### Residual Risk Assessment for the Phosphate Fertilizer and Phosphoric A cid Source Categories in Support of the July 2015 Risk and Technology Review Final Rule

1 

## Residual Risk Assessment for the PHOSPHATE FERTILIZER PRODUCTION and PHOSPHORIC ACID MANUFACTURING SOURCE CATEGORIES in Support of the July 2015 Risk and Technology Review Final Rule

by EPA’s Office of Air Quality Planning and Standards Office of Air and Radiation July 201 5

### Residual Risk Assessment for the Phosphate Fertilizer and Phosphoric A cid Source Categories in Support of the July 2015 Risk and Technology Review Final Rule

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## Table of Contents

1. Introduction

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

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

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

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

................................ ........................... 8 2.4. Screening for potential acute impacts of concern

................................ ........................ 9 2.5. Multipathway human health and e nvironmental risk screening

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

................................ ................................ ........... 12 2.6. Dose

- response assessment

................................ ................................ .......................... 16 2.6.1. Sources of chronic dose

- response information

................................ ................... 16 2.6.2. Sources of acute dose

- response information

................................ ....................... 24 2.7. Risk characterization

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

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

................................ ................................ ................................ .............. 29 2.7.3. Facility

- wide risks

................................ ................................ ............................... 30 2.7.4. MACT

- allowable risks

................................ ................................ ........................ 30 3. Risk Results for Phosphate Fertilizer Prod uction

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

................................ ................................ ..... 31 3.2 Baseline risk characterization

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

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

................................ ................................ ..... 38 4.2 Risk characterization

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

....................... 48 5 .1 Exposure modeling uncertainties

................................ ................................ .................. 48 5.2 Uncertainties in the dose

- response relationships

................................ ........................... 49 6 References

................................ ................................ ................................ ................................ . 58

### Residual Risk Assessment for the Phosphate Fertilizer and Phosphoric A cid Source Categories in Support of the July 2015 Risk and Technology Review Final Rule

3 Index of Tables

Table 2.2

- 1 AERMOD version 11103 model options for RTR modeling

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

- 1 (a) Dose

- Response Values for Chronic Inhalation Exposure to Carcinogens

..... 21 Table 2.6

- 1(b) Dose

- Response Values for Chronic Oral Exposure to Carcinogens

................ 22 Table 2.6

- 2 (a) Dose

- Response Values for Chronic Inhalation Exposure to Noncarcinogens 22 Table 2.6

- 2 (b) Dose

- Response Values for Chronic Oral Exposure to No ncarcinogens

......... 24 Table 2.6

- 3 Dose

- Response Values for Acute Exposure

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

- 1 Summary of Emissions from the Phosphate Fert ilizer Production Source Category and Availability of Dose

- Response Values

................................ ............................... 32 Table 3.2

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

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

- 2 Summary of the “Tier 1 and 2” Multipathway Screening Analysis for the Phosphate Fertilizer Production Source Category

................................ ................................ .... 36 Table 3. 2

- 3 Summary of the “Tier 2 and 3” Multipathway Screening Analysis for Phosphate Fertilizer Production for Mississippi Phosphates (NEIMS059305)

................................ ......... 36 Table 3.2

- 4 Summary of Environmental Risk Scr eening Results for the Phosphate Fertilizer Production Source Category

................................ ................................ ................................ ..... 38 Table 4.1

- 1 Summary of Emissions from the Phosphoric Acid Manufacturing Source Category and Availability of Dose

- Respo nse Values

................................ ............................... 40 Table 4.2

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

................................ ................................ ................................ ........................... 44 Table 4.2

- 2 Sum mary of Acute Refined Results for Phosphoric Acid Manufacturing

............ 45 Table 4.2

- 3 Summary of the “Tier 1 and 2” Multipathway Screening Analysis for the Phosphoric Acid Manufacturing Source Catego ry

................................ ................................ ... 47 Table 4.2

- 4 Summary of Environmental Risk Screening Results for the Phosphoric Acid Manufacturing Source Category

................................ ................................ ............................... 48

Appendices

Appendix 1 Technical Su pport Document for HEM3 Modeling Appendix 2 Meteorological Data for HEM3 Modeling Appendix 3 Analysis of data on short

- term emission rates relative to long

- term emission
rates Appendix 4 Draft Detailed Risk Modeling Results Appendix 5 Technical Suppor t Document for TRIM

- Based Multipathway Tiered Screening Scenario for RTR: Summary of Approach and Evaluation Appendix 6 Dispersion Model Receptor Revisions and Additions Appendix 7 Environmental Risk Screen ing Appendix 8 Acute Noncancer Risk Analysis f or Phosphate Fertilizer and Phosphoric Acid Manufacturing Appendix 9 Technical Support Document : Human Health Multipathway Residual Risk Screening Assessment for the Phosphate Fertilizer Production and Phosphoric Acid Manufacturing Source Categories

### Residual Risk Assessment for the Phosphate Fertilizer and Phosphoric A cid Source Categories in Support of the July 2015 Risk and Technology Review Final Rule

4 Index of Acronyms

AERMOD American Meteorological Society/EPA Regulatory Model AEGL Acute exposure guideline level ASTDR US Agency for Toxic Substances and Disease Registry CalE PA California Environmental Agency ERPG Emergency Response Planning Guideline HAP Hazardous Air Pollutant HEM Human Exposure Model HI Hazard index HQ Hazard quotient IRIS Integrated Risk Information System LOAEL Lowest Observed Adverse Effe ct Level MACT Maximum Achievable Control Technology MIR Maximum Individual Risk MOA Mode of action NAC National Advisory Committee NAAQS National Ambient Air Quality Standard NATA National Air Toxics Assessment NEI National Emissions Invent ory NOAEL No Observed Adverse Effect Level NPRM Notice of Proposed Rulemaking PB

- HAP Persistent and Bioaccumulative

- HAP POM Polycyclic organic matter REL Reference exposure level RfC Reference concentration RfD Reference dose RTR Risk and Technology Review TOSHI Target

- organ

- specific hazard index URE Unit risk estimate

### Residual Risk Assessment for the Phosphate Fertilizer and Phosphoric A cid Source Categories in Support of the July 2015 Risk and Technology Review Final Rule

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## 1. Introduction
Section 112 of the Clean Air Act (CAA) establishes a two

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

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

- based standards at least every eight years and revise a standard, as necessary, “taking into account developments in practices,
processe s 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 add itional risk reductions are necessary to protect public health with an ample margin of safety or to prevent an adverse environmental effect, EPA must develop standards to address these remaining risks. This second stage of the regulatory process is known as the residual risk stage. For each source category for which EPA issued MACT standards, the residual risk stage must be completed within eight years of promulgation of the initial technology

- based standard.

In December of 2006 we consulted with a panel from the EPA's Science Advisory Board (SAB) on the “Risk and Technology Review (RTR) Assessment Plan” and in June of 2007, we received a letter with the results of that consultation. Subsequent to the consultation, in July of 2009 a meeting was held with an SAB panel for a formal peer review of the “Risk and Technology Review (RTR) Assessment Methodologies” [ 2 ]. We received the final SAB report on this review in May of 2010 [ 3 ]. Where appropriate, we have responded to the key messages from this review i n developing our current risk assessment s 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 th e SAB are outlined in the me mo entitled, “EPA’s Actions in Response
t o Key Recommendations from the SAB Review of RTR R isk Assessment Methodologies” [ 4 ].

This document contains the methods and the results of baseline risk assessments (i.e., after the i mplementation of the MACT standard) performed for the phosp hate fertilizer production an d phosphoric acid manufacturing source categories. The methods discussion includes descriptions of the methods used to develop refined estimates of chronic inhalation exposures and human health risks for cancer and noncancer endpoints, as well as descriptions of the methods used to screen for acute health risks, chronic non

- inhalation health risks, and adverse environmental effects.

### Residual Risk Assessment for the Phosphate Fertilizer and Phosphoric A cid Source Categories in Support of the July 2015 Risk and Technology Review Final Rule

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## 2. Methods

2.1. Emissions and source data

Data from a CAA section 114 information collection request (ICR) were used for this assessment. In April 20 10

, we issued the ICRs to all U.S. phosphate fertilizer and phosphoric acid manufacturers to gather information needed to conduct the technology re view and residual risk requirements of the CAA. The ICRs requested available information regarding process equipment, control devices, phosphate and acid production, inventory data for all point and fugitive emissions, practices used to control fugitive emissions, and other aspects of facility operations, including stack parameters and locations. Additional survey information from this source category was collected in January 2014 and used in the assessment, namely new mercury and hydrofluoric acid (HF) emissions collected from plants that utilize calciners. Next, EPA engineers who have extensive knowledge of the characteristics of this industry performed an engineering and quality assurance/quality control review of the data to identify limitations and issues. Finally, EPA engineers contacted facility and industry representatives to clarify details and resolve issues with their data submissions.

The 2005 National

- Scale Air Toxics Assessment (NATA) National Emissions Inventory (NEI) was updated for th ese source categor ies with data obtained from industry thr o u gh the ICR process

. The emissions data and corrections are described in more detail in the memo entitled “ Emissions Data Used in Residual Risk Modeling: Phosphoric Acid and P hosphate Fertilizer Production Source Categories ” available in the docket for this action

.


### 2.2. Dispersion modeling for inhalation exposure assessment Both long

- and short

- term inhalation exposure concentrations and associated health risk from each facility in the source categor y of interest were estimated using the Human Exposure Model in combination with the American Meteorological Society/EPA Regulatory Model (AERMOD) dispersion modeling system (HEM

- 3) . The approach used in applying this modeling system is outlined below, and further details are provided in Appendix 1

. The HEM

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

The dispersion model in the HEM

- 3 system, utilizing EPA’s AERMOD version 11103 The version available at the time of the analysis ) , Gaussian plume dispersion model for modeling point, area, and volume sources of continuous air emissions from facility applications [ 5 ]. Further details on AERMOD can be found in the AERMOD Users Guide [ 6 ]. The model is used to develop annual average ambient concentrations through the simulation of hour

- by

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

### Residual Risk Assessment for the Phosphate Fertilizer and Phosphoric A cid Source Categories in Support of the July 2015 Risk and Technology Review Final Rule

7 The first step in the application of the HEM3 modeling system is to estimate ambient concentrations at locations of interest. The AERMOD model options employed are summarized in Table 2.2

- 1 and are discussed fur ther below.

Table 2.2

- 1 AERMOD version 11103 mo del options for RTR modeling

Modeling Option Selected Parameter for chronic exposure

Type of calculations Hourly Ambient Concentration

Source type Point, a rea represented as pseudo point source

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

- supplied receptors

Terrain characterization Actual from USGS 1

- degree DEM data

Building downwash Not Included

Plume deposition/depletion Not Included

Urban source option No

Meteorology 1 year representative NWS from nearest site ( over 200 stations)



### In HEM

- 3 , meteorological data are ordinarily selected from a list of over 200 National Weather Service (NWS) surface ob servation stations across the continental United States, Alaska, Hawaii, and Puerto Rico (the number of stations available at the time of the analysis)

. In most cases the nearest station is selected as representative of the conditions at the subject facil ity. Ideally, when considering off

- site meteorological data

, most site

- specific dispersion modeling efforts will employ up to five years of data to capture variability in weather patterns from year to year. However, because we had an insufficient number of appropriately formatted model input files derived from available meteorological data, we modeled only a single year, typically 1991. While the selection of a single year may result in under

estimation of long

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

estimation at others. For each facility identified by its characteristic latitude and longitude coordinates, the closest meteorological station was used in the dispersion modeling. Appendix 2 ( Meteorological Data Processing U sing AERMET for HEM

- 3 ) provides a complete listing of stations and assumptions along with further details used in processing the data through AERMET. The sensitivity of model results to the selection of the nearest weather station and the use of one year of meteorological data is discussed in “ Risk and Technology Review (RTR) Risk Assessment Methodologies ” [ 2 ].

The HEM

- 3 system estimates ambient concentrations at the geographic centroids of census blocks (using the 2010 Census), and at other receptor loca tions that can be specified by the

user. See Appendix 6 to this document ( Dispe r sion Model Receptor Revisions and Additions ) for a discussion of user receptors and centroid location changes specific to these source categories. The model accounts for the effects of multiple facilities when estimating concentration impacts at each block centroid. Typically we combined only the impacts of

### Residual Risk Assessment for the Phosphate Fertilizer and Phosphoric A cid Source Categories in Support of the July 2015 Risk and Technology Review Final Rule

8 facilities within the same source category, and assessed chronic exposure and risk only for census blocks with at least one resident (i.e., locations where people may reasonably be assumed to reside rather than receptor points at the fenceline of a facility). Chronic ambient concentrations were calculated as the annual average of all estimated short

- term (one

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

In contrast to the development of ambient concentrations for evaluating long

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

- case short

- term (one

- hour) concentrations were estimated both at the census block centroids and a t points nearer the facility that represent locations where people may be present for short periods, but generally no nearer than 100 meters from the center of the facility (note that for large facilities, this 100

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

- term emission rates were needed to screen for the potential for hazard via acute exposures, and since the ICR contains only annual emission totals, we generally apply the assumption to all source categories that the maximum one

- hour emission rate from any source is ten times the average annual hourly emission rate for that source. 1 

### More description of our short

- term assessment can be found in section 2.4 of this document

, Acute Risk Screening and Refined Assessme nt

.

Census block elevations for HEM

- 3 modeling were determined nationally from the US Geological Service 1/3 Arc Second National Elevation Dataset, which have a spatial resolution of about 10 meters. Elevations of polar grid points used in estimating s hort

- and long

- term ambient concentrations were assumed to be equal to the highest elevation of any census block falling within the polar grid sector corresponding to the grid point. If a sector does not contain any blocks, the model defaults the elevatio n to that of the nearest block. If an elevation is not provided for the emission source, the model uses the average elevation of all sectors within the innermost model ring. AERMOD adjusts the plume’s flow if nearby elevated hills are expected to influen ce the wind patterns. For details on how hill heights were estimated and used in the AERMOD modeling see Appendix 2 of this document, Technical Support Document for HEM3 Modeling

.


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

- term or long

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

We did not address short

- term human activity for two reasons. First, our experience with NATA assessments (which modeled daily activ ity using EPA’s HAPEM model) suggests that, given our current understanding of m icro environment concentrations and daily activities, modeling short

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

1 In situations where we have in formation on hourly emissions, we will adjust the multiplier when evaluating acute risks.

### Residual Risk Assessment for the Phosphate Fertilizer and Phosphoric A cid Source Categories in Support of the July 2015 Risk and Technology Review Final Rule

9 particulate HAPs; it w ould also reduce risk estimates for gaseous HAPs, but typically by much less. Second, basing exposure estimates on average ambient concentrations at census block centroids may underestimate or overestimate actual exposure concentrations at some residences. Further reducing exposure estimates for the most highly exposed residents by modeling their short

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

We did not address long

- term migration nor population growth or decrease over 70 years, instead basi ng the assessment on the assumption that each person’s predicted exposure is constant over the course of their lifetime which is assumed to be 70 years. In assessing cancer risk, we generally estimated three metrics: the maximum individual risk (MIR), whi ch is defined as the risk associated with a lifetime of exposure at the highest concentration (note that the MIR can be estimated for each source as well as for the entire source category); the population risk distribution; and the cancer incidence. The a ssumption of not considering short or long

- term population mobility does not bias the estimate of the MIR nor does it affect the estimate of cancer incidence since the total population number remains the same. It does, however, affect the shape of the dis tribution 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 speci fic risk levels.



### 2.4. S creening for potential acute impacts of concern In establishing a scientifically defensible approach for the assessment of potential health risks due to acute exposures to HAP, we f ollowed the same general approach that has been used for developing chronic health risk assessments under the residual risk program. That is, we developed a tiered, iterative approach. This approach to risk assessment was endorsed by the National Academy of Sciences in its 1993 publication “Science and Judgmen t in Risk Assessment” and subsequently was adopted in the EPA’s “Residual Risk Report to Congress” in 1999.

The assessment methodology is designed to eliminate from further consideration those facilities for which we have confidence that no acute advers e 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 begin with a screening assessment, which relies on readily available data and uses conservative assumptions that in combination approximate a potential worst

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

no potential acute health risks (i.e., it “screens out”), or that it requires further , more refined
assessment. A refined assessment could u s e industry

- or site

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

- specific d ata would be needed to refine the assessment; in others, lesser amount s of site

- specific data c an be used to determine that acute exposures are not a concern, and significant additional data collection is not necessary.

Acute health risk screening was p erformed for each facility as the first step. When we identify acute impacts which exceed their relevant benchmarks, we pursue refining our acute screening estimates to the extent possible using available data . In some cases, this may

### Residual Risk Assessment for the Phosphate Fertilizer and Phosphoric A cid Source Categories in Support of the July 2015 Risk and Technology Review Final Rule

10 include the use of a facility

- specific emissions multiplier to estimate the peak hourly emission rates from the average rates (rather than the default factor of 10). In other cases, this may entail determining the actual physical layout and boundaries of a facility and surr ounding communities to more accurately gauge where people might reasonably be exposed for an hour.

