.2 Hg 2

- geometric mean Leonard et al . 1998, John 1972, Hogg et al. 1978; MHg assumed, based on Hogg et al. 1978. t

- alpha for root

- root zone bulk soil day 21 21 21 Assumption

. Demethylation rate 1/day N/A N/A 0 Assumption

. Methylation rate 1/day 0 0 0 Assumption

. Oxidation rate 1/day 0 0 0 Assumption

. Reduction rate 1/day 0 0 0 Assumption

. **Stem Compartment Type**

**- Grasses and Herbs**  **a** Transpiration stream concentration factor (TSCF) m 3 [soil pore water]/m 3 [xylem fluid] 0 0.5 0.2 Calculation from Norway spruce, Scots pine, Bishop et al. 199 8. Demethylation rate 1/day N/A N/A 0.03 Calculated from Bache et al. 1973. Methylation rate 1/day 0 0 0 Assumption

. Oxidation rate 1/day 0 0 0 Assumption

. Reduction rate 1/day 0 0 0 Assumption

.

a Roots and stems are not modeled for deciduous or conife rous forest in the current version of TRIM.FaTE.

Attachment A A- 33 October 2014 TRIM.FaTE InputsTRIM

- Based Tiered Screening Methodolo gy for RTR



### Exhibit A- 22

. PAH Chemical

- Specific Parameters for Plant Compartments in the TRIM.FaTE Screening Scenario

Parameter Name U nits Value
2Methyl 712DMB Acenaph

thene Acenaph

thylene BaA BaP BbF BghiP BkF
**Leaf Compartment Type** Transfer factor to leaf particle 1/day 1.00E

- 04 1.00E

- 04 1.00E

- 04 1.00E

- 04 1.00E

- 04 1.00E

- 04 1.00E

- 04 1.00E

- 04 1.00E

- 04

Half

- life day 3.50 3.50 3.50 3 .50 3.50 3.50 3.50 3.50 3.50 **Particle on Leaf Compartment Type** Transfer factor to leaf 1/day 1.00E

- 04 1.00E

- 04 1.00E

- 04 1.00E

- 04 1.00E

- 04 1.00E

- 04 1.00E

- 04 1.00E

- 04 1.00E

- 04

Half

- life day 2.31 2.31 2.31 2.31 1.84 2.31 3.56 2.31 17.80 **Root Compartment T ype**  **- Grasses and Herbs**  **a** Half

- life day 34.60 34.60 34.60 34.60 34.60 34.60 34.60 34.60 34.60 Root soil

- water interaction

- alpha unitless 0.95 0.95 0.95 0.95 0.95 0.95 0.95 0.95 0.95

**Stem Compartment Type**

**- Grasses and Herbs**  **a** Half

- life day 3.50 3.50 3. 50 3.50 3.50 3.50 3.50 3.50 3.50

a Roots and stems are not modeled for deciduous or coniferous forest in the current version of TRIM.FaTE.

Attachment A A- 34 October 2014 TRIM.FaTE InputsTRIM

- Based Tiered Screening Methodolo gy for RTR



### Exhibit A- 23

. PAH Chemical

- Spe cific Parameters for Plant Compartments in the TRIM.FaTE Screening Scenario

Parameter Name Units Value Reference Chr DahA Fluoran thene Fluorene IcdP
**Leaf Compartment Type** Transfer factor to leaf particle 1/day 1.00E

- 04 1.00E

- 04 1.00E

- 04 1.00E

- 04 1. 00E

- 04 Assumption

.

Half

- life day 3.50 3.50 3.50 3.50 3.50 Edwards 1988 ( calculated from metabolic rate constant )

. **Particle on Leaf Compartment Type** Transfer factor to leaf 1/day 1.00E

- 04 1.00E

- 04 1.00E

- 04 1.00E

- 04 1.00E

- 04 Assumption

.

Half

- life day 4.1 2 17.80 2.31 2.31 17.80 Calculated as 2 times the measured photolysis half

- life from Mackay et al. 1992. Exceptions include values that have been set equal to Benzo(a)pyrene (2

- Methylnaphthalene; 7,12

- Dimethylbenz(a)anthracene; Acenaphthene; Acenaphthylen e; Benzo(ghi)perylene; Fluoranthene; and Fluorene)

. **Root Compartment Type**

**- Grasses and Herbs**  **a** Half

- life day 34.60 34.60 34.60 34.60 34.60 Edwards 1988 ( calculated from metabolic rate constant )

. Root soil water interaction

- alpha unitless 0.95 0.95 0.9 5 0.95 0.95 Assumption

.
**Stem Compartment Type**

**- Grasses and Herbs**  **a** Half

- life day 3.50 3.50 3.50 3.50 3.50 Edwards 1988 ( calculated from metabolic rate constant )

.

a Roots and stems are not modeled for deciduous or coniferous forest in the current version of TRIM.FaTE.

Attachment A A- 35 October 2014 TRIM.FaTE InputsTRIM

- Based Tiered Screening Methodolo gy for RTR



### Exhibit A-2 3. Dioxin Chemical

- Specific Parameters for Plant Compartments in the TRIM.FaTE Screening Scenario

Parameter Name Units Value Reference All Dioxins **Leaf Compartment Type** Transfer factor to leaf particle 1/day 0.003 Calculat ed as 1 percent of transfer factor to leaf; highly uncertain. Half

- life day 70 Arjmand and Sandermann 1985, as cited in Komoba et al. 1995; soybean root cell culture metabolism test data for DDE. **Particle on Leaf Compartment Type** Transfer factor to leaf 1/day 0.3 Assumption b ased on USEPA 2000 b (an estimate for mercury) and Trapp 1995; highly uncertain. Half

- life day 4.4 McCrady and Maggard 1993; photodegradation sorbed to grass foliage in sunlight; assumed 10 % sunlight per day. **Root Compartment Type**

**- Grasses and Herbs**  **a** Half

- life day 70 Arjmand and Sandermann 1985, as cited in Komoba et al. 1995; soybean root cell culture metabolism test data for DDE. Root s oil

- w ater i nteraction

- a lpha unitless 0.95 Assumption

. **Stem Compartment Type**

**- Grasses and H**  **erbs a**  Half

- life day 70 Arjmand and Sandermann 1985, as cited in Komoba et al. 1995; soybean root cell culture metabolism test data for DDE.

a Roots and stems are not modeled for deciduous or coniferous forest in the current version of TRIM.FaTE.

Attachment A A- 36 October 2014 TRIM.FaTE InputsTRIM

- Based Tiered Screening Methodolo gy for RTR



### Exhibi t A- 24

. Cadmium Chemical

- Specific Parameters for Aquatic Species in the TRIM.FaTE Screening Scenario

Parameter Name Units Value Reference

**Zooplankton Compartment Type** Absorption rate constant L[water]/kg[fish wet wt]

- day 1500 Goulet 2007

. Assimilation efficiency from algae unitless 0.5 Goulet 2007

. Elimination rate constant 1/day 0.03 Goulet 2007

. **Benthic Invertebrate Compartment Type** Sediment p artitioning

- a lpha of
e quilibrium unitless 0.95 Assumption.

Sediment partitioning

- p artition
c oefficient kg[bulk sed/kg[invertebrate wet wt] 0.27 Assumption.

Sediment p artitioning

- t ime to r each
a lpha of e quilibrium day 21 Hare et al. 2001

.
**Benthic Omnivore Compartment Type**  Assimilation efficiency from food unitless 0.1 Assumption b ased on Yan and Wang 2002. Absorption r ate c onstant unitless 1.23 Calculated based on body weight from regression in Hendriks and Heikens 200 1. Elimination r ate c onstant unitless 1.73E

- 02 Assumption. **Benthic Carnivore Compartment Type** Assimilation efficiency from food unitless 0.1 Assumption b ased on Yan and Wang 2002. Absorption r ate c onstant unitless 0.66 Calculated based on body weight from regression in Hendriks and Heikens 200 1. El imination r ate c onstant unitless 1.68E

- 03 Assumption. **Water**

**- column Herbivore Compartment Type**  Assimilation efficiency from food unitless 0.1 Assumed value based on Yan and Wang 2002. Assimilation efficiency from plants unitless 0.1 Assumed value based o n Yan and Wang 2002. Absorption rate constant unitless 2.46 C alculated based on body weight from regression in Hendriks and Heikens 200 1. Elimination rate constant unitless 1.73E

- 02 Assumption.

Attachment A A- 37 October 2014 TRIM.FaTE InputsTRIM

- Based Tiered Screening Methodolo gy for RTR



### Exhibit A- 24 . Cadmium Chemical

- Specific Parameters for Aquatic Species in the TRIM.FaTE Screening Scenario

Parameter Name Units Value Reference **Water**

**- column Omnivore Compartment Type**  Assimilation efficiency from food unitless 0.1 Assumption b ased on Yan and Wang 2002. Assimilation efficiency from plants unit less 0.1 Assumption based on Yan and Wang 2002. Absorption rate constant unitless 1.23 Calculated based on body weight from regression in Hendriks and Heikens 200 1. Elimination rate constant unitless 1.73E

- 02 Assumption. **Water**

**- column Carnivore Compartme**  **nt Type** Assimilation efficiency from food unitless 0.1 Assumption based on Yan and Wang 2002. Absorption rate constant unitless 0.66 Calculated based on body weight from regression in Hendriks and Heikens 200 1. Elimination rate constant unitless 1.73E

- 0 2 Assumption

Attachment A A- 38 October 2014 TRIM.FaTE InputsTRIM

- Based Tiered Screening Methodolo gy for RTR



### Exhibit A- 25

. Mercury Chemical

- Specific Parameters for Aquatic Species in the TRIM.FaTE Screening Scenario

Parameter Name Units Value Reference Hg(0) Hg( 2) MHg **Zooplankton Compartment Type** Assimilation Efficiency From Algae unitless 0.2 0.015 0.5 Environment Canada 2002. Half

- life day 1.0E+09 1.0E+09 1.0E+09 Assumption

. How Much Faster Hg Elimination Is Than For MHg unitless 3 3 1 Assumption

.
Methyla tion rate 1/day 0 0 0 Assumption

. Oxidation rate 1/day 0 1.0E+06 0 Assumption

. Reduction rate 1/day 0 0 0 Assumption

. **Benthic Invertebrate Compartment Type** Alpha of equilibrium for sediment partitioning unitless 0.95 0.95 0.95 Selected valu e (i.e., pro portion of equilibrium achieved by time “t”). Benthic invertebrate

- bulk sediment partition coefficient kg[bulk sediment]/kg[invertebrate wet wt] 0.0824 0.0824 5.04 Hg(0)

- assumed based on Hg(2) value; Hg(2) and MHg - Saouter et al. 1991.
t

- alpha for equ ilibrium for sediment partitioning day 14 14 14 Experiment duration from Saouter et al. 1991. **All Fish Compartments Types a**  Elimination adjustment factor unitless 3 3 1 Trudel and Rasmussen 1997. Assimilation efficiency from food unitless 0.06 0.06 0.5 W illiams et al. 2010. Demethylation rate 1/day N/A N/A 0 Assumption

. Methylation rate 1/day 0 0 0 Assumption

. Oxidation rate 1/day 1.0E+06 0 0 Assumption

. Reduction rate 1/day 0 0 0 Assumption

. **Water**

**- column Herbivore Compartment Type**  Assimilation effi ciency from plankton unitless 0.06 0.06 0.5 Williams et al. 2010.

a Screening scenario includes: Benthic Omnivore, Benthic Carnivore, Water

- column Herbivore, Water

- column Omnivore, and Water

- column Carnivore.

Attachment A A- 39 October 2014 TRIM.FaTE InputsTRIM

- Based Tiered Screening Methodolo gy for RTR



### Exhibit A- 26

. PAH Chemical

- Specific Parameters for Aquatic Species in the TRIM.FaTE Screening Scenario

Parameter Name Units Value
2Methyl 712DMB Acenaph thene Acenaph thylene BaA BaP BbF BghiP BkF
**Zooplankton C ompartment Type**  Absorption rate constant L[water]/kg[fish wet wt]

- day 790 42650.94 42231 42302.18 42650.81 42652.78 42650.68 42655.77 42652.5

Assimilation efficiency from algae unitless 0.5 0.25 0.5 0.5 0.46 0.25 0.25 0.25 0.25

Elimination rate constant 1/day 169.68 2.03 148.07 123.44 2.073 1.3864 2.12 0.33 1.48

Half

- life day 0.007788 17 0.00239 0.00239 1.284 16.5 17 17 17 **Benthic Invertebrate Compartment Type** Clearance constant unitless 100.6 100.6 100.6 100.6 100.6 100.6 100.6 100.6 100.6

V d (ratio of concentration in benthic invertebrates to concentration in water) ml/g 7235 7235 7235 7235 7235 7235 7235 7235 7235

Half

- life day 0.722 17 0.722 0.722 1.284 16.5 17 17 17 **All Fish Compartment Types a**  Gamma fish unitless 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0. 2 0.2 Assimilation efficiency from food unitless 0.5 0.15 0.5 0.32 0.15 0.15 0.15 0.15 0.15

Half

- life day 0.2 2 0.2 0.2 0.408 1.925 2 2 2

a Screening scenario includes: Benthic Omnivore, Benthic Carnivore, Water

- column Herbivore, Water

- column Omnivore, a nd Water

- column Carnivore.

Attachment A A- 40 October 2014 TRIM.FaTE InputsTRIM

- Based Tiered Screening Methodolo gy for RTR



### Exhibit A-2 6. PAH Chemical

- Specific Parameters for Aquatic Species in the TRIM.FaTE Screening Scenario

Parameter Name Units Value Reference Chr DahA Fluoran thene Fluorene IcdP
**Zooplankton Compartment Type** Absorption ra te constant L[water]/kg[fish wet wt]

- day 42649.95 42655.48 142000 15000 42655.93 K ow from Arnot et al. 2004. Exception is Berrojalbiz et al. 2009 (2

- Methylnaphthalene, Fluoranthene, and Fluorene)

. Assimilation efficiency from algae unitless 0.46 0.25 0.49 0.5 0.25 K ow from Arnot et al. 2004. Exception is maximum value from Wang and Wang 2006 (7,12

- Dimethylbenz(a)anthracene, Benzo(a)pyrene, Benzo(b)fluoranthene, Benzo(g,h,i)perylene, Benzo(k)fluoranthene, Dibenz(a,h)anthracene, and Indeno(1,2,3

- cd)pyrene)

. Elimination rate constant 1/day 2.3746 0.4331 8.678 81.87 0.269 K ow from Arnot et al. 2004

.

Half

- life day 0.495 17 0.00239 0.0002476 17 McElroy 1990. Exceptions include Berrojalbiz et al. 2009 (2

- Methylnaphthalene, Fluoranthene, and Fluorene) and Moermond et al. 2007 (Benz(a)anthracene and Benzo(a)pyrene)

. **Benthic Invertebrate Compartment Type** Clearance constant unitless 100.6 100.6 100.6 100.6 100.6 Stehly et al. 1990

. V d (ratio of concentration in benthic invertebrates to concentration in water) ml/g 7235 7235 7235 7235 7235 Stehly et al. 1990

.

Half

- life day 0.495 17 0.722 0.722 17 Moermond et al. 2007

.

Attachment A A- 41 October 2014 TRIM.FaTE InputsTRIM

- Based Tiered Screening Methodolo gy for RTR



### Exhibit A- 26 . PAH Chemical

- Specific Parameters for Aquatic Species in the TRIM.FaTE Screening Scenario

Parameter Name Units Value Reference Chr DahA Fluoran

thene Fluorene IcdP
**All Fish Compartment Types a**  Gamma fish unitless 0.2 0.2 0.2 0.2 0.2 Thomann 1989

. Assimilation efficiency from food unitless 0.15 0.15 0.14 0.14 0.15 Lemair et al. 1992. Exceptions include Barber 2008 (2

- Methylnaphthal ene and Acenaphthene) and Niimi and Palazzo 1986 (Acenaphthylene, Fluoranthene, and Fluorene)

. Half

- life day 0.533 2 0.165 0.2 2 Moermond et al. 2007

.

a Screening scenario includes: Benthic Omnivore, Benthic Carnivore, Water

- column Herbivore, Water

- column Omnivore, and Water

- column Carnivore.

Attachment A A- 42 October 2014 TRIM.FaTE InputsTRIM

- Based Tiered Screening Methodolo gy for RTR



### Exhibit A- 27 . Dioxin Chemical

- Specific Parameters for Aquatic Species in the TRIM.FaTE Screening Scena rio

Parameter Name Units Value1,2,3,4,6,7,8,9-OCDD1,2,3,4,6,7,8,9-OCDF1,2,3,4,6,7,8-HpCDD1,2,3,4,6,7,8-HpCDF1,2,3,4,7,8,9-HpCDF1,2,3,4,7,8-HxCDD1,2,3,4,7,8-HxCDF1,2,3,6,7,8-HxCDD1,2,3,6,7,8-HxCDF

**Zooplankton Compartment** Absorption r ate c onstant L[water]/kg[fish wet wt]

- da y 8640 8640 8640 8640 8640 8640 8640 8640 8640

Assimilation e fficiency from a lgae unitless 0.08 0.05 0.21 0.09 0.2 0.31 0.31 0.31 0.31 Elimination r ate c onstant 1/day 0.0102 0.016 0.016 0.0616 0.1829 0.0252 0.1474 0.0099 0.0194 Half

- life day 7E+06 7E+06 7E+06 7E+06 7E+06 7E+06 7E+06 7E+06 7E+06 **Benthic Invertebrate Compartment** Clearance c onstant unitless 0 0 0 0 0 0 0 0 0 Sediment p artitioning p artition
c oefficient kg/kg 0.0013 0.0017 0.0055 0.0012 0.042 0.033 0.0081 0.013 0.02

Sediment p artitioning a lpha of
e quilibrium unitless 0.95 0.95 0.95 0.95 0.95 0.95 0.95 0.95 0.95

Sediment p artitioning t ime to

r each a lpha of e quilibrium days 120 42 120 42 42 120 42 120 42

V d (ratio of concentration in benthic invertebrates to concentration in water) ml/g 0 0 0 0 0 0 0 0 0

Half

- life day 5776.2 5776.2 5776.2 5776.2 5776.2 5776.2 5776.2 5776.2 5776.2 **All Fish Compartments a**  Assimilation e fficiency from f ood unitless 0.08 0.05 0.21 b 0.09 0.2 0.3 1 c 0.31 0.31 0.31 Chemical u ptake r ate v ia g ill L[water]/kg[fish wet wt]

- day 11 6 56 25 50 102 200 300 200

Half

- life day 70 70 70 70 70 70 70 70 70

Attachment A A- 43 October 2014 TRIM.FaTE InputsTRIM

- Based Tiered Screening Methodolo gy for RTR



### Exhibit A-2 7. Dioxin Chemical

- Specific Parameters for Aquatic Species in the TRIM.FaTE Screening Scenario

Parameter Name Units Value1,2,3,7,8,9-HxCDD1,2,3,7,8,9-HxCDF1,2,3,7,8-PeCDD1,2,3,7,8-PeCDF2,3,4,6,7,8-HxCDF2,3,4,7,8-PeCDF2,3,7,8-TCDD2,3,7,8-TCDF

**Zooplankton Compartment** Absorption Rate Constant L[water]/kg[fish wet wt]

- day 8640 8640 8640 8640 8640 8640 8640 8640

Assimilation Efficiency from Algae unitle ss 0.31 0.31 0.42 0.42 0.31 0.42 0.41 0.51 Elimination Rate Constant 1/day 0.0099 0.0413 0.0819 0.2316 0.0192 0.4331 0.2268 1.0375 Half

- life day 7E+06 7E+06 7E+06 7E+06 7E+08 7E+08 7E+06 7E+08 **Benthic Invertebrate Compartment** Clearance Constant unitles s 0 0 0 0 0 0 0 0 Sediment Partitioning Partition Coefficient kg/kg 0.015 0.067 0.098 0.024 0.072 0.17 0.205 0.056

Sediment Partitioning Alpha of Equilibrium unitless 0.95 0.95 0.95 0.95 0.95 0.95 0.95 0.95

Sediment Partitioning Time to Reach Alpha of E quilibrium days 120 42 120 42 42 42 120 42

V d (ratio of concentration in benthic invertebrates to concentration in water) ml/g 0 0 0 0 0 0 0 0

Half

- life day 5776.2 5776.2 5776.2 5776.2 5776.2 5776.2 5776.2 5776.2 **All Fish Compartments a**  Assimilation e ff iciency from f ood unitless 0.31 0.31 0.42 0.42 0.31 0.42 0.41 0.51 Chemical u ptake r ate v ia g ill L[water]/kg[fish wet wt]

- day 300 200 700 300 200 400 600 400

Half

- life day 70 70 70 70 70 70 70 70

Attachment A A- 44 October 2014 TRIM.FaTE InputsTRIM

- Based Tiered Screening Methodolo gy for RTR



### Exhibit A-2 7. Dioxin Chemical

- Specific Parameters for Aq uatic Species in the TRIM.FaTE Screening Scenario

Parameter Name Units Reference

**Zooplankton Compartment** Absorption r ate c onstant L[water]/kg[fish wet wt]

- day Zhang et al. 2011; used copepod k u value

.

Assimilation e fficiency from a lgae unitless Morr ison et al. 1999. Exceptions include Niimi and Oliver 19 8 6 (1,2,3,4,6,7,8,9

- OCDD, 1,2,3,4,6,7,8,9

- OCDF), Berntssen et al. 2007 (1,2,3,4,6,7,8

- HpCDD, 1,2,3,4,6,7,8

- HpCDF), and 1,2,3,4,7,8,9

- HpCDF set conservatively as approximate linear interpolation betwee n values for 1,2,3,4,7,8

- HxCDD and 1,2,3,4,6,7,8

- HpCDD / 1,2,3,4,6,7,8

- HpCDF (i.e., 0.3 to 0.1

– 0.2). Elimination r ate c onstant 1/day Arnot and Gobas 2004; used K ow value

. Half

- life day Morrison et al. 1999; used metabolic rates for invertebrates

. **Benth ic Invertebrate Compartment**  Clearance c onstant unitless Assumption. Sediment p artitioning p artition c oefficient kg/kg Rubinstein et al. 1990; used TCDD data for sandworm

. Sediment p artitioning a lpha of e quilibrium unitless Rubinstein et al. 1990

. Sedim ent Partitioning Time to Reach Alpha of Equilibrium days Rubinstein et al. 1990

.

V d (ratio of concentration in benthic invertebrates to concentration in water) ml/g Assumption.

Half

- life day Rubinstein et al. 1990; used TCDD data for sandworm

. **All Fish Compartmentsa**  Assimilation Efficiency from Food unitless Morrison et al. 1999. Exceptions include Niimi and Oliver 1996 (1,2,3,4,6,7,8,9

- OCDD, 1,2,3,4,6,7,8,9

- OCDF), Van den Berg et al. 1994 (1,2,3,4,6,7,8

- HpCDD), Berntssen et al. 2007 ( 1,2,3,4,6,7,8

- HpC DF), and 1,2,3,4,7,8,9

- HpCDF set conservatively as approximate linear interpolation between values for 1,2,3,4,7,8

- HxCDD and 1,2,3,4,6,7,8

- HpCDD / 1,2,3,4,6,7,8

- HpCDF (i.e., 0.3 to 0.1

– 0.2). Chemical Uptake Rate Via Gill L[water]/kg[fish wet wt]

- day Muir et al. 1985. Exception is Opperhuizen et al. 1986 (1,2,3,7,8,9

- HxCDF, 1,2,3,7,8

- PeCDD, 1,2,3,7,8

- PeCDF, 2,3,4,7,8

- PeCDF, 2,3,7,8

- TCDD, 2,3,7,8

- TCDF)

. Half

- life day Berntssen et al. 2007

.

a Screening scenario includes: Benthic Omnivore, Benthic Carnivore, Water

- column Herbivore, Water

- column Omnivore, and Water

- column Carnivore.

b 0.21 used for benthic omnivore, water column omnivore, and water column herbivore; 0.13 used for benthic carnivore and water column carnivore.

c 0.31 used for benthic omnivore, water column omnivore, benthic carnivore, and water column carnivore; 0.37 used for water column herbivore.

Attachment A A- 45 October 2014 TRIM.FaTE InputsTRIM

- Based Tiered Sc reening Methodolo gy for RTR



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- Based Tiered Sc reening Methodolo gy for RTR



### Muh lb aier, J ., a n d G

. T. Tiss ue

. 1980 . Ca dm ium io n t he sou t he rn b asin of Lake Mi chigan

. Water, Air and Soil Pollution (15):45

- 49

. Mu ir, D.C., W.K. M ars h all, an d G

. R. We bs ter. 19 85 . B ioconcen tr ati on of PC DD s by fis h: E ffe c ts o f
mo lecu lar s tr uctu re an d w at e r c hemis tr y. Chemosph ere 14 (6 /7 ):8 29 -833.

Mu ller, H. and Pr o hl, G. 1993. ECOSYS

- 87: A dynamic model for assessing radiological consequences of nuclear accidents. Health Ph ysics 64(3):232

- 252.

NLM ( National Library of Medicine ). 2002

. Haz ardou s Sub stance Da ta Ba nk ( HSD B). Av aila b le at: h ttp://to x ne t .n l m.n i h.gov/ cgi-bi n/ sis /htm l gen?HSD B.

N ii m i, A ., a n d B. O li ve r. 1986 . Bi olog ical h alf -lives of chlo ri n at e d dibenzo-p-dio xi ns an d dibe nz ofu ra ns in rai nb ow trout ( Sal mo ga irdene ri ). Enviro nm en tal Toxicology 5: 49

- 53.

N ii m i, A. J, . a n d V

. Pala zzo

. 19 86 . B iolog ical h alf

- li ves o f ei gh t polycy clic ar oma tic hyd ro car bons (P AH s) in rai nb ow trout ( S al mo g air dne ri ). Wa te r Research 20 (4 ):5 03 -50 7.

N ria gu , J. O. 19 80

. Cadmium in t he env ir on me n t. Part I: Ecolog ical cy cli ng

. New Yo rk: John Wil ey & Son s. C hapte r 15: Up ta ke an d effects of cadmium in hi gh er p la n ts. pp

. 608-6 09.

O pp erhuizen, A. , W .J . Wa gen aar, F .W. M. v an d er Wiel en, M. v an den Berg, K . Ol ie, an d F.A.P .C. Gob as. 19 86 . U p take an d elimin at io n of PCDD/PCD F co ng ene rs by fish a fter a queo us exposu re to a fl y- ash e xtra ct from a mu n ici p al inci ne rato r. Chemos ph ere 15(9–12):2 04 9-2053

. doi: 10

. 10 16 /0045- 65 35 (86)905 11 -4.
Passivirta, J., Sinkkonen, S., Mikkelson, P., Rantio, T., Wania, F. (1999) Estimation of vapor pressures,
so lubilities and Henry’s law constants of selected persistent organic pollutants as functions of temperature. C hemosphere 39, 811

– 832.

P at erson, S. , D. Mac ka y, an d A . Gla dman. 19 91 . A f ugacity model o f c h em i cal up t ake by p la n ts from
so il a n d air. Chemos ph ere 2 3: 539-5 65.

Pe te rs en, G., A

. Ive rf eldt, an d J

. M un the

. 1995

. Atmos ph e ric me rc u ry species ove r C en tral a n d No rt he rn E urope. Mo de l calc ula ti ons an d c omp ari so n w i th observ at ions from the Nord ic Ai r a n d Precip itat io n Netwo rk for 1987 an d 1988

. At mo sphe ric Env ir

onment 29: 47

- 68.

Po rv ari, P ., a nd M . Ve r ta. 1995

. M et hylme rc u ry productio n in f loode d so ils: A la bo ra to ry st ud y. Wa te r, A ir, an d So il Po ll ution 80:7 65 -77 3.

Rie dere r, M. 1 99 5

. P ar tit i on in g a n d tra nspo rt of organ ic chem icals betw ee n t he at mos ph e ric
env ir on me nt an d l eaves. In: Tra pp , S . a n d J . C. McF arl ane, eds. P la nt contamin at i on : Mode li n g a n d simu lat io n of organ ic chem ical processes. Bo ca R aton, FL: Le wis P ub lis hers. pp

. 15 3-1 90.

R ub instein, N. I., R .J. Prue ll, B.K . Ta p li n, J.A

. LiVo lsi, and C. B. Norwood

. 1990 . B ioav aila b ility o f 2,3,7,8- TC DD, 2,3,7,8-TCD F a n d PC Bs to m ari ne benthos from P assaic ri ve r s edi me nts

. Chemos ph ere 20:1 09 7-11 02

. Sangster, J. (1993) LOGKOW, A Databank of Evaluated Octanol

- Water Partition Coefficients. 1st Edition, Montreal, Quebec, Canada.

Saoute r, E., F . Ri beyre, A

. Bo ud ou, an d R. Mauryb rac he t. 1991

. Hexagen ia ri gida (E ph emeropte ra) as a biolog ical mode l in a qu a tic ecoto xic ology - Ex pe ri ment al st udies on me rc ury tra nsfe rs from sedi me n t. Env ir

on me nt al Po ll ution 69: 51

- 67. Attachment A A- 51 October 2014 TRIM.FaTE InputsTRIM

- Based Tiered Sc reening Methodolo gy for RTR



### S ij m, D .T. H.M

., H. Weve r, P.J

. de Vri es, an d A. O pp erhuizen

. 19 89

. Octan -1-ol/wate r p ar ti ti o n c oe ff icien ts o f polychlorinate d dibe nz o-p-dio xi ns and dibe nz ofu ra ns: experi me nt al v al ues determine d with a s tirri n g method

. Chemos ph ere 19(1-6):263- 26 6.

Si mo n ic h, S. L ., a n d R .A

. Hi te s. 19 94 . Importance o f vege tat io n in r em ov in g polycy clic ar oma tic hydro car bons from the atmos ph ere. N at ure 370: 49

- 51.
Sp ero, J., B. De Vito, an d L. Theodore

. [eds.]

. 2 00 0 . R e gu lat o ry chem icals ha ndbo ok. New York, N Y: CRC Press, p

. 1072.
Stehly, G.R., P .F . La nd rum, M. G. H enry, an d C. Kle mm . 19 90 . To xi co kin etics of PAHs in Hexagen ia Env ir

on me nt al Toxicology an d C h em is tr y

9:1 67 -174.

Thibodeau x, L .J

. 1996 . E nviro nm en tal c hemodynam ic s: Move me nt of chem icals i n air, wa te r, an d so il. New York, N Y: Joh n Wi ley an d Sons, Inc.

T ho ma nn , R .V

. 19 89 . B ioaccumu lat ion mo del of organic-ch em i cal d istri bu tio n in a quatic food-ch ai ns. Env ir

on me nt al S cie nce and Technology 23(6):699- 707.
Trapp , S

. 1995

. Model for uptake o f xenobio tics in to plan ts. I n: Tra pp , S . a n d J . C. McF arla ne, eds. P la n t c ontamin at ion: Mode li n g an d si mu lat io n of organ ic ch emical processes. Bo ca R aton, FL: Le wis P ub lis he rs. pp

. 1 07 -15 1.

Trud e l, M

., an d J. B. Ra smussen

. 1997

. Mode li n g t he elimin at io n of me rc u ry by fi sh

. Env ir

onmen tal Science and Technolo gy 31:17 16 -17 22.

Tsir os, I.X.; R.B. Ambrose, an d A . C hronopoulou-Ser eli. 19 99 . A ir -veg etat io n p ar titionin g of to xic ch em ic als in env ir on me ntal simu lat ion mo de li ng . Gl ob al NES T: The Int ernational Journ al 1(3):1 77

- 18 4.

US EP A ( U.S. Environmental Protection Agency ). 20 07 . Dr aft risk asses sment fo r t he S ie mens Wa t er Tec hn ologies Corp. car bon re act iv ati on facili ty P ark er, A ri zon a. App e nd ix F . C hemi cal

- ph y sical p ara me te rs for compo und s not in USEP A' s HH RAP

. Av aila b le at: h tt p ://w ww .epa .g ov/ re gi on 9/ w ast e/siemens/ pd f/ Ri skAssessment/siemens- ris k assessApp x F.pd f.

USEPA. 20 05

. Hum an Heal th Risk Assessment Protoco l f o r Haz ar dous Wa s te Com bu stio n F aciliti es. C om panio n D ata b as e

. Av aila b le a t: h tt p://w ww .epa .g ov/regi on 6/6 pd / rcr a _c /protocol/protocol .h t m. USEPA 2003. Toxicological Review of 2

- Methylnaphthalene. Available online at: http://www.epa.gov/iris/toxreviews/1006tr.pdf

. USEPA . 20 00 a. Estima ti on Prog ram Inte rfa ce (EP I) suite . Offi ce o f Po ll ution Preventio n a n d To xics (OPP T). Av aila b le a t: h tt p://w ww .epa .g o v/ o pp t/ ex posure/docs/ep is u ite dl.htm.

USEPA . 20 00 b. D raft exposu re an d hu m an he alth r eas ses sment o f 2,3,7,8-tetr ac hlorodibe nzo -p-dio xin TC DD ) an d r e la te d c ompo und s, Volume 3: Prope rt ies, env ir on me n tal l evels, and b ack gro un d exposures, Chapte r 2: Physi cal a n d c hemi cal proper ties an d fate a n d App e nd ix A . E PA/600/P00/001 Bc. Av aila b le at: h tt p ://w ww .epa .g ov/ncea /pd fs /dio xi n/p art 1/volum e3 /ch ap -2 .pd f.

USEPA . 19 99

. Scr ee nin g le v el ecolog ical risk asses s me nt protoco l f o r haz ar dous wa s te combustio n facil i ties. Pee r R eview D ra f t, Nov em be r. Av ail ab le at: h tt p://w ww .epa .g ov /e pao sw er/haz was te/com bu st/eco risk .h tm

Attachment A A- 52 October 2014 TRIM.FaTE InputsTRIM

- Based Tiered Sc reening Methodolo gy for RTR



### USEPA . 19 98 . Ch emical fate h alf

- li v es for To xics Re l ease Inventor y (TRI) ch em icals. July

. S RC TR 98- 0 08

. USEPA . 19 97 a. Supp l eme n tal backgro un d doc um en t; No ng ro und wate r p at h way risk as s es sment; P et ro le um pr ocess w aste listing determin at ion

. App e nd ix B. C ompo un d spe cif ic i npu t v al ues. Av aila b le at: h tt p://w ww .epa .g o v/ osw /h az ar d/ was tetypes/ was teid/petroref/a pp e ndb.pd f.

USEPA . 19 97 b

. U .S . E nv ir onmen tal Prot ect io n Ag ency. M erc ur y st ud y repo rt to co ng ress. Vol um e III: Fa te and tra ns po rt of me rc u ry in t he envir on me n t. Office o f A ir Q u ality P la nn in g a n d S ta nd ar ds an d Office of Re s earch an d Develo pm en t.

V a n d e n B erg, M

., J. De Jon gh , H

. Po ige r, an d J. R. Ol s on

. 19 94 . The toxicokine tics an d me tabo li sm o f polychlo ri n at e d dibenzo-p-dio xi ns (PC DD s) an d dibe nz ofu ra ns (PCDFs) an d t he ir re le vance fo r toxi cit y

. CRC Critical Reviews in Toxicology 24(1) :1

- 74.

Va nd al, G.M., W .F

. F it zg erald, K. R. Ro lf hu s, an d C .H. Lamborg. 19 95 . Mode li n g t he ele me n tal me rc u ry cycle in P al lette L ak e, Wis consin, U SA

. W at e r, Ai r, and So il Po ll ution 80:7 89 -79 8.
Vulykh, N ., a n d V. Sh ata l ov . 20 01 . I nvestigation of Dioxin/ Fu ran C om po siti on in Emissions an d in Env ir on me nt al Med ia: Selection o f Co ng ene rs for M od e li ng . T ec hn ical No te 6/ 20 01 . Meteorolog ical Synthesizin g C ente r - East.

Wa ng , X., and W. X. Wa ng . 20 06 . B ioaccumu lat io n a nd tra nsfer o f be nz o (a) pyrene in a s imp lified

m ari n e f oo d ch ai n

. M ari ne Ecolo gy Progr ess S erie s 312:1 01 -1 11.
Williams, J.J., J. Dutton, C.Y. Chen, and N.S. Fisher. 2010. Metal (As, Cd, Hg, and CH 3 

### Hg) bioaccu mulation from water and food by the benthic amphipod Leptocherius plumulosus

. Environmental Toxicology and Chemistry 29(8):1755

- 1761.

Wil me r, C., a nd M

. F ric ke r. 19 96

. Stom ata. Secon d ed. New York, N Y: Chapm an an d H all. p

. 121.

WI DNR (Wi sconsin De p ar t me nt of N at u ral Resources) 20 07

. A G uide to U nd e rs ta nd in g t he Hydrolog ic Co nd iti on of Wis consin ’s La ke Sup erio r W ate rs heds. Wisc on sin De part me nt of N at u ral R esou rc es (DN R).

X ia o, Z .F. , D

. Stromberg, an d O. Li ndq v ist. 1995 . I n fl uen ce of hu m ic s ub stances o n ph otoly sis o f div al en t me rc ury in a qu eous soluti on

. Wat e r, A ir, an d So il Po ll u ti on 80:7 89 -79 8.

Ya n, X; an d Wa ng , WX. 2002. Ex posu re an d po ten tial f oo d c h ain tra nsfe r f ac tor o f Cd, Se an d Zn in

m ari n e fish Lu tja nu s ar gen ti maculatus

. Marine Ecology Progress Seri es 238: 173-1 86

. Zha ng , Q., L. Ya ng , an d W .X . Wa ng

. 2011 . B io acc umula ti o n a n d t ro ph ic t ra nsfer of dio xi ns in m ari n e copepods an d fis h

. Env ir

on me n tal Po ll ution 159(12):3390-3397.

Attachment A A- 53 October 2014 TRIM.FaTE Inputs[ This page intentionally left blank. ]TRIM

- Based Tiered Screening Methodolo gy for RTR



## Description of Multimedia Ingestion Risk Calculator (MIRC) Used for RTR Exposure and Risk Estimates

Attachment B i October 2014 Description of MIRC[ This page intentionally left blank. ]TRIM

- Based Tiered Scree ning Methodolo gy for RTR



## CONTENTS, ATTACHMENT B

1. Introductio n

................................ ..................................................................................... B-1
1.1. Purpose and Overview

................................ .......................................................... B-1
1.2. Scope of MIRC

................................ ...................................................................... B-1
1.3. Use in EP A’s Air Toxics Program

........................................................................... B-2
1.4. MIRC Highlights

................................ ..................................................................... B-2
1.5. Organization of This Attachment

............................................................................ B-3

2. MIRC Overview

................................ ............................................................................... B-3
2.1. Software

................................ ................................................................................ B-4
2.2. Exposure Pathways

................................ ............................................................... B-6
2.3. Receptor Groups

................................................................................................... B-7

3. Exposure Algorithms

................................ ....................................................................... B-9
3.1. Farm Food Chain Algorithms

................................................................................. B-9 3.1.1. Estimating Chemical Concentrations in Produce

................................... B- 10 3.1.2. Estimating Chemical Concentrations in Animal Products

....................... B- 16
3.2. Chemical Intake Calculations for Adults and Non

- Infant Children

........................ B- 19 3.2.1. Chemical Intake from Soil Ingestion

....................................................... B- 21 3.2.2. Chemical Intake from Fish Ingestion

...................................................... B- 21 3.2.3. Chemical Intake from Fruit Ingestion

..................................................... B- 23 3.2.4. Chemical Intake from Veg etable Ingestion

............................................. B- 23 3.2.5. Chemical Intake from Animal Product Ingestion

.................................... B- 25 3.2.6. Chemical Intake from Drinking Water Ingestion

..................................... B- 27
3.3. Total Chemical Intake

................................ .......................................................... B- 27
3.4. Chemical Intake Calculations for Nursing Infants

................................................. B- 28 3.4.1. Infant Average Daily Absorbed Dose

..................................................... B- 29 3.4.2. Chemical Concentration in Breast Milk Fat

............................................ B- 30 3. 4. 3. Chem ical Concentration in Aqueous Phase of Breast Milk

.................... B- 33 3.4.4. Alternative Model for Infant Intake of Methyl Mercury

............................ B- 35

4. Dose

- Resp onse Values Used for Assessment

.............................................................. B- 36
4.1. Cadmium

................................ ............................................................................. B- 39
4.2. Dioxins (2,3,7,8

- TCDD)

................................ ....................................................... B- 39
4.3. Mercury

................................ ............................................................................... B- 40
4.4. Polycyclic Organic Matter

.................................................................................... B- 40

5. Risk Estimation

................................ ............................................................................. B- 42
5.1. Cancer Risks

................................ ....................................................................... B- 42
5.2. Non

- cancer Hazard Quotients

................................ ............................................. B- 44 5.2.1. Hazard Quotients for Chemicals with a Chronic RfD

.............................. B- 45

Attachment B iii October 2014 Description of MIRCTRIM

- Based Tiered Scree ning Methodolo gy for RTR



### 5.2.2. Hazard Quotients for Chemicals with RfD Based on Developmental Effects

................................ .......................................... B- 45 5.2.3. Hazard Index for Chemicals with RfD s

.................................................. B- 45

6. Model Input Options

................................ ...................................................................... B- 46
6.1. Environmental Concentrations

............................................................................. B- 47
6.2. Farm

- Food

- Chain Parameter Values

................................ ................................... B- 47 6.2.1. List of Farm

- Food

- Chain (FFC) Parameters

.......................................... B- 47 6.2.2. Produce Parameter Values

................................ .................................... B- 49 6.2.3. Animal Product Parameter Values

......................................................... B- 62
6.3. Adult and Non

- Infant Exposure Parameter Values

............................................... B- 64 6.3.1. Body Weights

................................ ........................................................ B- 65 6.3.2. Water Ingestion Rates

................................ ........................................... B- 66 6.3.3. Local Food Ingestion Rates

................................................................... B- 66 6.3.4. Local Fish Ingestion Rates

................................ .................................... B- 70 6.3.5. Soil Ingestion Rates

................................ ............................................... B- 77 6.3.6. To tal Food Ingestion Rates

.................................................................... B- 77
6.4. Other Exposure Factor Values

................................ ............................................. B- 78 6.4.1. Exposure Frequency

................................ ............................................. B- 78 6.4.2. Fraction Contaminated

................................ .......................................... B- 79 6.4.3. Preparation and Cooking Losses

........................................................... B- 79 6.4.4. Food Preparation/Cookin g Adjustment Factor (FPCAF) for Fish B- 81
6.5. Breast

- Milk Infant Exposure Pathway Parameter Values

..................................... B- 82 6.5.1. Receptor

- specific Para meters

................................ ................................ B- 82 6.5.2. Chemical

- Specific Parameter Values

..................................................... B- 86

7. Summary of MIRC Default Exposure Parameter Settings

............................................. B- 89
7.1. D efault Ingestion Rates

...................................... ................................................. B- 89 7.1.1. Fish Ingestion Rates

.............................................................................. B- 90 7.1.2. Farm Food Chain Ingestion

................................ ................................... B- 90
7.2. Default Screening

- Level Population

- Specific Parameter Values

.......................... B- 92
7.3. Default Chemical

- Specific Parameter Value s for Screening Analysis

.................. B- 93
7.4. Screening

- Level Parameter Values for Nursing Infant Exposure

.......................... B- 94 7.4.1. Dioxins B- 94 7.4.2. Methyl Mercury

...................................................................................... B- 95

8. References

................................................................................................................... B- 96

Attachment B iv October 2014 Description of MIRCTRIM

- Based Tiered Scree ning Methodolo gy for RTR



### Exhibits, Attachment B

Exhibit B

- 1. Overview of MIRC Software Application for Performing Farm

- Food

Chain Ingestion Exposure and Risk Calculations

.............................................. B-5
Exhibit B

- 2. Transfer Pathways for Modeled Farm Food Chain (FFC) Media

......................... B-7
Exhibit B

- 3. Chemical Transfer Pathways for Produce

......................................................... B- 10
Exhibit B

- 4. Estimating Chemical Concentration in Aboveground Produce

............................ B- 10
Exhibit B

- 5. Chemical Transfer Pathways for Animal Products

.............................................. B- 16
Exhibit B

- 6. Oral Dose

- response Values Used to Calculate RTR Screening Threshold Emission Rates for PB

- HAP Chemicals a



### ........................................ B- 37
Exhibit B

- 7. WHO 2005 Toxic Equivalency Factors (TEFs) for Dioxin Congeners

................ B- 40
Exhibit B

- 8. Oral Dose

- response Values for Polycyclic Organic Matter (POM) Groups a



### ................................ .......................................................................... B- 41
Exhibit B

- 9. MIRC Parameters Used to Estimate Chemical Concentrations in Farm Foods

................................ .................................................................... B- 48
Exhibit B

- 10. Chemical

- Specific Inputs for Produce Parameters for Chemicals Included in MIRC

............................................................................................ B- 49
Exhibit B

- 11. Chemical

- Specific Inputs by Plant Type for Chemicals in MIRC

...................... B- 51
Exhibit B

- 12. Non

- Chemical

- Specific Produce Inputs

........................................................... B- 61
Exhibit B

- 13. Animal Product Chemical

- specific Inputs for Chemicals Included in MIRC

................................ .............................................................................. B- 62
Exhibit B

- 14. Soil and Plant Ingestion Rates for Animals

...................................................... B- 64
Exhibit B

- 15. Mean and Percentile Body Weight Estimates for Adults and Children

................................ .......................................................................... B- 65
Exhibit B

- 16. Estimated Daily Per Capita Mean and Percentile Water Ingestion Rates for Children and Adults a



### ................................ ........................................ B- 66
Exhibit B

- 17. Summary of Age

- Group Specific Food Ingestion Rates for Farm Food Items

...................................................................................................... B- 67
Exhibit B

- 18. Fish Ingestion Rates Used in Screening Assessment

..................................... B- 72
Exhibit B

- 19. Daily Mean and Percentile Consumer

- Only Fish Ingestion Rates for Children and Adults ( IR CO,y 

### ) a



### ...................................................................... B- 74
Exhibit B

- 20. Fraction of Population Consuming Freshwater/Estuarine Fish on a Single Day ( F PC,y 

### )

............................................................................................ B- 75
Exhibit B

- 21. Calculated Long

- term Mean and Percentile per capita Fish Inges tion Rates for Children and Adults ( IR PC,y 

### )

.............................................. B- 75
Ex hibit B

- 22. Calculated Mean and 90 th 

### Percentile Per capita Fish Ingestion Rates for Populations of Recreational Fishers (IR PC,y 

### )

.................................... B- 76
Exhibit B

- 23. Daily Mean and Percentile Soil Ingestion Rates for Children and Adults

................................ ............................................................................. B- 77
Exhibit B

- 24. Daily Mean and Percentile Per Capit a Total Food Intake for Children and Adults

................................ ......................................................... B- 78

Attachment B v October 2014 Description of MIRCTRIM

- Based Tiered Scree ning Methodolo gy for RTR



### Exhibit B

- 25. Fraction Weight Losses from Preparation of Various Foods

............................ B- 80
Exhibit B- 26. Scenario - and Receptor

- Specific Input Parameter Values Used to Estimate Infant Exposures via Breast Milk

...................................................... B- 83
Exhibit B

- 27. Average Body Weight for Infants

................................ ..................................... B- 83
Exhibit B

- 28. Time

- weighted Average Body Weight for Mothers

.......................................... B- 84
Exhibit B

- 29. Infant Breast Milk Intake Rates

................................ ....................................... B- 86
Exhibit B

- 30. Chemical

- specific Input Parameter Values for Breast Milk Exposure Pathway

................................ .......................................................... B- 87
Exhibit B

- 31. Farm Food Category Ingestion Rates for Health Protective Screenin g Scenario for Farming Households

.................................................. B- 91
Exhibit B

- 32. Mean Body Weight Estimates for Adults and Children a



### ................................... B- 92
Exhibit B

- 33. Chemical

- Specific Parameter Values for Input to MIRC a



### ................................ . B- 93
Exhibit B

- 34. Chemical and Animal

- Type Specific Biotransfer Factor (Ba) Values for Input to MIRC

............................................................................................ B- 94

Attachment B vi October 2014 Description of MIRCTRIM

- Based Tiered Scree ning Methodolo gy for RTR



### Equations, Attachment B
Equation B

- 1. Chemical Concentration in Aboveground Produce

.......................................... B- 11 Equation B

- 2. Chemical Concentration in Aboveground Produ ce Due to Root Uptake

................................ ............................................................................ B- 11 Equation B

- 3. Chemical Concentration in Aboveground Produce Due to Deposition of Particle

- phase Chemical

............................................................ B- 12 Equation B

- 4. Chemical Concentration in Aboveground Produce Due to Air

- to

Plant Transfer of Vapor

- phase Chemical

........................................................ B- 12 Equation B

- 5. Conversion of Aboveground Produce Chemical Concentration fr om Dry - to Wet

- Weight Basis

................................ ................................................ B- 13 Equation B

- 6. Chemical Concentration in Belowground Produce: Nonionic Organic Chemicals

................................ .......................................................... B- 14 Equatio n B

- 7. Chemical Concentration in Belowground Produce: Inorganic Chemicals

....................................................................................................... B- 15 Equation B

- 8. Conversion of Belowground Produce Chemical Concentration from Dry - to Wet

- Weight Basis

................................................................................ B- 15 Equation B

- 9. Chemical Concentration in Beef, Pork, or Total Dairy

..................................... B- 16 Equation B

- 10. Chemical Concentration in Poultry or Eggs

................................................... B- 17 Equation B

- 11. Incidental Ingestion of Chemical in Soil by Livestock

.................................... B- 17 Equation B

- 12. Ingestion of Chemical in Feed by Livestock

.................................................. B- 18 Equation B

- 13. Chemical Concentration in Livestock Feed (All Aboveground)

...................... B- 18 Equation B

- 14. Chemical Concentration in Livestock Feed Due to Root Uptake

.................. B- 19 Equation B

- 15. Average Daily Dose for Specified Age Group and Food Type

...................... B- 19 Equation B

- 16. Chemical Intake fro m Soil Ingestion

............................................................. B- 21 Equation B

- 17. Chemical Intake from Fish Ingestion

............................................................ B- 22 Equation B

- 18. Consumption

- weighted Chemical Concentration in Fish

.............................. B- 22 Equation B

- 19. Chemical Intake from Consumption of Exposed Fruits

................................ . B- 23 Equation B

- 20. Chemical Intake from Consumption of Protected Fruits

............................... B- 23 Equation B

- 21. Chemical Intake from Exposed Vegetables

.................................................. B- 24 Equation B

- 22. Chemical Intake from Protec ted Vegetables

................................................ B- 24 Equation B

- 23. Chemical Intake from Root Vegetables

........................................................ B- 24 Equation B

- 24. Chemical Intake from Ingestion of Beef

....................................................... B- 25
E qua tion B

- 25. Chemical Intake from Dairy Ingestion

.......................................................... B- 25 Equation B

- 26. Chemical Intake from Pork Ingestion

........................................................... B- 25 Equation B

- 27. Chemical Intake from Poultry Ingestion

........................................................ B- 26 Equation B

- 28. Chemical Intake from Egg Ingestion

............................................................ B- 26 Equation B

- 29. Chemical Intake from Drinking Water Ingestion

........................................... B- 27

Attachment B vii October 2014 Description of MIRCTRIM

- Based Tiered Scree ning Methodolo gy for RTR



### Equation B

- 30.

...................................................................................................................... B- 27 Equation B

- 31.

...................................................................................................................... B- 27 Equation B

- 32.

................................ ...................................................................................... B- 27 Equation B

- 33.

................................ ...................................................................................... B- 28 Equation B

- 34.

................................ ...................................................................................... B- 28 Equation B

- 35.

................................ ...................................................................................... B- 28 Equation B

- 36. Lifetime Average Daily Dose (LADD)

........................................................... B- 28 Equation B- 37. Average Daily Dose of Chemical to the Nursing Infant

................................ . B- 29 Equation B

- 38. Chemical Concentration in Breast Milk Fat

.................................................. B- 30 Equati on B

- 39. Daily Maternal Absorbed Intake

................................................................... B- 31 Equation B

- 40. Biological Elimination Rate Constant for Chemicals for Non

lactating Women

................................ ............................................................. B- 32 Equation B

- 41. Biological Elimination Constant for Lipophilic Chemicals for Lactating Women

................................ ............................................................ B- 32 Equation B

- 42. Chemical Concentration in Aqueous Phase of Breast Milk

........................... B- 33 Equation B

- 43. Fraction of Total Chemical in Body in the Blood Plasma Compartment

................................ .................................................................. B- 34 Equation B

- 44. Biological Elimination Rate Constant for Hydrophilic Chemicals

.................. B- 35 Equation B

- 45. Calculation of Infant Average Daily Absorbed Dose of Methyl Mercury

................................ ........................................................................... B- 36 Equation B

- 46. Calculation of Excess Lifetime Cancer Risk

................................................. B- 43 Equation B

- 47.

................................ ...................................................................................... B- 44 Equation B

- 48.

................................ ...................................................................................... B- 44 Equation B

- 49.

................................ ...................................................................................... B- 44 Equation B

- 50.

................................ ...................................................................................... B- 44 Equation B

- 51.

................................ ...................................................................................... B- 44 Equation B

- 52.

................................ ...................................................................................... B- 44 Equation B

- 53.

................................ ...................................................................................... B- 44
E qua tion B

- 54. Hazard Quotient for Chemicals with a Chronic RfD

...................................... B- 45 Equation B

- 55. Hazard Index Calculation

............................................................................. B- 46 Equation B

- 56. Calculation of Age

- Group

- Specific and Food

- Specific Ingestion Rates

................................ .............................................................................. B- 70 Equation B

- 57. Calculation of Alternative Age

- Group

- Specific Fish Ingestion Rates

................................ .............................................................................. B- 73

Attachment B viii October 2014 Description of MIRCTRIM

- Based Tiered Screening Methodolo gy for RTR



## 1. Introduction

1.1. Purpose and Overview This document provides a detailed description of the Multimedia I ngestion Risk Calculator (MIRC), a modeling tool and database designed to assist in estimating risks via multiple ingestion pathways, particularly for food products grown or raised at home or on a farm

. 27 

### MIRC was designed to estimate risks to humans from ingestion of produce or animal products, fish, and water in the vicinity of a source of chemical emissions to air. The user can evaluate either generalized (e.g., health protective default) or more site

- specific scenarios using the same tool. MIRC includ es a database of exposure parameter values, offering the user the option of selecting mean, median, and upper percentile values for many parameters, data permitting. Generally health protective default values were assigned to each parameter in the tool an d the default configuration is used for initial risk screening efforts by EPA’s Office of Air Quality Planning and Standards’ (OAQPS) for Risk and Technology Review (RTR) multimedia risk assessments. MIRC also allows the user to define the farm food chain (FFC) parameter values and receptor characteristics to better represent a site

- specific scenario.

With user

- input concentrations for one or more chemicals in air and soil and air

- to

- surface deposition rates, MIRC calculates the chemical’s concentrations in home - or farm

- grown produce and animal food products using FFC algorithms adapted from EPA’s Human Health Risk Assessment Protocol for Hazardous Waste Combustion Facilities (hereafter referred to as HHRAP; EPA 2005a). MIRC uses these calculated concentrations, along with user

- input chemical concentrations for fish and drinking water, to estimate chemical intake rates, as average daily doses (ADDs), for adults, children, and nursing infants. Users can obtain chemical input concentrations and deposition rates from measurements at an actual site or from a transport and fate model, such as TRIM.FaTE, as is done for RTR risk assessment.
For a specified set of chemical concentrations and MIRC parameter options, MIRC calculates ADDs separately for adults, four age groups of children, and infants to reflect differences in food ingestion rates and diet at different lifestages. MIRC estimates age

- specific hazard quotients (HQs) as the ratio of age

- specific ADDs to the reference dose (RfD) for a chemical. The mos t appropriate HQ for a chemical depends on its toxic mode of action and the duration of exposure required to produce an effect. MIRC also estimates average lifetime ADDs and compares those to cancer slope factors (CSFs) to estimate cancer risks. A breast milk ingestion pathway is available to estimate exposure and risks to nursing infants.
MIRC was developed to be a flexible, transparent application. The tool includes chemical transfer and ingestion exposure algorithms and a database of parameter values, many with several options, used by these equations

. The MIRC database includes values for the relevant physiochemical properties and toxicity reference values for more than 500 chemicals, including approximately 60 inorganics taken primarily from a datab ase developed for HHRAP (EPA 2005a). Although designed for OAQPS ’ RTR assessments for sources of hazardous air pollutants (HAPs), the tool is flexible in its design and can be used to assess risks in many other contexts where soil and air concentrations are predicted or measured.



### 1.2. Scope of MIRC For persistent and bioaccumulative (PB) chemicals, risks from direct inhalation of the chemical can be much less than risks from ingestion of the chemical in water, fish, and food products

27 Fully functional versions of MIRC have been developed in both Access TM- based and ExcelTM

- based formats; how ever, MIRC currently is not publicly available.

Attachment B B-1 October 2014 Description of MIRCTRIM

- Based Tiered Screening Methodolo gy for RTR



### grown in an area of chemi cal deposition. Vegetables and fruits in such areas can become contaminated directly by deposition of the airborne chemical to foliage, fruits, and vegetables or indirectly by root uptake of the chemical deposited to soils. Livestock can be exposed to th e PB chemicals via ingestion of contaminated forage and incidental ingestion of contaminated soils.
For PB chemicals, evaluation of the inhalation pathway for air pollutants may reveal only a portion of the risk to individuals in such populations. Households that consume high quantities of self

- caught fish or locally grown produce and animal products may be particularly susceptible to ingestion of chemicals transferred from air in the vicinity of an air emissions source. For PB chemicals in particular, therefore, EPA developed methods of estimating risk from indirect exposure pathways associated with the deposition of airborne chemicals to gardens and farms, as described in HHRAP (EPA 2005 a).



### 1.3. Use in EPA’s Air Toxics Program
For PB

- HAPs, indirect exposur e pathways, such as ingestion, might contribute more to total risk than the inhalation pathway. EPA therefore developed several computer software tools to assist in evaluating exposure and risk from non

- inhalation pathways. EPA developed the Total Risk I ntegrated Methodology (TRIM) Environmental Fate, Transport, and Ecological Exposure (TRIM.FaTE) computer program to simulate the release, transport, and fate of HAPs from a specific source throughout the area in which local (non

- source) chemical deposition is likely to be a concern. TRIM.FaTE models the transport of individual chemicals from the source through air by advection (wind) of particle - and vapor

- phase chemical and deposition of the chemical from air to terrestrial and aquatic ecosystems by wet and dry deposition. Movement of the chemical through a watershed via erosion and runoff, uptake by plants, and other abiotic and biotic transfer processes also are simulated. For the chemical that reaches surface waters, TRIM.FaTE models uptake and bioacc umulation 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 v arious types of fruits and vegetables, poultry, swine, and dairy (and beef) cattle. MIRC also calculates the associated risks for individuals who consume those foods. MIRC was designed to use specific TRIM.FaTE results to estimate FFC concentrations, ingestion exposures, and human health risks for OAQPS’ RTR assessments. It uses the same approach that OAQPS intends to implement directly in its TRIM system via three modules beyond TRIM.FaTE: TRIM Farm Food Chain, TRIM.Expo Ingestion 

### , and TRIM.Risk

. 28



### 1.4. MIRC H ighlights 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, a nd also can provide air deposition rates likely for the location(s) of interest based on local meteorology. The user can accept the default values for many exposure parameters and screen for small possibilities of risk, or the user can select other option s or overwrite parameter values to tailor the estimates to a specific scenario or location.

28 General information about the TRIM system is available at http://www.epa.gov/ttn/fera/trim_gen.html

.
Attachment B B-2 October 2014 Description of MIRCTRIM

- Based Tiered Screening Methodolo gy for RTR



### MIRC complies with EPA’s latest guidelines for exposure and risk assessment, including HHRAP ; the Agency’s 2005 Guidelines for Carcinogen Risk Assessment (Cancer Guidelines)

, Supplemental Guidance for Assessing Susceptibility from Early

- Life Exposure to Carcinogens (Supplemental Guidance), and Guidance on Selecting Age Groups for Monitoring and Assessing Childhood Exposures to Environmental Contaminants ( EPA 2005b, c,d); and its 2008 Child

- Specific Exposure Factors Handbook ( EPA 2008a)

. In particular, MIRC provides several important capabilities:

• 

### When provided air and soil concentrations, the MIRC software package allows rapid calculation of screening

- level exposure s 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 50 th



### , 90 th 

### , 95 th 

### , and 99 th 

### percentile values where data permit.

• 

### For carcinogens with a mutagenic mode of action, MIRC estimates a lifetime ADD using the three lifestages and potency adjustment factors recommended in EPA’s 2005 Cancer Guidelines and Supplemental Guidance.

• 

### The data for exposure parameters in the tool have been updated to include the latest recommende d values for children issued September 30, 2008, in the Agency’s Child

Specific Exposure Factors Handbook (CSEFH) (EPA 2008a).



### 1.5. O rganization of This Attachment Sections 2 through 5 of this attachment describe the exposure and risk models implemented in MIRC

. Section 2 provides an overview of the FFC exposure scenario and indicates options available to a user to tailor the scenario to specific applications. Section 3 describes the exposure algorithms used in MIRC, including how ADDs are calculated. Section 4 presents the toxicity reference values included in MIRC to calculate risks. Section 5 describes the risk characterization algorithms in MIRC. Section 6 of this document describes data input options for the model. Section 7 describes the default parameterization of MIRC for application to health protective risk screening assessments, and Section 8 provides the references.
Note that the default parameterization described in Section 7 was used to estimate Tier 1 screening threshold emission rates of PB

- HAPs from RTR facilitie s. These emissions levels are assumed to pose negligible risk to subsistence communities in the vicinity of a facility emitting the PB

- HAPs to air. Users of MIRC can modify the default values for many of the parameters to better represent a specific exposure scenario.


## 2. MIRC Overview

The Multimedia Ingestion Risk Calculator (MIRC) software package is designed to allow rapid calculation of screening

- level exposures and risks associated with subsistence and recreational farmer/fisher population s in the vicinity of a source of chemical emissions to air

. The tool allows a user to assess human exposures via ingestion pathways, including drinking water

Attachment B B-3 October 2014 Description of MIRCTRIM

- Based Tiered Screening Methodolo gy for RTR



### consumption, incidental soil ingestion, fish ingestion, and ingestion of ten types of farm food chain (FFC) pro ducts : exposed fruits, protected fruits, exposed vegetables, protected vegetables, root vegetables, beef, total dairy, pork, poultry, and eggs. The tool also includes a breast milk ingestion and risk module for nursing infants

. For fruits and vegetables, the terms “exposed” and “protected” refer to whether the edible portion of the plant is exposed to the atmosphere

. The remainder of this overview consists of three sections. The first (Section 2.1 of this attachment ) provid es 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 ( Section s 2.2 and 2.3 of this attachment ).



### 2.1. Software
The MIRC application includes the following components:

• 

### A graphical user interface through which the user locates and accesses various input and output tables.

• 

### Input tables in which the user can enter 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 facto rs.

• 

### Tabulated outputs of calculated chemical concentrations in the various farm food products (e.g., fruits, vegetables, beef, eggs) and ADDs for those foods and for water and fish ingestion for each receptor category.

• 

### Output tables with estimated cancer risks and non

- cancer hazard estimates associated with total ingestion exposure to each chemical for each receptor category.

Exhibit B-1 provides a flowchart displaying the types of required and optional inputs and the general flo w of calculations carried out by the tool.

Attachment B B-4 October 2014 Description of MIRCTRIM

- Based Tiered Screening Methodolo gy for RTR



### Exhibit B

- 1

. Overview of MIRC Software Application for Performing Farm

- Food

- Chain Ingestion Exposure and Risk Calculations

User Selects Receptor Characteristics From Options or Over

- write Body Weight User Specifies Environmental Concentrations for Chemical of Conc ern

Air Root

- zone and Surface Soils Fish Drinking Water

User Option to Add Breast Milk Pathway yes

no User Selects BMP Parameter Values Uptake by foliage / Uptake by roots: Vegetables, Fruits Grains Hay, Grass Animal Products

Home Grown Food Product Ingestion Rates Fish and Water Ingestion Rates

Duration Breast Feeding; maternal and infant characteristics Average Daily Dose (ADD) for Age Group y; y =1 to 5

Chemical Intake with Food/ Medium Type i; i = 1 to 10

Lifetime Average Daily Dose

Risk Characterization Module Farm Food Chain Biotransfer Calculations

Breast Milk Exposure Module

ADD Maternal [C] in milk
Infant Dose Adult ADD x absorption efficiency

Exposure Module

Chemical Toxicity Reference

Values:
SF and RfDs Age

- specific & Lifetime Exposure Doses
Lifetime Cancer Risks Age

- Specific Hazard Quotients User Selects Chemical(s ) of Concern: For Each, MIRC Calculates Risks

Attachment B B-5 October 2014 Description of MIRCTRIM

- Based Tiered Screening Methodolo gy for RTR



### A form within the graphical user interface enable s the user to construct specific scenarios by choosing ingestion sources, receptor ages, and other input choices (e.g., diet composition, body weight p ercentiles). This feature facilitate s the assessment o f various exposure scenarios. To begin an assessment , the user must supply values for the following chemical

- specific pa rameters for the scenario being evaluated:

• 

### Air concentration of total chemical,

• 

### Fraction chemical in air in vapor

- phase,

• 

### Wet and dry de position 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

• 

### Chemic al concentrations in upper trophic

- level fish.
Users can input measured values or values estimated by TRIM.FaTE or other models for these parameters.
The MIRC application uses the input data and a variety of empirical transfer factor values (included in its database) to estimate chemical concentrations in nine categories of FFC food types (see Section 2.2 of this attachment ). The FFC algorithms and transfer factor values included in MIRC are based on those presented in Chapter 5 of EPA’s HHRAP (EPA 2005 a)

. For outputs, MIRC is designed to calculate individual cancer risk and non

- cancer hazard quotients for one chemical at a time

. It is up to the risk assessor to determine if cancer risks or hazard quotients may be additive a cross two or more chemicals (i.e., if they cause toxic effects in the same target organ by the same mode of action, such as multiple polycyclic aromatic hydrocarbons ( PAHs ) that are carcinogenic by a mutagenic mode of action).

The tool assumes that an indi vidual is exposed via all of the pathways specified (e.g, fruits and vegetables, animal products, soil, etc.). The tool therefore is useful in estimating risk to the maximally exposed individuals in a risk assessment. To evaluate other receptor populatio ns, the user must specify the each exposure scenario separately.



### 2.2. Exposure Pathways
MIRC estimates the concentrations of chemicals i n FFC food categories grown in an area of airborne chemical deposition using algorithms and parameter values provided in HHRA P (EPA 2005a). FFC foods are evaluated in ten categories: exposed fruit, protected fruit, exposed vegetables, protected vegetables, root vegetables, beef, total dairy, pork, poultry, and eggs

. Exhibit B-2 summarize s the pathways by which chemicals are transferred to these food media

. Note that for a general Tier 1 screening

- level assessment, all of the pathways can be modeled,
as is the case for EPA’s Risk and Technology Review (RTR) calculation of screening threshold emission rates for persistent and bioaccumulative hazardous air pollutants (PB

- HAPs) ( EPA 2008b).

Attachment B B-6 October 2014 Description of MIRCTRIM

- Based Tiered Screening Methodolo gy for RTR



### Exhibit B

- 2

. Transfer Pathways for Modeled Farm Food Chain (FFC) Media

Farm Food M edia Chemical Transfer Pathways Exposed fruit and vegetables • Direct deposition from air of particle

- bound chemical

• Air

- to

- plant transfer of vapor phase chemical

• Root uptake from soil Protected fruit and vegetables (including root vegetables) • Root uptake from soil

Beef and total dairy (including milk) • Ingesti on of forage, silage, and grain a

• Soil ingestion Pork • Ingestion of silage and grain a

• Soil ingestion Poultry and eggs • Ingestion of grain a

• Soil ingestion

a Chemical concentrations in forage, silage, a nd grain are estimated via intermediate calculations analogous to those used for aboveground produce.


### Produce types included in the FFC can accumulate a chemical directly from air and/or soil. For exposed produce, chemical mass is assumed to be transferred to plants from the air in two ways. First, particle

- bound chemical can deposit directly on the plant surface. Second, the uptake of vapor

- phase chemicals by plants through their foliage can occur. For both exposed and protected produce, the concentr ation 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
co ncentration factor is applied. The algorithms used to estimate produce concentrations are presented in Section 3.1.1 of this attachment

. Chemical concentrations in animal products are estimated based on the amount of chemical consumed through the diet, including incidental ingestion of soil while grazing. The diet options for farm animals in MIRC include forage (plants grown on

- site for animal grazing, such as grass), silage (wet forage grasses, fresh

- cut hay, or other fresh plant material that has been stored and fermented), and feed grain products grown on the farm (e.g., corn, soybeans). All three animal feed products are assumed to accumulate chemical via root uptake from the soil. Forage and silage also c an accumulate chemical via direct deposition of particle

- bound chemical and vapor transfer.

T he algorithms in MIRC are based on the assumptions that beef and dairy cattle consume all three feed products, while pigs consume only silage and grain and chick ens consume only grain. The incidental ingestion of the chemical in soils during grazing or consumption of foods placed on the ground is estimated using empirical soil ingestion v alues . For secondary animal products (dairy products and eggs), chemical co ncentrations are estimated by applying a biotransfer factor to the estimated concentration in the “source” animal (cows and chickens, respectively). The algorithms used to estimate animal product concentrations are described in Section 3.1.2 of this attachment

. 

### 2.3. Receptor Groups As noted in EPA risk assessment guidelines ( EPA 2005b,c,d, 2008 a ), exposures of children are expected to differ from exposures of adults due to differences in body weights, ingestion rates, dietary prefer ences, 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 B B-7 October 2014 Description of MIRCTRIM

- Based Tiered Screening Methodolo gy for RTR



### EPA’s HHRAP ( Chapter 4, EPA 2005a) recommends assessing exposures for children and adults separately, but considers all non

- infant children in one category. Specifically, HHRAP recommends eight categories of receptor: farmer, child farmer, resident, child resident, fisher, child fisher, acute receptor, and nursing infant . Over time, different EPA programs have used different child age groupings to evaluate body weights, ingestion rates, and other parameter values needed to estimate chemical exposures and risks to children.
To improve the match between age groups used to estimate values across exposure parameters, in 2005, EPA recommend ed a standard set of child age categories for exposure and risk assessments ( EPA 2005b)

. EPA recommended four age groups for infants: birth to < 1 month; 1 to < 3 months; 3 to < 6 months; and 6 to < 12 months

. For young children, EPA recommended an additional four age groups: 1 to < 2 years; 2 to < 3 years; 3 to < 6 years; and 6 to < 11 years

. Two age groupings were recommended for teenagers and young adults: 11 to < 16 years; and 16 to < 21 yea rs

. These age groupings correspond to different developmental stages and reflect different food ingestion rates per unit body weight, with the highest ingestion rates occurring for the youngest, most rapidly growing, age groups.
For purposes of RTR assess ment using MIRC, the selection of age categories is limited by the categories for which most of the FFC food ingestion rates have bee n calculated. In Chapter 13 of both its Exposure Factors Handbook (EFH; EPA 2011a) and its Child

- Specific Exposure Factors 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 b ecause 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 tha t EPA recommended in 2005 for Agency exposure assessments ( EPA 2005b), they are the only age

- groupings for which such data are available. The U.S

. Department of Agriculture’s (USDA’s) 1987

- 1988 Nationwide Food Consumption Survey (USDA 1992, 1993, 1994a) remains the most recent survey of ingestion rates for home

- grown foods , and EPA’s analysis of those data, published in its 2011 EFH, remains the most recent ly published major analysis of those data. Because ingestion of home

- grown produce and animal produ cts are the primary exposure pathway s for which MIRC was developed, those are the age groupings used for all child parameter values used to estimate exposure and risk in MIRC.

Thus, i n MIRC, values for each exposure parameter were estimated for adults (20 to 70 years) and five children’s age groups:

• 

### infants under 1 year (i.e., 0 to < 1 year);

• 

### children ages 1 through 2 years (i.e., 1 to < 3 years);

• 

### children ages 3 through 5 years (i.e., 3 to < 6 years);

• 

### children ages 6 through 11 years (i.e., 6 to < 1 2 years) ; and

• 

### children ages 12 through 19 years (i.e., 12 to < 20 years).

See Sections 5.1 and 5.2 for descriptions of the risk characterization algorithms used to calculate cancer and non

- cancer effects, respectively, for the above age groupings. Exposure and risks to i nfants under 1 year of age are estimated only for the breast

- milk

- ingestion pathway

.

Attachment B B-8 October 2014 Description of MIRCTRIM

- Based Tiered Screening Methodolo gy for RTR



### For assessment of cancer risks from early

- life exposure, EPA recognizes that infants and chil dren 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 fo r adults

. To date, however, data by which to evaluate the relative sensitivity of children and adults to the same daily dose of a carcinogen remain limited

. Based on analyses of radioactive and other carcinogenic chemicals, EPA recommends evaluating two lifestages for children separately from adults for chemicals that cause cancer by a mutagenic mode of action (MOA): from birth to < 2 years and from 2 to < 16 years ( EPA 2005c,d)

. EPA also suggests that, as data become available regarding carcinogens with a mutagenic MOA , further refinements of these age groupings may be considered

. For assessing risks from exposures to carcinogenic chemicals that act via a mutagenic MOA

, the two early lifestages recommended by EPA ( EPA 2005c,d) also are included in MIRC :

• 

### children under the age of 2 years (i.e., 0 to < 2 years); and

• 

### children from 2 through 15 years (i.e., 2 to < 16 years).
Different age groupings are needed for the assessment of risks from carcinogenic chemicals with a mutagenic MOA and other carcinogen s with other or unknown MOAs. Currently in MIRC, t he only PB

- HAPs with a mutagenic mode of carcinogenesis are the carcinogenic PAHs . See Section 5.1 for a description of the age

- dependent adjustment factors (ADAFs) that are used to calculate cancer risks for chemicals with a mutagenic MOA

. 

## 3. Exposure Algorithms The exposure algorithms in MIRC are described below in four sections. Section 3.1 of this attachment presents the algorithms used to estimate chemical concentration s in FFC foods from chemical concentrations in soil and air

. Pathway

- specific algorithms used to estimate chemical intakes by adults and non

- infant children are described in Section 3.2 , and total chemi cal intake calculations are described in Section 3.3

. Finally, the sets of algorithms used to estimate chemical intake via consumption of breast milk by nursing infants are described in Section 3.4

. As noted previously, the exposure algorithms used in MIRC are based on those presented in HHRAP (EPA 2005b). Any differences between MIRC and HHRAP are explained in this section.



### 3.1. Farm Food Chain Algorithms
T he algorithms and parameters used to estimate chemical concentrations in produce and animal products are described in S ections 3.1.1 and 3.1.2 of this attachment , respectively. Discussions of the parameter value options and th e values selected as default s in MIRC for RTR risk assessment are provided in Section 6.2 . The use of TRIM.FaTE to model chemical fate and transport in the environment prior to FFC calculations drives the most significant diff erence between the FFC algorithms included in HHRAP and the equations used for RTR. The approach in HHRAP uses estimated ambient air concentrations and deposition rates from dispersion model simulations that use unit emission rates. Chemical

- specific emission rates (adjusted for vapor and particle

- bound fractions) are then incorporated into some of the HHRAP FFC algorithms to calculate concentrations in FFC media. Soil concentrations are calculated using a similar approach in HHRAP. For assessment of mu ltipathway exposures for RTR, TRIM.FaTE is used to estimate air concentrations, air

- to

- surface deposition rates, and soil concentrations, and these outputs are used in the FFC algorithms.

Attachment B B-9 October 2014 Description of MIRCTRIM

- Based Tiered Screening Methodolo gy for RTR



### 3.1.1. Estimating Chemical Concentrations in Produce Produce (vegetables an d fruits) can become contaminated directly by deposition of airborne chemical s to foliage and fruits or indirectly by uptake of chemical s deposited to the soil. Given these two contamination processes, produce is divided into two main groups: aboveground and belowground produce. Aboveground produce is divided into fruits and vegetables. These groups are further subdivided into “ exposed ” and “ protected ” depending on whether the edible portion of the plant is exposed to the atmosphere or is protected by a husk, hull, or other outer covering

. Exhibit B-3 lists the pathways by which ch emicals are transferred to the FFC produce categories

. Note that for a general screening

- level assessment, all of the pathways can be modeled, as was done for EPA’s calculation of Tier 1 screening threshold emission rates for PB

HAPs in its RTR assessments (EPA 2008b), and as described in the Technical Support Document . Sections 3.1.1.1 and 3.1.1 .2 below describe the transfer pathways and algorithms for aboveground and belowground produce, respectively.

Exhibit B-3

. Chemical Transfer Pathways for Produce

Farm Food Media Chemical Transfer Pathways Aboveground Produce Exposed fruits and vegetables Direct deposition from air of particle

- bound chemical Air

- to

- plant transfer of vapor phase chemical Root uptake from soil Protected fruits and vegetables Root uptake fr om soil

Belowground Produce Root vegetables Root uptake from soil



### 3.1.1.1. Aboveground Produce
For above ground exposed produce, chemical mass is assumed to be transferred to plants from the air in three ways, as illustrated in Exhibit B-4

. First, particle

- bound chemical can deposit directly on the plant surface via deposition ( Pd ). The amount of chemical accumulated is estimated based on the areal fraction of chemical deposition intercepted by the plant surface, minus a loss factor that is intended to account for removal of deposited chemical by wind and rain and changes in concent ration due to growth dilution

. Second, for chemical present in air in the vapor phase, the concentration of chemical accumulated by the plant’s foliage is estimated using an empirical air

- to

- plant biotransfer factor ( Pv )

. Third, the chemical concentration in the plant due to root uptake from the soil ( Pr AG

- produce 

### ) is estimated using an empirical bioconcentration factor ( Br AG

- produce 

### ) that relates the chemical Exhibit B

- 4

. Estimating Chemical Concentration in Aboveground Produce

Deposition of Particles ( Pd ) Root Uptake from Soil ( Pr AG

- produce ) Vapor Transfer

( Pv )

Chemical Concentration in

Aboveground Produce

Attachment B B- 10 October 2014 Description of MIRCTRIM

- Based Tiered Screening Methodolo gy for RTR



### concentration in the plant to the average chemical concentration in the soil at the root

- zone d epth 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 mechani sm through which aboveground protected produce becomes contaminated . As shown below, t he chemical concentration in the aboveground plant due to root uptake from soil ( Pr AG

- produce - DW 

### ) is estimated using an empirical bioconcentration factor ( Br AG

- produce

- DW 

### ) that relates the chemical concentration in the plant to the average chemical concentration in the soil at the root

- zone depth in the produce

- growing area ( Cs root

- zone_produce 

### ) . These equations all assume measurements on a dry

- weight (DW) basis.
Equation B-1

. Chemical Concentration in Aboveground Produce

where :

C

AG- produce

- DW(i) = Concentration of chemical in edible portion of aboveground produce type i, exposed or protecte d, on a dry

- weight (DW) basis (mg/kg produce DW)

Pd (i) = Chemical concentration in edible portion of aboveground produce type i due to deposition of particles (mg/kg produce DW); for protected aboveground produce, Pd equals zero

Pr

AG- produce -DW(i) = Chem ical concentration in edible portion of aboveground produce type i, exposed or protected, due to root uptake from soil at the root

- zone depth of the produce growing area (mg/kg produce DW)

Pv (i) = Chemical concentration in edible portion of aboveground produce type i due to air

- to

- plant transfer (μg/g [or mg/kg] produce DW); for protected aboveground produce, Pv equals zero


### Equation B-2

. Chemical Concentration in Aboveground Pro duce Due to Root Uptake

where:

Pr

AG- produce -DW(i) = Concentration of chemical in edible portion of aboveground produce type i

, exposed or protected , due to root uptake from soil at root

- zone depth in the produce

- growing area, on a dry

- weight (DW) basis (mg/kg produce DW)

Cs root

- zone_produce = Average chemical concentration in soil at root

- zone depth in produce

- growing area (mg/kg soil DW)

Br

AG- produce -DW(i) = Chemical

- specific plant/soil chemical bioconcentration factor for edible portion of aboveground produce type i

, exposed or protected (g soil DW / g produce DW) )()()( )( 

### P iii DW produce AG i DW produce AG 

### PvPd r C ++ =

−− −−

)( ce zone_produ

- root )( 

### P i DW produce AG i DW produce AG 

### Br Cs r

−− −− 

### × =

Attachment B B- 11 October 2014 Description of MIRCTRIM

- Based Tiered Screening Methodolo gy for RTR



### Equation B-3

. Chemical Concentration in Aboveground Produce Due to Deposition of Particle

- phase Chemical

whe re:

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 field per harvest of the edible portion of plant type i (yr)

Yp (i) = Yield or standing crop biomass of the edible portion of plant type i (kg produce DW/m 2 )



### Note that Equation B-3 differs from Equation 5

- 14 in HHRAP, from which it is derived

. In HHRAP, Equation 5

- 14 includes the term Q x ( 1 – Fv ) to indicate the emissions rate, in g/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 emis sion 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 depositio n rates might be values measured near that location or estimated using a fate and transport model, such as TRIM.FaTE, in conjunction with local meteorological information and emissions rate data

. The chemical emissions term used in HHRAP

, Q , therefore, is not used in MIRC ’s Equation B-3

. In addition, in MIRC, Drdp and Drwp , the average annual dry and wet

- particle

- phase deposition rates, respectively, are in units of g/m 2 

### -yr . Users of TRIM.FaTE should note that the dry - and wet

- particle

- deposition rates output from TRIM.FaTE are in units of g/m 2 

### -d ay; therefore, users must adjust the TRIM.FaTE output values to units of g/m 2



### - yr (i.e., multiply by 365 days/yr) before inputting values for Drdp and Drwp into MIRC.
Equation B-4

. Chemical Concentration in Aboveground Produce Due to Air

- to

- Plant Transfer of Vapor

- phase Chemical )()( )) (*)(( )( )( 

### ) 1( )) (( 000 ,1

ii i Tp ikp i i 

### kp Yp e Rp DrwpFwDrdp Pd
× −×××+×

= −

a i AG i AG i 

### VG Bv FvCa Pv ρ )( )( )( 

### ×××

=

Attachment B B- 12 October 2014 Description of MIRCTRIM

- Based Tiered Screening Methodolo gy for RTR



### w here:

Pv (i) = Concentration of chemical in edible portion of aboveground produce type i from air

- to

- plant transfer of vapor

- phase chemical on a dry

- weight (DW) basis (μg/g produce DW); set equal to zero for protected aboveground produce
Ca = Average annual total chemical concentration in air (g/m 3 )
Fv = Fraction of airborne chemical in va por phase (unitless)

Bv AG(i) = Air

- to

- plant biotransfer factor for aboveground produce type i for vapor

- phase chemical in air ([mg/g produce DW] / [mg/g air], i.e., g air/ g produce DW)

VG AG(i) = Empirical correction factor for aboveground exposed produc e type i to address possible overestimate of the diffusive transfer of chemical from the outside to the inside of bulky produce, such as fruit (unitless)

ρ a = Density of air (g/m 3 )



### Note that Equation B-4 differs from Equation 5

- 18 in HHRAP, from which it is derived

. In HHRAP, Equation 5

- 18 includes the term Q x Fv to indicate the emissions rate, in g/sec, of chemical from the source and the fraction of the chemical in vapor phase in the air

. HHRAP also includes the parameter Cyv , or the unitized yearly average air concentration of vapor

phase chemical in units of μg

- sec/g -m 3



### . For MIRC, the user inputs the average annual total ai r concentration of the chemical, Ca , for a specific location relative to the source in units of g /m 3 

### ; MIRC includes a chemical

- specific default value for Fv for chemicals included in its database. The air concentration might be a value measured near that location or a value estimated by a fate and transport model such as TRIM.FaTE . Users of TRIM.FaTE should n ote that the average annual concentration of the total chemical in air (i.e., total of both vapor and particulate phases)

, Ca , output from TRIM.FaTE is in units μ g/m 3 

### ; therefore, the user must adjust the value to units of g/m 3 

### (i.e., divide by 1,000 μg/g) before entering it in MIRC.
The calculations of chemical concentration in aboveground produce, ( C AG

- produce

- DW 

### ), shown above, are on a dry

- weight (D W) basis. The family FFC food ingestion rates, on the other hand, are on a fresh - or wet

- weight (WW) basis. MIRC therefore calculates the concentration in aboveground produce on a wet

- weight basis, C AG

- produce

- WW 

### , using Equation B-5 and the moisture content ( MAF ) of the FFC food category.
Equation B-5

. Conversion of Aboveground Produce Chemical Concentration from D ry - to W et -W eight Basis

where:

C

AG- produce

- WW(i) = Chemical concentration in edible portion of aboveground produce type i on a wet

weight (WW) basis (mg/kg produce WW)

C

AG- produce

- DW(i) = Chemical concentration in edible portion of aboveground produce type i on a dry

weight (DW) basis (mg/kg produce DW)

MAF (i) = Moisture adjustment factor for aboveground produce type i to convert the chemical concentration estimated for dry

- weight produce to the corresponding chemical concentration for full

- weight fresh produce (percent water ) 

###        

− × =

−− −− 

### 100 ) 100 ( )( )( )( i i DW produce AG i WW produce AG 

### MAF C C

Attachment B B- 13 October 2014 Description of MIRCTRIM

- Based Tiered Screening Methodolo gy for RTR



### 3.1.1.2. Belowground Produce The equations by which chemical concentrations are estimated in belowground produce are different for nonionic organic chemicals than for inorganic chemicals and ionic organic chemicals.



### 3.1.1.2.1. Nonionic Organic Chemicals For belowground pro duce , the nonionic organic chemical concentration in the tuber or root vegetable is derived from exposure to the chemical in soil and is estimated using an empirical root concentration factor ( RCF ) and the average chemical concentration in the soil at the root

zone depth in the produce

- growing area ( Cs root

- zone_produce 

### ), as shown in Equation B-6

. The RCF relates the chemical concentration in the plant on a wet

- weight basis to the average chemical concentration in the root

- zone soi l ( Cs root

- zone_produce 

### ) on a dry

- weight basis

. Belowground produce (i.e., tubers or root vegetables) are protected from the deposition and va por transfer by being covered by soil

. Therefore, root uptake of chemicals is the primary mechanism through which belowground produce becomes contaminated

. Equation B-6

. Chemical Concentration in Belowground Produce: Nonionic Organic Chemicals

where:

C BG- produce

- WW = Concentration of chemical in belowground (BG) produce (i.e., tuber or root vegetable) on a wet

- weight (WW) basis (mg chemical/kg produce WW)*

Cs root

- zone_produce = Average chemical concentration in soil at root

- zone depth in produce

- growing area, on a dry

- weight (DW) basis (mg chemical/kg soil DW)

RCF = Chemical

- specific root concentration factor for tubers and root produce (L soil pore water/kg root WW) *

VG rootveg = Empirical correction factor for belowground produce (i.e., tuber or root vegetable) to account for possible overestimate of the diffusive transfer of chemicals from the outside to the inside of bulky tubers or roots (based on carrots and potatoes) (unitless) *
Kds = Chemical

- specific soil/water partition coefficient (L soil pore water/kg soil DW)

UCF = Unit s conversion factor of 1 kg/L

* Note that there is only one type of BG produce; hence there are no plant

- type

- specific subscripts

. 

### The RCF, as developed by Briggs et al . (1982), is the ratio of the chemical concentration in the edible root on a wet

- weigh t basis to its concentration in the soil pore water. RCFs are based on experiments with growth solutions (hydroponic) instead of soils; therefore, it is necessary to divide the soil concentration by the chemical

- specific soil/water partition coefficient ( Kds )

. There is no conversion of chemical concentrations in belowground produce from DW to WW because the values are already on a WW basis

. For nonionic organic chemicals, it is possible to predict RCF values and Kds values ( for a specified soil organic carbon content) from an estimate of the chemical’s K ow 

### from empirically UCF Kds VG RCF Cs rootveg produce zoneroot



### × ××

= −

_ WW

- produce

- BG 

### C

Attachment B B- 14 October 2014 Description of MIRCTRIM

- Based Tiered Screening Methodolo gy for RTR



### derived regression models. Those models are shown in HHRAP Appendix A

- 2, Equations A-2- 14 and A -2- 15 ( RCF ) and in Equations A

- 29 and A -2- 10 ( Kds ). The RCF and Kds values so calculate d for many of the chemicals in HHRAP are included in the MIRC databas e (including the values for PAHs and dioxins)

. 

### 3.1.1.2.2. Inorganic and Ionic Organic Chemicals For inorganic chemicals and ionized organic chemicals, it is not possible to predict RCF or Kds value s from K ow 

### . For inorganic chemicals, chemical specific empirical values for the root/soil bioconcentration factor must be used. The root/soil bioconcentration factor, now specified as Br BG

- produce

- DW 

### , must be obtained from the literature for each inorganic chemical on a DW basis. For inorganic chemicals, therefore, Equation B-7 is used instead of Equation B-6

.

Equation B-7

. C hemical Concentration in Belowground Produce: Inorganic Chemicals

w here :

C BG- produce -DW = Concentration of chemical in edible portion of aboveground produce, due to root uptake from soil at root

- zone depth in the produce

- growing area, on a dry

weight (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 B-6 ) ( unitless )



### As for the aboveground produce , the DW estimate of concentration of chemical in the root

v egetables must be transformed to a WW estimate, as shown in Equation B-8

. Equation B-8 . Conversion of Belowground Produce Chemical Con centration from Dry - to Wet

- Weight Basis

where:

C BG- produce

- WW = Chemical concentration in edible portion of belowground produce on a weight

weight (WW) basis (mg/kg produce WW)

C BG- produce -DW = Concentration of chemical in edible portion of aboveground produce, due to root uptake from soil at root

- zone depth in the produce

- growing area, on a dry

weight (DW) basis (mg/kg produce DW)

MAF (BG) = Moisture adjustment factor (as in Equation B-5 , but single value for below ground produce) (percent water) 

### 1 _ rootveg DW produce BG produce zoneroot -DW BG-produce 

### VG Br Cs C × ×

= −− −



###      

− × =

−− −− 

### 100 ) 100 ( BG DW produce BG WW produce BG 

### MAF C C

Attachment B B- 15 October 2014 Description of MIRCTRIM

- Based Tiered Screening Methodolo gy for RTR



### 3.1.2. Estimating Chemical Concentrations in Animal Products Chemical concentrations in animal products are estimated based on the amount of chemical consumed by each animal group m through each plant feed type i ( Plant Ch

- In take ( i,m

) 

### ) and incidental ingestion of soil for ground

- foraging animals ( Soil Ch

- Intake (m) 

### )

. Exhibit B-5 summarizes the pathways by which chemicals are transferred to these home - or farm

- raised animal food products

. Note that for a general screening

- level assessment, all of the pathways can be modeled, as is done for EPA’s RTR calculation of screening threshold emission rates for PB

HAPs (EPA 2008b).

The feed options for farm animals in MIRC include forage (plants grown on

- site for animal grazing, such as grass), silage (wet forage grasses, fresh

- cut hay, or other fresh plant material that has been stored and fermented), and grain products grown on the farm

. As seen in Exhibit B-5

, the algorithms in MIR C for chemical intake with plant feed s ( Plant Ch

- Intake (i,m) 

### ) are based on the assumptions that beef and dairy cattle consume all three plant feed products, while pigs consume only silage and grain

, and chickens consume only grain

.
Exhibit B

- 5

. Chemical Transfer Pathways for Animal Products

Farm Food Media Chemical Transfer Pathways Animal Products Beef and total dairy (including milk) • Ingesti on of forage, silage, and grai n a

• Incidental soil ingestion
Pork • Ingestion of silage and grain a

• Incidental soil ingestion Poultry and eggs • Ingestion of grain a

• Incidental soil ingestion

a Chemical concentrations in plant feed (i.e., forage, silage, and grain) are estimated via interm ediate calculations (see Equation B

- 13

, Equation B

- 14

, Equation B

- 3 , and Equation B

- 4 ).


### Forage and silage are exposed to the air and can accumulate chemical s via direct deposition of particle

- bound chemical and tra nsfer of vapor

- phase chemical, while all animal feed grains are assumed to be protected from the air by a husk or pod (e.g., corn, soybeans)

. All three animal feed products are assumed to accumulate chemical via root uptake

. Chemical concentrations are estimated for animal feeds using algorithms analogous to those for aboveground farm produce described above . MIRC uses Equation B-9 to calculat e the concentration of chemical in beef, por k, or total dairy and Equation B- 10 to calculat e the concentration of chemical in poultry or eggs

. The chemical concentration in mammalian farm animals (i.e., beef and pigs) is adjusted using a metabolism factor ( MF ) that accounts for endogenous degradation o f the chemical (see Equation B-9 )

. MF is set to 1.0 for chemicals that are not metabolized and for chemicals for which the metabolic degradation rate is unknown

. Although oth er vertebrates, including birds, are likely to have similar metabolic pathways for most chemicals, the health protective assumption is that birds do not metabolize any chemicals; therefore, the MF is omitted from Equation B- 10 for poultry and eggs.
Equation B-9

. Chemical Concentration in Beef, Pork, or Total Dairy



###        
+ ××= 

# ∑

= − − n

i mi Intake Ch m Intake Ch m m mammal 

### Plant Soil MF Ba C

1 ),( )( )()(

Attachment B B- 16 October 2014 Description of MIRCTRIM

- Based Tiered Screening Methodolo gy for RTR



### where:

C mammal(m) = Concentration of chemical in mammalian animal product m , where m = be ef, pork, or total dairy (mg chemical/kg animal product WW)

Ba (m) = Chemical

- specific biotransfer factor for chemical in diet to chemical in animal food product m , where m = beef, pork, or total dairy ([mg chemical/kg animal product WW] / [mg chemical 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 placed on the ground (mg/day); see Equation B- 11 below

Plant Ch- Intake(i,m) = For livestock (animal product) type m , ingestion of chemical from plant feed type i (mg chemical/kg livestock WW); see Equation B- 12 below (If m = beef or total dairy, then n = 3 and i = forage, silage, and grain; m = pork, then n = 2 and i = silage and grain; m = poultry, then n = 1 and I = grain.)


### Equation B- 10

. Chemical Concentration in Poultry or Eggs

where:

C poultry(m) = Concentration of chemical in food product m , where m = poultry or eggs (mg chemical/kg animal product WW)

Ba (m) = Chemical

- specific biotransfer factor for food product m , where m = poultry or eggs (day/kg animal product WW)

Soil Ch- Intake(m) = Incidental ingestion of chemical in surface soils by consumption of food on the ground (mg chemical/day) where m = poultry; see Equation B- 11

Plant Ch- Intake(i,m) = For poultry (and eggs), animal m , ingestion of the chemical in plant feed type i (mg chemical/day), which for poultry is limited to grain; see Equation B- 12


### In MIRC, t he incidental ingestion of the chemica l in soils by livestock during grazing or consumption of feed placed on the ground ( Soil Ch

- Intake (m) 

### ) is estimated using empirical soil ingestion rate s ( Qs ) and a soil bioavailability factor for livestock ( Bs ), as shown in Equation B- 11 . At this time, the default value for Bs in MIRC for all chemicals is 1.0 (i.e., t he chemical in soil is assumed to be 100 percent bioavailable to the animal). This assumption may be reasonably accurate for the soil surface to which airborne chemic al is deposited. MIRC allows the user to enter a surface soil concentration for areas where livestock forage, Cs S-livestock 

### , that is distinct from the surface soil concentration input for areas where produce may be grown and where humans might incidentall y ingest soils (see Section 6.1 of this attachment )

. Equation B- 11

. Incidental Ingestion of Chemical in Soil by Livestock 

# ( ) ),( )( )()( mi Intake Ch m Intake Ch m m poultry 

### Plant Soil Ba C

− − 

### + ×=

Bs Cs Qs Soil livestock s m Intake Ch 

### × ×=

− − )(

Attachment B B- 17 October 2014 Description of MIRCTRIM

- Based Tiered Screening Methodolo gy for RTR



### where:

Soil Ch- Intake(m) = Incidental ingestion of the chemical in surface soils by livestock type m during grazing or consumption of foods placed on the ground (mg chemical/day)
Qs (m) = Quantity of soil eaten by animal type m each day (kg soil DW/day)

Cs s-livestock = Chemical concentration in surface soil in contaminated area where livestock feed (mg chemical/kg soil DW)

Bs = Soil bioavailability factor for livestock (unitless) (assumed to be the same for birds and mammals)


### Animal ingestion of the chemical in feed is 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 f eed type, i

, Plant Ch

- Intake( i,m

) 

### , is calculated separately according to Equation B- 12 . Note that the animal feed ingestion rates are on a dry

- weight (DW) basis; hence, no DW to wet weight (WW) conversion is needed.
Equation B- 12

. Ingestion of Chemical in Feed by Livestock

where:

Plant Ch-Intake(i,m) = Ingest ion of chemical in plant feed type i (mg chemical/day), where i = forage, silage, or grain, for livestock type m

F (i,m) = Fraction of plant feed type i obtained from contaminated area used to grow animal feed, where I = forage, silage, or grain (unitless) for livestock type m
Qp (i,m) = Quantity of plant feed type i consumed per animal per day (kg plant feed DW/day), where i = forage, silage, or grain, for livestock type m

C feed(i) = Concentration of chemical in ingested plant feed type i (mg chemical/kg plant feed DW), where i = forage, silage, or grain


### The concentrations of chemical in the three different types of plant feeds for livestock are calculated according to Equation B- 13

. The equation is the same as that for abovegro und produ ce in Equation B-1 , w ith the exception that the concentrations are for plants used as animal feeds (not produce consumed by humans) and all types of plant feed (i.e., forage, silage, and grain) are aboveground

. Equation B- 13

. Chemical Concentration in Livestock Feed (All Aboveground)

where:

C feed(i) = Concentration of chemical in plant feed type i on a dry

- weight (DW) basis (mg chemical/kg p lant feed DW), where i = forage, silage, or grain )( ),(),(),( i feed mi mimi Intake Ch 

### C Qp F Plant ××=

−

(i) (i) feed(i) feed(i) 

### Pv PdPr C ++=

Attachment B B- 18 October 2014 Description of MIRCTRIM

- Based Tiered Screening Methodolo gy for RTR

Pr

feed(i) = Concentration of chemical in plant feed type i due to root uptake from soil (mg/kg DW), where i = forage, silage, or grain; see Equation B- 14 below

Pd (i) = Concentrat ion of chemical in plant feed type i due to wet and dry deposition of particle

- phase chemical (mg/kg DW), where i = forage, silage, or grain; when i = grain, the Pd term equals zero

Pv (i) = Concentration of chemical in plant feed type i due to air

- to

- pla nt transfer of vapor

- phase chemical (μg/g [or mg/kg] DW) where i = forage, silage, or grain; when i = grain, the Pd term equals zero


### MIRC calculates the chemical concentration in animal feed due to root uptake from the soil using Equation B

- 14

. The equation is the same as Equation B-2 , except that a Br value appropriate to grasses is used and MIRC allows for different soil concentrations in the area used to grow animal feed than in the area used to grow produce for human consumption (see Section 6.1 of this attachment , user inputs) . Note that for feed type i = grains , the Pd and Pv terms do not apply (are set to zero), because the feed products (i.e., corn ke rnels, soy beans) are protected from the air (i.e., by husks, pods).
Equation B- 14 . Chem ical Concentration in Livestock Feed Due to Root Uptake

where:

Pr feed(i) = Concentratio n of chemical in plant feed type i due to root uptake from soil on a dry

- weight (DW) basis (mg chemical/kg plant feed DW), where i = forage, silage, or grain

Cs root

- zone_feed(i) = Average chemical concentration in soil at root

- zone depth in area used to grow plant feed type i (mg chemical/kg soil DW), where i = forage, silage, or grain

Br

feed(i) = Chemical

- specific plant

- soil bioconcentration factor for plant feed type i (kg soil DW/kg plant feed DW), where i = forage, silage, or grain


### The algorithms used to calculate Pd (i) 

### and Pv (i) 

### when plant feed type i = forage and silage are identical to those used to calculate Pd (i) 

### and Pv (i) 

### for aboveground exposed produce (i.e., Equation B-3 and Equation B-4 , re spectively ).

There are no conversion s of DW feed to WW feed , because all feed ingestion rates for liv es tock are based on DW feed.



### 3.2. Chemical Intake Calculations for Adults and Non

- Infant Children MIRC calculates human chemical intake rates from the ingestion of home

- grown foods as average daily doses (ADDs) normalized to body weight for each age grou p, chemical, and food type separately

. ADDs, calculated using Equation B- 15 , are expressed in milligrams of chemical per kilogram of receptor body weight per day (mg/kg

- day).
Equation B- 15

. Average Daily Dose for Specified Age Group and Food Type )( )(_ )( 

### P i feed i feed zoneroot i feed 

### Br Cs r × =

−



###                

× ×××

= days 365 (y)

(y) (y) (y) (i) (y,i) (i) (y,i) 

### EF
ATBW ED FC IR C ADD

Attachment B B- 19 October 2014 Description of MIRCTRIM

- Based Tiered Screening Methodolo gy for RTR



### w here :

ADD (y,i) = Average daily dose for age group y from food type or ingestion medium i (mg chemical/kg body weight

- day)

C (i) = Concentration of chemical in food type i harvested from the contaminated area (mg chemical/kg food or mg food/L water)
IR (y,i) = Ingestion rate for age group y of food type i (kg/day or L/day)
FC (i) = Fraction of food type i that was harvested from contaminated area (unitless)
ED (y) = Exposure duration for age group y (years)
BW (y) = Body weight for age group y (kg)

AT (y) = Averaging time for calculation of daily dose (years) for age group y , set equal to ED in MIRC

EF (y) = Annual exposure frequency for age group y (days)


### Equation B- 15 takes into account the chemical concentration in each food type i (or in water), the quantity of food brought into the h ome for consumption, the loss of some of the mass of the foods due to preparation and cooking, how much of the food is consumed per year, the amount of the food obtained from contaminated areas, and the consumer’s body weight (EPA 2011a, 2003a). In MIRC, ADDs are calculated separately for each chemical, home

- grown food type, and consumer age group.
ADD values, expressed as intakes, not absorbed doses, are appropriate for comparison with RfDs and for use with cancer slope factors ( C SFs) to estimate risk, as discussed in Section 5 of this attachment

. An exception is for the breast

- milk exposure pathway, where calculating the dose available to and absorbed by the nursing infant is rel ated to the dose absorbed by the mother as discussed in Section 3.4 of this attachment

. MIRC evaluates only one exposure scenario at a time. For screening

- level assessments, all components of this equation are assumed to remain constant for consumers in a given age gro up over time (e.g., seasonal and annual variations in diet are not explicitly taken into account)

. To calculate an ADD (y ,i ) 

### from the contaminated area for food group i over an entire lifetime of exposure, age

- group

- specific ingestion rates and body weight s are used for the age groups described in Section 2.3 of this attachment . In MIRC, the averaging time used to calculate the daily dose for an age group ( AT y 

### ) is equal to the exposure duration for that group ( ED y 

### ); therefore these variables drop out of Equation B- 15

. For each chemical included in a screening scenario, total average daily exposure for age group y ( ADD (y) 

### ) is estimated as the sum of chemical intake from all ingestion pathways combined: Note that the last exposure pathway is limited to infants

.

• 

### Incidental soil ingestion;

• 

### Ingestion of fish;

• 

### Ingestion of homegrown fruits (exposed and protected);

• 

### Ingestion of homegrown vegetables (exposed, protected, and root);

• 

### Ingestion of animal products from home

- raised animals:

– Milk and other dairy products from cows,

Attachment B B- 20 October 2014 Description of MIRCTRIM

- Based Tiered Screening Methodolo gy for RTR



### – Beef products,

– Pork products, and

– Poultry and eggs;

• 

### Ingestion of drinking water from specified source; and

• 

### Ingestion of breast milk by infants.
The algorithms for the f irst six exposure pat hways listed above are described in Sections 3.2.1 through 3.2.6 of this attachment

. The algorithms for the breast

- milk ingestion pathway are described in Section 3.4

. 

### 3.2.1. Chemical Intake from Soil Ingestion Equation B- 16 shows the equation used to estimate chemical intake through incidental ingestion of soil

. Equation B- 16

. Chemical Intake from Soil Ingestion

where:

ADD Soil(y) = Average daily chemical intake from incidental ingestion of soil or ingestion by child in age group y (mg chemical/kg body weight

- day)

C Soil = Concentration of chemical in soil from contamina ted area on a dry

- weight (DW) basis (μg/g soil DW)
IR Soil(y) = Soil ingestion rate for age group y (g DW/day)

FC Soil = Fraction of soil ingested that is from contaminated area (unitless)
BW (y) = Body weight for age group y (kg)

EF = Exposure frequency; number of days per year of exposure for family(ies) as specified for scenario ( ≤ 365 days)

Note: MIRC saves soil ingestion rates in units of mg/day (not g/day); therefore, there is an additional 0.001 g/mg conversion unit in the actual MIRC algorithm tha n shown here

. 

### 3.2.2. Chemical Intake from Fish Ingestion Ingestion of locally caught fish is included as a possible exposure pathway in MIRC ( Equation B- 17 ). Two types of fish are included in the exposure algorithm: tr ophic level 3.5 (abbreviated as TL3) fish, equivalent to benthic carnivores such as catfish and trophic level 4 (TL4) fish in the water column, equivalent to game fish such as lake trout and walleye. The chemical concentration in fish in Equation B- 17 is estimated as the consumption

- weighted chemical concentration using Equation B- 18

. 

###        
   

 

   

 
×××

= days 365 EF BW μg mg 0.001 FC IR C ADD
(y) Soil Soil(y) Soil
(y) Soil

Attachment B B- 21 October 2014 Description of MIRCTRIM

- Based Tiered Screening Methodolo gy for RTR



### Equation B- 17

. Chemical Intake from Fish Ingestion

Equation B- 18

. Consumption

- weighted Chemical Concentration in Fish


### C Fish 

### = ( C FishTL3 

### × F TL3 

### ) + ( C FishTL4 

### × F TL4 

### )

w here:

ADD Fish(y) = Average daily chemical intake from ingestion of local fish for age group y (mg/kg

day)

L1 Fish * = Weight of fish brought into home that is discarded during preparation (e.g., head, bones, liver, other viscera, belly fat, skin with fat) (unitless)

L2 Fish * = Loss of weight during cooking, such as evaporation and loss of fluids into pan (unitless)

C Fish TL3 = Chemical concentration in whole fish for trophic level 3.5 (TL3) fish on a wet

weight (WW) basis (mg/kg WW)

C Fish TL4 = Chemical concentration in whole fish for trophic level 4 ( TL4 ) fish on a wet

- weight (WW) basis (mg/kg WW)
F TL3 = Fraction of fish intake that is from TL3 (unitless)
F TL4 = Fraction of fish intake that is from TL4 (unitless)

C Fish = Consumption

- weighted mean chemical concentration in total fish (i.e., as specified by Equation B- 18 ) (mg/kg WW)
FC Fish = Fraction of local fish consumed derived from contaminated area (unitless)
BW (y) = Body weight for age y (kg)
IR Fish(y) * = Local fish ingestion rate f or age y (g WW/day)

EF = Exposure frequency; number of days per year of exposure for family(ies) as specified for scenario ( ≤ 365 days)

* Parameter values must be internally consistent. In contrast to the tables included in MIRC for ingestion rate opti ons for homegrown food products, which are based on the products as brought into the home from the field (see Section 6.3.3 of this attachment ), the tables of fish ingestion rate options included in MIRC are on an “as consumed” basis (i.e., after preparation and cooking losses), and L1 and L2 therefore are set equal to zero. If the user wishes to enter local fish ingestion rates on an “as harvested” basis, the user also should enter L1 and L2 values as specified in Section 6.4.3 of this attachment

. 

### When whole fish are prepared for cooking, it is usual for the viscera, head, and fins to be removed, particularly for larger fish

. Many persons also remove (or do not eat) the skin, bones, and belly fat

. EPA has, therefore, estimated the proportion of the weight of whole fish that tends to be lost during preparation and cooking across a variety of fish species ( EFH; EPA 2011a ) and included those losses in its HHRAP algorithms for chemical intake from fish (L1 Fish 

### and L2 Fish 

### in Equation B- 17 )

. 

## ( ) ( ) days365 g kg 001 0 2111        

×        
× ××

×−×−= EF BW FC

. IR C L L ADD
(y) Fish Fish(y) Fish

Fish Fish Fish(y)

Attachment B B- 22 October 2014 Description of MIRCTRIM

- Based Tiered Screening Methodolo gy for RTR



### 3.2.3. Chemical Intake from Fruit Ingestion Average daily doses of a chemical from homegrown exposed fruits are calculated separately for exposed and protected fruits ( E quation B- 19 and Equation B- 20 , respectively).
Equation B- 19 . Chemical Intake from Consumption of Exposed Fruits

Equation B- 20 . Chemical Intake from Consumption of Protected Fruits

where:

ADD ExpFruit(y) ADD ProFruit(y) = Average daily chemical intake from ingestion of exposed fruit or protected fruit (depending on subscrip t) (mg chemical/kg body weight

- day)

L1 ExpFruit = Mean reduction in fruit weight resulting from removal of skin or peel, core or pit, stems or caps, seeds and defects, and from draining liquids from canned or frozen forms (unitless)

L1 ProFruit = Mean redu ction in fruit weight that results from paring or other preparation techniques for protected fruits (unitless)

L2 ExpFruit = Mean reduction in fruit weight that results from draining liquids from cooked forms of the fruit (unitless)

C ExpFruit C ProFruit = Chemical concentration in whole exposed fruits or whole protected fruits (depending on subscript) on a wet

- weight (WW) basis (mg chemical/kg exposed fruit WW)

EF = Exposure frequency; number of days per year of exposure for family(ies) as specified for sc enario ( ≤ 365 days)
FC ExpFruit FC ProFruit = Fraction of exposed fruits or protected fruits (depending on subscript) obtained from contaminated area (unitless)

IR ExpFruit(y) IR ProFruit(y) = Ingestion rate of home

- grown exposed fruits or protected fruits (depending on subscript) for age y (g WW/kg body weight

- day)


### Fruit ingestion rates in the survey were based on weights of unprepared fruits (e.g., one apple; one pear) or the weight of a can of fruit (e.g., 8 oz can)

. The weight of the fruit ingested is less than the initial weight owing to common preparation actions ( L1 ExpFruit 

### and L1 ProFruit 

### ; e.g., coring apples and pears; pe e ling apples; pitting cherries)

. Cooking of exposed fruit (e.g., berries, apples, peaches) often results in further weight loss t hat results from liquids lost during cooking and drained from the cooking vessel ( L2 ExpFruit 

### ). EPA has assumed that cooking of protected fruit results in no loss of weight for the fruit.



### 3.2.4. Chemical Intake from Vegetable Ingestion MIRC includes three separat e algorithms for homegrown vegetables adapted from EPA’s HHRAP Modeling System (EPA 2005a ): one for exposed vegetables such as asparagus, broccoli, lettuce, and tomatoes ( although they are actually a fruit); one for protected vegetables 

# ( ) ( )        

×        

× × × × −× −= days 365 g kg 001 0 2111 EF FC

. IR C L L ADD ExpFruit ) ExpFruit(y ExpFruit ExpFruit ExpFruit ) ExpFruit(y



## ( )        

×        

× × × × −= days 365 g kg 001 0 11 EF FC

. IR C L ADD ProFruit ) ProFruit(y ProFruit ProFruit ) ProFruit(y

Attachment B B- 23 October 2014 Description of MIRCTRIM

- Based Tiered Screening Methodolo gy for RTR



### such as corn, cabba ge, soybeans, and peas; and one for root vegetables such as carrots, beets, and potatoes (see Equation B- 21

, Equation B- 22 , and Equation B- 23 , respectively ).
Equation B- 21 . Chemical Intake from Exposed Vegetables

Equation B- 22 . Chemical Intake from Protected Vegetables

Equation B- 23 . Chemical Intake from Root Vegetables

where:

ADD ExpVeg(y) ADD ProVeg(y) ADD RootVeg(y) = Average chemical intake from ingestion of exposed vegetables, protected
ve getables, or root vegetables (depending on subscript) for age group y (mg chemical/kg body weight

- day)

L1 ExpVeg = Mean net preparation and cooking weight loss for exposed vegetables (unitless); includes removing stalks, paring skins, discarding damaged le aves

L1 ProVeg = Mean net cooking weight loss for protected vegetables (unitless); includes removing husks, discarding pods of beans and peas, removal of outer leaves

L1 RootVeg = Mean net cooking weight loss for root vegetables (unitless); includes losses from removal of tops and paring skins

L2 RootVeg = Mean net post cooking weight loss for root vegetables from draining cooking liquids and removal of skin after cooking (unitless)
C ExpVeg C ProVeg C RootVeg = Chemical concentration in exposed vegetables, p rotected vegetables, or root vegetables (depending on subscript) on a wet

- weight (WW) basis (mg chemical/kg vegetable WW)

EF = Exposure frequency; number of days per year of exposure for family(ies) as specified for scenario ( ≤ 365 days) FC ExpVeg FC ProVeg FC RootVeg = Fraction of exposed vegetables, protected vegetables, or root vegetables (depending on subscript) obtained from contaminated area (unitless)

IR ExpVeg(y) IR ProVeg(y) IR RootVeg(y) = Ingestion rate of exposed vegetables, protected vegetables, or root vegetables (depending on subscript) for age group y (g vegetable WW/kg body weight

- day) 

# ( )        

×        

× × ×× −= days 365 g kg 001 0 11 EF FC

. I R C L A DD ExpVeg ExpVeg(y) ExpVeg ExpVeg ExpVeg(y)



# ( )        

×        

× × ××−= days 365 g kg 001 0 11 EF FC

. IR C L ADD ProVeg ProVeg(y) ProVeg ProVeg ProVeg(y)



# ( ) ( )        

×        

× × × × −× −= days 365 g kg 001 0 2111 EF FC

. IR C L L ADD RootVeg RootVeg(y) RootVeg RootVeg RootVeg RootVeg(y)

Attachment B B- 24 October 2014 Description of MIRCTRIM

- Based Tiered Screening Methodolo gy for RTR



### 3.2.5. Chemical Intake from Animal Product Ingestion Calculations of chemical intake from the consumption of farm animals and related food products are provided below i n Equation B- 24 through Equation 2

- 28 for homegrown beef, dairy (milk), pork, poultry, and eggs, respectively.
Equation B- 24

. Chemical Intake from Inge stion of Beef

where:

ADD Beef(y) = Average daily chemical intake from ingestion of beef for age group y (mg/kg

day)
L1 Beef = Mean net cooking loss for beef (unitless)
L2 Beef = Mean net post cooking loss for beef (unitless)

C Beef = Concentration of cont aminant in beef (mg/kg WW))

EF = Exposure frequency; number of days per year of exposure for family(ies) as specified for scenario ( ≤ 365 days)

IR Beef(y) = Ingestion rate of contaminated beef for age group y (g WW/kg

- day)

FCBeef = Fraction of beef cons umed raised on contaminated area or fed contaminated silage and grains (unitless)


### Equation B- 25

. Chemical Intake from Dairy Ingestion

where :

ADD Dairy(y) = Average daily chemi cal intake from ingestion of total dairy for age group y (mg/kg

- day)
C Dairy = Average concentration of contaminant in total dairy (mg/kg WW)

EF = Exposure frequency; number of days per year of exposure for family(ies) as specified for scenario ( ≤ 365 day s)
IR Dairy(y) = Ingestion rate of contaminated total dairy for age group y (g WW/kg

- day)

FC Dairy = Fraction of total dairy products from contaminated area (unitless)


### Equation B- 26

. Chemical Intake from Pork Ingestion 

## ( ) ( )        

×        

× ×××−×−= days 365 g kg 001 0 2111 EF FC

. IR CL L ADD Beef Beef(y) Beef Beef Beef Beef(y)



###        

×        

× ××= days 365 g kg 001 0 EF FC

. IR C ADD Dairy Dairy(y) Dairy Dairy(y)



## ( ) ( )        

×        

× ×××−×−= days 365 g kg 001 0 2111 EF FC

. IR CL L ADD Pork Pork(y) Pork Pork Pork Pork(y)

Attachment B B- 25 October 2014 Description of MIRCTRIM

- Based Tiered Screening Methodolo gy for RTR



### where:

ADD Pork(y) = Average daily chemical intake from ingestion of pork for age group y (mg/kg

day)

L1 Pork = Mean net cooking loss for pork (unitless); includes dripping and volatile losses during cooking; averaged over various cuts and preparation methods

L2 Pork = Mean net post cooking loss for pork (unitless); includes losses from cutting, shrinkage, excess fat, bones, scraps, and juices; averaged over various cuts and preparation methods
C Pork = Concentra tion of contaminant in pork (mg/kg WW)

EF = Exposure frequency; number of days per year of exposure for family(ies) as specified for scenario ( ≤ 365 days)
IR Pork(y) = Ingestion rate of contaminated pork for age y (g WW/kg

- day)

FC Pork = Fraction of pork obtained from contaminated area (unitless)


### Equation B- 27

. Chemical Intake from Poultry Ingestion

where:

ADDPoultry(y) = Average daily dose (chemical intake) from ingestion of poultry (mg/kg

- day) L1Poultry = Mean net cooking loss for poultry (unitless) L2Poultry = Mean net post cooking loss for poultry (unitless)

CPoultry = Concentration of chemical in poultry (mg/kg WW)

EF = Exposure frequency; number of days per year of exposure for family(ies) as specified for scenario ( ≤ 365 days) IRPoultry(y) = Ingestion rate of poultry for age group y (g WW/kg

- day)

FC Poultry = Fraction of poultry from contaminated area or fed contaminated grains (unitless)


### The reduction in the weigh t of beef, pork , and poultry during and after cooking may correlate with an increase or decrease in the concentration of the chemical in the food as consumed depending on the chemical and depending on the cooking method.
Equation B- 28 . Chemical Intake from Egg Ingestion

where:

ADD Egg(y) = Average daily chemical intake from ingestion of eggs for age group y (mg/kg

day) 

# ( ) ( )        

×        

× × ×× −×−= days 365 g kg 001 0 2111 EF FC

. IR C L L ADD Poultry Poultry(y) Poultry Poultry Poultry Poultry(y)



###        

×        

× ××= days 365 g kg 001 0 EF FC

. I R C A DD Egg Egg(y) Egg Egg(y)

Attachment B B- 26 October 2014 Description of MIRCTRIM

- Based Tiered Screening Methodolo gy for RTR

C Egg = Concentration of contaminant in eggs (mg/kg WW)

EF = Exposure frequency; number of days per year of exposure for family(ies) as specified for scenario ( ≤ 365 days)
IR Egg(y) = Ingestion rate of contaminated eggs for age group y (g WW/kg

- day)

FC Egg = Fraction of eggs obtained from contaminated area (unitle ss)



### 3.2.6. Chemical Intake from Drinking Water Ingestion If the user chooses to evaluate chemical ingestion via drinking water , the user specifies a chemical concentration in g/L (equivalent to mg/mL) based on their particular scenario. The chemical concentrati on could represent water from groundwater wells, community water, nearby surface waters, or other source

. For this exposure pathway, ingestion rates are in units of milliliters of water per day (mL/day ) (see Equation B- 29 ).
Equ ation B- 29 . Chemical Intake from Drinking Water Ingestion

where:

ADD DW(y) = Average daily chemical intake from ingestion of drinking water from local residential water source for age group y (mg/kg

- day)
C DW = Concentration of contaminant in drinking water (g/L)
IR DW(y) = Drinking water ingestion rate for age group y (mL/day)

FC DW = Fraction of drinking water obtained from contaminated area (unitless)
BW (y) = Body weight of age group y (kg)

EF = Exposure frequency; number of days per year of exposure for family(ies) as specified for scenario ( ≤ 365 days)



### 3.3. Total Chemical Intake
To estimate the total ADD

, or intake of a chemical from all of the exposure media that a single ind ividual in each age group is expected to contact (e.g., soil, local fish, five types of home

grown produce, and five types of home

- raised animals or animal products), the media

- specific chemical intakes are summed for each age group

. Total average daily e xposure for a particular age group y ( ADD (y) 

### ) is estimated as the sum of chemical intake from all ingestion pathways combined, as illustrated in Equation B

- 30 through Equation B

- 35 below.
Equations B

- 30 to B- 35. Total Average Daily Dose of a Chemical for Different Age Groups Equation B

- 30

. Equation B- 31

. Equation B- 32

.        

×         ××

= days 365 EF BW FC IR C ADD
(y) DW DW(y) DW DW(y)

breastmilk 

### ADD ADD = < )1( 

# ∑

= −

− 

### = n
i i 

### ADD ADD 1 ),21( )21( 

# ∑

= −

− 

### = n
i i 

### ADD ADD 1 ),53( )53(

Attachment B B- 27 October 2014 Description of MIRCTRIM

- Based Tiered Screening Methodolo gy for RTR



### Equation B- 33

. Equation B- 34

. Equation B- 35

.

where i represents the i th 

### food type or ingestion medium and n equals the total number of food types or ingestion media, and ADD parameters are defined below:

ADD (<1) = T otal average daily dose of chemical for infants less than one year from ingestion of breast milk (mg/kg

- day)

ADD (1– 2) = Total average daily dose of chemical from all ingestion sources for children ages 1 through 2 years (mg/kg

- day)
ADD (3– 5) = Total average daily dose for children ages 3 through 5 years (mg/kg

- day)
ADD (6– 11) = Total average daily dose for children ages 6 through 11 years (mg/kg

- day)
ADD (12

– 19) = Total average daily dose for children ages 12 through 19 years (mg/kg

- day)

ADD (adult) = Tot al average daily dose for adult age 20 up to 70 years (mg/kg

- day)


### The lifetime average daily dose ( LADD ) is calculated as the time

- weight average of the ADD values for each age group ( Equation B- 36 ).
Equation B- 36

. Lifetime Average Daily Dose (LADD)

The time

- weighting factors simply equal the duration of exposure for the specified age category in years divided by the total lifespan, as sumed to be 70 years

. 

### 3.4. Chemical Intake Calculations for Nursing Infants The scientific literature indicates that infants can be exposed to some chemical s via their mothers’ breast milk. The magnitude of the exposure can be estimated from information on t he mother’s exposure, data on the partitioning of the chemical into various compartments of the mother’s body and into breast milk, and information on the infant ’ s consumption of milk and absorption of the chemica l

. To add this exposure pathway to the MIR C application

, we adapted exposure algorithms and default assumptions from EPA’s Methodology for Assessing Health Risks Associated with Multiple Pathways of Exposure to Combustor Emissions ( EPA 1998)

, hereafter referred to as MPE, as explained below

. Not e that this pathway generally is of most concern for lipophilic bioaccumulative chemicals (e.g., dioxins) that can cause developmental effects

. The period of concern for the more hydrophilic chemicals that cause developmental effects generally is earlier, that is

, from conception to birth . Hydrophilic chemicals generally exchange well between the maternal and fetal blood supplies at the placenta. 

# ∑

= − − 

### = n
i i 

### ADD ADD 1 ), 11 6( ) 11 6( 

# ∑

= −

− 

### = n
i i 

### ADD ADD 1 ), 1912 ( ) 1912 ( 

# ∑

= 

### = n
i i adult adult 

### ADD ADD 1 ),( )(



###       +       +       +       +       +      

= < 

### 70 50 70 8 70 6 70 3 70 2 70

1 (adult) (12-19) (6-11) (3-5) (1-2) 1) ( 

### ADD ADD ADD ADD ADD ADD LADD

Attachment B B- 28 October 2014 Description of MIRCTRIM

- Based Tiered Screening Methodolo gy for RTR



### 3.4.1. Infant Average Daily Absorbed Dose The average daily dose of chemical absorbed by the infant ( DAI inf 

### ) is estim ated in MIRC with Equation B- 37 . This basic exposure equation relies on the concentration of the chemical in the breast milk, the infant’s breast

- milk ingestion rate ( IR milk 

### ), the absorption efficiency of the chemical by the oral route of exposure ( AE inf



### ), the bodyweight of the infant ( BW inf



### ), and the duration of breast feeding ( ED ). Equation B- 37 is EPA’s (EPA 1998) modification of an average daily dose for the infant model first published by Smith (198 7) and includes variables for both the concentration of the chemical in the breast milk fat ( C milkfat 

### ) and the concentration of the chemical in the aqueous phase of breast milk ( C aqueous 

### ). The remainder of the DAI inf 

### - associated equations assume that most chemicals of concern will partition either to the lipid phase or to the aqueous phase of breast milk, although some chemicals may partition significantly to both phases of milk. Thus, the remaining equations in MIRC assume that either C milkfat 

### or C aqueous 

### is equal to zero and hence drops out of the equation.
For the parameters in Equation B- 37 (and the equations that follow) that are not calculated from another equation, an EPA default value and options for other values availabl e in MIRC for the infant breast

- milk

- exposure pathway are described in Section 6.4 of this attachment . The user also can overwrite those parameter values with a different value from the literature as appropriate.
Equation B- 37

. Average Daily Dose of Chemical to the Nursing Infant

w here:

DAI inf = Average daily dose of chemical absorbed by infant (mg chemical/kg body weight

- day)

C milkfat = Concentra tion of chemical in lipid phase of maternal milk (mg chemical/kg milk lipid; calculated using Equation B- 38 )
f mbm = Fraction of fat in breast milk (unitless)

C aqueous = Concentration of chemical in aqueous phase of maternal mil k (mg chemical/kg aqueous phase milk; calculated using Equation B- 42 )
IR milk = Infant milk ingestion rate over the duration of nursing (kg milk/day)

AE inf = Absorption efficiency of the chemical by the oral route of exposure (i .e., chemical

- specific fraction of ingested chemical that is absorbed by the infant) (unitless)
ED = Exposure duration, i.e., duration of breast feeding (days)
BW inf = Body weight of infant averaged over the duration of nursing (kg)

AT = Averaging tim e associated with exposure of interest; equal to ED (days)



### As mentioned above, Equation B- 37 includes terms for the chemical in both the lipid - and non

lipid phases of milk. The remaining equations, however, assume that a chemical of concern will partition to the lipid or aqueous phase of breast milk, but not to both. Different models are used to estimate C milkfat 

### (described in Section 3.4.2 of this attachment ) and C aqueous 

### (described in Section 3.4.3 of this attachment ). 

# ( ) ( ) 

# ( ) 

# [ ]

ATBW EDAE IR f CfC DAI

inf inf milk mbm aqueous mbm milkfat inf 

### × ×××−× +×

= 1

Attachment B B- 29 October 2014 Description of MIRCTRIM

- Based Tiered Screening Methodolo gy for RTR



### 3.4.2. C hemical Concentration in Breast Milk Fat When developing the Methodology for Assessing Health Risks Associated with Multiple Pathways of Exposure to Combustor Emissions (MPE) (EPA 1998), EPA reviewed th ree first

order kinetics models for estimating chemical concentration in breast milk fat. The model selected for use in MIRC is the model selected in MPE. The other two models were not considered in MPE because one used a biotransfer factor (BTF) approach considered more of a screening model than a predictive tool ( Travis et al. 1988 ) and the other assumed that the contaminant concentration in the maternal fat compartment is at steady state and that the concentration in breast milk fat is the same as in maternal body fat ( Smith 1987 ). The model in MIRC is a changing

- concentration model that EPA adapted from a model by Sullivan et al

. (1991). The model, shown in Equation B- 38 , estimates the average chemical concentration in the b reast milk over the entire period of breast feeding by reference to a maximum theoretical steady

- state concentration. Studies of lipophilic chemicals such as dioxins suggest that concentrations in the maternal milk are highest during the first few weeks of breast feeding and then decrease over ti me (ATSDR 1998). Equation B- 38 accounts for the changing concentration in breast milk fat, but estimates one average value to represent the concentration over the entire duration of breas t feeding. The model is dependent on the maternal body burden of the chemical and assumes that the chemical concentration in breast milk fat is the same as the concentration in general maternal body fat. According to reviewers of the model, this assumption warrants further investigation because milk fat appears to be synthesized in the mammary glands and may have lower chemical concentrations than general body fat stores (EPA 2001a).
Equation B- 38

. C hem ical Concentration in Breast Milk Fat

w here:

C milkfat = Concentration of chemical in lipid phase of maternal milk (mg chemical/kg lipid)

DAI mat = Daily absorbed maternal chemical dose (mg chemical/kg maternal body weight

day; calculated using Equation B- 39 )

f f = Fraction of total maternal body burden of chemical that is stored in maternal fat (mg chemical in body fat / mg total chemical in whole body; value from literature or EPA default - see Section 6.5 of this attachment )

k

elim = Chemical

- specific total elimination rate constant for elimination of the chemical by non

- lactating women (per day; e.g., via urine, bile to feces, exhalation; value from literature or calculated using Equation B- 40 )
f fm = Fraction of maternal body weight that is fat stores (unitless)

k

fat_elac = Chemical

- specific rate constant for total elimination of chemical in the lipid phase of milk during nursing (per day; value from literature or calculat ed using Equation B- 41 )
t bf = Duration of breast feeding (days)

t pn = Duration of mother’s exposure prior to parturition and initiation of breast feeding (days) 

# ( )        

−        

−− × + × × ×

= − − bfelac fat pn elim tk

elac fat

elim tk

bfelac fat elac fat

elim

fm elim f

mat milkfat 

### e k k e tkk

k fk f DAI C

_ 

### 1 1 1

_ _ _

Attachment B B- 30 October 2014 Description of MIRCTRIM

- Based Tiered Screening Methodolo gy for RTR



### Equation B- 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. Thi s product, divided by the chemical

- specific elimination rate constant ( k elim 

### ) for non

lactating adult women and the fraction of the mother’s weight that is fat ( f fm 

### ) , represents the maximum theoretical steady

- state concentration of the chemical in an adult woman. If used alone to estimate the chemical concentration in breast milk fat, the equation as explained thus far is likely to overestimate the chemical concentration in milk fat because it does not account for losses due to breast feeding. Alone, this term ( DAI mat 

### f f 

### / k elim 

### f

fm 

### ) also assumes that the biological half

- life of the chemical in the mother’s breast milk fat is small relative to the duration of the mother’s exposure. However, for chemicals with half

- lives that are longer than the exposure d uration, which are the chemicals of concern in the applications of MIRC to date, an additional term is needed to determine the average concentration in the milk fat over the duration of her exposure.
To account for breast feeding losses and longer chemic al half

- lives in the mother than the exposure duration, an additional term is included in Equation B- 38 . This term includes a fraction dependent on two rate constants, k elim 

### and the elimination constant for a lipophilic chemical in lactating women via the lipid phase of breast milk ( k fat



### _ elac 

### ), the duration of the mother’s chemical exposure prior to nursing ( t pn 

### ), and the duration of breast feeding ( t bf



### ). The whole body concentration ( DAI mat 

### f f 

### / k elim 

### f

fm 

### ), the maximum theoretic al steady

- state concentration, is multiplied by the rate of elimination averaged over the duration of the mother’s exposure, including her exposure prior to and during lactation. To review the derivation of Equation B- 38

, see App endix B of MPE ( EPA 1998).
To estimate an average daily dose absorbed by an infant’s mother, or DAI mat 

### , the average daily dose ( ADD ) (in mg/kg

- day) for the chemical from all sources that MIRC calculates for adults ( ADD (adult) 

### , described in Section 3.3 of this attachment

, Equation B- 35 ), is multiplied by an absorption efficiency ( AE mat 

### ) or fraction of the chemical absorbed by the oral route of exposure, as shown in Equation B- 39 . The value for AE mat 

### can be estimated from absorption efficiencies for adults in general. Available data for some chemicals, in particular some inorganic compounds, indicate AE values for ingestion exposures of substantially less than 100 percent

. For a few of these chemicals, data also indicate lower AEs for the chemical when ingested in food or in soil than when ingested in water (e.g., cadmium)

. For a screening level assessment, however, it is reasonable to either assume 100 percent for the AE mat 

### or to use the higher AE mat 

### of the food and water AE mat 

### values if available; hence, a single AE mat 

### parameter is included in Equation B- 39

. Equation B- 39

. Daily Maternal Absorbed Intake

w here:

DAI mat = Daily maternal dose of chemical absorbed from medium i (mg/kg

- day)

ADD (adult) = Average daily dose to the mother (mg/kg

- day) (calculated by MIRC – see Section 3.3 of this attachment

, Equation B- 35 )

AE mat = Absorption efficiency of the chemical by the oral exposure route (i.e., chemical

specific fraction of ingested chemical that is absorbed) by the mother (unitless) (value from literature or EPA defaul t – see Section 6.4 of this attachment ) mat (adult) mat 

### AE ADD DAI × =

Attachment B B- 31 October 2014 Description of MIRCTRIM

- Based Tiered Screening Methodolo gy for RTR



### Equation B- 35

, used to calculate ADD (adult), 

### is based on many medium

- specific ingestion rates that are normalized to body weight. The adult body weights to wh ich the homegrown food ingestion rates are normalized are the body weights of the consumers in the original USDA survey (see Section 6.3.3 of this attachment ), which included both males and females

. An assumption in the breast

- milk exposure pathway is that those ingestion rates also are applicable to nursing mothers. The original data for ingestion rates for soil, drinking water, and fish are on a per person basis for males and females combined. MIRC divides those chemical intakes by an adult body weight for males and females combined as specified by the user (e.g., 71.4 kg mean value) to estimate the ADD normalized to body weight from those sources. If the user finds that those exposure media contribute the majority of the chemical intake for the risk scenario under consideration, the user may use alternative ingestion rates for those media and alternative body weights for nursing women, as described in Section 6.5 of this attachment

. Elimination rates for chemicals often are reported as the half

- life of the chemical in the body following a known dose of chemical. Many chemicals exhibit a two

- phase elimination process, the first being more rapid than the second. For screening risks for persistent and bioaccumulative chemicals, the half

- life of the slower phase of elimination, presumably from
non

- blood compartments of the body, is the more important of the two. Assuming first

- order kinetics, Equation B- 40 is used to conve rt a measured half

- life for elimination of a chemical for adults or non

- lactating women to an elimination rate constant (EPA 1998). The equation can be used to estimate any kind of chemical loss rate constant from a measured chemical half

- life.
Equation B- 40

. Biological Elimination Rate Constant for Chemicals for Non

- lactating Women

where:

k elim = Chemical

- specific elimination rate constant for elimination of the chemical fo r non

- lactating women (per day; e.g., via urine, bile to feces, exhalation)
ln2 = Natural log of 2 (unitless constant)

h = Chemical

- specific biological half

- life of chemical for non

- lactating women (days)


### For chemicals transferred from the body of lactating women to breast milk, the rate of chemical elimination is augmented by the rate of chemical loss via the milk through breast feeding. The total elimination rate for lactating women sometimes is measured directly and reported in the literature. Wher e direct measurements are not available, and for chemicals that partition predominantly to the lipid

- phase of milk, EPA has used Equation B- 41 to estimate the total chemical elimination rate for lactating women, k fat



### _ elac 

### (EPA 199 8)

. Equation B- 41

. Biological Elimination Constant for Lipophilic Chemicals for Lactating Women

where: 

### h k elim 

### 2 ln

=

mat fm mbm f milk elim elac fat 

### BW f ff IR kk
× ×× +=

_

Attachment B B- 32 October 2014 Description of MIRCTRIM

- Based Tiered Screening Methodolo gy for RTR

k

fat_elac = Rate constant for total elimination of chemical during nu rsing (per day); accounts for both elimination by adults in general and the additional chemical elimination via the lipid phase of milk in nursing women

k

elim = Elimination rate constant for chemical from adults, including non

- lactating women (per day; e .g., via urine, bile to feces, exhalation; chemical

- specific; value from literatur e or calculated from half

- life using Equation B- 40 )
IR milk = Infant milk ingestion rate over the duration of nursing (kg/d)

f f = Fraction of total maternal body burden of chemical that is stored in maternal fat (mg chemical in body fat / mg chemical total in body; value from literature or EPA default)
f mbm = Fraction of fat in breast milk (unitless)

f fm = Fraction of maternal body weight that is fat stores (unitless)

BW mat = Maternal body weight over the entire duration of the mother’s exposure to the chemical including during pregnancy and lactation (kg)


### Equation B- 41 is based on a model from Smith (1987) and accounts for the additional elimination pathway for lipophilic chemicals via the breast milk fat. The term K fat



### _ elac 

### is estimated by adding an estimate of the first

- order elimination constant for breast feeding losses to k elim 

### , which is the chemical

- specific tota l elimination rate constant for non

- lactating women. The breast feeding losses are estimated from the infant’s intake rate of breast milk ( IR milk 

### ), the fraction of the total maternal body burden of the chemical that is stored in maternal body fat ( f f 

### ), th e fraction of the mother’s breast milk that consists of fat (lipids) ( f mbm 

### ), the mother’s body weight ( BW mat 

### ), and the fraction of the mother’s weight that is body fat ( f fm 

### ). In Equation B- 41

, the value for the mother’s body weight should be specific to women of child

- bearing age, as opposed to a body weight value for both males and females that is used to estimate an adult average daily dose and the mother’s absorbed daily intake in Equation B- 39 . Body weight values for the mother are described in Section 6.5 of this attachment . Smith’s (1987) model assumes that the chemical partitions to the lipid

- phase of breast milk to the same degree that it partitions into the mother’s body fat. For highly lipophilic compounds, losses from breast feeding can be larger than losses by all other pathways (EPA 1998).



### 3.4.3. Chemical Concentration in Aqueous Phase of Breast Milk When developing MPE (EPA 1998), EPA also considered models to estimate chemical concentrations in the aqueous phase of breast milk ( C aqueous 

### ). EPA adapted Smith’s (1987) steady state concentration model for estimating C milkfat 

### and developed the C aqueous 

### model shown in Equation B- 42 (EPA 1998). Che micals that would partition to the aqueous phase of human milk include water

- soluble chemicals, such as salts of metals, and other hydrophilic chemicals that may be in equilibrium with bound forms of the chemical in different tissues

. The C aqueous 

### equatio n assumes that the chemical concentration in the aqueous phase of milk is directly proportional to the chemical concentration in the mother’s blood plasma

. The portion of chemical sequestered in red blood cells (e.g., bound to RBC proteins) is assumed to be unavailable for direct transfer to breast milk

. Equation B- 42

. Chemical Concentration in Aqueous Phase of Breast Milk

pm elacaq bm plmat aqueous 

### fk Pc f DAI C
× ××

=

_

Attachment B B- 33 October 2014 Description of MIRCTRIM

- Based Tiered Screening Methodolo gy for RTR



### where:

C aqueous = Concentration of chemical in aqueo us phase of maternal milk (mg/kg)

DAI mat = Daily absorbed maternal chemical dose (mg/kg

- day; calculated by Equation B- 39 )

f pl = Fraction of chemical in the body (based on absorbed intake) that is in the blood pla sma compartment (unitless; value from literature or calculated by Equation B

- 43 )

Pc bm = Partition coefficient for chemical between the plasma and breast milk in the aqueous phase (unitless); assumed to equal 1.0

k

aq_elac = Chem ical

- specific rate constant for total elimination of chemical in the aqueous phase of milk during nursing (per day; value from literature or calculated in Equation B- 44 )

f pm = Fraction of maternal weight that is blood plasma (uni tless)


### Equation B- 42 is a steady

- state concentration model that, like the Equation B- 38 for C milkfat 

### , is dependent on the maternal absorbed daily intake ( DAI mat 

### ). In Equation B- 42

, DAI mat 

### is multiplied by the fraction of the absorbed chemical that is circulating in the blood plasma compartment ( f pl 

### ) and a partitioning coefficient for the chemical between plasma and the aqueous phase of breast milk ( Pc bm 

### ). For highly water

- solub le chemicals that are not transported via special carrier molecules, the chemical is assumed to diffuse passively from the mother’s blood serum to the aqueous phase of her milk, in which case Pc bm 

### would equal 1.0. The denominator includes the biological e limination constant for the chemical in the aqueous phase of breast milk in lactating women ( k aq_elac 

### ) and the fraction of the mother’s weight that is plasma ( f pl 

### ). Because the model assumes steady

- state, it does not account for chemical species with long half

- lives in the body or for body burden losses due to lactation

. These factors are important for highly lipophilic chemicals and for non

- lipophilic chemicals such as methyl mercury, lead, and cadmium that partition into body compartments such as red bl ood cells and bone

. While these latter chemicals or forms of these chemicals are water

- soluble when free, they have relatively long half

- lives because they are in equilibrium with the chemical bound to macromolecules in some tissue compartments

. Lead is of particular concern because it can be released from the bone into the blood during lactation, and thus into the breast milk (EPA 2001a). Due to this limitation, the model may over - or underestimate exposure to the infant.

Because Equation B- 42 is based on the relationship between the chemical concentrations in the aqueous phase of breast milk and the blood plasma, a value for the fraction of the chemical in the mother’s blood plasma ( f pl 

### ) is required. Ideally, an empirical value for f pl 

### should be used. If empirical values are not available, f pl 

### can be estimated from Equation B- 43 , provided that an empirical value can be found for the fraction of the chemical in the body that is in the mother’s whole blood compartment ( f bl 

### ; EPA 1998).
Equation B- 43

. Fraction of Total Chemical in Body in the Blood Plasma Compartment

w here:

f pl = Fraction of chemical in body (based on absorb ed intake) that is in the blood plasma compartment (unitless); chemical

- specific 

# ( ) bp RBC bp bpbl pl 

### f Pc f ff f −+ ×

= 1

Attachment B B- 34 October 2014 Description of MIRCTRIM

- Based Tiered Screening Methodolo gy for RTR

f bl = Fraction of chemical in body (based on absorbed intake) in the whole blood compartment (unitless); chemical

- specific
f bp = Fraction of whole blood that is plasma (unit less)

PcRBC = Partition coefficient for chemical between red blood cells and plasma (unitless); chemical

- specific


### If the fraction of the total chemical in the body that is in the whole blood compartment ( f bl 

### ) is known for a given chemical, then the 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 B- 42 is the total chemical elimination rate for lactating women for hydrophilic chemicals, k aq_elac 

### . As for k fat_elac 

### for lipophilic chemicals, k aq_elac 

### for hydrophilic chemicals would be equal to k elim 

### plus the loss rate for the chemical in the aqueous phase of breast

- milk during lactation. In the case of hydrophilic chemicals, EPA has yet to propose a term for the additional elimination of a chemical in the aqueous phase of milk from breast feeding. Given basi c physiological mechanisms, we assume that chemical loss rates via urine are likely to be significantly higher than loss rates from nursing, however. This is because the counter

- current anatomy of kidney tubules allows substantial concentration of chemicals in the tubules for elimination in urine compared with the concentration in circulating blood and because of active secretion of some chemicals into urine. Therefore, the best estimation of elimination of hydrophilic chemicals by lactating women is simp ly k elim 

### , the elimination of the chemical from a non

- lactating woman, as shown in Equation B- 40 . The extent to which k elim 

### is an underestimate of k aq_elac 

### for a given chemical will determine the extent of health protective bias i n k aq_elac



### . Equation B- 44

. Biological Elimination Rate Constant for Hydrophilic Chemicals

w here :

k aq_elac = Chemical

- specific rate constant for total elimination of chemical by lactating women for hydrophilic chemicals (per day)

k

elim = Chemical

- specific rate constant for total elimination of chemical by non

- lactating women (per day; e.g., via urine, bile to feces, exhalation; value from literature or calculated from half

- li fe using Equation B- 40 )



### 3.4.4. Alternative Model for Infant Intake of Methyl Mercury In this version of MIRC, we were unable to fully parameterize the aqueous model for mercury. In particular, no empirical value could be found for the s teady

- state fraction of total hydrophilic chemical body burden in the mother that is in the blood plasma (f pl 

### , see Exhibit B- 29 ). This parameter could be estimated using Equation B- 43 if a suitable chemic al

- specific fraction of chemical in the body that is in the whole blood (f bl 

### ) could be found. However, the value found for f bl 

### is based on a single

- dose study and is not considered reliable for use in chronic exposure calculations.

We therefore conducted a literature search to identify existing physiologically based toxicokinetic (P BTK) models of lactational transfer of methylmercury (MeHg) in humans. Most PBTK models that we identified focused on gestational transfer of mercury between mother and elim elacaq 

### kk =

_

Attachment B B- 35 October 2014 Description of MIRCTRIM

- Based Tiered Screening Methodolo gy for RTR



### fetus, including a PBTK dynamic compartmental model for gestational transfer of MeHg in humans developed by Gearhart et al . (1995, 1996), and reparameterized by Clewell et al

. (1999).
We did find, however, that Byczkowski and Lipscomb (2001) had added a lactational transfer module to the Clewell et al . (1999) model. Byczkowski and Lipscomb compared their model’s predictions to epidemiological data from mother

- nursing

- infant pairs obtained following an accidental high

- dose poisoning in Iraq (Amin

- Zaki et al . 1976) and from 34 mother

- nursing

infant pairs examined in a low

- dose, chronic exposure environment (Fujita and Takabatake 1977). Using data from the Iraq incident, Byczkowski and Lipscomb (2001) found good agreement between their model’s predictions and the clinical data relating MeHg concentrations in breast milk to MeHg concentrations in infant’s blood with time following the poisoning. To compare their model’s predictions to data from chronic exposure to low doses of MeHg, Byczkowski and Lipscomb (2001) simulated MeHg intake for 500 days prior to conception, continued through gestation, and 6.5 months (200 days) of lactation. Their model’s predictions were consistent with Fujita and Takabatake’s (1977) study, although use of hair/blood partition coeffici ents based on the results of the 1977 study precluded use of this comparison as model validation. Both the model predictions and the mean values from the 1977 data indicated that the concentration of MeHg in the blood of nursing infants was close to the MeHg concentration in their mothers’ blood (approximately 0.025 to 0.027 mg/L, Figure 4 of report). At those blood concentrations, the PBTK model estimated the average maternal intake of MeHg to be 0.68 ± 0.33 (SD) μg/kg

- day and the average infant intake of MeHg to be 0.80 ± 0.38 μg/kg

- day. Therefore, for purposes of MIRC, the DAI inf 

### of MeHg is estimated to be the same as the maternal intake per unit body weight ( Equation B- 42 ).
Equation B- 45

. Calculation of Infant Average Daily Absorbed Dose of Methyl Mercury

where:

DAI inf_MeHg = Average daily dose of MeHg absorbed by infant from breast milk (mg/kg

- day)

DAI mat_MeHg = Average daily dose of methyl mercury absorbed by the mother, predominantly from fish (mg/kg

- day)


## 4. Dose

- Response Values Used for Assessment

Chemical dose

- response values included in MIRC i nclude carcino genic potency slope factors f or ingestion and non

- cancer oral reference doses (RfDs) for chronic exposures. The dose

response values currently used in MIRC for RTR assessments are shown in Exhibit B-6 For some chemicals, OAQPS has identified dose

- response values for use in RTR (EPA 2007a), and these dose

- response values are used in MIRC for RTR assessments. In general, OAQPS chose these values based on the following hierarchy of sources: EPA’s Integrated Risk Information System (IRIS); the Centers for Disease Control’s Agency for Toxic Substances and Disease Registry (ATSDR); and the California Environmental Protection Agency’s (CalEPA’s) Office of Environmental Health Hazard Assessment ( OEHHA ) Toxicity Criteria Database . For PB

- HA P chemicals that are currently evaluated in MIRC but do not currently have dose

- response values identified by OAQPS for RTR, alternative methods for deriving values were used (see Sections 4.2 and 4. 4 of this attachment ). mat_MeHg inf_MeHg 

### DAI DAI =

Attachment B B- 36 October 2014 Description of MIRCTRIM

- Based Tiered Screening Methodolo gy for RTR



### Exhibit B

- 6 . Oral Dose

- response Values Used to Calculate RTR Screening Threshold Emission Rates for PB

- HAP Chemicals a

Chemical CAS No. C ancer S lope F actor R e f erence D ose

Value (mg/kg

- day ) -1 Source Value (mg/kg

day ) Source

**Inorganics** Cadmium compounds in food b 7440439 not available 1.0E

- 03 IRIS Mercury (elemental) 7439976 not available not available Mercuric chloride 7487947 not available 3.0E

- 0 4 IRIS Methyl mercury (MeHg) 22967926 not available 1.0E

- 04 IRIS **Dioxins** 1,2,3,4,6,7,8

- Heptachlorodibenzo

- p

- dioxin 35822469 1.50E+03 Derived from WHO 2005 TEFs c not available
1,2,3,4,6,7,8

- Heptachlorodibenzofuran 67562394 1.50E+03 Derived from WHO 20 05 TEFs c not available
1,2,3,4,7,8,9

- Heptachlorodibenzofuran 55673897 1.50E+03 Derived from WHO 2005 TEFs c not available
1,2,3,4,7,8

- Hexachlorodibenzo

- p

- dioxin 39227286 1.50E+04 Derived from WHO 2005 TEFs c not available
1,2,3,4,7,8

- Hexachlorodibenzof uran 70648269 1.50E+04 Derived from WHO 2005 TEFs c not available
1,2,3,6,7,8

- Hexachlorodibenzo

- p

- dioxin 57653857 1.50E+04 Derived from WHO 2005 TEFs c not available
1,2,3,6,7,8

- Hexachlorodibenzofuran 57117449 1.50E+04 Derived from WHO 2005 TEFs c not ava ilable
1,2,3,7,8,9

- Hexachlorodibenzo

- p

- dioxin 19408743 6.20E+03 IRIS not available
1,2,3,7,8,9

- Hexachlorodibenzofuran 72918219 1.50E+04 Derived from WHO 2005 TEFs c not available
2,3,4,6,7,8

- Hexachlorodibenzofuran 60851345 1.50E+04 Derived from WHO 20 05 TEFs c not available
1,2,3,4,6,7,8,9

- Octachlorodibenzo

- p

- dioxin 3268879 4.50E+01 Derived from WHO 2005 TEFs c not available
1,2,3,4,6,7,8,9

- Octachlorodibenzofuran 39001020 4.50E+01 Derived from WHO 2005 TEFs c not available
1,2,3,7,8

- Pentachlorodiben zo

- p

- dioxin 40321764 1.50E+05 Derived from WHO 2005 TEFs c not available
1,2,3,7,8

- Pentachlorodibenzofuran 57117416 4.50E+03 Derived from WHO 2005 TEFs c not available
2,3,4,7,8

- Pentachlorodibenzofuran 57117314 4.50E+04 Derived from WHO 2005 TEFs c not av ailable
2,3,7,8

- Tetrachlorodibenzo

- p

- dioxin 1746016 1.50E+05 EPA ORD 7E

- 10 IRIS
2,3,7,8

- Tetrachlorodibenzofuran 51207319 1.50E+04 Derived from WHO 2005 TEFs c not available

Attachment B B- 37 October 2014 Description of MIRCTRIM

- Based Tiered Screening Methodolo gy for RTR



### Exhibit B

- 6 . Oral Dose

- response Values Used to Calculate RTR Screening Threshold Emission Rates for PB

- HAP Chemicals a

Chemical CAS No. C ancer S lope F actor R e f erence D ose

Value (mg/kg

- day ) -1 Source Value (mg/kg

day ) Source

**Polycyclic Organic Matter**

1- Methylnaphthalene 90120 5.0E

- 01 EPA 1999, POM Grou p 72002 d 7.0E

- 02 ATSDR

2- Acetylaminofluorene 53963 1.0E+01 EPA 1999, POM Group 75002 d not available

2- Chloronaphthalene 91587 5.0E

- 01 EPA 1999, POM Group 72002 d 8.0E

- 02 IRIS

2- Methylnaphthalene 91576 5.0E

- 01 EPA 1999, POM Group 72002 d 5.0E

- 02 ATSDR

3

- M ethylcholanthrene 56495 2.2E+1 CalEPA not available 7,12

- Dimethylbenz(a)anthracene 57976 2.5 E+0 2 CalEPA not available
Acenaphthene 83329 5.0E

- 01 EPA 1999, POM Group 72002 d 6.0E

- 02 IRIS

Acenaphthylene 208968 5.0E

- 01 EPA 1999, POM Group 72002 d not availa ble

Anthracene 120127 0 e IRIS 3.0E

- 01 IRIS Benz(a)anthracene 56553 1.2E+00 CalEPA not available Benzo(a)pyrene 50328 7.3 E+0 0 IRIS not available Benzo(b)fluoranthene 205992 1.2E+00 CalEPA not available
Benzo(e)pyrene 192972 5.0E

- 01 EPA 1999, POM Group 72002 d not available

Benzo(g,h,i)perylene 191242 5.0E

- 01 EPA 1999, POM Group 72002 d not available
Benzo(k)fluoranthene 207089 1.2E+00 CalEPA not available Carbazole 86748 2.0E

- 02 HEAST not available Chrysene 218019 1.2E

- 01 CalEPA not available Diben zo(a,h)anthracene 53703 4.1E+00 CalEPA not available Dibenzo(a,i)pyrene 189559 1.2E+0 2 CalEPA not available Dibenzo(a,j)acridine 224420 1.2E+00 CalEPA not available

Fluoranthene 206440 5.0E

- 01 EPA 1999, POM Group 72002 d 4.0E

- 02 IRIS

Fluorene 86737 5.0 E- 01 EPA 1999, POM Group 72002 d 4.0E

- 02 IRIS
Indeno(1,2,3

- c,d)pyrene 193395 1.2E+00 CalEPA not available
PAH, total 234 5.0E

- 01 EPA 1999, POM Group 71002 d not available

Perylene 198550 5.0E

- 01 EPA 1999, POM Group 72002 d not available

Phenanthrene 8501 8 0 e IRIS not available
Polycyclic Organic Matter 246 5.0E

- 01 EPA 1999, POM Group 71002 d not available

Attachment B B- 38 October 2014 Description of MIRCTRIM

- Based Tiered Screening Methodolo gy for RTR



### Exhibit B

- 6 . Oral Dose

- response Values Used to Calculate RTR Screening Threshold Emission Rates for PB

- HAP Chemicals a

Chemical CAS No. C ancer S lope F actor R e f erence D ose

Value (mg/kg

- day ) -1 Source Value (mg/kg

day ) Source

Pyrene 129000 0 e IRIS 3.0E

- 02 IRIS

Retene 483658 5.0E

- 01 EPA 1999, POM Group 72002 d not available

ATSDR = Agency for Toxic Substances and Disease Reg istry, IRIS = Integrated Risk Information System

, EPA OAQPS = EPA’s Office of Air Quality Planning and Standards

, CalEPA = California EPA

, EPA ORD = EPA’s Office of Research and Development

, HEAST = EPA Health Effects Assessment Tables, TEF = toxic equivalency factor

a Values as of June 2012; these values may be updated as newer ones become available.

b There are RfDs for both water ingestion and food ingestion for cadmium – the RfD for food is used.

c Dose

- response values for these dioxin congeners are not available from EPA sources. CSFs for these congeners were derived as discussed in Section 4.2 of this attachment

. d The method to assign oral cancer slope factors to polycyclic organic matter (POM) without CSFs available from other EPA sources is the same as that used in the 1999 National Air Toxics Assessment (EPA 1999). A complete description of the methodology is available at: http://w ww.epa.gov/ttn/atw/nata1999/99pdfs/pomapproachjan.pdf and is summarized in Section 4.4 of this attachment

. e W eight of evidence evaluation s indicated that the available data were adequate to determine that this chemical was not carcinogenic (EPA 2010 a),



### 4.1. Cadmium EPA has developed two chronic RfDs for cadmium (Cd) , one for food and one for water, based on data in IRIS indicating a lower absorption efficiency of cadmium from food than from water

. The default RfD set in MIRC is the higher RfD for Cd compounds in food ( as described in Section A.2.3, the drinking water exposure pathway is not modeled in the screening scenario because the likelihood that humans would use a lake as a drinking water source is assumed to be low ). User s of MIRC who assess exposures via drinking water would need to use the RfD for Cd compounds in water (i.e., 5.0E

- 04 mg/kg

- day)

. 