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

- site location as the surrogate for the maximum potential acute exposure concentration for any individual. These hourly concentrations are based on hourly emissions, as described above. Since information is not usually available on short

- term emission rates, we generally appl y the assumption that the maximum one

- hour emission rate from any source is ten times the average annual hourly emission rate for that source. (The average hourly emissions rate is defined as the total emissions for a year divided by the total number of o perating hours in the year.) This choice of a factor of ten for acute screening was originally based on engineering judgment

.
To develop a more robust peak

- to

- mean emissions factor, and in response to one of the key messages from the SAB consultation o n our RTR Assessment Plan, we performed an analysis using a short

- term emissions data set from a number of source s located in Texas (originally reported on by Allen et al

. 2004) [ 7 ]. In that report, the Texas Environmental Research Consortium Project compa red hourly and annual emissions data for volatile organic compound s for all facilities in a heavily

- industrialized 4

- county area (Harris, Galveston, Chambers, and Brazoria Counties ) over an eleven

- month time period in 2001 . We obtained the dataset and per formed our own analysis, focusing on sources which reported emitting high quantities of HAP over short periods of time (see Appendix 3 of this document, Analysis of data on short

- term emission rates relative to long

- term emission rates ). Most peak emissio n events were less than twice the annual average, and the highest was a factor of 74 times the annual average , and the 99 th 

### percentile ratio of peak hourly emission rate to the annual hourly emission rate was 9. Based on these results, we generally chose the factor of ten for all initial screening. This factor is intended to cover all possible hourly peaks associated with routinely

variable emissions. While there have been some documented emission excursions above this level, our analysis of the data fro m the Texas Environmental Research Consortium suggests that this factor should cover more than 99 percent of the short

- term peak gaseous or volatile HAP emissions from typical industrial sources. We have no data relating specifically to peak short

- term em issions of particulate HAP. In the absence of source category

- specific data, we use this same default approach for particulate emissions as well. For phosphate fertilizer production facilities an acute multiplier of 10 (x) the average 1

- hr emission rates was applied to all sources.

For phosphoric acid manufacturing facilities a multiplier of 10 (x) was applied for point sources based on the ICR data and engineering knowledge of the source category processes and for emissions from fugitive sources (gyp sum ponds and cooling ponds) an acute multiplier of 1 was applied.

### Residual Risk Assessment for the Phosphate Fertilizer and Phosphoric A cid Source Categories in Support of the July 2015 Risk and Technology Review Final Rule

11 As mentioned above, when we identify acute impacts which exceed their relevant benchmarks, we pursue refining our acute screening estimates to the extent possible. For the phosphate fe rtilizer production source category, all facilities screened out for acute risks from the initial HEM run , refer to Appendix 8 to this document ( Acute Noncancer Risk Analysis for Phosphate Fertilizer and Phosphoric Acid Manufacturing) , Table 1 to view init ial acute screen risk values

.
The hydrogen fluoride emissions from the phosphoric acid manufacturing gypsum ponds drove the acute risks. This emission source was considered stable and homogeneous and not subject to major fluctuations in hourly emissions over the year and therefore an acute multiplier of 1 was applied. This was applied to all area sources in the entire source category except for one facility that had site

- specific data for their gypsum ponds, an acute multiplier of 2 was applied for this f acility. This data was considered unique to the facility and not applicable for any other site. For further information on the acute multipliers applied for these source categories refer to the memo entitled “ Emissions Data Used in Residual Risk Modeling : Phosphoric Acid and Phosphate Fertilizer Production Source Categories ” available in the docket for this action.

In summary, we used conservative assumptions for emission rates, meteorology, and exposure location, and refined data where available. We used the following assumptions in our acute assessment approach:

• Peak 1

- hour emissions were based upon specific emission process groups varying from 1 to 1 0 times the average 1

- hour emission rates except for area source emissions from phosphoric acid man ufacturing facilities , an acute hourly multiplier of 1 was used except for 1 site we had site

- specific data to calculate an hourly multiplier

.

• For facilities with multiple emission points, peak 1

- hour emissions were assumed to occur at all emission points at the same time.

• For facilities with multiple emission points, 1

- hour concentrations at each receptor were assumed to be the sum of the maximum concentrations due to each emission point, regardless of whether those maximum concentrations occurred during t he 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 pers on was assumed to be located downwind at the point of maximum impact during this same 1

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

• The maximum impact was compared to multiple short

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

### Residual Risk Assessment for the Phosphate Fertilizer and Phosphoric A cid Source Categories in Support of the July 2015 Risk and Technology Review Final Rule

12 

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

**Multipathway H**  **uman H**  **ealth S**  **creening**



### The potential for significant human heal th risks due to exposures via routes other than inhalation (i.e., multipathway exposures) was screened by first determining whether any sources emitted any hazardous air pollutants known to be persistent and bioaccumulative in the environment (PB

- HAP) 2 

### . T he PB

- HAP compounds or compound classes are identified for the screening from the EPA’s Air Toxics Risk Assessment Library [ 8 ]. Examples of PB

- HAP are cadmium compounds, chlordane, chlorinated dibenzodioxins and furans, DDE, heptachlor, hexachlorobenzene, hexachlorocyclohexane, lead compounds, mercury compounds, methoxychlor, polychlorinated biphenyls (PCB) , polycyclic organic matter (POM) , toxaphene, and triflurali n. There were PB

- HAP emissions identified for both of the source categories addressed in th is risk document. For the phosphorate fertilizer production source category, we identified emissions of cadmium compounds, lead compounds , and mercury compounds. For the phosphoric acid manufacturing source category, we identified emissions of cadmium co mpounds, lead compounds , mercury compounds, POM, and dioxin.

With respect to PB

- HAP emissions other than lead, emissions were evaluated for potential

non

- inhalation risks using a tiered screening approach which was developed for use with the EPA’s peer

- reviewed Total Risk Integrated Methodology

– Fate, Transport, and Ecological Exposure (TRIM.FaTE 3 

### ) model. With this approach, we first determine whether the facility

specific emission rates of each of the emitted PB

- HAP are large enough to create the pot ential for significant non

- inhalation human health risks under reasonable worst

- case conditions. To facilitate this step, we developed emission rate levels for each PB

- HAP using a hypothetical
upper

- end screening exposure scenario developed for use in con junction with TRIM.FaTE. The exposure scenario includes a generic farming/fishing exposure scenario that simulates a subsistence environment. We conducted a sensitivity analysis on the screening scenario to ensure that its key design parameters would rep resent the upper end of the range of possible values, such that it would represent a conservative but not impossible scenario. The PB

- HAP emissions from each facility in the source category were compared to the emission rate levels for each of the PB

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

non

- inhalation pathways. For the purpose of developing emission rates for the multipathway screen, we derived emission levels for each PB

- HAP (other than lead) at which the maximum human health risk would be 1 in a million for lifetime cancer risk or a hazard quotient of 1.0 for noncancer impacts. We call this application of the TRIM.FaTE model the Tier 1 Screen. Se e Appendix 5 of this document, ( Technical Support Document for TRIM

- Based Multipathway Screening Scenario for RTR: Summary of Approach and Evaluation ) for a complete discussion of the development and testing of the screening scenario, as well as for the values of facility

- level emission rates developed for screening pote ntially significant

2 Although the two

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

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

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

3 EPA’s Total Risk Integrated Methodology (General Information) http://www2.epa.gov/fera/total

- risk

integrated

- methodology

- trim

- general

### Residual Risk Assessment for the Phosphate Fertilizer and Phosphoric A cid Source Categories in Support of the July 2015 Risk and Technology Review Final Rule

13 multipathway impacts.

If the PB

- HAP emissions for a facility exceed the Tier screening emission rate, we conduct a Tier 2 multipathway screen. In the Tier 2 screen, the location of each facility that exceeded the Tier 1 emission lev el is used to refine some of the assumptions associated with the environmental scenario while maintaining the exposure scenario assumptions. We then adjust the risk

- based Tier 1 screening level for each PB

- HAP for each facility based on an understanding o f how exposure concentrations estimated for the screening scenario change with meteorology and environmental assumptions. The screening scenario s developed include 2 separate receptors; 1) subsistence fisher and 2) subsistence farmer. This step creates

a facility

- specific emission level for each PB

- HAP ( i.e., the level for cadmium could be different for every facility), unlike the Tier 1 emission level which was constant for the same PB

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

The subsistence fisher scenario assumes a high

- end fish consumption rate of 373 grams/day

. 4 

### Also, the assessment uses the assumption that the biological productivity limitation of each lake is 1 gram of fish per acre of water Thus, in order to fulfill the adult ingestion rate, we may need to model more than one lake , refer to Appendix 5 of this document ( Technical Support Document for TRIM

- Based Multipathway Screening Scenario for RTR: Summary of Approach and Evaluation ) for a comp lete discussion of the bioassay studies used to support this assumption.

If we need to include more than one lake to achieve the 373 g/day ingestion rate, we begin with the lake with highest chemical concentration of a given PB

- HAP group, and “fish” up to the lake’s biological productivity. We then systematically proceed to other lakes based on concentration, until the 373 g/day limit is met. A maximum travel radius of 50 km relative to the facility is used to maintain a realistic scenario. The final T ier 2 screening result for the fisher can be expressed as the sum of the risk from each lake that is fished (which is based on the amount of fish ingested for each lake multiplied by the fish concentration). If the highest

concentration lake is at least 37 3 acres, the ingestion rate is not altered (i.e., remains 373 g/d for adults). If the cumulative size of multiple visited lakes exceeds 373 acres, the model includes from the final lake only the amount of fish necessary to satisfy the ingestion rate (i.e., to reach the 373 g/d). If there are not 373 acres of lake, the risk reflects a reduced ingestion rate based on the lake acreage. The farmer scenario (same approach for all Tiers) involves an individual living for a 70

- year lifetime on a farm homestead in the vicinity of the source and consuming produce grown on, and meat and animal products raised on, the farm. The ingestion rate used for these food groups are set at the 90 th 

### percentile of EPA’s “Exposure Factors Handbook: 2011 Edition”, and is considere d an upper

- bound level. [ 9 ]

Facility emissions of PB

- HAP that do not exceed these new Tier 2 screening levels are considered to pose no significant risks. When facilities exceed the Tier 2 screening levels, it does not mean that multipathway impacts are significant, only that we cannot rule out that possibility based on the results of the screen. Se e Appendix 5 of this document ( Technical

4 Burger, J. 2002. Daily consumption of wild fish and game: Exposures of high end recreationalists. Enviro nmental Health Research. 12(4):343

– 354

### Residual Risk Assessment for the Phosphate Fertilizer and Phosphoric A cid Source Categories in Support of the July 2015 Risk and Technology Review Final Rule

14 Support Document for TRIM

- Based Multipathway Screening Scenario for RTR: Summary of Approach and Evaluation ) for a complete discussion of the Tier 2 screen.

If the PB

- HAP emissions for a facility exceed the Tier 2 screening emissions rate, and data are available, we may decide to conduct a more refined multipathway assessment. This next iteration of assessment cou ld be an extensive refined assessment that replaces as many of the assumptions made in the Tier 2 screen with site

- specific data as possible or it could be focused on a few parameters such as adequacy of the lake, and/or the effective plume height and the use of time

- series meteorology in conjunction with hourly effective release heights

. We refer to the latter as the Tier 3 screen. In the Tier 3 screen, the lakes included in the screen are scrutinized for accessibility (i.e., private or public) and appro priateness (i.e., fishable lake v. industrial waste pond). In addition, a Tier 3 screen may include an examination of the site

- specific meteorological mixing height relative to the effective plume rise. As with the Tier 2 screen, the Tier 3 step creates a facility

- specific emission rate level for each PB

- HAP (i.e., the level for cadmium could be different for every facility). Also as with the Tier 2 screen, if facilities exceed the Tier 3 screening levels, it does not mean that multipathway impacts are s ignificant, only that we cannot rule out that possibility based on the results of the screen. See Appendix 5 of this document ( Technical Support Document for TRIM

- Based Multipathway Screening Scenario for RTR: Summary of Approach and Evaluation ) for a co mplete discussion of the Tier 3 screen. For the phosphate fertilizer production s ource category, we did conduct a Tier 3 screening assessment for Mississippi Phosphates Corporation in Pascagoula , Mississippi. In evaluating the potential multipathway risk s from emissions of lead compounds, rather than developing a screening emission rate for them , we compared maximum estimated chronic atmospheric concentrations with the current National Ambient Air Quality Standard (NAAQS) for lead. Values below the NAAQS were considered to have a low potential for multipathway risks.

The NAAQS value, a public health policy judgment, incorporated the Agency’s most recent health evaluation of air effects of lead exposure for the purposes of setting a national ambient air quality standard. In setting this value, the Administrator promulgated a standard that was requisite to protect public health with an adequate margin of safety. We consider values below the level of the primary NAAQS to protect against multipathway risks because as mentioned above, the primary NAAQS is set as to protect public health with an adequate margin of safety. However, ambient air lead concentrations above the NAAQS are considered to pose the potential for increased risk to public health. We con sider 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) th e site

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

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

### Residual Risk Assessment for the Phosphate Fertilizer and Phosphoric A cid Source Categories in Support of the July 2015 Risk and Technology Review Final Rule

15 setting the standard, also recognized that no evidence

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

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

Environmental Risk Screening

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

• Five persistent bioaccumulative HAP (PB

- HAP)

– cadmium, dioxins/furans, POM, mercury (both inorganic mercury and methyl mercury), and lead,

• Two acid gases

– hydrogen chloride (HCl) and hydrogen fluoride (HF).

HAP that persist and bioaccumulate are of particular environmental concern because they accumulate in the soil, sediment, and water. The acid gases

– HCl and HF

– were included due to their well

- documented potential to cause direct damage to terrestrial plants.

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

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

- case environm ental conditions to determine whether the facility

- specific off

- site emission rates of each of the emitted environmental HAP exceeded ecological benchmarks represented as emissions levels. If off

- site emissions from a facility do not exceed the Tier 1 lev els the facility “passes” the screen, and therefore, is not evaluated further under the screening approach. If off

- site emissions from a facility exceed the Tier 1 levels, we evaluate the facility further in Tier 2.

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

- site emissions from a facility do not exceed the Tier 2 lev els, the facility passes the screen, and is typically not evaluated further. If off

- site emissions from a facility exceed the Tier 2 levels, the facility does not pass the screen, and, therefore, may have the potential to cause adverse environmental effec ts. Such facilities are

### Residual Risk Assessment for the Phosphate Fertilizer and Phosphoric A cid Source Categories in Support of the July 2015 Risk and Technology Review Final Rule

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

For acid gases, the environmental screening analysis evaluates the potential phytotoxicity and reduced productivity of p lants due to chronic exposure to acid gases. The environmental risk screening methodology for acid gases is a single

- tier screen that compares the average off

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

For lead c ompounds, we currently do not have the ability to calculate media concentrations using the TRIM.FaTE model. However, air concentrations of lead are already calculated as part of the human health exposure and risk analysis using the HEM3

- AERMOD air dispers ion 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 Qua lity 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, vegetati on, 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.” 5



### 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 inventories for the source categories in this documen t were based on the EPA Office of Air Quality Planning and Standards’ existing recommendations for HAPs [ 10 ], which were also used for NATA [ 11 ]. This information has been obtained from various sources and prioritized according to (1) conceptual consistency with EPA risk assessment guidelines and (2) level of peer review received. The prioritization process was aimed at incorporating into our assessments the best

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

### Residual Risk Assessment for the Phosphate Fertilizer and Phosphoric A cid Source Categories in Support of the July 2015 Risk and Technology Review Final Rule

17 available science with respect to dose

- response information. The recommendations are based

on the following sources, in order of priority :

1) EPA has developed dose

- response assessments for chronic exposure for many of the pollutants in this study. These assessments typically provide a qualitative statement regarding the strength of scientific data and specify a reference concentration (RfC, for inhalation) or reference dose (RfD, for ingestion) to protect against effects other than cancer and/or a unit risk estimate (URE, for inhalation) or slope factor (SF, for ingestion) to estimate the probabili ty of developing cancer. The RfC is defined as an “ estimate (with uncertainty spanning perhaps an order of magnitude) of a continuous inhalation exposure to the human population (including sensitive subgroups) that is likely to be without an appreciable r isk of deleterious effects during a lifetime. ” Similarly, the RfD is “a n estimate (with uncertainty spanning perhaps an order of magnitude) of a daily oral exposure to the human population (including sensitive subgroups) that is likely to be without an ap preciable 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, approxi mating 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 info rmation in several forms, based on the level of review. The Integrated Risk Information System (IRIS) [ 12 ] is an EPA database that contains scientific health assessment information, including dose

- response information. All IRIS assessments since 1996 have also undergone independent external peer review. The current IRIS process includes review by EPA scientists, interagency reviewers from other federal agencies, and the public, and peer review by independent scientists external to EPA. New IRIS values

are developed and old IRIS values are updated as new health effects data become available. Refer to the “IRIS Track” website for detailed information on status and scheduling of current individual IRIS assessments and updates ( http://cfpub.epa.gov/ncea/iristrac/index.cfm )

. 

### EPA’s science policy approach , under the current carcinogen guidelines, is to use linear low

- dose extrapolation as a default option for carcinogens for which the mode of action (MO A) 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 c arcinogen 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 Min imum Risk Levels (MRLs) [ 13 ] for inhalation and oral exposure to many toxic substances. As stated on the ATSDR web site: “ Following discussions with scientists within the Department of Health and Human Services (HHS) and the EPA, ATSDR chose to adopt a
pra ctice similar to that of the EPA's Reference Dose (RfD) and Reference Concentration (RfC) for deriving substance specific health guidance levels for non neoplastic endpoints .” The MRL is defined as “an estimate of daily human exposure to a substance that is likely to be without an appreciable risk of adverse effects (other than cancer) over a specified

### Residual Risk Assessment for the Phosphate Fertilizer and Phosphoric A cid Source Categories in Support of the July 2015 Risk and Technology Review Final Rule

18 duration of exposure.” ATSDR describes MRLs as substance

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

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

- response assessments for many substances, based both on carcinogenicity and health effects other than cancer. The process for developing these assessments is similar to that used by EPA to develop IRIS values and incorporates significant external scientific peer review. As cited in the CalEPA Technical Support Document for developing their chronic assessments 6 

### : “The guidelines for developing chronic inhalation exposure levels incorporate many recommendations of the U.S. EPA [ 14 ] and NAS [ 15 ].” The non

- cancer information includes available inhalation health risk guidance values expressed as chronic inhalation reference exposure levels (RELs) [ 16 ]. CalEPA defines the REL as “the concentratio n level at or below which no health effects are anticipated in the general human population.” CalEPA's quantitative dose

- response information on carcinogenicity by inhalation exposure is expressed in terms of the URE [ 17 ], defined similarly to EPA's URE.