### 4.2. Dioxins (2,3,7,8

- TCDD) For chemicals for which the critical health effect is developmental, either in utero and/or during the first months or years of life, the exposure duration and timing of exposure for comparison with the RfD (or comparable values) require special consideration. The most sensitive health endpoints for both mercury and 2,3,7,8

- TCDD are neurological effects dur ing development that have long

- lasting effects on learning and social behaviors. To ensure a protective risk characterization for these chemicals, it is important to use the shortest exposure duration appropriate, at the appropriate life stage, for comparison with the toxicity reference values. This approach avoids “dilution” of an estimated average ADD that would result from averaging the lower daily chemical intake rates normalized to body weight for older children and adults with the potentially higher daily intake rates of infants over a longer exposure averaging period.
The convention for assessing risk from mixtures of dioxins is by application of toxic equivalency factors (TEFs) to dioxin concentrations, which are then expressed as toxic equivalents (TEQs). Of the dioxin congeners, 2,3,7,8

- TCDD is the most widely studied and considered to be one of the most toxic congeners. It is therefore assigned a TEF of 1, with the other dioxin congener TEQ concentrations scaled relative to 2,3,7,8

- TCDD concentrations on the basis of toxicity. For risk assessment of dioxins for RTR (with one exception), the World Health Organization (WHO) 2005 TEFs presented in Exhibit B-7 were used to derive the CSFs (shown in Exhibit B-6 ) for dioxin congeners without available EPA dose response values. The one exception is 1,2,3,7,8,9

- Hexachlorodibenzo -p- dioxin , whose TEF was based on data from IRIS.

Attachment B B- 39 October 2014 Description of MIRCTRIM

- Based Tiered Screening Methodolo gy for RTR



### Exhibit B

- 7 . WHO 2005 Toxic Equivalency Factors (TEFs) for Dioxin Congeners

Dioxin Congener CAS No. WHO 2005 Toxic Equivalency Factor a 1,2,3,4,6,7,8

- Heptachlorodibenzo

- p

- dioxin 35822469 0.01 1,2,3,4,6,7,8

- Heptachlorodibenzofuran 67562394 0.01 1 ,2,3,4,7,8,9

- Heptachlorodibenzofuran 55673897 0.01 1,2,3,4,7,8

- Hexachlorodibenzo

- p

- dioxin 39227286 0.1 1,2,3,4,7,8

- Hexachlorodibenzofuran 70648269 0.1 1,2,3,6,7,8

- Hexachlorodibenzo

- p

- dioxin 57653857 0.1 1,2,3,6,7,8

- Hexachlorodibenzofuran 57117449 0.1 1,2,3,7,8,9

- Hexachlorodibenzo

- p

- dioxin b 19408743 0.04 1,2,3,7,8,9

- Hexachlorodibenzofuran 72918219 0.1 2,3,4,6,7,8

- Hexachlorodibenzofuran 60851345 0.1 1,2,3,4,6,7,8,9

- Octachlorodibenzo

- p

- dioxin 3268879 3E

- 04 1,2,3,4,6,7,8,9

- Octachlorodibenzofuran 390010 20 3E

- 04 1,2,3,7,8

- Pentachlorodibenzo

- p

- dioxin 40321764 1 1,2,3,7,8

- Pentachlorodibenzofuran 57117416 0.03 2,3,4,7,8

- Pentachlorodibenzofuran 57117314 0.3 2,3,7,8

- Tetrachlorodibenzo

- p

- dioxin 1746016 1 2,3,7,8

- Tetrachlorodibenzofuran 51207319 0.1

a Sourc e: van den Berg et al. 2006, with the one exception in the next footnote.

b For 1,2,3,7,8,9

- HexCDD, OAQPS identified an oral cancer slope factor from IRIS. For the purposes of these multipathway assessments , EPA uses the TEF derived from this IRIS oral CSF (6200 1/mg/kd/d, equaling a TEF of 0.041) rather than the WHO 2005 TEF of 0.1.



### 4.3. Mercury The RfD applies to the pregnant mother as well as young children. EPA has not specified the minimum exposure duration at the RfD level of exposure that is appropriate to use in characterizing risk; we assume 10 years for women of childbearing age and 1 year for infants. We note that human exposures to MeHg are primarily through the consumption of fish and shellfish (EPA 2001b). EPA found that, on average, approximate ly 76 percent of the exposure to MeHg for women of childbearing age could be attributed to ingestion of mercury in freshwater and estuarine fish and shellfish, with the remaining 24 percent derived from marine fish and shellfish. Other sources accounted for less than 0.06 percent of total exposures (EPA 2001b).



### 4.4. Polycyclic Organic Matter
Dose

- response values for some of the POM species that are included in the screening assessment were not identified by OAQPS; for these POM species, an alternative methodo logy for identifying CSFs was needed. Previously, for risk assessment of inhalation exposures to polycyclic organic matter (POM) for EPA’s National

- Scale Air Toxics Assessments (NATA) and for RTR, OAQPS developed an approach for characterizing risks associated with the individual POM species and POM groups reported in the National Emissions Inventory (NEI). I ndividual POM s were assigned to one of eight POM groups according to cancer potencies derived by EPA for IRIS and by CalEPA, and based on assumptions regarding relative carcinogenicity. OAQPS then estimated an inhalation CSF for each POM group. The same approach was used to derive oral CSFs for POMs without available CSFs. Each POM group (with all its member

Attachment B B- 40 October 2014 Description of MIRCTRIM

- Based Tiered Screening Methodolo gy for RTR



### POM species reported in NEI, not just the species currently evaluated in this assessment ) and the corresponding CSFs using this methodology are presented in Exhibit B-8

. These group CSFs are used only when OAQPS has not, for the purposes of RTR, identified a CSF specific to the individual chemical.

Exhibit B

- 8 . Oral Dose

- response Values for Polycyclic Organic Matter (POM) Groups a

Individual POM or POM Group CAS No. C ancer S lope F act or b (mg/kg

- day) -1
**POM Group 71002** Benz(a)anthracene/chrysene (7

- PAH) 103

0.5 Total PAH 234 Polycyclic organic matter 246 16

- PAH 40 16

- PAH

– 7

- PAH 75040 **POM Group 72002** Anthracene 120127

0.5 Pyrene 129000 Benzo (g,h,i) perylene 191242 Benzo(e) pyrene 192972 Benzo(c)phenanthrene 195197 Perylene 198550 Benzo(g,h,i) f luoranthene 203123 Benzo(a)fluoranthene 203338 Fluoranthene 206440 Acenaphthylene 208968 1

- Methylpyrene 2381217 12

- Methylbenz(a)anthracene 2422794 Methylbenzopyrenes 2 47 Methylchrysene 248 Methylanthracene 26914181 Benzofluoranthenes 56832736 9

- Methylbenz(a)anthracene 779022 1

- Methylphenanthrene 832699 Acenaphthene 83329 Phenanthrene 85018 Fluorene 86737 2

- Methylnaphthalene 91576 2

- Chloronaphthalene 91587 **POM Group 73002** 7,12

- Dimethylbenz(a)anthracene 57976 1000 **POM Group 74002** Dibenzo(a,i)pyrene 189559 100 Dibenzo(a,h)pyrene 189640
Attachment B B- 41 October 2014 Description of MIRCTRIM

- Based Tiered Screening Methodolo gy for RTR



### Exhibit B

- 8 . Oral Dose

- response Values for

Polycyclic Organic Matter (POM) Groups a

Individual POM or POM Group CAS No. C ancer S lope F act or b (mg/kg

- day) -1
**POM Group 75002** 3

- Methylcholanthrene 56495

10 Dibenzo(a,e)pyrene 192654 5

- Methylchrysene 3697243 Benzo ( a ) p yrene 50328 Dibenzo(a,h)anthracene 53703 **POM Group 76002** Benzo(b+k)fluoranthene 102

1 Indeno(1,2,3

- c,d)pyrene 193395 Benzo(j)fluoranthene 205823 Benzo(b)fluoranthene 205992 Benzo(k)fluoranthene 207089 Dibenzo(a,j)acridine 224420 Benz(a)ant hracene 56553 **POM Group 77002** Chrysene 218019 0.1 **POM Group 78002**  7

- PAH 75 0.5

a These group CSFs are used only when OAQPS has not, for the purposes of RTR, identified a CSF specific to the individual chemical.

b The method to assign oral cancer slope factors to POM groups was the same as that used in the 1999 National Air Toxics Assessment (EPA 1999). A complete description of the methodology is available at: http://www.epa.g ov/ttn/atw/nata1999/99pdfs/pomapproachjan.pdf

. 

## 5. Risk Estimation For persistent and bioaccumulative hazardous air pollutants (PB

- HAPs), risks from inhalation of a chemical directly from air generally will be negligible compared with risks from ingestion of the chemical from foodstuffs grown in an area subject to air deposition of the chemical. Risk characterization for carcinogens with a linear mode of action at low doses is described in Section 5.1 of this attachment . Risk c haracterization for chemicals likely to exhibit a threshold for response (e.g., non

- cancer hazards) is described in Section 5.2

. 

### 5.1. Cancer Risks
The estimated risk of developing cancer from exposure to a chemical from a specified source is characterized as the excess l ifetime c ancer r isk (ELCR)

. The ELCR represents the incremental probability of an individual developing cancer over a lifetime as a result of lifetime exposure to the chemical. For a known or suspected carcinogen with a low

- dose linear mode of action , the estimated ELCR is calculated as the product of the lifetime average daily dose (LADD) and the
cancer slope factor (CSF) :

Attachment B B- 42 October 2014 Description of MIRCTRIM

- Based Tiered Screening Methodolo gy for RTR



### Equation B- 46

. Calculation of Excess Lifetime Cancer Risk

ELCR = LADD × C SF

w here :

ELCR = Estimated excess lifetime cancer risk from a chemical summed across all exposure pathways and media (unitless)

LADD = Lifetime average total daily dose from all exposure pathwa ys and media (mg/kg

day)

CSF = Oral carcinogenic potency slope factor for chemical (per mg/kg

- day)



### As described in Section 3.3 of this attachment , t he LADD (in mg/kg

- day) for a chemical is calculated to reflect age

- related di fferences in exposure rates that are experienced by a hypothetical individual throughout his or her lifetime of exposure

. Th e tot al chemical intake is normalized to a lifetime, which for the purposes of this assessment is assumed to be 70 years

. EPA con siders the possibility that children might be more sensitive than adults to toxic chemicals, including chemical carcinogens (EPA 2005b,c). Where data allow, EPA recommends development of lifestage

- specific cancer potency C SFs

. To date, EPA has developed a separate slope factor for early lifestage exposure for only one chemical ( i.e., 1,1,1

- trichloroethane ; EPA 2007b), and current data availability for most chemicals preclude this approach. EPA has, therefore, examined options for default adjustments of t he CSF to protect children. To date, the only mode of action (MOA) for carcinogenesis for which EPA has adequate data to develop a reasonable quantitative default approach is mutagenesis ( EPA 2005 b, c) . For carcinogens with a mutagenic MOA for cancer, EPA concluded that the carcinogenic potency of a chemical may be approximately tenfold greater for the first 2 years of life (i.e., birth up to second birthday) and threefold greater for the next 14 years of life (i.e., ages 2 through 15) than for adults (EPA 2005c). These conclusions are represented by age

dependent adjustment factors (ADAFs) of 10, 3, and 1 for the first two lifestages and for adults, respectively

. These three lifestages do not match the age categories for the home

- grown food ingestion
ra tes, the age categories in MIRC. As a consequence, ADAFs for the age groups in MIRC are adapted as time

- weighted average values as follows:

To estimate total lifetime risk from a lifetime of exposure to such a chemical, EPA recommends estimating the cancer risk for each of the three lifestages separately and then adding the risks for i = 1 to 6 age groups. 

## ( ) ( )

3 56 2 yr13 yr 1 10 10

53 21



### = = ×+×

= = <

)-( )-(

1) (



### ADAF . ADAF ADAF 

## ( ) ( )

1 2 8 yrs 41 yrs 43 3

1912

11 6



### = = ×+×

= =

(adult) )-( )-(



### ADAF ADAF ADAF

Attachment B B- 43 October 2014 Description of MIRCTRIM

- Based Tiered Screening Methodolo gy for RTR



### Equations B

- 47 to B- 53 . Lifetime Cancer Risk: Chemicals with a Mutagenic MOA for Cancer Equation B

- 47

. Risk (<1) 

### = ADD (0

– <1) 

### × 10 × CSF × (1 yr/70 yr) Equation B

- 48

. Risk (1

– 2) 

### = ADD (1

– 2) 

### × 6.5 × CSF × (2 yr/70 yr) Equation B

- 49

. Risk (3

– 5) 

### = ADD (3

– 5) 

### × 3 × CSF × (3 yr/70 yr) Equation B

- 50

. Risk (6

– 11) 

### = ADD (6

– 11) 

### × 3 × CSF × (6 yr/70 yr) Equ ation B

- 51

. Risk (12

– 19) 

### = ADD (12

– 19) 

### × 2 × CSF × (8 yr/70 yr) Equation B

- 52

. Risk (adult) 

### = ADD (adul t) 

### × 1 × CSF × (50 yr/70 yr) Equation B- 53

. **ELCR**

In other words, Equation B- 53 indicates that the total excess lifetime cancer risk ( ELCR) equal s the sum of the age

- group

- specific risks estimated by Equation B- 47 through Equation B- 52

, where:

Risk (<1) = R isk from chemical ingestion in first year of life
Risk (1- 2) = Risk from chemical ingestion f rom first birthday through age 2 years
Risk (3- 5) = Ri sk from chemical ingestion from age 3 through 5 years of age
Risk (6- 11) = Risk from chemical ingestion from age 6 through 11 years of age
Risk (12

- 19) = Risk from chemical ingestion from age 12 through 19 years of age
Risk (adult) = Risk from chemical ingestion from age 20 to 70 years age

ADD (<1) = Average daily dose for infants under one year of age (mg/kg

- day)
ADD (1- 2) = Average daily dose from first birthday through age 2 years of age (mg/kg

- day)
ADD (3- 5) = Average daily dose from age 3 through 5 years of age (mg/kg

- day)
ADD (6- 11) = Average daily dose from age 6 through 11 years of age (mg/kg

- day)
ADD (12

- 19) = Average daily dose from age 12 through 19 years of age (mg/kg

- day)
ADD (adult) = Aver age daily dose for adults age 20 to 70 years of age (mg/kg

- day)
CSF = Oral carcinogenic potency slope factor for chemical (per mg/kg

- day)
Risk (i) = R isk f rom chemical ingestion for the i th age group ELCR = T otal extra lifetime cancer risk (incremental or extra risk)

n = Number of age groups (i.e., 6)



### 5.2. Non

- cancer Hazard Quotients
Non

- cancer risks are presented as hazard quotients (HQs), that is, the ratio of the estimated daily intake (i.e., ADD) to the reference dose (e.g., chronic RfD) . If the HQ for a chemical is equal to or less than 1, EPA believes t hat there is no appreciable risk that non

- cancer health effects will occur . I f the HQ is greater than 1 then there is at least some possibility for an adverse health effect

. The larger the HQ value, the more likely it is that an adverse health effect may occur. 

# ∑

= 

### = n
i i 

### Risk 1 )(

Attachment B B- 44 October 2014 Description of MIRCTRIM

- Based Tiered Screening Methodolo gy for RTR



### 5.2.1. Hazard Quotients for Chemicals with a Chronic RfD For chemicals with a chronic RfD, MIRC calculates an HQ for each age group separately using Equation B- 54 to indicate the potential for adverse health effects associated with chronic exposure via ingestion pathways. T he HQ is the ratio of a long

- term, daily average exposure normalized to the receptor's body weight (i.e., ADD) to the RfD for that chemical . HQs are thresh old effects and are not additive across age groups.
Equation B- 54

. Hazard Quotient for Chemicals with a Chronic RfD

where:

HQ = Hazard quotient for chemical (unitless)

ADD = Average daily ingested dose of chemical (mg/kg

- day) from all food types and ingested media for the age group

RfD = Chronic oral reference dose for chemical (mg/kg

- day)



### 5.2.2. Hazard Quotients for Chemicals with RfD Based on Developmental Effects
For chemical s for which the toxicity reference value is an RfD based on developmental effects

, a shorter exposure duration ( ED ) and averaging time ( AT ) may be required

. For this type of chemical (e.g., methylmercury, 2,3,7,8

- TCDD), the appropriate ED/AT and sensitive lifestage for exposure may need to be estimated from the information provided in the critical developmental study(ies) from which the RfD was derived (e.g., in consultation with the RfD documentation in EPA’s IRIS or in a toxicological profile developed for the chemical)

. For screening

- level risk assessments, however, a health protective approach is to compare the highest ADD from among the child age categories provided in MIRC to the RfD , as is done for all PB

- HAPs. This approach ensures that the highest exposure from among the various age groups evaluate d is taken into consideration, regardless of which age group might be most relevant to the health effect of interest ( i.e., the age group on which the RfD is based).



### 5.2.3. Hazard Index for Chemicals with RfD s When conducting screening

- level assessments for multiple chemicals, it can be informative to calculate a hazard index (HI) for toxicologically similar chemicals (EPA 2000). The HI is the sum of HQs across chemicals (not age groups) as shown in Equation B- 55

. As with the HQ, if the HI value is less than 1, adverse health effects are not expected for that suite of chemicals

. If the screening level HI exceeds 1, however, the risk assessor may in some instances, evaluate the assumpt ions of the screening

- level assessment to determine if more realistic local values are available for parameters that drive risk. In addition, the risk assessor may need to examine the mode of action (MOA) and target organ(s) for the chemicals with the hig hest HQs to develop an appropriate approach to assessing their potential joint action. 

### RfD ADD HQ =

Attachment B B- 45 October 2014 Description of MIRCTRIM

- Based Tiered Screening Methodolo gy for RTR



### Equation B- 55

. 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 in some circumstances, may consider whether chemical interactions play a role in chemical toxicity ( EPA 2000)

. Exposures to more than one chemical can result in a greater or lesser toxic response than might be predicted on the basis of one or the other chemical acting alone (toxicologically independent) or acting in concert (toxicologically similar chemicals)

. Users are referred to EPA’s Supp lementary Guidance for Conducting Health Risk Assessment of Chemical Mixtures for approaches to assessing the potential for adverse health effects from exposure to multiple chemicals ( EPA 2000)

. Note that users of MIRC are responsible for determining how to interpret HQs for multiple chemicals

. 

## 6. Model Input Options This section describes the input options currently included in MIRC . Required user inputs for environmental media concentrations and air deposition rates, such as those predicted by (output of) TRIM.FaTE, are described in Section 6.1 of this attachment . Values for farm

- food

chain (FFC) parameters for specific types of produce and animal products are discussed in Section 6.2 . Options for parameterizing receptor characteristics are described in Section 6.3

, including age

- group

- specific values for body weight, water ingestion, and food ingestion by food type. Options for other exposure parameter val ues in MIRC, such as exposure frequency and loss of chemical during food preparation and cooking, are provided in Section 6.4

. Where values for chemical

- specific parameters are presented, values are presented only for PB

- HAP chemicals currently evaluated using the TRIM

- based RTR screening scenario. The database included with MIRC contains chemical

- specific parameter values for a large number of chemicals (more than 500), because all of the chemical

- specific input data compiled by EPA fo r use in HHRAP were uploaded into MIRC. However, only chemicals that are PB

- HAPs evaluated for RTR are discussed in this document (see Sections 1.1.1 and 2.2.2 ), and the HHRAP inputs provided for other chemicals have not been reviewed or verified. The data presented in this chapter were reviewed and used to develop the set of modeling defaults used to calculate screening threshold emission rates for RTR. Note that the default values used to estima te RTR screening thresholds, and the justification for selecting a specific value from the data sets described in this chapter, are discussed in Chapter 7.

Attachment B B- 46 October 2014 Description of MIRCTRIM

- Based Tiered Screening Methodolo gy for RTR



### 6.1. Environmental Concentrations
As noted in Section 2 of this attachment , MIRC is intended to estimate exposures and risks to self

- sufficient farming and fishing families from ingestion of FFC media in an area of airborne chemical deposition

. The tool analyzes one exposure scenario at a time (e.g., adult farmer exposed to dio xin from ingestion of beef); therefore, it is best used to evaluate a maximally exposed individual (MEI) or family when MIRC is used to screen for possible risks.
The following values specific to the air pollutant of concern are required inputs to MIRC:

• 

### a single air concentration (in g/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 μg/g dry weight), one each for:

– surface soil in produce growing area,

– surface soil where livestock fee d,

– root

- zone soil in produce growing area, and

– root

- zone soil in livestock feed growing area.
The MIRC software is configured to estimate ingestion exposures via drinking water for a specified chemical concentration in the drinking water source (e.g., grou ndwater well)

. However, no exposure via drinking water is assumed to occur when calculating the Tier 1 screening thresholds. As discussed in Section 2.2.3 , t he drinking water exposure pathway is not modeled for the scenario developed for the Tier 1 assessment because the likelihood that humans would use a lake as a drinking water source is assumed to be low.
The user must provide the inputs listed above; no default values are included for these parameters in MIRC

. Media concentrations output by TRIM.FaTE can be entered into the tool manually from model output files or can be imported directly from the TRIM.FaTE output files

. For RTR evaluations, a tool to facilitate this process was developed using a Microsoft ® 

### Excel TM routine written in Visual Basic

. 

### 6.2. Farm

- Food

- Chain Parameter Values Using the chemical information specified in Section 6.1 above as inputs, MIRC calculate s chemical concentrations in foods that are commonly grown or raised on family farms: exposed and protected fruit s; exposed and protected vegetables; root vegetables; beef; total dairy products; pork; and poultry and eggs

. 

### 6.2.1. List of Farm

- Food

- Chain (FFC) Parameters
MIRC estimates chemical concentrations in the produce identified abov e using algorithms from HHRAP (EPA 2005a) as described in Section 3.2 of this attachment . P arameter values required for these HHRAP algorithms, including chemical

- specific med ia transfer factors (e.g., Attachment B B- 47 October 2014 Description of MIRCTRIM

- Based Tiered Screening Methodolo gy for RTR



### soil

- to

- plant transfer coefficients) and plant - and animal

- specific properties (e.g., plant interception fraction, quantity of forage consumed by cattle), are included in tables in MIRC. As described in Section 7 of this attachment , the HHRAP

- recomme nded parameter values are the default values in MIRC; however, these and other inputs in MIRC can be revised as needed. Exhibit B-9 describes the parameters that are included in the algorithms used to estimate chemical concentrations in the farm food categories. The parameter names and symbols are referenced in this section for plants/produce and animal products

.
Exhibit B

- 9

. MIRC Parameters U sed 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 Uni tless ( g soil DW / g produce DW )

Bv AG(i) Chemical

- specific a ir

- to

- plant biotransfer factor for aboveground produce type i for vapor

- phase chemical in air U nitless ([mg chemical / g DW plant] / [mg chemical / g air]) Fw Fraction of wet deposition that adh eres to plant surfaces; 0.2 for anions, 0.6 for cations and most organics Unitless

Kd s Chemical

- specific s oil/water partition coefficient L soil pore water / kg soil DW kp (i) Plant

- specific surface loss coefficient for aboveground exposed produce and ani mal forage and silage yr - 1

MAF (i) Moisture adjustment factor for aboveground produce type i to convert the chemical concentration estimated for dry

- weight produce to the corresponding chemical concentration for full

weight fresh produce Percent water

RC F Chemical

- specific r oot concentration factor for tubers and root produce on a wet

- weight (WW) basis L soil pore water/ kg root WW Rp (i) Plant

- specific i nterception fraction for the edible portion of aboveground exposed produce or animal forage and silage Unitless

Tp (i) Length of plant 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 Unitless

VG rootveg Empirical correction factor for belowground produce (i.e., tuber or root vegetable) to account for possible overestimate of th e 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 y ield or standing crop biomass of the edible portion of produce or animal feed kg produce DW/m 2

**Animal Products**  Bs Soil bioavailability factor for livestock Unitless MF Chemical

- specific mammalian metabolism factor that accounts for endogenous degradation of the chemical Unitless

Attachment B B- 48 October 2014 Description of MIRCTRIM

- Based Tiered Screening Methodolo gy for RTR



### Exhibit B

- 9

. MIRC Parameters U sed to Estimate Chemical Concentrations in Farm Foods

Parameter Description Units Ba (b eef ) Chemical

- specific b iotransfer factor for chemical in diet of cow to chemical in beef on a fresh

- wet (FW; equivalent to WW) basis mg chemical/kg FW tissue/mg chemical/day or day/kg FW tissue Ba (d airy ) Biotransfer factor in dairy day/kg FW tissue Ba (p ork ) Biotransfer factor in pork day/kg FW tissue Ba (p oultry ) Biotrans fer factor in poultry day/kg FW tissue Ba (e ggs ) Biotransfer factor in eggs day/kg FW tissue Qs (m) Quantity of soil eaten by animal type m each day kg/day Qp (i,m) Quantity of plant feed type i consumed per animal type m each day kg/day

Source: EPA Sou rce: EPA 2005 a DW = dry weight; FW = fresh weight; WW = wet weight



### 6.2.2. Produce Parameter Values
Exhibit B- 10 and Exhibit B- 11 provide the chemical

- specific input values that are the current defaults for p roduce FFC food types in MIRC. Exhibit B- 12 present s additional non

- chemical

specific input values for parameters used in the algorithms that calculate chemical concentrations in produce. Unless otherwise noted, the default para meter values were obtained from HHRAP

. Options for other parameter values are not included in MIRC at this time; however, the user can overwrite values if appropriate

. Refer to HHRAP ( EPA 2005 a , Chapter 5 and associated appendices) for detailed descriptions of these parameters and documentation of input values

.
Exhibit B

- 10

. Chemical

- Specific Inputs for Produce Parameters for Chemicals Included in MIRC

Chemical Fractio n of Wet Deposition ( **Fw** ) (unitless) a Root Concentration Factor ( **RCF** ) (belowground) (L/kg) b Soil

- Water Partition Coefficient ( **Kds** ) (L/kg) c Chemical Air

- to

Plant Biotransfer Factor ( **Bv AG(i)**  ) (unitless) d **Inorganics** Cadmium compounds 0.6 NA 7.5E+01 NA e Me rcury (elemental) 0.6 NA 1.0E+03 0 f
Mercuric chloride 0.6 NA 5.8E+04 1.8E+03 Methyl mercury 0.6 NA 7.0E+03 0 f
**PAHs** 2

- Methylnaphthalene 0.6 2.2E+02 5.0E+01 1.4E+00 7,12

- Dimethylbenz(a)anthrace
ne 0.6 6.8E+03 4.0E+03 4.2E+04

Acenaphthene 0.6 2.4E+02 3.9 E+01 4.6E+00 Acenaphthylene 0.6 2.8E+02 6.8E+01 8.1E+00 Benz(a)anthracene 0.6 6.7E+03 2.9E+03 6.8E+03 Benzo(a)pyrene 0.6 9.2E+03 7.8E+03 1.7E+05

Attachment B B- 49 October 2014 Description of MIRCTRIM

- Based Tiered Screening Methodolo gy for RTR



### Exhibit B

- 10

. Chemical

- Specific Inputs for Produce Parameters for Chemicals Included in MIRC

Chemical Fractio n of Wet Deposition ( **Fw** ) (unitless) a Root Concentration Factor ( **RCF** ) (belowground) (L/kg) b Soil

- Water Partition Coefficient ( **Kds** ) (L/kg) c Chemical Air

- to

Plant Biotransfer Factor ( **Bv AG(i)**  ) (unitless) d Benzo(b)fluoranthene 0.6 6.6E+03 3.8E+03 1.7E+05 Benzo(g,h,i)perylene 0.6 3.0E+04 2.6E+04 2.3E+06 Benzo(k )fluoranthene 0.6 8.7E+03 5.5E+03 2.8E+05 Chrysene 0.6 6.0E+03 3.4E+03 1.4E+04 Dibenz(a,h)anthracene 0.6 2.3E+04 1.4E+04 6.2E+06 Fluoranthene 0.6 2.2E+03 3.9E+02 9.0E+02 Fluorene 0.6 3.8E+02 6.2E+01 1.6E+01 Indeno(1,2,3

- cd)pyrene 0.6 3.5E+04 3.2E+04 2 .8E+06 **Dioxins** OctaCDD, 1,2,3,4,6,7,8,9

- 0.6 4.8E+05 7.8E+05 2.4E+06
OctaCDF, 1,2,3,4,6,7,8,9

- 0.6 3.4E+05 4.9E+05 2.3E+06 HeptaCDD, 1,2,3,4,6,7,8

- 0.6 3.4E+05 4.9E+05 9.1E+05 HeptaCDF, 1,2,3,4,6,7,8

- 0.6 1.2E+05 1.2E+05 8.3E+05 HeptaCDF, 1,2,3,4,7,8 ,9

- 0.6 4.8E+04 3.9E+04 8.3E+05 HexaCDD, 1,2,3,4,7,8

- 0.6 2.4E+05 3.1E+05 5.2E+05 HexaCDF, 1,2,3,4,7,8

- 0.6 5.7E+04 4.9E+04 1.6E+05 HexaCDD, 1,2,3,6,7,8

- 0.6 4.9E+05 8.0E+05 5.2E+05 HexaCDF, 1,2,3,6,7,8

- 0.6 2.9E+05 4.1E+05 1.6E+05 HexaCDD, 1,2,3,7,8, 9

- 0.6 4.9E+05 8.0E+05 5.2E+05 HexaCDF, 1,2,3,7,8,9

- 0.6 1.6E+05 1.9E+05 1.6E+05 HexaCDF, 2,3,4,6,7,8

- 0.6 2.9E+05 4.1E+05 1.6E+05 PentaCDD, 1,2,3,7,8

- 0.6 9.2E+04 9.2E+04 2.4E+05 PentaCDF, 1,2,3,7,8

- 0.6 3.9E+04 3.0E+04 9.8E+04 PentaCDF, 2,3,4,7,8

- 0.6 2.3E+04 1.6E+04 9.8E+04

Attachment B B- 50 October 2014 Description of MIRCTRIM

- Based Tiered Screening Methodolo gy for RTR



### Exhibit B

- 10

. Chemical

- Specific Inputs for Produce Parameters for Chemicals Included in MIRC

Chemical Fractio n of Wet Deposition ( **Fw** ) (unitless) a Root Concentration Factor ( **RCF** ) (belowground) (L/kg) b Soil

- Water Partition Coefficient ( **Kds** ) (L/kg) c Chemical Air

- to

Plant Biotransfer Factor ( **Bv AG(i)**  ) (unitless) d TetraCDD, 2,3,7,8

- 0.6 4.0E+04 3.1E+04 6.6E+04 TetraCDF, 2,3,7,8

- 0.6 1.2E+04 6.2E+03 4.6E+04 Source: EPA 2005a. NA = not applicable.

a 6E

- 01 is the value for cations and most organic chemicals. As described in HHRAP (EPA 2005a), Appendix B (available at http://www.epa.gov/osw/hazard/tsd/td/combust/finalmact/ssra/05hhrapapb.pdf), EPA estimated this value (EPA 1994a, 1995a) from a study by Hoffman et al. (1992) in which soluble gamma

- emitting radionuclides and insoluble particles tagged with gamma

emitting radionuclides were deposited onto pasture grass via simulated rain. Note that the values developed experimentally f or pasture grass may not accurately represent all aboveground produce

- specific values. Also note that values based on the behavior of insoluble particles tagged with radionuclides may not accurately represent the behavior of organic compounds unde r site

- specific conditions.

b For nonionic organic chemicals, as described in HHRAP (EPA 2005a), Appendix A (available at http://www.epa.gov/osw/hazard/tsd/td/combust/finalmact/ssra/05hhrapapa.pdf ), RCF is used to calculate the below

- ground transfer of contaminants from soil to a root vegetable on a wet

- weight basis as shown in Equation B-6 . EPA estimated chemical

specific values for RCF from empirical regression equations developed by Briggs et al. (1982) based on their experiments measuring uptake of compounds into barley roots from growth solution. Briggs’ regression equations allow calculation of RCF values from log Kow. For metals and mercuric compounds, empirical values for soil to root vegetable transfer on a dry

- weight basis are available in the literature, thus the RCF was not needed.

c As discussed in HHRAP (EPA 2005a), Appendix A, Kds describes the partitioning of a compound between soil pore

- water and soil particles and strongly influences the release and movement of a compound into the subsurface soils and underlying aquife r. Kds values for mercuric compounds were obtained from EPA (1997b). Kds for cadmium compounds were obtained from EPA 1996. For all PAHs and dioxins, Kds was calculated by multiplying Koc times the screening scenario’s fraction organic carbon content (0.008). Empirical information for Koc was available for acenaphthene, benz(a)anthracene, benzo(a)pyrene, dibenz(a,h)anthracene, fluoranthene, and fluorene in USEAP 1996. For all other organic compounds, the Koc was calculated using the correlation equations presented in USEAP 2005a.

d As discussed in HHRAP (EPA 2005a), Appendix A, the value for mercuric chloride was obtained from EPA 1997b. BvAG(i) values for PAHs were calculated using the correlation equation derived for azalea leaves as cited in Bacci et al. (1992), the n reducing this value by a factor of 100, as suggested by Lorber (1995), who concluded that the Bacci factor reduced by a factor of 100 was similar to his own observations in various studies. The values for dioxins were obtained from Lorber and Pinsky (200 0).

e It is assumed that metals, with the exception of vapor

- phase elemental mercury, do not transfer significantly from air into leaves. f Speciation and fate and transport of mercury from emissions suggest that BvAG(i) values for elemental and methyl mercury are likely to be zero (EPA 2005a).



### Exhibit B

- 11 . Chemical

- Specific Inputs by Plant Type for Chemicals in MIRC

Compound Name Plant Part Plant

- Soil Bio

Concentration Factor ( **Br** AG

-produce-DW(i) ) (unitless ) a Empirical Correction Factor - Belowground Produce ( **VG** rootveg ) (unitless) b Empirical Correction Factor Aboveground Produce ( **VG AG(i)**  ) (unitless) c **Inorganics**

Cadmium compounds Exp. Fruit 1.3E

- 01

- 1.0E+00 Exp. Veg. 1.3E

- 01

- 1.0E+00 Forage 3.6E

- 01

- 1.0E+00 Grain 6.2E

- 02

- - Prot. Fruit 1.3E

- 01

- - Prot. Veg. 1.3E

- 01

- - Root 6.4E

- 02 1.0E+00

- Silage 3.6E

- 01

- 5.0E

- 01

Attachment B B- 51 October 2014 Description of MIRCTRIM

- Based Tiered Screening Methodolo gy for RTR



### Exhibit B

- 11 . Chemical

- Specific Inputs by Plant Type for Chemicals in MIRC

Compound Name Plant Part Plant

- Soil Bio

Concentration Factor ( **Br** AG

-produce-DW(i) ) (unitless ) a Empirical Correction Factor - Belowground Produce ( **VG** rootveg ) (unitless) b Empirical Correction Factor Aboveground Produce ( **VG AG(i)**  ) (unitless) c

Mercury (elemental) Exp. Fruit

- - 1.0E+00 Exp. Veg.

- - 1.0E+00 Forage

- - 1.0E+00 Grain

- - - Prot. Fruit

- - - Prot. Veg.

- - - Root

- 1.0E+00

- Silage

- - 5.0E

- 01

Mercuric chloride Exp. Fruit 1.5E

- 02

- 1.0E+00 Exp. Veg. 1.5E

- 02

- 1.0E+00 Forage 0.0E+00

- 1.0E+00 Grain 9.3E

- 03

- - Prot. Fruit 1.5E

- 02

- - Prot. Veg. 1.5E

- 02

- - Root 3.6E

- 02 1.0E+0 0

- Silage 0.0E+00

- 5.0E

- 01

Methyl mercury Exp. Fruit 2.9E

- 02

- 1.0E

- 02 Exp. Veg. 2.9E

- 02

- 1.0E

- 02 Forage 0.0E+00

- 1.0E+00 Grain 1.9E

- 02

- - Prot. Fruit 2.9E

- 02

- - Prot. Veg. 2.9E

- 02

- - Root 9.9E

- 02 1.0E

- 02

- Silage 0.0E+00

- 5.0E

- 01 **PAHs**

2- Methylnaphthalene Exp. Fruit 2.3E

- 01

- 1.0E+00 Exp. Veg. 2.3E

- 01

- 1.0E+00 Forage 2.3E

- 01

- 1.0E+00 Grain 2.3E

- 01

- - Prot. Fruit 2.3E

- 01

- - Prot. Veg. 2.3E

- 01

- - Root 4.4E+00 1.0E+00

- Silage 2.3E

- 01

- 5.0E

- 01

7,12

- Dimethylbenz(a)anthra
cene Exp. Fruit 1.7E

- 02

- 1.0E

- 02 Exp. Veg. 1.7E

- 02

- 1.0E

- 02 Forage 1.7E

- 02

- 1.0E+00 Grain 1.7E

- 02

- - Prot. Fruit 1.7E

- 02

- - Prot. Veg. 1.7E

- 02

- - Root 1.7E+00 1.0E

- 02

- Silage 1.7E

- 02

- 5.0E

- 01

Attachment B B- 52 October 2014 Description of MIRCTRIM

- Based Tiered Screening Methodolo gy for RTR



### Exhibit B

- 11 . Chemical

- Specific Inputs by Plant Type for Chemicals in MIRC

Compound Name Plant Part Plant

- Soil Bio

Concentration Factor ( **Br** AG

-produce-DW(i) ) (unitless ) a Empirical Correction Factor - Belowground Produce ( **VG** rootveg ) (unitless) b Empirical Correction Factor Aboveground Produce ( **VG AG(i)**  ) (unitless) c

Acenaphthene Exp. Fruit 2.1E

- 01

- 1.0E+00 Exp. Veg. 2.1E

- 01

- 1.0E+00 Forage 2.1E

- 01

- 1.0E+00 Grain 2.1E

- 01

- - Prot. Fruit 2.1E

- 01

- - Prot. Veg. 2.1E

- 01

- - Root 6.2E+00 1.0E+00

- Silage 2.1E

- 01

- 5.0E

- 01

Acenaphthylene Exp. Fruit 1.9E

- 01

- 1.0E

- 02 Exp. Veg. 1.9E

- 01

- 1 .0E

- 02 Forage 1.9E

- 01

- 1.0E+00 Grain 1.9E

- 01

- - Prot. Fruit 1.9E

- 01

- - Prot. Veg. 1.9E

- 01

- - Root 4.1E+00 1.0E

- 02

- Silage 1.9E

- 01

- 5.0E

- 01

Benz(a)anthracene Exp. Fruit 1.7E

- 02

- 1.0E

- 02 Exp. Veg. 1.7E

- 02

- 1.0E

- 02 Forage 1.7E

- 02

- 1. 0E+00 Grain 1.7E

- 02

- - Prot. Fruit 1.7E

- 02

- - Prot. Veg. 1.7E

- 02

- - Root 2.3E+00 1.0E

- 02

- Silage 1.7E

- 02

- 5.0E

- 01

Benzo(a)pyrene Exp. Fruit 1.4E

- 02

- 1.0E

- 02 Exp. Veg. 1.4E

- 02

- 1.0E

- 02 Forage 1.4E

- 02

- 1.0E+00 Grain 1.4E

- 02

- - Pro t. Fruit 1.4E

- 02

- - Prot. Veg. 1.4E

- 02

- - Root 1.2E+00 1.0E

- 02

- Silage 1.4E

- 02

- 5.0E

- 01

Attachment B B- 53 October 2014 Description of MIRCTRIM

- Based Tiered Screening Methodolo gy for RTR



### Exhibit B

- 11 . Chemical

- Specific Inputs by Plant Type for Chemicals in MIRC

Compound Name Plant Part Plant

- Soil Bio

Concentration Factor ( **Br** AG

-produce-DW(i) ) (unitless ) a Empirical Correction Factor - Belowground Produce ( **VG** rootveg ) (unitless) b Empirical Correction Factor Aboveground Produce ( **VG AG(i)**  ) (unitless) c

Benzo(b)fluoranthene Exp. Fruit 1.8E

- 02

- 1.0E

- 02 Exp. Veg. 1.8E

- 02

- 1.0E

- 02 Forage 1.8E

- 02

- 1.0E+00 Grain 1.8E

- 02

- - Prot. Fruit 1.8E

- 02

- - Pr ot. Veg. 1.8E

- 02

- - Root 1.7E+00 1.0E

- 02

- Silage 1.8E

- 02

- 5.0E

- 01

Benzo(g,h,i)perylene Exp. Fruit 5.7E

- 03

- 1.0E

- 02 Exp. Veg. 5.7E

- 03

- 1.0E

- 02 Forage 5.7E

- 03

- 1.0E+00 Grain 5.7E

- 03

- - Prot. Fruit 5.7E

- 03

- - Prot. Veg. 5.7E

- 03

- - Ro ot 1.1E+00 1.0E

- 02

- Silage 5.7E

- 03

- 5.0E

- 01

Benzo(k)fluoranthene Exp. Fruit 1.4E

- 02

- 1.0E

- 02 Exp. Veg. 1.4E

- 02

- 1.0E

- 02 Forage 1.4E

- 02

- 1.0E+00 Grain 1.4E

- 02

- - Prot. Fruit 1.4E

- 02

- - Prot. Veg. 1.4E

- 02

- - Root 1.6E+00 1.0E

- 02

- Si lage 1.4E

- 02

- 5.0E

- 01

Chrysene Exp. Fruit 1.9E

- 02

- 1.0E

- 02 Exp. Veg. 1.9E

- 02

- 1.0E

- 02 Forage 1.9E

- 02

- 1.0E+00 Grain 1.9E

- 02

- - Prot. Fruit 1.9E

- 02

- - Prot. Veg. 1.9E

- 02

- - Root 1.7E+00 1.0E

- 02

- Silage 1.9E

- 02

- 5.0E

- 01

Attachment B B- 54 October 2014 Description of MIRCTRIM

- Based Tiered Screening Methodolo gy for RTR



### Exhibit B

- 11 . Chemical

- Specific Inputs by Plant Type for Chemicals in MIRC

Compound Name Plant Part Plant

- Soil Bio

Concentration Factor ( **Br** AG

-produce-DW(i) ) (unitless ) a Empirical Correction Factor - Belowground Produce ( **VG** rootveg ) (unitless) b Empirical Correction Factor Aboveground Produce ( **VG AG(i)**  ) (unitless) c

Dibenz(a,h)anthracene Exp. Fruit 6.8E

- 03

- 1.0E

- 02 Exp. Veg. 6.8E

- 03

- 1.0E

- 02 Forage 6.8E

- 03

- 1.0E+00 Grain 6.8E

- 03

- - Prot. Fruit 6.8E

- 03

- - Prot. Veg. 6.8E

- 03

- - Root 1.6E+00 1.0E

- 02

- Silage 6.8E

- 03

- 5.0E

- 01

Fluoranthene Exp. Fruit 4.0E

- 02

- 1.0 E

- 02 Exp. Veg. 4.0E

- 02

- 1.0E

- 02 Forage 4.0E

- 02

- 1.0E+00 Grain 4.0E

- 02

- - Prot. Fruit 4.0E

- 02

- - Prot. Veg. 4.0E

- 02

- - Root 5.6E+00 1.0E

- 02

- Silage 4.0E

- 02

- 5.0E

- 01

Fluorene Exp. Fruit 1.5E

- 01

- 1.0E

- 02 Exp. Veg. 1.5E

- 01

- 1.0E

- 02 Forage 1.5E

- 01

- 1.0E+00 Grain 1.5E

- 01

- - Prot. Fruit 1.5E

- 01

- - Prot. Veg. 1.5E

- 01

- - Root 6.2E+00 1.0E

- 02

- Silage 1.5E

- 01

- 5.0E

- 01

Indeno(1,2,3

- cd)pyrene Exp. Fruit 5.1E

- 03

- 1.0E

- 02 Exp. Veg. 5.1E

- 03

- 1.0E

- 02 Forage 5.1E

- 03

- 1.0E+0 0 Grain 5.1E

- 03

- - Prot. Fruit 5.1E

- 03

- - Prot. Veg. 5.1E

- 03

- - Root 1.1E+00 1.0E

- 02

- Silage 5.1E

- 03

- 5.0E

- 01

Attachment B B- 55 October 2014 Description of MIRCTRIM

- Based Tiered Screening Methodolo gy for RTR



### Exhibit B

- 11 . Chemical

- Specific Inputs by Plant Type for Chemicals in MIRC

Compound Name Plant Part Plant

- Soil Bio

Concentration Factor ( **Br** AG

-produce-DW(i) ) (unitless ) a Empirical Correction Factor - Belowground Produce ( **VG** rootveg ) (unitless) b Empirical Correction Factor Aboveground Produce ( **VG AG(i)**  ) (unitless) c **Dioxins**

OctaCDD, 1,2,3,4,6,7,8,9 - Exp. Fruit 7.1E

- 04

- 1.0E

- 02 Exp. Veg. 7.1E

- 04

- 1.0E

- 02 Forage 7.1E

- 04

- 1.0E+00 Grain 7 .1E

- 04

- - Prot. Fruit 7.1E

- 04

- - Prot. Veg. 7.1E

- 04

- - Root 6.1E

- 01 1.0E

- 02

- Silage 7.1E

- 04

- 5.0E

- 01

OctaCDF, 1,2,3,4,6,7,8,9 - Exp. Fruit 9.2E

- 04

- 1.0E

- 02 Exp. Veg. 9.2E

- 04

- 1.0E

- 02 Forage 9.2E

- 04

- 1.0E+00 Grain 9.2E

- 04

- - Prot. F ruit 9.2E

- 04

- - Prot. Veg. 9.2E

- 04

- - Root 6.8E

- 01 1.0E

- 02

- Silage 9.2E

- 04

- 5.0E

- 01

HeptaCDD, 1,2,3,4,6,7,8 - Exp. Fruit 9.2E

- 04

- 1.0E

- 02 Exp. Veg. 9.2E

- 04

- 1.0E

- 02 Forage 9.2E

- 04

- 1.0E+00 Grain 9.2E

- 04

- - Prot. Fruit 9.2E

- 04

- - Pr ot. Veg. 9.2E

- 04

- - Root 6.8E

- 01 1.0E

- 02

- Silage 9.2E

- 04

- 5.0E

- 01

HeptaCDF, 1,2,3,4,6,7,8 - Exp. Fruit 2.0E

- 03

- 1.0E

- 02 Exp. Veg. 2.0E

- 03

- 1.0E

- 02 Forage 2.0E

- 03

- 1.0E+00 Grain 2.0E

- 03

- - Prot. Fruit 2.0E

- 03

- - Prot. Veg. 2.0E

- 03

- - Root 9.4E

- 01 1.0E

- 02

- Silage 2.0E

- 03

- 5.0E

- 01

Attachment B B- 56 October 2014 Description of MIRCTRIM

- Based Tiered Screening Methodolo gy for RTR



### Exhibit B

- 11 . Chemical

- Specific Inputs by Plant Type for Chemicals in MIRC

Compound Name Plant Part Plant

- Soil Bio

Concentration Factor ( **Br** AG

-produce-DW(i) ) (unitless ) a Empirical Correction Factor - Belowground Produce ( **VG** rootveg ) (unitless) b Empirical Correction Factor Aboveground Produce ( **VG AG(i)**  ) (unitless) c

HeptaCDF, 1,2,3,4,7,8,9 - Exp. Fruit 4.0E

- 03

- 1.0E

- 02 Exp. Veg. 4.0E

- 03

- 1.0E

- 02 Forage 4.0E

- 03

- 1.0E+00 Grain 4.0E

- 03

- - Prot. Fruit 4.0E

- 03

- - Prot. Veg. 4.0E

- 03

- - Root 1.2E+00 1.0E

- 0 2

- Silage 4.0E

- 03

- 5.0E

- 01

HexaCDD, 1,2,3,4,7,8 - Exp. Fruit 1.2E

- 03

- 1.0E

- 02 Exp. Veg. 1.2E

- 03

- 1.0E

- 02 Forage 1.2E

- 03

- 1.0E+00 Grain 1.2E

- 03

- - Prot. Fruit 1.2E

- 03

- - Prot. Veg. 1.2E

- 03

- - Root 7.6E

- 01 1.0E

- 02

- Silage 1.2E

- 03

- 5 .0E

- 01

HexaCDF, 1,2,3,4,7,8 - Exp. Fruit 3.5E

- 03

- 1.0E

- 02 Exp. Veg. 3.5E

- 03

- 1.0E

- 02 Forage 3.5E

- 03

- 1.0E+00 Grain 3.5E

- 03

- - Prot. Fruit 3.5E

- 03

- - Prot. Veg. 3.5E

- 03

- - Root 1.2E+00 1.0E

- 02

- Silage 3.5E

- 03

- 5.0E

- 01

HexaCDD, 1,2,3,6 ,7,8 - Exp. Fruit 7.0E

- 04

- 1.0E

- 02 Exp. Veg. 7.0E

- 04

- 1.0E

- 02 Forage 7.0E

- 04

- 1.0E+00 Grain 7.0E

- 04

- - Prot. Fruit 7.0E

- 04

- - Prot. Veg. 7.0E

- 04

- - Root 6.1E

- 01 1.0E

- 02

- Silage 7.0E

- 04

- 5.0E

- 01

Attachment B B- 57 October 2014 Description of MIRCTRIM

- Based Tiered Screening Methodolo gy for RTR



### Exhibit B

- 11 . Chemical

- Specific Inputs by Plant Type for Chemicals in MIRC

Compound Name Plant Part Plant

- Soil Bio

Concentration Factor ( **Br** AG

-produce-DW(i) ) (unitless ) a Empirical Correction Factor - Belowground Produce ( **VG** rootveg ) (unitless) b Empirical Correction Factor Aboveground Produce ( **VG AG(i)**  ) (unitless) c

HexaCDF, 1,2,3,6,7,8 - Exp. Fruit 1.0E

- 03

- 1.0E

- 02 Exp. Veg. 1.0E

- 03

- 1.0E

- 02 Forage 1.0E

- 03

- 1.0E+00 Grain 1.0E

- 03

- - Prot. Fruit 1.0E

- 03

- - Prot. Veg. 1.0E

- 03

- - Root 7.1E

- 01 1.0E

- 02

- Silage 1.0E

- 03

- 5.0E

- 01

HexaCDD, 1,2,3,7,8,9 - Exp. Fruit 7.0E

- 04

- 1.0E

- 02 Exp. Veg. 7 .0E

- 04

- 1.0E

- 02 Forage 7.0E

- 04

- 1.0E+00 Grain 7.0E

- 04

- - Prot. Fruit 7.0E

- 04

- - Prot. Veg. 7.0E

- 04

- - Root 6.1E

- 01 1.0E

- 02

- Silage 7.0E

- 04

- 5.0E

- 01

HexaCDF, 1,2,3,7,8,9 - Exp. Fruit 1.6E

- 03

- 1.0E

- 02 Exp. Veg. 1.6E

- 03

- 1.0E

- 02 Forag e 1.6E

- 03

- 1.0E+00 Grain 1.6E

- 03

- - Prot. Fruit 1.6E

- 03

- - Prot. Veg. 1.6E

- 03

- - Root 8.5E

- 01 1.0E

- 02

- Silage 1.6E

- 03

- 5.0E

- 01

HexaCDF, 2,3,4,6,7,8 - Exp. Fruit 1.0E

- 03

- 1.0E

- 02 Exp. Veg. 1.0E

- 03

- 1.0E

- 02 Forage 1.0E

- 03

- 1.0E+00 Gr ain 1.0E

- 03

- - Prot. Fruit 1.0E

- 03

- - Prot. Veg. 1.0E

- 03

- - Root 7.1E

- 01 1.0E

- 02

- Silage 1.0E

- 03

- 5.0E

- 01

Attachment B B- 58 October 2014 Description of MIRCTRIM

- Based Tiered Screening Methodolo gy for RTR



### Exhibit B

- 11 . Chemical

- Specific Inputs by Plant Type for Chemicals in MIRC

Compound Name Plant Part Plant

- Soil Bio

Concentration Factor ( **Br** AG

-produce-DW(i) ) (unitless ) a Empirical Correction Factor - Belowground Produce ( **VG** rootveg ) (unitless) b Empirical Correction Factor Aboveground Produce ( **VG AG(i)**  ) (unitless) c

PentaCDD, 1,2,3,7,8 - Exp. Fruit 2.4E

- 03

- 1.0E

- 02 Exp. Veg. 2.4E

- 03

- 1.0E

- 02 Forage 2.4E

- 03

- 1.0E+00 Grain 2.4E

- 03

- - Prot. F ruit 2.4E

- 03

- - Prot. Veg. 2.4E

- 03

- - Root 1.0E+00 1.0E

- 02

- Silage 2.4E

- 03

- 5.0E

- 01

PentaCDF, 1,2,3,7,8 - Exp. Fruit 4.6E

- 03

- 1.0E

- 02 Exp. Veg. 4.6E

- 03

- 1.0E

- 02 Forage 4.6E

- 03

- 1.0E+00 Grain 4.6E

- 03

- - Prot. Fruit 4.6E

- 03

- - Prot. Veg. 4.6E

- 03

- - Root 1.3E+00 1.0E

- 02

- Silage 4.6E

- 03

- 5.0E

- 01

PentaCDF, 2,3,4,7,8 - Exp. Fruit 6.8E

- 03

- 1.0E

- 02 Exp. Veg. 6.8E

- 03

- 1.0E

- 02 Forage 6.8E

- 03

- 1.0E+00 Grain 6.8E

- 03

- - Prot. Fruit 6.8E

- 03

- - Prot. Veg. 6.8E

- 03

- - Root 1 .5E+00 1.0E

- 02

- Silage 6.8E

- 03

- 5.0E

- 01

TetraCDD, 2,3,7,8 - Exp. Fruit 4.5E

- 03

- 1.0E

- 02 Exp. Veg. 4.5E

- 03

- 1.0E

- 02 Forage 4.5E

- 03

- 1.0E+00 Grain 4.5E

- 03

- - Prot. Fruit 4.5E

- 03

- - Prot. Veg. 4.5E

- 03

- - Root 1.3E+00 1.0E

- 02

- Silage 4 .5E

- 03

- 5.0E

- 01

Attachment B B- 59 October 2014 Description of MIRCTRIM

- Based Tiered Screening Methodolo gy for RTR



### Exhibit B

- 11 . Chemical

- Specific Inputs by Plant Type for Chemicals in MIRC

Compound Name Plant Part Plant

- Soil Bio

Concentration Factor ( **Br** AG

-produce-DW(i) ) (unitless ) a Empirical Correction Factor - Belowground Produce ( **VG** rootveg ) (unitless) b Empirical Correction Factor Aboveground Produce ( **VG AG(i)**  ) (unitless) c

TetraCDF, 2,3,7,8 - Exp. Fruit 1.2E

- 02

- 1.0E

- 02 Exp. Veg. 1.2E

- 02

- 1.0E

- 02 Forage 1.2E

- 02

- 1.0E+00 Grain 1.2E

- 02

- - Prot. Fruit 1.2E

- 02

- - Prot. Veg. 1.2E

- 02

- - Root 1.9E+00 1.0E

- 02

- Silage 1.2E

- 02

- 5.0E

- 01

a As discu ssed in HHRAP ( EPA 2005a), the Br AG-

produce

- DW(i) for aboveground produce and forage accounts for the uptake from soil and the subsequent transport of contaminants through the roots to the aboveground plant parts

. For organics, correlation equations to calculate values for Br on a dry weight basis were obtained from Travis and Arms (1988)

. For cadmium, Br values were derived from uptake slope factors provided in EPA 1992

. Uptake slope is the ratio of contaminant concentration in dry weight plant tissue t o the mass of contaminant applied per hectare soil

. Br aboveground values for mercuric chloride and methyl mercury were calculated using methodology and data from Baes, et al. (1984)

. Br forage values for mercuric chloride and methyl mercury (on a dry weight basis) were obtained from EPA 1997b

. The HHRAP methodology assumes that elemental mercury doesn’t deposit onto soils

. Therefore, it’s assumed that there is no plant uptake through the soil.

b As discussed in HHRAP ( EPA 2005 a ), Appendix B, VG rootveg r epresents an empirical correction factor that reduces produce concentration

. Because of the protective outer skin, size, and shape of bulky produce, transfer of lipophilic chemicals ( i.e., log K ow greater than 4) to the center of the produce is not likely

. In addition, typical preparation techniques, such as washing, peeling, and cooking, further reduce the concentration of the chemical in the vegetable as consumed by removing the high concentration of chemical on and in the outer skin, leaving the flesh with a lower concentration than would be the case if the entire vegetable were pureed without washing

. For belowground produce, HHRAP ( EPA 2005 a ) recommends using a VG r ootveg value of 0.01 for PB

- HAP with a log K ow greater than 4 and a value of 1.0 for PB

- HAP with a log K ow less than 4 based on information provided in EPA 1994b

. In developing these values, EPA (1994b) assumed that the density of the skin and the whole vegetable are equal (potentially overestimating the concentration of PB

- HAP in belowground produce due to root uptake).

c As discussed in HHRAP ( EPA 2005 a ), Appendix B, VG ag represents an empirical correction factor that reduces ab oveground produce concentration and was developed to estimate the transfer of PB

- HAP into leafy vegetation versus bulkier aboveground produce (e.g., apples)

. Because of the protective outer skin, size, and shape of bulky produce, transfer of lipophilic PB

- HAP (log K ow greater than 4) to the center of the produce is not likely

. In addition, typical preparation techniques, such as washing, peeling, and cooking, further reduces residues

. For aboveground produce, HHRAP ( EPA 2005 a ) recommends using a VG ag value of 0.01 for PB

- HAP with a log K ow greater than 4 and a value of 1.0 for PB

- HAP with a log K ow less than 4 based on information provided in EPA 1994 b. In developing these values, EPA (1994 b ) assumed the following: (1) translocation of compounds deposited on the surface of aboveground vegetation to inner parts of aboveground produce would be insignificant (potential ly underestimating the concentration of PB

- HAP in aboveground produce due to air

- to

- plant transfer); (2) the density of the skin and the whole vegetable are equal (potentially overestimating the concentration of PB

- HAP in aboveground produce due to air

- to

- plant transfer); and (3) the thickness of vegetable skin and broadleaf tree skin are equal (effects on the concentration of PB

- HAP in aboveground produce due to air

- toplant transfer unknown)

. For forage, HHRAP recommends a VG ag value of 1.0, also based on information provided in EPA 1994 b. A VG ag value for silage is not provided in EPA 1994 b ; the VGag value for silage of 0.5 was obtained from NC DEHNR (1997); however, NC DEHNR does not present a specific rationale for this recommendation

. Depending on the composition of the site

specific silage, this value may under - or overestimate the actual value

.

Attachment B B- 60 October 2014 Description of MIRCTRIM

- Based Tiered Screening Methodolo gy for RTR



### Exhibit B

- 12

. Non

- Chemical

- Specific Produce Inputs

Plant Part Interception Fraction

( **Rp (i)**  ) (unitless) a Plant Surface

Loss Coefficient ( **kp (i)**  ) (1/year) b Length of Plant Exposure to Deposition ( **Tp (i)**  ) (year) c Yield or Standing

Crop Biomass

( **Yp (i)**  ) (kg/m 2 ) d Plant Tissue

Specific Moisture Adjustment Factor ( **MAF (i)** 

) (percent ) e Exposed Vegetable 0.982 18 0.16 5.66 92 Protected Fruit NA NA NA NA 90 Protected Vegetable NA NA NA NA 80
Forage (animal feed) 0.5 18 0.12 0.24 92
Exposed Fruit 0.053 18 0.16 0.25 85 Root Vegetables NA NA NA NA 87 Silage (animal feed) 0.46 18 0.1 6 0.8 92 Grain (animal feed) NA NA NA NA 90 Source: EPA 2005a. NA = not applicable.

a Baes et al

. (1984) used an empirical relationship developed by Chamberlain (1970) to identify a correlation between initial Rp values and pasture grass productivity (standing crop biomass [ Yp ]) to calculate Rp values for exposed vegetables, exposed fruits, forage, and silage

. Two key uncertainties are associated with using these values for Rp : (1) Chamberlain’s (1970) empirical relationship developed for pasture gras s may not accurately represent aboveground produce

. (2) The empirical constants developed by Baes et al

. (1984) for use in the empirical relationship developed by Chamberlain (1970) may not accurately represent the site

- specific mixes of aboveground produce consumed by humans or the site

- specific mixes of forage or silage consumed by livestock.

b The term kp is a measure of the amount of chemical that is lost to natural physical processes (e.g., wind, water) over time

. The HHRAP

- recommended value of 18 yr -1 (also recommended by EPA 1994a and 1998) represents the midpoint of a range of values reported by Miller and Hoffman (1983)

. There are two key uncertainties associated with using these values for kp : (1) The recommended equation for calculating kp inclu des a health protective bias in that it does not consider chemical degradation processes

. ( 2) Given the reported range of kp values from 7.44 to 90.36 yr -1 , plant concentrations could range from about 1.8 times higher to about 5 times lower than the plant concentrations estimated in FFC media using the midpoint kp value of 18.

c HHRAP ( EPA 2005a) recommends using a Tp value of 0.16 years for aboveground produce and cattle silage

. This is consistent with earlier reports by EPA (1994a, 1998) and NC DEHNR (1997), which recommended treating Tp as a constant based on the average period between successive hay harvests

. Belcher and Travis (1989) estimated this period at 60 days

. Tp is calculated as 60 days ÷ 365 days/year = 0.16 years

. For forage, the average o f the average period between successive hay harvests (60 days) and the average period between successive grazing (30 days) is used (that is, 45 days), and Tp is calculated as (60 days + 30 days)/ 2 ÷ 365 days/yr = 0.12 yr

. Two key uncertainties are associated with use of these values for Tp : (1) The average period between successive hay harvests (60 days) may not reflect the length of the growing season or the length between successive harvests for site

- specific aboveground produce crops

. The concentration of chemical in aboveground produce due to direct (wet and dry) deposition ( Pd ) will be underestimated if the site

- specific value of Tp is less than 60 days, or overestimated if the site

specific value of Tp is more than 60 days.

d Yp values for aboveground produce and forage were calculated using an equation presented in Baes et al

. (1984) and Shor et al

. (1982): Yp = Y hi / A hi , where Y hi = Harvest yield of i th crop (kg DW) and A hi = Area planted to i th crop (m2), and using values for Y h and A h from USDA ( 1994b and 1994c)

. A production

- weighted U.S

. average Yp of 0.8 kg DW/m2 for silage was obtained from Shor et al

. 1982.

e MAF represents the plant tissue

- specific moisture adjustment factor to convert dry

- weight concentrations into wet

- weight concentration s (which are lower owing to the dilution by water compared with dry

- weight concentrations)

. Values obtained from Chapter 10 of EPA's 2003 SAB Review materials for 3MRA Modeling System, Volume II, “Farm Food Chain and Terrestrial Food Web Data” ( EPA 2003a) , which references EPA 1997c

. Note that the value for grain used as animal feed is based on corn and soybeans, not seed grains such as barley, oats, or wheat.

Attachment B B- 61 October 2014 Description of MIRCTRIM

- Based Tiered Screening Methodolo gy for RTR



### 6.2.3. Animal Product Parameter Values MIRC also requires chemical

- specific inputs for many of the animal product algorithms. The relevant values are shown in Exhibit B- 13 for the chemicals included in MIRC to date. The HHRAP algorithms require additional inputs for the animal products calculations that are not specific to PB

- HAPs , but are specific to the animal and animal product type. The soil and plant ingestion rates recommended in HHRAP for beef cattle, dairy cattle, swine, and chicken are provided in Exhibit B- 14

.
Exhibit B

- 13

. Animal Product Chemical

- specific Inputs for Chemicals Included in MIRC

Compound Name Soil Bio

Availability Factor ( **Bs** ) (unitless) Biotransfer Factors (Ba m ) (day/kg FW tissue) a and Met abolism Factors (MF) ( unitless) b
Mammal Non

- mammal Beef ( **Ba beef**  ) Dairy ( **Ba dairy**  ) Pork ( **Ba pork**  ) **MF** Eggs ( **Ba eggs**  ) Poultry ( **Ba poultry**  ) **MF**
**Inorganics** Cadmium compounds 1 1.2E

- 04 6.5E

- 06 1.9E

- 04 1 2.5E

- 03 1.1E

- 01 NA Mercury (elemental) 1 0 0 0 1 0 0 N A Mercuric chloride 1 1.1E

- 04 1.4E

- 06 3.4E

- 05 1 2.4E

- 02 2.4E

- 02 NA Methyl mercury 1 1.2E

- 03 1.7E

- 05 5.1E

- 06 1 3.6E

- 03 3.6E

- 03 NA **PAHs** 2

- Methylnaphthalene 1 2.4E

- 02 5.0E

- 03 2.9E

- 02 0.01 1.0E

- 02 1.7E

- 02 NA 7,12

- Dimethylbenz(a)anthra
cene 1 3.9E

- 02 8.3E -03 4.8E

- 02 0.01 1.7E

- 02 2.9E

- 02 NA

Acenaphthene 1 2.5E

- 02 5.2E

- 03 3.0E

- 02 0.01 1.0E

- 02 1.8E

- 02 NA Acenaphthylene 1 2.6E

- 02 5.5E

- 03 3.1E

- 02 0.01 1.1E

- 02 1.9E

- 02 NA Benz(a)anthracene 1 3.9E

- 02 8.3E

- 03 4.8E

- 02 0.01 1.7E

- 02 2.9E

- 02 NA Benzo(a)pyrene 1 3.8E

- 02 8.0E

- 03 4.6E

- 02 0.01 1.6E

- 02 2.8E

- 02 NA Benzo(b)fluoranthene 1 3.9E

- 02 8.3E

- 03 4.8E

- 02 0.01 1.7E

- 02 2.9E

- 02 NA Benzo(g,h,i)perylene 1 2.9E

- 02 6.1E

- 03 3.5E

- 02 0.01 1.2E

- 02 2.1E

- 02 NA Benzo(k)fluoranthene 1 3.8E

- 02 8.0E

- 03 4.6E

- 02 0.01 1.6E

- 02 2.8E

- 02 NA Chrysene 1 4.0E

- 02 8.4E

- 03 4.8E

- 02 0.01 1.7E

- 02 2.9E

- 02 NA Dibenz(a,h)anthracen
e 1 3.1E

- 02 6.5E

- 03 3.8E

- 02 0.01 1.3E

- 02 2.3E

- 02 NA

Fluoranthene 1 4.0E

- 02 8.5E

- 03 4.9E

- 02 0.01 1.7E

- 02 3.0E

- 02 NA Fluorene 1 2.9E

- 02 6.1E

- 03 3.5E

- 02 0.01 1.2E

- 02 2.1E

- 02 NA Indeno(1,2,3

- cd)pyrene 1 2.7E

- 02 5.8E

- 03 3.3E

- 02 0.01 1.2E

- 02 2.0E

- 02 NA

**Dioxins** OctaCDD, 1,2,3,4,6,7,8,9

- 1 6.9E

- 03 1.4E

- 03 8.3E

- 03 1 2.9E

- 03 5.1E

- 03 NA
OctaCDF, 1,2,3,4,6,7,8,9

- 1 8.8E

- 03 1.8E

- 03 1.1E

- 02 1 3.7E

- 03 6.5E

- 03 NA
HeptaCDD, 1,2,3,4,6, 7,8

- 1 8.8E

- 03 1.8E

- 03 1.1E

- 02 1 3.7E

- 03 6.5E

- 03 NA

Attachment B B- 62 October 2014 Description of MIRCTRIM

- Based Tiered Screening Methodolo gy for RTR



### Exhibit B

- 13

. Animal Product Chemical

- specific Inputs for Chemicals Included in MIRC

Compound Name Soil Bio

Availability Factor ( **Bs** ) (unitless) Biotransfer Factors (Ba m ) (day/kg FW tissue) a and Met abolism Factors (MF) ( unitless) b
Mammal Non

- mammal Beef ( **Ba beef**  ) Dairy ( **Ba dairy**  ) Pork ( **Ba pork**  ) **MF** Eggs ( **Ba eggs**  ) Poultry ( **Ba poultry**  ) **MF**
HeptaCDF, 1,2,3,4,6,7,8

- 1 1.6E

- 02 3.5E

- 03 2.0E

- 02 1 6.9E

- 03 1.2E

- 02 NA
HeptaCDF, 1,2,3,4,7,8,9

- 1 2.4E

- 02 5.1E

- 03 3.0E

- 02 1 1.0E

- 02 1.8E

- 02 NA
HexaCDD, 1,2,3,4,7,8

- 1 1.1E

- 02 2.3E

- 03 1.3E

- 02 1 4.6E

- 03 8.1E

- 03 NA HexaCDF, 1,2,3,4,7,8

- 1 2.3E

- 02 4.8E

- 03 2.8E

- 02 1 9.6E

- 03 1.7E

- 02 NA HexaCDD, 1,2,3,6,7,8

- 1 6.8E

- 03 1.4E

- 03 8.2E

- 03 1 2.9E

- 03 5.0E

- 03 NA HexaCDF, 1,2,3,6,7,8

- 1 9.7E

- 03 2.0E

- 03 1.2E

- 02 1 4.1E

- 03 7.1E

- 03 NA HexaCDD, 1,2,3,7,8,9

- 1 6.8E

- 03 1.4E

- 03 8.2E

- 03 1 2.9E

- 03 5.0E

- 03 NA
HexaCDF, 1,2,3,7,8,9

- 1 1.4E

- 02 2.9E

- 03 1.7E

- 02 1 5.8E

- 03 1.0E

- 02 NA HexaCDF, 2,3,4,6,7,8

- 1 9.6E

- 03 2.0E

- 03 1.2E

- 02 1 4.1E

- 03 7.1E

- 03 NA PentaCDD, 1,2,3,7,8

- 1 1.8E

- 02 3.9E

- 03 2.2E

- 02 1 7.8E

- 03 1.4E

- 02 NA PentaCDF, 1,2,3,7,8

- 1 2.6E

- 02 5.5E

- 03 3.2E

- 02 1 1.1E

- 02 1.9E

- 02 NA PentaCDF, 2,3,4,7,8

- 1 3.1E

- 02 6.5E

- 03 3.8E

- 02 1 1.3E

- 02 2.3E

- 02 NA TetraCDD, 2,3,7,8

- 1 2.6E

- 02 5.5E

- 03 3.2E

- 02 1 1.1E

- 02 1.9E

- 02 NA TetraCDF, 2,3,7,8

- 1 3.6E

- 02 7.7E

- 03 4.4E

- 02 1 1.5E

- 02 2.7E

- 02 NA Source: EPA 2005a, unless otherwise indicated. NA = not applicable.

a As discussed in HHRAP (EPA 2005a), Appendix A, biotransfer factors for mercury compounds were obtained from EPA 1997b. Consi dering speciation, fate, and transport of mercury from emission sources, elemental mercury is assumed to be vapor

- phase and hence is assumed not to deposit to soil or transfer into aboveground plant parts. As a consequence, there is no transfer of elemental mercury into a nimal tissues. Biotransfer factors for cadmium compounds were obtained from EPA 1995b. Biotransfer factors for dioxins and PAHs were calculated from chemical octanol

- water partitioning coefficients (K ow values) using the correlation equation from RTI (2005) and assuming the following fat c ontents: milk - 4%; beef - 19%; pork - 23%; poultry

- 14%; and eggs - 8%.

b As discussed in HHRAP (EPA 2005a), EPA (1995c) recommends using a metabolism factor (MF) to account for metabolism of PAHs by mammals to offset the amount of bioaccumulation suggested by biotransfer factors. EPA has recommended an MF of 0.01 for bis(2

- ethylhexyl)phthalate (BEHP) and 1.0 for all other chemicals (EPA 1995d). For MIRC, an MF of 0.01 is also used to calculate concentrations of PAHs in food products from mammalian specie s based on the work of Hofelt et al. (2001). This factor takes into account the P450

- mediated metabolism of PAHs in mammals; applying this factor in our approach reduced the concentrations of chemicals in beef, pork, and dairy by two orders of magnitude.

Attachment B B- 63 October 2014 Description of MIRCTRIM

- Based Tiered Screening Methodolo gy for RTR



### Exhibit B

- 14

. Soil and Plant Ingestion Rates for Animals

Animal Soil Ingestion Rate

– **Qs (m)**  (kg/day) a Plant Part Consumed by Animal Plant Ingestion Rate

– **Qp (I,m)**  (kg /day)

Beef cattle b 0.5 Silage 2.5 Forage 8.8 Grain 0.47

Dairy cattle c 0.4 Silage 4.1 Forage 13.2 Grain 3.0

Swine d 0.37 Silage 1.4 Grain 3.3 Chicken (eggs) e 0.022 Grain 0.2

Source: EPA 2005 a HHRAP (Chapter 5).

a Beef cattle : NC DEHNR (19 97) and EPA (1994 b ) 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) c ited an average beef cattle weight of 590 kg, and a daily dry matter intake rate (non

- lactating cows) of 2 percent of body weight

. This results in a daily dry matter intake rate of 11.8 kg DW/day and a daily soil ingestion rate of about 0.5 kg/day

. Dair y cattle : NC DEHNR (1997 ) and EPA (1994 b ) recommended a soil ingestion rate for dairy cattle of 0.4 kg/day based on Fries (1994) and NAS (1987)

. As discussed in HHRAP, Fries (1994) reported soil ingestion to be 2 percent of the total dry matter intake

. NAS (1987) cited an average beef cattle weight of 630 kg and a daily dry matter intake rate (non

- lactating cows) of 3.2 percent of body weight

. This resulted in a daily dry matter intake rate of 20 kg/day DW, and a daily soil ingestion rate of approximate ly 0.4 kg/day

. Uncertainties associated with Qs include the lack of current empirical data to support soil ingestion rates for dair y cattle and the assumption of uniform contamination o f soil ingested by cattle

. Swine : NC DEHNR (1997) recommended a soil ingestion rate for swine of 0.37, 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 ass umption 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)

. Unc ertainties include the lack of current empirical data to support soil ingestion rates for chicken and the assumption of uniform contamination of soil ingested by ch icken.

b The beef cattle ingestion rates of forage, silage, and grain are based on the total daily intake rate of about 12 kg DW/day (based on NAS [1987] reporting a daily dry matter intake that is 2 percent of an average beef cattle body weight of 590 kg) and are supported by NC DEHNR (1997), EPA (1994 b and 1990), and Boone et al

. (1981)

. The principal uncertainty associated with these Qp values is the variability between forage, silage, and grain ingestion rates for cattle

. c The dairy cattle ingestion rates of forage, silage, and grain are based on the total daily intake rate of about 20 kg DW/day (NAS 1987; EPA 199 2 ) as recommended by NC DEHNR (1997)

. Uncertainties include the proportion of each food type in the diet, which varies from location to location

. Assuming uniform contamination of plant materials consumed by cattle also introduces uncertainty.

d Swine are not grazing animals and are assumed not to eat forage (EPA 1998). EPA (1994 b and 1998) and NC DEHNR (1997) recommended including only silage and grains in the diet of swine. EPA ( 1995c) recommended an ingestion rate of 4.7 kg DW/d ay for a swine, referencing NAS (1987)

. Assuming a diet of 70 percent grain and 30 percent silage ( EPA 1990), HHRAP estimated ingestion rates of 3.3 kg DW/day (grain) and 1.4 kg DW/day (silage). Uncertainties associated with Qp i nclude variability of the proportion of grain and silage in the diet, which varies from location to location

. e Chickens consume grain provided by the farmer. The daily quantity of grain feed consumed by chicken is assumed to be 0.2 kg/day (Ensminger ( 1980 ) , Fries ( 1982 ) , and NA S ( 1987)

. Uncertainties associated with this variable include the variability of actual grain ingestion rates from site to site

. In addition, assuming uniform contamination of plant materials consumed by chicken introduces some uncertainty.



### 6.3. Adult and N on - Infant Exposure Parameter Values The exposure parameters included in MIRC and the ir default and other value options are summarized in the following subsections. The default values were selected to result in a highly health protective screening scenario . Parameter value options were primarily obtained or estimated from EPA’s Exposure Factors Handbook (EFH ; EPA 2011a ) and Child

- Specific Exposure Factors Handbook (CSEFH; EPA 2008a)

. Where values were reported for age groupings other than those used in MI RC (see Section 2.3 above for MIRC age groups), time

weighted average values were estimated for the MIRC age groups from the available data

.

Attachment B B- 64 October 2014 Description of MIRCTRIM

- Based Tiered Screening Methodolo gy for RTR



### In MIRC, ingestion rates for h ome -p roduce d 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) (EPA 2011a). The body weight parameter v alues presented in Exhibit B- 15

, therefore, are not applied in the chemical intake (ADD) equations for these food types.
In MIRC, i ngestion rates also are included for drinking water (mL/day), soil (mg/day), and fish (g/day). These ingestion rates, however, are on a per person basis (i.e., not normalized for body weight). The body weight parameter values presented in Exhibit B- 15 , therefore, are applied in the chemical intake (ADD) equations for these media.



### 6.3.1. Body Weights Body weight ( BW ) options included in MIRC include mean, 5 th 

### , 10 th 

### , 50 th 

### , 90 th 

### , and 95 th 

### percentile value s for adults and the five children’s age groups : < 1 year; 1–2 years; 3–5 years; 6– 11 years; and 12

– 19 years

. For its default scree ning assessment, EPA uses the mean BW for each age group. The BWs currently in the MIRC database are listed in Exhibit B- 15

. In general, BW values for the five children’s age groups were calculated from the summary data provide d in Table 8

- 3 of EPA’s 2008 CSEFH . For purposes of comparison, alternative BW values f or children ages 12 through 19 years also were estimated using data from Portier et al

. ( 2007 ). These values are listed in the last row of Exh ibit B- 15 , but are not included in MIRC. The means calculated using the two methods for children ages 12 through 19 years were essentially identical at 64 kg

. The other percentile values for this age group using the two methods differed by approximately 10 percent or less.

Exhibit B

- 15

. Mean and Percentile Body Weight Estimates for Adults and Children

Lifestage

(years) Duration (years) Body Weight (kg) Mean 5 th 10 th 50 th 90 th 95 th
Adult a (20

- 70) 50 80.0 a 52.9 56 69.3 89.7 97.6 Child < 1 b 1 7.83 6.03 6.38 7.76 9.24 9.66 Child 1

- 2 c 2 12.6 9.9 10.4 12.5 14.9 15.6 Chi ld 3

- 5 d 3 18.6 13.5 14.4 17.8 23.6 26.2 Child 6

- 11 e 6 36.0 22.1 24.0 33.5 51.2 58.6 Child 12

- 19 f 8 64.2 39.5 45 64.2 83.5 89 [Child 12

- 19 g 8 64.3 41.1 44.6 60.9 88.5 98.4]

a BW represents the recommended body weight from EPAs 2011 EFH. Although the 18 to 74 year age category in EPA’s EFH does not match exactly the age 20 to 70 year categorization of adults in MIRC, the magnitude of error in the mean and percentile body weights is likely to be very small (i.e., less than 1%).

b Each BW represents a time

- weighted average of body weights for age groups birth to <1 month, 1 to <3 months, 3 to <6 months, and 6 to <12 months from Table 8

- 3 of the 2008 CSEFH

. Original sample sizes for each of these age groups can also be found in Table 8

- 3.

c Each BW represents a time

- weighted average of body weights for age groups 1 to <2 years and 2 to <3 years from Table 8

- 3 of the 2008 CSEFH

. Original sample sizes for each of these age groups can also be found in Table 8

- 3.

d BWs obtained directly from Table 8

- 3 of the 2008 CSEFH (age group 3 to <6 years).

e Each BW represents a time

- weighted average of body weights for age groups 6 to <11 years and 11 to <16 years from Table 8

- 3 of the 2008 CSEFH

. Original sample sizes for each of these age groups can also be found in Table 8 -3.
f Mean BW estimated using Table 8

- 22 of the 2008 CSEFH, which is based on NHANES IV data as presented in Portier et al. (2007) . This estimate was calculated as the average of the 8 single

- year age groups from 12 to 13 years through 19 to 20 years

. Values for the other percentiles were estimated using Portier et al., 2007

. g Each BW represents a tim e- weighted average of body weights for age groups 11 to <16 years and 16 to <21 years from Table 8- 3 of the 2008 CSEFH . E stimated values include 11

- year

- olds and individuals through age 20, which contributes to uncertainty in the estimates for 12 to 19 ye ars . Those values are provided for comparison purposes only and are not included in MIRC.

Attachment B B- 65 October 2014 Description of MIRCTRIM

- Based Tiered Screening Methodolo gy for RTR



### 6.3.2. Water Ingestion Rates MIRC also includes the option of calculating chemical ingestion via drinking water obtained from surface

- water sources or from wells (i.e., from groundwater) in the contaminated area

. Users have the option in MIRC to set drinking water ingestion rates to zero or to revise the drinking water ingestion rates in MIRC to better reflect site

- specific water uses

. The 2008 CSEFH recommends values for drinking water ingestion rates for children based on a study reported by Kahn and Stralka (2008)

. Table 3

- 4 of the CSEFH provides per capita estimates of community water ingestion rates by age categories

. Community water ingestion includes both direct a nd indirect ingestion of water from the tap. Direct ingestion is defined as direct consumption of water as a beverage, while indirect ingestion includes water added during food or beverage preparation

. The source of these data is the 1994

- 1996 and 1998 U .S. Department of Agriculture’s ( USDA ’s) Continuing Survey of Food Intakes by Individuals ( CSFII ) ( USDA 2000 )

. Exhibit B- 16 includes the drinking water ingestion rates for children that are included in MIRC

. Mean and percentile adult drinking water ingestion rates were obtained from EPA (2004 b ), which presents estimated per capita water ingestion rates for various age categories based on data collected by the USDA’s 1994

– 1996 and 1998 CSFII (USDA 2000 )

. Adult ingestion rates, pr esented in Exhibit B- 16 , represent community water ingestion, both direct and indirect as defined above, for males and females combined, ages 20 years and older

.

Exhibit B

- 16

. Estimated Daily Per Capita Mean and Percentile Water Ingestio n Rates for Children and Adults a

Lifestage (years) Ingestion Rates, Community Water (mL/day) Mean 50 th 90 th 95 th 99 th
Child <1 b 504 482 969 1113 1440 Child 1

- 2 c 332 255 687 903 1318 Child 3

- 5 d 382 316 778 999 1592 Child 6

- 11 e 532 417 1149 1499 2274 Child 12

- 19 f 698 473 1641 2163 3467 Adult g 1219 981 2534 3087 4567
Sources: EPA 2004, 2008a *T he sample size does not meet minimum reporting requirements as described in EPA 2008 a. For some of these MIRC age groupings, the values are based on the time

- weighted average value for 2 or more age ranges from CSEFH Table 3 -4 . O ne or more age ranges within the group may not meet the minimum reporting requirements, but not necessarily all of them fall within this category.

a Source is Kahn and Stralka 2008, also presented in the CSEFH ( EPA 2008 a).

b Each IR represents a time

- weighted average of ingestion rates for age groups birth to <1 month, 1 to <3 months, 3 to <6 m onths, and 6 to <12 months from Table 3

- 4 of the 2008 CSEFH

. c Each IR represents a time

- weighted average of ingestion rates for age groups 1 to <2 years and 2 to <3 years from Table 3

- 4 of the 2008 CSEFH.

d Each IR represents the ingestion rate for age group 3 to <6 years from Table 3

- 4 of the 2008 CSEFH.

e Each IR represents the ingestion rate for age group 6 to <11 years from Table 3

- 4 of the 2008 CSEFH. This value represents a health protective (i.e., slightly low) estimate of IR for ages 6 through 11 years since 11

- year olds are not included in this CSEFH age group. f Each IR represents a time

- weighted average of ingestion rates for age groups 11 to <16 years, 16 to <18, and 18 to <21 years from Table 3

- 4 of the 2008 CSEFH

. Note that estimated values include 11

- year

- olds and individuals through age 20, which contributes to uncertainty in the estimates for 12 to 19 years.

g Adult drinking water ingestion rates were obtained from EPA (2004 b ), Appendix E, Part I, Table A1 for community water, both sexes (age s 20+), direct plus indirect water ingestion.



### 6.3.3. Local Food Ingestion Rates
MIRC includes m ean, median, 90 th 

### , 95 th 

### , and 99 th 

### percentile food

- specific ingestion rates (IRs) for consumers

- only of farm food chain (FFC) media for adults and children

. The mean and
Attachment B B- 66 October 2014 Description of MIRCTRIM

- Based Tiered Screening Methodolo gy for RTR



### percentile values are from EPA’s analysis of data from the USDA’s 1987 to 1988 Nationwide Food Consumption Survey (NFCS) (USDA 1993), as presented in Chapter 13 of the Agency’s Exposure Factors Handbook (i.e., Intake Rates for Various Home Produced Food Items) ( EPA 2011a). Consumers

- only means that individuals who did not report eating a specified type of food during the three

- day period covered by the food ingestion part of the survey were not included in the analysis of ingestion rates for that food type. The questionnaire included the options for a household to self

- identify in one or more of five categories: as a household that gardens, raises animals, hunts, fishes, or farms. As of September 2008, that survey was the most recent NFCS available ( E PA 2008a, CSEFH), and we are not aware of any that might be more recent. 29


### For the adult age group in MIRC, data were compiled on food

- specific IRs separately for two types of households as indicated in the “Response to Questionnaire” ( EPA 2011a , Chapter 13): (1) households that farm (F) and (2) households that garden or raise animals (HG for homegrown)

. This division reflects EPA’s data analysis

. EPA tabulated IRs for fruits and vegetables separately for households that farm and households that garden

. Similarly, EPA tabulated IRs for animals and animal products for households that farm and for households that raise animals

. Thus, the first type of household, F, represents farmers who may both grow crops and raise animals and who are likely to consume more homegrown/raised foods than the second type of household

. The second type of household, HG, represents the non

- farming households that may consume lower amounts of home

- grown or raised foods (i.e., HG encompasses both households that garden and households that raise animals)

. Th e food

- specific ingestion rates are based on the amount of each food type that households that farm (F) or households that garden and raise animals (HG) produced and brought into their homes for consumption and the number of persons consuming the food. EPA averaged the actual consumption rate for home

- grown foods over the 1

- week survey period.

The default food

- specific ingestion rates in MIRC for adults are those for farming households (F) in Exhibi t B- 17 . The user can specify use of the non

- farming household (HG) ingestion rates if they are more appropriate for the user’s exposure scenario.

Exhibit B

- 17

. Summary o f Age

- Group Specific Food Ingestion Rates for Farm Food Items

Product Child (age in yr) Adult (20

– 70

yrs) <1 1–2 3–5 6– 11 12

– 19 **Mean ingestion rates (**  **g/kg**

**- day**  **)** Beef a N/A 4.14 4.00 3.77 1.72 1.93 Dairy b N/A 91.6 50.9 27.4 13.6 2.96 Eggs a N/A 2.46 1 .42 0.86 0.588 0.606 Exposed Fruit a N/A 6.14 2.60 2.52 1.33 1.19 Exposed Vegetable a N/A 3.48 1.74 1.39 1.07 1.38 Pork a N/A 2.23 2.15 1.50 1.28 1.10 Poultry a N/A 3.57 3.35 2.14 1.50 1.37

29 Note that EPA’s 2008 CSEFH does not distinguish between exposed and protected fruits and vegetables when recommending food ingestion rates based on the same data set for the same age categories

. EPA’s 1997 analysis for its EFH therefore remains the most appropriate data source for use in MIRC.

Attachment B B- 67 October 2014 Description of MIRCTRIM

- Based Tiered Screening Methodolo gy for RTR



### Exhibit B

- 17

. Summary o f Age

- Group Specific Food Ingestion Rates for Farm Food Items

Product Child (age in yr) Adult (20

– 70

yrs) <1 1–2 3–5 6– 11 12

– 19

Protected Fruit a N/A 16.6 12.4 8.50 2.96 5.19 Protected Vegetabl e a N/A 2.46 1.30 1.10 0.78 0.862 Root Vegetable a N/A 2.52 1.28 1.32 0.94 1.03 Water (mL/day) c N/A 332 382 532 698 1218 **Median ingestion rates (g/kg** 

**- day)**  Beef a N/A 2.51 2.49 2.11 1.51 1.55 Dairy b N/A 125 66.0 34.4 15.5 2.58 Eggs a N/A 1.51 0.83 0.561 0 .435 0.474 Exposed Fruit a N/A 1.82 1.11 0.61 0.62 0.593 Exposed Vegetable a N/A 1.89 1.16 0.64 0.66 0.812 Pork a N/A 1.80 1.49 1.04 0.89 0.802 Poultry a N/A 3.01 2.90 1.48 1.30 0.922 Protected Fruit a N/A 7.59 5.94 3.63 1.23 2.08 Protected Vegetable a N/A 1.94 1.04 0.79 0.58 0.564 Root Vegetable a N/A 0.46 0.52 0.57 0.56 0.59 Water (mL/day) c N/A 255 316 417 473 981 **90 th**  **percentile ingestion rates (g/kg** 

**- day)**  **d** Beef a N/A 9.49 8.83 11.4 3.53 4.41 Dairy b N/A 185 92.5 57.4 30.9 6.16 Eggs a N/A 4.90 3.06 1.90 1.30 1.31 Exposed Fruit a N/A 12.7 5.41 6.98 3.41 2.37 Exposed Vegetable a N/A 10.7 3.47 3.22 2.35 3.09 Pork a N/A 4.90 4.83 3.72 3.69 2.23 Poultry a N/A 7.17 6.52 4.51 3.13 2.69 Protected Fruit a N/A 44.8 32.0 23.3 7.44 15.1 Protected Vegetable a N/A 3.8 8 2.51 2.14 1.85 1.81 Root Vegetable a N/A 7.25 4.26 3.83 2.26 2.49 Water (mL/day) c N/A 687 778 1149 1640 2534 **95 th**  **percentile ingestion rates (g/kg** 

**- day)**  Beef a N/A 12.9 12.5 12.5 3.57 5.83 Dairy b N/A 167 89.9 56.0 32.3 7.80 Eggs a N/A 5.38 3.62 2.37 1. 43 1.59 Exposed Fruit a N/A 14.6 6.07 11.7 4.78 3.38 Exposed Vegetable a N/A 11.9 6.29 5.47 3.78 4.46 Pork a N/A 6.52 6.12 4.73 6.39 2.60 Poultry a N/A 8.10 7.06 5.07 3.51 3.93 Protected Fruit a N/A 48.3 35.1 26.9 11.4 19.2 Protected Vegetable a N/A 9.42 5 .10 3.12 2.20 2.83 Root Vegetable a N/A 10.4 4.73 5.59 3.32 3.37

Attachment B B- 68 October 2014 Description of MIRCTRIM

- Based Tiered Screening Methodolo gy for RTR



### Exhibit B

- 17

. Summary o f Age

- Group Specific Food Ingestion Rates for Farm Food Items

Product Child (age in yr) Adult (20

– 70

yrs) <1 1–2 3–5 6– 11 12

– 19 Water (mL/day) c N/A 903 999 1499 2163 3087 **99 th**  **percentile ingestion rates (g/kg** 

**- day)**  Beef a N/A 20.9 19.8 13.3 4.28 6.84 Dairy b N/A 180 87.2 54.8 34.7 9.20 Eggs a N/A 16.2 11.2 8.19 4.77 1.83 Exposed Fruit a N/A 25.2 32.5 15.7 5.9 13.0 Exposed Vegetable a N/A 12.1 7.36 13.3 5.67 8.42 Pork a N/A 8.71 9.74 6.61 4.29 3.87 Poultry a N/A 9.63 10.24 6.12 4.60 4.93 Protected Fruit a N/A 109 71.2 58.2 19.1 34.4 Protected Vegetable a N/A 9.42 5.31 5.40 2.69 5.56 Root Vegetable a N/A 10.4 4.73 7.47 5.13 7.57 Water (mL/day) c N/A 1318 1592 2274 3467 4567

a Primary source for values was the 1987

– 1988 NFCS survey; compiled results are presented in Chapter 13 of 2011 Exposure Factors Handbook ( EPA, 2011 a) . When data were unavailable for a particular age group, intake rate for all age groups was used multiplied by the age

- specific ratio of intake based on national population intake rates from CSFII.

b Primary source for values was 1987

– 1988 NFCS survey, compiled results presented in Chapter 13 of 2011 Exposure Factors Handbook ( EPA, 2011 a) . When data were unavailable for a particular age group, intake rate for all age groups was used multiplied by the age

- specific ratio of intake based on national population intake rates from an NHANES 2003

– 2006 analysis in Chapter 11 of the Exposure Factors Handbook.

c Primary source for children less than 3 years of age was a Ka h n and Stralka ( 2008 ) analysis of CSFII data, and from EPA’s analysis of NHANES 2003

– 2006 data for children and adults greater than three. All data tables that were used and justifications for data sources are presented in Chapter 3 of the 2011 Exposure Factors’ Handbook.

d Default ingestion rate percentile used in MIRC for Tier 1 assessments and chemical threshold calculations

. 

### For children, EPA estimated food

- specific IRs for four age categories ( EPA 2011a ): 1– 2 years, 3– 5 years, 6

– 11 years, and 12

– 19 years

. Sample sizes were insufficient to distinguish IRs for children in different types of households; hence, for children, a single IR value represents both F and HG households for a given food type and age category ( Exhibit B- 17 )

. For some food types and age categori es, there were insufficient data for EPA to provide consumer

- only intake rates (i.e., data set for the subpopulation consisted of fewer than 20 observations ). T he HHRAP methodology , Section 6.2.2.2, recommend s a method by which to calculate the “missing” age

specific consumer

- only ingestion rate s , as explained below. Fo od

- specific intake rates (IRs) for those child age groups and food items not included in Chapter 13 of the 2011 EFH , that is IR age_group _x 

### , were derived using the following information:

• 

### M ean or percentile

- specific consumer

- only intake of the farm food item, as brought into the home, for the total NFCS survey population (from EFH Chapter 13) – IRCO_total;

• 

### Mean or percentile

- specific per capita intake of the food type from all sources, as
co nsumed, for the specific child age group, from Chapter 3 of the CSFII Analysis of Food Intake Distributions (EPA 2003c) – IRPC, age_group_x; and

• 

### Mean or percentile

- specific per capita intake of the farm food item for the total CSFII survey population (fro m Chapter 3 of EPA 2003c) – IRPC_total.

Attachment B B- 69 October 2014 Description of MIRCTRIM

- Based Tiered Screening Methodolo gy for RTR



### 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 a s 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 B- 56 was used to calculate the “missing” age

- specific consumer

- only IRs : Equation B- 56

. Calculation of Age

- Group

- Specific and Food

- Specific Ingestion Rates

where:

IR CO, age_group_x = Mean or percentile

- specific consumer

- only intake of the food type from all sources, as consumed, for the specific c hild 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 p opulation


### In this discussion, per capita (as opposed to consumer

- onl y

) 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 beca use 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 c hild

- 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 calcu lation.



### 6.3.4. Local Fish Ingestion Rates
6.3.4.1. Screening Scenario The USDA’s 1987 to 1988 Nationwide Food Consumption Survey (NFCS) (USDA 1993, 1994a), as presented in Chapter 13 of the Agency’s Exposure Factors Handbook (i.e., Intake Rates for 

### IR IR IR IR
PC_total oup_x PC, age_gr CO_total oup_x CO, age_gr 

### ×

=

Attachment B B- 70 October 2014 Description of MIRCTRIM

- Based Tiered Screening Methodolo gy for RTR



### Various Home Produced Food Items) (EPA 2011a)

, includes family

- caught fish ingestion rates by age category. There are several disadvantages, however, to using that data source to estimate fish ingestion rates. First, due to inadequate sample sizes, EPA did not report fish inge stion rates for children less than 6 years of age. Second, the NFCS data were collected more than two decades ago. Third, the reported fish ingestion rates are for ages 6 to 11 and 12 to 19 and are based on 29 and 21 individuals in each age category, res pectively (EPA 2011a, Table 13

- 20). Finally, the ingestion rates from NFCS data are based on total weight of fish as brought into the home, and do not include losses from preparation of the fish (i.e., removal of inedible parts and, possibly, the skin). Estimates of preparation losses for fish intended to apply to the NFCS fish ingestion rate data are very uncertain and are based on a wide variety of freshwater, estuarine, and marine fish, and squid (EPA 2011a, Table 13

- 69). Additionally, when considerin g the multipathway screening methodology, it is important that potential health effects to those individuals who are most likely to have the greatest PB

- HAP exposure are not underestimated and, therefore, ingestion rates that are reflective of subsistence fisher ingestion rates are desired. Therefore, a more recent survey was sought that included larger sample sizes, data for children younger than six years, ingestion rates for the parts of fish actually consumed, and ingestion rates reflective of subsistence fisher ingestion rates.
Taking all of these issues into consideration, the default adult fish ingestion rate selected for use in MIRC is 373 g/day, which is the the estimated 99 th 

### percentile of fish ingestion rates for woman fisher s as reported by Burge r (2002). This rate is based upon ingestion of “wild

- caught” fish, which includes freshwater, estuarine, and marine species, while our screening scenarios focus only on freshwater fish from lakes. This is notable because a number of studies indicate that fish ingestion rates are limited by species and habitat (i.e., lake, river, estuary, and ocean) and that the majority of the fish consumed in the United States are from river, marine and estuarine habitats versus lakes. Thus, although the fish ingestion rate for this group of subsistence fishers is not the highest fish ingestion rate available for use by EPA, we do believe it strikes the appropriate balance between being health protective and having screening scenarios so conservative that they are of limited use in the decision making process. This high

- end fish ingestion rate is appropriate in the context of the conservative screening scenario used in the RTR process. This methodology is particularly applicable for national rulemakings given that it is very likely that subsistence woman fishers of child bearing age are located throughout the United States. Finally, we note that using a high

- end subsistence fish ingestion rate is consistent with section 112 of the CAA, which focuses on risks associated w ith maximally exposed individuals.
Because Burger (2002) did not estimate fish ingestion rates for children, another data source was needed to develop ingestion rates for the child age categories that are used in MIRC. The child ingestion rates need to be consistent with the Burger adult ingestion rate, reflective of subsistence fisher ingestion rates, and based on adequate sample sizes. To satisfy these requirements, data on child ingestion rates from EPA’s Estimated Per Capita Fish Consumption in the Un ited States (EPA 2002) were selected for use. Specifically, the estimated 99 th 

### percentile of as

- prepared

, consumer

- only ingestion rates for finfish and shellfish were selected (see Section 4.2.1.1 Table 5 of EPA 2002).The original data were collected as pa rt of the 1994

– 96 and 1998 USDA Continuing Survey of Intakes by Individuals (CSFII; USDA 2000). Values reflect “as prepared” ingestion rates for which cooking and preparation losses ( L1 and L2 ) did not need to be considered. “Total” fish as reported in t his source represent s consumption of finfish plus shellfish.
Because the child age categories used in MIRC differ from the CSFII age categories presented in EPA 2002, the CSFII data were adjusted for use in MIRC. The CSFII data did not provide ingestion rates for the 1

– 2 year age category. To estimate ingestion rates for this age group,

Attachment B B- 71 October 2014 Description of MIRCTRIM

- Based Tiered Screening Methodolo gy for RTR



### EPA used the ingestion rate for the 3

– 5 year age category scaled downward by the ratio of the mean body weight of the 1

– 2 year age group to the mean body weight of the 3 –5 year age group. Because MIRC uses a 3

– 5 year age category, no adjustment was needed for CSFII data from that age category. For the 6

– 11 and 12

– 19 age categories, time

- weighted average ingestion rates were calculated based on the CSFII ingestion rates. Exhibit B

- 18 provides the fish ingestion rates used in the screening assessment

.
Exhibit B

- 18

. Fish I ngestion R ates Used in Scree ning Assessment
Ingestion Rates (g/day) Infants

<1 yr Child 1

– 2 yrs Child 3

– 5 yrs Child 6

– 11 yrs Child 12

– 19 yrs Adult 20

– 70 yrs NA 107.7 a 159.0 b 268.2 c 331.0 c 373 d

a A fish ingestion rate for ages 1

- 2 years was not available . The v alue represe nts the consumer

- only fish ingestion rate for ages 3 to 5 from EPA (2002) (Section 4 .2.1.1 Table 5 [freshwater/estuarine habitat]), scaled down by the ratio of the mean Child 1

- 2 body weight to the mean Child 3

- 5 body weight

. b This value represents the c onsumer

- only fish ingestion rate for ages 3 to 5 from EPA (2002), Section 4 .2.1.1 Table 5 (freshwater/estuarine habitat) rounded to the nearest full number

. c These values represent time

- weighted average consumer

- only fish ingestion rate s based on ingestion rates from EPA (2002), Section 4 .2.1.1 Table 5 (freshwater/estuarine habitat)

. d This value represents the 99 th percentile ingestion rate of wild caught fish for women as reported by Burger (2002)

. 

### 6.3.4.2. Other Values Available in MIRC EPA’s (2002) analysis of freshwater and estuarine fish consumption from the USDA’s Continuing Survey of Food Intake by Individuals (CSFII) for 1994

- 96 and 1998 was chosen to provide fish ingestion rate options by age category in MIRC. Although the fish consumption rates reported in the CSFII include all sources, commercial and self

- caught, for purposes of screening level risk assessments, it was assumed that all freshwater and estuarine fish consumed are self

- caught. The inclusion of commercially obtained and estuarine fish will overestimate locally caught freshwater fish ingestion rates for most populations in the United States; however, it also might underestimate locally caught fish ingestion rates for some populations (e.g., Native Americans, Asian and Pacific Island communities, rural African American communities). Because consumption of locally caught fish varies substantially from region to region in the United States and from one population or ethnic group to the next, users of MIRC are encouraged to use more locally relev ant data whenever available.
For children, MIRC also includes values for the mean and the 90th, 95th, and 99th percentile fish per

- capita ingestion rate s (freshwater and estuarine fish only) based on EPA’s analysis of 1994

- 96 and 1998 CSFII data ( EPA 200 2, 2008a) . Those rates include individuals who eat fish and those who do not eat fish. As shown in EPA’s 2008 CSEFH, Table 10

- 7, the 90 th 

### percentile per capita ingestion rates estimated from the two

- day CSFII recall period are zero for some child age gro ups

. Although not presented in CSEFH Table 10

- 7, median ingestion rate s for all child age groups would be zero (considering the “consumer only” sample sizes [CSEFH Table 10

- 9] relative to the “ per

- capita ” sample sizes in Table 10

- 7)

. The high percentil e fish ingestion rates that are zero result from the short duration of the CSFII recall period (two days) compared with the averaging time of interest (a year) and the relatively infrequent consumption of fish (e.g., on the order of once a week to once a month or less) compared with the near daily ingestion of other types of food products (e.g., dairy, produce, meat)

. Use of zero for fish ingestion rates, however, is not useful in MIRC

. As a result, an alternative method was used to estimate fish ingestio n rates for children and adults that could provide reasonable, non

- zero values for all age groups and percentiles

.

Attachment B B- 72 October 2014 Description of MIRCTRIM

- Based Tiered Screening Methodolo gy for RTR



### The alternative, age

- group

- specific fish ingestion rate s were derived using values for each age group, y :

• 

### Mean or other appropriate perce ntile consumer

- only fish ingestion rate s for age group y , IRCO, y , from EPA’s Estimated Per Capita Fish Consumption in the United States (EPA 2002), Section 5.2.1.1, Table 5, for freshwater/estuarine habitat. 30

• 

### Fraction of the population consuming freshwate r/estuarine fish, FPC, y , calculated as
consumer

- only sample size / U.S. population sample for age group y . The data to calculate th o se fractions are available in the 2008 CSEFH and EPA 2002.
Equation B- 57 was used to calculate the alternative, per capita fish ingestion rate s by age group 

### ( IR PC,y 

### ):
Equation B- 57

. Calculation of Alternative Age

- Group

- Specific Fish Ingestion Rates

where:

IR PC,y = Per c apita 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 rat her than the subset of the population that ingests the particular food category. Here, per capita ingestions are recommended by the HHRAP methodology because
no consumer

- only percentile

- specific intakes are provided for the different age groups.

The mean and percentile consumer

- only fish ingestion rates for children and adults and the fraction of the population consuming freshwater/estuarine fish used in calculating long

- term per capita fish ingestion rates by age group are presented in Exhibit B- 19 and Exhibit B- 20 . The mean and percentile per capita fish ingestion rates estimated using this methodology are summarized in Exhibit B- 21 and are available in MIRC. The fish ingest i on rates provided in Exhibit B- 21 and included in MIRC are intended to represent the harvest and consumption of fish in surface waters in a hypothetical depositional area

. For site

- specific application of this tool, users should consider using more localized survey data to estimate more appropriate fish ingestion rates

. The fishing season varies substantially across the United States by latitude, and fish consumption patterns also vary by type of water body (e.g., ponds, lakes, rivers, streams, estuaries, coastal marine), cultural heritage, and general geographic area

. Therefore, use of more localized information is encouraged

. Note that as indicated in Exhibit 16 and Exhibit 18 , in developing the screening threshold emission rates, health protective fish ingestion rates for child and adult fish consumers that more closely represent exposures of a high

- end recreational fisher were used.

30 Most of these data also are provided in Table 10

- 9 of the CSEFH; the median values, however, are not presented in the CSEFH, and values for the mean and all other percentiles are slightly different due to rounding. y PC, y CO, y PC, 

### F IR IR ×=

Attachment B B- 73 October 2014 Description of MIRCTRIM

- Based Tiered Screening Methodolo gy for RTR



### As noted in Section 6.4.3 of this attachment , if the user overwrites the fish IRs shown in Exhibit B- 21 with fresh

- weight as caught values (e.g., values obtained from a local creel survey), the user is advised to set non

- zero values for the preparation and cooking loss factors L1 and L2 in Equation B- 15 . Suggested values are presented in Section 6.4.3

.
Exhibit B

- 19

. Daily Mean and Percentile **Consumer**

**- Only**  Fish Ingestion Rates for Children and Adults ( **IR CO,y**  

### ) a

Lifestage (years) Ingestion Rates, All Fish (g/day) Mean 50th 90 th 95 th 99 th
Child <1 NA NA NA NA NA Child 1

- 2 b 27.31 15.61 64.46 87.60 138.76 * Child 3

- 5 c 40.31 23.04 95.16 129.31 204.84 * Child 6

- 11 d 61.49 28.46 156.86 * 247.69 * 385.64 * Child 12

- 19 e 79.07 43.18 181.40 * 211.15 * 423.38 * Adult f 81.08 47.39 199.62 * 278.91 505.65 *
Sources: EPA 2002, 2008a N A = not applicable; it is assumed that children < 1 year of 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 P er capita fish ingestion (FI) rates for children by age group are available from Chapter 10 of the CSEFH ( EPA 2008a); however, all 50th and some 90th percentile ingestion rates are zero

. Per capita FI rates were therefore estimated as described in Equation B- 57 to provide reasonable, non

- zero values for all age groups and percentiles.

b A fish IR for ages 1

- 2 years was not available . The v alue represents the consumer

- only fish ingestion rate for ages 3 to 5 from EPA (2002) (Section 5.2.1.1 Table 5 [freshwater/estuarine habitat]), scaled down by the ratio of the mean Child 1

- 2 body weight to the mean Child 3

- 5 body weight

. c These values represent the consumer

- only fish ingestion rate for ages 3 to 5 from EPA (2002), Section 5.2.1. 1 Table 5 (freshwater/estuarine habitat)

. Sample size = 442.

d These values represent the consumer

- only fish ingestion rate for ages 6 to 10 from EPA (2002), Section 5.2.1.1 Table 5 (freshwater/estuarine habitat)

. Sample size = 147.

e These values represen t the time

- weighted average per capita fish ingestion rate for ages 11 to 15 and 16 to 17 years from EPA (2002), Section 5.1.1.1 Table 5 (freshwater/estuarine habitat); the value may underestimate ingestion rate for ages 12 to 19 years

. Sample size = 135.

f These values r epresent the consumer

- only fish ingestion rate for individuals 18 years and older from EPA (2002), Section 5.2.1.1 Table 4 (freshwater/estuarine habitat)

. Sample size = 1,633.

Attachment B B- 74 October 2014 Description of MIRCTRIM

- Based Tiered Screening Methodolo gy for RTR



### Exhibit B

- 20

. Fraction of Population Consuming Freshwater/Estuarine Fish on a Single Day ( **F PC,y**  

### )
Lifestage (years) Fraction Consuming Fish Child 3

- 5 0.0503 a Child 6

- 11 0.0440 b Child 12

- 19 0.0493 c Adult 0.08509 d

Sources: EPA 2002, 2008a

a This value was 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 i ndividuals who consumed fish on at least one of two survey days) to match the one

- day ingestion rate.

b As in footnote a, the value was calculated using the ages 6 to 10 sample size for consumers only divided by the sample size f or U.S. population divided b y 2.

c The value was calculated by summing the ages 11 to 15 and 16 to 17 sample sizes for consumers only and dividing by both by the sum of the sample sizes for U.S. population and by a factor of 2.

d The value was calculated using the ages 18 and older sample size for consumers only divided by the sample size for U.S. population from Section 5.1.1.1 Table 4. The result was divided by 2 to represent a one

- day sampling period in order to match the
one

- day ingestion rate.



### Exhibit B

- 21

. Calculated Long

- term Mean and Percentile **per capita**  Fish Ingestion Rate s for Children and Adults ( **IR PC,y**  

### )

Lifestage (years) Ingestion Rates, All Fish (g/day) Mean 50th 90 th 95 th 99 th
Chil d <1 NA NA NA NA NA Child 1

- 2 a 1.37 0.79 3.24 4.41 6.98 Child 3

- 5 b 2.03 1.16 4.79 6.51 10.3 Child 6

- 11 c 2.71 1.25 6.90 10.9 17.0 Child 12

- 19 d 3.90 2.13 8.95 10.4 20.9 Adult e 6.90 4.03 16.99 23.73 43.02

Sources: EPA 2002, 2008a N A = not applicable; it is assumed that children < 1 year of age do not consume fish.

a Values were 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 o f 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 c alculated as (consumer

- only IR for Adults) x (fraction of population consuming fish for Adults).

Attachment B B- 75 October 2014 Description of MIRCTRIM

- Based Tiered Screening Methodolo gy for RTR



### MIRC also includes values for the mean and the 90th percentile fish ingestion rates for
recre ational fisher s, black and female recreational fisher s, and fisher s of Hispanic, Laotian, and Vietnamese descent which are shown in Exhibit B

- 22 . These latter three populations are culturally or economically disposed to higher rates of fish ingestion than the general population. Recreational fisher values are from the EFH (EPA, 2011a). Black and female recreational fisher s ingestion rates are presented in Burger (2002). The fish ingestion rates for Hispanic, Laot ian, and Vietnamese populations were derived from a study by Shilling et al . (2010) of contaminated fish consumption in California’s Central Valley Delta. Shilling et al . (2010) reported mean and 95 th 

### percentile ingestion rates for each subpopulation. In part due to the low sample size in the Shilling study (n of 30 to 45), 95 th 

### percentile values were believed to be unrealistically high. The 90 th 

### percentile ingestion rate estimates presented in Exhibit B

- 22 for Hi spanic, Laotian, and Vietnamese fishers were derived by EPA using information from Shilling et al . (2010; EPA, 2010).

Exhibit B

- 22 . Calculated Mean and 90 th 

### Percentile Per capita Fish Ingestion Rates for Populations of Recreational Fishers (IR PC,y 

### )
Subpopulation

Percentile Units Recrea

tional Fisher a Female Recrea

tional Fisher b Black Recrea

tional Fisher b Hispanic Recrea

tional Fisher c Laotian Recrea

tional Fisher c Vietn amese Recrea

tional Fisher c
**Ingestion of Fish**
Mean g/day 8 39.1 171 25.8 47.2 27.1
90th g/day 11 123 446 98 144.8 99.1

a 1997 Exposure Factors Handbook (USEPA, 1997 a )

b Burger (2002) weights are “as consumed” for locally caught fish.

c Shilling, F., A. W hite, L. Lippert, and M. Lubell. 2010. Contaminated fish consumption in California’s Central Valley Delta. Environmental Research 110:334

- 344.


### Applications to date of MIRC have used whole fish concentrations estimated by TRIM.FaTE

. The proportion of lip id 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 ex ample, EPA estimated a consumption

- weighted mean lipid value for fillets of fish from TL3 to be 2.6 percent and from TL4 to be 3.0 percent (Table 6

- 9 in EPA 2003b)

. If a user of MIRC wishes to account for reduced chemical concentration in fillet compared with whole fish for lipophilic chemicals, the user can specify a “preparation” loss of chemical (see Section 6.4 of this attachment ).
For lipophilic chemicals (e.g., log K ow 

### greater than 4), which partition primarily into the f atty tissues of fish, much of the higher concentration tissues might be stripped from the fish during preparation (e.g., belly fat, viscera which includes fat in liver, etc

. , fat under skin)

. The degree to which the concentration of chemical in a fillet is less than the average total concentration in the whole fish is chemical specific

. Assuming the chemical concentration in the fillet is the same as in the whole fish may result in a health protective bias for highly lipophilic chemicals . For persons who prefer to consume fillets with the skin on and do not discard belly fat

, assuming the same concentration of chemical in the fish consumed as in the whole fish also is protective.

Attachment B B- 76 October 2014 Description of MIRCTRIM

- Based Tiered Screening Methodolo gy for RTR



### 6.3.5. Soil Ingestion Rates Adult gardeners and farmers may incidentally ingest soi ls 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 2008 a)

. Data on soil ingestion rates are sparse; the soil ingestion rates listed in Exhibit B- 23 and included in MIRC are based on very limited data, as is evident from the values listed. The studies evaluated by EPA for children generally focused on children between the ages of 1 and 3 to 6 years and were not specific to families that garden or farm. The default ingestion rates in MIRC are the 90 th 

### percentile values, as for other ingestion rate parameters.

Exhibit B

- 23 . Daily Mean and Percentile Soil Ingestion Rates for Children and Adults

Age Group

(years) Soil Ingestion Rate (mg/day) Mean a 50 th a 90 th 95 th 99 th
Child < 1 NA Child 1

- 2 50 50 200 b 200 b 200 b Child 3

- 5 50 50 200 b 200 b 200 b Child 6

- 11 50 50 201 c 331 d 331 d Child 12

- 19 50 50 201 c 331 d 331 d Adult 20

- 70 20 20 201 c 331 d 331 d

Sources: EPA 2008 a , EPA 2011 a

a For mean and 50 th percentile soil ingestion rates for children, value represents a “central tendency” estimate from EPA’s 2008 CSEFH, Table 5 -1 . For adults, value is the recommended mean value for adults from EPA’s 2011 EFH, Chapter 5, Table 5

- 1.

b V alues are the recommended “upper percentile” value for children from EPA’s 2011 EFH, Chapter 4, Table 4

- 23. The 2008 CSEFH and 2011 E F H included a h igh

- end value associated with pica only, but this value has not been used.

c V alues are 90 th percentile adult ingestion rates calculated in Stanek et al. 1997 ; used to represent older children and adults.

d V alues are 95 th percentile adult ingestion rates ca lculated in Stanek et al. 1997 ; used to represent older children and adults.



### 6.3.6. Total Food Ingestion Rates Although not included in MIRC for deterministic screening

- level exposure and risk assessments, total food ingestion rates w ould be included in any pro babilistic module develop ed for MIRC

. The total food ingestion rates presented in Exhibit B- 24 will be used to normalize or to truncate the sum of food

- specific ingestion rates to reasonable values

. This procedure is particularl y 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 B B- 77 October 2014 Description of MIRCTRIM

- Based Tiered Screening Methodolo gy for RTR



### Exhibit B

- 24 . Daily Mean and Percentile **Per Capita** Total Food Intake for Children an d 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 1,74 6 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 7 9 125 144 177 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

. 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 r epresents
a health prote ctive (i.e., slightly low) estimate for ages 6 through 11 years since 11

- year olds are not included in this CSEFH age group.

e These values represent a time

- weighted average for age groups 11 to <16 years (N=975) and 16 to <21 (N=743) years from Table 14

- 3 of the 2008 CSEFH

. Note that estimated values include 11

- year

- olds and individuals through age 20, which contributes to uncertainty in the estimates.
f These values represent a time

- weighted average for age groups 20 to 39 years (N=2,950) and 40 to 69 year s (N=4,818) from Table 5B of the 2005 EPA analysis of CSFII

. g These values represent a time

- weighted average for age groups 20 to 39 years (N=2,950) and 40 to 69 years (N=4,818) from Table 5A of the 2005 EPA analysis of CSFII

. h Percents consuming foods from Table 14

- 3 of the 2008 CSEFH include: 67.0% (birth to <1 month); 74.7% (1 to <3 months); 93.7% (3 to <6 months); and 99.7% (6 to <12 months)

. Infants under the age of 1 that consume breast milk are classified as “non

consumers” of food.



### 6.4. Other Exp osure 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 d ays per year that an individual
consumes home

- produced food items that are contaminated with the chemical being evaluated. In MIRC, the default value for EF is 365 days/year for all exposure sources and all potential receptors. This assumption is consistent with the food ingestion rates used in MIRC (i.e., daily intake rates equivalent to annual totals divided by 365 days) and does not imply that residents
nece ssarily consume home

- produced food products every day of the year

. Attachment B B- 78 October 2014 Description of MIRCTRIM

- Based Tiered Screening Methodolo gy for RTR



### If the user wishes to evaluate daily intake rates based on shorter averaging times, the user can overwrite both the food

- specific ingestion rates and the EF for each home

- grown food produ ct. Users of MIRC might want to specify a lower EF values for various food types where residents obtain some of their diet from commercial sources and where consumption of home grown produce is seasonal.



### 6.4.2. Fraction Contaminated
The fraction contaminated (FC) represents the fraction of each food product consumed that is contaminated by the chemical at a level consistent with environmental concentrations in the area of concern (e.g., area with maximum deposition rates). Obviously, the most health protectiv e assumption is that all food products consumed (i.e., 100 percent) are from the location represented by the chemical concentrations input into MIRC.

For non

- infant children and the adult age cohorts, MIRC includes the default FC of 1, assuming that 100 percent of the food product consumed is produced by households that farm, garden, or raise animals

. The user can vary this default FC value for individual food products to tailor the assessment to a particular exposure scenario

. 

### 6.4.3. Preparation and Cooking Losses Food preparation and c ooking loss es are included in the FFC exposure calculations to account for the amount of a food product as brought into the home that is not ingested due to loss durin g preparation, cooking, or post

- cooking . These losses need to be accounted for in the ADD equations because the food ingestion rates calculated from the USDA 1 987 to 1988 NFCS are based on the weight of home grown produce and animal products brought from the field into the house prior to any type of preparation. Not all of the produce or products were eventually ingested. In general, some parts of the produce and products are discarded during preparation while other parts might not be consumed even after cooking (e.g., bones). Thus, the actual food ingested is generally less than the amount brought 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 loss es for exposed fruit, protected fruit, exposed vegetables, protected vegetables, root vegetables, beef, pork, poultry, and fish . Different types of loss es apply to different types of foods. Therefore, the losses can be represented by only two parameters, L1 and L2 , the definitions of which vary according to the food type as explained in the endnotes in Exhibit B- 25 . All prepara tion and cooking loss parameter values were estimated as specified in the exhibit’s endnotes from data presented in Chapter 13 of the EPA’s 1997 and 2011 EFH (EPA 1997a and 2011a).

Attachment B B- 79 October 2014 Description of MIRCTRIM

- Based Tiered Screening Methodolo gy for RTR



### Exhibit B

- 25

. Fraction Weight Losses from Preparation of Various Foods

Product Mean Cooking, Paring, or Preparation Loss (Cooking Loss Type 1 [L1]) (unitless) a Mean Net Post Cooking (Cooking Loss Type 2 [L2]) (unitles s) 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 1997 a and 2011a NA = Not Available

a For fruits , includ es 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 ingredien ts

. 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 fo rms

. 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 average s of means for all fruits with available data (except oranges) (Table 13

- 6).

d This value r epresents an average of means for all exposed vegetables with available data (Table 13

- 7)

. Exposed vegetables include asparagus, broccoli, cabbage, cucumber, lettuce, okra, peppers, snap beans, and tomatoes.

e This value was se t equal to the v alue for oranges (Table 13

- 6).

f This value r epresents an average of means for all protected vegetables with available data (Table 13

- 7)

. Protected vegetables include pumpkin, corn, peas, and lima beans.

g These values r epresent average s of means for all root vegetables with available data (Table 13

- 7)

. Root vegetables include beets, carrots, onions, and potatoes.

h This value r epresents an average of means for chicken and turkey (Table 13

- 5).

i If the user changes fish ingestion rates to matc h a survey of the whole weight of fish brought into the home from the field (divided by the consumers of the fish), an appropriate value for L1 would be 0.3 1 and an appropriate L2 would be 0.11 ( EPA 2011a).



### There are substantial uncertainties associated with the L1 and L2 parameters, including the wide variation in values across produce types that were averaged together to recommend a central tendency value for each. For example, t he L2 factor does not distinguish between weight loss during cooking by wa ter evaporation, which might leave the chemical in the food (chemical not lost) , and pouring the cooking liquid down the drain (chemical lost) or using the liquid to create a sauce (chemical not lost) . In addition, the concentration of chemical might be h ighest in the skin, which often is discarded , and lowe r in the consumed portion of many bulky fruits and vegetable s. Finally, the data EPA used to evaluate L1 included negative losses (i.e., weight gains) due to hydration of dried vegetables (e.g., peas a nd 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 1 993, 1994 a) as reported in EPA’s EFH , which reported food as brought into the home, as is the case for the other food categories

. Instead, the fish IR data included in MIRC are based on parts actually consumed, and so no loss processes for preparation are needed

. If the user manually changes fish ingestion rates to match a local survey of the whole weight of fish brought into the home from the field (divided by number of persons consuming the fish), the user should also set the L1 and L2 parameter to non

- zero values.
Attachment B B- 80 October 2014 Description of MIRCTRIM

- Based Tiered Screening Methodolo gy for RTR



### 6.4.4. Food P reparation/ C ooking A djustment F actor (FPCAF) for Fish In addition to estimating the weight of the food that is lost to preparation and cooking, there also can be changes in the chemical concentrations due to cooking. Because the fish consumption rates are “as consumed” and the fish concentration is based on uncooked fish, adjustments should be made to reflect the chemical concentrations in fish after cooking. In order to account for this phenomenon, an FPCAF can be applied to the u ncooked fish concentration to estimate a concentration in cooked fish. The following sections discuss FPCAFs for each of the 4 PB

HAPs.



### 6.4.4.1. Mercury In the U.S. EPA Revised Technical Support Document: National

- Scale Assessment of Mercury Risk to Populations with High Consumption of Self

- caught Freshwater Fish (EPA 2011 b ), a food preparation/cooking adjustment factor (FPCAF) of 1.5 was used to adjust methyl

- mercury (MeHg) concentrations in consumed fish (i.e., a 50% increase in MeHg concentration due to cooking)

. C ooking fish typically increases MeHg levels per unit fish (as consumed) because MeHg concentrates in the muscle

, while preparation involves removal primarily of non

- muscle elements of the fish. The value is based on a study by Morgan et al. (1997).



### 6.4.4.2. Cad mium Similar to mercury, cadmium will bind to muscle and will be retained during the cooking process. As such, the same FPCAF of 1.5 that is used for mercury is assumed for cadmium.



### 6.4.4.3. Dioxin Dioxins are lipophilic and have been demonstrated to be lost during cooking. Based on a literature review, an FPCAF of 0.7 to is applied to account for these losses during the cooking process. A brief summary of supporting literature follows.

• 

### Schecter et al. (1998) found that the mass of PCDD and PCDF in fresh catfish fillet (skin on) decreased by about 50 percent per serving portion during cooking. Given the simultaneous losses of moisture/fats during broiling of the catfish, the PCDDs and PCDFs concentrations decreased by 33 percent (i.e., multiply uncooked concentration in fresh fish by a factor of 0.66 = 0.70 to one significant digit).

• 

### Reinert et al. (1972) reported higher losses of another highly lipophilic chemical, DDT, from cooking fish fillets of bloaters, yellow perch, lake trout, and coho salmon. Concentrations of DDT in fish fillet portions for lake trout and coho salmon, top predators, were reduced by 64 to 72 percent by frying or broiling, primarily through preferential loss of fat (and lipophilic DDT) during cooking. The investigators did not report skin on or off; however, they used steak cuts instead of flat fillets, which provide a smaller ratio of skin to muscle than is the case for fillets that constitute one side of the fish.

• 

### Zabik and Zabik (1995) quantified the reduction in TCDD concentration of cooked, with the skin off, fillets compared with uncooked fillet with skin for fish harvested from the Great Lakes. Concentrations in the cooked fish with the skin off were reduced relative to the raw fillet with the skin on by approximately 44 percent for walleye, 80 percent for white bass, and 61 percent for lake trout. Comparing losses of TCDD for fillets cooked with the skin on versus fillets that were both skinned and cooked, Zabik and Zabik (1995) found reductions in TCDD concentrations of approximately 43 p ercent for Chinook Salmon cooked with the skin on and 57 percent for c hinook s almon cooked with

Attachment B B- 81 October 2014 Description of MIRCTRIM

- Based Tiered Screening Methodolo gy for RTR



### the skin off. They found a 37 percent reduction of TCDD concentration for carp fillets cooked with the skin on and 54 percent reduction if the skin was removed.
The three studies listed above indicate that the 0.7 factor is not likely to overestimate loss of PCDD/PCDFs from fish during cooking (pan frying, broiling, grilling). Reductions in TCDD concentrations could be much higher with skin removal and trimming o f fat.



### 6.4.4.4. PAHs While it is reasonable to assume that there might be losses of lipophilic PAHs during the cooking process, there is insufficient information to distinguish what the net loss (or gain) during cooking might be because cooking can create PAHs from proteins in the tissue. The literature acknowledges these competing forces, but does not provide information to disentangle the gain and loss mechanisms. As such, a neutral approach was taken, which is to assume an adjustment factor of 1.0 (i.e., no adjus tment) for PAHs.



### 6.5. Breast

- Milk Infant Exposure Pathway Parameter Values Values used for parameters in the breast

- milk exposure pathway algorithms (see Section 3.4 of this attachment ) can be scenario

- specific, receptor

- specific, and/or chemical

- specific and might be empirically derived or estimated by an appropriate model. For parameters that are 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 B- 26 lists the parameters and their default values. The text that follows describes the input value or value options for each exposure parameter required by MIRC to calculate the infant absorbed chemical intake rate, or DAI inf 

### . For para meter values that can be estimated when empirical values are not available, see the equation description in Section 3.4 of this attachment

.

Attachment B B- 82 October 2014 Description of MIRCTRIM

- Based Tiered Screening Methodolo gy for RTR



### Exhibit B

- 26 . Scenario

- and Receptor

- Specific Input Parameter Values Used to Estimate Infant Exposures via Breast Mil k

Parameter Description Default Value

AT Averaging time for infant’s exposure via breast milk, i.e., duration of nursing (days) =ED

BW inf Body weight of infant (kg) averaged over 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 B

- 3 6 ED Exposure duration for infant, i.e., duration of breast feeding (days) =AT AT/ED Averaging time divided by exposure duration 1.0 f bp Fraction of mother’s whole blood that is plasma (unitless) 0.65 f fm Fraction of mother’s body weight that is fat (unitless) 0.30 f mbm Fraction of fat in 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 over duration of nursing (kg/day) 0.709
t bf Duration of breast feed ing (days) 365
t pn Duration of maternal chemical exposure prior to nursing (days) 3285



### Averaging time ( AT ) and exposure duration ( ED ). AT refers to the time over which the infant’s exposure to the chemical of concern is averaged. ED refers to the duration of the infant’s exposure. For the exposure scenario considered for this age group, both AT and ED equal the duration of the nursing period, and they therefore cancel each other out in the infant average daily dose equation

. Infant body weight ( BW **i nf** 



### ). 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 averaged from birth to the first birthday

. Similarly, if an infant breast feeds for 6 months, the user should select the body weight for an infant that is averaged from birth to six months. Becaus e the default breast feeding duration ( t bf



### ) is one year (i.e., 365 days), the default infant body weight is 7.8 kg

, which is the time

- weighted average for the mean infant body weight between birth and the first birthday from EPA’s 2008 Child Specific Expos ure Factors Handbook ( CSEFH; EPA 2008 a)

. Exhibit B- 27 presents additional values for the infant body weight parameter that the user can select instead of the MIRC default.

Exhibit B

- 27

. 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
Attachment B B- 83 October 2014 Description of MIRCTRIM

- Based Tiered Screening Methodolo gy for RTR



### Exhibit B

- 27

. Average Body Weight for Infants

Statistic 0 to < 6 months (kg) 0 to < 12 months (kg) 0 to < 18 months (kg) 0 to < 24 months (kg) Mean 6.5 7.8 a 9.0 9.6 90 th percentile 7.7 9.2 10.6 11.3 95 th percentile 8.0 9.7 11.1 11.8

Source: EPA ( 2008a ) ; each value is the time

- weighted average 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., 0.7 5 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 lacta ting. The MIRC default BW mat 

### of 66 kg is based on CSFII data compiled by EPA for non

- lactating and non

pregnant women between the ages of 15 and 44 (i.e., women of child

- bearing age), lactating women, and pregnant women (EPA 2004). Exhibit B- 28 presents additional values for the maternal body weight parameter which the user may choose to use instead of the MIRC default. The BW mat 

### value is not the value that MIRC uses to estimate the mother’s absorbed daily intake ( DAI mat 

### ). The daily ingestion rates for home

- grown/raised food products in MIRC are for men and women combined, with the rates normalized to body weight. The ingestion rates for soil, water, and fish included in MIRC are not normalized to body weight but are based on b oth men and women. For those ingestion rates, MIRC uses an average body weight value for males and females to estimate the average daily dose (intake) of the chemical in mg/kg

- day. These values are subject to the assumption that the body

- weight normalized ingestion rates and resulting ADD values are applicable to nursing mothers.

Exhibit B- 28

. Time

- weighted Average Body Weight for Mothers

Statistic Weight (kg)

Mean 66.0 a

5 th 47.1 10 th 50.2 25 th 54.3 50 th 62.0 75 th 72.0 90 th 85.7 95 th 97.0

Source: EPA 2004

a MIRC default value


### Exposure duration ( ED ). See discussion of AT and ED above. Fraction of mother’s whole blood that is plasma ( f **bp** 

### ). Steinbeck (1954) reporte d that plasma volume accounts for approximately 60 percent of the total blood volume in non

- lactating human females (EPA 1998). Harrison (1967) and Ueland (1976) reported plasma volumes between 63 to 70 percent in postpartum women (EPA 1998). The default value in MIRC of 65 percent ( 0.65 )
Attachment B B- 84 October 2014 Description of MIRCTRIM

- Based Tiered Screening Methodolo gy for RTR



### is the value recommended by EPA in its Methodology for Assessing Health Risks Associated with Multiple Pathways of Exposure to Combustor Emissions (MPE, EPA 1998).
Fraction of mother’s body weight that is fat ( f f **m** 

### ). A l imitation of using a steady

- state, instead of a dynamic partitioning, model for lactational transfer of chemicals is that several key parameters change over the course of exposure. For example, Equation B- 38 , used to estimate the concentration of a lipophilic chemical in breast milk fat, assumes that the mother’s body fat will remain constant over the entire duration of breast feeding (t bf 

### ), which is unlikely to be true (EPA 2001a). Another limitation of the single analytic model is that chemical transfer rates from blood to milk are unlikely to be the same as the rate of mobilization of the chemical from fat stores to the blood (EPA 2001a). Studies cited in ATSDR’s toxicological profile for chlorinated dibenzo -pdioxins show a correlation between percent body fat and the elimination rate of dioxins, with longer half

- lives for dioxins in individuals with a higher proportion of fat in their bodies (ATSDR 1998). In the context of a screening model, however, EPA recommends a default value for the fraction of a mother’s body comprised of fat of 0.3 based on data and discussions presented by Smith (1987) and Sullivan et al . (1991) (EPA 1998). A fraction of 0.3 indicates that 30 percent of the mother’s body weight is fat, which is a health protective value (EPA 2001a). To establish a health protective screening scenario, the MIRC default value for f fm 

### is 0.30

. Fraction of fat in mother’s breast milk ( f **mbm** 

### ). The C milkfat 

### model ( Equation B- 38 ) assumes that a
co nstant fraction of breast milk is fat, even though there is evidence that indicates variation in the fat content of breast milk throughout lactation (Sim and McNeil 1992). Different studies suggest a fat content of breast milk in humans of between 1 and 5 percent (Jensen 1987, Schecter et al

. 1994, Hong et al

. 1994, McLachlan 1993, Bates et al

. 1994, NAS 1991, Butte et al . 1984, Maxwell and Burmaster 1993, EPA 2011a, Smith 1987, Sullivan et al . 1991). The MIRC default value for f mbm 

### of 0.04 (i.e., 4 perce nt) 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 2011a, Table 15

- 3). To estimate an “average” value, EPA first estimated study

- sample -siz e weighted average values for 1 through 12 months of age and then developed time

- weighted average milk ingestion rates from those (EPA 2011a). EPA estimated an upper percentile (upper bound) value as the mean plus two standard deviations. MIRC converts t he ingestion rates measured volumetrically (mL/day) to mass

- based estimates (kg/day) assuming the density of human milk to be 1.03 g/mL (reported by NAS 1991 and recommended by EPA 2011a). The resulting values are shown in the first two rows of Exhibit B- 29 . The MIRC screening

- level default of 980 mL/day is an upper

bound estimate based on a one

- year nursing period.

Exhibit B- 29 also includes the recommended values for four non

- overlapping age categori es from the CSEFH (EPA 2008a, Table 15

- 1). The values demonstrate that although infants grow substantially from birth to one year of age, the “upper bound” estimates of their milk ingestion rates are very close to 1 liter per day at all stages of developm ent in the first year.

Attachment B B- 85 October 2014 Description of MIRCTRIM

- Based Tiered Screening Methodolo gy for RTR



### Exhibit B

- 29

. Infant Breast Milk Intake Rates

Age Category Average (mL/d) Average (kg/d) “Upper Bound” (mL/d) “Upper Bound” (kg/d) Reference

1 to 6 months 742 0.764 1,033 1.064 EPA 2011a † 0 to < 12 months 688 0.709 980 a 1.01 a EPA 2011a †

0 to < 1 month 510 0.525 950 0.979 EPA 2008 a †† 1 to < 3 months 690 0.711 980 1.01 EPA 2008 a † 3 to < 6 months 770 0.793 1,000 1.03 EPA 2008 a † 6 to < 12 months 6 20 0.639 1,000 1.03 EPA 2008 a †

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 i ts MPE Methodology, EPA asserts a health protective value for the duration of breast feeding of 1 year (i.e., 365 days) and a central tendency estimate of 6 months (180 days) (EPA 1998). Reviewers of MPE noted that 365 days may be overly health protective, given that only 20 percent of infants are breast fed for 6 months, at which point alternative foods are introduced, at least in addition to breast milk (EPA 2001a). Nonetheless, to establish a health protective screening scenario, the MIRC default for t bf 

### is 365 days.
Duration of the mother’s exposure to the chemical of concern prior to nursing ( t **pn** 

### ). The model shown as Equation B- 38 includes this parameter to reduce the over

- estimate of chemical concentration in milk fat that occurs if the model is applied to a 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 ) r elative to the chemical half

- life. As the duration of an exposure scenario increases to me et and exceed the chemical half

- life, however, the overestimate that occurs without this parameter is reduced. For example, assume a chemical biological half

- life o f 8 years and a nursing period of 1 year. If exposure of the mother starts at the beginning of nursing, using Equation B- 38 without the t pn 

### term results in an over

- estimate of the concentration of the chemical in breast milk by a factor of 28.1 compared with the prediction using Equation B- 38 with the t pn 

### term (EPA 1998, Table 9

- 6). However, at longer pre

- natal exposures of the mother, the magnitude of the over

- estimate is reduced: for a 10

- year exposure , the magnitude of the overestimate without the t pn 

### term is 2.28, and for a 30

- year exposure, the overestimate is reduced to 1.39.
For purposes of the screening

- level assessment , we assume an exposure duration equal to the MIRC default half

- life for diox ins , or 10 years

. Only 3

, 285 days of that period are pre

- natal (i.e., 3650 minus 365 days, assuming 1 year lactation period). Although longer exposure periods are possible for the screening scenario, there is sufficient uncertainty in the model to merit accepting a health protective bias for this parameter value.



### 6.5.2. Chemical

- Specific Parameter Values
The chemical

- specific parameters in the breast

- milk pathway in MIRC are listed in Exhibit B- 30

. Note that the parameters for which values are needed are different for the lipophilic chemicals (i.e., dioxins), for which lactational transfer is assumed to occur via milk fat, and inorganic chemicals, for which the transfer is assumed to occur via the aqueous phase of breast milk (i.e.,

m ercury). All dioxin congeners were assumed to manifest identical values as TCDD in regard to breast milk

- related parameters.
Attachment B B- 86 October 2014 Description of MIRCTRIM

- Based Tiered Screening Methodolo gy for RTR



### Exhibit B- 30

. Chemical

- specific Input Paramete r 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 (de fault) 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 bur den 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 non

lactating women (per day; related to chemical half

life) 1.9E

- 04 b 1.4E

- 02 c

k fat_elac Rate constant for total elimination of lipophilic chemicals by lactating women (per day) Est. using Equation B

- 41 NA

Pc bm Partition coefficient for hydrophilic chemical between maternal blood plasma and aqueous phase of breast milk (g milk/g plasma; model assumption) NA 1.0 (model assumption)

Pc RBC Partition coefficient for hydrophilic or protein

- bound chemical between red blood cells (RBC) and plasma in maternal blood (mL whole blood/mL RBC) NA 40 (Hollins et al

. 1975 )

NA = not applicable . ND = not yet determined from literature.

a This value is based on a single

- dose study and may not be appropriate for a chronic exposure model.

b An empirical value for this variable is currently missing for application of model.

c This value was calculated from biological half

- life ( h ) using Equation B- 40

. 

### Absorption efficiency of the chemical by the oral route of exposure for the infant ( AE **inf**



### ). The models included in MIRC assume that the AE inf 

### from th e 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 chemical

- specific values come from studies that account for absorption of the chemical from milk, because absorption from other matrices (e.g., solid foods) may not be relevant (EPA 2001a). If chemical

- specific data are not available

Attachment B B- 87 October 2014 Description of MIRCTRIM

- Based Tiered Screening Methodolo gy for RTR



### for adults or infants, a health protective default value for AE inf 

### for a screening level assessment is 1 .0, which assumes 100 percent absorption (EPA 1998).

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).
Absorption efficiency of the chemical by the oral route of exposure for the mother ( AE **mat** 

### )

. The default value for bo th dioxins and MeH g 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 fo r MeHg in MIRC, 0.059, is fro m Kershaw et al

. (1980), which reported kinetics of blood deposition and clearance of MeHg in humans

. Individuals consumed one meal of fish that contained between 18 and 22 μg Hg/kg body weight

. The fraction of the dose deposited in the blood volume after mercury was fully distributed in tissues was 5.9 percent or 0.059. Thi s study used a single

- dose and thus may not be appropriate for a chronic exposure analysis.
Fraction of total maternal chemical body burden that is in body fat ( f **f** 

### ). Based on ATSDR’s Toxicological Profile for Selected PCBs (ATSDR 1992) and Sullivan et al

. (1991), EPA concluded that the “fraction of ingested contaminant stored in fat may be >90%” for lipophilic chemicals such as PCBs and dioxins (EPA 1998). This statement was interpreted to mean that 90 percent of the maternal body burden of chemical at “steady state” is located in body fat for dioxins at steady state.
Fraction of total maternal chemical body burden that is in blood plasma ( f **pl** 

### ). For hydrophilic chemicals, this parameter represents the steady

- state fraction of the total chemical in the b ody 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 u sing Equation B- 43 . However, this equation requires a reliable value for f bl 

### , and the value found for mercury may not be appropriate for a chronic exposure analysis (see above).

Chemical half

- life in non

- lactating women ( h ) . In general, highly lipophilic chemicals tend to have relatively long biological half

- lives. EPA estimates that the half

- life for dioxins is between 7 and 10 years (EPA 1994a). ATSDR estimates that the half

- life for 2,3,7,8

- TCDD in particular may be as long as 12 years (ATSDR 1998)

. To establish a health protective screening scenario, the MIRC default half

- life for dioxins is set to 10 years or 3650 days

. The half

- life for methylmercury is on the order of weeks, not years

. Greenwood et al

. (1978) measured blood clearance rates for MeHg in lactating Iraqi women exposed accidentally to MeHg via bread prepared from wheat treated with a fungicide that contained MeHg

. The data indicated a mean half

- life for MeHg of approximately 42 days

. Sherlock et al . (1984) reported
an average measured half -l ife 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 paramet er 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
Attachment B B- 88 October 2014 Description of MIRCTRIM

- Based Tiered Screening Methodolo gy for RTR



### reasonable assumption for water soluble chemicals is that k aq_elac 

### is equal to k elim 

### as discussed for Equation B- 43 . The extent to which k elim 

### is an underestimate of k aq_elac 

### for a given chemical will determine the extent of health protective bias in k aq_elac



### . Chemical elimination rate constant for non

- lactating women ( k e **lim** 

### ). Although values for this parameter often are reported directly in the literature, MIRC estimates k elim 

### from chemical half

li fe assuming first

- order kinetics as shown in Equation B- 40 . For example, for a biological half

life of 3,650 days for dioxins, k elim 

### is estimated to be 1.9E

- 04 per day. Assuming a biological half

- life of 50 days for MeHg, the va lue for k elim 

### is estimated to be 0.014 per day

. Rate constant for total elimination of lipophilic chemicals by lactating women ( k **fat_elac** 

### ). Although values for this parameter might be found in the scientific literature for some chemicals, in MIRC, k

fat_elac 

### for dioxins is calculated from Equation B- 41 . When the parameters in that equation are set to the default values in MIRC for dioxins, MIRC estimates a value of 0.0015 per day for k

fat_elac



### . Partition coefficient for chemical b etween maternal blood plasma and aqueous phase of breast milk ( Pc **bm** 

### ). The aqueous model, presented in Equation B- 42 , assumes that the concentrations in the plasma and aqueous phase of breast milk are directly proportional (EPA 1998). Therefore, the default value for this parameter for MeHg in MIRC is 1.0.
Partition coefficient for chemical between red blood cells and plasma in maternal blood ( Pc **RBC** 

### ). Chemical

- specific values for this parameter should be located in the scientific literature. If chemical

- specific values are unavailable and it is assumed that there is equal distribution of the chemical in the plasma and red blood cells, EPA suggests a default value of 1.0 ( EPA 1998)

. F or MeHg, MIRC includes a va lue of 40 based on Hollins et al . (1975) study of cats exposed to MeHg, which reported a ratio of radio

- labeled mercury in red blood cells to plasma of 97.7 to 2.3 (i.e., ratio of 42.5).



## 7. Summary of MIRC Default Exposure Parameter Settings

T he default settings included in MIRC are intended to be characteristic of a health protective (but plausible) exposure scenario that results in a negligible or extremely low chance of underestimating risk. These default parameter values were used to derive the screening threshold emiss ion rates used for screening emissions of PB

- HAPs from sources included in RTR risk assessments. These values are the default, or initial setting, for parameter values in MIRC as described in Section 6 of this attachment . Thi s section summarizes the default parameter values used to calculate screening thresholds.
This chapter is organized to present the chemical - and scenario

- specific inputs to MIRC by data type. The screening

- level analysis uses 90 th 

### percentile ingestion rates for soil, breast milk, and farm food items and 99 th 

### percentile ingestion rates for fish (presented in Section 7.1 ) and population

- specific characteristic assumptions (presented in Section 7.2 ), t hat are generally health protective in nature. Screening thresholds were derived for five RTR chemical species: benzo(a)pyrene, cadmium, mercuric chloride, methyl mercury, and 2,3,7,8

- TCDD; Section 7.3 presents chemical

- specif ic parameter inputs for these five chemicals. Finally, Section 7.4 presents default parameter values for the nursing infant exposure scenario, which applied only to dioxin and methyl mercury as discussed in Section 3.4

. 

### 7.1. Default Ingestion Rates The screening

- level (or default ) values for ingestion rates for soil, breast milk, and for each farm food item are equal to the 90 th 

### percentile of the distribution of national data for that ingestion

Attachment B B- 89 October 2014 Description of MIRCTRIM

- Based Tiered Screening Methodolo gy for RTR



### medium. In general, these values were obtained from the 2011 Exposure Factors Handbook or the 2008 Child

- Specific Exposure Factors Handbook (see Exhibit B- 31 ). Fish ingestion rates are also available from these sources; however, as described in Section 6.3.4 , these sources were not used to obtain fish ingestion rates.



### 7.1.1. Fish Ingestion Rates
Th e adult fish ingestion rate was obtained from Burger (2002) , a study that examined daily consumption of wild

- caught fis h for high

- end recreationalists (white, black and female) in South Carolina. For female high

- end consumers of wild

- caught fish, Burger identified average and higher

- percentile consumption rates as follows: 39.1 g/day (mean), 123 g/day (90 th 

### percentile), 1 72 g/day (95 th 

### percentile), and 373 g/day (99 th 

### percentile). As shown in Exhibit B- 31 and discussed in Section 6.3.4 , f or adults, the rate of fish ingestion assumed in the screening scenario is 373 g/day , which corresponds to the 99 th 

### percentile value estimated by Burger for adult females . T his value was selected to be representative of subsistence fishers

. For the child age groups, as discussed in Section 6.3.4 , the baseline fish ingestion rates for the screening scenario are based on “as prepared” total freshwater/estuarine fish ingestion rates at the 99 th 

### percentile of the distribution for the consumer

- only population (i.e., inclusive only of people who consume fish, rat her than per

- captita rates, which include both consumers and non

consumers), as estimated in EPA (2002)

, Section 4 .2.1.1

. Some adjustments were necessary because the age groups evaluated for RTR (which correspond to the age groups for which farm food ingestion rates are available) do not all directly correspond to the age groups in the EPA (2002) report. As described in Section 6.3.4 , these adjustments convert the available age

specific data on fish ingestion rates to the age -s pecific values needed for MIRC.
For the screening

- level fish ingestion exposure scenario, the consumer evaluated is an individual who regularly consumes a large amount of fish that he or she has caught locally over the course of a 70

- year lifetime. Model ed exposures are intended to encompass those of a subsistance fisher whose diet comprises a substantial proportion of fish. The scenario is not, however, intended to represent the maximum possible exposure an individual subsistence fisher might experience.
Although the fish ingestion rates presented here are representative of the 99 th 

### percentile of the evaluated data set, the use of these inputs (compared with 90 th 

### percentile values used for other food types) is not considered to be inconsistent. This is due to the idiosyncrasies of the survey data on fish consumption, the fact that the data sets for homegrown foods and fish are not parallel, and the consideration of rates appropriate for subsistence fishers, as described above.
As discussed above, EPA believes that use of these fish ingestion rates strikes the appropriate balance between being health protective and having screening scenarios so conservative that they are of limited use in the decision making process. This high

- end fish ingestion rate is appropriate in the context of the conservat ive screening scenario used in the RTR process and is applicable for national rulemakings given that it is very likely that subsistence woman fisher s of child bearing age are located throughout the United State s. Using a high

- end subsistence fish ingestion rate also is consistent with section 112 of the CAA, which focuses on risks associated with maximally exposed individuals.



### 7.1.2. Farm Food Chain Ingestion
The default settings assume that a ll food types are obtained from the area of chemical deposition specified by the user (i.e., fraction of food from contaminated area = 1.0).

Attachment B B- 90 October 2014 Description of MIRCTRIM

- Based Tiered Screening Methodolo gy for RTR



### For estimates of screening threshold emission rates for PB

- HAPS, environmental concentrations and air deposition rates were estimated using TRIM.FaTE for the area of maximal deposition in the vicinity of a hypothetical facility, and thus represent risks estimated for a maximally exposed individual/farm/family.

Exhibit B

- 31 also includes a sum of the 90th percentile ingestion rates for homegrown food categories and 99th percentile fish ingestion to show the implied total food ingestion rate associated with setting multiple food

- type

- specific ingestion rates at upper percentiles. Because these upper p ercentile values for each farm food category are likely to reflect different individuals, it is likely that addition of multiple upper percentile intake values will exceed the total food ingestion rates expected for the general population. This sum is sho wn on the third row from the bottom (Total Food : Homegrown Only).
The second row from the bottom presents the 90 th 

### percentile of the distribution of individual total food ingestion rates from the USDA’s 1994

- 96 and 1998 Continuing Survey of Food Intakes b y Individuals (CSFII) (USDA 2000) data sets, as analyzed by EPA (EPA 2005e). The total ingestion rate for the farming households (third row from bottom) takes into account the cooking losses typical of each food category to provide a better comparison with the 90 th 

### percentile individual total food ingestion rates from CSFII (which are based on consumption of prepared foods). The final row of Exhibit B- 31 shows the likely magnitude of the overestimates by age category by presentin g the ratio of the two preceding rows. The values in this row demonstrate the potential for overestimating intake by using upper percentile values for all food groups. This bias may be considered when evaluating the results estimated by MIRC.