As mentioned above, in order to characterize non

- cancer chronic effects, and in response to key recommendations from the SAB, the EPA selects dose

- response values that reflect the best available science for all HAP included in RTR risk assessments 7 

### . Mo re specifically, for a given HAP, EPA examines the availability of inhalation reference values from the sources included in our tiered approach (e.g., IRIS first, ATSDR second, Cal EPA third) and determines which inhalation reference value represents the b est available science. Thus, as new inhalation reference values become available, EPA will typically evaluate them and determine whether they should be given preference over those currently being used in RTR risk assessments. 

### In developing chronic risk es timates, we adjusted dose

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

1) There is an existing IRIS RfC for manganese (Mn) published in 1993 8 

### . This value was in the RTR risk assessment supporting the 2011 Ferroalloys Notice of Proposed Rulemaking. However, since the 2011 proposal ATSDR has published an assessment of Mn toxicity (2012) which includes a chronic inhalation reference value (i.e., an ATSDR Minimal Risk Level or MRL) 9 

### . Both the 1993 IRIS RfC and the 2012 ATSDR MRL w ere based on the same study (Roels et al., 1993). In developing their assessment, ATSDR used updated dose

- response modeling methodology (benchmark dose approach) and considered recent pharmacokinetic

6 Air Toxi cs Hot Spots Program, Risk Assessment Guidelines, Part III

- Technical Support Document For the Determination of Non

- cancer Chronic Reference Exposure Levels. Air Toxicology and Epidemiology Section, Office of Environmental Health Hazard Assessment, Calif ornia Environmental Protection Agency. February 2000 ( http://www.oehha.ca.gov/air/chronic_rels/pdf/relsP32k.pdf )

7 The SAB peer review of RTR Risk Assessment Methodologies is avail able at: http://yosemite.epa.gov/sab/sabproduct.nsf/4AB3966E263D943A8525771F00668381/$File/EPA

- SAB

- 10

- 007

- unsigned.pdf

. 8 USEPA I ntegrated Risk Information System Review of Manganese (1993) available at http://www.epa.gov/iris/subst/0373.htm

. 9 Agency for Toxic Substances & Disease Registry Toxicological Profile for Manganese ( 2012) available at http://www.atsdr.cdc.gov/toxprofiles/tp.asp?id=102&tid=23

.

### Residual Risk Assessment for the Phosphate Fertilizer and Phosphoric A cid Source Categories in Support of the July 2015 Risk and Technology Review Final Rule

19 findings to support their MRL derivation. In the inter est of using the best available science, the EPA has chosen to rely on the ATSDR MRL for Mn in the current secondary aluminum proposal.



### 2) In the case of nickel compounds, to provide a health

- protective estimate of potential cancer risks, we used the IRIS U RE value for nickel subsulfide in this risk assessment. Based on past scientific and technical considerations, the determination of the percent of nickel subsulfide was considered a major factor for estimating the extent and magnitude of the risks of canc er due to nickel

- containing emissions. Nickel speciation information for some of the largest nickel

- emitting sources (including oil combustion, coal combustion, and others) suggested that at least 35 percent of total nickel emissions may be soluble
compou nds and that the URE for the mixture of inhaled nickel compounds (based on nickel subsulfide, and representative of pure insoluble crystalline nickel) could be derived to reflect the assumption that 65 percent of the total mass of nickel may be carcinogeni c.

Based on consistent views of major scientific bodies (i.e., National Toxicology Program (NTP) in their 12 th 

### Report of the Carcinogens (ROC) 10 

### , International Agency for Research on Cancer (IARC) 11 

### , and other international agencies) 12 

### that consider all ni ckel compounds to be carcinogenic, we currently consider all nickel compounds to have the potential of being carcinogenic to humans. The 12th Report of the Carcinogens states that the “combined results of epidemiological studies, mechanistic studies, and carcinogenic studies in rodents support the concept that nickel compounds generate nickel ions in target cells at sites critical for carcinogenesis, thus allowing consideration and evaluation of these compounds as a single group.” Although the precise nic kel compound (or compounds) responsible for the carcinogenic effects in humans is not always clear, studies indicate that nickel sulfate and the combinations of nickel sulfides and oxides encountered in the nickel refining industries cause cancer in humans (these studies are summarized in a review by Grimsrud et al., 2010 13 

### ). The major scientific bodies mentioned above have also recognized that there are differences in toxicity and/or carcinogenic potential across the different nickel compounds.

Therefor e, in the inhalation risk assessment for this supplemental proposal, to take a conservative approach, we considered all nickel compounds to be as carcinogenic as nickel subsulfide and applied the IRIS URE for nickel subsulfide without a factor to reflect t he assumption that 100 percent of the total mass of nickel may be as carcinogenic as pure nickel subsulfide. However, given that there are two additional URE values 14

10 National Toxicology Program (NTP), 2011. Report on carcinogens. 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

11 International Agency for Research on Cancer (IARC), 1990. IARC monographs on the evaluation of carcinogenic risks to humans. Chromium, nickel, and welding. Vol. 49. Lyons, France: International Agency for Research on Cancer, World Health Organization Vol. 49:256.

12 World Health Organi zation (WHO, 1991) and the Europea n Union’s Scientific Committee o n Health and Environmental Risks (SCHER, 2006)

. 13 Grimsrud TK and Andersen A. Evidence of carcinogenicity in humans of water

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

- 7. Availa ble online at http://www.occup

- med.com/content/5/1/7

. 14 Two UREs (other than the current IRIS values) have been derived for nickel compounds as a group : one developed by the California Department of Health Services

### Residual Risk Assessment for the Phosphate Fertilizer and Phosphoric A cid Source Categories in Support of the July 2015 Risk and Technology Review Final Rule

20 derived for exposure to mixtures of nickel compounds, as a group, that are 2

- 3 fold lower than the IRIS URE for nickel subsulfide, the EPA also considers it reasonable to use a value that is 50 percent of the IRIS URE for nickel subsulfide for providing an estimate of the lower end of the plausible range of cancer potency values for different mixtures of nickel compounds

.
3) Where possible for emissions of unspecified mixtures of HAP categories such as metal compounds and POM, we apply category

- specific chemical speciation profiles appropriate to the source category to develop a composite dose

- response value for the category.

4) Where POM emissions were not speciated into individual compounds, we applied the same simplifying a ssumptions to assessments that are used in NATA [ 18 ]. The NATA approach partitions POM into eight different non

- overlapp ing “groups” that are modeled as separate pollutants. Each POM group comprises POM species of similar carcinogenic potency, for which we can apply the same URE.

The emissions inventory for the phosphate fertilizer production and phosphoric acid manufactu ring source categories includes emissions of HAP with available chronic quantitative inhalat ion dose

- response values. Of these

, 2 2 are classified as known, probable, or possible carcinogens, with quantitative cancer dose

- response values available. These 2 2 HAPs, their quantitative inhalation chronic cancer dose

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

- 1(a). The POM compounds and dioxin with chronic oral cancer dose

- response values available (for which multipathway screening assessments were performed) are listed in Table 2.6

- 1(b). Fifty HAP have quantitative inhalation chronic noncancer threshold values available, four of these HAP (mercury , lead, dioxin, and cadmium ), for which a multipathway assessment was performed, also have quantitative oral chronic noncancer threshold values available. These 50 HAP, their threshold values, and the source of the value are listed in Table 2.6

- 2(a) and Table 2.6

- 2(b).

( http://www.arb.ca.gov/toxics/id/summary/nickel_tech_b.pdf ) and the other by the Texas Commission on Environmental Quality ( http://www.epa.gov/ttn/atw/nata1999/99pdfs/healtheffectsinfo.pdf ).

### Residual Risk Assessment for the Phosphate Fertilizer and Phosphoric A cid Source Categories in Support of the July 2015 Risk and Technology Review Final Rule

21 Table 2.6

- 1 (a) Dose

- Response Values for Chronic Inhalation E xposure to Carcinogens

URE (unit risk estimate for cancer) 15 

### = cancer risk per μg/m 3 

### of average lifetime exposure. Sources: IRIS = EPA Integrated Risk Information System, EPA OAQPS = interim value recommended by the EPA Office of Air Quality Planning & Standards, CAL = California EPA Office of Environmental Health Hazard Assessment

, Conv. Oral = Oral unit risk converted to inhalation. Pollutant CAS Number 16 

### URE 7 

### (1/μg/m 3 

### ) Source

1,2,3,6,7,8

- hexachlorodibenzo

- p

- dioxin 57653857 3.3 EPA

- ORD 2,4

- dinitrotoluene 121142 0.000089 CAL Acetaldehyde 75070 0.0000022 IRIS Arsenic compounds 7440382 0.0043 IRIS Benzene 17 71432 0.0000078 IRIS Benzyl c hloride 100447 0.000049 CAL Beryllium compounds 7440417 0.0024 IRIS Bromoform 75252 0.0000011 IRIS Cadmium compounds 7440439 0.0018 IRIS Chromium (VI) compounds 18540299 0.01 2 IRIS Ethyl b enzene 100414 0.0000025 CAL Ethylene dibromide 106934 0.0006 IRIS Ethylene d ichloride 107062 0.000026 IRIS Ethylene o xide 75218 0.000088 CAL Formaldehyde 18 50000 0.000013 IRIS Methyl tert

- butyl e ther 1634044 0.00000026 CAL Methylene c hl oride 75092 0.00 0 0000 1 6 IRIS Naphthalene 91203 0.000034 CAL Nickel compounds 7440020 0.00048 EPA OAQPS Polycyclic Organic Matter 19 (POM) Benzo[a]pyrene 

### 50328 0.00176 CAL
Tetrachloroeth ene 

### 127184 0.00000026 IRIS

15 The URE is the upper

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

16 Chemical Abstract Services identification number. For groups of compounds that lack a CAS number we have used a surrogate 3

- digi t identifier corresponding to the group’s position on the CAA list of HAPs.

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

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

- 06 per ug/m 3

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

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

19 Assigned the URE associated with a mixture of POM compounds having a similar potency. Details of this method, also use d in the National Air Toxics Assessments, are available at http://www.epa.gov/nata2002/methods.html#pom

### Residual Risk Assessment for the Phosphate Fertilizer and Phosphoric A cid Source Categories in Support of the July 2015 Risk and Technology Review Final Rule

22 Table 2 .6

- 1(b) Dose

- Response Values for Chronic Oral Exposure to Carcinogens

SF (oral slope factor for cancer) = cancer risk per mg/kg/d of average lifetime exposure. Sources : IRIS = EPA Integrated Risk Information System, CAL = California EPA Office of Environ mental Health Hazard Assessment, EPA/OAQPS = interim value recommended by the EPA Office of Air Quality Planning and Standards, EPA ORD = EPA Office of Research and Development, HEAST = EPA Health Effects Assessment Tables

Pollutant CAS Number SF (1/mg/kg /d) Source Dioxin/Furans Hexachlorodibenzo

- p

- dioxin 19408743 6200 IRIS 2,3,7,8

- Tetrachlorodibenzo

- p

- dioxin 1746016 150000 EPA ORD Methylene chloride 75092 0.00 2 IRIS Polycyclic organic matter (POM) Benzo(a)pyrene 50328 7.3 IRIS



### T able 2.6

- 2 ( a ) Dose

- Response Values for Chronic In halation 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 popu lation (including sensitive subgroups) that is likely to be without an appreciable risk of deleterious effects during a lifetime. Sources: IRIS = EPA Integrated Risk Information System, CAL = California EPA Office of Environmental Human Health Assessment, ATSDR = US Agency for Toxic Substances Disease Registry, HEAST = EPA Health Effects Assessment Tables, EPA OAQPS = EPA Office of Air Quality Planning & Standards Pollutant CAS Number 8 

### RfC (mg/m 3 

### ) Source 20

2,4

- dinitrotoluene 

### 121142 0.007 CAL
2

- chloroacet ophenone 

### 532274 0.00003 IRIS

– L/ M Acetaldehyde 75070 0.009 IRIS

- L Acrolein 107028 0.00002 IRIS

- M Antimony compounds 7440360 0.0002 IRIS

- L Arsenic compounds 7440382 0.000015 CAL Benzene 71432 0.03 IRIS

- M Beryllium compounds 7440417 0.00002 IR IS

- M Cadmium compounds 7440439 0.00001 ATSDR Carbon disulfide 75150 

### 0.7 IRIS

- M Chlorobenzene 108907 1 CAL Chloroform 67663 0.098 ATSDR Chromium (VI) compounds 18540299 0.0001 IRIS

- M Cobalt compounds 7440484 0.0001 ATSDR Cumene 98828 0.4 IRIS

- M Cyanide & Cyanide c pds Cyanide compounds 21 57125 0.00 08 IRIS

– L /M

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

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

### Residual Risk Assessment for the Phosphate Fertilizer and Phosphoric A cid Source Categories in Support of the July 2015 Risk and Technology Review Final Rule

23 Diethanolamine 111422 0.003 CAL Ethyl benzene 100414 1 IRIS

- L Ethyl chloride 75003 10 IRIS

- M Ethylene dibromide 106934 0.009 IRIS

- M Ethylene dichloride 107062 2.4 AT SDR Formaldehyde 50000 0.0098 ATSDR Hydrochloric acid 7647010 0.02 IRIS

- L Hydrofluoric acid 7664393 0.014 CAL Isophorone 78591 2 CAL Lead compounds 7439921 0.00015 EPA OAQPS Manganese compounds 7439965 0.000 3 ATSDR Mercury c ompounds

Elementa l gaseous mercury 7439976 0.0003 IRIS

- M Gaseous divalent mercury 201 0.0003 IRIS

- M Particulate divalent mercury 202 0.0003 IRIS

- M Methyl bromide 74839 0.005 IRIS

- H Methyl chloride 74873 0.09 IRIS

- M Methyl isobutyl ketone 108101 3 IRIS

- L/M Methyl methacrylate 80626 0.7 IRIS

- M/H Methyl tert

- butyl ether 1634044 3 IRIS

- M Methylene chloride 75092 0.6 IRIS

– M/H Naphthalene 91203 0.003 IRIS

- M

n

- hexane 110543 0.7 IRIS

- M Nickel compounds 7440020 0.00009 ATSDR Phenol 108952 0.2 CA L Propionaldehyde 123386 0.008 IRIS

– L/M Selenium compounds 7782492 0.02 CAL Styrene 100425 1 IRIS

- M Tetrachloroethene 

### 127184 0.04 

### IRIS

- M Toluene 108883 5 IRIS

- H Vinyl acetate 108054 0.2 IRIS

- H Xylenes (mixed) 1330207 0.1 IRIS

- M

m

- Xy lene 22 108383 0.1 IRIS

- M

o

- Xylene 22 95476 0.1 IRIS

- M
p

- Xylene 22 106423 0.1 IRIS

- M

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

### Residual Risk Assessment for the Phosphate Fertilizer and Phosphoric A cid Source Categories in Support of the July 2015 Risk and Technology Review Final Rule

24

Table 2.6

- 2 (b) Dose

- Response Values for Chronic Oral Exposure to Noncarcinogens

RfD (reference dose) = an estimate (with uncertainty spanning perhaps an order of magnitude) of a continuous oral exposure to the human population (including sensitive subgroups) that is likely to be without an appreciable risk of deleterious effects during a lifetime. Sources: IRIS = EPA Integrated Risk In formation System, ATSDR = US Agency for Toxic Substances and Disease Registry, HEAST = EPA Health Effects Assessment Tables

Pollutant CAS Number RfD (mg/kg/d) Source Cadmium compounds 7440439 0.0005 IRIS

- H Mercury and Mercury Compounds 23
Mer curic chloride 7439976 0.0003 IRIS

- H Methyl mercury 22967926 0.0001 IRIS

- H Phenylmercuric acetate 62384 0.00008 IRIS

- L 2,3,7,8

- Tetrachlorodibenzo

- p

- dioxin 1746016 0.0000000007 IRIS

- H Tetrachloroethene 127184 0.006 IRIS

- M Tetraethyl lead 78002 0.0000001 IRIS

- M



### 2.6.2. Sources of acute dose

- response information
Hazard identification and dose

- response assessment information for preliminary acute inhalation exposure assessments is based on the existing recommendations of OAQPS for HAPs [ 19 ]

. Depending on availability, the results from screening acute assessments are compared to both “no effects” reference levels for the general public, such as the California Reference Exposure Levels (RELs), as well as emergency response levels, such as Acu te 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 def inition of that level and any limitations expressed therein. Comparisons among different available inhalation health effect reference values (both acute and chronic) for selected HAPs can be found in an EPA document of graphical arrays. 24



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

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

The acute R EL ( 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 . [ 20 ]

. RELs are based on the most sensitive, relevant, adverse health effect reported in the medical and toxicological literature. RELs are designed to protect the most sensitive individuals in the population by the inclusion of margins of safety. Since mar gins of safety are incorporated to address

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

24 See : U.S. EPA. Graphical Arrays of Chemical

- Specific Health Effect Reference Values for Inhalatio n Exposures [Final Report]. (U.S. Environmental Protection Agency, Washington, D.C., EPA/600/R

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

### Residual Risk Assessment for the Phosphate Fertilizer and Phosphoric A cid Source Categories in Support of the July 2015 Risk and Technology Review Final Rule

25 data gaps and uncertainties, exceeding the REL does not automatically indicate an adverse health impact. ” Acute RELs are developed for 1

- hour (and 8

- hour) exposures. The values incorporate uncertainty factors sim ilar 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 Commit tee (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 exposures ranging from 10 min to 8 h.” Their intended application is “for conducting risk assessments to aid in the development of emergency preparedness and prevention plans, as well as real time emergency response actions, for accidental chemical releases at fixed facilities and from transport carriers.” The document states: “the primar y purpose of the AEGL program and the NAC/AEGL Committee is to develop guideline levels for once

- in

- a

- lifetime , short

term exposures to airborne concentrations of acutely toxic, high

- priority chemicals.” In detailing the intended application of AEGL value s, 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, planni ng, and prevention programs. More specifically, the AEGL values will be used for conducting various risk assessments to aid in the development of emergency preparedness and prevention plans, as well as real

- time emergency response actions, for accidental chemical releases at fixed facilities and from transport carriers.” The NAC/AEGL defines AEGL

- 1 and AEGL

- 2 as:

“ AEGL

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

### ) of a substance above which it is predicted that the general population, inclu ding 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 concen tration (expressed as ppm or mg/m 3 

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

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

“ 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 airbo rne concentrations above each AEGL, there is a progressive increase in the likelihood of occurrence and the severity of effects described for each corresponding AEGL. Although the AEGL values represent threshold levels for the general public, including su sceptible subpopulations, such as infants, children, the elderly, persons with asthma, and those with other illnesses, it is recognized that individuals, subject to unique or idiosyncratic

### Residual Risk Assessment for the Phosphate Fertilizer and Phosphoric A cid Source Categories in Support of the July 2015 Risk and Technology Review Final Rule

26 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 Planning Guidelines (ERPGs) [ 21 ] for acute exposures at three different levels of severity. These guidelines represent concentrations for exposure of the general population (but not particularly sensitive persons) for up to 1 hour associated with effects expected to be mild or transient (ERPG

- 1), irreversible or serious (ERPG

- 2), and potentiall y life

- threatening (ERPG

- 3).