Exhibit B

- 31 . Farm Food Category Ingestion Rates for Health Protective Screening Scenario for Farming Households

Product Screening

- Level Consumer Ingestion Rate Units Infants

<1 y r Child 1

– 2 yrs Child 3

– 5 yrs Child 6

– 11 yrs Child 12

– 19 yrs Adult 20

– 70 yrs
**Farm Food Item**
Beef a NA 9.49 8.83 11.4 3.53 4.41 g/kg

- day Dairy b NA 185 92.5 57.4 30.9 6.16 g/kg

- day Eggs a NA 4.90 3.06 1.90 1.30 1.31 g/kg

- day Exposed Fruit a NA 12.7 5. 41 6.98 3.41 2.37 g/kg

- day Exposed Vegetable a NA 10.7 3.47 3.22 2.35 3.09 g/kg

- day Pork a NA 4.90 4.83 3.72 3.69 2.23 g/kg

- day Poultry a NA 7.17 6.52 4.51 3.13 2.69 g/kg

- day Protected Fruit a NA 44.8 32.0 23.3 7.44 15.1 g/kg

- day Protected Vegetable a NA 3 .88 2.51 2.14 1.85 1.81 g/kg

- day Root Vegetable a NA 7.25 4.26 3.83 2.26 2.49 g/kg

- day
**Other**
Breast milk c 1.01 NA NA NA NA NA kg/day Soil (dry) NA 200 d 200 d 201 e 201 e 201 e mg/day Fish (per individual) f NA 107.7 g 159.0 g 268.2 h 331.0 h 373 g/day

Attachment B B- 91 October 2014 Description of MIRCTRIM

- Based Tiered Screening Methodolo gy for RTR



### Exhibit B

- 31 . Farm Food Category Ingestion Rates for Health Protective Screening Scenario for Farming Households

Product Screening

- Level Consumer Ingestion Rate Units Infants

<1 y r Child 1

– 2 yrs Child 3

– 5 yrs Child 6

– 11 yrs Child 12

– 19 yrs Adult 20

– 70 yrs
**Total Fo od Ingestion Rates for Comparison Only (not in MIRC; excludes soil and water)**  Total Food: Homegrown only i NA 259 142 99 51 35.5 g/kg

- day

Total Food: All Sources j NA 125 91 61 34 23.7 g/kg

- day Overestimate (ratio of Homegrown/Total) NA 2.1 1.6 1.6 1.5 1 .3 (unitless)

Sources: EPA 2011 a , EPA 2008 a , unless otherwise noted. NA = not applicable

a Primary source for values was the 1987

– 1988 NFCS survey; compiled results are presented in Chapter 13 of the 2011 Exposure Factors Handbook ( EPA, 2011 a) . When data were unavailable for a particular age group, the intake rate for all age groups was multiplied by the age

- specific ratio of intake based on national population intake rates from CSFII.

b Primary source for values was 1987

– 1988 NFCS survey, compiled results are presented in Chapter 13 of the 2011 Exposure Factors Handbook ( EPA, 2011 a) . When data were unavailable for a particular age group, the intake rate for all age groups was multiplied by the age

- specific ratio of intake based on national population intake rates from an NHANES 2003

– 2006 analysis in Chapter 11 of the Exposure Factors Handbook.

c Infants are assumed to consume only breast milk for one year.

d These values are the recommended “upper percentile” value for children from EPA’s 2011 EFH, Chapter 4, Table 4

- 23. The 2008 CSEFH and 2011 EFH included a high

- end value associated with pica only, but this value has not been used.

e These values are 90th percentile adult ingestion rates calculated in Stanek et al. 1997, and they are used to represent older children and adults.
f The ingestion rate for adults was obtained from Burger (2002) and is the 99 th percentile value for adult females considered high

- end recreationists ; this value is believed to be representative of subsistence fishers. The 99 th percentile values for children were d erived based on EPA’s E stimated Per Capita Fish Consumption in the United States (2002)

— Section 4. 2 .1.1 Table 5 (for child age categories) adjusted and scaled. Values reflect “as prepared” ingestion rates.

g The fish ingestion rate for children aged 3

– 5 years was obtained dire ctly from Section 4.2.1.1, Table 5 in the EPA (2002) report (value presented is rounded); for these children, the RTR age

- group range matches the EPA (2002) age category. Fish ingestion rates for children less than 3 years old, however, were not provided. Therefore, for children aged 1

– 2 years, the fish ingestion rate was calculated using the ingestion rate for children aged 3

– 5 years scaled downward by the ratio of the mean body weight of children aged 1

– 2 years to the mean body weight of children aged 3 –5- years.
h Time

- weighted average ingestion rates were calculated using the EPA (2002) fish ingestion estimates in order to adjust for the differences between the age group ranges used for the RTR screening and those presented in the 2002 EPA report.

i Sum of post

- cooking food ingestion rates. This estimate is calculated by multiplying the food ingestion rates on previous rows (excluding soil and water) by (1 -L 1 )x(1 -L 2 ), where L 1 and L 2 are the loss rates from Exhibit B- 25

. The rows are then summed to get the total post

cooking ingestion rate. j 90 th percentile total food intake rates from EPA 2008a and 2005e based on CSFII data 1994

- 96 and 1998; see Section 6.3.6 of this document.



### 7.2. Default Screening

- Le vel Population

- Specific Parameter Values The screening

- level values for body weights (BWs) for the RTR screening threshold analysis, which serve as the default values in MIRC, are mean values and are presented in Exhibit B- 32

. A s stated in Section 6 of this attachment

, EPA recommends using the mean BW for each age group when using upper percentile values for medium ingestion rates. Use of the mean body weights introduces no bias toward over - or under estimating risk.

Exhibit B

- 32

. Mean Body Weight Estimates for Adults and Childre n a
Lifestage (years) Duration (years) Mean Body Weight (kg) Adult b (20

- 70) 50 80.0 Child < 1 c 1 7.83 Child 1

- 2 c 2 12.6 Child 3

- 5 d 3 18.6

Attachment B B- 92 October 2014 Description of MIRCTRIM

- Based Tiered Screening Methodolo gy for RTR



### Exhibit B

- 32

. Mean Body Weight Estimates for Adults and Childre n a
Lifestage (years) Duration (years) Mean Body Weight (kg) Child 6

- 11 e 6 36.0 Child 12

- 19 f 8 64.2

a Sources: EPA 1997, 2008a

b EPA

- recommended value (EPA 2011a).

d These values were obtained directly f rom Table 8

- 3 of the 2008 CSEFH.

e Each BW represents a time -w eighted average of BWs for age groups 6 to <11 years and 11 to <16 years from Table 8

- 3 of the 2008 CSEFH

. Original sample sizes for each of these age groups can also be found in Table 8

- 3.

f These values were calculated as time

- weighted average BW for age groups 11 to <16 years and 16 to <21 years from Table 8

- 3 of the 2008 CSEFH. The direction of the possible bias is unknown. The values match the estimate based on Table 8

- 22 of the NHANES IV data as presented by Portier et al. (2007).



### 7.3. Default Chemical

- Specific Parameter Values for Screening Analysis
Exhibit B- 33 presents chemical

- specific parameter values for input to MIRC for the screening

level analysis. Values for bioavailability when ingested in soil ( Bs ), mammalian meta bolism 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 B- 33

.

Exhibit B

- 33 . Chemical

- Specific Parameter Values for Input to MIRC a

Parameter Description Benzo(a)

- pyrene Cadmium Mercuric chloride Methyl mercury 2,3,7,8

- TCDD Units

Bs Soil bioavailability factor for livestock 1 1 1 1 1 unitless

MF Mammalian metabolism factor 0.01 1 1 1 1 unitless

VGrootveg Empirical correction factor for belowground produce, i.e., tuber or root vegetable, to account for possibl e overestimate of the transfer of chemicals from the outside to the inside of bulky tubers or roots (based on carrots and potatoes) 0.01 1 1 0.01 0.01 unitless

Fw Fraction of wet deposition that adheres to plant surfaces; 0.2 for anions, 0.6 for cations a nd most organics 0.6 0.6 0.6 0.6 0.6 unitless

Attachment B B- 93 October 2014 Description of MIRCTRIM

- Based Tiered Screening Methodolo gy for RTR



### Exhibit B

- 33 . Chemical

- Specific Parameter Values for Input to MIRC a

Parameter Description Benzo(a)

- pyrene Cadmium Mercuric chloride Methyl mercury 2,3,7,8

- TCDD Units

BvAG Air

- to

- plant biotransfer factor for aboveground produce for vapor

phase chemical in air 174,523 0 1,800 0 65,500 [mg/g produce DW]/[mg/g air]

RCF Chemical

- specific root concentration factor for tubers an d root produce 9,180 0 0 0 40,002 L soil pore water/kg root WW

Kds Chemical

- specific soil/water partition coefficient 7,750 75 58,000 7,000 31,126 L soil pore water/kg soil DW

a Values presented in this exhibit are also presented in previous exhibits; how ever exact values used in the assessment are presented here, rather than values restricted by significant figures. In addition, only values for those chemicals that are specifically used in the screening assessment are provided here.


### Only single estimat es were developed for each of these parameters for HHRAP (EPA 2005a), and the potential direction and magnitude of bias toward over - or underestimating risks were not investigated in this assessment . The inputs that are both chemical

- specific and plant

- ty pe

specific, as presented in Exhibit B- 11 , are not repeated here. Again, only single estimates were developed for these parameters and the potential direction and magnitude of bias toward over or underestimating risks were not investigated. Finally, Exhibit B- 34 presents biotransfer factors for each of the chemicals and animal types for which screening threshold emissions were calculated.

Exhibit B

- 34 . Chemical and Animal

- Type Specific Biotransfer Factor (Ba) Values for Input to MIRC ([mg chemical/kg WW tissue or dairy] / [mg chemical intake/day] = day/kg WW tissue or dairy) Chemical Beef Dairy Pork Eggs Poultry Benzo( a)pyrene 3.8E

- 02 8.0E

- 03 4.6E

- 02 1.6E

- 02 2.8E

- 02 Cadmium 1.2E

- 04 6.5E

- 06 1.9E

- 04 2.5E

- 03 1.1E

- 01 Mercuric c hloride 1.1E

- 04 1.4E

- 06 3.4E

- 05 2.4E

- 02 2.4E

- 02 Methyl m ercury 1.2E

- 03 1.7E

- 05 5.1E

- 06 3.6E

- 03 3.6E

- 03 2,3,7,8

- TCDD 3.6E

- 02 7.7E

- 03 4.4E

- 02 1.5E

- 02 2.7E

- 02



### 7.4. Screening

- Level Parameter Values for Nursing Infant Exposure MIRC is configured to evaluate risk to nursing infants exposed to dioxins and to methylmercury (MeHg) in their mother’s milk for a family farming and catching fish in the area of ma ximal air deposition of chemical. Input values were summarized in Section 6.5 of this attachment

. 

### 7.4.1. Dioxins For dioxins, chemical intake via breast milk by nursing infants was estimated using the model presented in EPA’s Metho dology for Assessing Health Risks Associated with Multiple Pathways of Exposure to Combustor Emissions (MPE, EPA 199 8). The assumption that lactational transfer of dioxins to the infant occurs via the lipid

- phase of milk appears reasonable. The

Attachment B B- 94 October 2014 Description of MIRCTRIM

- Based Tiered Screening Methodolo gy for RTR



### following screening

- level assumptions used in that model should bias the results toward health

protective estimates of risks.

• 

### Duration of nursing is a full year and no other foods or liquids are consumed by the infant; a more “typical” value would be six months.

• 

### Ab sorption efficiency of dioxin in food or milk by mother and infant are 100 percent; this assumption might overestimate absorption but probably by no more than 15 percent (see Section 6.5.2 ).

• 

### The fat content of human milk is assumed to be 4 percent, a value toward the high end of the reported range of values (1 to 5 percent).

• 

### The maternal chemical intake is estimated using upper percentile ingestion rates for the different homegrown foods (see discussion for Exhibit B- 31 ); this assumption might overestimate total ingestion of homegrown foods by a factor of more than 2 (see Exhibit B- 31 ).

• 

### If the fraction of the maternal body burden of dioxin that is in the body fat compartment is greater than 90 percent, as suggested by ATSDR (1998), then actual exposures of the infant may be less than estimated.
There also are parameter values and model assumptions for the lipid

- phase breast

- milk pathway for which possible bias is unknown.

• 

### The acc uracy 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 underesti mating risks. The model results are sensitive to the biological half

- life of the chemical in the mother relative to the length of her exposure prior to the lactation period. Using an exposure duration for the mother equal to the assumed half

- life for dioxins, 10 years, may underestimate the duration of exposure of the mother.



### 7.4.2. Methyl Mercury For MeHg, empirical data from a single human study (Fujita and Takabatake 1977) was used in conjunction with a physiologically based pharmacokinetic (PBPK) model of la ctational transfer of MeHg developed and partially validated by Byczkowski and Lipscomb (2001) to support a very simple predictive model

. Both the human data and the PBPK model indicated that for relatively low MeHg exposures, the concentration of MeHg in the nursing infant’s blood is similar to its concentration in the mother ’ s blood

. The PBPK model suggested in addition that the average daily dose of MeHg absorbed from milk by the nursing infant ( 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 B B- 95 October 2014 Description of MIRCTRIM

- Based Tiered Screening Methodolo gy for RTR



## 8. References
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. Attachment B B- 98 October 2014 Description of MIRCTRIM

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- Based Tiered Screening Methodolo gy for RTR



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. EPA

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. C. EPA

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- 530/D

- 03

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. EPA

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. Office of Water, Office of Science and Technology, Washington, D.C. EPA

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. EPA

. 2003c. CSFII Analysis of Food Intake Distributions. Office of Research and Development, National Center for Environmental Assessment, Washington, D.C. EPA

- 600 -R- 03

- 29. Available at: http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=56610

. EPA

. 2004. Estimated Per capita Water Ingestion and Body Weight in the United States – An Update. Office of Water, Office of Science and Technology, Washington, D.C. EPA

- 822 -RAttachment B B- 101 October 2014 Description of MIRCTRIM

- Based Tiered Screening Methodolo gy for RTR



### 00

- 001. October. Available at: http://www.epa.gov/waterscience/criteria/drinking/percapita/2004.pdf

EPA

. 2004c. Risk Assessment Guidance for Superfund. Volume I: Human Health Evaluation Manual (Part E, Supplemental Guidance for Dermal Risk Assessment) , Final . Office of Superfund Remediation and Technology Innovation, Washington, D.C. EPA/540/R/99/005; OSWER 9285.7

- 02EP; NTIS PB99

- 963312. July.

EPA

. 2005a. Human Health Risk Assessment Protocol for Hazardou s Waste Combustion Facilities. Office of Solid Waste and Emergency Response, Washington, DC. EPA

- 530 -R05

- 006. September. Available at: http://www.epa.gov/osw/hazard/tsd/td/combust/risk.htm

. EPA

. 2005b. Guidance on Selecting Age Group s 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.g ov/ncea/cfm/recordisplay.cfm?deid=146583

. EPA

. 2005c. Guidelines for Carcinogen Risk Assessment. Risk Assessment Forum, Washington, DC. EPA/630/P

- 03/001F. March. Available fr om: http://www.epa. gov/IRIS/cancer032505

- final.pdf

. EPA

. 2005d. Supplemental Guidance for Assessing Susceptibility from Early

- Life Exposure to Carcinogens. Risk Assessment Forum: Washington, D.C. EPA

- 630/R

- 03

- 003F. March. Available at: http://www.epa.gov/ttn/atw/childrens_supplement_final.pdf

. EPA

. 2005e. Analysis of Total Food Intake and Composition of Individual's Diet Based on the U.S. Department of Agriculture's 1994

- 96, 1998 Continuing Survey of Food Intakes By Individuals (CSFII) (Final). Office of Research and Development, National Center for Environmental Assessment, Washington, D.C. EPA/600/R

- 05/062F. Available at: http:/ /cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=132173

. EPA

. 2007a. Prioritized Chronic Dose

- Response Values for Screening Risk Assessments (Table 1). Office of Air Quality Planning and Standards; June 12, 2007. Available at: http://www.epa.gov/ttn/atw/toxsource/summary.html

. EPA

. 2007b. Toxicological Review of 1,1,1

- Trichloroethane (CAS No. 71

- 55

- 6) In Support of Summary Information on the Integrated Risk Information System (IRIS). Office of Research and Development, Washington, DC. EPA/635/R

- 03/006. August. Available at: http://www.epa.gov/iris

EPA

. 2008a. Child

- Specific Exposure Factors Handbook. Office of Research and Development, Washington, D .C

. EP A/600/R

- 06/096F. September. Available at: http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=199243

. EPA

. 2008b. Draft Report on EPA OAQPS Risk and Technology Review Methodologies : For Review by the EPA Science Advisory Board; Case Studies – MACT I Petroleum Refining Sources, Portland Cement Manufacturing. Office of Air Quality Planning and Standards, Office of Air and Radiation, Research Triangle Park, NC. July 14, 2008.

EPA

. 2010

. U.S. EPA. Development of a Relative Potency Factor (RPF) Approach for Polycyclic Aromatic Hydrocarbon (PAH) Mixtures (External Review Draft). Washington, DC, EPA/635/R

- 08/012A February. Available at: http://cfpub.epa.gov/ncea/iris_drafts/recordisplay.cfm?deid=194584

Attachment B B- 102 October 2014 Description of MIRCTRIM

- Based Tiered Screening Methodolo gy for RTR



### EPA

. 2011a

. Exposure Factors Handbook: 2011 Edition

. Office of Research and Development, Washington, D.C. EPA/600/R

- 090/052F. September

. Available at: http://cfpub.epa.gov/ncea/risk/recordisplay.cfm?deid=236252

. EPA

. 2011b

. Revised Technical Support Document: National

- Scale Assessment of Mercury Risk to Populations with High Consumption of Self

- caught Freshwater Fish; In Support of the Appropriate and Necessary Finding for Coal - and Oil

- Fired Electric Generating Units. Office of Air Quality Planning and Standards

, Research Triangle Park, NC

. EPA

- 452/R

- 11

- 009 . Dec ember

. 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 Organi za tion reevaluation of human and m ammalian toxic equivalency factors for dioxins and dioxin

- like compounds. Toxicol Sci. 93(2): 223

- 41.
Zabik, ME; Zabik, MJ. 1995. Tetra

- chlorodibenzo -p- dioxin residue reduction by cooking/processing of fish fillets harvested from the Great Lakes. Bulletin of Environmental Contamination and Toxicology. 55:264

- 269.

Attachment B B- 103 October 2014 Description of MIRC[This page intentionally left blank.]TRIM

- Based Tiered Screening Methodolo gy for RTR



## Dermal Risk Screening

Attachment C i October 2014 Dermal Risk Screening[ This page intentionally left blank. ]TRIM

- Based Tiered Screening Methodolo gy for RTR



## CONTENTS , ATTACHMENT C

1. Hazard Identification and Dose Response Assessment

.................................................. C-1

2. Dermal Exposure Estimation

................................ ........................................................... C-2
2.1. Equations for Estimating Dermal Exposure

............................................................ C-2
2.2. Exposure Factors and Assumptions

...................................................................... C-3
2.3. Receptor

- Specific Parameters

................................ ............................................... C-4
2.4. Scenario

- Specific Parameters

................................ ............................................... C-4
2.5. Chemical

- Specific Parameters

................................ ............................................... C-5

3. Screening

- Level Cancer Risks and Non

- Cancer Hazard Quotients

................................ . C-6
3.1. Dermal Cancer Risk

................................ ............................................................... C-6
3.2. Dermal Hazard Quotient

................................ ........................................................ C-7

4. Dermal Screening Results

................................ .............................................................. C-7

5. References

................................ ................................................................................... C- 10

Attachment C iii October 2014 D ermal Risk ScreeningTRIM

- Based Tiered Screening Methodology for RTR



### Exhibi t s, Addendum 3

Exhibit C

- 1. Cancer Slope Factors and Reference Doses Based on Absorbed Dose

................................................................................................................. C-2
Exhibit C

- 2. Receptor

- Specific Bod y Surface Area Assumed to be Exposed to Chemicals

......................................................................................................... C-4
Exhibit C

- 3. Scenario

- Specific Exposure Values for Water and Soil Contact

.......................... C-5
E xhibit C

- 4. Chemical

- Specific Dermal Exposure Values for Water and Soil Contact

................................ ............................................................................. C-6
Exhibit C

- 5. Summary of Dermal Non

- Cancer Hazards

.......................................................... C-8
Exhibit C

- 6. Summary of Dermal Cancer Risks

................................ ...................................... C-9

Attachment C iv October 2014 Dermal Risk S creeningTRIM

- Based Tiered Screening Methodolo gy for RTR



### Non

- inhalation exposure to PB

- HAPs can occur by way of the dermal pathway through contact with PB

- HAP

- contaminated soil and water. H owever, dermal absorption of chemicals that are originally airborne is generally a relatively minor pathway of exposure compared to other exposure pathwa ys ( EPA 2006, Cal/EPA 2000) . This section demonstrates that for the conservative tiered screening scenario developed for RTR multipathwa y evaluation, the dermal exposure route is not a significant risk pathway when compared to the ingestion pathway. In general, the RTR multipathway assessment followed the protocol for evaluating a reasonable maximum exposure as described in EPA’s Risk Ass essment Guidance for Superfund (RAGS), Volume I: Human Health Evaluation Model, Part E, Supplemental Guidance for Dermal Risk Assessment ( EPA 2004)

. 

## 1. Hazard Identification and Dose Response Assessment To assess the potential contribution of dermal exposure to non

- inhalation exposure, w e evaluated the potential for cancer and chronic non

- cancer effects for the four PB

- HAPs currently assessed in the multipathway scre ening evaluation for RTR: cadmium, divalent mercury, 2,3,7,8

- TCDD, and benzo(a)pyrene

. EPA h as not developed carcinogenic potency slope factors ( C SFs) and non

- cancer reference doses (RfDs) specifically for evaluating potential human health concerns associated with dermal exposure to PB

- HAPs. Instead, dermal toxicity values can be derived from oral toxicity values via route

- to

- route extrapolation by adjusting for gastrointestinal (GI) absorption

. EPA recommends making this adjustment only when GI absorption of the chemical is significantly less than 100% (i.e., less than 50 percent)

. Otherwise, a default value of complete (100 percent) oral absorption is assumed, and no adjustment is made ( EPA 2004).
The absorbed cancer slope factor ( C SF ABS 

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

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

### ), as follows:

where:

CSF ABS = Absorbed slope factor (mg/kg

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

- day) -1

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



### T he absorbed reference dose (RfD ABS 

### ) is ba sed on the oral reference dose (RFD O 

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

### ) , as shown below

.

where:

RfD ABS = Absorbed reference dose (mg/kg

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

- day)

ABS GI = Fraction of ch emical absorbed in gastrointestinal tract (unitless) GI o ABS 

### ABS CSF CSF =

GI o ABS 

### ABS RfD RfD ×=

Attachment C C-1 October 2014 Dermal Risk ScreeningTRIM

- Based Tiered Screening Methodolo gy for RTR



### The GI absorptions for 2,3,7,8

- TCDD and all polycyclic aromatic hydrocarbons (PAHs) (which includes benzo[a]pyrene) were estimated to be greater than 50 percent. Therefore, as shown in Exhibit C-1 , no adjustments to the available oral CSFs were required. For cadmium and divalent mercury, adjustments were made based on absorption data provided in RAGS Part E, Exhibit 4

- 1. The RfDs for dermal exposure to cadmium and divalent mercury are also shown in Exhibit C-1

. Exhibit C-1 . Cancer Slope Factors and Reference Doses Based on Absorbed Dose

PB

- HAP F raction of C ontaminant A bsorbed in GI T ract ( ABS GI ) (unitless) A bsorbed C ancer Slope F actor ( CSF ABS ) a (mg/kg

- day) -1 Absorbed R eference D ose ( RfD ABS ) a

(mg/kg

- day) Cadmium Compounds 0.05 NA 2.5E

- 05

Divalent Mercury 0.07 NA 2.1E

- 05

2,3,7,8

- TCDD No adjustment required b 1.5E+05 1.0E

- 09

Benzo[a]pyrene No adjustment required b 1.0E+01 NA

NA = Not applicable

a Oral dose response values are presented in Appendix 2. Only the resulting adjusted dose response values are presented in this table.

b According to RAGS Part E, Exhibit 4

- 1, GI absorption is expected to be greater than 50%.



## 2. Dermal Exposure Estimation

Dermal exposures and risks resulting from absorption of the chemical through the skin from contact with contaminated water and soil w ere evaluated for the RTR screening scenari o. 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 wa ter evaluation, individuals were assumed to be exposed to contaminated surface water with the same PB

- HAP concentration as the TRIM.FaTE screening scenario lake over their entire bodies on a daily basis.



### 2.1. Equations for Estimating Dermal Exposure The genera l equation used to estimate dermal absorbed dose (DAD) for water or soil is shown below, and is expressed in milligrams of PB

- HAP per kilogram of receptor body weight per day (mg/kg

- day). DADs are calculated separately for the water and soil pathways and then added together for each age group

.

where:

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) 

### ATBW SAEFEDEV DA DAD event



### × ××××

=

Attachment C C-2 October 2014 Dermal Risk ScreeningTRIM

- Based Tiered Screening Methodolo gy for RTR

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)



### DA event 

### is e stimated 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:

Water – Inorganic Chemicals:

Soil – All Chemicals:



### where:

DA event = Absorbed dose per event (mg/cm 2

- event) C w C s = Chemical concentration in water (mg/cm 3 ) or soil (mg/kg)

K

p = Chemical

- specific dermal permeability coefficient of compound in water (cm/hr )

FA = Chemical

- specific fraction absorbed; accounts for loss due to the regular shedding of skin cells of some chemical originally dissolved into skin (unitless)



# τ

event = Chemical

- specific lag time per event (hr/event)

t event = Receptor

- specific event duration (hr/event)
AF = Receptor - and activity

- specific adherence factor of soil to skin (mg/cm 2

- event)
ABS = Chemical

- specific dermal absorption fraction (unitle ss)

CF = Conversion factor (10 -6 kg/mg)



### 2.2. Exposure Factors and Assumptions The exposure parameters included in this assessment and their default and other value options are summarized in this subsection

. Default values were selected to result in a highly conservative estimate of exposure (i.e., exposures are likely overestimated). Parameter values were primarily obtained or estimated from RAGS Part E ( EPA 2004) and the Child

- Specific Exposure Factors Handbook ( CSEFH

, EPA 2008)

. Receptor

- and scenario

- spe cific exposure assumptions are discussed first, and a discussion of chemical

- specific parameters values follows . Estimated water and soil exposure concentrations are presented at the end of this subsection. 

### π τ event

event p w event 

### t6 K FA 2C DA ×× ×××=

event pw event 

### tKC DA ××=



### CF ABSAF C DA s event 

### ×××=

Attachment C C-3 October 2014 Dermal Risk ScreeningTRIM

- Based Tiered Screening Methodolo gy for RTR



### 2.3. Receptor

- Specific Parameters Dermal exposures an d risks were estimated for the same age groups used in the ingestion exposure assessment: adults (ages 20 to 70 years) and five child age groups: <1 year; 1 to 2 years; 3 to 5 years; 6 to 11 years; and 12 to 19 years. The body weight values used in the ingestion exposure assessment were used in the dermal exposure assessment.
Body surface areas (SAs) for water and soil exposures for adults were calculated using Appendix C, Exhibit C -1, of RAGS Part E. For children, SAs for water and soil exposures for the five children’s age groups were estimated using Table s 7-1 and 7

- 2 of the CSEFH, respectively. For SA (water), individuals were assumed to shower or bathe in the water with 100 percent of their body exposed. For SA (soil), it was assumed that indivi duals were exposed on a fraction of their total body, specifically their head, forearms, hands, lower legs, and feet. Based on information provided in RAGS Part E, the SA for forearms was calculated using the SA for arms and assuming a forearm

- to

- arm rati o of 0.45, and the SA for lower legs was estimated using the SA for legs and assuming a lower leg

- to

- leg ratio of 0.4.
Values for body SA by age group are summarized in Exhibit C-2

. Exhibit C-2 . Receptor

- Specific Body Surface Area Assumed to be Exposed to Chemicals
Age Group a (years) Surface Area for Water Exposure (cm 2 ) Surface Area for Soil Exposure (cm 2 ) Adult 20

- 70 18,150 g 6,878 h Child <1 b 3 ,992 1,772 Child 1

- 2 c 5,700 2,405 Child 3

- 5 d 7,600 3,354 Child 6

- 11 e 10,800 4,501 Child 12

- 19 f 17,150 6,906

a Sources for the child groups included Table 7

- 1 (total body surface area for SA

- Water), and Table 7

- 2 (fraction of total body surface area for SA

- Soil) of the 2008 CSEFH.

b Represents a time

- weighted average for age groups birth to <1 month, 1 to <3 months, 3 to <6 months, and 6 to <12 months.

c Represents a time

- weighted average for age groups 1 to <2 years and 2 to <3 years.

d Values for age group 3 to <6 years in the 2008 CSEFH.

e Values for age group 6 to <11 years in the 2008 CSEFH. Represents a conservative (i.e., slightly low) estimate for ages 6 through 11 years since 11

- year olds are not included in this CSEFH age group.

f Represents a t ime

- weighted average for age groups 11 to <16 years and 16 to <21 years. Note that estimated values include 11

- year

- olds and individuals through age 20, which contributes to uncertainty in the estimates for 12 to 19 years.

g Represents the average total su rface area of adults from Table C

- 1 of RAGS Part E.

h Represents the average surface area of adults for head, forearms, hands, lower legs, and feet from Table C

- 1 of RAGS Part E.



### 2.4. Scenario

- Specific Parameters
Exhibit C-3 summarize s the exposure values related to frequency and duration of contact. In general, these are the recommended defaults for calculating a reasonable maximum exposure (RME) for a residential scenario as proposed by EPA in RAGS Part E, Chapter 3.

Attachment C C-4 October 2014 Dermal Risk ScreeningTRIM

- Based Tiered Screening Methodolo gy for RTR



### Exhibit C-3 . Scenario

- Specific Exposure Values for Water and Soil Contact
Exposure Parameter Receptor Value Source **Water Contact** Event Duration (t event ) (hr/event) Child 1 Reasonable maximum exposure scenario for showering/bathing from RAGS Part E, Exhibit 3

- 2 Adult 0.58

**Soil Contact** Soil Adherence Factor (AF) (mg/cm 2 ) Child 0.2 For children, value is geometric mean value for children playing (wet soil) and for adults, value is geometric mean value for an adult farmer from RAGS Part E, Exhibit 3 -3 Adult 0.1

**Both Media** Event Frequency (EV) (events/day) All 1 Reasonable maximum exposure scenario from RAGS Part E, Exhibits 3

- 2 and 3- 5. Exposure Frequency (EF) (days/year) All 3 50

Exposure Duration (ED) (years) Child <1 1 Represents the number of years included in the age group; also used in ingestion exposure calculations. Child 1

- 2 2 Child 3

- 5 3 Child 6

- 11 6 Child 12

- 19 8 Adult 20

- 70 50 Averaging Time (AT) (da ys) For cancer assessment, an AT equal to a lifetime (70 years) × 365 days/year is used. Same value used in ingestion exposure calculations. For non

- cancer assessment, an AT equal to the exposure duration (ED)
× 365 days/year is used, so AT will vary by r eceptor group. Same value used in ingestion exposure calculations.



### 2.5. Chemical

- Specific Parameters
The chemical

- specific parameters required to quantitatively evaluate dermal pathway exposures are listed in Exhibit C-4 . For the water concentration in the dermal analysis, t he modeled TRIM.FaTE chemical concentration in the screening scenario pond at the screening threshold emission rate was used. For the soil concentration, the modeled TRIM.FaTE chemical concentration in surface soil in parcel N1 ( untilled soil, closest to facility ) of the screening scenario at threshold emission rate was used. This same soil concentration was also used in ingestion exposure calculations for soil ingestion.
Dermal absorption of chemicals in water is based on the use of a dermal permeability coefficient (K p 

### ), which measures the rate that a chemical penetrates the skin. Dermal absorption of soil

bound chemicals is based on the use of a dermal absorption fraction (ABS), which is a measure of how much of a chemical the skin absorbs through contact with soil.

Attachment C C-5 October 2014 Dermal Risk ScreeningTRIM

- Based Tiered Screening Methodolo gy for RTR



### Exhibit C-4 . Chemical

- Specific Dermal Exposure Values for Water and Soil Contact

PB

- HAP Cadmium Divalent Mercur y 2,3,7,8

- TCDD Benzo[a] pyrene Source

Chemical concentration in Water (Cw) (mg/cm 3 ) 2.4E

- 08 1.9E

- 09 2.6E

- 18 2.1E

- 13 TRIM.FaTE modeled concentration in screening scenario pond Chemical concentration in Soil (Cs) (mg/kg) 6.9E

- 02 6.3E

- 02 2.2E

- 10 1.4E

- 04 TRI M.FaTE modeled concentration in surface soil in parcel N1 in screening scenario Permeability coefficient in water (Kp) (cm/hour) 0.001 0.001 0.81 0.7 Values from RAGS Part E, Exhibits B

- 3 (organics) and B

4 (inorganics) Fraction absorbed water (FA) (unit less) NA NA 0.5 1.00 Values from RAGS Part E, Exhibits B

- 3; only used for organic chemicals Lag time per event ( event) (hr/event) NA NA 6.82 2.69 Values from RAGS Part E, Exhibits B

- 3; only used for organic chemicals Dermal absorption fraction (ABS) from soil (unitless) 0.001 0.045 a 0.03 0.13 Values from RAGS Part E, Exhibit 3

- 4, unless otherwise noted

a Value obtained from Bioavailability in Environmental Risk Assessment (Hrudey et al. 1996).


## 3. Screening

- Level Cancer Risks and Non

- Cancer Hazard Quotients Toxicity values we re used in conjunction with exposure information to evaluate the potential for cancer risks and non

- ca ncer health hazards. Risk estimation methods are presented below.



### 3.1. Dermal Cancer Risk
Cancer risk for the dermal route was calculated as the product of the age -specific DAD s and the absorbed CSF for each chemical, as follows:

Dermal Cancer Risk = DAD × C SF ABS



### where:

DAD = Dermal Absorbed Dose (mg/kg

- day)

CSF ABS = Absorbed cancer slope factor (mg/kg

- day) -1



### Lifetime dermal cancer r isks were calculated for 2,3,7, 8- TCDD and benzo[a] pyrene

. The total risk accounts for dermal exposures that an individual m ight receive from these PB

- HAP s in water plus soil over his or her lifetime (70 years).

Attachment C C-6 October 2014 Dermal Risk ScreeningTRIM

- Based Tiered Screening Methodolo gy for RTR



### 3.2. Dermal Hazard Quotient Dermal hazard quotient (HQ) was estimated as the ratio of age

- specific DAD s to the absorbed RfD for each chemical , as shown below:

Dermal HQ = DAD / RfD ABS



### where:

DAD = Dermal Absorbed Dose (mg/kg

- day)

RfD ABS = Absorbed reference dose (mg/kg

- day)


### The aggregate HQ accounts for exposures that an individual in a receptor group may receive from the PB

- HAP in water and soil over the exposure duration. Non

- cancer hazard is not additive across the age groups evaluated here

. 

## 4. Dermal Screening Results

Exhibit C-5 presents a summary of estimated dermal non

- cancer hazards by age group. A summary of estimated lifetime dermal cancer risks is provided in Exhibit C-6 . The highest HQ value w as 0.006 (representing divalent mercury exposure for children less than 1 year of age) or less. This is approximately1 70 times less than the potential ingestion hazard quotients associated with the screening scenario (i.e. emissions of divalent mercury in the screening scenario resulted in an ingestion hazard quotient of 1) . The highest estimated individual lifetime cancer risk associated with potential dermal exposures was 4.1 E-0 9 for benzo[a]pyrene; this value is approximately 240 times smaller than the ingestion risk (i.e., 1E

- 06) estimated for the
s ame screening threshold emission rate.

Attachment C C-7 October 2014 Dermal Risk ScreeningTRIM

- Based Tiered Screening Methodolo gy for RTR



### Exhibit C-5 . Summary of Dermal Non

- Cancer Hazards

0 0.0000005 0.000001 0.0000015 0.000002 0.0000025 0.000003 0.0000035 0.000004 0.0000045

Child <1 Child 1

- 2 Child 3

- 5 Child 6

- 11 Child 12

- 19 Adult 20

- 70Hazard Quotient

Receptor 2,3,7,8

- TCDD Water

Soil

Total

0 0.0001 0.0002 0.0003 0.0004 0.0005 0.0006 0.0007

Child <1 Child 1

- 2 Child 3

- 5 Child 6

- 11 Child 12

- 19 Adult 20

- 70Hazard Quotient

Receptor Cadmium Water

Soil

Total

0 0.001 0.002 0.003 0.004 0.005 0.006 0.007

Child <1 Child 1

- 2 Child 3

- 5 Child 6

- 11 Child 12

- 19 Adult 20

- 70Hazard Quotient

Receptor Divalent Mercury Water

Soil

Total

Attachment C C-8 October 2014 Dermal Risk ScreeningTRIM

- Based Tiered Screening Methodolo gy for RTR



### Exhibit C-6 . Summary of Dermal Cancer Risks

PB

- HAP Dermal Lifetime Cancer Risk Magnitude of Difference a
**2,3,7,8**

**- TCDD**  Water 2.64E

- 10 >3,700 Soil 1.49E

- 11 >67,300 Total 2.79E

- 10 >3,500 **Benzo[a]pyrene** Water 1.50E

- 09 >600 Soil 2.63E

- 09 >3 00 Total 4.12E

- 09 >200

a Represents the magnitude of difference b etween the e stimated d ermal r isk and the p otential i ngestion r isk associated with the s creening s cenario

. 

### Based on these results and taking into consideration the extremely conservative nature of the dermal exposure calculations, it was assumed that it is not necessary to incorporate dermal exposures in calculating multipathway screening threshold levels. Specifically, the daily exposure durations of 0.58 hour for adults and 1 hour for chi ldren used to calculate dermal exposure from water are highly conservative and assume that the individual is bathing in surface water taken directly from a contaminated lake or is swimming in the lake for 350 days of the year. The exposure frequency of 35 0 days and corresponding skin surface area available for contact with contaminated soils (i.e., head, hands, arms, legs, and feet) likely also grossly overestimates dermal exposure to soil.

Attachment C C-9 October 2014 Dermal Risk ScreeningTRIM

- Based Tiered Screening Methodolo gy for RTR



## 5. References Cal/EPA ( California Environmental Protection Agency ) O ffice of Environmental Health Hazard Assessment (OEHHA ). 2000. Air Toxics Hot Spots Program Risk Assessment Guidelines; Part IV, Exposure Assessment and Stochastic Analysis Technical Support Document. Section 6, Dermal Exposure Assessment. September. Available at: http://www.oehha.ca.gov/air/hot_spots/pdf/chap6.pdf

. Hrudey, S.E., W. Chen, and C.G. Roussex, 1996. Bioavailability in environmental risk assessment. CRC Press, Inc, Lewis publish ers.

EPA (Environmental Protection Agency). 2004. Risk Assessment Guidance for Superfund Volume 1: Human Health Evaluation Manual (Part E, Supplemental Guidance for Dermal Risk Assessment). EPA/540/R99/005. Available at: http://www.epa.gov/oswer/riskassessment/ragse/index.htm

EPA . 2006. Risk and Technology Review (RTR) Assessment Plan. Office of Air and Radiation. November 20. Available at: http://www.epa.gov/sab/panels/consul_risk_and_tech_assessment_plan.htm

. EPA . 2008. Child

- Specific Exposure Factors Handbook. Office of Research and Development, Washington, D.C. EPA/600/R

- 06/096F. September. Availab le at: http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=199243

.

Attachment C 10 October 2014 Dermal Risk ScreeningTRIM

- Based Tiered Scree ning Methodolo gy for RTR



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

Attachment D i October 2014 RIM.FaTE Parameters[This page intentionally left b lank.]TRIM

- Based Tiered Screening Methodolo gy for RTR



### Exhibits, Addendum 1

Exhibit Add B1

- 1. TRIM.FaTE Parameters Considered for Inclusion in Tier 2 Analysis

............................................................................................................ D-1

Attachment B, Addendum 1 iii October 2014 Tier 2 TRIM.FaTE Parameters[This page intentionally left blank.]TRIM

- Based Tiered Screening Methodolo gy for RTR



### E xhibit D

- 1 . TRIM.FaTE Parameters Considered for Inclusion in Tier 2 Assessment

Parameter Mechanism of Potential Influence in TRIM Assessment of Parameter Significance and Ease in Implementation Uncertainty in Site

- Specific Data for Facilities Priority for Inclusion
**Meteorological Parameters**
Wind direction (% of time wind blows toward the lake and farm) In previous runs, direct deposition accounted for the bulk of chemical input onto farms and into lakes. Because wind direction is strongly correlated to direct deposition in a given location , media concentrations are potentially highly sensitive to this parameter. Also, because the percentage of time the prevailing wind blo ws in the direction of lakes and farms can vary considerably across locations, differences in this parameter might also result in significant changes in important environmental concentrations. Highly Significant : Previous sensitivity analyses have confirm ed this to be a very sensitive parameter in the Tier 1 Screening modeling set

- up. Changing the fraction of time the wind blows toward the lake and farm by a factor of two corresponds to a change in the risk by a factor of two.
Low Effort to Implement: Th is variable is relatively straightforward to vary in the Tier 2 screening scenario s. Low to Moderate: The average fraction of time the wind blows in a given direction can be estimated for any surface meteorological station. Then, facilities can be linked t o the closest surface meteorological station. High

Wind speed Wind speed can affect the location of the “peak” concentration and deposition patterns in a given model configuration, as well as the risk

- distance profile. Highly Significant : Previous sensiti vity analyses have confirmed this to be a very sensitive parameter. However, wind speed does not vary widely across U.S. locations which could reduce its potential influence.

Low Effort to Implement: 

### This variable is relatively straightforward to vary in the Tier 2 screening scenario s

. Low to Moderate: 

### The annually

averaged wind speed can be estimated for any surface meteorological station. Then, facilities can be linked to the closest surface meteorological station. High

Attachment D D-1 October 2014 Tier 2 TRIM.FaTE ParametersTRIM

- Based Tiered Screening Methodolo gy for RTR



### E xhibit D

- 1 . TRIM.FaTE Parameters Considered for Inclusion in Tier 2 Assessment

Parameter Mechanism of Potential Influence in TRIM Assessment of Parameter Significance and Ease in Implementation Uncertainty in Site

- Specific Data for Facilities Priority for Inclusion
Precipitation Chemicals for which wet vapor or wet particle deposition processes are important are likely to be sensitive to the assumed level of precipitation

. Highly Significant : Previous sensitivity analyses have indicated a relatively high sensitivity of risk to precipitation for most PB

HAPs (PAHs, cadmium, and mercury)

. Moderate Effort to Implement: In implementing changes in precipitation in TRIM, care must be taken to also preserve the overall water balance in the model. Low to Moderate: The annually

averaged precipitation rate can be estimated for the subset of surface meteorological stations that capture rainfall data. Then, facilities can be linked to the closest surface meteorological station with available data. High

Attachment D D-2 October 2014 Tier 2 TRIM.FaTE ParametersTRIM

- Based Tiered Screening Methodolo gy for RTR



### E xhibit D

- 1 . TRIM.FaTE Parameters Considered for Inclusion in Tier 2 Assessment

Parameter Mechanism of Potential Influence in TRIM Assessment of Parameter Significance and Ease in Implementation Uncertainty in Site

- Specific Data for Facilities Priority for Inclusion
Mixing height Greater mixing heights increase the dispers ion of pollutants in the atmosphere and consequently reduce deposition to the ground in the areas around the stack. This is likely to be a highly sensitive parameter if there is a sizeable variation in mixing heights between facilities. Highly Significant : Previous sensitivity analyses have shown risk to be very sensitive to mixing height.
Low Effort to Implement: This variable is relatively straightforward to vary in the Tier 2 screening scenario s. Moderate to High: Mixing height estimates are available f or upper air meteorological stations, and this set of stations is more limited than the set of surface meteorological stations. Each surface station can be linked to the closest upper air station to estimate the average mixing height. Then, facilities can be linked to the closest surface meteorological station. The relative uncertainty in mixing height for a given facility is high, given diurnal variations in mixing height and the smaller number of upper air stations. High

Attachment D D-3 October 2014 Tier 2 TRIM.FaTE ParametersTRIM

- Based Tiered Screening Methodolo gy for RTR



### E xhibit D

- 1 . TRIM.FaTE Parameters Considered for Inclusion in Tier 2 Assessment

Parameter Mechanism of Potential Influence in TRIM Assessment of Parameter Significance and Ease in Implementation Uncertainty in Site

- Specific Data for Facilities Priority for Inclusion
**Configurational Parameters** Dista nce of lake from stack Deposition is known to decrease with distance from stack, although this relationship also depends on meteorological parameters such as wind speed and wind direction. Significance Difficult to Determine: Limited results from previous TRIM model runs show an inconclusive relationship between risk and distance from stack, possibly as a result of limited statistical power. Some studies in the literature show a definite decreasing risk gradient with distance but others report too many co nfounding factors to isolate the precise relationship.

Moderate Effort to Implement: This variable requires updates to the layout coordinates and requires more effort to vary in the Tier 2 screening scenario s than the meteorological parameters. Low: The lakes within a given radius of each facility can be found using ArcGIS TM (see section 4). High

Distance of farm from stack Deposition is known to decrease with distance from stack, although this relationship also depends on meteorological parameters such as wind speed and wind direction. Significance Difficult to Determine: Limited results from previous TRIM model runs show an inconclusive relationship between risk and distance from stack, possibly as a result of limited statistical power. Some studies in the literature show a definite decreasing risk gradient with distance but others report too many confounding factors to isolate the precise relationship.

Moderate Effort to Implement: This variable requires updates to the layout coordinates and requires more effort to vary in the Tier 2 screening scenario s than the meteorological parameters. High: Although the distance to the farm will likely affect risk, it is difficult to determine the precise land parcels near each facility that are actually used for farming now or in the future. Medium

Attachment D D-4 October 2014 Tier 2 TRIM.FaTE ParametersTRIM

- Based Tiered Screening Methodolo gy for RTR



### E xhibit D

- 1 . TRIM.FaTE Parameters Considered for Inclusion in Tier 2 Assessment

Parameter Mechanism of Potential Influence in TRIM Assessment of Parameter Significance and Ease in Implementation Uncertainty in Site

- Specific Data for Facilities Priority for Inclusion

Watershed: lake area ratio A higher watershed:lake area ratio potentially increases the chemical input of water

- soluble or particle

attached chemicals into the lake. But the associated higher flush rate will likely
re duce this effect. Significance Difficult to Determine : Changes in the watershed to lake ratio affect risk, but the interaction depends on other variables involved in the water balance.

Moderate Effort to Implement: In implementing changes in the watershed :lake ratios in TRIM, care must be taken to also preserve the overall water balance in the model. High: The portion of land serving as a watershed to a particular lake is difficult to determine. Medium

Area and depth of lake A higher lake area would
ca pture more deposition but this effect might be counterbalanced by the ensuing larger volume of water, which reduces chemical concentration. Similarly, a deeper lake would also reduce concentrations, but this effect might be counterbalanced by the ensuing l ower flush rates at a constant level of precipitation/runoff. Significance Difficult to Determine : The impact of these parameters is inconclusive based on current studies using the TRIM model.

Moderate Effort to Implement: The lake area variable requires updates to the layout coordinates and requires more effort to vary in the Tier 2 screening scenario s than the meteorological parameters. In implementing changes in these variables in TRIM, care must be taken to also preserve the overall water balance in the model. High: While the area of lakes near a facility can be determined using GIS, the depth cannot. Medium

Attachment D D-5 October 2014 Tier 2 TRIM.FaTE ParametersTRIM

- Based Tiered Screening Methodolo gy for RTR



### E xhibit D

- 1 . TRIM.FaTE Parameters Considered for Inclusion in Tier 2 Assessment

Parameter Mechanism of Potential Influence in TRIM Assessment of Parameter Significance and Ease in Implementation Uncertainty in Site

- Specific Data for Facilities Priority for Inclusion
**Physical Parameters** Flush rate A higher flush rate out of the lake would result in a higher rate of chemical output from the lake, assuming c onstant inflow and volume. Significance Difficult to Determine : The impact of this parameter is inconclusive based on current studies using the TRIM model.

Moderate Effort to Implement: In implementing changes in the flush rate in TRIM, care must be taken to also preserve the overall water balance in the model. High: The flush rate of a lake cannot be determined easily for any lake found near a facility. In addition, erosion rates, watershed information, and lake depth needed to estimate the flushing rate are not readily available. Medium

Runoff rate and fraction A higher runoff rate (or fraction) would likely result in greater chemical input into the lake for some chemicals but also potentially a higher flush rate out of the lake. Significance Difficult to Determine : The impact of this parameter is inconclusive based on current studies using the TRIM model.

Moderate Effort to Implement: In implementing changes in the runoff rate and fraction in TRIM, care must be taken to also preserve the overall water balance in the model. High: As with the flush rate, the runoff rate and fraction for any lake near a facility cannot be readily determined. Medium

Erosion rate and fraction A higher erosion rate would likely result in greater chemical input into the lake for particle

- bound chemicals. It would also result in greater chemical transport onto farmlands, but this might be counterbalanced by equally greater erosion off farmland. Highly Significant : Previous analyses have shown risk to be sensitive to this param eter for some chemicals.

Moderate Effort to Implement: In implementing changes in the erosion rate and fraction in TRIM, care must be taken to also preserve the overall water balance in the model. High: As with the flush rate, the erosion rate and fracti on for any lake near a facility cannot be readily determined. Medium

Attachment D D-6 October 2014 Tier 2 TRIM.FaTE ParametersTRIM

- Based Tiered Screening Methodolo gy for RTR



### E xhibit D

- 1 . TRIM.FaTE Parameters Considered for Inclusion in Tier 2 Assessment

Parameter Mechanism of Potential Influence in TRIM Assessment of Parameter Significance and Ease in Implementation Uncertainty in Site

- Specific Data for Facilities Priority for Inclusion

**Chemical Parameters** Methylation/ demethylation rates (Hg) For Hg, methylation and demethylation rates in lake sediment and surface water are potentially sensitive parameters affecting risk. A literature survey has indicated a relatively high range for rate constants describing these processes. Highly Significant : Previous analyses run in TRIM have confirmed the high sensitivity of these parameters for Hg.
Low Effort to Implement: This variable is relatively straightforward to vary in the Tier 2 screening scenario s. High: The specific methylation / deme t hylation rates for mercury in the vicinity of a specific facility cannot be readily determined. Low

Total phosphorus levels in the l ake The total phosphorus content of a lake is used as part of the TRIM.FaTE parameterization process to estimate the biomass content of different trophic levels. These biomass levels affect the biomagnification of chemicals up the food chain and potential ly risk to human consumers of fish. Not Significant : Previous analyses have shown limited sensitivity to total phosphorus levels. This is likely because the empirical equations predicting biomass in each trophic level depend in similar ways on the level of total phosphorus. So changes in total phosphorus do not significantly affect the ratio of biomass between the different trophic levels.
Low Effort to Implement: This variable is relatively straightforward to vary in the Tier 2 screening scenario s

. High: The total phosphorus levels in lakes near a specific facility cannot be readily determined. Low

Attachment D D-7 October 2014 Tier 2 TRIM.FaTE Parameters[This page intentionally left blank.]TRIM

- Based Tiered Screening Methodolo gy for RTR



## Analysis of Lake Size and Sustainable Fish Population

Attachment E i October 2014 Lake Size Analysis[This page intentionally left blank.]TRIM

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## CONTENTS , ADDENDUM 2

1. Introduction

................................ ..................................................................................... E-1

2. Angler Behavior

................................ .............................................................................. E-1

3. Fish Biology

.................................................................................................................... E-2
3.1. Lake Productivity

................................................................................................... E-2
3.2. Proportion of Fish Biomass by Trophic Level

......................................................... E-4
3.3. Minimum Viable Population Size

............................................................................ E-5

4. Summary of Assumptions for the Lake Size Analysis

...................................................... E-6

5. Equations Used to Determine Lake Fish Populations

...................................................... E-6

6. References

................................ ..................................................................................... E-8

Attachment E iii October 2014 Lake Size AnalysisTRIM

- Based Tiered Screening Methodolo gy for RTR

[This page intentionally left blank.]

Attachment E iv October 2014 Lake Size AnalysisTRIM

- Based Tiered Screening Methodolo gy for RTR



## 1. Introduction Identifying the smallest size of a lake that might maintain self

- sustaining populations of fish from trophic levels (TL) 3 and 4 and is sufficient to support at least one fisher at a specified fish ingestion rate requires consideration of many factors. Some factors depend on assumptions about the behavior of fisher s who consume fish from a lake ( see Section 2 below ). Other factors depend upon the general biology of fish populations in North American ecoregions (see Section 3 ). Based on evaluation of these factors, a set of assumptions was developed to support the estimation of minimum lake sizes that are needed to sustain a particular total human ingestion rate in grams/day (see Section 4 ) . Then, equations were developed (see Section 5 ) that were used to create Exhibit 26 (in Section 3.4 of the main body of the report) , which was used to determine the threshold lake size of 25 acres.


## 2. Fisher Behavior Several assumptions regarding fisher behavior are important for estimating a minimum lake size that is fishable. The first is a conservative assumption that fisher s (and their family members) consume about 50:50 top carnivore fish from both the benthos and from the water column. Benthic carnivores (BC), such as catfish and chub that consume benthic invertebrates and small benthic fish, can grow to large sizes (e.g., 2 kg). Assuming a diet of 50 percent benthic in vertebrates and 50 percent small benthic fish (e.g., young of the year that feed on algae or detritus), the BC fish category would represent trophic level (TL) 3.5. Pelagic piscivores

— water column carnivores (WCC)

— include species such as largemouth bass, lake trout, pickerel, and walleye. The WCC are modeled as TL 4, with 100 percent of their diet comprised of water column omnivores (WCO, e.g., pan fish such as bluegill and white perch), and the diet of the WCO is assumed to be 100 percent minnow

- sized fish that feed on zooplankton and algae . The assumption of 50:50 WCC and BC consumption is conservative , because smaller, pan fish, are not included. Although fisher s might prefer to catch and consume the pelagic (TL4) game fish species, their generally low er abundance compared with fish that also consume benthic invertebrates dictates that fisher s will more often capture the benthic (TL3.5) carnivores. Some TL2 (herbivorous) fish such as carp attain “catchable” size, but they generally are not popular fish for consumption and are not considered here.

. A second assumption is that fisher s and their family members consume only the fillet portion of a fish. According to Ebert et al. (1993)

, the edible fraction of fish as a proportion of total fresh body we ight is 0.4 for salmon, 0.78 for smelt, and 0.3 for all other species. EPA recommends use of 0.30 for the consumable fraction of fish (USEPA, 1989). For this assessment , a 0.33 edible fraction for TL4 fish was assumed

. That factor is roughly one

- third

, which we considered preferable to 0.30 to account for some proportion of salmon likely in the diet. This factor is used in the lake size analysis to estimate total fish biomass required to support specified human fish consumption rates.
A third assumption relates to ingestion rates of the fisher or fisher family. Fish ingestion rates used for the purpose of the Tier 2 assessment are the same as those in Tier 1 and are consistent with subsistence fisher ingestion rates (see Exhibit 18

. Summary of RTR Tier 1 Screening Scenario Assumptions ).
The final assumption is that the lake must support a sustainable fishery despite fish harvesting by humans; in other words, the lake shouldn’t be “fished out” by the harvest r ate required to meet the subsistence fisher fish ingestion rate. The productivity of any particular fishery (local population of a species of fish) and the proportion of adult fish that can be harvested for human consumption are difficult values to estimate, and models to predict sustainable harvests of different fisheries are numerous and complex. Species

- specific parameters of key to such
Attachment E E-1 October 2014 Lake Size AnalysisTRIM

- Based Tiered Screening Methodolo gy for RTR



### models include fecundity with age and size; survivorship of eggs, fry, and juveniles to sexual maturity (recruitment); natural predation pressures; and temporal variation in food availability. For the purpose of this analysis, simplifying assumptions are required. In the analysis by Håkanson and Boulion (2004), which included a survey of 122 lakes, the authors noted t hat a typical loss from fishing by birds, mammals, and humans would be approximately 10 percent of the fish biomass in the prey fish compartment (T L 3) and 10 percent of the biomass in the predator fi sh compartment (TL4). The authors also found that as ove rall lake productivity increased, the biomass of prey (TL3) fish increased more rapidly than the biomass of predator (TL4) fish. For our lake size analysis, we assumed that fisher s can harvest 10 percent of the biomass of pelagic WCC fish without diminish ing the fish population size

. 

## 3. Fish Biology Fish life histories also are key to estimating the minimum surface area of a pond or lake that could support a sustainable fish population of WCC

. The productivity and trophic structure of fish communities in ponds and lakes across the United States are varied. Thus, any set of assumptions is unlikely to all hold true at any given location. Nonetheless, three factors are important to any estimate of a minimum lake surface area for sustainable pelagic TL4 fishi ng: the general productivity of a lake (expressed as grams of fish wet weight per meter squared, g ww/m 2 

### ); the maximum likely proportion of the total fish biomass in a lake that is comprised of the top trophic level fish; and the minimum viable population (MVP) size required for the fish species to be self

- sustaining in the short term (for at least a few decades).



### 3.1. Lake Productivity The general productivity of a lake depends on many factors, including latitude, seasonal temperatures, and nutrients supporting the base of the food web. For lakes at approximately the same latitude in the same climate, nutrients play a key role in the total fish biomass that a lake might support. In a regression analysis of data on total phosphorus (TP) and fish biomass for 31 lakes across North America, Europe, and Russia, Nürnberg (1996) summarized the “limits” among three TP

- defined lake trophic status categories with respect to total fish wet weight biomass per unit area:

Oligo

- meso (TP = 10 μg/L) = 1.9 g ww/m 2
Meso

- eutro (TP = 30 μg/L) = 3.7 g ww/m 2

Eutro

- hypereutro (TP = 100 μg/L) = 8.5 g ww/m 2


### Nürnberg (1996) also summarized total fish biomass limits from Bachmann et al. (1996) for the
same lake trophic status categories based on a sample of 60 lakes in Florida:

Oligo

- meso (TP = 10 μg/L) = 7.4 g ww/m 2
Meso

- eutro (TP = 30 μg/L) = 10.6 g ww/m 2

Eutro

- hypereutro (TP = 100 μg/L) = 15.6 g ww/m 2


### As might be expected, for the same TP concentrations, standing fish biomass per unit area in the Florida lakes is two to three times higher than standing fish biomass for more northerly lakes with shorter growing seasons.
Hanson and Legget (1982) estimated the relationship betwe en TP and standing stock of fish using a regression model based on samples from 21 lakes ranging in surface area from 0.1

– 25,000 hectares (~0.25

– 62,000 acres) and located between 0° and 56° N latitude and
Attachment E E-2 October 2014 Lake Size AnalysisTRIM

- Based Tiered Screening Methodolo gy for RTR



### 121° E to 122° W longitude. Their linear regression relating TP to total fish standing biomass ( B) had a correlation coefficient ( r 2 

### ) of 0.84 :

B = 0.792 + 0 .07 2 (TP)

where:

B = total fish biomass (kg/h ectare )

TP = total phosphorous ( μg/L )



### T he regression model of Hanson and Legget (1982) predict ed total fish biomass densities in lakes of 3.0

– 9.5 g w w/m 2 

### for TP concentrations ranging from 10

– 50 μg/L for oligo

- mesotrophic to mid

- range eutrophic lakes. Another regression model from Hoyer and Canfield (1991) predicted fish biomass densities in streams of 2.6

– 6.6 g ww/m 2 

### over the same range of TP concentrations.
In general, for very small lakes, relatively low fish productivity is likely. For example, Demers et al. (2001) found fish standing biomass values of 2.73 and 3.81 g ww/m 2 

### in two lakes of 27 and 22 acres (11 and 9 hectares), respectively, in sout h- central Ontario. Brönmark and Weisner (1996) reported on aquatic communities from a sample of 44 small ponds in southern Sweden (most were less than 5 hectares ≈ 12 acres ). They found no fish in 5 of the smaller ponds (mean surface area of 0.20 ± 0.097 acres)

— which also exhibited lower TP concentrations than the larger ponds

— and no pisc ivorous fish in another 11 of the 44 ponds (mean surface area of 0.46 ± 0.27 acres). For the 28 ponds with piscivorous (TL4) fish present, the mean pond surface area was 1.4 (±1.3 SD) acres.
Scientists have also examined the relationship between TP and total fish biomass in reservoirs. Yurk and Ney (1989) examined the relationsh ip between TP and standing stock of fish in 22 reservoirs in southern Appalachia sampled in 1973. Their logarithmic regression relating TP to total fish standing biomass (B) used the following equation and had an r 2 

### of 0.75:

Log 10 

### (B) = 1.07 + 1.14 × Log 10 

### (TP)
Use of the equations from Hanson and Legget (1982) and Yurk and Ney (1989) yielded similar predications of total fish biomass at low to intermediate TP concentrations. At low TP (e.g., 10 μg/L), predictions of total fish biomass were 3.0 g ww/m 2 

### (Y urk and Ney, 1989) and 1.6 g ww/m 2 

### (Hanson and Legget, 1982); at high TP (e.g., 100 μg/L), fish biomass predicted by the two models were 15.5 and 22.4 g ww/m 2 

### , respectively.
Ideally, one would have data indicating TP levels in lakes in the vicinity of facilities for a Tier 2 assessment . Such data, however, are rarely readily available. For purposes of the screening assessment, therefore, we assume that fish productivity per unit area is independent of lake size over a wide range of lake sizes and that TP levels are unknown.
Leidy and Jenkins (1977) reported analyses of several large data sets to support modeling of fish productivity and carrying capacity in reservoirs across the United States for the National Reservoir Research Program. The analyses included studies of fish standing biomass by species in 61 reservoirs across the midwestern and eastern United States sampled at different times between 1952 and 1975. Only reservoirs of at least 500 acres (202 hectares) in size were included, with some excee ding 65,000 acres (in the Missouri drainage basin). Considering all 61 reservoirs, the mean biomass density of fish was 41.3 (± 30.4 SD) g ww/m 2 

### . The minimum
Attachment E E-3 October 2014 Lake Size AnalysisTRIM

- Based Tiered Screening Methodolo gy for RTR



### and maximum total fish biomass densities were 3.2 and 133.2 g ww/m 2 

### , respectively, and the media n value was 30.9 g ww/m 2 

### . Reservoirs typically have large drainage basins, which in some areas can receive excess TP from large expanses of agricultural areas.
In summary, the fish productivity in lakes and reservoirs can vary by more than three orders of magnitude. The reservoirs surveyed by Leidy and Jenkins (1977) in general were much larger (and were often more shallow and nutrient rich) than the natural lakes surveyed by others d iscussed above

. T he mean standing fish biomass of approximately 41.3 g ww /m2 from the reservoir survey is likely to be higher than a mean value for representative sample s of natural lakes in the United States

. For the purpose of estimating the minimum lake size that would sup port a sustainable WCC fishery, we rounded that va lue down to a single significant digit of 40 g ww/m 2 

### as the upper limit for total fish biomass in a lake. That standing biomass is higher than predicted by the regression models of Hanson and Legget (1982), Yurk and Ney (1989), and Nürnberg (1996) at a high total phosphorus of 100 μg/L (where phosphorous is the limiting nutrient) . Less productive lakes would support fewer fish per unit area, and, therefore, would have to be larger to support a specified fish ingestion rate.



### 3.2. Proportion of Fish Biomass by Trophic Level
As indicated previously , for the Tier 2 assessment , the proportion of fish in a fisher ’s diet that consists of WCC (TL4) and BC (TL3.5) is assumed to be 50:50 by biomass (not numbers) for lakes that support the four trophic levels. In smaller lakes, TL4 fish are likely to be missing or rare, with TL3 fish in the water column (e.g., sunfish) being the highest trophic level supported by the primary productivity (algal/plant production) in some lakes. As a “rule of thumb” in ecology, 10 percent or less of the energy produced at one trophic level usually can be converted to biomass in the next trophic level (i.e., approximately 90 percent loss of energy) per trophic step. However, with different species having different energy assimilation efficien cies and with smaller species generally having higher turnover rates than larger species, the 10 percent energy rule does not necessarily translate into a standing biomass pyramid of similar proportions. In this section, the proportion of fish (based on biomass) that might be expected in the WCC and the BC fish compartments relative to total standing fish biomass are examined assuming that the lake is large enough to support WCC (pelagic TL4 fish).
Examination of several studies of fish biomass by trophic level indicated that top trophic level fish might comprise up to 20 percent of the standing fish biomass in many locations. Ploskey and Jenkins (1982) estimated that piscivorous fish, both those that are generally free

- swimming or pelagic (e.g., pike, gar, walleye) and those that rest and forage primarily in the benthos (e.g., various species of catfish, suckers ) comprise 22 percent of the total fish biomass in DeGray Lake, Arkansas (averaged across several years). Leidy and Jenkins (1977) estimated that 18 percent of the fish biomass across the 61 reservoirs they examined was piscivorous (minimum of 14 percent and maximum 24 percent). Demers et al. (2001) categorized 2 percent and 15 percent of the total fish biomass in two small lakes of 27 and 22 acres in size, respectively, as piscivorous/benthivorous fish ( e.g., largemouth bass

, creek chub) ; primary benthivores (e.g., catfish, suckers) dominated at >70 percent in both lakes.

One of the more recent food web models for freshwater lakes is that of Håkanson and Boulion (2004). They designed their model to predict productivity and standing crop of prey and predatory fishes in lakes of northern Europe. The authors acknowledged that fish feeding patterns are complicated by the fact that fish change their feeding preferences as they age. Some fish species consume zooplankton or benthic invertebrates in their first year, and switch to small fish and then to larger fish as they mature and grow in size

.

Attachment E E-4 October 2014 Lake Size AnalysisTRIM

- Based Tiered Screening Methodolo gy for RTR



### Håkanson and Boulion (2004) created a “distribution co efficient” to indicate what proportion of the total fish biomass in a lake is prey versus predatory fish. Based on data from 122 lakes in Europe and North America, they concluded that 27 percent by biomass is a “normal” portion of predatory fish in a bala nced system. They noted further, however, that for eutrophic lakes with TP levels >100 μg/L, the proportion of fish represented by piscivores declines to less than 20 percent

. The piscivores included both benthic and pelagic species. We note that most benthic piscivores also consume benthic macroinvertebrates.
Based on the information above, the top trophic level fish are assumed to comprise 21 percent of the total fish biomass. With the bulk of productivity in lakes originating from detritus in the benthos, the total biomass of strictly pelagic game fish is expected to be less than that of benthic fish. Therefore, for purpose of this lake size analysis, the piscivorous/benthivorous fish were separated into two compartments, with 17.5 percent of the total fish biomass in a benthic carnivore ( TL3 .5 or BC) compartment and 3.5 percent of the total in a pelagic piscivore ( WCC ) compartment. Thus, the TL4 fish, when present, represent the limiting compartment for fish harvesting and consumption.



### 3.3. Minimum Viable Population Size The final step in estimating the minimum lake size that can support sustainable fishing of its WCC fish species is to invoke the concept of minimum viable population (MVP) size. MVP is a concept used frequently in conservation biology for animals and is defined as the smallest population that will persist for a specified duration (e.g., 100 years) with a given probability (e.g., 95 percent). MVP for any given species and location depends on many attributes of the species biology (e.g., body size, reproductive rate, home range size, corridors between populations, variabilit y in environmental characteristics that impact fecundity and survival). At lower numbers, the likelihood of population extinction increases due to environmental and demographic stochasticity (Menzie et al., 2008). As for fisheries biology, entire text bo oks have been dedicated to applied population ecology with population simulations incorporating demographic and life

- history characteristics, spatial separation of habitat patches and metapopulations, the probability of local catastrophes, genetic variation (e.g., drift), and other factors with predictions of time

- to

- extinction or probability of extinction within specified time periods (e.g., Soulé, 1987; Akçakaya et al., 1999). Consideration of such models in population

level ecological risk assessment has begun, but faces many challenges (Barnthouse et al., 2008). Moreover, that level of effort is beyond the resources available for screening

- level assessments

. Much of the initial work on MVP investigated the genetic minima required for short

- term survival

, continuing adaptation to environmental change, and ultimately evolution. Inbreeding has been considered the primary threat to short

- term survival and genetic drift the principal threat to losing the genetic variation required for adaptation (Shaffer, 19 87). Several analyses (Senner

, 1980 ; Franklin

, 1980 ; Soulé

, 1980 ; Frankel and Soulé, 1981; Lande and Barrowclough

, 1987) have led to the conclusion that minimum effective population sizes on the order of 50 are required for short

- term survival (e.g., seve ral generations, decades), while effective population sizes on the order of 500 are necessary to provide adequate genetic variation for continuing adaptation over the long term (e.g., tens of generations, centuries for some animals) ( Shaffer, 1981; 1987 ). Effective population size, N e 

### , is a measure of the rate of genetic drift (loss of genetic diversity or inbreeding), and its definition generally depends on the population in question (Rieman and Allendorft, 2001). N e 

### can be estimated mathematically based on stochastic behavior of gene frequencies in a diploid population. Simple models assume a fixed population size, constant fecundity, specified sex ratio, and no overlap between generations (see studies cited in NRC, 1986). For animals with 50:50 sex ra tios, the effective population size is essentially the same as the actual breeding adult population size (Ewens et al., 1987). One of the most extensive Attachment E E-5 October 2014 Lake Size AnalysisTRIM

- Based Tiered Screening Methodolo gy for RTR



### population viability analyses in the United States has been conducted on the spotted owl (Boyce, 1993)

. Given the number and complexity of factors that influence MVP, however, including the definitions of time horizon (e.g., 100 years) and probability of survival ( e.g., 95 percent ), population biologists caution against using a “rule of thumb ” for MVP acr oss circumstances (Ewens et al.

, 1987).
Note that the MVP is appropriate for a single species of fish, not for generic categories of fish such as WCC or TL4. For this Tier 2 assessment

, the MVP of 50 associated with short

- term population survival was assu med for a TL4 fish species isolated in a lake. In reality, short

- term extirpations from a lake can be countered by purposeful introductions from other lakes or during flooding events. Thus, an MVP of 500 was not considered necessary for game fish in lake s.


## 4. Summary of Assumptions for the Lake Size Analysis The following assumptions were used in processing lake data for the Tier 2 assessment and in estimat ing the relationship between fish ingestion and sustainable harvest rates and lake size (see Section 3.4.1 of Attachment B ).

1. Piscivorous fish ( WCC and BC ), when present, comprise approximately 21 percent of the standing biomass of fish (ignoring seasonal changes). The BC fish represent 17 .5 percent of the standing fish biomass; WCCs account for 3.5 percent of the total fish biomass. Thus, WCC fish, when present, represent the limiting compartment for fish harvesting and consumption.
2. H umans can harvest 10 percent of the biomass of a fish compartment without thr eatening the population due to overharvesting.
3. The MVP size for a single WCC species is at least 50 adult fish for a local population to survive over the short term (more than a decade)

. 4. O nly 33 percent of the fish is edible fillet muscle

. 

## 5. Equations Used to Determine Lake Fish Populations

The standing biomass of WCC ( TL4 ) fish supported in Lake X can be calculated as the total standing biomass of fish (Total SB) multiplied by 0.035, based on the assumption that TL4 fish represent approximately 3.5 percent of the standing biomass in Lake X

.

WWWWWW SS SS = TT SSSS xx FF FFFF FF WWWWWW ( Equation 1)

wher e:

WCC SB = Standing biomass of WCC fish ( g wet weight [ww]/m 2 ) in Lake X
Total SB = Total standing biomass of fish ( g ww/m 2 ) in Lake X

Fraction WCC = Fraction of WCC fish in La ke X ( i.e., 0.035)


### Using WCC SB and the size of Lake X (Lake Size), the total number of WCC fish supported in Lake X can be calculated using Equation 2 below

.

NN

. WWWWWW = LLLLLLLL SSSSSS xx WW SSSS xx CC
WW

aa 

### ( Equation 2)

where: Attachment E E-6 October 2014 Lake Size AnalysisTRIM

- Based Tiered Screening Methodolo gy for RTR

No. WCC = Total number of WCC fish in Lake X
Lake Size = Size of Lake X ( acres )

WCC SB = Standing biomass of WCC fish ( g ww/m 2 ; from Equation 1 )
CF = 4047 (unit conversion factor m 2 / acre )

BW a = Body weight of adult TL4 fis h ( 2000 g; assumed )


### The likely a nnual p roductivity of WCC fish (kg/year) in Lake X can be estimated using Equation 3

.

PP PP PP WWWWWW = LLLLLLLL SSSSSS xx WW SSSS xx CC 1
CC 2 

### (Equation 3)

where:
Productivity WCC = Likely annual productivity of WCC fish in Lake X (kg/year)

Lake Size = Size of the Lake X ( acres )

WCC SB = Standing biomass of WCC f ish ( g ww/m 2 ; from Equation 1 )
CF1 = 4047 (unit conversion factor 1, m 2 / acre )

CF2 = 1000 (unit conversion factor 2, g/kg )


### The maximum fish ingestion rate (g/day) for WCCs plus BCs associated with sustainable fishin g can be predicted using Equation 4 . It assumes that the fisher s consume 50 percent WCC and 50 percent BC, represented by the factor of 2 in Equation 4

.

MM SS IIII ( + WWWWWW ) = 2 xx PPPPPPPPPPPPPP PP PPPP WW xx xx xx CC 1
CC 2 

### ( Equation 4 )

where:

Max Sustain IR ( BC + WCC ) = Predicted maximum sustainable ingestion rate for BC and WCC fish (g/day) Productivity WCC = Likel y annual productivity of WCC fish in Lake X (kg /year; from Equation 3 )
FF = Fillet fraction; represents the assumed edible portion of fish ( 0.33; unitless )
HF = Annual harvest fraction (0.10; unitless )
CF1 = 1000 ( unit conversion factor 1, g/kg )

CF2 = 365 (unit conversion factor 2, days/year )

Attachment E E-7 October 2014 Lake Size AnalysisTRIM

- Based Tiered Screening Methodolo gy for RTR



## 6. References Akçakaya, H.R., Burgman, M.A., and Ginzburg, L.R. 1999. Applied Population Ecology: Principles and Computer Exercises using RAMAS EcoLab. Sunderland, MA: Sinauer Associates, Inc. Publishers.
Bachmann, RW; Jones, BL; Fox, DD; Hoyer, M; Bull, LA; Canfield, DE. 1996. Relations between trophic state indicators and fish in Florida (USA) lakes. Canadian Journal of Fisheries and Aquatic Sciences. 53:842

- 855.
Barnthouse, L .W., Munns, W.R. Jr., and Sorensen, M.T. 2008. Population

- Level Ecological Risk Assessment. Boca Raton, FL: CRC Taylor & Francis Group; Pnesacola, FL: Society of Environmental Toxicology and Chemistry (SETAC).
Boyce, M.S. 1993. Population viability analysis: adaptive management for threatened and endangered species. In: Wildlife Management Institute, Transactions of the 58th North American Wildlife and Natural Resources Conferences. Proceedings WWRC

- 93

- 19, pp. 520

- 527.
Brönmark, C. and S.E.B. Weisner. 1996. Decoupling of cascading trophic interactions in a freshwater benthic food chain. Oecologia 108: 534

- 541.
Demers, E., McQueen, D.J., Ramcharan, C.W., and A. Perez

- Fuentetaja. 2001. Did piscivores regulate changes in fish community structure? Advanc. Limnol. 56: 49

- 80.
Ebert, E., N. Harrington, K. Boyle, J. Knight, and R. Keenan. 1993. Estimating consumption of freshwater fish among Maine anglers. N. Am. J. Fisheries Manage. 13: 737

- 745.
Ewens, W.J., Brockwell, P.J., Gani, J.M., and Resnick, S.I. 1987. Minim um viable population sizes in the presence of catastrophes. In: M.E. Soulé (ed.) Viable Populations for Conservation. United Kingdom, Cambridge: Cambridge University Press, pp. 59

- 68.
Frankel, O.H., and Soulé, M.E. 1981. Conservation and evolution. United Kingdom, Cambridge: Cambridge University Press.
Franklin, I.R. 1980. Evolutionary change in small populations. In: M.E. Soulé and B.A. Wilcox (eds.) Conservation Biology: An Evolutionary

- Ecological Perspective. Sunderland, MA: Sinauer Associates; pp. 135

- 150. As cited by Shaffer, 1987.
Håkanson, L. and V.V. Boulion. 2004. Modeling production and biomasses of prey and predatory fish in lakes. Hydrobiologia 511: 125

– 150.
Hanson, J.M., and W.C. Leggett. 1982. Empirical prediction of fish biomass and yield. Ca n. J. Fish. Aquat. Sci. 39: 257

- 263.
Hoyer, M.V., and D.E. Canfield, Jr. 1991. A phosphorus

- fish standing crop relationship for streams? Lake Reserv. Manage. 7: 25

- 32.
Lande, R., and Barrowclough, G.F. 1987. Effective population size, genetic variation, a nd their use in population management. In: M.E. Soulé (ed.) Viable Populations for Conservation. United Kingdom, Cambridge: Cambridge University Press, pp. 87

- 123.
Leidy, G.R., and Jenkins, R.M. 1977. The development of fishery compartments and population rate coefficients for use in reservoir ecosystem modeling. Contract Report Y

- 77

- 1 prepared
Attachment E E-8 October 2014 Lake Size AnalysisTRIM

- Based Tiered Screening Methodolo gy for RTR



### by the National Reservoir Research Program, U.S. Fish and Wildlife Service, for the U.S. Army Corps of Engineers, Waterways Experiment Station, Vicksburg, Mississipp i, USA.
Menzie, C., Bettinger, N., Fritz, A., Kapustka, L., Regan, H., Moller, V., and Noel, H. 2008. Population protection goals. In: Barnthouse, L.W., Munns, W.R. Jr., and Sorensen, M.T. 2008. Population

- Level Ecological Risk Assessment. Boca Raton, FL: CRC Taylor & Francis Group; Pnesacola, FL: Society of Environmental Toxicology and Chemistry (SETAC), pp. 41

- 68.
NRC (National Research Council). 1986. Ecological Knowledge and Environmental Problem Solving. Committee on the Applications of Ecological Theory to Environmental Problems. Washington, DC: National Academies of Science Press.
Nürnberg, G.K. 1996. Trophic state of clear and colored, soft - and hardwater lakes with special consideration of nutrients, anoxia, phytoplankton and fish. Journal of Lake a nd Reservoir Management 12(4): 432

- 447.
Ploskey, G.R., and R.M. Jenkins. 1982. Biomass model of reservoir fish and fish

- food interactions, with implications for management. North American Journal of Fisheries Management 2(2): 105

- 121.
Rieman, B.E., and All endorf, F.W. 2001. Effective population size and genetic conservation criteria for bull trout. N. A. J. Fisheries Manage. 21:756

- 764.
Senner, J.W. 1980. Inbreeding depression and the survival of zoo populations. In: M.E. Soulé and B.A. Wilcox (eds.) Conser vation Biology: An Evolutionary

- Ecological Perspective. Sunderland, MA: Sinauer Associates; pp. 209

- 244. As cited by Shaffer, 1987.
Scaffer, M.L. 1981. Minimum population sizes for species conservation. Bioscience 31: 131

- 134.
Shaffer, M. 1987. Minimum via ble populations: coping with uncertainty. In: M.E. Soulé (ed.) Viable Populations for Conservation. United Kingdom, Cambridge: Cambridge University Press, pp. 69

- 86.
Soulé, M.E. 1980. Thresholds for survival: maintaining fitness and evolutionary potential. In: M.E. Soulé and B.A. Wilcox (eds.) Conservation Biology: An Evolutionary

- Ecological Perspective. Sunderland, MA: Sinauer Associates; pp. 151

- 170. As cited by Shaffer, 1987.
Soulé, M.E. (ed.) 1987. Viable Populations for Conservation. United Kingdom, Ca mbridge: Cambridge University Press.
USEPA (US Environmental Protection Agency). 1989. Assessing human health risks from chemically contaminated fish and shellfish: a guidance manual. EPA

- 503/8

- 89

- 002. Washington, DC.
Yurk, J.J., and J.J. Ney. 1989. Phosp horus

- fish community biomass relationships in southern Appalachian reservoirs: can lakes be too clean for fish? Lake and Reservoir Management 5(2): 83

- 90.

Attachment E E-9 October 2014 Lake Size Analysis[This page intentionally left blank.]

# A ppendix 6

– Environmental Risk Screen

### Table 1

– PB

- HAP Benchmarks Included in the Environmental Risk Screen

EcoHAP Assessment Endpoint Benchmark Effects Level Benchmark Value Benchmark Source Tier 1 Eco

Screening Threshold (TPY)
Cadmium Fish

- Avian Piscivores NOAEL (merganser) 0.7 (mg/kg BW/day) d 2.03E

- 01 Cadmium Fish

- Avian Piscivores LOAEL (merganser) 1 (mg/kg BW/day) d 2.90E

- 01 Cadmium Fish

- Mammalian Piscivores NOAEL (mink) 0.742 (mg/kg BW/day) e 4.06E

- 01 Cadmium Fish

- Mammalian Piscivores LOAEL (mink) 7.42 (mg/kg BW/day) e 4.06E+00 Cadmium Sediment Community No

- effect Level 0.6 (mg/kg dry wt) b 1.73E+00 Cadmium Sediment Community Threshold Level 1.2 (mg/kg dry wt) a 3.45E+00 Cadmium Sediment Community Probable

- effect Level 3.5 (mg/kg dry wt) b 1.01E+01 Cadmium Surface Soil

- Dist. 1

- 312 m Threshold

- Mammalian Insectivores (shrew) 0.36 (mg/kg dry wt) a 3.64E

- 01
Cadmium Surface Soil

- Dist. 1

- 312 m Threshold

- Avian Ground Insectivores (woodcock) 0.77 (mg/kg dry wt) a 7.79E

- 01 Cadmium Surface Soil

- Dist. 1

- 312 m Threshold Level

- Plant Community 32 (mg/kg dry wt) a 3.24E+01 Cadmium Surface Soil

- Dist. 1

- 312 m Threshold Level

- Invertebrate Community 140 (mg/kg dry wt) a 1.42E+02 Cadmium Surface Soil

- Dist. 2

- 850 m Threshold

- Mammalian Insectivores (shrew) 0.36 (mg/kg dry wt) a 5.21E

- 01
Cadmium Surface Soil

- Dist. 2

- 850 m Threshold

- Avian Ground Insectivores (woodcock) 0.77 (mg/kg dry wt) a 1.11E+00 Cadmium Surface Soil

- Dist. 2

- 850 m Threshold Level

- Plant Community 32 (mg/kg dry wt) a 4.63E+01 Cadmium Surface Soil

- Dist. 2

- 850 m Threshold Level

- Invertebrate Community 140 (mg/kg dry wt) a 2.03E+02 Cadmium Surface Soil

- Dist. 3

- 1,500 m Threshold

- Mammalian Insectivores (shrew) 0.36 (mg/kg dry wt) a 7.53E

- 01
Cadmium Surface Soil

- Dist. 3

- 1,500 m Threshold

- Avian Ground Insectivores (woodcock) 0.77 (mg/kg dry wt) a 1.61E+00 Cadmium Surface Soil

- Dist. 3

- 1,500 m Threshold Level

- Plant Community 32 (mg/kg dry wt) a 6.69E+01 Cadmium Surface Soil

- Dist. 3

- 1,500 m Threshold Level

- Invertebrate Community 140 (mg/kg dry wt) a 2.93E+02 Cadmium Surface Soil

- Dist. 4

- 3,500 m Threshold

- Mammalian Insectivores (shrew) 0.36 (mg/kg dry wt) a 1.05E+00
Cadmium Surface Soil

- Dist. 4

- 3,500 m Threshold

- Avian Ground Insectivores (woodcock) 0.77 (mg/kg dry wt) a 2.24E+00 Cadmium Surface Soil

- Dist. 4

- 3,500 m Threshold Level

- Plant Community 32 (mg/kg dry wt) a 9.32E+01 Cadmium Surface Soil

- Dist. 4

- 3,500 m Threshold Level

- Invertebrate Community 140 (mg/kg dry wt) a 4.08E+02 Cadmium Surface Soil

- Dist. 5

- 7,500 m Threshold

- Mammalian Insectivores (shrew) 0.36 (mg/kg dry wt) a 2.99E+00
Cadmium Surface Soil

- Dist. 5

- 7,500 m Threshold

- Avian Ground Insectivores (woodcock) 0.77 (mg/kg dry wt) a 6.40E+00 Cadmium Surface Soil

- Dist. 5

- 7,500 m Threshold Level

- Plant Community 32 (mg/kg dry wt) a 2.66E+02 Cadmium Surface Soil

- Dist. 5

- 7,500 m Threshold Level

- Invertebrate Community 140 (mg/kg dry wt) a 1.16E+03 C admium Water

- column Community Threshold Level 0.25 (ug/L) c 7.66E

- 01 Cadmium Water

- column Community Frank

- effect Level 2 (ug/L) c 6.12E+00

### Table 1

– PB

- HAP Benchmarks Included in the Environmental Risk Screen

Dioxin Fish

- Avian Piscivores NOAEL (merganser) 0.0000014 (mg/kg BW/day) f 3.17E

- 05 Dioxin Fish

- Avian Piscivores LOAEL (merganser) 0.000014 (mg/kg BW/day) f 3.17E

- 04
Dioxin Fish

- Mammalian Piscivores NOAEL (mink) 0.000000771 (mg/kg BW/day) f 4.12E

- 05 Dioxin Fish

- Mammalian Piscivores LOAEL (mink) 0.00000771 (mg/kg BW/day) f 4.12E

- 04 Dioxin Sediment Community Threshold Level 0.00000116 (mg/kg dry wt) g 3.10E

- 05 Dioxin Surface Soil

- Dist. 1

- 312 m Threshold

- Mammalian Insectivores (shrew) 0.0000002 (mg/kg dry wt) h 6.69E

- 07 Dioxin Surface Soil

- Dist. 2

- 850 m Threshold

- Mammalian Insectivores (shrew) 0.0000002 (mg/kg dry wt) h 9.48E

- 07
Dioxin Surface Soil

- Dist. 3

- 1,500 m Threshold

- Mammalian Insectivores (shrew) 0.0000002 (mg/kg dry wt) h 1.28E

- 06 Dioxin Surface Soil

- Dist. 4

- 3,500 m Threshold

- Mammalian Insectivores (shrew) 0.0000002 (mg/kg dry wt) h 2.08E

- 06 Dioxin Surface Soil

- Dist. 5

- 7,500 m Threshold

- Mammalian Insectivores (shrew) 0.0000002 (mg/kg dry wt) h 4.39E

- 06 Dioxin Water

- column Community Threshold Level 0.000012 (ug/L) i 3.84E

- 03 Dioxin Water

- column Community Frank

- effect Level 0.1 (ug/L) i 3.20E+01
Mercuric Chloride Sediment Community Threshold Level 0.15 (mg/kg dry wt) a 9.11E

- 03 Mercuric Chloride Sediment Community Probable

- effect Level 0.7 (mg/kg dry wt) j 4.25E

- 02 Mercuric Chloride Surface Soil

- Dist. 1

- 312 m Threshold Level

- Plant Community 0.3 (mg/kg dry wt) k 9.25E

- 03 Mercuric Chloride Surface Soil

- Dist. 1

- 312 m Threshold Level

- Invertebrate Community 0.1 (mg/kg dry wt) k 3.08E

- 03 Mercuric Chloride Surface Soil

- Dist. 2

- 850 m Threshold Level

- Plant Community 0.3 (mg/kg dry wt) k 1.35E

- 02 Mercuric Chloride Surface Soil

- Dist. 2

- 850 m Threshold Level

- Invertebrate Community 0.1 (mg/kg dry wt) k 4.50E

- 03 Mercuric Chloride Surface Soil

- Dist. 3

- 1,500 m Threshold Level

- Plant Community 0.3 (mg/kg dry wt) k 1.78E

- 02 Mercuric Chloride Surface Soil

- Dist. 3

- 1,500 m Threshold Level

- Invertebrate Community 0.1 (mg/kg dry wt) k 5.93E

- 03 Mercuric Chloride Surface Soil

- Dist. 4

- 3,500 m Threshold Level

- Plant Community 0.3 (mg/kg dry wt) k 2.70E

- 02 Mercuric Chloride Surface Soil

- Dist. 4

- 3,500 m Threshold Level

- Invertebrate Community 0.1 (mg/kg dry wt) k 8.99E

- 03 Mercuric Chloride Surface Soil

- Dist. 5

- 7,500 m Threshold Level

- Plant Community 0.3 (mg/kg dry wt) k 7.23E

- 02 Mercuric Chloride Surface Soil

- Dist. 5

- 7,500 m Threshold Level

- Invertebrate Community 0.1 (mg/kg dry wt) k 2.41E

- 02 Mercuric Chloride Water

- column Community Threshold Level 0.77 (ug/L) c 7.78E

- 01 Mercuric Chloride Water

- column Community Frank

- effect Level 1.4 (ug/L) c 1.42E+00 Mercury (methyl) Fish

- Avian Piscivores NOAEL (merganser) 0.013 (mg/kg BW/day) f 7.55E

- 03 Mercury (methyl) Fish

- Avian Piscivores LOAEL (merganser) 0.078 (mg/kg BW/day) f 4.53E

- 02 Mercury (methyl) Fish

- Mammalian Piscivores NOAEL (mink) 0.0247 (mg/kg BW/day) e 4.01E

- 02 Mercury (methyl) Fish

- Mammalian Piscivores LOAEL (mink) 0.123 (mg/kg BW/day) e 2.00E

- 01 Mercury (methyl) Sediment Community Threshold Level 0.2 (mg/kg dry wt) l 5.43E+00 Mercury (methyl) Sediment Community Probable

- effect Level 1 (mg/kg dry wt) l 2.72E+01 Mercury (methyl) Surface Soil

- Dist. 1

- 312 m Threshold Level

- Invertebrate Community 0.67 (mg/kg dry wt) I 1.25E+00

### Table 1

– PB

- HAP Benchmarks Included in the Environmental Risk Screen

References :

a. U.S. EPA OSWER b. Environment Canada c. U.S. EPA OW d. CA DTSC HERD (2009) e. Sample, et. al, 1996 f. U.S. EPA 1995 g. U.S. EPA Region 3, 4, and 5. h. U.S. EPA Region i. U.S. EPA Region 4 j. Florida DEP, NOAA k. ORNL and EPA R4, R6 l. MacDonald, et. al. 2000 m. U.S. EPA Region 6 n. NAAQS for Lead, Secondary Standard Mercury (methyl) Surface Soil

- Dist. 2

- 850 m Threshold Level

- Invertebrate Community 0.67 (mg/kg dry wt) i 1.82E+00 Mercury (methyl) Surface Soil

- Dist. 3

- 1,500 m Threshold Level

- Invertebrate Community 0.67 (mg/kg dry wt) i 2.40E+00 Mercury (methyl) Surface Soil

- Dist. 4

- 3,500 m Threshold Level

- Invertebrate Community 0.67 (mg/kg dry wt) i 3.62E+00 Mercury (methyl) Surface Soil

- Dist. 5

- 7,500 m Threshold Level

- Invertebrate Community 0.67 (mg/kg dry wt) i 9.71E+00 Mercury (methyl) Water

- column Community Threshold Level 0.0028 (ug/L) c 3.30E

- 01 Mercury (methyl) Water

- column Community Frank

- effect Level 0.099 (ug/L) c 1.17E+01
PAH Fish

- Mammalian Piscivores NOAEL (mink) 0.417 (mg/kg BW/day) e 4.72E+02
PAH Fish

- Mammalian Piscivores LOAEL (mink) 4.17 (mg/kg BW/day) e 4.72E+03
PAH Sediment Community No

- effect Level 0.032 (mg/kg dry wt) b 4.73E+00
PAH Sediment Community Threshold Level 0.15 (mg/kg dry wt) l, h 2.22E+01
PAH Sediment Community Probable

- effect Level 1.45 (mg/kg dry wt) l 2.14E+02
PAH Surface Soil

- Dist. 1

- 312 m Threshold

- Mammalian Insectivores (shrew) 1.52 (mg/kg dry wt) h 3.45E+00
PAH Surface Soil

- Dist. 2

- 850 m Threshold

- Mammalian Insectivores (shrew) 1.52 (mg/kg dry wt) h 5.09E+00
PAH Surface Soil

- Dist. 3

- 1,500 m Threshold

- Mammalian Insectivores (shrew) 1.52 (mg/kg dry wt) h 8.22E+00
PAH Surface Soil

- Dist. 4

- 3,500 m Threshold

- Mammalian Insectivores (shrew) 1.52 (mg/kg dry wt) h 2.02E+01
PAH Surface Soil

- Dist. 5

- 7,500 m Threshold

- Mammalian Insectivores (shrew) 1.52 (mg/kg dry wt) h 5.06E+01
PAH Water

- column Community Threshold Level 0.014 (ug/L) a, h, m 1.84E+01
PAH Water

- column Community Frank

- effect Level 0.24 (ug/L) a 3.16E+02
Lead Ambient Air NAAQS Secondary Standard 0.15 ug/m 3 n NA

### Table 2

– Acid Gas Benchmarks Included in the Environmental Risk Screen

a

- Note that the human health RfC is 20 μg/m3 , which is lower than the ecological benchmark.



### Acid Gas Chronic 90

- day Benchmark in μg/m3 Hydrochloric acid

- LOEL 50 a 

### Hydrofluoric acid

– Plant Community LOEL 0.5 Hydrofluoric acid

– Plant Community LOEL 0.4

### Appendix 7 Detailed Risk Modeling ResultsFacility NEI ID Facility Name Address City State

01015223 Anniston Army Depot, Anniston Alabama 7 Frankford Avenue Anniston AL

01045482 U.S. Army Aviation Center of Excellence and Fort Rucker Dilly Branch Road, Bldg. 1120, Attn: ATZQ-DPW Fort Rucker AL 010515017 GKN WESTLAND AEROSPACE, INC. 3951 ALABAMA HWY 229 TALLASSEE AL

01073446 Alabama Aircraft Industries Incorporated - Birmingham 1943 50th Street North Birmingham AL 01083198 Pratt & Whitney Automation 15091 AL HWY 20 Madison AL 010892023 George C Marshall Space Flight Center Marshall Space Flight Center Huntsville AL 02090348 Eielson Air Force Base 2310 Central Avenue, Suite 100 Eielson AFB AK

02090529 United States Army Garrison Fort Wainwright, Alaska 1060 Gaffney Road #6000 Fairbanks North Star Borough AK 02150683 U.S. Coast Guard Base Support Unit Kodiak Bldg. N38 Rezanof Hwy and Cape Sarichef Road Kodiak AK 04013510 Ohlinger Industries Inc. 2111 West Melinda Lane Phoenix AZ

04013602 US Airways Maintenance and Technical Operations Center 4000 East Sky Harbor Boulevard Phoenix AZ 04019300 Learjet Inc. 1255 East Aero Park Boulevard Tucson AZ 04019433 Davis-Monthan Air Force Base, Tucson, AZ 3791 S. Third Street Tucson AZ 050272001 American Fuel Cells and Coated Fabrics 601 Firestone Drive Magnolia AR 05051409 Triumph Fabrications Hot Springs 1923 Central Ave Hot Springs AR 05113377 Crider Aircraft Painting, Inc. 104 Airport Lane Mena AR 05143331 Pratt & Whitney PSD Inc. 275 E. Robinson Ave. Springdale AR 060292024 Naval Air Weapons Station 1 Administration Circle China Lake CA 06029545 Edwards Air Force Base 5 Popson Ave., Building 2650A Edwards Air Force Base CA 060372022 Palmdale 3520 East Avenue M Palmdale CA 06037429 The Boeing Company - C-17 Facility 2401 E Wardlow Rd Long Beach CA 06037490 El Segundo 500 N Douglas Ave El Segundo CA 060376004 Gulfstream Aerospace Corporation 4150 Donald Douglas Drive Long Beach CA 06059538 DUCOMMUN AEROSTRUCTURES INC 1885 N BATAVIA ST ORANGE CA 06065520 Rohr, Inc. 8200 Arlington Avenue Riverside CA 06071540 Ducommun AeroStructures El Mirage Facility 4001 El Mirage Road El Mirage CA 06073306 Naval Air Station North Island NAS North Island San Diego CA

060812015 United Airlines Technical Operations Maintenance Center 800 S. Airport Blvd San Francisco CA 09001684 Sikorsky Aircraft - Bridgeport 1 Facility 1210 South Avenue Bridgeport CT 09001688 Helicopter Support Inc. 116/124 Quarry Road Trumbull CT

09001693 Sikorsky Aircraft Corporation - Stratford Facility 6900 Main Street Stratford CT 090032025 EDAC Technologies 1798 New Britain Avenue Farmington CT

09003626 Kaman Aerospace Corporation - Helicopters Division 50 Old Windsor Road Bloomfield CT

Table 1 - Facility Identification Information

1 of 5Facility NEI ID Facility Name Address City State Table 1 - Facility Identification Information

090075021 Kaman Aerospace Corporation, Precision Products Division 217 Smith Street Middletown CT

10003349 DASSAULT FALCON JET - WILMINGTON CORP 191 North Dupont Hwy New Castle DE 12005313 Tyndall AFB 325 FW / Tyndall AFB Panama City FL 12009358 Patrick Air Force Base 1201 Edward H. White II Street Patrick Air Force Base FL

12023284 TIMCO Aviation Services, Inc. - Lake City Base Maintenance Operations 102 SE Academic Avenue Lake City FL 12031634 U.S. Naval Station Mayport Old Mayport Road Jacksonville FL 12061380 Piper Aircraft, Inc. 2926 Piper Drive Vero Beach FL

12083270 LOCKHEED MARTIN MISSILES AND FIRE CONTROL 498 OAK ROAD OCALA FL 120862016 AAR LANDING GEAR SERVICES 9371 NW 100 STREET MIAMI FL

12099695 Sikorsky Aircraft Corporation - Jupiter, Florida Facility 17900 Beeline Highway (SR-710) JUPITER FL

13009224 Triumph Aerostructures - Vought Integrated Programs Division 90 Hwy 22 W Milledgeville GA 130516005 Gulfstream Aerospace Corporation P.O. Box 2206 Savannah GA

13063388 Delta Air Lines, Inc. Technical Operations Center 1775 Aviation Blvd. Atlanta GA

13067506 U.S. Air Force Plant 6 Lockheed Martin Aeronautics Company 86 South Cobb Drive Marietta GA 131276002 Gulfstream Aerospace Corporation 550 Connole Street Brunswick GA 13153431 Robins Air Force Base Highway 247 and Watson Boulevard Robins Air Force Base

GA

132332017 Meggitt (Rockmart), Inc. (formerly Engineered Fabrics Corp) 669 Goodyear Street Rockmart GA 15003346 Joint Base Pearl Harbor Hickam Commander Navy Region Hawaii 850 Ticonderoga Street, Suite 110 Pearl Harbor HI 18097487 Rolls-Royce Corporation 2001/2355 S. Tibbs Avenue Indianapolis IN 181035004 Grissom Air Reserve Base 7207 South Grissom Avenue Grissom Air Reserve Base IN 181412011 Honeywell International Inc; South Bend 3520 Westmoor Street South Bend IN 19163356 Carleton Life Support Systems Inc. 2734 Hickory Grove Road Davenport IA 20035486 GE Engine Services, LLC 7577 4th and A St. Arkansas City KS 201252005 Cessna Aircraft Company One Cessna Boulevard Independence KS 20173193 Globe Engineering Company, Inc. 1539 South St. Paul Wichita KS

201732006 Cessna Aircraft Company - Mid-Continent Facility One Cessna Boulevard Wichita KS 201732007 Cessna Aircraft Company 5800 E. Pawnee Street Wichita KS 201732027 Spirit Aerosystems, Inc. 3801 South Oliver Wichita KS 20173453 Learjet, Inc One Learjet Way Wichita KS 2017365 Hawker Beechcraft Corporation 9709 East Central Wichita KS 24005458 Middle River Aircraft Systems 103 Chesapeake Park Plaza Baltimore MD
2 of 5Facility NEI ID Facility Name Address City State Table 1 - Facility Identification Information

24015282 Alliant Techsystems Inc. 55 Thiokol Road Elkton MD

24017525 NAVFAC Washington, PWD South Potomac, Naval Support Facility Indian Head Environmental Division, 3972 Ward Rd, Ste. 101 Indian Head MD 25009450 Raytheon Integrated Air Defense Center 350, 358 and 362 Lowell St Andover MA 25009477 General Electric Aviation 1000 Western Avenue Lynn MA 26065644 Pratt & Whitney AutoAir, Inc. 5640 Enterprise Drive Lansing MI 27053389 Delta Air Lines, Inc. MSP TechOps 7500 Airline Drive Minneapolis MN 280475024 1108 TASMG Hangar 1, Hewes Ave. Gulfport MS 28089367 L-3 Vertex Aerospace 555 Industrial Drive South Madison MS 29183329 The Boeing Company-St. Charles 2600 North 3rd Street St. Charles MO 29189332 The Boeing Company - St. Louis Airport and McDonnell Blvd. St. Louis MO 29189513 GKN Aerospace - St. Louis 142 James S. McDonnell Boulevard Hazelwood MO 33011652 PGM of New England LLC 5 Perimeter Road Manchester NH

36103518 EDO Corporation Antenna Products & Technologies 585 Johnson Ave Bohemia NY 36103521 EDO Marine and Aircraft Systems 1250-1500 New Horizon Blvd North Amityville NY 36111630 Ametek Rotron, Inc. 55 Hasbrouck Lane Woodstock NY

370492008 Dept. of the Navy, Fleet Readiness Center East A Street - Marine Corps Air Station Cherry Point NC 37049483 Marine Corps Air Station - Cherry Point NC Highway 101 at US 70 West Cherry Point NC

37051340 Headquarters XVIII Airborne Corps and Fort Bragg 2175 Reilly Road, Stop A Fort Bragg NC 37119600 US Airways, Inc. - CLT (Charlotte, NC) 5020 Hangar Road Charlotte NC 37133455 US Marine Corps Base Camp Lejeune 1 Camp Lejeune Camp Lejeune NC 39017343 CTL Aerospace, Inc. 5616 Spellmire Drive Cincinnati OH 39021672 Honeywell Aerospace 550 State Route 55 Urbana OH 39057430 Wright-Patterson AFB 1450 Littrell Road Dayton OH 39061524 GE Aviation, Evendale One Neumann Way Cincinnati OH 39109419 Goodrich 101 Waco St Troy OH

391335027 SAINT-GOBAIN PERFORMANCE PLASTICS CORPORATION 335 N. DIAMOND STREET RAVENNA OH 39151479 PCC Airfoils, LLC 3860 Union Ave. S.E. Minerva OH 401092026 Tinker Air Force Base, OK 72 ABW CEAN, 7535 5th Street, BLDG 400 Tinker AFB OK 401432003 Bizjet International 1925 N. Sheridan Tulsa OK 401432013 Nordam I&S and NTRSD Divisions 6910 and 6911 N. Whirlpool Dr. Tulsa OK 401432014 Quality Plating Co. of Tulsa 2665 N. Darlington Tulsa OK

401432021 American Airlines Maintenance and Engineering Center 3800 N MINGO RD TULSA OK 40143460 NORDAM Repair Division 11200 East Pine Tulsa OK 40143494 Spirit AeroSystems 3330 North Mingo Road Tulsa OK 40143523 Southwest United Industries, Inc. 422 S. St. Louis Ave. Tulsa OK
3 of 5Facility NEI ID Facility Name Address City State Table 1 - Facility Identification Information

42003603 US Airways Pittsburgh International Airport 500 Tower Road Coraopolis PA 42029689 Sikorsky Global Helicopters 110 E. Stewart Huston Drive Coatesville PA

42045473 The Boeing Company, Defense, Space & Security, Rotorcraft Programs, Philadelphia Stewart Avenue and Route 291 Ridley Township PA 42055233 Letterkenny Army Depot 1 Overcash Avenue Chambersburg PA 42081660 Avco Corporation d/b/a Lycoming Engines 652 Oliver Street Williamsport PA 45045417 Lockheed Martin Aeronautics Company 244 Terminal Road Greenville SC 45063428 Eagle Aviation Inc 2700 Aviation Way West Columbia SC 4703758 Triumph Aerostructures, LLC 1431 Vultee Blvd Nashville TN

48027213 Department of the Army, U.S. Army Garrison-Fort Hood 4622 Engineer Dr. Fort Hood TX 48029468 The Boeing Company - San Antonnio 375 Airlift Drive 2nd Floor/C43 San Antonio TX 480295025 Randolph Air Force Base 1 Washington Circle Randolph AFB TX 481136003 Dallas Love Field P.O. Box 7415 Dallas TX 481215032 American Airlines - AFW 2000 Eagle Parkway Fort Worth TX 482015019 NASA Ellington Field 12400 Brantly Ave. Houston TX 482015020 NASA Lyndon B. Johnson Space Center 2101 NASA Pkwy Houston TX 48231376 L-3 Communications Intergrated Systems MID 10001 Jack Finney Blvd Greenville TX 483553000 US Army Corpus Christi Army Depot 308 Crecy St. Corpus Christi TX 483672020 WEATHERFORD AEROSPACE INC. 610 W. THIRD ST. WEATHERFORD TX 484392009 Bell Helicopter Textron Plant 1 600 East Hurst BLVD Ft. Worth TX 484392010 Bell Helicopter Textron Plant 5 1700 N Highway 360 Grand Prairie TX

48439416 Lockheed Martin Corporation d.b.a. Lockheed Martin Aeronautics Company 1 Lockheed Boulevard Fort Worth TX 48439508 Goodrich Interiors Fort Worth 1201 Forum Way South Fort Worth TX 49003696 ATK Launch Systems, Promontory 9160 North Highway 83 Promontory UT 490115010 Alliant Techsystems Aerospace Structures Building C-14 Clearfield Freeport Center Cleafield UT 490115012 ATK Launch Systems, Promontory Buildings H-7, H-5, G-12, G-13 Freeport Center Clearfield UT 49011663 Hill Air Force Base 75 CEG/CEV Hill Air Force Base UT 490355013 ATK Launch Systems, Bacchus 5000 South 8400 West West Valley City UT

516505023 LANGLEY AIR FORCE BASE JOINT BASE LANGLEY-EUSTIS 37 SWEENEY BOULEVARD LANGLEY AFB VA 51700334 Joint Base Langley-Eustis 1407 Washington Blvd. Fort Eustis VA 51710293 Naval Station Norfolk, Norfolk Virginina Naval Station Norfolk Norfolk VA 51810292 Naval Air Station Oceana, Virginia Beach, VA 1750 Tomcat Blvd Virginia Beach VA 53029438 Naval Air Station Whidbey Island (Ault Field) 1115 W. Lexington Street (building 103) Oak Harbor WA 53033383 Boeing Commercial Airplane Group - Renton 800 Logan Ave. N Renton WA

53033384 Boeing Commercial Airplane Group-North Boeing Field 7700 E. Marginal Way Seattle WA 53033406 Hexcel Corporation 19819 84th Avenue South Kent WA 53033532 Boeing Auburn 700 15th St. SW Auburn WA
4 of 5Facility NEI ID Facility Name Address City State Table 1 - Facility Identification Information

53053447 Boeing Commercial Airplanes Fabrication Division 18001 Canyon Rd Puyallup WA 53061398 Boeing Everett 3003 West Casino Road Everett WA 530615014 AVIATION TECHNICAL SERVICES, INC. 3121 109TH ST SW EVERETT WA 54033246 Pratt and Whitney Engine Services 1525 Midway Park Drive Bridgeport WV 540572002 Allegany Ballistics Laboratory 210 State Route 956 Rocket Center WV 54067418 BE Aerospace / SMR Technologies 93 Nettie Fenwick Road Fenwick WV 55075288 Goodrich Cabin Seating Systems 701 Maple Street Peshtigo WI 55079687 Derco Repair Services 8065 West Fairlane Avenue Milwaukee WI 550876001 Gulfstream Aerospace Corporation W6365 Discovery Drive Appleton WI

5 of 5Cancer MIR Cancer

Incidence Noncancer

Max HI Target

Organ Cancer

MIR Noncancer

Max HI Target

Organ 01015223 2.10E-12 3.92E-10 2.46E-07 respiratory 3.50E-06 4.00E-01 neurological 0% 01045482 5.55E-10 3.39E-08 1.49E-02 neurological 1.18E-06 4.32E-02 kidney 0% 01073446 4.00E-10 1.05E-07 5.62E-05 neurological 7.01E-09 1.51E-04 kidney 6% 01083198 1.51E-09 1.42E-07 6.61E-05 respiratory 1.51E-09 6.61E-05 respiratory 100% 2017365 4.25E-07 3.87E-05 7.60E-03 reproductive 5.70E-07 7.60E-03 reproductive 75% 02090348 7.08E-11 2.61E-09 1.95E-04 respiratory 7.45E-08 9.35E-02 developmental 0% 02090529 5.17E-07 1.87E-05 4.47E-04 respiratory 5.17E-07 4.48E-04 respiratory 100% 02150683 1.17E-07 1.51E-07 9.70E-05 respiratory 1.17E-07 4.86E-03 neurological 100% 04013510 0.00E+00 0.00E+00 5.82E-04 neurological 8.70E-06 2.49E-02 respiratory 0% 04013602 5.41E-09 3.64E-06 1.54E-05 neurological 5.41E-09 1.54E-05 neurological 100% 04019300 1.49E-07 4.30E-05 3.47E-04 neurological 1.49E-07 3.47E-04 neurological 100% 04019433 2.10E-07 9.98E-05 2.62E-02 respiratory 2.04E-07 2.62E-02 respiratory 100% 4703758 2.09E-06 5.43E-04 1.10E-02 respiratory 2.09E-06 1.11E-02 respiratory 100% 05051409 1.07E-05 4.29E-04 4.47E-02 respiratory 1.23E-05 4.59E-02 respiratory 87% 05113377 1.69E-07 8.05E-07 1.49E-01 respiratory 1.78E-07 1.56E-01 respiratory 95% 05143331 3.11E-10 1.62E-08 1.69E-05 neurological 3.24E-10 1.76E-05 neurological 96% 06029545 6.30E-09 6.78E-07 6.63E-06 respiratory 1.09E-08 9.07E-05 kidney 58% 06037429 2.51E-08 4.69E-05 3.17E-04 respiratory 3.31E-08 9.51E-04 respiratory 76% 06037490 1.97E-08 7.08E-06 1.79E-04 neurological 5.94E-08 3.02E-04 respiratory 33% 06059538 2.87E-08 1.49E-05 2.76E-03 neurological 1.29E-07 6.31E-03 neurological 22% 06065520 2.77E-07 4.19E-05 8.04E-03 respiratory 2.87E-07 1.24E-02 respiratory 96% 06071540 3.05E-07 3.79E-06 2.98E-02 neurological 3.05E-07 2.98E-02 neurological 100% 06073306 1.86E-09 1.16E-07 2.95E-03 respiratory 1.14E-06 2.40E-02 respiratory 0% 09001684 0.00E+00 0.00E+00 5.81E-07 developmental 0.00E+00 5.37E-07 developmental

- 09001688 0.00E+00 0.00E+00 7.30E-07 neurological 0.00E+00 8.55E-07 neurological

- 09001693 1.84E-07 7.03E-05 9.18E-03 respiratory 6.18E-06 4.00E-02 respiratory 3% 09003626 3.65E-08 2.38E-06 3.74E-03 respiratory 3.28E-07 3.78E-03 respiratory 11% 10003349 5.56E-08 1.12E-05 8.92E-05 neurological 3.13E-07 3.24E-04 respiratory 18% 010515017 6.48E-09 3.50E-07 7.65E-03 respiratory 6.48E-09 7.65E-03 respiratory 100% 010892023 9.35E-08 3.98E-05 3.59E-03 liver 1.53E-06 6.88E-03 kidney 6% 12005313 5.22E-09 9.91E-07 3.70E-03 respiratory 5.23E-09 3.70E-03 respiratory 100% 12009358 1.55E-09 9.77E-08 3.00E-05 respiratory 7.65E-09 3.00E-05 respiratory 20% 12023284 3.15E-07 4.12E-06 5.12E-03 respiratory 3.10E-07 5.12E-03 respiratory 100% 12031634 2.25E-07 3.00E-05 1.60E-03 respiratory 2.26E-07 1.60E-03 respiratory 100% 12061380 7.06E-07 4.67E-05 4.70E-03 respiratory 7.06E-07 4.70E-03 respiratory 100% 12083270 2.36E-10 1.41E-08 8.20E-07 respiratory 2.75E-10 9.50E-04 neurological 86% 12099695 0.00E+00 0.00E+00 4.42E-10 developmental 4.24E-10 9.44E-06 kidney 0% 13009224 1.12E-07 1.49E-05 2.44E-02 respiratory 1.15E-07 2.54E-02 respiratory 97% 13063388 5.32E-07 3.56E-04 1.28E-02 respiratory 8.61E-07 1.68E-02 kidney 62% 13067506 1.07E-06 9.57E-04 5.22E-03 respiratory 1.09E-06 5.27E-03 respiratory 99% 13153431 3.47E-06 3.32E-04 2.06E-01 respiratory 3.71E-06 2.12E-01 respiratory 93% 15003346 1.31E-08 1.62E-06 2.65E-04 respiratory 1.31E-08 2.65E-04 respiratory 100% 18097487 2.21E-08 5.06E-06 2.85E-04 liver 1.16E-07 2.05E-03 respiratory 19% 19163356 1.30E-07 1.01E-05 1.20E-03 respiratory 1.30E-07 1.20E-03 respiratory 100% 20035486 3.79E-08 1.34E-06 8.02E-02 respiratory 9.78E-07 8.50E-02 respiratory 4% 20173193 1.34E-08 4.50E-07 1.07E-05 respiratory 1.34E-08 3.42E-05 skeletal 100% 20173453 1.03E-07 7.74E-06 2.57E-03 respiratory 6.29E-07 6.37E-03 respiratory 16% 24005458 5.14E-06 1.64E-03 1.43E-02 reproductive 5.31E-06 1.48E-02 reproductive 97% 24015282 1.97E-07 1.32E-05 1.44E-03 neurological 2.00E-07 1.77E-02 respiratory 99% 24017525 2.83E-10 4.05E-07 4.03E-06 neurological 2.22E-08 7.45E-03 respiratory 1% 25009450 1.50E-10 3.02E-08 1.89E-05 neurological 3.53E-09 5.25E-05 kidney 4% 25009477 1.73E-11 1.84E-09 2.51E-04 reproductive 3.82E-08 1.01E-03 kidney 0% 26065644 2.37E-08 5.80E-06 3.93E-03 respiratory 2.37E-08 3.93E-03 respiratory 100% 27053389 1.69E-08 1.79E-05 2.39E-03 respiratory 4.12E-08 2.42E-03 respiratory 41% 28089367 7.71E-08 1.90E-06 1.41E-04 reproductive 7.71E-08 1.41E-04 reproductive 100% 29183329 1.12E-07 5.68E-06 1.75E-03 respiratory 1.26E-07 1.85E-03 respiratory 89% 29189332 1.65E-07 4.77E-05 3.37E-02 respiratory 2.23E-06 3.44E-02 respiratory 7% 29189513 3.91E-07 1.78E-04 1.23E-02 respiratory 7.07E-07 1.24E-02 respiratory 55% 33011652 2.94E-09 9.45E-07 1.99E-04 respiratory 2.94E-09 1.99E-04 respiratory 100% 36103518 0.00E+00 0.00E+00 4.15E-03 neurological 0.00E+00 4.15E-03 neurological

- 36103521 3.45E-09 6.61E-07 1.08E-04 neurological 3.57E-09 1.19E-04 neurological 97% 36111630 6.00E-08 5.39E-07 5.63E-03 neurological 6.00E-08 5.63E-03 neurological 100% 37049483 6.67E-10 4.79E-08 1.37E-04 respiratory 5.77E-06 9.22E-02 developmental 0% 37051340 5.58E-09 6.43E-07 1.01E-03 respiratory 1.24E-06 9.74E-03 respiratory 0% Table 2a – Maximum Predicted HEM-3 Chronic Risks

Actual Emissions

Facility NEI ID Category Chronic Risk 1 Facility Chronic Risk 1 SC % of Facility-wide Cancer Risk

1 of 3Cancer MIR Cancer

Incidence Noncancer

Max HI Target

Organ Cancer

MIR Noncancer

Max HI Target

Organ Table 2a – Maximum Predicted HEM-3 Chronic Risks

Actual Emissions

Facility NEI ID Category Chronic Risk 1 Facility Chronic Risk 1 SC % of Facility-wide Cancer Risk 37119600 9.99E-09 5.83E-06 2.79E-04 respiratory 1.02E-08 2.81E-04 respiratory 98% 37133455 5.44E-09 3.52E-07 2.63E-04 respiratory 1.12E-06 1.28E-01 respiratory 0% 39017343 1.53E-07 3.35E-05 1.30E-04 respiratory 1.53E-07 1.30E-04 respiratory 100% 39021672 4.71E-07 3.04E-05 1.02E-01 respiratory 4.78E-07 1.03E-01 respiratory 98% 39057430 2.08E-10 2.28E-08 5.09E-05 respiratory 8.55E-08 4.49E-03 developmental 0% 39061524 3.10E-06 8.06E-04 4.14E-02 respiratory 3.11E-06 4.14E-02 respiratory 100% 39109419 9.17E-08 1.02E-06 6.00E-02 respiratory 8.83E-07 6.03E-02 respiratory 10% 39151479 5.44E-06 2.18E-04 1.07E-01 respiratory 5.44E-06 1.07E-01 respiratory 100% 40143460 2.21E-06 2.30E-04 8.11E-03 respiratory 2.21E-06 8.11E-03 respiratory 100% 40143494 9.09E-08 4.20E-05 6.55E-03 respiratory 9.24E-08 6.56E-03 respiratory 98% 40143523 9.49E-06 3.18E-04 4.58E-01 kidney 1.16E-05 4.58E-01 respiratory 82% 42003603 1.05E-07 1.14E-05 2.77E-03 respiratory 1.07E-07 2.78E-03 respiratory 98% 42029689 0.00E+00 0.00E+00 2.32E-06 developmental 0.00E+00 2.59E-06 developmental

- 42045473 6.69E-08 4.23E-05 5.26E-03 respiratory 2.96E-07 6.11E-03 respiratory 23% 42055233 3.25E-11 1.15E-08 9.49E-07 neurological 4.12E-08 3.50E-04 kidney 0% 42081660 2.05E-06 1.51E-04 7.99E-02 kidney 2.36E-06 3.95E-01 neurological 87% 45045417 2.26E-08 1.02E-05 4.70E-04 respiratory 2.91E-08 5.41E-04 respiratory 78% 45063428 1.07E-07 3.96E-06 2.38E-03 liver 1.32E-08 2.38E-03 liver 100% 48027213 1.64E-10 4.12E-08 3.42E-04 respiratory 2.98E-07 3.77E-03 kidney 0% 48029468 3.67E-06 3.45E-04 1.07E-01 respiratory 3.56E-06 1.04E-01 respiratory 100% 48231376 9.07E-08 1.18E-05 5.51E-04 neurological 9.26E-08 6.27E-04 neurological 98% 48439416 2.90E-07 1.84E-05 1.55E-01 respiratory 3.07E-06 1.57E-01 respiratory 9% 48439508 2.16E-11 2.07E-08 1.97E-06 reproductive 2.16E-11 1.97E-06 reproductive 100% 49003696 7.05E-08 8.21E-07 8.24E-03 respiratory 8.51E-08 8.24E-03 respiratory 83% 49011663 6.56E-06 7.58E-04 9.20E-03 respiratory 6.68E-06 1.63E-02 respiratory 98% 050272001 2.35E-07 1.53E-06 3.75E-03 neurological 2.84E-07 3.81E-03 neurological 83% 51700334 7.29E-11 4.41E-08 6.07E-08 respiratory 1.67E-07 3.35E-03 kidney 0% 51710293 8.47E-06 2.54E-03 2.86E-02 respiratory 8.48E-06 2.86E-02 respiratory 100% 51810292 1.87E-06 4.41E-04 4.89E-02 respiratory 1.87E-06 4.89E-02 respiratory 100% 53029438 9.78E-07 4.63E-05 9.62E-04 respiratory 1.05E-06 1.46E-03 respiratory 93% 53033383 2.37E-06 7.21E-04 1.30E-02 reproductive 2.38E-06 1.30E-02 reproductive 100% 53033384 2.67E-06 3.76E-04 2.60E-02 respiratory 2.65E-06 2.59E-02 respiratory 100% 53033406 8.75E-08 1.17E-05 2.17E-04 neurological 8.85E-08 7.42E-03 respiratory 99% 53033532 4.11E-07 1.87E-04 1.30E-02 respiratory 8.07E-07 1.36E-02 respiratory 51% 53053447 5.30E-07 1.83E-04 1.63E-03 respiratory 5.40E-07 1.71E-03 respiratory 98% 53061398 6.03E-07 1.05E-03 1.01E-02 respiratory 6.16E-07 1.03E-02 respiratory 98% 54033246 2.27E-10 3.96E-08 1.89E-07 respiratory 2.30E-10 4.96E-06 neurological 99% 54067418 1.21E-08 1.45E-08 4.34E-04 neurological 3.49E-08 5.52E-04 kidney 35% 55075288 4.95E-09 1.09E-07 4.20E-05 neurological 3.26E-08 2.97E-04 kidney 15% 55079687 0.00E+00 0.00E+00 5.55E-09 neurological 0.00E+00 5.46E-09 neurological

- 060292024 4.27E-11 6.75E-09 1.96E-07 reproductive 3.28E-06 1.32E-02 kidney 0% 060372022 8.36E-08 9.93E-06 2.40E-03 respiratory 2.96E-07 3.60E-02 respiratory 28% 060376004 6.59E-08 1.15E-04 5.52E-04 neurological 7.51E-08 5.40E-04 neurological 88% 060812015 2.65E-08 1.34E-05 1.08E-02 respiratory 4.02E-07 1.54E-02 respiratory 7% 090032025 7.42E-11 3.75E-09 2.21E-06 neurological 7.42E-11 2.21E-06 neurological 100% 090075021 1.36E-07 1.83E-06 1.02E-02 neurological 1.67E-07 1.02E-02 neurological 81% 120862016 1.02E-07 9.40E-05 2.38E-04 neurological 2.89E-07 2.38E-04 respiratory 35% 130516005 9.79E-07 7.27E-05 1.22E-02 respiratory 9.80E-07 1.22E-02 respiratory 100% 131276002 2.47E-08 8.77E-07 1.23E-04 reproductive 2.47E-08 1.23E-04 reproductive 100% 132332017 2.32E-06 5.00E-05 5.97E-02 neurological 2.32E-06 5.97E-02 neurological 100% 181035004 1.88E-08 1.60E-06 9.61E-03 respiratory 1.62E-06 9.30E-03 respiratory 1% 181412011 1.90E-07 2.18E-05 1.58E-04 respiratory 2.68E-07 1.20E-03 kidney 71% 201252005 1.70E-08 5.06E-07 5.28E-04 respiratory 1.71E-08 5.31E-04 respiratory 99% 201732006 3.12E-08 1.03E-05 3.86E-02 neurological 5.17E-08 3.91E-02 neurological 60% 201732007 2.54E-06 3.93E-04 4.90E-02 respiratory 2.56E-06 4.90E-02 respiratory 99% 201732027 1.42E-05 5.25E-03 1.09E-01 neurological 1.49E-05 1.13E-01 neurological 96% 280475024 5.60E-10 1.30E-07 5.17E-05 reproductive 9.36E-06 7.80E-03 respiratory 0% 370492008 7.20E-06 4.92E-04 3.09E-02 respiratory 1.82E-05 1.28E-01 developmental 40% 391335027 1.46E-08 6.18E-07 1.36E-03 neurological 1.46E-08 1.36E-03 neurological 100% 401092026 1.54E-06 4.96E-04 8.44E-02 respiratory 1.54E-06 8.44E-02 respiratory 100% 401432003 5.82E-09 2.16E-06 5.40E-04 respiratory 5.82E-09 5.40E-04 respiratory 100% 401432013 1.19E-06 6.15E-05 8.73E-02 respiratory 1.19E-06 8.73E-02 respiratory 100% 401432014 5.42E-06 1.87E-04 3.35E-01 respiratory 5.44E-06 3.36E-01 respiratory 100% 401432021 2.80E-06 5.36E-04 2.27E-01 respiratory 3.00E-06 2.27E-01 respiratory 93%

2 of 3Cancer MIR Cancer

Incidence Noncancer

Max HI Target

Organ Cancer

MIR Noncancer

Max HI Target

Organ Table 2a – Maximum Predicted HEM-3 Chronic Risks

Actual Emissions

Facility NEI ID Category Chronic Risk 1 Facility Chronic Risk 1 SC % of Facility-wide Cancer Risk 480295025 1.02E-08 7.74E-07 1.69E-01 respiratory 1.05E-07 3.31E-01 respiratory 10% 481136003 8.30E-08 5.78E-05 5.01E-04 respiratory 1.11E-07 5.40E-04 respiratory 75% 481215032 2.17E-08 1.94E-05 1.76E-02 respiratory 2.17E-08 1.76E-02 respiratory 100% 482015019 5.68E-09 2.53E-06 8.60E-04 respiratory 5.95E-09 8.71E-04 respiratory 95% 482015020 1.23E-09 8.96E-07 3.31E-06 respiratory 2.03E-08 2.05E-04 kidney 6% 483553000 3.28E-07 4.33E-05 2.47E-02 respiratory 1.99E-06 4.39E-01 respiratory 16% 483672020 3.09E-07 1.19E-05 2.97E-02 neurological 3.09E-07 2.97E-02 neurological 100% 484392009 1.13E-06 1.01E-04 1.45E-01 respiratory 1.16E-06 1.42E-01 respiratory 97% 484392010 1.79E-10 4.98E-08 1.78E-05 reproductive 1.80E-09 1.62E-04 neurological 10% 490115010 3.71E-07 6.36E-05 3.07E-04 respiratory 3.41E-07 2.89E-04 respiratory 100% 490115012 2.07E-08 4.44E-06 1.66E-03 respiratory 7.42E-07 6.80E-03 respiratory 3% 490355013 1.66E-07 6.15E-05 2.99E-03 respiratory 1.66E-07 2.99E-03 respiratory 100% 516505023 9.43E-09 1.87E-06 4.81E-04 respiratory 3.72E-07 7.74E-03 kidney 3% 530615014 5.83E-11 2.98E-08 2.12E-03 reproductive 7.17E-12 2.12E-03 reproductive 100% 540572002 4.00E-07 9.54E-06 3.78E-03 respiratory 2.10E-06 5.44E-02 respiratory 19% 550876001 7.05E-07 8.68E-05 3.67E-03 liver 7.17E-07 3.64E-03 liver 98%

1 BOLD indicates a cancer risk great than 1 in a million or a noncancer risk greater than 1

3 of 3Cancer MIR Cancer

Incidence Noncancer

Max HI Target

Organ 01015223 2.11E-12 3.94E-10 2.46E-07 respiratory 01045482 5.57E-10 3.40E-08 1.49E-02 neurological 01073446 4.08E-10 1.07E-07 5.73E-05 neurological 01083198 1.54E-09 1.45E-07 6.74E-05 respiratory 2017365 4.33E-07 3.94E-05 7.75E-03 reproductive 02090348 7.22E-11 2.66E-09 1.97E-04 respiratory 02090529 5.20E-07 1.88E-05 4.50E-04 respiratory 02150683 1.20E-07 1.54E-07 9.89E-05 respiratory 04013510 0.00E+00 0.00E+00 5.94E-04 neurological 04013602 5.51E-09 3.71E-06 1.57E-05 neurological 04019300 1.52E-07 4.39E-05 3.54E-04 neurological 04019433 2.08E-07 9.91E-05 2.67E-02 respiratory 4703758 2.09E-06 5.43E-04 1.10E-02 respiratory 05051409 1.09E-05 4.37E-04 4.51E-02 respiratory 05113377 1.51E-07 6.15E-07 1.52E-01 respiratory 05143331 3.11E-10 1.62E-08 1.70E-05 neurological 06029545 6.43E-09 6.92E-07 6.74E-06 respiratory 06037429 2.55E-08 4.77E-05 3.22E-04 respiratory 06037490 1.97E-08 7.08E-06 1.80E-04 neurological 06059538 2.93E-08 1.52E-05 2.82E-03 neurological 06065520 2.80E-07 4.23E-05 8.20E-03 respiratory 06071540 3.11E-07 3.86E-06 3.04E-02 neurological 06073306 1.75E-09 1.03E-07 2.98E-03 respiratory 09001684 0.00E+00 0.00E+00 5.92E-07 developmental 09001688 0.00E+00 0.00E+00 7.45E-07 neurological 09001693 1.84E-07 7.04E-05 9.29E-03 respiratory 09003626 3.67E-08 2.37E-06 3.76E-03 respiratory 10003349 5.67E-08 1.14E-05 9.10E-05 neurological 010515017 6.48E-09 3.46E-07 7.65E-03 respiratory 010892023 4.94E-08 2.08E-05 3.66E-03 liver 12005313 5.32E-09 1.01E-06 3.77E-03 respiratory 12009358 1.57E-09 9.89E-08 3.04E-05 respiratory 12023284 3.16E-07 4.04E-06 5.22E-03 respiratory 12031634 2.29E-07 3.06E-05 1.63E-03 respiratory 12061380 7.20E-07 4.76E-05 4.80E-03 respiratory 12083270 2.39E-10 1.43E-08 8.25E-07 respiratory 12099695 0.00E+00 0.00E+00 4.51E-10 developmental 13009224 1.13E-07 1.51E-05 2.49E-02 respiratory 13063388 5.42E-07 3.63E-04 1.30E-02 respiratory 13067506 1.09E-06 9.74E-04 5.25E-03 respiratory 13153431 3.53E-06 3.36E-04 2.10E-01 respiratory 15003346 1.34E-08 1.65E-06 2.70E-04 respiratory 18097487 1.45E-08 2.77E-06 2.91E-04 liver 19163356 1.33E-07 1.03E-05 1.22E-03 respiratory 20035486 3.79E-08 1.34E-06 8.02E-02 respiratory 20173193 1.37E-08 4.57E-07 1.09E-05 respiratory 20173453 1.05E-07 7.89E-06 2.62E-03 respiratory 24005458 5.14E-06 1.64E-03 1.43E-02 reproductive 24015282 1.98E-07 1.33E-05 1.47E-03 neurological 24017525 2.89E-10 4.13E-07 4.11E-06 neurological 25009450 1.38E-10 2.50E-08 1.89E-05 neurological 25009477 1.73E-11 1.84E-09 2.54E-04 reproductive 26065644 2.37E-08 5.80E-06 3.93E-03 respiratory 27053389 1.72E-08 1.82E-05 2.43E-03 respiratory 28089367 7.86E-08 1.94E-06 1.44E-04 reproductive 29183329 1.14E-07 5.79E-06 1.76E-03 respiratory 29189332 1.68E-07 4.83E-05 3.38E-02 respiratory 29189513 3.98E-07 1.81E-04 1.23E-02 respiratory 33011652 3.00E-09 9.62E-07 2.03E-04 respiratory 36103518 0.00E+00 0.00E+00 4.24E-03 neurological 36103521 3.52E-09 6.74E-07 1.11E-04 neurological 36111630 6.00E-08 5.39E-07 5.63E-03 neurological 37049483 6.80E-10 4.88E-08 1.40E-04 respiratory 37051340 5.69E-09 6.55E-07 1.03E-03 respiratory Facility NEI ID Category Chronic Risk 1 Table 2b – Maximum Predicted HEM-3 Chronic Risks

Allowable Emissions

1 of 3Cancer MIR Cancer

Incidence Noncancer

Max HI Target

Organ Facility NEI ID Category Chronic Risk 1 Table 2b – Maximum Predicted HEM-3 Chronic Risks

Allowable Emissions

37119600 1.02E-08 5.94E-06 2.79E-04 respiratory 37133455 5.55E-09 3.59E-07 2.68E-04 respiratory 39017343 1.56E-07 3.42E-05 1.32E-04 respiratory 39021672 4.79E-07 3.09E-05 1.04E-01 respiratory 39057430 1.99E-10 2.19E-08 5.19E-05 respiratory 39061524 3.11E-06 8.09E-04 4.14E-02 respiratory 39109419 9.36E-08 1.04E-06 6.12E-02 respiratory 39151479 5.55E-06 2.22E-04 1.09E-01 respiratory 40143460 2.24E-06 2.34E-04 8.23E-03 respiratory 40143494 9.12E-08 4.21E-05 6.65E-03 respiratory 40143523 9.66E-06 3.24E-04 4.67E-01 kidney 42003603 1.06E-07 1.15E-05 2.79E-03 respiratory 42029689 0.00E+00 0.00E+00 2.36E-06 developmental 42045473 6.49E-08 4.12E-05 5.26E-03 respiratory 42055233 3.31E-11 1.18E-08 9.66E-07 neurological 42081660 2.09E-06 1.54E-04 8.15E-02 kidney 45045417 2.31E-08 1.04E-05 4.70E-04 respiratory 45063428 1.34E-08 5.22E-07 2.43E-03 liver 48027213 1.67E-10 4.16E-08 3.42E-04 respiratory 48029468 3.67E-06 3.45E-04 1.07E-01 respiratory 48231376 9.25E-08 1.20E-05 5.52E-04 neurological 48439416 2.92E-07 1.85E-05 1.57E-01 respiratory 48439508 2.17E-11 2.08E-08 1.97E-06 reproductive 49003696 7.16E-08 8.30E-07 8.24E-03 respiratory 49011663 6.56E-06 7.57E-04 9.24E-03 respiratory 050272001 2.40E-07 1.56E-06 3.83E-03 neurological 51700334 7.43E-11 4.39E-08 6.19E-08 respiratory 51710293 8.47E-06 2.54E-03 2.88E-02 respiratory 51810292 1.86E-06 4.40E-04 4.99E-02 respiratory 53029438 9.66E-07 4.56E-05 9.66E-04 respiratory 53033383 2.39E-06 7.27E-04 1.32E-02 reproductive 53033384 2.72E-06 3.83E-04 2.65E-02 respiratory 53033406 8.91E-08 1.20E-05 2.19E-04 neurological 53033532 4.18E-07 1.90E-04 1.33E-02 respiratory 53053447 5.30E-07 1.83E-04 1.63E-03 respiratory 53061398 6.06E-07 1.07E-03 1.03E-02 respiratory 54033246 2.32E-10 4.04E-08 1.93E-07 respiratory 54067418 1.21E-08 1.45E-08 4.38E-04 neurological 55075288 5.05E-09 1.11E-07 4.29E-05 neurological 55079687 0.00E+00 0.00E+00 5.66E-09 neurological 060292024 4.32E-11 6.83E-09 2.00E-07 reproductive 060372022 8.37E-08 9.94E-06 2.40E-03 respiratory 060376004 6.72E-08 1.17E-04 5.60E-04 neurological 060812015 2.66E-08 1.35E-05 1.09E-02 respiratory 090032025 7.42E-11 3.75E-09 2.24E-06 neurological 090075021 1.39E-07 1.87E-06 1.04E-02 neurological 120862016 1.04E-07 9.59E-05 2.42E-04 neurological 130516005 9.98E-07 7.41E-05 1.22E-02 respiratory 131276002 2.52E-08 8.92E-07 1.23E-04 reproductive 132332017 2.32E-06 5.00E-05 5.98E-02 neurological 181035004 1.91E-08 1.64E-06 9.80E-03 respiratory 181412011 1.94E-07 2.22E-05 1.61E-04 respiratory 201252005 1.70E-08 5.06E-07 5.28E-04 respiratory 201732006 3.10E-08 1.02E-05 3.86E-02 neurological 201732007 2.53E-06 3.92E-04 4.90E-02 respiratory 201732027 1.45E-05 5.35E-03 1.12E-01 neurological 280475024 5.72E-10 1.33E-07 5.28E-05 reproductive 370492008 7.35E-06 5.01E-04 3.11E-02 respiratory 391335027 1.47E-08 6.19E-07 1.36E-03 neurological 401092026 1.56E-06 5.02E-04 8.60E-02 respiratory 401432003 5.82E-09 2.16E-06 5.40E-04 respiratory 401432013 1.19E-06 6.18E-05 8.75E-02 respiratory 401432014 5.46E-06 1.88E-04 3.35E-01 respiratory 401432021 2.82E-06 5.40E-04 2.30E-01 respiratory

2 of 3Cancer MIR Cancer

Incidence Noncancer

Max HI Target

Organ Facility NEI ID Category Chronic Risk 1 Table 2b – Maximum Predicted HEM-3 Chronic Risks

Allowable Emissions

480295025 1.00E-08 7.42E-07 1.73E-01 respiratory 481136003 7.67E-08 5.31E-05 5.11E-04 respiratory 481215032 2.20E-08 1.97E-05 1.77E-02 respiratory 482015019 5.14E-09 2.32E-06 8.62E-04 respiratory 482015020 1.11E-09 8.41E-07 3.37E-06 respiratory 483553000 3.27E-07 4.31E-05 2.47E-02 respiratory 483672020 3.15E-07 1.21E-05 3.03E-02 neurological 484392009 1.13E-06 1.02E-04 1.45E-01 respiratory 484392010 1.18E-10 3.76E-08 1.78E-05 reproductive 490115010 3.78E-07 6.48E-05 3.13E-04 respiratory 490115012 2.07E-08 4.44E-06 1.66E-03 respiratory 490355013 1.66E-07 6.15E-05 2.99E-03 respiratory 516505023 9.44E-09 1.87E-06 4.81E-04 respiratory 530615014 7.31E-12 3.74E-09 2.16E-03 reproductive 540572002 4.00E-07 9.54E-06 3.78E-03 respiratory 550876001 6.55E-07 8.19E-05 3.74E-03 liver

1 BOLD indicates a cancer risk great than 1 in a million or a noncancer risk greater than 1

3 of 3REL AEGL1 AEGL2 ERPG1 ERPG2 01015223 Formaldehyde 2.65E-05 1.32E-06 8.57E-08 1.21E-06 1.21E-07 01015223 Xylenes (mixed) 3.04E-06 1.20E-07 1.67E-08 0.00E+00 0.00E+00 01015223 Toluene 8.15E-07 4.02E-08 6.70E-09 1.59E-07 2.74E-08 01015223 Ethyl benzene 0.00E+00 5.61E-08 1.64E-09 0.00E+00 0.00E+00 01045482 Xylenes (mixed) 7.63E-06 3.00E-07 4.20E-08 0.00E+00 0.00E+00 01045482 Toluene 2.22E-06 1.09E-07 1.82E-08 4.32E-07 7.46E-08 01045482 Ethyl benzene 0.00E+00 3.48E-07 1.02E-08 0.00E+00 0.00E+00 01045482 Cumene 0.00E+00 9.67E-09 1.61E-09 0.00E+00 0.00E+00 01073446 Toluene 2.63E-04 1.30E-05 2.16E-06 5.12E-05 8.84E-06 01073446 Xylenes (mixed) 3.03E-05 1.19E-06 1.67E-07 0.00E+00 0.00E+00 01073446 Ethyl benzene 0.00E+00 2.13E-07 6.22E-09 0.00E+00 0.00E+00 01083198 2,4-Toluene diisocyanate 0.00E+00 3.34E-05 7.93E-06 6.59E-05 4.25E-06 01083198 Xylenes (mixed) 4.63E-06 1.82E-07 2.55E-08 0.00E+00 0.00E+00 01083198 Triethylamine 2.89E-06 0.00E+00 0.00E+00 0.00E+00 0.00E+00 01083198 Ethyl benzene 0.00E+00 7.12E-07 2.08E-08 0.00E+00 0.00E+00 01083198 Toluene 4.60E-07 2.27E-08 3.78E-09 8.95E-08 1.55E-08 01083198 Cumene 0.00E+00 2.15E-10 3.58E-11 0.00E+00 0.00E+00 2017365 Propyl cellosolve 7.14E-02 0.00E+00 0.00E+00 0.00E+00 0.00E+00 2017365 2-Butoxyethyl acetate 1.05E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00 2017365 Formaldehyde 2.43E-04 1.21E-05 7.85E-07 1.11E-05 1.11E-06 2017365 Xylenes (mixed) 7.25E-05 2.85E-06 3.99E-07 0.00E+00 0.00E+00 2017365 Arsenic compounds 3.82E-05 0.00E+00 0.00E+00 0.00E+00 0.00E+00 2017365 Toluene 2.18E-05 1.07E-06 1.79E-07 4.24E-06 7.32E-07 2017365 Mercury (elemental) 3.80E-06 0.00E+00 1.34E-09 0.00E+00 1.14E-09 2017365 Ethyl benzene 0.00E+00 1.50E-07 4.37E-09 0.00E+00 0.00E+00 2017365 Methyl methacrylate 0.00E+00 1.23E-07 1.76E-08 0.00E+00 0.00E+00 2017365 Triethylamine 4.48E-08 0.00E+00 0.00E+00 0.00E+00 0.00E+00 2017365 Methanol 7.92E-09 3.21E-10 8.21E-11 8.53E-10 1.71E-10 2017365 Cumene 0.00E+00 7.26E-11 1.21E-11 0.00E+00 0.00E+00 02090348 Diethylene glycol monobutyl ether 4.96E-04 0.00E+00 0.00E+00 0.00E+00 0.00E+00 02090348 Methylene diphenyl diisocyanate 0.00E+00 0.00E+00 0.00E+00 0.00E+00 3.03E-05 02090348 Triethylamine 5.01E-06 0.00E+00 0.00E+00 0.00E+00 0.00E+00 02090348 Xylenes (mixed) 1.63E-06 6.41E-08 8.97E-09 0.00E+00 0.00E+00 02090348 Toluene 4.35E-07 2.14E-08 3.57E-09 8.47E-08 1.46E-08 02090348 Formaldehyde 1.40E-07 6.98E-09 4.52E-10 6.40E-09 6.40E-10 02090348 Ethyl benzene 0.00E+00 2.88E-08 8.39E-10 0.00E+00 0.00E+00 02090348 Cumene 0.00E+00 4.47E-09 7.46E-10 0.00E+00 0.00E+00 02090529 Diethylene glycol monobutyl ether 1.42E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00 02090529 2-Butoxyethyl acetate 1.32E-04 0.00E+00 0.00E+00 0.00E+00 0.00E+00 02090529 Xylenes (mixed) 1.92E-05 7.54E-07 1.06E-07 0.00E+00 0.00E+00 02090529 Toluene 1.07E-05 5.26E-07 8.76E-08 2.08E-06 3.58E-07 02090529 Methylene chloride 3.78E-06 7.66E-08 2.78E-08 5.29E-08 2.03E-08 02090529 Chlorobenzene 0.00E+00 3.50E-06 2.33E-07 0.00E+00 0.00E+00 02090529 Ethylene glycol methyl ether 3.10E-06 0.00E+00 0.00E+00 0.00E+00 0.00E+00 02090529 Methanol 1.09E-06 4.44E-08 1.13E-08 1.18E-07 2.36E-08 02090529 Ethyl benzene 0.00E+00 5.67E-07 1.65E-08 0.00E+00 0.00E+00 02090529 n-Hexane 0.00E+00 0.00E+00 3.80E-08 0.00E+00 0.00E+00 02090529 Cumene 0.00E+00 2.78E-10 4.63E-11 0.00E+00 0.00E+00 02150683 Benzene 7.31E-06 5.59E-08 3.65E-09 5.94E-08 1.98E-08 02150683 Methanol 2.71E-06 1.10E-07 2.81E-08 2.92E-07 5.84E-08 02150683 Phenol 1.34E-06 1.34E-07 8.74E-08 2.05E-07 4.10E-08 02150683 Toluene 1.11E-06 5.47E-08 9.12E-09 2.16E-07 3.73E-08

Facility NEI ID Pollutant Table 3 – Maximum Predicted Acute Risks

Actual Emissions

Maximum Hazard Quotient 1

1 of 27REL AEGL1 AEGL2 ERPG1 ERPG2

Facility NEI ID Pollutant Table 3 – Maximum Predicted Acute Risks

Actual Emissions

Maximum Hazard Quotient 1

02150683 Methylene chloride 1.25E-06 2.54E-08 9.22E-09 1.75E-08 6.73E-09 02150683 Xylenes (mixed) 9.45E-07 3.71E-08 5.20E-09 0.00E+00 0.00E+00 02150683 Ethyl benzene 0.00E+00 2.43E-08 7.08E-10 0.00E+00 0.00E+00 02150683 Cumene 0.00E+00 8.73E-10 1.45E-10 0.00E+00 0.00E+00 04013510 Xylenes (mixed) 2.15E-03 8.44E-05 1.18E-05 0.00E+00 0.00E+00 04013510 Toluene 3.31E-05 1.64E-06 2.73E-07 6.45E-06 1.11E-06 04013602 Xylenes (mixed) 9.93E-05 3.90E-06 5.46E-07 0.00E+00 0.00E+00 04013602 Toluene 6.09E-05 3.01E-06 5.01E-07 1.19E-05 2.05E-06 04013602 Hydrofluoric acid 6.86E-06 2.01E-06 8.24E-08 1.03E-06 1.03E-07 04013602 Ethyl benzene 0.00E+00 5.98E-06 1.74E-07 0.00E+00 0.00E+00 04013602 Methanol 3.80E-06 1.54E-07 3.94E-08 4.10E-07 8.19E-08 04013602 Benzene 9.00E-07 6.89E-09 4.50E-10 7.32E-09 2.44E-09 04013602 n-Hexane 0.00E+00 0.00E+00 1.17E-08 0.00E+00 0.00E+00 04019300 Xylenes (mixed) 4.45E-04 1.75E-05 2.45E-06 0.00E+00 0.00E+00 04019300 Toluene 2.18E-04 1.07E-05 1.79E-06 4.24E-05 7.32E-06 04019300 Methanol 6.03E-05 2.45E-06 6.25E-07 6.49E-06 1.30E-06 04019300 Ethyl benzene 0.00E+00 3.29E-05 9.61E-07 0.00E+00 0.00E+00 04019433 Phenol 3.77E-04 3.77E-05 2.46E-05 5.75E-05 1.15E-05 04019433 Methylene chloride 3.13E-04 6.34E-06 2.30E-06 4.38E-06 1.68E-06 04019433 Arsenic compounds 8.36E-05 0.00E+00 0.00E+00 0.00E+00 0.00E+00 04019433 Butyl carbitol acetate 7.23E-05 0.00E+00 0.00E+00 0.00E+00 0.00E+00 04019433 Xylenes (mixed) 3.37E-06 1.32E-07 1.85E-08 0.00E+00 0.00E+00 04019433 Toluene 1.39E-06 6.85E-08 1.14E-08 2.70E-07 4.67E-08 04019433 Ethyl benzene 0.00E+00 4.68E-07 1.36E-08 0.00E+00 0.00E+00 04019433 Cumene 0.00E+00 1.43E-08 2.38E-09 0.00E+00 0.00E+00 4703758 Ethylene glycol ethyl ether 6.02E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00 4703758 Diethylene glycol monoethyl ether 3.75E-04 0.00E+00 0.00E+00 0.00E+00 0.00E+00 4703758 Methylene diphenyl diisocyanate 0.00E+00 0.00E+00 0.00E+00 0.00E+00 2.03E-04 4703758 Toluene 1.46E-04 7.19E-06 1.20E-06 2.84E-05 4.90E-06 4703758 Xylenes (mixed) 1.55E-04 6.11E-06 8.55E-07 0.00E+00 0.00E+00 4703758 Methanol 1.32E-04 5.37E-06 1.37E-06 1.43E-05 2.85E-06 4703758 Propyl cellosolve 3.67E-05 0.00E+00 0.00E+00 0.00E+00 0.00E+00 4703758 Ethyl benzene 0.00E+00 8.53E-06 2.49E-07 0.00E+00 0.00E+00 4703758 Phenol 3.03E-07 3.03E-08 1.98E-08 4.63E-08 9.26E-09 4703758 Cumene 0.00E+00 9.46E-09 1.58E-09 0.00E+00 0.00E+00 05051409 Ethylene glycol ethyl ether 7.94E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00 05051409 Toluene 1.30E-03 6.40E-05 1.07E-05 2.53E-04 4.37E-05 05051409 Propyl cellosolve 1.12E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00 05051409 Formaldehyde 7.63E-04 3.82E-05 2.47E-06 3.50E-05 3.50E-06 05051409 Butyl carbitol acetate 8.10E-04 0.00E+00 0.00E+00 0.00E+00 0.00E+00 05051409 Xylenes (mixed) 7.15E-04 2.81E-05 3.93E-06 0.00E+00 0.00E+00 05051409 Methylene diphenyl diisocyanate 0.00E+00 0.00E+00 0.00E+00 0.00E+00 2.71E-04 05051409 2-Butoxyethyl acetate 1.25E-04 0.00E+00 0.00E+00 0.00E+00 0.00E+00 05051409 Phenol 1.23E-05 1.23E-06 8.02E-07 1.88E-06 3.76E-07 05051409 Arsenic compounds 1.42E-05 0.00E+00 0.00E+00 0.00E+00 0.00E+00 05051409 Methanol 5.25E-06 2.13E-07 5.45E-08 5.66E-07 1.13E-07 05051409 Ethyl benzene 0.00E+00 4.31E-07 1.26E-08 0.00E+00 0.00E+00 05051409 Beryllium compounds 0.00E+00 0.00E+00 0.00E+00 0.00E+00 3.73E-09 05051409 Cumene 0.00E+00 9.41E-11 1.57E-11 0.00E+00 0.00E+00 05113377 Ethylene glycol ethyl ether acetate 1.88E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 05113377 Propyl cellosolve 1.54E-01 0.00E+00 0.00E+00 0.00E+00 0.00E+00 05113377 2-Butoxyethyl acetate 1.12E-01 0.00E+00 0.00E+00 0.00E+00 0.00E+00