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

involved/AIHAGuidelineFoundation/EmergencyResponsePlanningG uidelines/Pages/default.a
spx ) are described in their supporting documentation as follows: “ Emergency Response Planning Guidelines (ERPGs) were developed for emergency planning and are intended as health based guideline concentrations for single exposures to chemicals. These guidelines (i.e., the ERPG Documents and ERPG values) are intended for use as planning tools for assessing the adequacy of accident prevention and emergency response plans, including transportation emergency planning and for developing comm unity emergency response plans. The emphasis is on ERPGs as planning values: When an actual chemical emergency occurs there is seldom time to measure airborne concentrations and then to take action. ” ERPG

- 1 and ERPG

- 2 values are defined by AIHA as follows:

“ ERPG

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

“ ERPG

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

In assessing the potential risks associated with acute exposures to HAP, we do not follow a prioritization scheme and therefore we consider available dose

- response values from multiple autho ritative sources.

The emissions inventory for the phosphate fertilizer production and phosphoric acid manufacturing source categories includes emissions of 3 5 HAP with relevant and available quantitative acute dose

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

- 3.

### Residual Risk Assessment for the Phosphate Fertilizer and Phosphoric A cid Source Categories in Support of the July 2015 Risk and Technology Review Final Rule

27 Table 2.6

- 3 Dose

- Response Values for Acute Exposure

Pollutant CAS Number AEGL

- 1 (1

- hr) (mg/m 3 

### ) AEGL

- 2 (1

- hr) (mg/m 3 

### ) ERPG

1 (mg/m 3 

### ) ERPG

- 2 ( mg/m 3 

### ) REL Acetaldehyde 75070 81 490 18 360 0.47 Acrolein 107028 0.069 0.23 0. 11 0. 34 0.0025 Arsenic and Arsenic Compounds Arsenic compounds 744038 2 0.0002 Benzene 71432 170 2600 16 0 48 0 1.3 Benzyl chloride 10044 7 5.2 52 0.24 Beryl lium c ompounds 7440417 0.025 Carbon d isulfide 75150 40 500 3.1 160 6.2 Chlorobenzene 108907 46 690 Chloroform 67663 310 24 0 0.15
Cumene 98828 250 1500 Dimethyl sulfate 7778 1 0.12 0.62 Ethyl b enzene 100414 140 4800 Ethylene d ibromid e 106934 130 180 Ethylene d ichloride 107062 200 810 Formaldehyde 50000 1.1 17 1. 2 1 2 0.055

n

- Hexane 110543 12000 Hydrochloric a cid 7647010 2.7 33 4.5 3 0 2.1 Hydrofluoric acid 7664393 0.82 20 1.6 16 0.24 Mercury (elemental) 743997 6 1.7 2 0.0006 Methyl b romide 74839 820 19 0 3.9 Methyl c hloride 74873 1900 83 0 Methyl hydrazine 6034 4 3.2 Methyl m ethacrylate 80626 70 490 Methyl tert

- butyl e ther 1634044 180 2100 Methylene c hloride 75092 690 1900 100 0 26 00 14 Phenol 108952 58 89 3 8 190 5.8 Propionaldehyde 123386 110 620 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 Vinyl a cetate 108054 24 630 18 260 Xylenes (mixed) 1330207 560 4000 22

m

- X ylene 22 108383 22

o

- Xylene 2 2 95476 22

p

- Xylene 2 2 106423 22

### Residual Risk Assessment for the Phosphate Fertilizer and Phosphoric A cid Source Categories in Support of the July 2015 Risk and Technology Review Final Rule

28 

### 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 a n 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 resour ces available. In all cases, major issues associated with determining the nature and extent of the risk are identified and discussed. Further, the EPA Administrator’s March 1995 Policy for Risk Characterization [ 22 ] specifies that a risk characterization “be prepared in a manner that is clear, transparent, reasonable, and consistent with other risk characterizations of similar scope prepared across programs in the Agency.” These principles of transparency and consistency have been reinforced by the Agency ’s Risk Characterization Handbook [ 23 ], in 2002 by the Agency’s information quality guidelines [ 24 ], and in the OMB/OSTP September 2007 Memorandum on Updated Principles for Risk Analysis 25 

### , 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, withi n the limitations of available time and resources. We provide summaries of risk metrics (including maximum individual cancer risks and noncancer hazards, as well as cancer incidence estimates) along with a discussion of the major uncertainties associated with their derivation to provide decision makers with the fullest picture of the assessment and its limitations.

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

- bound probability of developing cancer over a 70

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

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

zero.

For EPA’s list of carcinogenic HAPs that act by a mutagenic mode

- of

- action [ 25 ], we applied EPA’s Supplementa l Guidance for Assessing Susceptibility from Early

- Life Exposure to Carcinogens [ 26 ]

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

- 1), 3 (for children aged 2

- 15), or 1.6 (for 70 years of exposure beginning at birt h), 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

25 Memorand um 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, As sociate 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 )

### Residual Risk Assessment for the Phosphate Fertilizer and Phosphoric A cid Source Categories in Support of the July 2015 Risk and Technology Review Final Rule

29 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 entat ion policy [ 27 ] 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 fo r the entire receptor population within the area of analysis wa s estimated by multiplying the estimated lifetime cancer risk for each census block by the number of people residing in that block, then summing the results for the entire modeled domain. This lifetime population incidence estimate wa s divided by 70 years to obtain an estimate of the number of cancer cases per year.

Unlike linear dose

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

The risk character ization for chronic effects other than cancer is expressed in terms of the HQ for inhalation, calculated for each HAP at each census block centroid. As discussed above, RfCs incorporate generally conservative uncertainty factors in the face of uncertain

e xtrapolations, such that an HQ greater than one does not necessarily suggest the onset of adverse effects. The HQ cannot be translated to a probability that adverse effects will occur, and is unlikely to be proportional to adverse health effect outcomes i n a population

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

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

To combine risks across multiple carcinogens, our assessments use the mixtures guidelines’ [ 28

, 29 ] default assumption of additivity of effects, and combine risks by summing them using the independence formula in the mixtures guid elines.

### Residual Risk Assessment for the Phosphate Fertilizer and Phosphoric A cid Source Categories in Support of the July 2015 Risk and Technology Review Final Rule

30 In assessing noncancer hazard from chronic exposures, in cases where different pollutants cause adverse health effects via completely different modes of action, it may be inappropriate to aggregate HQs. In consideration of these mode

- of

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

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

- organ

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

. Because impacts on organs or systems that occur above the critical dose have not been included in the TOSHI calculations, some TOSHIs may have been underestimated. As with the HQ, the TOSHI should not be interpreted as a probability of adverse effects, or as strict delineation of “safe” and “unsafe” levels. Rather, the TOSHI is another measure of the potential for adverse health outcomes associated with pollutant exposure, and needs to be interpreted carefully by health scientists and risk managers.

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

2.7.3. Facility

- wide r isks To help place the source category risks in context, we examined “facility

- wide” risks using 2005 NEI data and modeling as described in Section 2.2. For the facilities in these so urce categories, we estimated the maximum inhalation cancer and chronic noncancer risks associated with all HAP emissions sources at the facility, including emissions sources that are not part of the source categories but are located within a contiguous ar ea and are under common control. We analyzed risks due to the inhalation of HAP for the populations residing within 50 kilometers of each facility. The results of these facility

- wide assessments are summarized below in the Risk Characterization sections of this document. The complete results of the facility

- wide assessments are provided in Appendix 4

.

2.7.4. MACT

- a llowable r isks The emissions data in the data sets for these seven source categories are estimates of actual emissions on an annual basis. The ris k results presented in the following sections are based on these actual emissions. For these two source categories, the MACT

- allowable emissions are estimated to be equivalent to the actual emissions except for certain emission sources

. See the memo randu m titled “Emissions Data Used in Residual Risk Modeling: Phosphoric Acid and Phosphate Fertilizer Production Source Categories ” available in the docket for this action. Therefore, except for certain sources, the risks based on MACT

- allowable emissions would be equivalent to the actual risks presented below. See the source category risk characterization sections for details on the MACT

- allowable risk.

### Residual Risk Assessment for the Phosphate Fertilizer and Phosphoric A cid Source Categories in Support of the July 2015 Risk and Technology Review Final Rule

31 

## 3. Risk Results for P hosphate Fertilizer Production

3.1 Source c ategory d escription and r esults Phosphate fe rtilizer production facilities use beneficiated phosphate rock with acids and/or nitrates to produce different types and grades of fertilizers. Phosphate fertilizers are used in agricultural applications as a plant nutrient. Phosphoric A cid M anufacturing ( 40 CFR part 63 subpart AA) and P hosphate F ertilizers P roduction (40 CFR part 63 subpart BB) were established as separate source categories by EPA. However, these operations are generally collocated, as phosphoric acid manufacturing facilities provide feeds tock for phosphate fertilizer production facilities and much of the phosphoric acid produced in the U.S. is consumed in the manufacture of fertilizers.

Phosphate fertilizers are classified according to the concentration of phosphorus pentoxide in the fin al product. Normal superphosphate is made by reacting phosphate rock with sulfuric acid. Granular triple superphosphate (GTSP) is made by reacting phosphoric acid (produced at a phosphoric acid facility) with beneficiated phosphate rock. Ammonium phosphat es, of which diammonium phosphate (DAP) is the most common, are made by reacting phosphoric acid, sulfuric acid, and anhydrous ammonia. Emission point s along the phosphate fertilizer production process include reactors, granulators, dryers, screens, and gr inding and drying
processes.

Emissions data

, collected via ICRs sent to all U.S. phosphate fertilizer, were used to conduct this assessment. The data include information regarding process equipment, control devices, phosphate and acid production, inve ntory data for all point and fugitive emissions, practices used to control fugitive emissions, and other aspects of facility operations, including stack parameters and locations.

We currently estimate that there are eleven phosphate fertilizer production facilities operating in the U nited S tates . Ten of these eleven facilities are co

- located with phosphoric acid manufacturing facilities. The 2005 National

- Scale Air Toxics Assessment (NATA) National Emissions Inventory (NEI) was updated for this category w ith data from industry and enhanced quality assurance of facility and emission locations. All eleven of these facilities are identified as major sources in the NEI.

The emissions for the phosphate fertilizer production source category ICR data set ( e leven facilities) are summarized in Table 3.1

- 1. The total HAP emissions for the source category are approximately 105 tons per year. Based on these data, the HAP emitted in the largest quantities across these eleven facilities is hydrofluoric acid . Th is c hemical account s for 9 6 percent of the total emissions by mass. Persistent and bioaccumulative HAP (PB HA P) 26 

### reported as emissions from these facilities include mercury, lead, and cadmium compounds. The following environmental HAP are emitted from the p h osphate f ertilizer p roduction source

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

- 453K

- 04

- 001A, and provided on the EPA’s Technology Transfer Network website for Fate, Exposure, and Risk Assessment at http://www2.epa.gov/fera/risk

- assessment

- and

- modeling

- air

- toxics

- risk

- assessment

reference

- library

### Residual Risk Assessment for the Phosphate Fertilizer and Phosphoric A cid Source Categories in Support of the July 2015 Risk and Technology Review Final Rule

32 category and are included in the environmental risk screen: mercury compounds, cadmium, lead, and hydrofluoric acid.

Table 3. 1

- 1 Summary of Emissions from the Phosphate Fertilizer Production Source C ategory and Availability of Dose

- Response Values

HAP a Emissions (tpy) Number of Facilities Reporting HAP (1 1 facilities in ICR data set) Prioritized I nhalation Dose

- Response Value Identified by OAQPS b

PB

HAP? Unit Risk Estimate for

Cancer? Reference C oncentration

for Chronic Noncancer? Health Benchmark Values for

Acute Noncancer? Hydrofluoric Acid 101 11   Manganese Compounds 0.5 11  Nickel Compounds 0.3 11   Mercury Compounds

Elemental Gaseous Mercury 0. 01 11    Gaseous Divalent Mercury 0.0 04 11   Particulate Divalent Mercury 0.0 04 11   Lead Compounds 0.10 11 c  Chromium Compounds

Chromium (III) Compounds 0.09 11 Chromium (VI) Compounds 0.04 11   Cadmium Compo unds 0.03 11    Arsenic Compounds 0.03 11    Selenium Compounds 0.02 11  Cobalt Compounds 0.02 11  Antimony Compounds 0.01 11  Beryllium Compounds 0.005 11   

a Notes for how HAP were speciated for risk assessment:

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

.

• For emissions reported generically as “chromium” or “chromium & compound,” emissions are speciated 75% “chromium (III) compounds” and 25% “chromium (VI) compounds” for this source category. Chromium speciation profiles can be found on the EPA’s Technolog y 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,” emissi ons are speciated for this category as 80% elemental gaseous mercury, 10% gaseous divalent mercury, and 10% particulate divalent
mercury. Mercury speciation profiles can be found on the EPA’s Technology Transfer Network website for emissions inventories at http://www.epa.gov/ttn/chief/net/2005inventory.html

. b Specific dose

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

- response

- assessment

- assessing

- health

- risks

- associated

- exposure

- hazardous

- air

pollutants

c There is no reference concentration for lead. In considering noncancer hazards for lead in this assessment, we compared maximum annual ambient exposure estimates to the National Ambient Air Quality Standards (NAAQS) for lead (0.15 μg/m 3 ). These NAAQS for lead were reviewed with revisions adopted in October 2008 ( http://www.epa.gov/air/lead/actions.html ). The primary (health

- based) standard is a maximum or not

- to

- be

- exceeded, rolling three

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

- based) standard is identical to the primary standard.

### Residual Risk Assessment for the Phosphate Fertilizer and Phosphoric A cid Source Categories in Support of the July 2015 Risk and Technology Review Final Rule

33



### 3.2 Baseline r isk c haracterization This section presents the results of the risk assessment for the phosphate fertilizer production source categor y. The basic chronic inhalation risk estimates presented here are the maximum individual lifetime cancer risk, the maximum chronic hazard index, and the cancer incidence. We also present results from our acute inhalation impact screening in the form of ma ximum 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 h azard index at the highest exposure location. A detailed summary of the facility

- specific risk assessment results is available in Appendix 4 of this document, Draft Detailed Risk Modeling Results

.

Baseline Inhalation Risk Assessment Results Based on Act ual Emissions

Table 3 .2

- 1 summarizes the preliminary inhalation risk results for this source category. The results indicate that maximum lifetime individual cancer risks are less than 1 in a million. The total estimated cancer incidence from this source category is 0.001 excess cancer cases
per year

. The maximum chronic non cancer target organ specific hazard index ( TOS HI) value for the source category could be up to 0. 003 associated with emissions of hydrogen fluoride

, indicating no significant potentia l for chronic noncancer impacts.

Worst

- case a cute hazard quotients (HQs) were calculated for every HAP shown in Table 2.6

- 3 that has an acute benchmark . Based on maximum hourly emission estimates an emissions multiplier of 10 was used to estimate the p eak hourly emission rates for this source category. See the memorandum entitled, “Emissions Data Used in Residual Risk Modeling: Phosphoric Acid and Phosphate Fertilizer Production Source Categories ” for detailed description of how the maximum hourly emis sions were developed for this source category (found in the docket for this rule making ). No HAP in this source category had an acute HQ of greater than 1 for any benchmark.