2 of 27REL AEGL1 AEGL2 ERPG1 ERPG2

Facility NEI ID Pollutant Table 3 – Maximum Predicted Acute Risks

Actual Emissions

Maximum Hazard Quotient 1

05113377 Phenol 9.31E-03 9.31E-04 6.07E-04 1.42E-03 2.84E-04 05113377 Methylene chloride 8.67E-03 1.76E-04 6.39E-05 1.21E-04 4.67E-05 05113377 Toluene 1.92E-03 9.46E-05 1.58E-05 3.74E-04 6.45E-05 05113377 Xylenes (mixed) 1.91E-03 7.51E-05 1.05E-05 0.00E+00 0.00E+00 05113377 Ethyl benzene 0.00E+00 6.75E-05 1.97E-06 0.00E+00 0.00E+00 05113377 Methanol 2.51E-05 1.02E-06 2.60E-07 2.70E-06 5.41E-07 05143331 Formaldehyde 2.10E-05 1.05E-06 6.79E-08 9.62E-07 9.62E-08 05143331 Toluene 5.83E-06 2.88E-07 4.80E-08 1.14E-06 1.96E-07 05143331 Xylenes (mixed) 4.71E-06 1.85E-07 2.59E-08 0.00E+00 0.00E+00 05143331 Methanol 9.74E-07 3.95E-08 1.01E-08 1.05E-07 2.10E-08 05143331 Phenol 1.99E-07 1.99E-08 1.30E-08 3.04E-08 6.07E-09 05143331 Ethyl benzene 0.00E+00 7.17E-09 2.09E-10 0.00E+00 0.00E+00 06029545 Diethylene glycol monobutyl ether 2.19E-05 0.00E+00 0.00E+00 0.00E+00 0.00E+00 06029545 Triethylamine 8.72E-06 0.00E+00 0.00E+00 0.00E+00 0.00E+00 06029545 Xylenes (mixed) 6.53E-06 2.57E-07 3.59E-08 0.00E+00 0.00E+00 06029545 Toluene 3.65E-07 1.80E-08 3.00E-09 7.10E-08 1.23E-08 06029545 Ethyl benzene 0.00E+00 1.42E-07 4.13E-09 0.00E+00 0.00E+00 06029545 Cumene 0.00E+00 3.57E-11 5.95E-12 0.00E+00 0.00E+00 06037429 Xylenes (mixed) 1.48E-04 5.83E-06 8.16E-07 0.00E+00 0.00E+00 06037429 Toluene 8.85E-05 4.36E-06 7.27E-07 1.72E-05 2.98E-06 06037429 Ethyl benzene 0.00E+00 1.17E-05 3.41E-07 0.00E+00 0.00E+00 06037429 Methyl methacrylate 0.00E+00 2.32E-06 3.31E-07 0.00E+00 0.00E+00 06037429 Methylene chloride 1.20E-06 2.43E-08 8.84E-09 1.68E-08 6.46E-09 06037429 Phenol 1.78E-07 1.78E-08 1.16E-08 2.71E-08 5.42E-09 06037429 Methanol 4.56E-08 1.85E-09 4.73E-10 4.91E-09 9.82E-10 06037490 Toluene 2.57E-04 1.27E-05 2.11E-06 5.00E-05 8.63E-06 06037490 Xylenes (mixed) 1.35E-05 5.32E-07 7.44E-08 0.00E+00 0.00E+00 06037490 Triethylamine 1.38E-07 0.00E+00 0.00E+00 0.00E+00 0.00E+00 06037490 Ethyl benzene 0.00E+00 9.20E-08 2.68E-09 0.00E+00 0.00E+00 06037490 Cumene 0.00E+00 1.55E-08 2.58E-09 0.00E+00 0.00E+00 06059538 Tetrachloroethene 2.50E-04 2.09E-05 3.13E-06 7.37E-06 3.58E-06 06065520 Xylenes (mixed) 2.88E-05 1.13E-06 1.58E-07 0.00E+00 0.00E+00 06065520 Toluene 1.47E-05 7.26E-07 1.21E-07 2.87E-06 4.95E-07 06065520 Formaldehyde 7.09E-06 3.55E-07 2.29E-08 3.25E-07 3.25E-08 06065520 Ethyl benzene 0.00E+00 3.13E-06 9.13E-08 0.00E+00 0.00E+00 06065520 Vinyl acetate 0.00E+00 1.10E-07 4.17E-09 1.46E-07 1.01E-08 06065520 Methanol 1.56E-08 6.33E-10 1.62E-10 1.68E-09 3.36E-10 06065520 Phenol 3.56E-09 3.56E-10 2.32E-10 5.43E-10 1.09E-10 06065520 1,4-Dioxane 5.71E-10 2.81E-11 1.43E-12 0.00E+00 0.00E+00 06065520 Cumene 0.00E+00 6.09E-12 1.02E-12 0.00E+00 0.00E+00 06071540 Tetrachloroethene 4.27E-02 3.56E-03 5.34E-04 1.26E-03 6.10E-04 06071540 Xylenes (mixed) 1.39E-03 5.46E-05 7.65E-06 0.00E+00 0.00E+00 06071540 Toluene 1.91E-04 9.42E-06 1.57E-06 3.72E-05 6.42E-06 06071540 Ethyl benzene 0.00E+00 1.43E-09 4.17E-11 0.00E+00 0.00E+00 06073306 2,4-Toluene diisocyanate 0.00E+00 1.76E-05 4.17E-06 3.46E-05 2.24E-06 06073306 Toluene 1.54E-05 7.59E-07 1.27E-07 3.00E-06 5.18E-07 06073306 Dimethyl formamide 0.00E+00 0.00E+00 2.76E-07 1.24E-05 2.48E-07 06073306 Methylene chloride 4.35E-06 8.82E-08 3.20E-08 6.08E-08 2.34E-08 06073306 Xylenes (mixed) 4.07E-06 1.60E-07 2.24E-08 0.00E+00 0.00E+00 06073306 Methanol 2.15E-06 8.73E-08 2.23E-08 2.32E-07 4.64E-08 06073306 Hydrofluoric acid 6.77E-07 1.98E-07 8.13E-09 1.02E-07 1.02E-08 06073306 Methyl methacrylate 0.00E+00 1.12E-07 1.60E-08 0.00E+00 0.00E+00

3 of 27REL AEGL1 AEGL2 ERPG1 ERPG2

Facility NEI ID Pollutant Table 3 – Maximum Predicted Acute Risks

Actual Emissions

Maximum Hazard Quotient 1

06073306 Ethyl benzene 0.00E+00 3.94E-08 1.15E-09 0.00E+00 0.00E+00 06073306 Styrene 2.11E-08 5.21E-09 8.06E-10 2.11E-09 4.03E-10 06073306 Phenol 3.39E-09 3.39E-10 2.21E-10 5.17E-10 1.03E-10 06073306 n-Hexane 0.00E+00 0.00E+00 3.39E-09 0.00E+00 0.00E+00 09001684 Methanol 1.84E-05 7.46E-07 1.91E-07 1.98E-06 3.96E-07 09001688 Toluene 1.30E-05 6.43E-07 1.07E-07 2.54E-06 4.39E-07 09001688 Methanol 2.80E-06 1.13E-07 2.90E-08 3.01E-07 6.02E-08 09001693 2-Butoxyethyl acetate 1.77E-02 0.00E+00 0.00E+00 0.00E+00 0.00E+00 09001693 Butyl carbitol acetate 1.19E-02 0.00E+00 0.00E+00 0.00E+00 0.00E+00 09001693 Phenol 2.62E-04 2.62E-05 1.71E-05 4.00E-05 8.00E-06 09001693 Methylene chloride 3.37E-04 6.84E-06 2.48E-06 4.72E-06 1.82E-06 09001693 Formaldehyde 2.87E-04 1.43E-05 9.27E-07 1.31E-05 1.31E-06 09001693 Xylenes (mixed) 2.16E-04 8.48E-06 1.19E-06 0.00E+00 0.00E+00 09001693 Diethylene glycol monobutyl ether 2.07E-04 0.00E+00 0.00E+00 0.00E+00 0.00E+00 09001693 Toluene 1.40E-04 6.89E-06 1.15E-06 2.72E-05 4.70E-06 09001693 Ethylene glycol ethyl ether acetate 1.38E-04 0.00E+00 0.00E+00 0.00E+00 0.00E+00 09001693 Methanol 6.99E-06 2.84E-07 7.25E-08 7.53E-07 1.51E-07 09001693 Ethylene glycol ethyl ether 1.10E-06 0.00E+00 0.00E+00 0.00E+00 0.00E+00 09001693 Ethyl benzene 0.00E+00 6.03E-07 1.76E-08 0.00E+00 0.00E+00 09001693 Cumene 0.00E+00 2.42E-08 4.04E-09 0.00E+00 0.00E+00 09001693 Methylene diphenyl diisocyanate 0.00E+00 0.00E+00 0.00E+00 0.00E+00 1.79E-08 09001693 n-Hexane 0.00E+00 0.00E+00 5.25E-11 0.00E+00 0.00E+00 09003626 2-Butoxyethyl acetate 2.79E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00 09003626 Ethylene glycol ethyl ether 2.56E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00 09003626 Butyl carbitol acetate 3.03E-04 0.00E+00 0.00E+00 0.00E+00 0.00E+00 09003626 Ethylene glycol methyl ether 2.53E-04 0.00E+00 0.00E+00 0.00E+00 0.00E+00 09003626 Methylene chloride 2.17E-05 4.39E-07 1.60E-07 3.03E-07 1.17E-07 09003626 Formaldehyde 1.43E-05 7.15E-07 4.63E-08 6.55E-07 6.55E-08 09003626 Phenol 1.05E-05 1.05E-06 6.83E-07 1.60E-06 3.20E-07 09003626 Xylenes (mixed) 1.07E-05 4.19E-07 5.86E-08 0.00E+00 0.00E+00 09003626 Toluene 6.52E-06 3.22E-07 5.36E-08 1.27E-06 2.19E-07 09003626 Methanol 1.68E-06 6.83E-08 1.74E-08 1.81E-07 3.62E-08 09003626 Ethyl benzene 0.00E+00 1.67E-07 4.86E-09 0.00E+00 0.00E+00 09003626 Tetrachloroethene 1.08E-07 9.00E-09 1.35E-09 3.18E-09 1.54E-09 09003626 Arsenic compounds 3.89E-08 0.00E+00 0.00E+00 0.00E+00 0.00E+00 09003626 Cumene 0.00E+00 2.34E-08 3.90E-09 0.00E+00 0.00E+00 09003626 Beryllium compounds 0.00E+00 0.00E+00 0.00E+00 0.00E+00 1.00E-11 10003349 Xylenes (mixed) 2.62E-05 1.03E-06 1.44E-07 0.00E+00 0.00E+00 10003349 Styrene 1.77E-06 4.38E-07 6.78E-08 1.77E-07 3.39E-08 10003349 Methanol 6.30E-07 2.55E-08 6.53E-09 6.78E-08 1.36E-08 10003349 Ethyl benzene 0.00E+00 6.64E-07 1.94E-08 0.00E+00 0.00E+00 10003349 Arsenic compounds 2.16E-07 0.00E+00 0.00E+00 0.00E+00 0.00E+00 10003349 Toluene 3.34E-08 1.65E-09 2.75E-10 6.51E-09 1.12E-09 010515017 2-Butoxyethyl acetate 3.73E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00 010515017 Methanol 4.26E-04 1.73E-05 4.41E-06 4.58E-05 9.17E-06 010515017 Xylenes (mixed) 1.80E-04 7.06E-06 9.89E-07 0.00E+00 0.00E+00 010515017 Ethylene glycol ethyl ether acetate 8.47E-05 0.00E+00 0.00E+00 0.00E+00 0.00E+00 010515017 Toluene 5.44E-05 2.69E-06 4.48E-07 1.06E-05 1.83E-06 010515017 Methylene chloride 5.04E-06 1.02E-07 3.72E-08 7.06E-08 2.71E-08 010515017 Phenol 2.43E-06 2.43E-07 1.59E-07 3.72E-07 7.43E-08 010515017 Acetaldehyde 1.22E-06 7.08E-09 1.17E-09 3.19E-08 1.59E-09 010515017 Ethyl benzene 0.00E+00 5.49E-07 1.60E-08 0.00E+00 0.00E+00

4 of 27REL AEGL1 AEGL2 ERPG1 ERPG2

Facility NEI ID Pollutant Table 3 – Maximum Predicted Acute Risks

Actual Emissions

Maximum Hazard Quotient 1

010515017 Methylene diphenyl diisocyanate 0.00E+00 0.00E+00 0.00E+00 0.00E+00 3.63E-07 010515017 Ethyl acrylate 0.00E+00 4.04E-10 9.15E-11 3.35E-07 1.14E-10 010515017 1,2-Epoxybutane 0.00E+00 8.99E-08 4.61E-08 0.00E+00 0.00E+00 010515017 Cumene 0.00E+00 2.30E-09 3.84E-10 0.00E+00 0.00E+00 010892023 Phenol 1.73E-01 1.73E-02 1.13E-02 2.65E-02 5.29E-03 010892023 Methylene chloride 4.95E-02 1.00E-03 3.65E-04 6.93E-04 2.66E-04 010892023 Xylenes (mixed) 2.72E-04 1.07E-05 1.50E-06 0.00E+00 0.00E+00 010892023 Ethyl benzene 0.00E+00 5.54E-06 1.62E-07 0.00E+00 0.00E+00 12005313 Butyl carbitol acetate 3.74E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00 12005313 Diethylene glycol dimethyl ether 8.60E-04 0.00E+00 0.00E+00 0.00E+00 0.00E+00 12005313 Methylene diphenyl diisocyanate 0.00E+00 0.00E+00 0.00E+00 0.00E+00 2.48E-04 12005313 Xylenes (mixed) 9.54E-05 3.75E-06 5.25E-07 0.00E+00 0.00E+00 12005313 Toluene 1.18E-05 5.85E-07 9.74E-08 2.31E-06 3.99E-07 12005313 Ethyl benzene 0.00E+00 1.18E-05 3.44E-07 0.00E+00 0.00E+00 12005313 Formaldehyde 1.03E-05 5.17E-07 3.34E-08 4.74E-07 4.74E-08 12005313 Triethylamine 3.11E-06 0.00E+00 0.00E+00 0.00E+00 0.00E+00 12005313 Cumene 0.00E+00 1.13E-08 1.88E-09 0.00E+00 0.00E+00 12009358 Phenol 2.71E-06 2.71E-07 1.76E-07 4.13E-07 8.26E-08 12009358 Methylene chloride 2.17E-06 4.41E-08 1.60E-08 3.04E-08 1.17E-08 12009358 Xylenes (mixed) 9.99E-07 3.92E-08 5.49E-09 0.00E+00 0.00E+00 12009358 Toluene 3.51E-07 1.73E-08 2.88E-09 6.83E-08 1.18E-08 12009358 Methanol 2.67E-07 1.08E-08 2.77E-09 2.87E-08 5.75E-09 12009358 Methylene diphenyl diisocyanate 0.00E+00 0.00E+00 0.00E+00 0.00E+00 2.33E-07 12009358 Ethyl benzene 0.00E+00 5.71E-08 1.67E-09 0.00E+00 0.00E+00 12009358 n-Hexane 0.00E+00 0.00E+00 4.79E-11 0.00E+00 0.00E+00 12023284 Glycol Ethers 7.97E-02 0.00E+00 0.00E+00 0.00E+00 0.00E+00 12023284 Toluene 3.45E-03 1.70E-04 2.84E-05 6.72E-04 1.16E-04 12023284 Methylene chloride 1.32E-03 2.68E-05 9.75E-06 1.85E-05 7.13E-06 12023284 Phenol 7.10E-04 7.10E-05 4.63E-05 1.08E-04 2.17E-05 12023284 Xylenes (mixed) 3.00E-04 1.18E-05 1.65E-06 0.00E+00 0.00E+00 12023284 Styrene 4.87E-05 1.20E-05 1.86E-06 4.87E-06 9.31E-07 12023284 Methanol 9.11E-06 3.70E-07 9.45E-08 9.81E-07 1.96E-07 12023284 Ethyl benzene 0.00E+00 2.64E-06 7.69E-08 0.00E+00 0.00E+00 12023284 n-Hexane 0.00E+00 0.00E+00 1.01E-07 0.00E+00 0.00E+00 12031634 Butyl carbitol acetate 2.13E-04 0.00E+00 0.00E+00 0.00E+00 0.00E+00 12031634 Toluene 1.11E-04 5.50E-06 9.16E-07 2.17E-05 3.75E-06 12031634 Formaldehyde 2.70E-05 1.35E-06 8.73E-08 1.24E-06 1.24E-07 12031634 Xylenes (mixed) 2.01E-05 7.88E-07 1.10E-07 0.00E+00 0.00E+00 12031634 Phenol 1.07E-05 1.07E-06 6.98E-07 1.63E-06 3.27E-07 12031634 Methanol 2.56E-06 1.04E-07 2.66E-08 2.76E-07 5.52E-08 12031634 Ethyl benzene 0.00E+00 6.26E-07 1.82E-08 0.00E+00 0.00E+00 12031634 Methylene chloride 3.86E-07 7.84E-09 2.85E-09 5.41E-09 2.08E-09 12031634 1,1,1-Trichloroethane 2.51E-07 1.31E-08 5.17E-09 8.98E-09 4.49E-09 12031634 Styrene 4.33E-08 1.07E-08 1.65E-09 4.33E-09 8.26E-10 12031634 n-Hexane 0.00E+00 0.00E+00 3.35E-08 0.00E+00 0.00E+00 12031634 Cumene 0.00E+00 2.19E-08 3.65E-09 0.00E+00 0.00E+00 12031634 Benzene 1.34E-08 1.02E-10 6.69E-12 1.09E-10 3.62E-11 12061380 Ethylene glycol ethyl ether 4.24E-02 0.00E+00 0.00E+00 0.00E+00 0.00E+00 12061380 2-Butoxyethyl acetate 2.39E-02 0.00E+00 0.00E+00 0.00E+00 0.00E+00 12061380 Toluene 9.15E-04 4.51E-05 7.52E-06 1.78E-04 3.08E-05 12061380 Formaldehyde 1.58E-04 7.90E-06 5.11E-07 7.24E-06 7.24E-07 12061380 Methanol 1.86E-05 7.56E-07 1.93E-07 2.01E-06 4.01E-07

5 of 27REL AEGL1 AEGL2 ERPG1 ERPG2

Facility NEI ID Pollutant Table 3 – Maximum Predicted Acute Risks

Actual Emissions

Maximum Hazard Quotient 1

12061380 Ethyl benzene 0.00E+00 3.66E-06 1.07E-07 0.00E+00 0.00E+00 12061380 Arsenic compounds 1.24E-06 0.00E+00 0.00E+00 0.00E+00 0.00E+00 12061380 Cumene 0.00E+00 5.97E-09 9.96E-10 0.00E+00 0.00E+00 12083270 Formaldehyde 2.06E-05 1.03E-06 6.65E-08 9.42E-07 9.42E-08 12083270 Ethyl acrylate 0.00E+00 6.05E-09 1.37E-09 5.01E-06 1.71E-09 12083270 Xylenes (mixed) 3.11E-07 1.22E-08 1.71E-09 0.00E+00 0.00E+00 12083270 Toluene 2.44E-07 1.20E-08 2.01E-09 4.75E-08 8.21E-09 12083270 Ethyl benzene 0.00E+00 1.19E-08 3.46E-10 0.00E+00 0.00E+00 12083270 Cumene 0.00E+00 2.21E-10 3.69E-11 0.00E+00 0.00E+00 12099695 Methanol 2.46E-06 9.99E-08 2.55E-08 2.65E-07 5.30E-08 13009224 Formaldehyde 5.87E-03 2.94E-04 1.90E-05 2.69E-04 2.69E-05 13009224 Toluene 1.98E-03 9.75E-05 1.62E-05 3.85E-04 6.65E-05 13009224 Xylenes (mixed) 1.72E-03 6.75E-05 9.46E-06 0.00E+00 0.00E+00 13009224 Epichlorohydrin 2.45E-04 1.45E-05 3.51E-06 1.68E-05 4.20E-06 13009224 Ethyl benzene 0.00E+00 3.94E-05 1.15E-06 0.00E+00 0.00E+00 13009224 Methylene diphenyl diisocyanate 0.00E+00 0.00E+00 0.00E+00 0.00E+00 3.79E-05 13009224 Methanol 1.52E-05 6.17E-07 1.58E-07 1.64E-06 3.27E-07 13009224 Phenol 2.65E-06 2.65E-07 1.73E-07 4.04E-07 8.08E-08 13009224 Styrene 1.39E-06 3.44E-07 5.31E-08 1.39E-07 2.66E-08 13009224 Arsenic compounds 1.72E-06 0.00E+00 0.00E+00 0.00E+00 0.00E+00 13009224 Beryllium compounds 0.00E+00 0.00E+00 0.00E+00 0.00E+00 1.98E-10 13063388 Butyl carbitol acetate 2.39E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00 13063388 Diethylene glycol monobutyl ether 2.38E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00 13063388 Ethylene glycol ethyl ether 5.34E-04 0.00E+00 0.00E+00 0.00E+00 0.00E+00 13063388 Ethylene glycol methyl ether 5.20E-04 0.00E+00 0.00E+00 0.00E+00 0.00E+00 13063388 Formaldehyde 1.46E-04 7.32E-06 4.73E-07 6.71E-06 6.71E-07 13063388 Arsenic compounds 1.19E-04 0.00E+00 0.00E+00 0.00E+00 0.00E+00 13063388 Toluene 7.40E-05 3.65E-06 6.09E-07 1.44E-05 2.49E-06 13063388 Xylenes (mixed) 8.60E-05 3.38E-06 4.73E-07 0.00E+00 0.00E+00 13063388 Ethylene glycol ethyl ether acetate 3.19E-05 0.00E+00 0.00E+00 0.00E+00 0.00E+00 13063388 Phenol 1.88E-05 1.88E-06 1.23E-06 2.88E-06 5.75E-07 13063388 Methylene diphenyl diisocyanate 0.00E+00 0.00E+00 0.00E+00 0.00E+00 1.39E-05 13063388 Methylene chloride 9.67E-06 1.96E-07 7.13E-08 1.35E-07 5.21E-08 13063388 1,2-Dimethoxyethane 7.12E-06 0.00E+00 0.00E+00 0.00E+00 0.00E+00 13063388 Styrene 4.79E-06 1.18E-06 1.83E-07 4.79E-07 9.14E-08 13063388 2,4-Toluene diisocyanate 0.00E+00 1.92E-06 4.55E-07 3.78E-06 2.44E-07 13063388 Methanol 4.93E-06 2.00E-07 5.12E-08 5.31E-07 1.06E-07 13063388 Carbitol acetate 5.33E-06 0.00E+00 0.00E+00 0.00E+00 0.00E+00 13063388 2-Butoxyethyl acetate 4.13E-06 0.00E+00 0.00E+00 0.00E+00 0.00E+00 13063388 2-(Hexyloxy)ethanol 3.24E-06 0.00E+00 0.00E+00 0.00E+00 0.00E+00 13063388 Ethyl benzene 0.00E+00 2.66E-06 7.76E-08 0.00E+00 0.00E+00 13063388 Acrylic acid 1.97E-07 2.69E-07 8.45E-09 3.94E-07 7.88E-09 13063388 Benzene 7.54E-07 5.77E-09 3.77E-10 6.13E-09 2.04E-09 13063388 Epichlorohydrin 4.17E-07 2.47E-08 5.96E-09 2.85E-08 7.14E-09 13063388 Methyl methacrylate 0.00E+00 8.84E-08 1.26E-08 0.00E+00 0.00E+00 13063388 m-Xylene 8.74E-08 0.00E+00 0.00E+00 0.00E+00 0.00E+00 13063388 Cumene 0.00E+00 4.55E-08 7.58E-09 0.00E+00 0.00E+00 13063388 o-Xylene 3.49E-08 0.00E+00 0.00E+00 0.00E+00 0.00E+00 13063388 Chloroform 2.79E-08 0.00E+00 1.35E-11 0.00E+00 1.75E-11 13063388 n-Hexane 0.00E+00 0.00E+00 5.22E-09 0.00E+00 0.00E+00 13063388 Trichloroethylene 0.00E+00 1.68E-09 4.91E-10 2.18E-09 4.37E-10 13063388 Carbon tetrachloride 3.19E-09 2.16E-11 5.05E-12 4.66E-11 9.61E-12

6 of 27REL AEGL1 AEGL2 ERPG1 ERPG2

Facility NEI ID Pollutant Table 3 – Maximum Predicted Acute Risks

Actual Emissions

Maximum Hazard Quotient 1

13063388 Beryllium compounds 0.00E+00 0.00E+00 0.00E+00 0.00E+00 1.37E-09 13063388 Triethylamine 4.83E-10 0.00E+00 0.00E+00 0.00E+00 0.00E+00 13063388 Aniline 0.00E+00 7.60E-12 4.95E-12 0.00E+00 0.00E+00 13063388 1,4-Dioxane 4.06E-12 1.99E-13 1.00E-14 0.00E+00 0.00E+00 13063388 Propylene oxide 3.93E-12 7.20E-14 1.80E-14 1.01E-13 2.10E-14 13063388 Ethylene oxide 0.00E+00 0.00E+00 1.50E-13 0.00E+00 1.35E-13 13067506 Propyl cellosolve 5.76E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00 13067506 2-Butoxyethyl acetate 2.60E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00 13067506 Diethylene glycol monobutyl ether 1.47E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00 13067506 Xylenes (mixed) 6.13E-04 2.41E-05 3.37E-06 0.00E+00 0.00E+00 13067506 Diethylene glycol monomethyl ether 3.93E-04 0.00E+00 0.00E+00 0.00E+00 0.00E+00 13067506 Butyl carbitol acetate 2.42E-04 0.00E+00 0.00E+00 0.00E+00 0.00E+00 13067506 Toluene 1.00E-04 4.95E-06 8.26E-07 1.96E-05 3.38E-06 13067506 Triethylamine 1.13E-04 0.00E+00 0.00E+00 0.00E+00 0.00E+00 13067506 Ethylene glycol methyl ether 3.34E-05 0.00E+00 0.00E+00 0.00E+00 0.00E+00 13067506 Methylene diphenyl diisocyanate 0.00E+00 0.00E+00 0.00E+00 0.00E+00 2.94E-05 13067506 Arsenic compounds 1.55E-05 0.00E+00 0.00E+00 0.00E+00 0.00E+00 13067506 Phenol 8.53E-06 8.53E-07 5.56E-07 1.30E-06 2.60E-07 13067506 Methanol 3.64E-06 1.48E-07 3.77E-08 3.92E-07 7.84E-08 13067506 Formaldehyde 2.33E-06 1.16E-07 7.54E-09 1.07E-07 1.07E-08 13067506 Ethyl benzene 0.00E+00 1.39E-06 4.05E-08 0.00E+00 0.00E+00 13067506 Carbitol acetate 1.37E-06 0.00E+00 0.00E+00 0.00E+00 0.00E+00 13067506 Methyl methacrylate 0.00E+00 2.05E-07 2.94E-08 0.00E+00 0.00E+00 13067506 Propylene oxide 5.23E-08 9.53E-10 2.35E-10 1.35E-09 2.75E-10 13067506 Carbon tetrachloride 2.92E-08 1.98E-10 4.63E-11 4.27E-10 8.82E-11 13067506 n-Hexane 0.00E+00 0.00E+00 2.35E-08 0.00E+00 0.00E+00 13067506 Beryllium compounds 0.00E+00 0.00E+00 0.00E+00 0.00E+00 8.29E-09 13067506 Cumene 0.00E+00 6.40E-09 1.07E-09 0.00E+00 0.00E+00 13067506 p-Xylene 1.97E-09 0.00E+00 0.00E+00 0.00E+00 0.00E+00 13153431 Butyl carbitol acetate 5.01E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00 13153431 2,4-Toluene diisocyanate 0.00E+00 2.60E-04 6.17E-05 5.13E-04 3.31E-05 13153431 Phenol 3.65E-04 3.65E-05 2.38E-05 5.57E-05 1.11E-05 13153431 Methylene chloride 4.54E-04 9.21E-06 3.34E-06 6.35E-06 2.44E-06 13153431 2-Butoxyethyl acetate 3.89E-04 0.00E+00 0.00E+00 0.00E+00 0.00E+00 13153431 Xylenes (mixed) 2.29E-04 9.01E-06 1.26E-06 0.00E+00 0.00E+00 13153431 Toluene 8.99E-05 4.44E-06 7.40E-07 1.75E-05 3.03E-06 13153431 Methanol 3.91E-05 1.59E-06 4.05E-07 4.21E-06 8.42E-07 13153431 Ethyl acrylate 0.00E+00 4.79E-08 1.09E-08 3.97E-05 1.36E-08 13153431 Formaldehyde 2.96E-05 1.48E-06 9.58E-08 1.36E-06 1.36E-07 13153431 Ethyl benzene 0.00E+00 2.28E-05 6.66E-07 0.00E+00 0.00E+00 13153431 Benzene 1.29E-06 9.85E-09 6.44E-10 1.05E-08 3.49E-09 13153431 Cumene 0.00E+00 1.63E-07 2.72E-08 0.00E+00 0.00E+00 13153431 Ethylene oxide 0.00E+00 0.00E+00 2.01E-08 0.00E+00 1.81E-08 15003346 Toluene 2.38E-06 1.18E-07 1.96E-08 4.64E-07 8.02E-08 15003346 Xylenes (mixed) 6.14E-07 2.41E-08 3.38E-09 0.00E+00 0.00E+00 15003346 Ethyl benzene 0.00E+00 4.82E-08 1.41E-09 0.00E+00 0.00E+00 18097487 Phenol 7.56E-04 7.56E-05 4.93E-05 1.15E-04 2.31E-05 18097487 Methylene chloride 6.14E-04 1.24E-05 4.52E-06 8.59E-06 3.30E-06 18097487 Toluene 4.37E-05 2.16E-06 3.59E-07 8.51E-06 1.47E-06 18097487 Formaldehyde 6.48E-06 3.24E-07 2.10E-08 2.97E-07 2.97E-08 18097487 1,4-Dioxane 2.06E-06 1.01E-07 5.16E-09 0.00E+00 0.00E+00 18097487 Xylenes (mixed) 4.31E-07 1.69E-08 2.37E-09 0.00E+00 0.00E+00

7 of 27REL AEGL1 AEGL2 ERPG1 ERPG2

Facility NEI ID Pollutant Table 3 – Maximum Predicted Acute Risks

Actual Emissions

Maximum Hazard Quotient 1

18097487 Methanol 2.85E-07 1.15E-08 2.95E-09 3.06E-08 6.13E-09 18097487 Ethyl benzene 0.00E+00 2.94E-09 8.58E-11 0.00E+00 0.00E+00 19163356 Toluene 1.41E-04 6.98E-06 1.16E-06 2.75E-05 4.76E-06 19163356 Xylenes (mixed) 5.30E-06 2.08E-07 2.92E-08 0.00E+00 0.00E+00 19163356 Methanol 3.51E-06 1.42E-07 3.64E-08 3.78E-07 7.56E-08 19163356 Formaldehyde 3.02E-06 1.51E-07 9.77E-09 1.38E-07 1.38E-08 19163356 n-Hexane 0.00E+00 0.00E+00 3.10E-07 0.00E+00 0.00E+00 19163356 Ethyl benzene 0.00E+00 5.56E-08 1.62E-09 0.00E+00 0.00E+00 19163356 Cumene 0.00E+00 8.07E-09 1.34E-09 0.00E+00 0.00E+00 20035486 Diethylene glycol monobutyl ether 9.73E-02 0.00E+00 0.00E+00 0.00E+00 0.00E+00 20035486 2-Butoxyethyl acetate 1.85E-02 0.00E+00 0.00E+00 0.00E+00 0.00E+00 20035486 2,4-Toluene diisocyanate 0.00E+00 2.63E-03 6.24E-04 5.19E-03 3.35E-04 20035486 Formaldehyde 3.15E-03 1.58E-04 1.02E-05 1.44E-04 1.44E-05 20035486 Xylenes (mixed) 3.10E-03 1.22E-04 1.71E-05 0.00E+00 0.00E+00 20035486 Acrylic acid 6.17E-04 8.41E-04 2.64E-05 1.23E-03 2.47E-05 20035486 Toluene 1.03E-03 5.06E-05 8.43E-06 2.00E-04 3.45E-05 20035486 Ethylene glycol methyl ether 4.38E-04 0.00E+00 0.00E+00 0.00E+00 0.00E+00 20035486 Phenol 1.66E-04 1.66E-05 1.08E-05 2.53E-05 5.06E-06 20035486 Methanol 1.34E-04 5.42E-06 1.38E-06 1.44E-05 2.88E-06 20035486 Ethyl benzene 0.00E+00 9.24E-05 2.69E-06 0.00E+00 0.00E+00 20035486 Acrylonitrile 0.00E+00 1.59E-08 1.22E-09 7.21E-09 2.06E-09 20035486 n-Hexane 0.00E+00 0.00E+00 6.35E-11 0.00E+00 0.00E+00 20173193 Ethyl benzene 0.00E+00 1.32E-07 3.85E-09 0.00E+00 0.00E+00 20173193 Xylenes (mixed) 1.30E-07 5.10E-09 7.14E-10 0.00E+00 0.00E+00 20173193 Cumene 0.00E+00 1.62E-08 2.70E-09 0.00E+00 0.00E+00 20173453 2-Butoxyethyl acetate 1.72E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00 20173453 Xylenes (mixed) 2.63E-04 1.03E-05 1.45E-06 0.00E+00 0.00E+00 20173453 Toluene 1.48E-04 7.31E-06 1.22E-06 2.89E-05 4.99E-06 20173453 Ethyl benzene 0.00E+00 2.55E-06 7.44E-08 0.00E+00 0.00E+00 20173453 Methyl methacrylate 0.00E+00 1.75E-06 2.50E-07 0.00E+00 0.00E+00 20173453 Methanol 1.01E-07 4.10E-09 1.05E-09 1.09E-08 2.18E-09 24005458 Ethylene glycol methyl ether 2.31E-01 0.00E+00 0.00E+00 0.00E+00 0.00E+00 24005458 Propyl cellosolve 9.23E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00 24005458 Dimethyl formamide 0.00E+00 0.00E+00 2.66E-05 1.20E-03 2.39E-05 24005458 Glycol ethers 7.73E-04 0.00E+00 0.00E+00 0.00E+00 0.00E+00 24005458 Butyl carbitol acetate 7.29E-04 0.00E+00 0.00E+00 0.00E+00 0.00E+00 24005458 Toluene 1.37E-04 6.78E-06 1.13E-06 2.68E-05 4.62E-06 24005458 Xylenes (mixed) 5.03E-05 1.98E-06 2.77E-07 0.00E+00 0.00E+00 24005458 Mercury (elemental) 6.40E-06 0.00E+00 2.26E-09 0.00E+00 1.92E-09 24005458 Methanol 1.63E-06 6.62E-08 1.69E-08 1.76E-07 3.51E-08 24005458 Methylene diphenyl diisocyanate 0.00E+00 0.00E+00 0.00E+00 0.00E+00 5.11E-07 24005458 Ethyl benzene 0.00E+00 2.74E-09 8.00E-11 0.00E+00 0.00E+00 24015282 Tetrachloroethene 2.99E-04 2.49E-05 3.74E-06 8.80E-06 4.27E-06 24015282 Xylenes (mixed) 8.63E-06 3.39E-07 4.74E-08 0.00E+00 0.00E+00 24015282 Toluene 4.15E-06 2.05E-07 3.41E-08 8.08E-07 1.39E-07 24015282 Ethyl benzene 0.00E+00 7.31E-08 2.13E-09 0.00E+00 0.00E+00 24015282 Cumene 0.00E+00 2.98E-10 4.97E-11 0.00E+00 0.00E+00 24017525 Xylenes (mixed) 5.66E-05 2.22E-06 3.11E-07 0.00E+00 0.00E+00 24017525 Ethyl benzene 0.00E+00 2.53E-06 7.37E-08 0.00E+00 0.00E+00 24017525 Toluene 1.40E-06 6.89E-08 1.15E-08 2.72E-07 4.70E-08 24017525 Cumene 0.00E+00 2.83E-07 4.72E-08 0.00E+00 0.00E+00 25009450 Toluene 6.80E-06 3.36E-07 5.59E-08 1.33E-06 2.29E-07

8 of 27REL AEGL1 AEGL2 ERPG1 ERPG2

Facility NEI ID Pollutant Table 3 – Maximum Predicted Acute Risks

Actual Emissions

Maximum Hazard Quotient 1

25009450 Ethylene glycol methyl ether 5.05E-06 0.00E+00 0.00E+00 0.00E+00 0.00E+00 25009450 Diethylene glycol monobutyl ether 4.26E-06 0.00E+00 0.00E+00 0.00E+00 0.00E+00 25009450 Xylenes (mixed) 1.06E-06 4.16E-08 5.82E-09 0.00E+00 0.00E+00 25009450 Methylene chloride 4.77E-07 9.68E-09 3.51E-09 6.68E-09 2.57E-09 25009450 Methanol 1.31E-08 5.32E-10 1.36E-10 1.41E-09 2.82E-10 25009450 Ethyl benzene 0.00E+00 9.91E-09 2.89E-10 0.00E+00 0.00E+00 25009450 n-Hexane 0.00E+00 0.00E+00 1.08E-09 0.00E+00 0.00E+00 25009477 Diethylene glycol monobutyl ether 2.95E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00 25009477 Glycol ethers 7.70E-04 0.00E+00 0.00E+00 0.00E+00 0.00E+00 25009477 Toluene 1.03E-05 5.07E-07 8.45E-08 2.00E-06 3.46E-07 25009477 Methanol 1.01E-05 4.09E-07 1.05E-07 1.09E-06 2.17E-07 25009477 Formaldehyde 4.76E-07 2.38E-08 1.54E-09 2.18E-08 2.18E-09 25009477 Xylenes (mixed) 2.34E-08 9.19E-10 1.29E-10 0.00E+00 0.00E+00 25009477 Ethyl benzene 0.00E+00 7.49E-10 2.19E-11 0.00E+00 0.00E+00 26065644 2,4-Toluene diisocyanate 0.00E+00 2.88E-04 6.84E-05 5.68E-04 3.67E-05 26065644 Methanol 1.32E-04 5.36E-06 1.37E-06 1.42E-05 2.84E-06 26065644 Xylenes (mixed) 6.18E-05 2.43E-06 3.40E-07 0.00E+00 0.00E+00 26065644 Toluene 2.04E-05 1.01E-06 1.68E-07 3.97E-06 6.85E-07 26065644 Ethyl benzene 0.00E+00 1.10E-05 3.21E-07 0.00E+00 0.00E+00 26065644 Formaldehyde 1.40E-06 7.00E-08 4.53E-09 6.42E-08 6.42E-09 27053389 Diethylene glycol monobutyl ether 2.77E-05 0.00E+00 0.00E+00 0.00E+00 0.00E+00 27053389 Diethylene glycol monomethyl ether 2.17E-05 0.00E+00 0.00E+00 0.00E+00 0.00E+00 27053389 Ethylene glycol ethyl ether acetate 1.87E-05 0.00E+00 0.00E+00 0.00E+00 0.00E+00 27053389 2-Butoxyethyl acetate 1.52E-05 0.00E+00 0.00E+00 0.00E+00 0.00E+00 27053389 2,4-Toluene diisocyanate 0.00E+00 4.34E-06 1.03E-06 8.56E-06 5.52E-07 27053389 Xylenes (mixed) 1.04E-05 4.08E-07 5.71E-08 0.00E+00 0.00E+00 27053389 Toluene 4.66E-06 2.30E-07 3.83E-08 9.07E-07 1.57E-07 27053389 Methylene diphenyl diisocyanate 0.00E+00 0.00E+00 0.00E+00 0.00E+00 1.15E-06 27053389 Methanol 2.06E-07 8.35E-09 2.13E-09 2.22E-08 4.43E-09 27053389 Phenol 1.62E-07 1.62E-08 1.06E-08 2.48E-08 4.96E-09 27053389 Ethyl benzene 0.00E+00 1.73E-07 5.05E-09 0.00E+00 0.00E+00 27053389 Styrene 8.74E-08 2.16E-08 3.34E-09 8.74E-09 1.67E-09 27053389 Propylene oxide 9.66E-08 1.76E-09 4.34E-10 2.49E-09 5.07E-10 27053389 Formaldehyde 5.49E-08 2.75E-09 1.78E-10 2.52E-09 2.52E-10 27053389 Methyl methacrylate 0.00E+00 3.70E-08 5.29E-09 0.00E+00 0.00E+00 27053389 Acrylic acid 2.46E-09 3.36E-09 1.05E-10 4.92E-09 9.84E-11 27053389 n-Hexane 0.00E+00 0.00E+00 3.00E-09 0.00E+00 0.00E+00 27053389 Cumene 0.00E+00 9.17E-12 1.53E-12 0.00E+00 0.00E+00 28089367 Glycol Ethers 1.06E-01 0.00E+00 0.00E+00 0.00E+00 0.00E+00 28089367 Xylenes (mixed) 1.68E-03 6.61E-05 9.25E-06 0.00E+00 0.00E+00 28089367 Toluene 9.24E-04 4.56E-05 7.59E-06 1.80E-04 3.11E-05 28089367 Ethyl benzene 0.00E+00 4.79E-05 1.40E-06 0.00E+00 0.00E+00 28089367 Methanol 8.92E-06 3.62E-07 9.25E-08 9.60E-07 1.92E-07 29183329 Butyl carbitol acetate 9.22E-04 0.00E+00 0.00E+00 0.00E+00 0.00E+00 29183329 Xylenes (mixed) 3.57E-04 1.40E-05 1.97E-06 0.00E+00 0.00E+00 29183329 Glycol Ethers 7.81E-05 0.00E+00 0.00E+00 0.00E+00 0.00E+00 29183329 Methanol 2.02E-05 8.20E-07 2.09E-07 2.17E-06 4.35E-07 29183329 Toluene 1.29E-05 6.36E-07 1.06E-07 2.51E-06 4.34E-07 29183329 Methylene diphenyl diisocyanate 0.00E+00 0.00E+00 0.00E+00 0.00E+00 1.58E-05 29183329 Carbitol acetate 5.34E-06 0.00E+00 0.00E+00 0.00E+00 0.00E+00 29183329 Phenol 3.61E-06 3.61E-07 2.35E-07 5.51E-07 1.10E-07 29183329 Cumene 0.00E+00 3.06E-06 5.10E-07 0.00E+00 0.00E+00

9 of 27REL AEGL1 AEGL2 ERPG1 ERPG2

Facility NEI ID Pollutant Table 3 – Maximum Predicted Acute Risks

Actual Emissions

Maximum Hazard Quotient 1

29183329 Ethylene glycol methyl ether 3.52E-06 0.00E+00 0.00E+00 0.00E+00 0.00E+00 29183329 Propyl cellosolve 2.96E-06 0.00E+00 0.00E+00 0.00E+00 0.00E+00 29183329 p-Xylene 2.82E-06 0.00E+00 0.00E+00 0.00E+00 0.00E+00 29183329 Formaldehyde 1.51E-06 7.56E-08 4.89E-09 6.93E-08 6.93E-09 29183329 Ethylene glycol ethyl ether acetate 9.18E-07 0.00E+00 0.00E+00 0.00E+00 0.00E+00 29183329 Chlorobenzene 0.00E+00 3.23E-07 2.15E-08 0.00E+00 0.00E+00 29183329 Ethyl benzene 0.00E+00 2.00E-07 5.83E-09 0.00E+00 0.00E+00 29183329 Acrylic acid 1.85E-08 2.52E-08 7.92E-10 3.70E-08 7.39E-10 29183329 Ethylene glycol ethyl ether 5.95E-08 0.00E+00 0.00E+00 0.00E+00 0.00E+00 29183329 Ethyl acrylate 0.00E+00 3.78E-12 8.56E-13 3.13E-09 1.07E-12 29183329 Epichlorohydrin 2.48E-09 1.46E-10 3.54E-11 1.69E-10 4.23E-11 29183329 Benzene 9.06E-10 6.93E-12 4.53E-13 7.36E-12 2.45E-12 29183329 Arsenic compounds 5.60E-10 0.00E+00 0.00E+00 0.00E+00 0.00E+00 29183329 n-Hexane 0.00E+00 0.00E+00 3.60E-10 0.00E+00 0.00E+00 29183329 1,3-Butadiene 0.00E+00 1.70E-14 2.00E-15 1.18E-12 5.90E-14 29189332 2,4-Toluene diisocyanate 0.00E+00 6.06E-04 1.44E-04 1.19E-03 7.71E-05 29189332 Propyl cellosolve 7.59E-04 0.00E+00 0.00E+00 0.00E+00 0.00E+00 29189332 Butyl carbitol acetate 6.15E-04 0.00E+00 0.00E+00 0.00E+00 0.00E+00 29189332 Xylenes (mixed) 3.73E-04 1.46E-05 2.05E-06 0.00E+00 0.00E+00 29189332 Toluene 2.76E-04 1.36E-05 2.27E-06 5.38E-05 9.30E-06 29189332 Methylene diphenyl diisocyanate 0.00E+00 0.00E+00 0.00E+00 0.00E+00 2.07E-04 29189332 Methanol 1.39E-04 5.65E-06 1.44E-06 1.50E-05 3.00E-06 29189332 Diethylene glycol monobutyl ether 1.64E-04 0.00E+00 0.00E+00 0.00E+00 0.00E+00 29189332 Formaldehyde 8.21E-05 4.11E-06 2.66E-07 3.77E-06 3.77E-07 29189332 Phenol 6.59E-05 6.59E-06 4.29E-06 1.01E-05 2.01E-06 29189332 Ethyl acrylate 0.00E+00 8.30E-08 1.88E-08 6.89E-05 2.35E-08 29189332 Methylene chloride 3.66E-05 7.43E-07 2.70E-07 5.12E-07 1.97E-07 29189332 Ethylene glycol methyl ether 2.16E-05 0.00E+00 0.00E+00 0.00E+00 0.00E+00 29189332 Glycol Ethers 1.83E-05 0.00E+00 0.00E+00 0.00E+00 0.00E+00 29189332 Ethylene glycol ethyl ether acetate 1.26E-05 0.00E+00 0.00E+00 0.00E+00 0.00E+00 29189332 Diethylene glycol monoethyl ether 1.04E-05 0.00E+00 0.00E+00 0.00E+00 0.00E+00 29189332 Carbitol acetate 7.99E-06 0.00E+00 0.00E+00 0.00E+00 0.00E+00 29189332 Methyl methacrylate 0.00E+00 4.38E-06 6.26E-07 0.00E+00 0.00E+00 29189332 Cumene 0.00E+00 2.83E-06 4.72E-07 0.00E+00 0.00E+00 29189332 Acrylic acid 3.58E-07 4.88E-07 1.53E-08 7.16E-07 1.43E-08 29189332 Styrene 1.04E-06 2.56E-07 3.96E-08 1.04E-07 1.98E-08 29189332 Chlorobenzene 0.00E+00 1.34E-06 8.92E-08 0.00E+00 0.00E+00 29189332 Ethyl benzene 0.00E+00 1.36E-06 3.98E-08 0.00E+00 0.00E+00 29189332 Triethylamine 1.28E-06 0.00E+00 0.00E+00 0.00E+00 0.00E+00 29189332 Ethylene glycol ethyl ether 8.15E-07 0.00E+00 0.00E+00 0.00E+00 0.00E+00 29189332 p-Xylene 4.42E-07 0.00E+00 0.00E+00 0.00E+00 0.00E+00 29189332 1,2-Epoxybutane 0.00E+00 2.53E-07 1.29E-07 0.00E+00 0.00E+00 29189332 Ethylene oxide 0.00E+00 0.00E+00 1.21E-08 0.00E+00 1.09E-08 29189332 n-Hexane 0.00E+00 0.00E+00 4.32E-09 0.00E+00 0.00E+00 29189332 Benzene 1.87E-09 1.43E-11 9.36E-13 1.52E-11 5.07E-12 29189332 Epichlorohydrin 3.30E-10 1.95E-11 4.72E-12 2.26E-11 5.65E-12 29189332 Acrylonitrile 0.00E+00 3.70E-14 3.00E-15 1.70E-14 5.00E-15 29189513 Toluene 8.01E-04 3.95E-05 6.59E-06 1.56E-04 2.69E-05 29189513 Diethylene glycol monobutyl ether 5.27E-04 0.00E+00 0.00E+00 0.00E+00 0.00E+00 29189513 Glycol Ethers 1.33E-04 0.00E+00 0.00E+00 0.00E+00 0.00E+00 29189513 Methylene diphenyl diisocyanate 0.00E+00 0.00E+00 0.00E+00 0.00E+00 1.07E-04 29189513 Methanol 2.51E-05 1.02E-06 2.60E-07 2.70E-06 5.40E-07

10 of 27REL AEGL1 AEGL2 ERPG1 ERPG2

Facility NEI ID Pollutant Table 3 – Maximum Predicted Acute Risks

Actual Emissions

Maximum Hazard Quotient 1

29189513 Xylenes (mixed) 2.67E-05 1.05E-06 1.47E-07 0.00E+00 0.00E+00 29189513 Phenol 1.25E-05 1.25E-06 8.18E-07 1.92E-06 3.83E-07 29189513 Tetrachloroethene 1.15E-05 9.62E-07 1.44E-07 3.40E-07 1.65E-07 29189513 Methylene chloride 1.11E-05 2.25E-07 8.16E-08 1.55E-07 5.96E-08 29189513 Benzene 1.41E-06 1.08E-08 7.06E-10 1.15E-08 3.83E-09 29189513 Ethyl benzene 0.00E+00 5.39E-07 1.57E-08 0.00E+00 0.00E+00 29189513 n-Hexane 0.00E+00 0.00E+00 1.48E-07 0.00E+00 0.00E+00 33011652 Toluene 6.33E-05 3.12E-06 5.21E-07 1.23E-05 2.13E-06 33011652 Xylenes (mixed) 2.11E-05 8.30E-07 1.16E-07 0.00E+00 0.00E+00 33011652 Phenol 1.78E-06 1.78E-07 1.16E-07 2.72E-07 5.43E-08 33011652 Methylene chloride 1.84E-06 3.74E-08 1.36E-08 2.58E-08 9.93E-09 33011652 Ethyl benzene 0.00E+00 1.29E-06 3.75E-08 0.00E+00 0.00E+00 33011652 Formaldehyde 7.25E-07 3.62E-08 2.34E-09 3.32E-08 3.32E-09 33011652 Methanol 1.09E-08 4.41E-10 1.13E-10 1.17E-09 2.34E-10 33011652 Cumene 0.00E+00 6.77E-09 1.13E-09 0.00E+00 0.00E+00 33011652 Methylene diphenyl diisocyanate 0.00E+00 0.00E+00 0.00E+00 0.00E+00 3.63E-09 36103518 Xylenes (mixed) 1.76E-03 6.91E-05 9.67E-06 0.00E+00 0.00E+00 36103518 Toluene 7.90E-04 3.90E-05 6.49E-06 1.54E-04 2.66E-05 36103521 Xylenes (mixed) 9.26E-05 3.64E-06 5.09E-07 0.00E+00 0.00E+00 36103521 Toluene 2.94E-05 1.45E-06 2.42E-07 5.73E-06 9.90E-07 36103521 Ethyl benzene 0.00E+00 1.59E-06 4.65E-08 0.00E+00 0.00E+00 36111630 Glycol Ethers 6.54E-02 0.00E+00 0.00E+00 0.00E+00 0.00E+00 36111630 Xylenes (mixed) 1.66E-03 6.52E-05 9.13E-06 0.00E+00 0.00E+00 36111630 Toluene 1.05E-03 5.19E-05 8.65E-06 2.05E-04 3.54E-05 36111630 Formaldehyde 2.60E-04 1.30E-05 8.42E-07 1.19E-05 1.19E-06 36111630 Ethyl benzene 0.00E+00 8.74E-06 2.55E-07 0.00E+00 0.00E+00 37049483 Methanol 3.44E-05 1.40E-06 3.57E-07 3.71E-06 7.41E-07 37049483 Xylenes (mixed) 2.33E-05 9.15E-07 1.28E-07 0.00E+00 0.00E+00 37049483 Phenol 1.35E-05 1.35E-06 8.79E-07 2.06E-06 4.12E-07 37049483 Toluene 1.40E-05 6.92E-07 1.15E-07 2.73E-06 4.72E-07 37049483 Methylene chloride 1.27E-05 2.58E-07 9.35E-08 1.78E-07 6.84E-08 37049483 Methyl methacrylate 0.00E+00 6.22E-07 8.89E-08 0.00E+00 0.00E+00 37049483 Ethyl benzene 0.00E+00 3.86E-07 1.13E-08 0.00E+00 0.00E+00 37049483 Formaldehyde 2.94E-07 1.47E-08 9.50E-10 1.35E-08 1.35E-09 37049483 Benzene 7.51E-08 5.75E-10 3.76E-11 6.10E-10 2.03E-10 37049483 Cumene 0.00E+00 1.62E-08 2.69E-09 0.00E+00 0.00E+00 37051340 Xylenes (mixed) 1.42E-05 5.56E-07 7.79E-08 0.00E+00 0.00E+00 37051340 Toluene 2.89E-06 1.43E-07 2.38E-08 5.63E-07 9.73E-08 37051340 Ethyl benzene 0.00E+00 7.44E-07 2.17E-08 0.00E+00 0.00E+00 37051340 Cumene 0.00E+00 4.54E-10 7.57E-11 0.00E+00 0.00E+00 37119600 Xylenes (mixed) 3.48E-04 1.37E-05 1.91E-06 0.00E+00 0.00E+00 37119600 Toluene 2.77E-04 1.36E-05 2.27E-06 5.39E-05 9.30E-06 37119600 Formaldehyde 1.28E-04 6.39E-06 4.13E-07 5.86E-06 5.86E-07 37119600 Phenol 4.22E-05 4.22E-06 2.75E-06 6.44E-06 1.29E-06 37119600 Methylene diphenyl diisocyanate 0.00E+00 0.00E+00 0.00E+00 0.00E+00 2.85E-05 37119600 Ethyl benzene 0.00E+00 1.31E-05 3.83E-07 0.00E+00 0.00E+00 37119600 Hydrofluoric acid 3.47E-06 1.02E-06 4.17E-08 5.21E-07 5.21E-08 37119600 Styrene 2.96E-06 7.31E-07 1.13E-07 2.96E-07 5.65E-08 37119600 Methylene chloride 7.33E-07 1.49E-08 5.40E-09 1.03E-08 3.95E-09 37119600 Tetrachloroethene 5.13E-07 4.28E-08 6.41E-09 1.51E-08 7.33E-09 37119600 1,1,1-Trichloroethane 1.51E-07 7.89E-09 3.11E-09 5.40E-09 2.70E-09 37119600 Benzene 9.83E-08 7.52E-10 4.91E-11 7.99E-10 2.66E-10

11 of 27REL AEGL1 AEGL2 ERPG1 ERPG2

Facility NEI ID Pollutant Table 3 – Maximum Predicted Acute Risks

Actual Emissions

Maximum Hazard Quotient 1

37119600 n-Hexane 0.00E+00 0.00E+00 1.62E-08 0.00E+00 0.00E+00 37119600 Arsenic compounds 6.05E-09 0.00E+00 0.00E+00 0.00E+00 0.00E+00 37119600 Methanol 2.55E-09 1.04E-10 2.65E-11 2.75E-10 5.49E-11 37133455 Toluene 4.25E-05 2.10E-06 3.50E-07 8.29E-06 1.43E-06 37133455 2,4-Toluene diisocyanate 0.00E+00 8.65E-06 2.05E-06 1.71E-05 1.10E-06 37133455 Xylenes (mixed) 1.54E-05 6.06E-07 8.49E-08 0.00E+00 0.00E+00 37133455 Ethyl benzene 0.00E+00 9.06E-07 2.64E-08 0.00E+00 0.00E+00 37133455 Cumene 0.00E+00 7.84E-07 1.31E-07 0.00E+00 0.00E+00 37133455 Arsenic compounds 1.42E-07 0.00E+00 0.00E+00 0.00E+00 0.00E+00 37133455 Methanol 7.96E-09 3.23E-10 8.26E-11 8.57E-10 1.71E-10 37133455 Mercury (elemental) 4.39E-10 0.00E+00 1.55E-13 0.00E+00 1.32E-13 39017343 Ethylene glycol ethyl ether 2.88E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00 39017343 Butyl carbitol acetate 1.19E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00 39017343 2-Butoxyethyl acetate 8.02E-04 0.00E+00 0.00E+00 0.00E+00 0.00E+00 39017343 Propyl cellosolve 6.13E-04 0.00E+00 0.00E+00 0.00E+00 0.00E+00 39017343 Toluene 2.18E-06 1.07E-07 1.79E-08 4.24E-07 7.33E-08 39017343 Xylenes (mixed) 1.91E-06 7.51E-08 1.05E-08 0.00E+00 0.00E+00 39017343 2,4-Toluene diisocyanate 0.00E+00 5.07E-07 1.20E-07 9.99E-07 6.45E-08 39017343 Methanol 1.27E-06 5.16E-08 1.32E-08 1.37E-07 2.74E-08 39017343 Ethyl benzene 0.00E+00 5.83E-08 1.70E-09 0.00E+00 0.00E+00 39017343 Arsenic compounds 2.42E-08 0.00E+00 0.00E+00 0.00E+00 0.00E+00 39021672 2-Butoxyethyl acetate 9.02E-04 0.00E+00 0.00E+00 0.00E+00 0.00E+00 39021672 Xylenes (mixed) 5.50E-04 2.16E-05 3.02E-06 0.00E+00 0.00E+00 39021672 2,4-Toluene diisocyanate 0.00E+00 1.11E-04 2.64E-05 2.19E-04 1.42E-05 39021672 Formaldehyde 2.22E-04 1.11E-05 7.18E-07 1.02E-05 1.02E-06 39021672 Toluene 1.71E-04 8.41E-06 1.40E-06 3.32E-05 5.74E-06 39021672 Diethylene glycol monoethyl ether 1.86E-04 0.00E+00 0.00E+00 0.00E+00 0.00E+00 39021672 Ethylene glycol methyl ether 8.73E-05 0.00E+00 0.00E+00 0.00E+00 0.00E+00 39021672 Phenol 1.83E-05 1.83E-06 1.19E-06 2.79E-06 5.58E-07 39021672 Ethyl benzene 0.00E+00 2.00E-05 5.82E-07 0.00E+00 0.00E+00 39021672 Methylene chloride 1.52E-05 3.08E-07 1.12E-07 2.12E-07 8.16E-08 39021672 Epichlorohydrin 1.36E-06 8.02E-08 1.94E-08 9.29E-08 2.32E-08 39021672 Acrylic acid 1.25E-07 1.70E-07 5.34E-09 2.49E-07 4.99E-09 39021672 Cumene 0.00E+00 1.64E-07 2.74E-08 0.00E+00 0.00E+00 39021672 Benzene 1.81E-07 1.38E-09 9.04E-11 1.47E-09 4.90E-10 39021672 Arsenic compounds 5.80E-08 0.00E+00 0.00E+00 0.00E+00 0.00E+00 39021672 n-Hexane 0.00E+00 0.00E+00 5.51E-09 0.00E+00 0.00E+00 39021672 Beryllium compounds 0.00E+00 0.00E+00 0.00E+00 0.00E+00 1.88E-11 39057430 Glycol Ethers 1.10E-04 0.00E+00 0.00E+00 0.00E+00 0.00E+00 39057430 Toluene 2.87E-06 1.42E-07 2.36E-08 5.59E-07 9.65E-08 39057430 Xylenes (mixed) 2.00E-06 7.85E-08 1.10E-08 0.00E+00 0.00E+00 39057430 Methylene chloride 3.85E-07 7.82E-09 2.84E-09 5.40E-09 2.08E-09 39057430 Ethyl benzene 0.00E+00 2.99E-08 8.74E-10 0.00E+00 0.00E+00 39057430 Methanol 1.60E-08 6.48E-10 1.66E-10 1.72E-09 3.44E-10 39057430 Propylene oxide 9.68E-09 1.77E-10 4.35E-11 2.50E-10 5.09E-11 39057430 Benzene 8.27E-09 6.33E-11 4.14E-12 6.72E-11 2.24E-11 39057430 Cumene 0.00E+00 7.16E-10 1.19E-10 0.00E+00 0.00E+00 39057430 Acetaldehyde 2.02E-10 1.17E-12 1.94E-13 5.28E-12 2.64E-13 39061524 Diethylene glycol dimethyl ether 7.16E-05 0.00E+00 0.00E+00 0.00E+00 0.00E+00 39061524 Toluene 1.96E-05 9.65E-07 1.61E-07 3.81E-06 6.58E-07 39109419 Xylenes (mixed) 1.84E-06 7.25E-08 1.01E-08 0.00E+00 0.00E+00 39109419 Ethyl benzene 0.00E+00 2.90E-08 8.45E-10 0.00E+00 0.00E+00

12 of 27REL AEGL1 AEGL2 ERPG1 ERPG2

Facility NEI ID Pollutant Table 3 – Maximum Predicted Acute Risks

Actual Emissions

Maximum Hazard Quotient 1

40143460 2-Butoxyethyl acetate 5.48E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00 40143460 Butyl carbitol acetate 5.01E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00 40143460 Ethylene glycol ethyl ether acetate 4.41E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00 40143460 Ethylene glycol ethyl ether 3.75E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00 40143460 Ethylene glycol methyl ether 1.57E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00 40143460 Toluene 1.11E-03 5.49E-05 9.14E-06 2.17E-04 3.74E-05 40143460 Xylenes (mixed) 1.14E-03 4.50E-05 6.29E-06 0.00E+00 0.00E+00 40143460 Methylene diphenyl diisocyanate 0.00E+00 0.00E+00 0.00E+00 0.00E+00 7.63E-05 40143460 Ethyl benzene 0.00E+00 4.58E-05 1.34E-06 0.00E+00 0.00E+00 40143460 Formaldehyde 1.68E-05 8.41E-07 5.44E-08 7.71E-07 7.71E-08 40143460 Methanol 9.91E-07 4.02E-08 1.03E-08 1.07E-07 2.13E-08 40143494 Formaldehyde 4.26E-03 2.13E-04 1.38E-05 1.95E-04 1.95E-05 40143494 Phenol 3.63E-04 3.63E-05 2.37E-05 5.55E-05 1.11E-05 40143494 2-Butoxyethyl acetate 2.01E-04 0.00E+00 0.00E+00 0.00E+00 0.00E+00 40143494 Toluene 1.19E-04 5.85E-06 9.75E-07 2.31E-05 3.99E-06 40143494 Xylenes (mixed) 2.69E-05 1.06E-06 1.48E-07 0.00E+00 0.00E+00 40143494 Ethyl benzene 0.00E+00 7.88E-08 2.30E-09 0.00E+00 0.00E+00 40143494 Methylene diphenyl diisocyanate 0.00E+00 0.00E+00 0.00E+00 0.00E+00 6.97E-08 40143494 Cumene 0.00E+00 4.87E-09 8.12E-10 0.00E+00 0.00E+00 40143523 Ethylene glycol methyl ether acetate 7.32E-04 0.00E+00 0.00E+00 0.00E+00 0.00E+00 40143523 Xylenes (mixed) 6.69E-04 2.63E-05 3.68E-06 0.00E+00 0.00E+00 40143523 Toluene 3.17E-04 1.56E-05 2.60E-06 6.17E-05 1.07E-05 40143523 Phenol 3.93E-05 3.93E-06 2.56E-06 6.00E-06 1.20E-06 40143523 Ethyl benzene 0.00E+00 3.20E-05 9.35E-07 0.00E+00 0.00E+00 40143523 Methylene diphenyl diisocyanate 0.00E+00 0.00E+00 0.00E+00 0.00E+00 1.69E-05 40143523 Methanol 8.14E-06 3.30E-07 8.44E-08 8.76E-07 1.75E-07 40143523 Cumene 0.00E+00 1.84E-08 3.07E-09 0.00E+00 0.00E+00 42003603 Xylenes (mixed) 5.17E-05 2.03E-06 2.84E-07 0.00E+00 0.00E+00 42003603 Toluene 2.79E-05 1.37E-06 2.29E-07 5.43E-06 9.37E-07 42003603 Methylene diphenyl diisocyanate 0.00E+00 0.00E+00 0.00E+00 0.00E+00 3.25E-05 42003603 Phenol 1.22E-05 1.22E-06 7.93E-07 1.86E-06 3.72E-07 42003603 Formaldehyde 1.38E-05 6.92E-07 4.48E-08 6.34E-07 6.34E-08 42003603 Styrene 1.82E-06 4.50E-07 6.96E-08 1.82E-07 3.48E-08 42003603 Ethyl benzene 0.00E+00 1.21E-06 3.54E-08 0.00E+00 0.00E+00 42003603 Arsenic compounds 8.85E-07 0.00E+00 0.00E+00 0.00E+00 0.00E+00 42003603 Methanol 5.74E-07 2.33E-08 5.95E-09 6.18E-08 1.24E-08 42003603 n-Hexane 0.00E+00 0.00E+00 3.23E-09 0.00E+00 0.00E+00 42003603 Cumene 0.00E+00 8.64E-10 1.44E-10 0.00E+00 0.00E+00 42045473 Propyl cellosolve 6.62E-02 0.00E+00 0.00E+00 0.00E+00 0.00E+00 42045473 2-Butoxyethyl acetate 8.57E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00 42045473 Phenol 1.06E-03 1.06E-04 6.93E-05 1.62E-04 3.24E-05 42045473 Methylene chloride 1.20E-03 2.43E-05 8.82E-06 1.68E-05 6.45E-06 42045473 Toluene 4.05E-04 2.00E-05 3.33E-06 7.88E-05 1.36E-05 42045473 Xylenes (mixed) 3.78E-04 1.49E-05 2.08E-06 0.00E+00 0.00E+00 42045473 Ethyl acrylate 0.00E+00 2.53E-07 5.73E-08 2.10E-04 7.16E-08 42045473 Diethylene glycol monobutyl ether 1.61E-04 0.00E+00 0.00E+00 0.00E+00 0.00E+00 42045473 Butyl carbitol acetate 1.37E-04 0.00E+00 0.00E+00 0.00E+00 0.00E+00 42045473 Methylene diphenyl diisocyanate 0.00E+00 0.00E+00 0.00E+00 0.00E+00 5.33E-05 42045473 Ethylene glycol methyl ether 4.27E-05 0.00E+00 0.00E+00 0.00E+00 0.00E+00 42045473 Ethylene glycol ethyl ether acetate 2.25E-05 0.00E+00 0.00E+00 0.00E+00 0.00E+00 42045473 Ethyl benzene 0.00E+00 1.42E-05 4.15E-07 0.00E+00 0.00E+00 42045473 Formaldehyde 1.11E-05 5.54E-07 3.59E-08 5.08E-07 5.08E-08

13 of 27REL AEGL1 AEGL2 ERPG1 ERPG2

Facility NEI ID Pollutant Table 3 – Maximum Predicted Acute Risks

Actual Emissions

Maximum Hazard Quotient 1

42045473 Methyl methacrylate 0.00E+00 5.91E-06 8.44E-07 0.00E+00 0.00E+00 42045473 Methanol 5.32E-06 2.16E-07 5.52E-08 5.73E-07 1.15E-07 42045473 Epichlorohydrin 4.16E-06 2.46E-07 5.94E-08 2.84E-07 7.11E-08 42045473 Acrylic acid 1.19E-07 1.62E-07 5.10E-09 2.38E-07 4.76E-09 42045473 Benzene 5.86E-08 4.48E-10 2.93E-11 4.76E-10 1.59E-10 42045473 Styrene 1.74E-08 4.29E-09 6.63E-10 1.74E-09 3.32E-10 42045473 n-Hexane 0.00E+00 0.00E+00 2.08E-08 0.00E+00 0.00E+00 42045473 Acetaldehyde 6.06E-09 3.52E-11 5.82E-12 1.58E-10 7.92E-12 42055233 Xylenes (mixed) 5.30E-05 2.08E-06 2.91E-07 0.00E+00 0.00E+00 42055233 Toluene 1.04E-05 5.12E-07 8.54E-08 2.02E-06 3.49E-07 42055233 Ethyl benzene 0.00E+00 1.15E-06 3.36E-08 0.00E+00 0.00E+00 42055233 n-Hexane 0.00E+00 0.00E+00 2.69E-09 0.00E+00 0.00E+00 42081660 Diethylene glycol monobutyl ether 9.25E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00 42081660 Xylenes (mixed) 3.22E-04 1.27E-05 1.77E-06 0.00E+00 0.00E+00 42081660 Toluene 1.98E-04 9.75E-06 1.63E-06 3.85E-05 6.65E-06 42081660 Ethyl benzene 0.00E+00 2.53E-05 7.37E-07 0.00E+00 0.00E+00 42081660 Methylene diphenyl diisocyanate 0.00E+00 0.00E+00 0.00E+00 0.00E+00 9.44E-06 45045417 Propyl cellosolve 5.77E-04 0.00E+00 0.00E+00 0.00E+00 0.00E+00 45045417 Butyl carbitol acetate 2.77E-04 0.00E+00 0.00E+00 0.00E+00 0.00E+00 45045417 2-Butoxyethyl acetate 1.74E-04 0.00E+00 0.00E+00 0.00E+00 0.00E+00 45045417 Ethylene glycol ethyl ether acetate 1.64E-04 0.00E+00 0.00E+00 0.00E+00 0.00E+00 45045417 Toluene 5.67E-05 2.80E-06 4.67E-07 1.10E-05 1.91E-06 45045417 Xylenes (mixed) 1.50E-05 5.91E-07 8.27E-08 0.00E+00 0.00E+00 45045417 Methylene diphenyl diisocyanate 0.00E+00 0.00E+00 0.00E+00 0.00E+00 1.07E-05 45045417 Ethylene glycol methyl ether 8.20E-06 0.00E+00 0.00E+00 0.00E+00 0.00E+00 45045417 Diethylene glycol monoethyl ether 6.28E-06 0.00E+00 0.00E+00 0.00E+00 0.00E+00 45045417 Diethylene glycol monobutyl ether 5.66E-06 0.00E+00 0.00E+00 0.00E+00 0.00E+00 45045417 Methanol 2.55E-06 1.04E-07 2.65E-08 2.75E-07 5.49E-08 45045417 Phenol 4.01E-07 4.01E-08 2.61E-08 6.12E-08 1.22E-08 45045417 Formaldehyde 4.82E-07 2.41E-08 1.56E-09 2.21E-08 2.21E-09 45045417 Ethyl benzene 0.00E+00 4.44E-07 1.30E-08 0.00E+00 0.00E+00 45045417 Benzene 4.74E-08 3.62E-10 2.37E-11 3.85E-10 1.28E-10 45045417 Methylene chloride 4.46E-08 9.05E-10 3.29E-10 6.25E-10 2.40E-10 45045417 Methyl methacrylate 0.00E+00 1.68E-08 2.40E-09 0.00E+00 0.00E+00 45045417 n-Hexane 0.00E+00 0.00E+00 1.11E-08 0.00E+00 0.00E+00 45045417 Cumene 0.00E+00 2.15E-09 3.58E-10 0.00E+00 0.00E+00 45063428 Methylene chloride 4.93E-03 1.00E-04 3.63E-05 6.90E-05 2.65E-05 45063428 Phenol 2.93E-03 2.93E-04 1.91E-04 4.47E-04 8.94E-05 45063428 Xylenes (mixed) 4.63E-04 1.82E-05 2.55E-06 0.00E+00 0.00E+00 45063428 Toluene 2.73E-04 1.35E-05 2.25E-06 5.32E-05 9.19E-06 48027213 Methanol 5.00E-06 2.03E-07 5.19E-08 5.39E-07 1.08E-07 48027213 Toluene 1.01E-06 4.98E-08 8.29E-09 1.96E-07 3.39E-08 48027213 Xylenes (mixed) 1.06E-06 4.15E-08 5.82E-09 0.00E+00 0.00E+00 48027213 Methylene chloride 1.02E-07 2.07E-09 7.52E-10 1.43E-09 5.49E-10 48027213 Ethyl benzene 0.00E+00 3.20E-08 9.35E-10 0.00E+00 0.00E+00 48029468 Ethyl acrylate 0.00E+00 1.93E-06 4.38E-07 1.60E-03 5.48E-07 48029468 Formaldehyde 1.42E-03 7.12E-05 4.60E-06 6.52E-05 6.52E-06 48029468 Xylenes (mixed) 2.04E-04 8.00E-06 1.12E-06 0.00E+00 0.00E+00 48029468 Toluene 1.30E-04 6.39E-06 1.06E-06 2.52E-05 4.36E-06 48029468 2-Butoxyethyl acetate 8.39E-05 0.00E+00 0.00E+00 0.00E+00 0.00E+00 48029468 Benzene 3.93E-05 3.01E-07 1.97E-08 3.20E-07 1.07E-07 48029468 Methylene diphenyl diisocyanate 0.00E+00 0.00E+00 0.00E+00 0.00E+00 2.49E-05

14 of 27REL AEGL1 AEGL2 ERPG1 ERPG2

Facility NEI ID Pollutant Table 3 – Maximum Predicted Acute Risks

Actual Emissions

Maximum Hazard Quotient 1

48029468 Methanol 9.04E-06 3.67E-07 9.38E-08 9.74E-07 1.95E-07 48029468 Ethyl benzene 0.00E+00 8.48E-06 2.47E-07 0.00E+00 0.00E+00 48029468 Ethylene glycol methyl ether 6.75E-06 0.00E+00 0.00E+00 0.00E+00 0.00E+00 48029468 Phenol 3.06E-06 3.06E-07 1.99E-07 4.66E-07 9.33E-08 48029468 Styrene 1.41E-06 3.48E-07 5.37E-08 1.41E-07 2.69E-08 48029468 Methyl methacrylate 0.00E+00 1.41E-06 2.01E-07 0.00E+00 0.00E+00 48029468 Cumene 0.00E+00 2.73E-07 4.54E-08 0.00E+00 0.00E+00 48029468 p-Xylene 1.98E-07 0.00E+00 0.00E+00 0.00E+00 0.00E+00 48029468 Acrylonitrile 0.00E+00 1.41E-09 1.08E-10 6.40E-10 1.83E-10 48029468 n-Hexane 0.00E+00 0.00E+00 6.74E-10 0.00E+00 0.00E+00 48231376 Toluene 2.41E-04 1.19E-05 1.98E-06 4.69E-05 8.11E-06 48231376 Xylenes (mixed) 1.98E-04 7.79E-06 1.09E-06 0.00E+00 0.00E+00 48231376 Styrene 5.95E-05 1.47E-05 2.27E-06 5.95E-06 1.14E-06 48231376 Ethylene glycol methyl ether 2.16E-05 0.00E+00 0.00E+00 0.00E+00 0.00E+00 48231376 2,4-Toluene diisocyanate 0.00E+00 3.98E-06 9.44E-07 7.84E-06 5.06E-07 48231376 Methanol 1.56E-06 6.34E-08 1.62E-08 1.68E-07 3.36E-08 48231376 Ethyl benzene 0.00E+00 1.88E-07 5.47E-09 0.00E+00 0.00E+00 48231376 n-Hexane 0.00E+00 0.00E+00 7.90E-08 0.00E+00 0.00E+00 48439416 Butyl carbitol acetate 2.73E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00 48439416 Methylene diphenyl diisocyanate 0.00E+00 0.00E+00 0.00E+00 0.00E+00 3.14E-04 48439416 Xylenes (mixed) 7.73E-05 3.03E-06 4.25E-07 0.00E+00 0.00E+00 48439416 Diethylene glycol dimethyl ether 4.71E-05 0.00E+00 0.00E+00 0.00E+00 0.00E+00 48439416 Ethylene glycol methyl ether 2.75E-05 0.00E+00 0.00E+00 0.00E+00 0.00E+00 48439416 Toluene 1.97E-05 9.73E-07 1.62E-07 3.84E-06 6.63E-07 48439416 Dimethyl formamide 0.00E+00 0.00E+00 4.10E-07 1.85E-05 3.69E-07 48439416 Propyl cellosolve 1.02E-05 0.00E+00 0.00E+00 0.00E+00 0.00E+00 48439416 Formaldehyde 6.34E-06 3.17E-07 2.05E-08 2.91E-07 2.91E-08 48439416 2-Butoxyethyl acetate 1.79E-06 0.00E+00 0.00E+00 0.00E+00 0.00E+00 48439416 Arsenic compounds 1.41E-06 0.00E+00 0.00E+00 0.00E+00 0.00E+00 48439416 p-Xylene 1.42E-07 0.00E+00 0.00E+00 0.00E+00 0.00E+00 48439416 Ethyl benzene 0.00E+00 1.30E-07 3.80E-09 0.00E+00 0.00E+00 48439416 Cumene 0.00E+00 3.43E-08 5.71E-09 0.00E+00 0.00E+00 48439416 Methyl methacrylate 0.00E+00 1.58E-08 2.25E-09 0.00E+00 0.00E+00 48439416 Carbon tetrachloride 9.68E-09 6.57E-11 1.53E-11 1.41E-10 2.92E-11 48439416 n-Hexane 0.00E+00 0.00E+00 2.68E-09 0.00E+00 0.00E+00 48439416 Aniline 0.00E+00 6.13E-10 4.00E-10 0.00E+00 0.00E+00 48439416 Beryllium compounds 0.00E+00 0.00E+00 0.00E+00 0.00E+00 2.02E-10 48439508 2-Butoxyethyl acetate 5.09E-04 0.00E+00 0.00E+00 0.00E+00 0.00E+00 48439508 Tetrachloroethene 1.90E-06 1.58E-07 2.37E-08 5.58E-08 2.71E-08 48439508 Toluene 1.05E-06 5.16E-08 8.60E-09 2.04E-07 3.52E-08 48439508 Xylenes (mixed) 8.80E-07 3.46E-08 4.84E-09 0.00E+00 0.00E+00 48439508 Phenol 5.49E-08 5.49E-09 3.58E-09 8.37E-09 1.67E-09 48439508 Ethyl benzene 0.00E+00 4.59E-08 1.34E-09 0.00E+00 0.00E+00 48439508 Methanol 1.14E-08 4.61E-10 1.18E-10 1.22E-09 2.45E-10 49003696 1,1,1-Trichloroethane 1.88E-03 9.81E-05 3.87E-05 6.71E-05 3.36E-05 49003696 Xylenes (mixed) 1.55E-03 6.08E-05 8.52E-06 0.00E+00 0.00E+00 49003696 Methylene diphenyl diisocyanate 0.00E+00 0.00E+00 0.00E+00 0.00E+00 2.66E-04 49003696 Toluene 9.95E-05 4.91E-06 8.18E-07 1.94E-05 3.35E-06 49003696 Ethylene glycol ethyl ether 7.64E-05 0.00E+00 0.00E+00 0.00E+00 0.00E+00 49003696 Ethyl benzene 0.00E+00 3.62E-05 1.06E-06 0.00E+00 0.00E+00 49003696 Trichloroethylene 0.00E+00 4.76E-06 1.39E-06 6.17E-06 1.23E-06 49003696 Tetrachloroethene 1.06E-05 8.84E-07 1.33E-07 3.12E-07 1.52E-07