Table 3. 2

- 1 Summary of Source Category Level Inha lation Risks for Phosphate Fertilizer Production Result HAP “Drivers” Facilities in Source Category Number of Facilities Estimated to be i n Source Category 11 n/a

Number of Facilities Modeled in Risk Assessment 11 n/a

Cancer Risks Maximum Individual Lifetime Cancer Risk (in 1 million) 0.5 chromium (VI) compounds, nickel compounds

### Residual Risk Assessment for the Phosphate Fertilizer and Phosphoric A cid Source Categories in Support of the July 2015 Risk and Technology Review Final Rule

34 Table 3. 2

- 1 Summary of Source Category Level Inha lation Risks for Phosphate Fertilizer Production Result HAP “Drivers” Number of Facilities with Maximum Individual Lifetime Cancer Risk: Greater than or equal to 100 in 1 million 0 n/a
Greater than or equal to 10 in 1 million 0 Greate r than or equal to 1 in 1 million 0 Chronic Noncancer Risks Maximum Skeletal Hazard Index 0. 003 Hydrogen fluoride Number of Facilities with Maximum Skeletal l Hazard Index: Greater than 1 0 n/a Acute Noncancer Screening Results

Maximum Acute Hazard Quotient 0.4 0. 09 0.0 4 0.0 03 0.0 04 elemental gaseous mercury (REL) hydrofluoric acid (AEGL

- 1) hydrofluoric acid (ERPG

- 1) hydrofluoric acid (AEGL

- 2) hydrofluoric acid (ERPG

- 2) Number of Facilities With Potential for Acute Effects 0 n/a
Population Exposure Number of People Living Within 50 Kilometers of Facilities Modeled 5,000,000 n/a
Number of People Exposed to Cancer Risk: Greater than or equal to 100 in 1 million 0 n/a
Greater than or equal to 10 in 1 million 0 n/a Greater than or equal to 1 in 1 million 0 n/a Number of People Exposed to Noncancer Skeleta l Hazard Index: Greater than 1 0 n/a Estimated Cancer Incidence (excess cancer cases per year) 0.001 n/a
Contribution of HAP to Cancer Incidence chromium (VI) compounds 65% n/a nickel co mpounds 19% n/a arsenic compounds 11% n/a



### Baseline Inhalation Assessment Results Based on Allowable Emissions

Analysis of potential differences between actual emissions levels and the maximum emissions allowable under the MACT standards were also calculated for this source category. Based upon the above analysis we multiplied the modeled actual risks for the MIR facility with process site

- specific multipliers to estimate allowable risks under the MACT. This approach was deemed sufficient due to the low actual modeled risks for the source category.

### Residual Risk Assessment for the Phosphate Fertilizer and Phosphoric A cid Source Categories in Support of the July 2015 Risk and Technology Review Final Rule

35 The maximum lifetime individual cancer risks based upon allowable are still less than 1 in a million

. The maximum chronic noncancer TOSHI value for allowable emissions was estimated with an estimated H I = 0.003, the same as the actuals.

Baseline Multipathway Assessment Results Based on Actual Emissions

To identify potential multipathway health risks from PB

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

- HAP emitted from the phosphate fertilizer production s ource category (based on actual emissions) to screening emission rates ( see section 2.5) see Appendix 5 for a more detailed discussion of the screening emission rates

. The PB

- HAP emitted by facili ties in this category include mercury compounds (11 facilities), lead compounds (11 facilities), and cadmium compounds (11 facilities. We compared reported emissions of PB

- HAP to the Tier 1 screening emission thresholds established by the EPA for the purpo ses of the RTR risk assessments. One facility (NEIMS059305) emitted divalent mercury (Hg 2+ 

### ) above the Tier 1 screening threshold level, exceeding the screening threshold by a factor of 20 , and we found it necessary to conduct a Tier 2 screening assessment.

W e refined our Tier 2 screen with some additional site

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

- Based Multipathway Screening Scenario for RTR: Summary of Approach and Evaluation , for more information abo ut the Tier 2 screen.)

. The additional site

- specific information included the location of fishable l akes and local wind direction and speed. The result of this analysis was the development of site

- specific emission screening levels for each of the four P B

- HAP for the farmer and fisher scenarios.

Similar to the results from the Tier 1 screen, an exceedance of a screening level in Tier 2 cannot be equated with a risk value or a hazard quotient (or hazard index). Rather, it represents a high

- end estimat e of what the risk or hazard may be. For example, an exceedance of 2 for a non

- carcinogen can be interpreted to mean that we have high confidence that the hazard would be lower than 2. Similarly, an exceedance of 30 for a carcinogen means that we have hi gh confidence that the risk is lower than 30 in a million. Our confidence comes from the conservative, or health

- protective, assumptions that are in the screens: we choose inputs from the upper end of the range of possible values for the influential para meters used in the screens; and we assume that the exposed individual exhibits ingestion behavior that would lead to a high total exposure.

Based on this Tier 2 screening analysis, mercury emissions exceed ed the non

- cancer screening thresholds by a f actor of 3 for the subsistence fisher . A review of the 3 lakes the fisher needed to fish to support his fish ingestion intake of 373 grams/day indicated the lakes were fishable. Due to the theoretical construct of the screening model, these factors are n ot directly translatable into estimates of risk or hazard quotients for these facilities. Table 3.2

- 2 summarizes the results of the Tier 1 and Tier 2 screens. We further note that these results are biased high for purposes of screening and are therefore subject to significant uncertainties.

### Residual Risk Assessment for the Phosphate Fertilizer and Phosphoric A cid Source Categories in Support of the July 2015 Risk and Technology Review Final Rule

36 Table 3.2

- 2 Summary of the “Tier 1 and 2 ” Multipathway Screening Analysis for the Phosphate Fertilizer Production Source Category

To refine our mercury Tier 2 s creen for Mississippi Phosphates, we proceeded to a Tier 3

screen; a ) we examined the set of lakes from which the fisher ingested fish. Any lakes that appeared to not be fishable or not publicly accessible were removed from the assessment, and the screening assessment was repeated. After we made the determi nation the 3 critical lakes were fishable, we analyzed b ) plume rise data, a review of the hourly data indicated about a 33 percent decrease in the Tier 2 screening value resulting in a value of 2; we than proceeded to step c ) assessment of time

- series met eorology and effective stack release heights. Using TRIM

- FaTE for the Tier 2 screening scenario for Mississippi Phosphates we used time

- series meteorology to calculate the cumulative loss of chemical being released above the mixing height as well as fate and transport effects for each lake body . This refined screen reduce d the media concentration and exposure s for each of the lakes and decrease d the non

- cancer screening value to 1, refer to Table 3.2

- 3

.
For this source category our analysis indicates the multipathway risks are at levels that are acceptable and below a level of concern. For further discussion on the methods and results for each of the three Tier screen s , s ee Appendix 9 of this document, Technical Support Document: Human Health Multipat hway Residual Risk Screening Assessment for the Phosphate Fertilizer Production and Phosphoric Acid Manufacturing Source Categories

.

Table 3.2

- 3 Summary of the “Tier 2 and 3 ” Multipathway Screening Analysis for Phosphate Fertilizer Production for Missis sippi Phosphates (NEIMS059305)

### Residual Risk Assessment for the Phosphate Fertilizer and Phosphoric A cid Source Categories in Support of the July 2015 Risk and Technology Review Final Rule

37 The multipathway s creen s includes some hypothetical elements, namely the hypothetical farmer and fisher scenarios. We also included children in different age ranges and adults. It is important to note that even though the multipathway assessment has been conducted, no data exist to verify the existence of either the farmer or fisher scenario outlined above. The farmer scenario involves an individual living for a 70

- year lifetime on a farm homestead in the vicinity of t he source and consuming produce grown on, and meat and animal products raised on, the farm. The individual also incidentally ingests surface soil at the location of the farm homestead. The fisher scenario involves an individual who regularly consumes fis h caught in freshwater lakes in the vicinity of the source of interest over the course of a 70

- year lifetime. In addition, exposure estimates and risks for infants consuming contaminated breast milk were evaluated in the case of dioxins, with the assumpti on that the nursing mother was exposed to chemicals via one of the two scenarios described above.

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

### ) . Results of this analysis estimate that the NAAQS for lead w ould not be exceeded at any off

- site locations.

Baseline Environmental Risk Screening Based on Actual Emissions

We conducted a screening

- level evaluation of the potential for adverse environmental risks associated with emissions of the following environmental HAP from the p hosphate f ertilizer
p roduction source category: mercury, cadmium, lead compounds, and hydrofluoric acid. The results of the environmental scr eening analysis are summarized in Table 3.2

- 4 . In our Tier 1 analysis, emissions of mercury and cadmium did not exceed the threshold emission rates for any of the ecological benchmarks (either the LOAEL or NOAEL) for any facility in the source category

. Therefore, we did not conduct a Tier 2 environmental screening analysis. For lead, estimate s of air concentrations did not exceed the secondary lead NAAQS for any individually modeled point for any facility

. For hydrofluoric acid, the average modeled

con centration around each facility (i.e., the a verage concentration of all off site data points in the modeling domain) did not exceed any ecological benchmark (either the LOAEL or NOAEL). In addition , each individual modeled concentration of hydrofluoric aci d (i.e., each off site data point in the modeling domain) was below the ecological benchmarks for all

### Residual Risk Assessment for the Phosphate Fertilizer and Phosphoric A cid Source Categories in Support of the July 2015 Risk and Technology Review Final Rule

38 facilities.

Table 3.2

- 4 Summary of Environmental Risk Screen ing Results for the Phosphate Fertilizer Production Source Category

Environmental HAP Num ber of Facilities In Category Exceeding Percent of Modeled Area in Category Exceeding 2


### Tier 1 Screen Tier 2 Screen 1 

### NOAEL LOAEL NOAEL LOAEL NOAEL LOAEL

PB

HAP Hg None None

- - 0% 0% Cd N one N one

- - 0% 0% Pb N one N one

- - 0% 0% Acid Gases 3 

### HF NA N one

- - NA 0%

NA

– Not applicable. 1

- Tier 2 screen is only performed for PB

- HAP (other than lead) when there are exceedances of the Tier 1

screen. The acid gas and lead screen s are a single

- tier. 2

- A value of 0% indicates that none of the mode led 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 an d the other from the State of Washington. Although, they are both considered to be LOELs

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



### Facility

- wide Inhalation Assessment Results

The facility

- wide M IR and TOSHI, available in Appendix 4 , are based on actual emissions, as

c ollected in the 114 request, from all emissions sources at the identified facilities. The results indicate that 11 facilities with phosphate fertilizer production processes have facility

- w ide cancer MIR less than or equal to 1 in a million. The maximum facility

- wide TOSHI is less than 1.



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

4.1 Source c ategory d escription and r esults

Phosphoric acid manufacturing facilities pr oduce phosphoric acid from beneficiated phosphate rock. Phosphoric acid is used as a food additive, chemical feedstock, and as a raw material for certain types of fertilizers and animal feed supplements. Phosphoric A cid M anufacturing (40 CFR part 63 subpar t AA) and P hosphate F ertilizers P roduction (40 CFR part 63 subpart BB) were established as separate source categories by EPA. However, these operations are generally collocated, as phosphoric acid manufacturing facilities provide feedstock for phosphate fe rtilizer production facilities and much of the phosphoric acid produced in the U.S. is consumed in the manufacture of fertilizers.

### Residual Risk Assessment for the Phosphate Fertilizer and Phosphoric A cid Source Categories in Support of the July 2015 Risk and Technology Review Final Rule

39 Phosphate rock is the starting material for the production of all phosphate products. All phosphate rock deposits contain m ercury, fluorides, carbonates, and radionuclides along with the phosphate nodules of the ore. Once the ore reaches the phosphoric acid production facility, phosphoric acid is produced using the wet method, in which beneficiated ground phosphate rock is rea cted with sulfuric acid and weak phosphoric acid to produce phosphoric acid and phosphogypsum, a waste product. The phosphogypsum is disposed of on

- site in waste piles known as “gypsum stacks.” Emission point s along the phosphoric acid manufacturing proc ess include wet process phosphoric acid process lines, superphosphoric acid process lines (including vacuum evaporation processes and submerged combustion processes), phosphate rock dryers, phosphate rock calciners, dryers, cooling ponds, and gypsum stacks

.
Emissions data, collected via ICRs sent to all U.S. phosphoric acid manufacturers, were used to conduct this assessment. The data include information regarding process equipment, control devices, phosphate and acid production, inventory data for all p oint and fugitive emissions, practices used to control fugitive emissions, and other aspects of facility operations, including stack parameters and locations. We currently estimate that there are twelve phosphoric acid manufacturing facilities operating in the U nited S tates . Ten of these twelve facilities are co

- located with phosph ate fertilizer production facilities. The 2005 National

- Scale Air Toxics Assessment (NATA) National Emissions Inventory (NEI) was updated for this category with data from industr y and enhanced quality assurance of facility and emission locations. Eleven of these twelve facilities are identified as major sources in the NEI, and one facility is identified as an area source.

The emissions for the phosphoric acid manufacturing so urce category ICR data set (of twelve facilities) are summarized in Table 4.1

- 1 . The total HAP emissions for the source category are approximately 2,200 tons per year. Based on these data, the HAP emitted in the largest quantities across these twelve facil ities are hydrofluoric acid and hydrochloric acid . These two chemicals account for 99 percent of the total emissions by mass. Persistent and bioaccumulative HAP (PB HA P) 27 

### reported as emissions from these facilities include cadmium compounds, c hlorinated d i benzodioxins and f urans (analyzed as 2,3,7,8

- t etrachlorodibenzo

- p

- dioxin ), lead, mercury, and p olycyclic o rganic ma tter (analyzed as b enzo(a) p yrene ). The following environmental HAP are emitted from the p hosphoric a cid

m anufacturing source category and ar e included in the environmental risk screen: mercury compounds, cadmium, POM, dioxins/furans, lead, hydrochloric acid and hydrofluoric acid.

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

- 453K

- 04

- 001A, and provided on the EPA’s T echnology Transfer Network website for Fate, Exposure, and Risk Assessment at http://www2.epa.gov/fera/risk

- assessment

- and

- modeling

- air

- toxics

- risk

- assessment

reference

- library

### Residual Risk Assessment for the Phosphate Fertilizer and Phosphoric A cid Source Categories in Support of the July 2015 Risk and Technology Review Final Rule

40 Table 4.1

- 1 Summary of Emissions from the Phosphoric Acid Manufacturing Source Category and Availability of Dos e - Response Values

HAP a Emissions (tpy) Number of Facilities Reporting HAP (1 2 facilities in ICR data set) Prioritized I nhalation Dose

- Response Value Identified by OAQPS b

PB

HAP? Unit Risk Estimate for Cancer? Reference Concentration

for Chronic Nonca ncer? Health Benchmark Values for

Acute Noncancer? Hydrofluoric Acid 2194 12   Hydrochloric Acid 27 1   Methyl Isobutyl Ketone 9 1  Mercury Compounds

Elemental Gaseous Mercury 0. 1 12    Gaseous Divalent Mercury 0.00 1 12   Particulate Divalent Mercury 0.00 1 12   Nickel Compound s 0.2 12   Manganese Compounds 0.08 12  Chromium Compounds

Chromium (III) Compounds 0.06 11 Chromium (VI) Compounds 0.01 12   Cyanide Compounds 0.06 1  Selenium Compounds 0.05 12  Formaldehyde 0.04 1   
Benzene 0.03 1    Cadmium Compounds 0.02 12    Lead Compounds 0.02 12 c  Benzyl Chloride 0.02 1   Isophorone 0.01 1   Acetaldehyde 0.01 1    Methyl Chloride 0.01 1  
Toluene 0.01 1   Arsenic Compoun ds 0.009 12    Propionaldehyde 0.008 1  
Acrolein 0.006 1   Methylene Chloride 0.006 1    Cobalt Compounds 0.005 12  Methyl Hydrazine 0.004 1  Methyl Bromide 0.004 1  
Carbon Disulfide 0.003 1   Antimony Comp ounds 0.003 12  Ethyl Benzene 0.002 1    Polycyclic Organic Matter Polycyclic Organic Matter 0.002 1   Benzo[a]pyrene 0.0000009 1  
N

- hexane 0.001 1  

### Residual Risk Assessment for the Phosphate Fertilizer and Phosphoric A cid Source Categories in Support of the July 2015 Risk and Technology Review Final Rule

41 Table 4.1

- 1 Summary of Emissions from the Phosphoric Acid Manufacturing Source Category and Availability of Dos e - Response Values

HAP a Emissions (tpy) Number of Facilities Reporting HAP (1 2 facilities in ICR data set) Prioritized I nhalation Dose

- Response Value Identified by OAQPS b

PB

HAP? Unit Risk Estimate for Cancer? Reference Concentration

for Chronic Nonca ncer? Health Benchmark Values for

Acute Noncancer? Naphthalene 0.001 1  
Chloroform 0.001 1  
Dimet hyl Sulfate 0.001 1  Ethyl Chloride 0.0009 1  Ethylene Dichloride 0.0009 1   
Bromoform 0.0009 1  Xylenes (mixed) 0.0008 1   Methyl Tert

- butyl Ether 0.0008 1    Beryllium Compounds 0.0008 12   
Tetrachloroethene 0. 0007 1    Styrene 0.0006 1  
Chlorobenzene 0.0005 1   Methyl Methacrylate 0.0004 1  
Phenol 0.0004 1   Acetophenone 0.0003 1 Vinyl Acetate 0.0002 1   1,1,2,2

- tetrachloroethane 0.0002 1 2,4

- dinitrotoluene 0.00 02 1  
Cumene 0.0001 1  
O

- xylene 0.0001 1   Ethylene Dibromide 0.00003 1   
2

- chloroacetophenone 0.000006 1 
Dioxins and Furans 1,2,3,6,7,8

- hexachlorodibenzo

- p

- dioxin 0.000000006 1   

a Notes for how HAP were speciated for risk assessment:

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

• For emissions reported generically as “chromium” or “chromium & compound,” emissions are speciated 75% “chromium (III) compounds” and 25% “chromium (VI) compounds” for this source category. Chromium speciation profiles can be found on the EPA’s Technology Transfer Network website for emissions inventories at http://www.epa.gov/ttn/chief/net/2005inventory.html

.

• For emissions reported generically as “mercury” o r “mercury & compounds,” emissions are speciated for this category as 80% elemental gaseous mercury, 10% gaseous divalent mercury, and 10% particulate divalent
mercury. Mercury speciation profiles can be found on the EPA’s Technology Transfer Network websi te for emissions inventories at http://www.epa.gov/ttn/chief/net/2005inventory.html

. b Specific dose

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

- response

- assessment

- assessing

- health

- risks

- associated

- exposure

- hazardous

- air

pollutants

### Residual Risk Assessment for the Phosphate Fertilizer and Phosphoric A cid Source Categories in Support of the July 2015 Risk and Technology Review Final Rule

42 c There is no reference concentration for lead. In considering noncancer hazards for lead in this assessment, we compared maximum annual ambient exposure estimates to the National Ambient Air Quality Standards (NAAQS) for lead (0.15 μg/m 3 ). These NAAQS for lead were reviewed with revisions adopted in October 2008 ( http://www.epa.gov/air/lead/actions.html ). The primary (health

- based) standard is a maximum or not

- to

- be

- exceeded, rolling three

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

- based) standard is identic al to the primary standard.



### 4.2 Risk c haracterization This section presents the results of the risk assessment for the phosphoric acid manufacturing source category. The basic chronic inhalation risk estimates presented here are the maximum individual lifetime cancer risk, the maximum chronic hazard index, and the cancer incidence. We also present results from our acute inhalation impact screening in the form of maximum hazard quotients, as well as the results of our preliminary screen for potential non

- inhala tion risks from PB HAP. Also presented are the HAP “drivers,” which are the HAP that collectively contribute 90 percent of the maximum cancer risk or maximum hazard index at the highest exposure location. A detailed summary of the facility

- specific risk assessment results is available in Appendix 4 of this document, Draft Detailed Risk Modeling Results

.

Baseline Inhalation Risk Assessment Results Based on Actual Emissions

Table 4 .2

- 1 summarizes the preliminary inhalation risk results for this source ca tegory. The results indicate that maximum lifetime individual cancer risks are less than 1 in a million. The total estimated cancer incidence from this source category is 0.00 02 excess cancer cases per year

. The maximum chronic non cancer target organ sp ecific hazard index ( TOS HI) value for the source category could be up to 0.2 associated with emissions of hydrofluoric acid

, indicating no significant potential for chronic noncancer impacts.

Worst

- case acute hazard quotients (HQs) were calculated for e very HAP that has an acute benchmark , t wo facilities wer e identified with HQ values > 1. For cases where the screening acute HQ was greater than 1, we further refined the estimates by determining the highest HQ value that is outside facility boundaries. The highest refined worst

- case acute HQ value is 2 (based on the acute REL for hydrofluoric acid ) as shown in Table 4 .2

- 1. The HQ values of 2 represent an upper

- bound risk estimate for both facilities ; the off

- site locations for these sites

were either lo cated in a rural location in which public access is limited or in an off

- site area that maybe owned by the facility.

For phosphoric acid manufacturing facilities a multiplier of 10 (x) was developed for point sources based upon the ICR data and engineer ing knowledge of the source category processes and for emissions from area sources (gypsum ponds and cooling ponds) an acute multiplier of 1 was used. The hydrogen fluoride emissions from the phosphoric acid manufacturing gypsum ponds drove the acute risk s. This emission source was considered stable and homogeneous and not subject to major fluctuations in hourly emissions over the year and therefore an acute refined multiplier of 1 was applied. This was applied to all area sources in the entire source cat egory except for one facility that had site

- specific data for their gypsum ponds, an acute multiplier of 2 was applied for this facility. This data was considered unique to the facility and not applicable for any other site. For further information on th e acute multipliers applied for this source category refer to the memo entitled “ Emissions Data Used in Residual Risk Modeling: Phosphoric Acid and Phosphate Fertilizer Production Source

### Residual Risk Assessment for the Phosphate Fertilizer and Phosphoric A cid Source Categories in Support of the July 2015 Risk and Technology Review Final Rule

43 Categories ” available in the docket for this action. Table 4 .2

- 2 pr ovides more information on the refined acute risk estimates for HAP that had an acute HQ greater than 1 for any benchmark , refer to Appendix 8 to this document ( A cute Noncancer Ris k Analysis for Phosphate Fertilizer and Phosphoric Acid Manufacturing) Table 2 for further information.

For acute risks, 4 facilities exceeded the HEM

- 3 screen for hydrofluoric acid exceeding the REL by a factor of 2. A refined analysis of these sites identified 2 facilities with off

- site concentrations exceeding the REL. T he worst

- case maximum estimated 1

- hour exposure to hydrofluoric acid outside the facility fence line for the phosphoric acid manufacturing source category is 530 u g/m 3 

### . This estimated worst

- case exposure exceeds the 1

- hour REL by a factor of 2 (HQ REL 

### = 2 ) a nd is below the 1

- hour AEGL

- 1 (HQ AEGL

- 1 

### = 0. 6 ).

Worst

- case a cute hazard quotients (HQs) were calculated for every HAP shown in Table 4

- 1 that has an acute benchmark, and the highest acute HQ (and ass ociated HAP) is shown in Table 4 .2

- 1.

### Residual Risk Assessment for the Phosphate Fertilizer and Phosphoric A cid Source Categories in Support of the July 2015 Risk and Technology Review Final Rule

44 Table 4 .2

- 1 S ummary of Source Category Level Inhalation Risks for Phosphoric Acid Manufacturing

Result HAP “Drivers” Facilities in Source Category Number of Facilities Estimated to be i n Source Category 12 n/a

Number of Facilities Modeled in Risk Assessment 12 n/a

Cancer Risks Maximum Individual Lifetime Cancer Risk (in 1 million) 0.09 chromium (VI) compounds, arsenic compounds, nickel compounds Number of Facilities with Maximum Individual Lifetime Cancer Risk: Greater than or equal to 100 in 1 million 0 n/a Greater than or equal to 10 in 1 million 0 Greater than or equal to 1 in 1 million 0 Chronic Noncancer Risks Maximum Skeletal Hazard Index 0.2 hydrofluoric acid Maximum Neurological Hazard Index 0.002 manganese c ompounds Number of Facilities with Maximum Skeletal or Neurological Hazard Index: Greater than 1 0 n/a Acute Noncancer Refined Results

Maximum Acute Hazard Quotient 2 0.6 0.3 0.03 0.03 hydrofluoric acid (REL) hydrofluoric acid (AEGL

- 1) hydrofluoric ac id (ERPG

- 1) hydrofluoric acid (AEGL

- 2) hydrofluoric acid (ERPG

- 2) Number of Facilities With Potential for Acute Effects 2 hydrofluoric acid
Population Exposure Number of People Living Within 50 Kilometers of Facilities Modeled 5, 2 00,000 n/a

Number of People Exposed to Cancer Risk: Greater than or equal to 100 in 1 million 0 n/a Greater than or equal to 10 in 1 million 0 n/a Greater than or equal to 1 in 1 million 0 n/a Number of People Exposed to Noncancer Skeletal or Neurological Hazard Index: Greater than 1 0 n/a Estimated Cancer Incidence (excess cancer cases per year) 0.00 02 n/a
Contribution of HAP to Cancer Incidence chromium (VI) compounds 56 % n/a arsenic compounds 23 % n/a nickel compounds 1 2 % n/a

### Residual Risk Assessment for the Phosphate Fertilizer and Phosphoric A cid Source Categories in Support of the July 2015 Risk and Technology Review Final Rule

45 Table 4 .2

- 2 Summary of Acut e Refined Results for Phosphoric Acid Manufacturing

MAXIMUM ACUTE HAZARD QUOTIENTS ACUTE DOSE

- RESPONSE VALUES

Screening Results

Based on REL Based on AEGL

- 1/ ERPG

- 1 Based on AEGL

- 2/ ERPG

- 2 REL (mg/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 ) HAP Max. 1

- hr. Air Conc. (mg/m 3 ) Hydrofluoric acid 0.5 2 0.6 / 0.3 0. 03 / 0. 03 0.24 0.82 1.6 20 16

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

- response values. O nly 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, b ased on an analysis of currently available emissions data. Notes on Acute Dose

- Response Values: REL

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

- hour exposures. AEGL

– Acute exposure guideline leve ls represent emergency exposure (1

- hour) limits for the general public. AEGL

- 1 is the exposure level above which it is predicted that the general population, including susceptible individuals, could exp erience effects that are notable di scomfort, but which are transient and reversible upon cessation of exposure. AEGL

- 2 is the exposure level above which it is predicted that the general population, including susceptible individuals, could exp erience irreversible or other serious, long

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

– Emergency Removal Program guidelines represent emergency exposure (1

- hour) limits for the general public. ERPG

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

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

.

### Residual Risk Assessment for the Phosphate Fertilizer and Phosphoric A cid Source Categories in Support of the July 2015 Risk and Technology Review Final Rule

46 Baseline Inhalation Assessment Results Based on Allowable Emissio ns

Analysis of potential differences between actual emissions levels and the maximum emissions allowable under the MACT standards were also calculated for this source category. Based upon the above analysis we multiplied the modeled actual risks for the MIR facility with process site

- specific multipliers to estimate allowable risks under the MACT. This approach was deemed sufficient due to the low actual modeled risks for the source category. The maximum lifetime individual cancer risks based upon allowa ble are still less than 1 in a million

. The maximum chronic noncancer TOSHI value for allowable emissions was estimated at an HI = 0.3.

Baseline Multipathway Assessment Results Based on Actual Emissions

To identify potential multipathway health risks fro m PB

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

- HAP emitted from the phosph oric acid manufacturing s ource category (based on actual emissions) to screening emission rates ( see section 2.5) see Append ix 5 for a more detailed discussion of the screening emission rates

. The PB

- HAP emitted by facilities in this category include mercury compounds (1 2 facilities), le ad compounds (12 facilities), dioxin/furan compounds (1 facilit y ), POM compounds (1 facilit y ) and cadmium compounds (1 2 facilities ) . We compared reported emissions of PB

- HAP to the Tier 1 screening emission thresholds established by the EPA for the purposes of the RTR risk assessments. One facility , PCS in Aurora, NC (NEI 455 71 ) emitted divalent mercury (Hg 2+ 

### ) and cadmium compounds above the Tier 1 screening threshold level, exceeding the mercury screening threshold by a factor of 7 and cadmium by a factor of 2 , we than proceeded to conduct a Tier 2 screening assessment for di valent mercury and cadmium

.

For the se PB

- HAP ’s and facility that did not screen out during our initial Tier 1 multipathway screening analysis, we improved our analysis with some additional site

- specific i nformation to develop a Tier 2 screen. (See Append ix 5 of this document, Technical Support Document for TRIM

- Based Multipathway Screening Scenario for RTR: Summary of Approach and Evaluation , for more information about the Tier 2 screen.) The additional site

- specific information included the location of fishable l akes and local wind direction and speed. The result of this analysis was the development of site

- specific emission screening levels for each of the four PB

- HAP for the farmer and fisher scenarios.

Based on this Tier 2 screening analysis, th e cadmium is below the non

- cancer screening threshold with a value of 0.3 and at a value of 1 for mercury for the subsistence fisher scenario. Both screening values for cadmium and mercury are below a l evel of concern

. Due to the theoretical construct of the screening model, these factors are not directly translatable into estimates of risk or hazard quotients f or these facilities. Table 4 .2

- 3 summarizes the results of the Tier 1 and Tier 2 screens. We further note that these results are biased high for purposes of screening and are therefore subject to significant uncertainties.

### Residual Risk Assessment for the Phosphate Fertilizer and Phosphoric A cid Source Categories in Support of the July 2015 Risk and Technology Review Final Rule

47 Table 4 .2

- 3 Summary of the “Tier 1 and 2 ” Multipathway Screening Analysis for the Phosphoric Acid Manufacturing Source Category

The Tier 2 Screen includes some hypothet ical elements, namely the hypothetical farmer and fisher scenarios. It is important to note that even though the multipathway assessment has been conducted, no data exist to verify the existence of either the farmer or fisher scenario outlined abov e. The farmer scenario involves an individual living for a 70

- year lifetime on a farm homestead in the vicinity of the source and consuming produce grown on, and meat and animal products raised on, the farm. The individual also incidentally ingests surfa ce soil at the location of the farm homestead. The fisher scenario involves an individual who regularly consumes fish caught in freshwater lakes within a radius of 50 km of the emission source. In addition, exposure estimates and risks for infants consuming con taminated breast milk were evaluated in the case of dioxins, with the assumption that the nursing mother was exposed to chemicals via one of the two scen arios described above.

Baseline Environmental Risk Screening Based on Actual Emissions

We conducted a screening

- level evaluation of the potential for adverse environmental risks associated with emissions of the following environmental HAP from the p hosphoric a cid

m anufacturing source category: mercury compounds, cadmium, POM, dioxins, lead, hydrochlori c acid and hydrofluoric acid. The results of the environmental screening analysis are summarized in Table 4.2

- 4. In our Tier 1 analysis, emissions of mercury, cadmium, POM, and dioxins did not exceed the threshold emission rates for any of the ecological benchmarks (either the LOAEL or NOAEL) for any facility in the source category . Therefore, we did not conduct a Tier 2 environmental screening analysis. For lead, there were no exceedances of the secondary lead NAAQS for any individual data point for an y facility

.
For hydrochloric acid and hydrofluoric acid, the average modeled concentration around each facility (i.e., the average concentration of all offsite data points in the modeling domain) did not exceed any ecological benchmark (either the LOAE L or NOAEL). In addition , each individual modeled concentration of hydrochloric acid (i.e., ea ch off site data point in the modeling domain) was below the ecological benchmarks for all facilities. The results of the

### Residual Risk Assessment for the Phosphate Fertilizer and Phosphoric A cid Source Categories in Support of the July 2015 Risk and Technology Review Final Rule

48 environmental screen for hydrofluoric a cid are summarized for each facility in Table 3 of Appendix 7

. For hydrofluoric acid , less th an 1 percent of the off site modeling domain for the entire source category was above the Canadian LOEL ecological benchmark. The largest facility exceedance area based on the Canadian LOEL ecological benchmark represented 6 percent of th at facility’s total offsite model ed area

.

Table 4 .2

- 4 Summary of Environmental Risk Screen ing Results for the Phosphoric Acid Manufacturing Source Category

Environmental HAP Num ber of Facilities In Category Exceeding Percent of Modeled Area in Category Exceeding Tier 1 Screen Tier 2 Screen 1 

### NOAEL LOAEL NOAEL LOAEL NOAEL LOAEL

PB

HAP Hg None None

- - 0% 0% Cd N one N one

- - 0% 0% POM N one N one

- - 0% 0% Dioxins N one N one

- - 0% 0% Pb N one N one

- - 0% 0% Acid Gases 3 

### HCl NA N one

- - 0% 0% HF NA N one

- - NA 1%

NA

– Not applicable. 1

- Tier 2 screen is only performed for PB

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

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

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

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

- the level between a NOAEL and a LOAEL, we have listed the results under the LOAEL column. The results pre sented in the table are the same for both the LOEL from Canada and the LOEL from the State of Washington (i.e., no facility showing exceedance of averaged values; 1% of area in category showing and exceedance). For HCl, we evaluated one benchmark at the L OAEL level.



### Facility

- wide Inhalation Assessment Results

The facility

- wide MIR and TOSHI, available in Appendix 4, are based on emissions as identified in the NEI, from all emissions sources at the identified facilities. The results of the facility

- wide indicate that eleven facilities with phosphoric acid manufacturing processes have a facility

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

- wide TOSHI for the source category is 0.2.



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

5.1 Exposure 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

### Residual Risk Assessment for the Phosphate Fertilizer and Phosphoric A cid Source Categories in Support of the July 2015 Risk and Technology Review Final Rule

49 uncertainties in our emission inventory likely dominate the uncertainties in our exposure modeling estimates. The chronic exposure modeling uncertainties are considered relatively small in comparison, since we are using EPA’s refined local dispersion mode l 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 depl etion, and atmospheric degradation. Additionally, estimates of the maximum individual risk (MIR) contain 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 closur e 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 pot ential downward bias in both the MIR and population risk estimates.

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

We have purposely biased the acute screening results high, considering that they depend upon the joint occurrence of independent factors, such as hourly emissions rates, meteorology and human acti vity patterns . In cases where multiple acute threshold values are considered scientifically acceptable we have chosen the most conservative of these threshold values, erring on the side of overestimating potential health risks from acute exposures. In th e cases where these results indicated the potential for exceeding short

- term health thresholds post

controls, we refined our assessment by developing a better understanding of the geography of the facility relative to potential exposure locations.



### 5.2 Un certainties in the d ose - r esponse r elationships In the sections that follow, separate discussions are provided on uncertainty associated with cancer potency factors and noncancer reference values. Cancer potency values are derived for chronic (lifetime) e xposures. Noncancer reference values are generally derived for chronic exposures (up to a lifetime), bu t may also be derived for acute ( <24 hours), short

- term (>24 hours up to 30 days), and subchronic (>30 days up to 10 percent of lifetime) exposure durati ons, all of which are derived based on an assumption of continuous exposure throughout the duration specified. For the purposes of assessing all potential health risks associated wit h the emissions included in an assessment, we rely on both chronic (cance r and noncancer) and acute ( noncancer ) benchmarks, which are described in more detail below.