15 of 27REL AEGL1 AEGL2 ERPG1 ERPG2

Facility NEI ID Pollutant Table 3 – Maximum Predicted Acute Risks

Actual Emissions

Maximum Hazard Quotient 1

49003696 1,2-Epoxybutane 0.00E+00 4.54E-07 2.32E-07 0.00E+00 0.00E+00 49003696 Formaldehyde 5.14E-07 2.57E-08 1.66E-09 2.36E-08 2.36E-09 49003696 Cumene 0.00E+00 3.16E-07 5.27E-08 0.00E+00 0.00E+00 49003696 Methanol 5.05E-08 2.05E-09 5.24E-10 5.44E-09 1.09E-09 49003696 Hydrofluoric acid 2.06E-08 6.02E-09 2.47E-10 3.09E-09 3.09E-10 49003696 Arsenic compounds 3.75E-09 0.00E+00 0.00E+00 0.00E+00 0.00E+00 49011663 Propyl cellosolve 1.46E-02 0.00E+00 0.00E+00 0.00E+00 0.00E+00 49011663 Diethylene glycol dimethyl ether 9.01E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00 49011663 Butyl carbitol acetate 3.07E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00 49011663 Formaldehyde 8.18E-04 4.09E-05 2.65E-06 3.75E-05 3.75E-06 49011663 Phenol 4.92E-04 4.92E-05 3.21E-05 7.51E-05 1.50E-05 49011663 Methylene chloride 4.54E-04 9.22E-06 3.35E-06 6.36E-06 2.45E-06 49011663 Diethylene glycol monobutyl ether 3.99E-04 0.00E+00 0.00E+00 0.00E+00 0.00E+00 49011663 Xylenes (mixed) 3.44E-04 1.35E-05 1.89E-06 0.00E+00 0.00E+00 49011663 Toluene 2.61E-04 1.29E-05 2.15E-06 5.09E-05 8.79E-06 49011663 Methanol 1.41E-04 5.74E-06 1.47E-06 1.52E-05 3.05E-06 49011663 Ethylene glycol ethyl ether 1.28E-04 0.00E+00 0.00E+00 0.00E+00 0.00E+00 49011663 Ethylene glycol methyl ether 8.41E-05 0.00E+00 0.00E+00 0.00E+00 0.00E+00 49011663 Dimethyl formamide 0.00E+00 0.00E+00 1.59E-06 7.15E-05 1.43E-06 49011663 Glycol Ethers 5.61E-05 0.00E+00 0.00E+00 0.00E+00 0.00E+00 49011663 2-(Hexyloxy)ethanol 4.78E-05 0.00E+00 0.00E+00 0.00E+00 0.00E+00 49011663 Methylene diphenyl diisocyanate 0.00E+00 0.00E+00 0.00E+00 0.00E+00 3.00E-05 49011663 Diethylene glycol monomethyl ether 2.21E-05 0.00E+00 0.00E+00 0.00E+00 0.00E+00 49011663 2-Butoxyethyl acetate 1.28E-05 0.00E+00 0.00E+00 0.00E+00 0.00E+00 49011663 Acetaldehyde 6.61E-06 3.84E-08 6.34E-09 1.73E-07 8.63E-09 49011663 1,2-Epoxybutane 0.00E+00 4.26E-06 2.18E-06 0.00E+00 0.00E+00 49011663 Ethylene glycol ethyl ether acetate 3.72E-06 0.00E+00 0.00E+00 0.00E+00 0.00E+00 49011663 Acrylonitrile 0.00E+00 2.16E-06 1.66E-07 9.84E-07 2.81E-07 49011663 Ethyl benzene 0.00E+00 3.34E-06 9.74E-08 0.00E+00 0.00E+00 49011663 Carbon tetrachloride 2.63E-06 1.78E-08 4.16E-09 3.84E-08 7.93E-09 49011663 Benzene 2.40E-06 1.83E-08 1.20E-09 1.95E-08 6.49E-09 49011663 Tetrachloroethene 1.27E-06 1.06E-07 1.59E-08 3.74E-08 1.82E-08 49011663 Cumene 0.00E+00 7.92E-07 1.32E-07 0.00E+00 0.00E+00 49011663 Triethylamine 7.82E-07 0.00E+00 0.00E+00 0.00E+00 0.00E+00 49011663 Methyl methacrylate 0.00E+00 5.07E-07 7.25E-08 0.00E+00 0.00E+00 49011663 1,1,1-Trichloroethane 3.36E-07 1.76E-08 6.93E-09 1.20E-08 6.02E-09 49011663 Chlorobenzene 0.00E+00 3.10E-07 2.06E-08 0.00E+00 0.00E+00 49011663 Styrene 1.99E-07 4.92E-08 7.60E-09 1.99E-08 3.80E-09 49011663 Acrylic acid 5.21E-08 7.10E-08 2.23E-09 1.04E-07 2.08E-09 49011663 Aniline 0.00E+00 9.24E-08 6.03E-08 0.00E+00 0.00E+00 49011663 Trichloroethylene 0.00E+00 3.60E-08 1.05E-08 4.67E-08 9.34E-09 49011663 Ethyl acrylate 0.00E+00 9.00E-11 2.04E-11 7.46E-08 2.55E-11 49011663 n-Hexane 0.00E+00 0.00E+00 2.38E-08 0.00E+00 0.00E+00 49011663 m-Xylene 1.07E-08 0.00E+00 0.00E+00 0.00E+00 0.00E+00 49011663 Epichlorohydrin 8.77E-09 5.18E-10 1.25E-10 6.00E-10 1.50E-10 49011663 1,4-Dioxane 2.45E-10 1.20E-11 6.12E-13 0.00E+00 0.00E+00 49011663 Ethylene oxide 0.00E+00 0.00E+00 6.04E-13 0.00E+00 5.44E-13 050272001 Toluene 3.91E-03 1.93E-04 3.22E-05 7.62E-04 1.32E-04 050272001 Xylenes (mixed) 1.21E-03 4.76E-05 6.67E-06 0.00E+00 0.00E+00 050272001 Methanol 1.62E-04 6.56E-06 1.68E-06 1.74E-05 3.48E-06 050272001 Ethyl benzene 0.00E+00 5.44E-05 1.59E-06 0.00E+00 0.00E+00 050272001 n-Hexane 0.00E+00 0.00E+00 1.44E-06 0.00E+00 0.00E+00

16 of 27REL AEGL1 AEGL2 ERPG1 ERPG2

Facility NEI ID Pollutant Table 3 – Maximum Predicted Acute Risks

Actual Emissions

Maximum Hazard Quotient 1

51700334 Ethyl benzene 0.00E+00 2.24E-10 6.54E-12 0.00E+00 0.00E+00 51710293 2,4-Toluene diisocyanate 0.00E+00 1.01E-03 2.39E-04 1.99E-03 1.28E-04 51710293 Toluene 1.60E-03 7.87E-05 1.31E-05 3.11E-04 5.37E-05 51710293 Xylenes (mixed) 1.56E-03 6.13E-05 8.58E-06 0.00E+00 0.00E+00 51710293 Methylene chloride 1.46E-04 2.96E-06 1.07E-06 2.04E-06 7.85E-07 51710293 Formaldehyde 8.61E-05 4.30E-06 2.79E-07 3.95E-06 3.95E-07 51710293 Benzene 1.21E-05 9.28E-08 6.07E-09 9.86E-08 3.29E-08 51710293 Ethyl benzene 0.00E+00 5.92E-06 1.73E-07 0.00E+00 0.00E+00 51710293 Hydrofluoric acid 8.45E-07 2.47E-07 1.01E-08 1.27E-07 1.27E-08 51710293 Cumene 0.00E+00 1.93E-07 3.21E-08 0.00E+00 0.00E+00 51810292 Methylene chloride 8.10E-04 1.64E-05 5.97E-06 1.13E-05 4.36E-06 51810292 Phenol 5.70E-04 5.70E-05 3.72E-05 8.70E-05 1.74E-05 51810292 Xylenes (mixed) 1.59E-04 6.24E-06 8.74E-07 0.00E+00 0.00E+00 51810292 Toluene 7.26E-05 3.58E-06 5.97E-07 1.41E-05 2.44E-06 51810292 Methanol 3.71E-05 1.51E-06 3.85E-07 4.00E-06 8.00E-07 51810292 Methylene diphenyl diisocyanate 0.00E+00 0.00E+00 0.00E+00 0.00E+00 2.95E-06 51810292 Ethyl benzene 0.00E+00 5.78E-07 1.69E-08 0.00E+00 0.00E+00 51810292 Cumene 0.00E+00 1.23E-08 2.05E-09 0.00E+00 0.00E+00 51810292 n-Hexane 0.00E+00 0.00E+00 3.85E-09 0.00E+00 0.00E+00 53029438 Phenol 6.69E-04 6.69E-05 4.36E-05 1.02E-04 2.04E-05 53029438 Methylene chloride 6.43E-04 1.31E-05 4.74E-06 9.00E-06 3.46E-06 53029438 Toluene 8.65E-05 4.27E-06 7.11E-07 1.68E-05 2.91E-06 53029438 Benzene 1.10E-05 8.43E-08 5.52E-09 8.96E-08 2.99E-08 53029438 Ethyl benzene 0.00E+00 6.18E-06 1.80E-07 0.00E+00 0.00E+00 53029438 Xylenes (mixed) 8.13E-07 3.20E-08 4.47E-09 0.00E+00 0.00E+00 53029438 Cumene 0.00E+00 2.63E-07 4.39E-08 0.00E+00 0.00E+00 53029438 n-Hexane 0.00E+00 0.00E+00 4.57E-09 0.00E+00 0.00E+00 53033383 Diethylene glycol monobutyl ether 2.39E-01 0.00E+00 0.00E+00 0.00E+00 0.00E+00 53033383 Toluene 1.25E-03 6.15E-05 1.03E-05 2.43E-04 4.20E-05 53033383 2-Butoxyethyl acetate 1.43E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00 53033383 Xylenes (mixed) 7.65E-05 3.01E-06 4.21E-07 0.00E+00 0.00E+00 53033383 Methanol 2.74E-05 1.11E-06 2.84E-07 2.95E-06 5.90E-07 53033383 Tetrachloroethene 4.08E-06 3.40E-07 5.10E-08 1.20E-07 5.83E-08 53033383 Methylene chloride 3.89E-06 7.89E-08 2.87E-08 5.44E-08 2.09E-08 53033383 Ethyl benzene 0.00E+00 2.67E-06 7.80E-08 0.00E+00 0.00E+00 53033383 Arsenic compounds 2.71E-06 0.00E+00 0.00E+00 0.00E+00 0.00E+00 53033383 Cumene 0.00E+00 1.69E-08 2.81E-09 0.00E+00 0.00E+00 53033383 Beryllium compounds 0.00E+00 0.00E+00 0.00E+00 0.00E+00 8.78E-10 53033384 Diethylene glycol monobutyl ether 9.69E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00 53033384 Xylenes (mixed) 1.49E-04 5.84E-06 8.18E-07 0.00E+00 0.00E+00 53033384 2,4-Toluene diisocyanate 0.00E+00 4.53E-05 1.07E-05 8.93E-05 5.76E-06 53033384 2-Butoxyethyl acetate 9.86E-05 0.00E+00 0.00E+00 0.00E+00 0.00E+00 53033384 Toluene 5.00E-05 2.47E-06 4.11E-07 9.74E-06 1.68E-06 53033384 Phenol 1.08E-05 1.08E-06 7.06E-07 1.65E-06 3.31E-07 53033384 Methylene chloride 1.01E-05 2.05E-07 7.44E-08 1.41E-07 5.44E-08 53033384 Arsenic compounds 8.35E-06 0.00E+00 0.00E+00 0.00E+00 0.00E+00 53033384 Ethyl benzene 0.00E+00 5.77E-06 1.68E-07 0.00E+00 0.00E+00 53033384 Beryllium compounds 0.00E+00 0.00E+00 0.00E+00 0.00E+00 2.71E-09 53033384 Cumene 0.00E+00 2.01E-10 3.36E-11 0.00E+00 0.00E+00 53033406 Xylenes (mixed) 1.45E-04 5.70E-06 7.99E-07 0.00E+00 0.00E+00 53033406 Toluene 1.58E-05 7.80E-07 1.30E-07 3.08E-06 5.32E-07 53033406 Ethyl benzene 0.00E+00 4.84E-06 1.41E-07 0.00E+00 0.00E+00

17 of 27REL AEGL1 AEGL2 ERPG1 ERPG2

Facility NEI ID Pollutant Table 3 – Maximum Predicted Acute Risks

Actual Emissions

Maximum Hazard Quotient 1

53033406 Methanol 3.56E-10 1.44E-11 3.69E-12 3.83E-11 7.66E-12 53033532 2-Butoxyethyl acetate 1.82E-02 0.00E+00 0.00E+00 0.00E+00 0.00E+00 53033532 Toluene 2.91E-04 1.43E-05 2.39E-06 5.66E-05 9.78E-06 53033532 Xylenes (mixed) 1.23E-04 4.84E-06 6.77E-07 0.00E+00 0.00E+00 53033532 Methanol 6.02E-05 2.44E-06 6.24E-07 6.48E-06 1.30E-06 53033532 Mercury (elemental) 1.59E-05 0.00E+00 5.63E-09 0.00E+00 4.78E-09 53033532 Methylene chloride 1.14E-05 2.32E-07 8.42E-08 1.60E-07 6.16E-08 53033532 Phenol 6.83E-06 6.83E-07 4.45E-07 1.04E-06 2.08E-07 53033532 Arsenic compounds 7.44E-06 0.00E+00 0.00E+00 0.00E+00 0.00E+00 53033532 Ethyl benzene 0.00E+00 4.70E-06 1.37E-07 0.00E+00 0.00E+00 53033532 2,4-Toluene diisocyanate 0.00E+00 8.74E-07 2.07E-07 1.72E-06 1.11E-07 53033532 Formaldehyde 1.41E-06 7.05E-08 4.56E-09 6.46E-08 6.46E-09 53033532 Benzene 9.55E-07 7.31E-09 4.78E-10 7.76E-09 2.59E-09 53033532 Glycol Ethers 6.89E-07 0.00E+00 0.00E+00 0.00E+00 0.00E+00 53033532 Methylene diphenyl diisocyanate 0.00E+00 0.00E+00 0.00E+00 0.00E+00 4.71E-07 53033532 Maleic anhydride 0.00E+00 0.00E+00 0.00E+00 2.08E-07 2.08E-08 53033532 Triethylamine 6.94E-08 0.00E+00 0.00E+00 0.00E+00 0.00E+00 53033532 Cumene 0.00E+00 5.62E-09 9.37E-10 0.00E+00 0.00E+00 53033532 Ethylene oxide 0.00E+00 0.00E+00 3.06E-10 0.00E+00 2.75E-10 53033532 Hydrofluoric acid 1.37E-11 4.00E-12 1.64E-13 2.05E-12 2.05E-13 53033532 n-Hexane 0.00E+00 0.00E+00 2.14E-13 0.00E+00 0.00E+00 53053447 Glycol ethers 9.30E-04 0.00E+00 0.00E+00 0.00E+00 0.00E+00 53053447 Toluene 2.23E-04 1.10E-05 1.84E-06 4.35E-05 7.51E-06 53053447 Xylenes (mixed) 4.84E-05 1.90E-06 2.66E-07 0.00E+00 0.00E+00 53053447 2-Butoxyethyl acetate 2.52E-05 0.00E+00 0.00E+00 0.00E+00 0.00E+00 53053447 Methanol 9.51E-06 3.86E-07 9.86E-08 1.02E-06 2.05E-07 53053447 Ethyl benzene 0.00E+00 2.43E-06 7.07E-08 0.00E+00 0.00E+00 53053447 Phenol 5.84E-08 5.84E-09 3.81E-09 8.92E-09 1.78E-09 53053447 Arsenic compounds 4.49E-08 0.00E+00 0.00E+00 0.00E+00 0.00E+00 53053447 Benzene 1.96E-08 1.50E-10 9.80E-12 1.59E-10 5.31E-11 53053447 Beryllium compounds 0.00E+00 0.00E+00 0.00E+00 0.00E+00 1.31E-11 53061398 Diethylene glycol monobutyl ether 4.26E-02 0.00E+00 0.00E+00 0.00E+00 0.00E+00 53061398 Propyl cellosolve 4.03E-02 0.00E+00 0.00E+00 0.00E+00 0.00E+00 53061398 Arsenic compounds 1.08E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00 53061398 Toluene 5.76E-04 2.84E-05 4.74E-06 1.12E-04 1.94E-05 53061398 Styrene 4.23E-04 1.04E-04 1.61E-05 4.23E-05 8.07E-06 53061398 2,4-Toluene diisocyanate 0.00E+00 1.20E-04 2.85E-05 2.37E-04 1.53E-05 53061398 Mercury (elemental) 1.06E-04 0.00E+00 3.75E-08 0.00E+00 3.19E-08 53061398 Xylenes (mixed) 5.87E-05 2.31E-06 3.23E-07 0.00E+00 0.00E+00 53061398 Ethylene glycol ethyl ether acetate 4.53E-05 0.00E+00 0.00E+00 0.00E+00 0.00E+00 53061398 Formaldehyde 3.98E-05 1.99E-06 1.29E-07 1.82E-06 1.82E-07 53061398 Methylene chloride 3.93E-05 7.98E-07 2.90E-07 5.51E-07 2.12E-07 53061398 Phenol 2.93E-05 2.93E-06 1.91E-06 4.48E-06 8.96E-07 53061398 Methanol 2.95E-05 1.20E-06 3.06E-07 3.18E-06 6.36E-07 53061398 Epichlorohydrin 1.81E-05 1.07E-06 2.59E-07 1.24E-06 3.10E-07 53061398 Hydrofluoric acid 5.90E-06 1.73E-06 7.08E-08 8.84E-07 8.84E-08 53061398 Methylene diphenyl diisocyanate 0.00E+00 0.00E+00 0.00E+00 0.00E+00 5.23E-06 53061398 Trichloroethylene 0.00E+00 1.21E-06 3.53E-07 1.57E-06 3.14E-07 53061398 Ethyl benzene 0.00E+00 1.73E-06 5.04E-08 0.00E+00 0.00E+00 53061398 Benzene 1.44E-06 1.10E-08 7.22E-10 1.17E-08 3.91E-09 53061398 Tetrachloroethene 1.25E-06 1.04E-07 1.56E-08 3.68E-08 1.79E-08 53061398 Glycol Ethers 1.13E-06 0.00E+00 0.00E+00 0.00E+00 0.00E+00

18 of 27REL AEGL1 AEGL2 ERPG1 ERPG2

Facility NEI ID Pollutant Table 3 – Maximum Predicted Acute Risks

Actual Emissions

Maximum Hazard Quotient 1

53061398 Cumene 0.00E+00 3.01E-07 5.01E-08 0.00E+00 0.00E+00 53061398 Chlorobenzene 0.00E+00 2.42E-07 1.61E-08 0.00E+00 0.00E+00 53061398 Acrylic acid 5.04E-08 6.87E-08 2.16E-09 1.01E-07 2.01E-09 53061398 Acetaldehyde 1.78E-07 1.03E-09 1.70E-10 4.64E-09 2.32E-10 53061398 Propylene oxide 1.71E-07 3.11E-09 7.67E-10 4.41E-09 8.97E-10 53061398 Ethylene glycol ethyl ether 7.10E-08 0.00E+00 0.00E+00 0.00E+00 0.00E+00 53061398 Methyl methacrylate 0.00E+00 3.07E-08 4.39E-09 0.00E+00 0.00E+00 53061398 n-Hexane 0.00E+00 0.00E+00 2.05E-08 0.00E+00 0.00E+00 53061398 Ethyl acrylate 0.00E+00 1.95E-12 4.42E-13 1.62E-09 5.52E-13 53061398 Carbon tetrachloride 5.13E-10 3.48E-12 8.12E-13 7.50E-12 1.55E-12 53061398 1,4-Dioxane 1.31E-11 6.43E-13 3.30E-14 0.00E+00 0.00E+00 53061398 p-Xylene 2.12E-12 0.00E+00 0.00E+00 0.00E+00 0.00E+00 53061398 Acrylonitrile 0.00E+00 4.27E-13 3.30E-14 1.94E-13 5.60E-14 54067418 Toluene 3.35E-03 1.65E-04 2.75E-05 6.52E-04 1.13E-04 54067418 Formaldehyde 1.10E-03 5.48E-05 3.55E-06 5.02E-05 5.02E-06 54067418 Xylenes (mixed) 2.03E-04 7.97E-06 1.12E-06 0.00E+00 0.00E+00 54067418 Tetrachloroethene 7.09E-05 5.91E-06 8.86E-07 2.09E-06 1.01E-06 54067418 Ethyl benzene 0.00E+00 8.85E-06 2.58E-07 0.00E+00 0.00E+00 55075288 Toluene 3.77E-05 1.86E-06 3.10E-07 7.34E-06 1.27E-06 55075288 Xylenes (mixed) 4.27E-05 1.68E-06 2.35E-07 0.00E+00 0.00E+00 55075288 Methanol 1.39E-05 5.64E-07 1.44E-07 1.50E-06 3.00E-07 55075288 Ethyl benzene 0.00E+00 1.11E-06 3.25E-08 0.00E+00 0.00E+00 55075288 Arsenic compounds 1.17E-07 0.00E+00 0.00E+00 0.00E+00 0.00E+00 55075288 Cumene 0.00E+00 8.41E-09 1.40E-09 0.00E+00 0.00E+00 55079687 Toluene 1.26E-07 6.20E-09 1.03E-09 2.45E-08 4.23E-09 060292024 Propyl cellosolve 1.84E-04 0.00E+00 0.00E+00 0.00E+00 0.00E+00 060292024 Methylene chloride 7.96E-07 1.62E-08 5.87E-09 1.11E-08 4.29E-09 060292024 Xylenes (mixed) 1.27E-07 5.01E-09 7.01E-10 0.00E+00 0.00E+00 060292024 Toluene 1.01E-07 4.99E-09 8.32E-10 1.97E-08 3.40E-09 060292024 Methanol 3.18E-08 1.29E-09 3.30E-10 3.43E-09 6.86E-10 060292024 Hydrofluoric acid 1.01E-08 2.96E-09 1.22E-10 1.52E-09 1.52E-10 060292024 Ethyl benzene 0.00E+00 2.50E-09 7.30E-11 0.00E+00 0.00E+00 060372022 Xylenes (mixed) 3.23E-04 1.27E-05 1.78E-06 0.00E+00 0.00E+00 060372022 2,4-Toluene diisocyanate 0.00E+00 6.34E-05 1.50E-05 1.25E-04 8.07E-06 060372022 Carbitol acetate 2.07E-04 0.00E+00 0.00E+00 0.00E+00 0.00E+00 060372022 Toluene 1.29E-04 6.38E-06 1.06E-06 2.52E-05 4.35E-06 060372022 Formaldehyde 5.04E-05 2.52E-06 1.63E-07 2.31E-06 2.31E-07 060372022 Ethylene glycol methyl ether 3.14E-05 0.00E+00 0.00E+00 0.00E+00 0.00E+00 060372022 Ethylene glycol ethyl ether acetate 1.50E-05 0.00E+00 0.00E+00 0.00E+00 0.00E+00 060372022 2-Butoxyethyl acetate 1.10E-05 0.00E+00 0.00E+00 0.00E+00 0.00E+00 060372022 Benzene 1.45E-06 1.11E-08 7.27E-10 1.18E-08 3.94E-09 060372022 Styrene 1.03E-06 2.54E-07 3.92E-08 1.03E-07 1.96E-08 060372022 Methyl methacrylate 0.00E+00 6.40E-07 9.14E-08 0.00E+00 0.00E+00 060372022 1,3-Butadiene 0.00E+00 9.52E-09 1.19E-09 6.49E-07 3.25E-08 060372022 Methanol 4.69E-07 1.90E-08 4.86E-09 5.05E-08 1.01E-08 060372022 Methylene diphenyl diisocyanate 0.00E+00 0.00E+00 0.00E+00 0.00E+00 3.48E-07 060372022 Ethyl benzene 0.00E+00 2.65E-07 7.72E-09 0.00E+00 0.00E+00 060372022 Triethylamine 2.44E-07 0.00E+00 0.00E+00 0.00E+00 0.00E+00 060372022 Chlorobenzene 0.00E+00 1.65E-07 1.10E-08 0.00E+00 0.00E+00 060372022 Acrylic acid 1.76E-08 2.40E-08 7.54E-10 3.52E-08 7.04E-10 060372022 Cumene 0.00E+00 2.11E-08 3.51E-09 0.00E+00 0.00E+00 060372022 n-Hexane 0.00E+00 0.00E+00 4.59E-09 0.00E+00 0.00E+00

19 of 27REL AEGL1 AEGL2 ERPG1 ERPG2

Facility NEI ID Pollutant Table 3 – Maximum Predicted Acute Risks

Actual Emissions

Maximum Hazard Quotient 1

060376004 Xylenes (mixed) 2.22E-04 8.74E-06 1.22E-06 0.00E+00 0.00E+00 060376004 Toluene 1.40E-04 6.91E-06 1.15E-06 2.73E-05 4.71E-06 060376004 Styrene 8.38E-05 2.07E-05 3.20E-06 8.38E-06 1.60E-06 060376004 2-Butoxyethyl acetate 4.24E-05 0.00E+00 0.00E+00 0.00E+00 0.00E+00 060376004 Diethylene glycol monobutyl ether 3.63E-05 0.00E+00 0.00E+00 0.00E+00 0.00E+00 060376004 Methylene chloride 8.09E-06 1.64E-07 5.96E-08 1.13E-07 4.36E-08 060376004 Methanol 2.45E-06 9.95E-08 2.54E-08 2.64E-07 5.28E-08 060376004 Formaldehyde 1.47E-06 7.33E-08 4.74E-09 6.72E-08 6.72E-09 060376004 Trichloroethylene 0.00E+00 1.38E-07 4.02E-08 1.79E-07 3.58E-08 060376004 Phenol 1.93E-07 1.93E-08 1.26E-08 2.95E-08 5.89E-09 060376004 n-Hexane 0.00E+00 0.00E+00 2.45E-07 0.00E+00 0.00E+00 060376004 Carbon tetrachloride 2.10E-07 1.42E-09 3.32E-10 3.06E-09 6.32E-10 060376004 Ethylene glycol ethyl ether acetate 2.04E-07 0.00E+00 0.00E+00 0.00E+00 0.00E+00 060376004 Ethyl benzene 0.00E+00 1.13E-07 3.29E-09 0.00E+00 0.00E+00 060376004 Acetaldehyde 1.08E-07 6.27E-10 1.04E-10 2.82E-09 1.41E-10 060812015 Xylenes (mixed) 9.21E-06 3.62E-07 5.07E-08 0.00E+00 0.00E+00 060812015 Toluene 1.82E-06 8.99E-08 1.50E-08 3.55E-07 6.13E-08 090032025 Toluene 1.52E-05 7.51E-07 1.25E-07 2.96E-06 5.12E-07 090032025 Methanol 2.81E-06 1.14E-07 2.92E-08 3.03E-07 6.06E-08 090032025 Xylenes (mixed) 4.06E-07 1.59E-08 2.23E-09 0.00E+00 0.00E+00 090032025 Ethyl benzene 0.00E+00 1.93E-08 5.64E-10 0.00E+00 0.00E+00 090075021 Xylenes (mixed) 7.18E-06 2.82E-07 3.95E-08 0.00E+00 0.00E+00 090075021 Trichloroethylene 0.00E+00 6.15E-07 1.79E-07 7.97E-07 1.59E-07 090075021 Toluene 7.51E-07 3.71E-08 6.18E-09 1.46E-07 2.53E-08 090075021 Ethyl benzene 0.00E+00 7.13E-08 2.08E-09 0.00E+00 0.00E+00 120862016 Toluene 8.66E-03 4.27E-04 7.12E-05 1.69E-03 2.91E-04 120862016 Xylenes (mixed) 6.55E-04 2.57E-05 3.60E-06 0.00E+00 0.00E+00 120862016 Ethyl benzene 0.00E+00 2.45E-06 7.14E-08 0.00E+00 0.00E+00 130516005 2-Butoxyethyl acetate 1.42E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00 130516005 Toluene 4.04E-04 2.00E-05 3.33E-06 7.88E-05 1.36E-05 130516005 Diethylene glycol monoethyl ether 2.25E-04 0.00E+00 0.00E+00 0.00E+00 0.00E+00 130516005 Propyl cellosolve 1.19E-04 0.00E+00 0.00E+00 0.00E+00 0.00E+00 130516005 Ethylene glycol ethyl ether acetate 1.00E-04 0.00E+00 0.00E+00 0.00E+00 0.00E+00 130516005 Styrene 6.33E-05 1.56E-05 2.42E-06 6.33E-06 1.21E-06 130516005 Xylenes (mixed) 7.76E-05 3.05E-06 4.27E-07 0.00E+00 0.00E+00 130516005 Methanol 1.53E-05 6.20E-07 1.58E-07 1.65E-06 3.29E-07 130516005 Ethylene glycol ethyl ether 8.96E-06 0.00E+00 0.00E+00 0.00E+00 0.00E+00 130516005 Phenol 5.63E-06 5.63E-07 3.67E-07 8.59E-07 1.72E-07 130516005 Methylene chloride 3.60E-06 7.31E-08 2.66E-08 5.05E-08 1.94E-08 130516005 Ethyl benzene 0.00E+00 2.06E-06 6.00E-08 0.00E+00 0.00E+00 130516005 Diethylene glycol monobutyl ether 4.03E-07 0.00E+00 0.00E+00 0.00E+00 0.00E+00 130516005 Formaldehyde 3.43E-07 1.72E-08 1.11E-09 1.57E-08 1.57E-09 130516005 n-Hexane 0.00E+00 0.00E+00 2.69E-07 0.00E+00 0.00E+00 130516005 Carbon tetrachloride 1.69E-07 1.15E-09 2.68E-10 2.47E-09 5.11E-10 130516005 Cumene 0.00E+00 9.59E-08 1.60E-08 0.00E+00 0.00E+00 130516005 Benzene 3.35E-08 2.56E-10 1.67E-11 2.72E-10 9.06E-11 130516005 o-Xylene 2.65E-08 0.00E+00 0.00E+00 0.00E+00 0.00E+00 130516005 m-Xylene 2.65E-08 0.00E+00 0.00E+00 0.00E+00 0.00E+00 130516005 p-Xylene 2.65E-08 0.00E+00 0.00E+00 0.00E+00 0.00E+00 130516005 Acetaldehyde 7.97E-09 4.62E-11 7.64E-12 2.08E-10 1.04E-11 130516005 Methyl methacrylate 0.00E+00 6.03E-09 8.61E-10 0.00E+00 0.00E+00 130516005 Propylene oxide 4.30E-10 7.84E-12 1.93E-12 1.11E-11 2.26E-12

20 of 27REL AEGL1 AEGL2 ERPG1 ERPG2

Facility NEI ID Pollutant Table 3 – Maximum Predicted Acute Risks

Actual Emissions

Maximum Hazard Quotient 1

130516005 Aniline 0.00E+00 9.32E-11 6.08E-11 0.00E+00 0.00E+00 131276002 2-Butoxyethyl acetate 1.27E-02 0.00E+00 0.00E+00 0.00E+00 0.00E+00 131276002 Toluene 1.92E-04 9.48E-06 1.58E-06 3.74E-05 6.47E-06 131276002 Xylenes (mixed) 6.01E-05 2.36E-06 3.30E-07 0.00E+00 0.00E+00 131276002 Styrene 4.14E-05 1.02E-05 1.58E-06 4.14E-06 7.91E-07 131276002 Ethylene glycol ethyl ether acetate 2.17E-05 0.00E+00 0.00E+00 0.00E+00 0.00E+00 131276002 Ethyl benzene 0.00E+00 3.70E-06 1.08E-07 0.00E+00 0.00E+00 131276002 n-Hexane 0.00E+00 0.00E+00 1.60E-07 0.00E+00 0.00E+00 131276002 Cumene 0.00E+00 4.99E-10 8.32E-11 0.00E+00 0.00E+00 132332017 Toluene 1.79E-02 8.83E-04 1.47E-04 3.48E-03 6.02E-04 132332017 Xylenes (mixed) 4.71E-03 1.85E-04 2.59E-05 0.00E+00 0.00E+00 132332017 Methanol 7.13E-04 2.89E-05 7.39E-06 7.68E-05 1.54E-05 132332017 Phenol 3.12E-04 3.12E-05 2.03E-05 4.76E-05 9.52E-06 132332017 Ethyl benzene 0.00E+00 1.71E-04 5.00E-06 0.00E+00 0.00E+00 132332017 2,4-Toluene diisocyanate 0.00E+00 4.85E-05 1.15E-05 9.56E-05 6.17E-06 132332017 Formaldehyde 1.34E-04 6.71E-06 4.34E-07 6.15E-06 6.15E-07 132332017 Methylene diphenyl diisocyanate 0.00E+00 0.00E+00 0.00E+00 0.00E+00 5.58E-05 132332017 n-Hexane 0.00E+00 0.00E+00 3.43E-05 0.00E+00 0.00E+00 132332017 Tetrachloroethene 5.57E-06 4.64E-07 6.96E-08 1.64E-07 7.95E-08 132332017 Chlorobenzene 0.00E+00 5.90E-06 3.93E-07 0.00E+00 0.00E+00 132332017 Ethyl acrylate 0.00E+00 6.08E-09 1.38E-09 5.05E-06 1.72E-09 132332017 Benzene 7.59E-07 5.80E-09 3.79E-10 6.16E-09 2.05E-09 181035004 Butyl carbitol acetate 1.52E-05 0.00E+00 0.00E+00 0.00E+00 0.00E+00 181035004 Toluene 1.91E-06 9.40E-08 1.57E-08 3.71E-07 6.41E-08 181035004 Xylenes (mixed) 5.15E-07 2.02E-08 2.83E-09 0.00E+00 0.00E+00 181035004 Ethyl benzene 0.00E+00 3.91E-08 1.14E-09 0.00E+00 0.00E+00 181412011 Xylenes (mixed) 2.12E-04 8.33E-06 1.17E-06 0.00E+00 0.00E+00 181412011 Toluene 2.83E-05 1.40E-06 2.33E-07 5.51E-06 9.51E-07 181412011 Phenol 5.65E-06 5.65E-07 3.68E-07 8.62E-07 1.72E-07 181412011 Ethyl benzene 0.00E+00 9.15E-07 2.67E-08 0.00E+00 0.00E+00 201252005 Glycol Ethers 6.04E-02 0.00E+00 0.00E+00 0.00E+00 0.00E+00 201252005 Toluene 1.54E-04 7.58E-06 1.26E-06 2.99E-05 5.17E-06 201252005 Xylenes (mixed) 1.33E-04 5.21E-06 7.30E-07 0.00E+00 0.00E+00 201252005 Ethyl benzene 0.00E+00 3.55E-06 1.03E-07 0.00E+00 0.00E+00 201252005 n-Hexane 0.00E+00 0.00E+00 1.48E-07 0.00E+00 0.00E+00 201732006 Ethylene glycol ethyl ether 1.63E-01 0.00E+00 0.00E+00 0.00E+00 0.00E+00 201732006 Glycol Ethers 9.05E-02 0.00E+00 0.00E+00 0.00E+00 0.00E+00 201732006 Formaldehyde 1.12E-03 5.58E-05 3.61E-06 5.11E-05 5.11E-06 201732006 Styrene 4.94E-04 1.22E-04 1.89E-05 4.94E-05 9.43E-06 201732006 Toluene 2.38E-04 1.18E-05 1.96E-06 4.64E-05 8.02E-06 201732006 Methanol 1.15E-04 4.68E-06 1.20E-06 1.24E-05 2.48E-06 201732006 Xylenes (mixed) 1.03E-04 4.05E-06 5.66E-07 0.00E+00 0.00E+00 201732006 Methylene chloride 7.06E-05 1.43E-06 5.20E-07 9.88E-07 3.80E-07 201732006 2,4-Toluene diisocyanate 0.00E+00 1.68E-05 3.99E-06 3.31E-05 2.14E-06 201732006 Ethyl benzene 0.00E+00 4.38E-06 1.28E-07 0.00E+00 0.00E+00 201732006 Triethylamine 1.29E-06 0.00E+00 0.00E+00 0.00E+00 0.00E+00 201732006 n-Hexane 0.00E+00 0.00E+00 2.13E-07 0.00E+00 0.00E+00 201732006 Phenol 3.82E-09 3.82E-10 2.49E-10 5.83E-10 1.17E-10 201732007 Glycol Ethers 3.40E-02 0.00E+00 0.00E+00 0.00E+00 0.00E+00 201732007 Toluene 9.07E-04 4.48E-05 7.46E-06 1.77E-04 3.05E-05 201732007 Methylene chloride 6.09E-04 1.24E-05 4.49E-06 8.53E-06 3.28E-06 201732007 Xylenes (mixed) 4.08E-04 1.60E-05 2.24E-06 0.00E+00 0.00E+00

21 of 27REL AEGL1 AEGL2 ERPG1 ERPG2

Facility NEI ID Pollutant Table 3 – Maximum Predicted Acute Risks

Actual Emissions

Maximum Hazard Quotient 1

201732007 Methylene diphenyl diisocyanate 0.00E+00 0.00E+00 0.00E+00 0.00E+00 3.17E-05 201732007 Styrene 1.59E-05 3.92E-06 6.06E-07 1.59E-06 3.03E-07 201732007 Ethyl benzene 0.00E+00 9.56E-06 2.79E-07 0.00E+00 0.00E+00 201732007 Methanol 7.62E-07 3.09E-08 7.90E-09 8.20E-08 1.64E-08 201732007 n-Hexane 0.00E+00 0.00E+00 3.72E-08 0.00E+00 0.00E+00 201732027 2-Butoxyethyl acetate 4.21E-02 0.00E+00 0.00E+00 0.00E+00 0.00E+00 201732027 Tetrachloroethene 1.04E-02 8.68E-04 1.30E-04 3.06E-04 1.49E-04 201732027 Arsenic compounds 8.76E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00 201732027 Toluene 5.15E-03 2.54E-04 4.24E-05 1.00E-03 1.73E-04 201732027 Xylenes (mixed) 1.75E-03 6.89E-05 9.65E-06 0.00E+00 0.00E+00 201732027 Formaldehyde 1.60E-03 7.98E-05 5.16E-06 7.31E-05 7.31E-06 201732027 Benzene 4.85E-04 3.71E-06 2.42E-07 3.94E-06 1.31E-06 201732027 Glycol Ethers 1.79E-04 0.00E+00 0.00E+00 0.00E+00 0.00E+00 201732027 Diethylene glycol monobutyl ether 1.31E-04 0.00E+00 0.00E+00 0.00E+00 0.00E+00 201732027 Ethyl benzene 0.00E+00 5.24E-05 1.53E-06 0.00E+00 0.00E+00 201732027 Methanol 2.25E-05 9.15E-07 2.34E-07 2.43E-06 4.86E-07 201732027 Cumene 0.00E+00 7.53E-08 1.25E-08 0.00E+00 0.00E+00 201732027 2,4-Toluene diisocyanate 0.00E+00 2.51E-08 5.96E-09 4.95E-08 3.20E-09 201732027 n-Hexane 0.00E+00 0.00E+00 2.92E-10 0.00E+00 0.00E+00 280475024 2-Butoxyethyl acetate 1.26E-02 0.00E+00 0.00E+00 0.00E+00 0.00E+00 280475024 Xylenes (mixed) 5.17E-05 2.03E-06 2.85E-07 0.00E+00 0.00E+00 280475024 Toluene 6.20E-06 3.06E-07 5.10E-08 1.21E-06 2.08E-07 280475024 Ethyl benzene 0.00E+00 1.81E-06 5.27E-08 0.00E+00 0.00E+00 280475024 Cumene 0.00E+00 1.25E-07 2.08E-08 0.00E+00 0.00E+00 370492008 Butyl carbitol acetate 1.64E-02 0.00E+00 0.00E+00 0.00E+00 0.00E+00 370492008 Diethylene glycol monoethyl ether 8.02E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00 370492008 Propyl cellosolve 6.97E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00 370492008 2-Butoxyethyl acetate 4.82E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00 370492008 Methylene chloride 2.44E-03 4.96E-05 1.80E-05 3.42E-05 1.32E-05 370492008 Ethylene glycol ethyl ether acetate 1.03E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00 370492008 Toluene 1.50E-04 7.39E-06 1.23E-06 2.92E-05 5.04E-06 370492008 Methanol 1.57E-04 6.38E-06 1.63E-06 1.69E-05 3.39E-06 370492008 Arsenic compounds 1.32E-04 0.00E+00 0.00E+00 0.00E+00 0.00E+00 370492008 Xylenes (mixed) 1.24E-04 4.87E-06 6.81E-07 0.00E+00 0.00E+00 370492008 Diethylene glycol dimethyl ether 9.75E-05 0.00E+00 0.00E+00 0.00E+00 0.00E+00 370492008 2,4-Toluene diisocyanate 0.00E+00 2.92E-05 6.92E-06 5.75E-05 3.71E-06 370492008 Formaldehyde 8.34E-05 4.17E-06 2.70E-07 3.82E-06 3.82E-07 370492008 Ethylene glycol ethyl ether 4.51E-05 0.00E+00 0.00E+00 0.00E+00 0.00E+00 370492008 Ethylene glycol methyl ether 2.04E-05 0.00E+00 0.00E+00 0.00E+00 0.00E+00 370492008 Phenol 6.02E-06 6.02E-07 3.92E-07 9.19E-07 1.84E-07 370492008 Carbitol acetate 3.80E-06 0.00E+00 0.00E+00 0.00E+00 0.00E+00 370492008 Styrene 1.91E-06 4.73E-07 7.30E-08 1.91E-07 3.65E-08 370492008 Ethyl benzene 0.00E+00 2.30E-06 6.71E-08 0.00E+00 0.00E+00 370492008 Cumene 0.00E+00 1.43E-06 2.39E-07 0.00E+00 0.00E+00 370492008 Tetrachloroethene 1.42E-06 1.18E-07 1.77E-08 4.17E-08 2.03E-08 370492008 Methyl methacrylate 0.00E+00 4.90E-07 7.01E-08 0.00E+00 0.00E+00 370492008 Propylene oxide 5.27E-07 9.61E-09 2.37E-09 1.36E-08 2.77E-09 370492008 Acetaldehyde 1.44E-07 8.35E-10 1.38E-10 3.76E-09 1.88E-10 370492008 Acrylic acid 2.39E-08 3.26E-08 1.03E-09 4.95E-08 9.58E-10 370492008 Mercury (elemental) 5.94E-08 0.00E+00 2.10E-11 0.00E+00 1.78E-11 370492008 Hydrofluoric acid 2.77E-08 8.10E-09 3.32E-10 4.15E-09 4.15E-10 370492008 n-Hexane 0.00E+00 0.00E+00 3.21E-08 0.00E+00 0.00E+00

22 of 27REL AEGL1 AEGL2 ERPG1 ERPG2

Facility NEI ID Pollutant Table 3 – Maximum Predicted Acute Risks

Actual Emissions

Maximum Hazard Quotient 1

370492008 Benzene 2.31E-08 1.77E-10 1.16E-11 1.88E-10 6.27E-11 370492008 Methylene diphenyl diisocyanate 0.00E+00 0.00E+00 0.00E+00 0.00E+00 1.31E-08 370492008 Trichloroethylene 0.00E+00 3.67E-09 1.07E-09 4.76E-09 9.51E-10 370492008 Epichlorohydrin 7.15E-09 4.22E-10 1.02E-10 4.89E-10 1.22E-10 370492008 1,3-Butadiene 0.00E+00 3.20E-11 3.99E-12 2.18E-09 1.09E-10 370492008 1,4-Dioxane 6.74E-10 3.32E-11 1.69E-12 0.00E+00 0.00E+00 370492008 Ethylene oxide 0.00E+00 0.00E+00 7.96E-11 0.00E+00 7.17E-11 370492008 Vinyl chloride 3.06E-11 8.62E-12 1.78E-12 4.24E-12 4.24E-13 391335027 Ethylene glycol ethyl ether acetate 2.08E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00 391335027 Xylenes (mixed) 6.02E-04 2.37E-05 3.31E-06 0.00E+00 0.00E+00 391335027 Toluene 1.49E-04 7.36E-06 1.23E-06 2.91E-05 5.02E-06 391335027 Ethyl benzene 0.00E+00 2.36E-06 6.88E-08 0.00E+00 0.00E+00 401092026 Butyl carbitol acetate 1.31E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00 401092026 Methylene chloride 8.79E-04 1.78E-05 6.48E-06 1.23E-05 4.73E-06 401092026 Phenol 6.39E-04 6.39E-05 4.17E-05 9.76E-05 1.95E-05 401092026 Toluene 2.92E-04 1.44E-05 2.40E-06 5.69E-05 9.83E-06 401092026 Formaldehyde 1.94E-04 9.68E-06 6.26E-07 8.87E-06 8.87E-07 401092026 Ethylene glycol ethyl ether acetate 1.10E-04 0.00E+00 0.00E+00 0.00E+00 0.00E+00 401092026 Xylenes (mixed) 8.13E-05 3.19E-06 4.47E-07 0.00E+00 0.00E+00 401092026 Ethyl acrylate 0.00E+00 6.92E-08 1.57E-08 5.74E-05 1.96E-08 401092026 Hydrochloric acid 3.60E-06 2.80E-06 2.29E-07 1.68E-06 2.52E-07 401092026 Epichlorohydrin 5.83E-06 3.45E-07 8.34E-08 3.99E-07 9.98E-08 401092026 Methylene diphenyl diisocyanate 0.00E+00 0.00E+00 0.00E+00 0.00E+00 5.12E-06 401092026 Methanol 1.11E-06 4.52E-08 1.15E-08 1.20E-07 2.40E-08 401092026 Benzene 9.46E-07 7.24E-09 4.73E-10 7.69E-09 2.56E-09 401092026 Ethyl benzene 0.00E+00 5.19E-07 1.51E-08 0.00E+00 0.00E+00 401092026 Cumene 0.00E+00 1.37E-07 2.28E-08 0.00E+00 0.00E+00 401092026 p-Xylene 5.59E-08 0.00E+00 0.00E+00 0.00E+00 0.00E+00 401092026 Ethylene oxide 0.00E+00 0.00E+00 1.47E-08 0.00E+00 1.33E-08 401432003 Toluene 7.65E-05 3.77E-06 6.29E-07 1.49E-05 2.57E-06 401432003 Methanol 3.20E-05 1.30E-06 3.32E-07 3.44E-06 6.89E-07 401432003 Xylenes (mixed) 1.94E-05 7.64E-07 1.07E-07 0.00E+00 0.00E+00 401432003 Ethyl benzene 0.00E+00 5.12E-07 1.49E-08 0.00E+00 0.00E+00 401432013 Ethylene glycol ethyl ether 3.85E-01 0.00E+00 0.00E+00 0.00E+00 0.00E+00 401432013 2-Butoxyethyl acetate 2.43E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00 401432013 Formaldehyde 1.73E-03 8.63E-05 5.58E-06 7.91E-05 7.91E-06 401432013 Xylenes (mixed) 9.12E-04 3.58E-05 5.01E-06 0.00E+00 0.00E+00 401432013 Toluene 1.58E-04 7.77E-06 1.30E-06 3.07E-05 5.30E-06 401432013 Methanol 1.69E-04 6.88E-06 1.76E-06 1.82E-05 3.65E-06 401432013 2,4-Toluene diisocyanate 0.00E+00 3.07E-05 7.29E-06 6.06E-05 3.91E-06 401432013 Methylene diphenyl diisocyanate 0.00E+00 0.00E+00 0.00E+00 0.00E+00 5.11E-05 401432013 Ethyl benzene 0.00E+00 4.31E-05 1.26E-06 0.00E+00 0.00E+00 401432014 Xylenes (mixed) 9.99E-04 3.92E-05 5.49E-06 0.00E+00 0.00E+00 401432014 Toluene 8.59E-05 4.24E-06 7.06E-07 1.67E-05 2.89E-06 401432014 Ethyl benzene 0.00E+00 4.74E-05 1.38E-06 0.00E+00 0.00E+00 401432014 Methylene diphenyl diisocyanate 0.00E+00 0.00E+00 0.00E+00 0.00E+00 4.60E-05 401432014 Phenol 4.57E-06 4.57E-07 2.98E-07 6.97E-07 1.39E-07 401432014 Arsenic compounds 5.10E-06 0.00E+00 0.00E+00 0.00E+00 0.00E+00 401432014 Methanol 9.46E-07 3.84E-08 9.82E-09 1.02E-07 2.04E-08 401432014 Cumene 0.00E+00 4.77E-08 7.94E-09 0.00E+00 0.00E+00 401432014 Beryllium compounds 0.00E+00 0.00E+00 0.00E+00 0.00E+00 1.65E-09 401432021 Diethylene glycol monobutyl ether 8.65E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00

23 of 27REL AEGL1 AEGL2 ERPG1 ERPG2

Facility NEI ID Pollutant Table 3 – Maximum Predicted Acute Risks

Actual Emissions

Maximum Hazard Quotient 1

401432021 2-Butoxyethyl acetate 5.50E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00 401432021 Diethylene glycol monomethyl ether 1.25E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00 401432021 Toluene 3.57E-04 1.76E-05 2.94E-06 6.95E-05 1.20E-05 401432021 2,4-Toluene diisocyanate 0.00E+00 5.05E-05 1.20E-05 9.96E-05 6.43E-06 401432021 Xylenes (mixed) 5.52E-05 2.17E-06 3.04E-07 0.00E+00 0.00E+00 401432021 Styrene 2.03E-05 5.01E-06 7.75E-07 2.03E-06 3.87E-07 401432021 Methylene chloride 1.85E-05 3.75E-07 1.36E-07 2.59E-07 9.95E-08 401432021 Methanol 5.93E-06 2.41E-07 6.15E-08 6.38E-07 1.28E-07 401432021 1,1,1-Trichloroethane 4.65E-06 2.43E-07 9.58E-08 1.66E-07 8.32E-08 401432021 Methyl methacrylate 0.00E+00 3.91E-06 5.58E-07 0.00E+00 0.00E+00 401432021 Ethylene glycol ethyl ether acetate 3.99E-06 0.00E+00 0.00E+00 0.00E+00 0.00E+00 401432021 Phenol 1.66E-06 1.66E-07 1.08E-07 2.53E-07 5.07E-08 401432021 Ethyl benzene 0.00E+00 2.16E-06 6.30E-08 0.00E+00 0.00E+00 401432021 Formaldehyde 1.46E-06 7.28E-08 4.71E-09 6.68E-08 6.68E-09 401432021 Carbon tetrachloride 3.16E-07 2.15E-09 5.01E-10 4.62E-09 9.54E-10 401432021 Benzene 1.14E-07 8.74E-10 5.72E-11 9.29E-10 3.10E-10 401432021 n-Hexane 0.00E+00 0.00E+00 1.09E-07 0.00E+00 0.00E+00 401432021 Tetrachloroethene 3.16E-08 2.63E-09 3.95E-10 9.30E-10 4.52E-10 401432021 Cumene 0.00E+00 3.08E-08 5.14E-09 0.00E+00 0.00E+00 401432021 1,2-Epoxybutane 0.00E+00 8.82E-09 4.52E-09 0.00E+00 0.00E+00 401432021 Acrylic acid 2.49E-09 3.40E-09 1.07E-10 5.16E-09 9.97E-11 401432021 Epichlorohydrin 3.51E-10 2.08E-11 5.02E-12 2.40E-11 6.01E-12 480295025 Formaldehyde 1.37E-04 6.85E-06 4.44E-07 6.28E-06 6.28E-07 480295025 Xylenes (mixed) 1.98E-05 7.77E-07 1.09E-07 0.00E+00 0.00E+00 480295025 Methanol 1.13E-05 4.59E-07 1.17E-07 1.22E-06 2.44E-07 480295025 Toluene 1.01E-05 5.00E-07 8.33E-08 1.97E-06 3.41E-07 480295025 Methylene chloride 9.54E-06 1.94E-07 7.03E-08 1.34E-07 5.14E-08 480295025 Styrene 4.47E-06 1.10E-06 1.71E-07 4.47E-07 8.53E-08 480295025 Cumene 0.00E+00 1.11E-06 1.85E-07 0.00E+00 0.00E+00 480295025 Ethyl benzene 0.00E+00 8.74E-07 2.55E-08 0.00E+00 0.00E+00 480295025 Phenol 3.47E-07 3.47E-08 2.26E-08 5.29E-08 1.06E-08 480295025 n-Hexane 0.00E+00 0.00E+00 4.82E-09 0.00E+00 0.00E+00 481136003 2-Butoxyethyl acetate 6.60E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00 481136003 Phenol 1.60E-03 1.60E-04 1.04E-04 2.44E-04 4.87E-05 481136003 Methylene chloride 1.50E-03 3.05E-05 1.11E-05 2.11E-05 8.10E-06 481136003 Toluene 1.88E-04 9.26E-06 1.54E-06 3.65E-05 6.31E-06 481136003 Xylenes (mixed) 6.79E-05 2.67E-06 3.74E-07 0.00E+00 0.00E+00 481136003 Ethylene glycol ethyl ether acetate 5.95E-06 0.00E+00 0.00E+00 0.00E+00 0.00E+00 481136003 Ethyl benzene 0.00E+00 3.29E-06 9.60E-08 0.00E+00 0.00E+00 481136003 Trichloroethylene 0.00E+00 2.69E-07 7.86E-08 3.49E-07 6.98E-08 481136003 n-Hexane 0.00E+00 0.00E+00 5.33E-08 0.00E+00 0.00E+00 481136003 Styrene 1.46E-08 3.60E-09 5.56E-10 1.46E-09 2.78E-10 481215032 Diethylene glycol monobutyl ether 1.43E-02 0.00E+00 0.00E+00 0.00E+00 0.00E+00 481215032 Diethylene glycol monomethyl ether 6.66E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00 481215032 Xylenes (mixed) 6.70E-05 2.63E-06 3.69E-07 0.00E+00 0.00E+00 481215032 Toluene 1.79E-05 8.83E-07 1.47E-07 3.49E-06 6.02E-07 481215032 Methylene chloride 2.02E-05 4.10E-07 1.49E-07 2.83E-07 1.09E-07 481215032 Ethyl benzene 0.00E+00 3.17E-06 9.24E-08 0.00E+00 0.00E+00 481215032 Methanol 2.39E-06 9.70E-08 2.48E-08 2.58E-07 5.15E-08 481215032 n-Hexane 0.00E+00 0.00E+00 6.08E-09 0.00E+00 0.00E+00 482015019 Methylene chloride 7.46E-05 1.51E-06 5.50E-07 1.05E-06 4.02E-07 482015019 Phenol 4.93E-05 4.93E-06 3.21E-06 7.52E-06 1.50E-06

24 of 27REL AEGL1 AEGL2 ERPG1 ERPG2

Facility NEI ID Pollutant Table 3 – Maximum Predicted Acute Risks

Actual Emissions

Maximum Hazard Quotient 1

482015019 2-Butoxyethyl acetate 3.18E-05 0.00E+00 0.00E+00 0.00E+00 0.00E+00 482015019 Formaldehyde 1.76E-05 8.80E-07 5.69E-08 8.06E-07 8.06E-08 482015019 Toluene 8.55E-06 4.22E-07 7.03E-08 1.66E-06 2.88E-07 482015019 Xylenes (mixed) 2.42E-06 9.51E-08 1.33E-08 0.00E+00 0.00E+00 482015019 1,1,1-Trichloroethane 6.14E-07 3.21E-08 1.26E-08 2.20E-08 1.10E-08 482015019 Arsenic compounds 8.89E-08 0.00E+00 0.00E+00 0.00E+00 0.00E+00 482015019 Ethyl benzene 0.00E+00 5.51E-08 1.61E-09 0.00E+00 0.00E+00 482015019 Methanol 9.67E-09 3.92E-10 1.00E-10 1.04E-09 2.08E-10 482015019 Hydrofluoric acid 5.70E-10 1.67E-10 6.84E-12 8.55E-11 8.55E-12 482015020 Methylene chloride 4.36E-05 8.85E-07 3.21E-07 6.11E-07 2.35E-07 482015020 Xylenes (mixed) 3.06E-06 1.20E-07 1.68E-08 0.00E+00 0.00E+00 482015020 Toluene 6.78E-07 3.35E-08 5.58E-09 1.32E-07 2.28E-08 482015020 Methanol 3.38E-07 1.37E-08 3.51E-09 3.64E-08 7.29E-09 482015020 Ethyl benzene 0.00E+00 8.83E-08 2.57E-09 0.00E+00 0.00E+00 482015020 Formaldehyde 6.03E-09 3.02E-10 1.95E-11 2.76E-10 2.76E-11 482015020 m-Xylene 3.04E-09 0.00E+00 0.00E+00 0.00E+00 0.00E+00 482015020 n-Hexane 0.00E+00 0.00E+00 1.02E-10 0.00E+00 0.00E+00 483553000 2-Butoxyethyl acetate 1.06E-02 0.00E+00 0.00E+00 0.00E+00 0.00E+00 483553000 Toluene 2.90E-04 1.43E-05 2.39E-06 5.65E-05 9.77E-06 483553000 Methylene chloride 3.15E-04 6.38E-06 2.32E-06 4.40E-06 1.69E-06 483553000 Xylenes (mixed) 1.54E-04 6.04E-06 8.46E-07 0.00E+00 0.00E+00 483553000 Formaldehyde 1.91E-05 9.53E-07 6.17E-08 8.74E-07 8.74E-08 483553000 Phenol 3.66E-06 3.66E-07 2.39E-07 5.59E-07 1.12E-07 483553000 Ethyl benzene 0.00E+00 3.64E-06 1.06E-07 0.00E+00 0.00E+00 483553000 Methanol 8.82E-07 3.58E-08 9.15E-09 9.50E-08 1.90E-08 483553000 Cumene 0.00E+00 2.27E-07 3.79E-08 0.00E+00 0.00E+00 483553000 1,2-Epoxybutane 0.00E+00 7.35E-08 3.76E-08 0.00E+00 0.00E+00 483553000 Hydrofluoric acid 4.10E-08 1.20E-08 4.92E-10 6.15E-09 6.15E-10 483553000 Methylene diphenyl diisocyanate 0.00E+00 0.00E+00 0.00E+00 0.00E+00 3.99E-08 483553000 n-Hexane 0.00E+00 0.00E+00 1.12E-08 0.00E+00 0.00E+00 483553000 Trichloroethylene 0.00E+00 2.99E-09 8.72E-10 3.88E-09 7.75E-10 483553000 Methyl methacrylate 0.00E+00 1.44E-11 2.06E-12 0.00E+00 0.00E+00 483553000 Benzene 8.30E-14 1.00E-15 0.00E+00 1.00E-15 0.00E+00 483672020 Tetrachloroethene 5.70E-03 4.75E-04 7.13E-05 1.68E-04 8.15E-05 484392009 Propyl cellosolve 4.49E-02 0.00E+00 0.00E+00 0.00E+00 0.00E+00 484392009 Butyl carbitol acetate 1.44E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00 484392009 Methylene chloride 2.67E-04 5.41E-06 1.96E-06 3.73E-06 1.44E-06 484392009 2,4-Toluene diisocyanate 0.00E+00 4.91E-05 1.16E-05 9.68E-05 6.25E-06 484392009 Toluene 5.32E-05 2.63E-06 4.38E-07 1.04E-05 1.79E-06 484392009 Glycol ethers 4.95E-05 0.00E+00 0.00E+00 0.00E+00 0.00E+00 484392009 Ethylene glycol ethyl ether 4.25E-05 0.00E+00 0.00E+00 0.00E+00 0.00E+00 484392009 o-Xylene 2.92E-05 0.00E+00 0.00E+00 0.00E+00 0.00E+00 484392009 Xylenes (mixed) 2.20E-05 8.64E-07 1.21E-07 0.00E+00 0.00E+00 484392009 Ethyl acrylate 0.00E+00 2.44E-08 5.54E-09 2.03E-05 6.93E-09 484392009 Formaldehyde 1.54E-05 7.68E-07 4.97E-08 7.04E-07 7.04E-08 484392009 Triethylamine 7.08E-06 0.00E+00 0.00E+00 0.00E+00 0.00E+00 484392009 Diethylene glycol monobutyl ether 6.28E-06 0.00E+00 0.00E+00 0.00E+00 0.00E+00 484392009 Ethyl benzene 0.00E+00 2.58E-06 7.53E-08 0.00E+00 0.00E+00 484392009 Benzene 1.27E-06 9.72E-09 6.36E-10 1.03E-08 3.44E-09 484392009 Methanol 1.72E-07 6.98E-09 1.78E-09 1.85E-08 3.70E-09 484392009 Cumene 0.00E+00 6.18E-08 1.03E-08 0.00E+00 0.00E+00 484392009 p-Xylene 1.87E-08 0.00E+00 0.00E+00 0.00E+00 0.00E+00

25 of 27REL AEGL1 AEGL2 ERPG1 ERPG2

Facility NEI ID Pollutant Table 3 – Maximum Predicted Acute Risks

Actual Emissions

Maximum Hazard Quotient 1

484392010 Propyl cellosolve 1.81E-04 0.00E+00 0.00E+00 0.00E+00 0.00E+00 484392010 Toluene 6.19E-05 3.06E-06 5.09E-07 1.21E-05 2.08E-06 484392010 Methylene chloride 5.98E-06 1.21E-07 4.40E-08 8.37E-08 3.22E-08 484392010 Xylenes (mixed) 1.57E-07 6.19E-09 8.66E-10 0.00E+00 0.00E+00 484392010 Diethylene glycol monobutyl ether 1.24E-08 0.00E+00 0.00E+00 0.00E+00 0.00E+00 484392010 Ethyl benzene 0.00E+00 4.04E-09 1.18E-10 0.00E+00 0.00E+00 484392010 Cumene 0.00E+00 7.52E-10 1.25E-10 0.00E+00 0.00E+00 484392010 Ethyl acrylate 0.00E+00 9.55E-13 2.17E-13 7.92E-10 2.71E-13 484392010 Formaldehyde 5.90E-10 2.95E-11 1.91E-12 2.71E-11 2.71E-12 484392010 Benzene 4.05E-11 3.10E-13 2.00E-14 3.29E-13 1.10E-13 484392010 Methanol 5.01E-12 2.03E-13 5.20E-14 5.40E-13 1.08E-13 484392010 Triethylamine 6.90E-14 0.00E+00 0.00E+00 0.00E+00 0.00E+00 490115010 Xylenes (mixed) 2.49E-05 9.78E-07 1.37E-07 0.00E+00 0.00E+00 490115010 Toluene 1.20E-06 5.91E-08 9.86E-09 2.33E-07 4.03E-08 490115010 Ethyl benzene 0.00E+00 7.01E-07 2.05E-08 0.00E+00 0.00E+00 490115012 Xylenes (mixed) 4.13E-05 1.62E-06 2.27E-07 0.00E+00 0.00E+00 490115012 Toluene 1.02E-05 5.05E-07 8.41E-08 1.99E-06 3.44E-07 490115012 1,1,1-Trichloroethane 6.34E-06 3.32E-07 1.31E-07 2.27E-07 1.13E-07 490115012 Ethyl benzene 0.00E+00 2.07E-06 6.05E-08 0.00E+00 0.00E+00 490355013 2,4-Toluene diisocyanate 0.00E+00 7.37E-05 1.75E-05 1.45E-04 9.38E-06 490355013 Xylenes (mixed) 1.70E-04 6.69E-06 9.37E-07 0.00E+00 0.00E+00 490355013 Methylene diphenyl diisocyanate 0.00E+00 0.00E+00 0.00E+00 0.00E+00 1.63E-04 490355013 Toluene 1.06E-04 5.21E-06 8.68E-07 2.06E-05 3.55E-06 490355013 Methylene chloride 2.91E-05 5.91E-07 2.14E-07 4.07E-07 1.57E-07 490355013 1,1,1-Trichloroethane 2.02E-05 1.06E-06 4.17E-07 7.24E-07 3.62E-07 490355013 Ethyl benzene 0.00E+00 6.11E-06 1.78E-07 0.00E+00 0.00E+00 490355013 Formaldehyde 4.83E-06 2.41E-07 1.56E-08 2.21E-07 2.21E-08 490355013 Methanol 1.49E-06 6.04E-08 1.54E-08 1.60E-07 3.21E-08 490355013 Benzene 5.14E-07 3.93E-09 2.57E-10 4.18E-09 1.39E-09 490355013 Cumene 0.00E+00 2.14E-07 3.56E-08 0.00E+00 0.00E+00 490355013 1,3-Butadiene 0.00E+00 3.26E-09 4.07E-10 2.22E-07 1.11E-08 490355013 n-Hexane 0.00E+00 0.00E+00 9.65E-09 0.00E+00 0.00E+00 516505023 Xylenes (mixed) 2.76E-05 1.08E-06 1.52E-07 0.00E+00 0.00E+00 516505023 Toluene 1.10E-05 5.45E-07 9.09E-08 2.15E-06 3.72E-07 516505023 Phenol 5.60E-07 5.60E-08 3.65E-08 8.54E-08 1.71E-08 516505023 Methylene chloride 5.22E-07 1.06E-08 3.84E-09 7.30E-09 2.81E-09 516505023 Ethyl benzene 0.00E+00 9.92E-08 2.89E-09 0.00E+00 0.00E+00 516505023 Triethylamine 3.26E-08 0.00E+00 0.00E+00 0.00E+00 0.00E+00 530615014 Butyl carbitol acetate 1.36E-02 0.00E+00 0.00E+00 0.00E+00 0.00E+00 530615014 Diethylene glycol monobutyl ether 1.36E-02 0.00E+00 0.00E+00 0.00E+00 0.00E+00 530615014 Toluene 2.98E-04 1.47E-05 2.45E-06 5.79E-05 1.00E-05 530615014 Methylene chloride 1.91E-06 3.87E-08 1.41E-08 2.67E-08 1.03E-08 530615014 Phenol 3.29E-07 3.29E-08 2.14E-08 5.02E-08 1.00E-08 540572002 Xylenes (mixed) 3.03E-04 1.19E-05 1.67E-06 0.00E+00 0.00E+00 540572002 Toluene 1.84E-04 9.10E-06 1.52E-06 3.59E-05 6.20E-06 540572002 Formaldehyde 1.03E-04 5.17E-06 3.35E-07 4.74E-06 4.74E-07 540572002 Diethylene glycol monomethyl ether 1.36E-05 0.00E+00 0.00E+00 0.00E+00 0.00E+00 540572002 Phenol 4.22E-06 4.22E-07 2.75E-07 6.45E-07 1.29E-07 540572002 Ethyl benzene 0.00E+00 4.51E-06 1.31E-07 0.00E+00 0.00E+00 540572002 Methanol 3.56E-06 1.44E-07 3.69E-08 3.83E-07 7.66E-08 540572002 Ethylene glycol ethyl ether 9.42E-07 0.00E+00 0.00E+00 0.00E+00 0.00E+00 540572002 Styrene 1.67E-08 4.13E-09 6.38E-10 1.67E-09 3.19E-10

26 of 27REL AEGL1 AEGL2 ERPG1 ERPG2

Facility NEI ID Pollutant Table 3 – Maximum Predicted Acute Risks

Actual Emissions

Maximum Hazard Quotient 1

540572002 Methylene diphenyl diisocyanate 0.00E+00 0.00E+00 0.00E+00 0.00E+00 1.78E-08 540572002 n-Hexane 0.00E+00 0.00E+00 3.50E-10 0.00E+00 0.00E+00 550876001 Phenol 1.67E-02 1.67E-03 1.09E-03 2.55E-03 5.09E-04 550876001 2-Butoxyethyl acetate 7.30E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00 550876001 Methylene chloride 6.94E-03 1.41E-04 5.11E-05 9.71E-05 3.74E-05 550876001 Ethylene glycol ethyl ether acetate 2.70E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00 550876001 Toluene 5.26E-04 2.60E-05 4.33E-06 1.02E-04 1.77E-05 550876001 Xylenes (mixed) 5.34E-04 2.10E-05 2.94E-06 0.00E+00 0.00E+00 550876001 Diethylene glycol monoethyl ether 3.23E-04 0.00E+00 0.00E+00 0.00E+00 0.00E+00 550876001 Methanol 2.45E-04 9.94E-06 2.54E-06 2.64E-05 5.28E-06 550876001 Styrene 3.44E-05 8.51E-06 1.31E-06 3.44E-06 6.57E-07 550876001 Diethylene glycol monobutyl ether 3.65E-05 0.00E+00 0.00E+00 0.00E+00 0.00E+00 550876001 Ethyl benzene 0.00E+00 9.14E-06 2.67E-07 0.00E+00 0.00E+00 550876001 Ethylene glycol methyl ether 6.54E-06 0.00E+00 0.00E+00 0.00E+00 0.00E+00 550876001 Ethylene glycol ethyl ether 5.63E-06 0.00E+00 0.00E+00 0.00E+00 0.00E+00 550876001 Formaldehyde 4.33E-06 2.16E-07 1.40E-08 1.98E-07 1.98E-08 550876001 Glycol Ethers 4.50E-06 0.00E+00 0.00E+00 0.00E+00 0.00E+00 550876001 Propyl cellosolve 2.33E-06 0.00E+00 0.00E+00 0.00E+00 0.00E+00 550876001 Methyl methacrylate 0.00E+00 1.40E-06 2.00E-07 0.00E+00 0.00E+00 550876001 Acetaldehyde 8.31E-07 4.82E-09 7.97E-10 2.17E-08 1.09E-09 550876001 Methylene diphenyl diisocyanate 0.00E+00 0.00E+00 0.00E+00 0.00E+00 6.85E-07 550876001 n-Hexane 0.00E+00 0.00E+00 4.73E-07 0.00E+00 0.00E+00 550876001 Carbon tetrachloride 1.49E-07 1.01E-09 2.36E-10 2.18E-09 4.50E-10 550876001 m-Xylene 6.48E-08 0.00E+00 0.00E+00 0.00E+00 0.00E+00 550876001 Propylene oxide 2.84E-08 5.18E-10 1.28E-10 7.34E-10 1.49E-10 550876001 Benzene 1.20E-08 9.21E-11 6.02E-12 9.78E-11 3.26E-11 550876001 Cumene 0.00E+00 1.09E-09 1.81E-10 0.00E+00 0.00E+00 550876001 o-Xylene 1.22E-09 0.00E+00 0.00E+00 0.00E+00 0.00E+00 550876001 p-Xylene 1.22E-09 0.00E+00 0.00E+00 0.00E+00 0.00E+00 550876001 Aniline 0.00E+00 4.11E-11 2.68E-11 0.00E+00 0.00E+00 201732006a Glycol Ethers 1.02E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00 201732006a Toluene 6.41E-04 3.16E-05 5.27E-06 1.25E-04 2.16E-05 201732006a Methanol 3.86E-04 1.56E-05 4.00E-06 4.15E-05 8.31E-06 201732006a n-Hexane 0.00E+00 0.00E+00 1.77E-06 0.00E+00 0.00E+00 201732006a Phenol 3.17E-08 3.17E-09 2.06E-09 4.83E-09 9.66E-10 39021672a Toluene 7.22E-08 3.56E-09 5.93E-10 1.41E-08 2.43E-09 39021672a Benzene 1.13E-09 8.64E-12 5.65E-13 9.18E-12 3.06E-12

Note: BOLD indicates acute risks greater than 1 1 Some maximum acute impacts may be at onsite locations.

27 of 27Facility NEI ID Pollutant Tier 1 1 Tier 2 1

10892023 Cadmium 2.66E-01 Below Tier 1 Threshold

4019433 Cadmium 1.00E-01 Below Tier 1 Threshold

5051409 Cadmium 1.33E-06 Below Tier 1 Threshold

60372022 Cadmium 3.43E-05 Below Tier 1 Threshold

9001693 Cadmium 5.53E-07 Below Tier 1 Threshold

9003626 Cadmium 2.00E-09 Below Tier 1 Threshold

12005313 Cadmium 1.04E-05 Below Tier 1 Threshold

12023284 Cadmium 2.02E-05 Below Tier 1 Threshold

12031634 Cadmium 7.59E-04 Below Tier 1 Threshold

120862016 Cadmium 6.39E-03 Below Tier 1 Threshold

13009224 Cadmium 4.32E-08 Below Tier 1 Threshold

13063388 Cadmium 6.54E-05 Below Tier 1 Threshold

13067506 Cadmium 8.75E-03 Below Tier 1 Threshold

201732006 Cadmium 1.98E-03 Below Tier 1 Threshold

201732027 Cadmium 2.65E-02 Below Tier 1 Threshold

2017365 Mercury (methyl) 1.86E-03 Below Tier 1 Threshold

24005458 Mercury (methyl) 5.68E-03 Below Tier 1 Threshold

370492008 Cadmium 1.33E-01 Below Tier 1 Threshold

370492008 Mercury (methyl) 5.34E-06 Below Tier 1 Threshold

37119600 Cadmium 3.64E-06 Below Tier 1 Threshold

37133455 Mercury (methyl) 1.14E-07 Below Tier 1 Threshold

39021672 Cadmium 4.12E-04 Below Tier 1 Threshold

401432014 Cadmium 1.38E-06 Below Tier 1 Threshold

401432021 Cadmium 1.29E+00 Below Tier 1 Threshold

40143523 Cadmium 1.26E+00 Below Tier 1 Threshold

42003603 Cadmium 1.25E-04 Below Tier 1 Threshold

42081660 Cadmium 1.11E+00 Below Tier 1 Threshold

484392009 Cadmium 1.12E-02 Below Tier 1 Threshold

484392010 Cadmium 1.55E-08 Below Tier 1 Threshold

49011663 Cadmium 4.15E-02 Below Tier 1 Threshold

53033383 Cadmium 3.51E-06 Below Tier 1 Threshold

53033384 Cadmium 5.78E-06 Below Tier 1 Threshold

53033532 Cadmium 5.64E-03 Below Tier 1 Threshold

53033532 Mercury (methyl) 1.54E-02 Below Tier 1 Threshold

53053447 Cadmium 3.50E-08 Below Tier 1 Threshold Table 4 – Multipathway Screening Analysis Results

Actual Emissions

1 of 2Facility NEI ID Pollutant Tier 1 1 Tier 2 1 Table 4 – Multipathway Screening Analysis Results

Actual Emissions

53061398 Cadmium 5.54E-03 Below Tier 1 Threshold

53061398 Mercury (methyl) 6.02E-01 Below Tier 1 Threshold

1 Numeric value indicates number of times facility level emissions above screening threshold.

2 of 2

### Appendix 8 Acute Impacts Refined Analysis

### Refined Acute Modeling Approach
Initial acute screening risk calculations were performed with the HEM- 3 model. HEM-3 estimates acute (1-hour) impacts at both polar and census block receptors. It is assumed for this short period of time that an exposed individual could be located at any off-site location. The lack of readily available detailed property boundary information for many of the facilities evaluated made it difficult to determine whether receptors were on- or off-site. In the absence of such information, the first ring of polar receptors was placed 100 meters from the plant center for many facilities. However, these polar rings often transected on-site locations, restricting public access to exposures at these levels and thereby overestimating exposures.
The screening approach used by HEM-3 to estimate maximum 1-hour exposures also likely overestimates. To estimate maximum 1-hour concentrations at each receptor, HEM-3 sums the maximum concentrations attributed to each source, regardless of whether those maximum concentrations occurred during the same hour. In other words, HEM-3 assumes that the maximum impact from each source at each receptor occurs at the same time. In actuality, maximum impacts from different sources may occur at different times.

This appendix includes the results of refined acute assessments performed for facilities that exceeded short-term health benchmarks using the HEM-3 approach. The refinements can address both areas of conservatism described above. The simplest refinement is to plot the HEM-3 polar grid results on aerial photographs of the facilities; This allows the assessment of off-site locations that may be accessible to the public ( e.g., roadways and public buildings.). The attached figure present the estimated hazard quotient values (the modeled 1-hour concentration of a pollutant divided by its short-term health benchmark) at the polar receptors near the facilities.

### HEM-3 (Screening) Refined

Results

5113377 Ethylene glycol

ethyl ether acetate REL 

### 2 2 Max off-site HQ at centroid of census tract (populated) Petroleum Refining Source Sector

1 

### Indicates modeling technique used to refined estimates; see figures depicting off-site impacts Table 1 – Maximum Predicted Acute Risks Greater than 1 (Refined Approach)



### Facility NEI ID Pollutant Criteria Refined Modeling Approach 1 

### Baseline - Actuals

# Figure 1

– 05113377

Acute Ethylene Glycol Ethyl Ether Acetate HQ (REL)