Although every effort is made to identify peer

- review ed dose

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

- reviewed cancer potency values or reference values for chronic non

- cancer or acute effects (inhalation or

### Residual Risk Assessment for the Phosphate Fertilizer and Phosphoric A cid Source Categories in Support of the July 2015 Risk and Technology Review Final Rule

50 ingestion) . Since exposures to these pollutants cannot be included in a quantitative risk estimate, an understatement of risk for these pollutants at environmental exposure levels is possible.

Additionally, chronic dose

- response values for certain compounds included in the assessment may be under EPA IRIS review

, and revised assessments may determine that these pollutants are more or less potent than currently thought. We will re

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

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

.

Cancer assessment

The discussion of dose

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

- reviewed cancer potency factors used where EPA

- developed values are not yet available. To place this discussion in context, we provide a quote from the EPA’s Guidelines for Carcinogen Risk Assessment [ 30 ] (here in referred to as Cancer Guidelines ). “The primary goal of EPA actions is protection of human health; accordingly, as an Agency policy, risk assessment procedures, including default options that are used in the absence of scientific data to the contrary, should be health protective.” The approach adopted in this document is consistent with this approach as described in the Cancer Guidelines

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

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

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

- reviewed methodologies. The EPA Cancer Guidelines describes the Agency’s recommendations for metho dologies for cancer risk assessment. EPA believes that cancer risk estimates developed following the procedures described in the Cancer Guidelines and outlined below generally provide an upper bound estimate of risk. That is, EPA’s upper bound estimates represent a “plausible upper limit to the true value of a quantity” (although this is usually not a true statistical confidence limit). 28 

### In some circumstances, the true risk could be as low as zero; however, in other circumstances the risk could also be g reater. 29

28 IRIS glossary ( http://ofmpub.epa.gov/sor_internet/registry/termreg/searchandretrieve/glossariesandkeywordlists/search.do?deta ils=&glossaryName=IRIS%2 0Glossary ).

29 The exception to this is the URE for benzene, which is considered to cover a range of values, each end of which is considered to be equally plausible, and which is based on maximum likelihood estimates.

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51 When developing an upper bound estimate of risk and to provide risk values that do not underestimate risk, EPA generally relies on conservative default approaches. 30 

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

Such health risk assessments have associated uncertainties, some which may be considered quantitatively, and others which generally are expressed qualitatively. Uncertainties may vary substantially among cancer risk assessments associated with exposures to different pollutants, since the assessments employ different databases with different strengths and limitat ions and the procedures employed may differ in how well they represent actual biological processes for the assessed substance. EPA’s Risk Characterization Handbook also recommends that risk characterizations present estimates demonstrating the impact on t he assessment of alternative choices, data, models and assumptions [ 31 ]. Some of the major sources of uncertainty and variability in deriving cancer risk values are described more fully below.

(1) The qualitative similarities or differences between tumo r responses observed in experimental animal bioassays and those which would occur in humans are a source of uncertainty in cancer risk assessment. In general, EPA does not assume that tumor sites observed in an experimental animal bioassay are necessarily predictive of the sites at which tumors would occur in humans. 31 

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

- estimation or over

- estimation of human cancer risks.

(2) Uncertainties regarding the most appropriate dose metric for an assessment can also lead to differences in risk estimates

. For example, the measure of dose is commonly expressed in units of mg/kg/d ingested or the inhaled concentration of the pollutant. However, data may support development of a pharmacokinetic model for the absorption, distribution, metabolism and excretio n of an agent, which may result in improved dose metrics (e.g., average blood concentration of the pollutant or the quantity of agent metabolized in the body). Quantitative uncertainties result when the appropriate choice of a dose metric is uncertain or when dose metric estimates are themselves uncertain (e.g., as can occur when alternative

30 According to the NRC report Scienc e 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 unk nown or uncertain.” The 1983 NRC report Risk Assessment in the Federal Government: Managing the Process defined default option as “the option chosen on the basis of risk assessment policy that appears to be the best choice in the absence of data to the co ntrary” (NRC, 1983a, p. 63). Therefore, default options are not rules that bind the agency; rather, the agency may depart from them in evaluating the risks posed by a specific substance when it believes this to be appropriate. In keeping with EPA’s goal of protecting public health and the environment, default assumptions are used to ensure that risk to chemicals is not underestimated (although defaults are not intended to overtly overestimate risk). See EPA 2004 An Examination of EPA Risk Assessment Princ iples and Practices, EPA/100/B

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

- final.pdf

31 Per the EPA Cancer Guidelines: “The default option is that positive effects in animal cancer studi es 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.”

### Residual Risk Assessment for the Phosphate Fertilizer and Phosphoric A cid Source Categories in Support of the July 2015 Risk and Technology Review Final Rule

52 pharmacokinetic models are available for a compound). Uncertainty in dose estimates may lead to either over or underestimation of risk.

(3) For the quantitative extr apolation 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 base d 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 experim ental animals and humans may be either greater than or less than that estimated by baseline scientific scaling predictions due to uncertainties associated with limitations in the test data and the correctness of scaled estimates.

(4) EPA cancer risk est imates, whether based on epidemiological or experimental animal data, are generally developed using a benchmark dose (BMD) analysis to estimate a dose at which there is a specified excess risk of cancer, which is used as the point of departure (or POD) fo r the remainder of the calculation. Statistical uncertainty in developing a POD using a benchmark dose (BMD) approach is generally addressed though use of the 95 percent lower confidence limit on the dose at which the specified excess risk occurs (the BMD L), decreasing the likelihood of understating risk. EPA has generally utilized the multistage model for estimation of the BMDL using cancer bioassay data (see further discussion below).

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

- specific, bi ologically

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

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

- dose (straight line from POD) approach is used when available mode of action data suppor t 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 C ancer Guidelines generally consider a linear dose

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

- dose linear responses, although the slope of this relationship may not be the same as the slope estimated by the straight line approach.

EPA most frequently utilizes a linear low

- dose extrapolation approach as a baseline science

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

- specific determination. This approach is designed to not underestimate risk in the face of uncertainty

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53 and variability. EPA believes that linear dose

- response models, when appropriately applied

a s part of EPA’s cancer risk assessment process, provide an upper bound estimate of risk and generally provide a health protective approach. Note that another source of uncertainty is the characterization of low

- dose nonlinear, non

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

- probit models) in representing dose response relationships due to the variability in response within human populations. Another National Research Coun cil (NRC) report [ 32 ] suggests that models based on distributions of individual thresholds are likely to lead to sigmoidal

- shaped dose

- response functions for a population. This report notes sources of variability in the human population: “One might expect these individual tolerances to vary extensively in humans depending on genetics, coincident exposures, nutritional status, and various other susceptibility factors...” Thus, if a distribution of thresholds approach is considered for a carcinogen risk as sessment, 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 ari se 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 assess ment typically utilizes a straight line approach from the BMDL. This is equivalent to using an upper confidence limit on the slope of the straight line extrapolation. The impact of the choice of the BMDL on bottom line risk estimates can be quantified by comparing risk estimates using the BMDL value to central estimate BMD values, although these differences are generally not a large contributor to uncertainty in risk assessment (Subramaniam et. al., 2006) [ 33 ]. It is important to note that earlier EPA ass essments, including the majority of those for which risk values exist today, were generally developed using the multistage model to extrapolate down to environmental dose levels and did not involve the use of a POD. Subramaniam et. al. (2006) also provide comparisons indicating that slopes based on straight line extrapolation from a POD do not show large differences from those based on the upper confidence limit of the multistage model.

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

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

- specific data to evaluate this variability are generally not available. There may also be important life stage differences in the quantitative potency of carcinogens and, with the exception of the recommendations in EPA’s S upplemental Cancer Guidance for carcinogens with a mutagenic mode of action, risk assessments do not generally quantitatively address life stage differences. However, one approach used commonly in EPA assessments that may help address variability in respo nse is to extrapolate human response from results observed in the most sensitive species and sex tested, resulting typically in the highest URE which can be supported by reliable data, thus supporting estimates that are designed not to underestimate risk i n the face of uncertainty and variability.

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54 Chronic noncancer assessment

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

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

– ATSDR) utilize an approach that is intended not to underestimate risk in the face of uncertainty and variability. When there are gaps in the available information, uncertainty factors ( UFs) are applied to derive reference values that are intended to be protective against appreciable risk of deleterious effects . Uncertainty factors are commonly default values 32 

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

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

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

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

For non

- cancer endpoints r elated to chronic exposures, EPA derives a Reference Dose (RfD) for exposures via ingestion and a Reference Concentration (RfC) for inhalation exposures. These values provide an estimate (with uncertainty spanning perhaps an order of magnitude) of a daily oral exposure (RfD) or a continuous inhalation exposure (RfC) to the human population (including sensitive subgroups) that is likely to be without an appreciable risk of deleterious effects during a lifetime. 33 

### To derive values that are intended to be “wi thout appreciable risk,” EPA’s methodology relies upon an uncertainty factor (UF) approach [ 34 ],[ 35 ] which includes consideration of both uncertainty and variability.

EPA begins by evaluating all of the available peer

- reviewed literature to determine non

cancer endpoints of concern, evaluating the quality, strengths and limitations of the available studies. EPA typically chooses the relevant endpoint that occurs at the lowest dose, often using statistical modeling of the available data, and then determin es the appropriate point of departure (POD) for derivation of the reference value. A POD is determined by (in order of preference): (1) a statistical estimation using the benchmark dose (BMD) approach; (2) use of the dose or concentration at which the to xic response was not significantly elevated (no observed adverse effect level

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

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

- assessment process when the correct scientific model is unknown or uncerta in.” The 1983 NRC report Risk Assessment in the Federal Government: Managing the Process defined default option as “the option chosen on the basis of risk assessment policy that appears to be the best choice in the absence of data to the contrary” (NRC, 1 983a, p. 63). Therefore, default options are not rules that bind the agency; rather, the agency may depart from them in evaluating the risks posed by a specific substance when it believes this to be appropriate. In keeping with EPA’s goal of protecting pu blic health and the environment, default assumptions are used to ensure that risk to chemicals is not underestimated (although defaults are not intended to overtly overestimate risk). See EPA 2004 An examination of EPA Risk Assessment Principles and Practi ces, EPA/100/B

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

- final.pdf

33 See IRIS glossary

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55 A series of downward adjustments using default UFs is then applied to the POD to estimate the reference value [ 36 ] . While collectively termed “UFs”, these factors account for a number of different quantitative considerations when utilizing observed animal (usually rodent) or human toxicity data in a risk assessment. The UFs are intended to account for: (1) variation in susceptibility among the members of the human population (i.e., inter

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

- than

- lifetime exposure (i.e., extrapolating from subchronic to chronic exposure); (4) uncertainty in extrapolating from a LOAEL in the absence of a NOAEL; and (5) uncertainty when the database is incomplete or there are prob lems 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 assessmen t) , EPA typically applies an UF value of 10 or 3 with the cumulative UF value leading to a downward adjustment of 10

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

- fold factor. If an extrapolation step

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

1) Heterogeneity among human s 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 e pidemiologic studies), to a larger, more diverse population. In the absence of pollutant

- specific data on human variation, a 10

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

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

- standing scientific support for the use of animal studies as indicators of potential toxicity to humans , there are uncertainties in such extrapolations. In the absence of data to the contrary, the typical approach is to use the most rel evant endpoint from the most sensitive species and the most sensitive sex in assessing risks to the average human. Typically

, compound specific data to evaluate relative sensitivity in humans versus rodents are lacking, thus leading to uncertainty in this extrapolation. Size

- related differences (allometric relationships) indicate that typically humans are more sensitive than rodents when compared on a mg/kg/day basis. The default choice of 10 for the interspecies UF is consistent with these differences. For a s pecific chemical, differences in species responses may be greater or less than this value

.
Pharmacokinetic models are useful to examine species differences in pharmacokinetic processing and associated uncertainties; however, such dosimetric adjust ments are not always

### Residual Risk Assessment for the Phosphate Fertilizer and Phosphoric A cid Source Categories in Support of the July 2015 Risk and Technology Review Final Rule

56 possible. Information may not be available to quantitatively assess toxicokinetic or toxicodynamic differences between animals and humans, and in many cases a 10

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

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

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

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

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

4) Toxicity data are typically limited as to the dose or exposure levels that have been tested in individual studies; in an animal study, for example, treatment groups may differ in exposure by up to an order of magnitude. The preferred approach to arrive at a POD is to use BMD analysis; however, this approach requires adequate quantitative results for a meaningful analysis, which is not always possible. Use of a NOAEL is the next preferred approach after BMD analysis in determining a POD for deriving a health effect reference value. However, many stu dies lack a dose or exposure level at which an adverse effect is not observed (i.e., a NOAEL is not identified). When using data limited to a LOAEL, a UF of 10 or 3

- fold is often applied.

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

- fold is typically applied.

Acute noncancer asses sment

Many of the UFs used to account for variability and uncertainty in the development of acute reference values are quite similar to those developed for chronic durations, but more often using individual UF values that may be less than 10. UFs are app lied 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 follow ing paragraph). The UFs applied in acute reference value derivation include: 1) heterogeneity among humans; 2) uncertainty in extrapolating from animals to humans; 3) uncertainty in LOAEL to NOAEL adjustments; and 4) uncertainty in accounting for an inco mplete database on toxic effects of potential concern. Additional adjustments are often applied to account for uncertainty in extrapolation from observations at one exposure duration (e.g., 4 hours) to arrive at a POD for derivation of an acute reference value at another exposure duration (e.g., 1 hour).

Not all acute reference values are developed for the same purpose and care must be taken when interpreting the results of an acute assessment of human health effects relative to the reference value or v alues being exceeded. W here relevant to the estimated exposures, the

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57 lack of threshold values at different levels of severity should be factored into the risk characterization as potential uncertainties.

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58 

## 6 References

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

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

.
2 US EPA, 2009. Risk and Technology Review (RTR) Risk Assessment Methodologies: For

Review by the EPA’s Science Advisory Board with Case Studies

– MACT I Petroleum Refini ng Sources and Portland Cement Manufacturing

. EPA

- 452/R

- 09

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

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

http://yosemite.epa.gov/sab/sabproduct.nsf/4AB3966E263D943A8525771F00668381/$File/EP A

- SAB

- 10

- 007

- unsigned.pdf

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

5

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

General Purpose (Flat and Complex Terrain) Dispersion Mode l 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

– AERMO D. EPA

- 454/B

03

- 001

. http://www.epa.gov/scram001/7thconf/aermod/aermodugb.pdf

.
7

. Allen , D. , C

. Murphy, Y

. Kimura, W

. Vizuete, T

. Edgar

, H

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. - U

. Kim, M

. Webster

, and M

. Symons , 2004. Variable industrial VOC emissions and their impact on ozone formation in the Houston Galveston Area. Final Report: Texas Environmental Research Consortium Project H

- 13. http://files.harc.edu/Projects/AirQuality/Projects/H013.2003/H13FinalReport.pdf

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

- 453

- K

- 04

- 001A. http://www2.epa.gov/fera/dose

- response

- assessment

- assessing

- health

- risks

- associated

exposure

- hazardous

- air

- pollutants

9 . US EPA, September 2011. Exposure Factors Handbook: 20 11 Edition; http://www.epa.gov/ncea/efh/pdfs/efh

- complete.pdf

10

. US EPA, 2005. Table 1. Prioritized Chronic Dose

- Response Values (2/28/05). Office of

Air Quality Planning and S tandards. http://www2.epa.gov/fera/dose

- response

- assessment

assessing

- health

- risks

- associated

- exposure

- hazardous

- air

- pol lutants

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

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

12

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

.

### Residual Risk Assessment for the Phosphate Fertilizer and Phosphoric A cid Source Categories in Support of the July 2015 Risk and Technology Review Final Rule

59 13

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

.
14

. US EPA, 1994

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

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

15

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

16

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

.

17

. CA Office of Environmental Health Hazard Assessment, 2005. Technical Support Document for Describing Available Cancer Potency Factors, May 2005. http://www.oehha.ca.gov/air/hot_spots/pdf/may2005Hotspots.pdf

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

.
19 . US EPA, 2014. Table 2. Acute Dose

- Response Values for Screening Risk Assessments ( 5 /0 9 /20 14 ). Office of Air Quality Pl anning and Standards; http://www2.epa.gov/fera/dose

response

- assessment

- assessing

- health

- risks

- associated

- exposure

- hazard ous

- air

- pollutants

20 . CA Office of Environmental Health Hazard Assessment, 2014. All Acute Reference Exposure Levels developed by OEHHA as of January 2014. http://www.o ehha.ca.gov/air/allrels.html

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

involved/AIHAGuidelineFoundation/EmergencyResponsePlanningGuidelines/Pages/default.a
spx

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

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

- B

- 00

- 002.

24 . US EPA, 2002. EPA’s Guidelines for Ensuring and Maximizing the Quality, Objectivity, Utility, and Integrity of Information Disseminated by the Environmental Prot ection Agency. EPA Office of Environmental Information. EPA/260R

- 02

- 008

. http://www.epa.gov/quality/informationguidelines/documents/EPA_InfoQualityGuid elines.pdf

25

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

### Residual Risk Assessment for the Phosphate Fertilizer and Phosphoric A cid Source Categories in Support of the July 2015 Risk and Technology Review Final Rule

60 26

. US EPA, 2005. Supplemental guidance for assessing early

- life exposure to carcinogens. EPA/ 630/R

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

.

27

. US EPA, 2005. Science Policy Council Cancer Guidelines Implementation Workgroup

Communication I: Memo fro m W.H. Farland dated 4 October 2005 to Science Policy Council. http://www.epa.gov/osa/spc/pdfs/canguid1.pdf

28

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

- 630

- R

- 98

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

29

. US EPA, 2000. Supplementary Guidance for Conducting Health Risk Assessment of Chem ical Mixtures. EPA

- 630/R

- 00

- 002. http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=20533

30

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

- 03/001F, 2005. http://www.epa.gov/cancerguidelines/

31

. US EPA, 2000

. Risk Characterization Handbook. EPA 100

- B

- 00

- 002.

32

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

.
33

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

– 830.

34

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

35

. US EPA. 1994. Methods for Derivation of Inhalation Refer ence Concentrations and Applic ation of Inhalation Dosimetry. http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=71993

36

. US EPA. 2002. A Review of the Reference Dose and Reference C oncentration Processes. http://www.epa.gov/raf/publications/pdfs/rfd

- final.pdf

### Appendix 1 Technical Support Docume nt for HEM-3 Modeling

# Modeling for the Residual Risk and Technology Review Using the Human Exposure Model 3 – AERMOD Version

Updated 8/7/2008

Technical Support Document

Prepared for:

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

Prepared by:

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

### ii 

## Disclaimer

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

### iii 

# Contents

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

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

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

.. 14 3.2 Pollutant Cross-Referencing ............................................................................................. 15 3.3 Meteorological Data.......................................................................................................... 15 3.4 Model Options Selected ...................................................................................................

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

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

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

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

5. Uncertainties ............................................................................................................................. 24

### iv 6. References ................................................................................................................................. 31



# Figures

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

........ 23



# Tables

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

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

### 1 

# 1. Introduction

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

Section 2 of this report provides an over view of the HEM3 system; and Section 3 describes inputs and choices made in implementing the model for the RTR program. Quality assurance efforts undertaken in the modeling effort are discussed in Sec tion 4, and uncertainties associated with the modeling effort are discussed in Section 5.

### 2 

# 2. Overview of the HEM3– AERMOD System

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

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

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

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

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

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

The following sections outline the methodologies used in the HEM3 system. Section 2.1 describes the preparation of di spersion modeling inputs, Secti on 2.2 describes the running of AERMOD, Section 2.3 describes calculations performed by HEM3 to calculate risks and exposures, and Section 2.4 details the sources and methods used to produce HEM3’s data libraries. The HEM3 User’s Manual provides add itional details on the input data and algorithms used in the model. 3 

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



## 2.1 Preparation of Dispersion Modeling Inputs

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

2.1.1 Compiling Emission Source Data

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

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

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

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

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

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

2.1.2 Defining the Modeling Domain and Receptors

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

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

Treatment of Nearby Census Blocks and Screening for Overlapping Blocks

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

Parameter Typical value
Modeling domain size – maximum distance to be modeled a 50 km
Cutoff distance for modeling of individual blocks b 2,000 – 3,000 m
Overlap distance – where receptors will be considered to be on plant property b 30 m
Polar receptor network
Distance to the innermost ring a 100 m

N umber of concentric rings 13

N umber of radial directions 16

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

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

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

Polar receptor network

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

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

Elevations and hill heights for model receptors

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

### 6 Elevations and controlling hill heights for th e polar grid receptors are also estimated based on values from the Census library. HEM3 divides the modeling doma in into sectors based on the polar receptor network, with each Census block assigned to the sector corresponding to the closest polar grid receptor. Each polar grid receptor is then assigne d an elevation based on the highest elevation for any Census block in its sector. The contro lling hill height is also set to the maximum hill height within the sector. If a sector does not contain any blocks, the model defaults to the elevation and controlling hill he ight of the nearest block outside the sector.

2.1.3 Selection of Meteorological Data

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



## 2.2 Running of AERMOD

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

2.2.1 AERMOD Dispersion Options Used by HEM3

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

### These options include the following:

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

! use of calm processing routines

! use of missing-data processing routines

! consideration of terrain effects

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

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

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

! consideration of building wake effects

! calculation of short term impacts

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

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

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

2.2.2 Use of Dilution Factors

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

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



## 2.3 Postprocessing of AERMOD Results in HEM3

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

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

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

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

2.3.1 Calculation of Impacts at Modeled Receptors

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

For cancer risk:
CR T 

### = 

##  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 pollu tant types j (parti culate or gas) CR i, j 

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

### = dilution factor [(  g/m 3 

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

##  k 

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

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

### = cancer unit risk estimate for pollutant k

### 9 TOSHI T 

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

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

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

If the cancer unit risk estimate is not availabl e for a given chemical, then that chemical is not included in the calculation of cancer risk. Likewise, if the noncancer reference concentration is not available for a given chemical, that chem ical is not included in the calculation of hazard indices. Note also that separa te reference concentrations are used for acute and chronic hazard indices.

HEM3 computes short term concentrati ons and records the highest short term concentration for each pollutant. In addition, the user can opt to compute and record the short term and long concentrations at each receptor. Concentrations are computed as follows.

Long term concentrations:
AC T, k 

### = 

##  i 

### AC i, k



### AC i, k 

### = DF i, j 

###  CF  E i, k



### Short term concentrations:
AC T 

### = 

##  i 

### AC i, k



### AC i, k 

### = DF i, j 

###  CF  E i, k 

###  M

where: AC T, k 

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

##  i 

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

### ) AC i, k 

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

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

### ) M = ratio between the estimated maximum s hort term emission rate and the long term average emission rate (dimensionless)

2.3.2 Interpolation of Impacts at Outer Census Blocks

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

I a, r 

### = I 1, r 

### + (I 2, r 

### – I 1, r 

### )  (a – A1)  (A2 – A1)

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

2.3.3 Calculation of Population Exposures and Incidence

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

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

TCR = 

## 
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 within distance the modeling domain CR m



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

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

2.3.4 Model Outputs

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

! Maximum long term impacts at populated locations

" maximum lifetime individual cancer risk

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

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

" locations of the maximum cancer risk and TOSHI

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

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

! Maximum acute impacts

" maximum short term ambient concentration for each chemical

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

" locations of the maximum impacts for different chemicals

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

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

! Outputs for all receptors

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

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

" estimated deposition (optional)

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

! Population exposures and tota l cancer risk, or incidence

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

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

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



## 2.4 Data Libraries Used in HEM3

2.4.1 Chemical Health Effects Information

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

! unit risk estimate for cancer

! reference concentration for chronic noncancer health effects

! reference concentrations for acute health effects

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

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



### 2.4.2 Census Block Locations and Elevation Data

The HEM3 Census library includes Census block identification codes, locations, populations, elevations, and controlling hill height s for all of the over 5 million Census blocks identified in the 2000 Census. The location coordi nates reflect the intern al point of the block, which is a point selected by the Census to be ro ughly in the center of the block. For complex shapes, the internal point may not be in the geographic center of the block. Locations and population data for Census blocks in the 50 stat es and Puerto Rico were extracted from the LandView® database. 5 

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

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

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

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

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

2.4.3 Meteorological Data

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

2.4.4 Gaseous Deposition Parameters

HEM3 provides options to compute the deposi tion of air pollutants, and to take into account the impacts of plume depletion due to depos ition of gaseous and particulate pollutants. If either of these options is selected by the model user for gaseous pollutants, a number of pollutant properties are required by AERMOD. Thes e include the diffusivity of the pollutant in air, the diffusivity of the pollutant in water, the Henry’s Law constant, and a parameter reflecting the cuticular resistance to uptak e of the pollutant by leaves r CL 

### ). 8 

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

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

### 14 

# 3. Modeling for the Residual Risk Technology Review

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



## 3.1 Emission Source Inputs

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

! pollutants emitted

! emission rate for each pollutant

! emission source coordinates

! stack height (or emission height for fugitive sources)

! stack diameter (or configuration of fugitive sources)

! emission velocity

! emission temperature

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

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

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

### 15 

## 3.2 Pollutant Cross-Referencing

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

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



## 3.3 Meteorological Data

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

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

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

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

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

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

subgroup (m 3 /g)

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

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

### 17 POM 77002 Chr y sene 0.00001



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

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

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

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

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

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

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

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

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

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



## 3.4 Model Options Selected

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

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

### but no facility-specific

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

Table 3-2. Modeling Domain and Model Set-Up Options in the HEM-3 Model As Used in the Residual Risk and Tec hnology Review Assessments Option Selection Dispersion model: AERMOD or ISCST3 AERMOD Type of analysis: chronic, acute, or both Both Averaging time for short term impacts 1-hour Multiplier for short term emissions 10 a Urban or rural dispersion characteristics Rural Include terrain impacts Yes Include building wake effects No Calculate deposition (wet, dry, or both) No Include impacts of plume depletion due to deposition No User-specified receptor locations (for plant bou ndary sites or other sites of interest) No Modeling domain size – maximum distance to be modeled 50 km Cutoff distance for modeling of individual blocks 3 km b
Overlap distance where receptors are considered to be on plant property – measured from each source 30 m c

Polar receptor network specifications Distance from the plant center to the innermost ring 100 m c Number of rings 13

Number of directions 16 Meteorology data Closest site

a For the secondary aluminum production category, th e ratio of maximum short-term emissions to long-term average emissions was set to 100, based on the engineering review.
b The individual block modeling cutoff was increased for categories and for so me facilities to ensure that the maximum individual risk values were not interpolated.

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



### 3.4.1 Urban or Rural Dispersion Characteristics

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

3.4.2 Deposition and Plume Depletion

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

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

3.4.3 Cutoff Distance for Modeling of Individual Blocks

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

3.4.4 Plant Boundary Assumptions

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



## 3.5 Modeling of Multiple Facilities

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

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

### 21 

# 4. Quality Assurance

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

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



## 4.1 Engineering Review

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

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



## 4.2 Geographic Checks

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

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

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

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

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

" any maximum TOSHI above 10

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

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

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

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

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

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

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

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### 24 

# 5. Uncertainties

This analysis is subject to a number of un certainties. For instance, model verification studies for AERMOD showed predicted maximu m annual concentrations ranging from 0.3 to 1.6 times measured values, with an average of 0.9. Predicted maximum short term (1 to 24 hours) concentrations were 0.25 to 2.5 times m easured values, with an average of 1.0. 16



### In addition, a number of simplifying assumptions have been made in this modeling analysis. First, the c oordinates reported by the Department of Census for Census block “internal points” have been used as a surrogate for long-term population exposures . Locations of actual residences have not been modeled. In addition, the current version of HEM3 does not take into account the movement of people from one Census block to another dur ing the course of their lives, or commuting patterns during a given day. Nor doe s the model take into account the attenuation of pollutant from outside emission sources in indoor air. Ideall y, risks to individuals would be modeled as they move through their communities and undertake different activities. However, such modeling is time- and resource-intensive and can only capture a portion of the uncertainty associated with the full range of human activities. In general, it is e xpected that long-term exposures will be overstated for high-end estimate s (as most individuals will not spend all their time at their highly affected residences), but ma y understate the total popul ation exposed (as some individuals living outside the m odeled area may regularly commute into the area for work or school).

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

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

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

### 25 In addition, meteorological observations are based on the closest station in the HEM3 meteorological library (see Figure 2-1). Altern ative meteorological st ations may be more appropriate for some facilities. Ideally, facility-specific meteor ological observations would be used. A single year of meteorological data (1 991) was selected for dispersion modeling. When considering off-site meteorological data most s ite specific dispersion modeling efforts will employ up to five years of data to capture variability in weathe r patterns from year to year. However, because of the large number of f acilities in the analysis and the extent of the dispersion modeling analysis (national scale), it was not practical to m odel five years of data. Other national studies such as NATA have also considered only a single year of meteorological data. A sensitivity analyses performed by the NATA assessment found that variability attr ibutable to the selection of the meteorology location/time (both temporal and spatial) resulted in a 17-84% variation in predicted concentrations at a given station 17



### .
Finally, risk and exposure fact ors are also subject to uncer tainty. Not all individuals experience the same degree of e xposure or internal dos e of a given pollutantdue to individualspecific parameters such as weight, age, and gend er. While the health benchmarks used in the analysis crudely account for sensitive populat ions, a prototypical human (e.g., body weight, ventilation rate) is used to define the benchmark. Because of the variability of these parameters in the population, this factor will resu lt in a degree of uncertainty in the resulting risk estimate.

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

! emissions inventory

! fate and transport modeling

! exposure assessment

! toxicity assessment

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

### 31 

# 6. References

1. EPA. 2004. AERMOD: Description of Model Formulation . EPA-454/R-03-004, U.S. Environmental Protection Agency, Research Triangle Park, NC. www.epa.gov/scram001/dispersion_prefrec.htm#aermod

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

17 

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

### Appendix 2 Meteorological Data for HEM-3 Modeling

### 2 

## DRAFT

METEOROLOGICAL DATA PROCESSING USING AERMET

FOR

HEM-3

Submitted by:

MACTEC Federal Programs, Inc.

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

July 28, 2006 Revisions May 14, 2012Meteorological Data Processing using AERMET July 28, 2006 For HEM-AERMOD



### i TABLE OF CONTENTS

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

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

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

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

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

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

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

LIST OF TABLES

Table

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

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

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

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

LIST OF FIGURES Figure

1 SURFACE AND UPPER AIR STATIONS ............................................................4Meteorological Data Processing using AERMET July 28, 2006 Rev. 05/12 For HEM-AERMOD



### 1 PROJECT DESCRIPTIO N AND BACKGROUND

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

METEOROLOGICAL DATA PROCESSING

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

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

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

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

TABLE 1

SURFACE STATIONS WITH PA IRED UPPER AIR STATION

SURFACE WBAN LAT LON UPPER AIR WBAN LAT LON Paducah, KY 03816 37.07 -88.77 Paducah, KY 03816 37.07 -88.77 Huntsville, AL 03856 34.65 -86.78 Nashville, TN 13897 36.13 -86.68 Huntington, WV 03860 38.37 -82.55 Huntington, WV 03860 38.37 -82.55 Greenville, SC 03870 34.90 -82.22 Athens, GA 13873 33.95 -83.32 Fort Worth, TX 03927 32.90 -97.02 Stephenville, TX 13901 32.22 -98.18 Wichita, KS 03928 37.65 -97.45 Topeka, KS 13996 39.07 -95.63 Lake Charles, LA 03937 30.12 -93.23 Lake Charles, LA 03937 30.12 -93.23 Jackson, MS 03940 32.32 -90.08 Jackson, MS 03940 32.32 -90.08Meteorological Data Processing using AERMET July 28, 2006 Rev. 05/12 For HEM-AERMOD



### 2 SURFACE WBAN LAT LON UPPER AIR WBAN LAT LON Kansas City, MO 03947 39.30 -94.73 Topeka, KS 13996 39.07 -95.63 San Juan, PR 11641 18.43 -66.02 San Juan, PR 11641 18.43 -66.00 Orlando, FL 12815 28.45 -81.32 Tampa, FL 12842 27.70 -82.40 Gainesville, FL 12816 29.68 -82.27 Waycross, GA 13861 31.25 -82.40 Tampa, FL 12842 27.97 -82.53 Ta mpa Bay, FL 12842 27.97 -82.53 W. Palm Beach, FL 12844 26.68 -80.10 W. Palm Beach, FL 12844 26.68 -80.12 Victoria, TX 12912 28.85 -96.92 Corp us Christi, TX 12924 29.77 -97.50 New Orleans, LA 12916 30.00 -90.25 Slidell, LA 53813 30.33 -89.82 Port Arthur, TX 12917 29.95 -94.02 Lake Charles, LA 03937 30.12 -93.23 Brownsville, TX 12919 25.90 -97.43 Brownsville, TX 12919 25.90 -97.43 San Antonio, TX 12921 29.53 -98.47 Del Rio, TX 22010 29.37 -100.92 Corpus Christi, TX 12924 27.77 -97.52 Corpus Christi, TX 12924 27.77 -97.52 Houston, TX 12960 29.98 -95.37 Lak e Charles, LA 03937 30.12 -93.23 Raleigh, NC 13722 35.87 -78.78 Greensboro, NC 13723 36.10 -79.95 Greensboro, NC 13723 36.08 -79.95 Greensboro, NC 13723 36.08 -79.95 Norfolk, VA 13737 36.90 -76.20 Wa llops Island, VA 93739 37.93 -75.47 Philadelphia, PA 13739 39.87 -75.23 Atlantic City, NJ 93755 39.75 -74.67 Richmond, VA 13740 37.50 -77.32 Sterling, VA 93734 38.98 -77.47 Roanoke, VA 13741 37.32 -79.97 Greensboro, NC 13723 36.10 -79.95 Wilmington, NC 13748 34.27 -77.90 Charleston, SC 13880 32.90 -80.03 Wilmington, DE 13781 39.67 -75.60 At lantic City, NJ 93755 39.75 -74.67 Meridian, MS 13865 32.33 -88.75 Jackson, MS 03940 32.32 -90.08 Charleston, WV 13866 38.38 -81.58 Huntington, WV 03860 38.37 -82.55 Atlanta, GA 13874 33.63 -84.45 Athens, GA 13873 33.95 -83.33 Birmingham, AL 13876 33.57 -86.75 C enterville, AL 03881 32.90 -87.25 Bristol, TN 13877 36.47 -82.40 Huntington, WV 03860 38.37 -82.55 Charleston, SC 13880 32.90 -80.03 Charleston, SC 13880 32.90 -80.03 Charlotte, NC 13881 35.22 -80.93 Greensboro, NC 13723 36.08 -79.95 Columbia, SC 13883 33.95 -81.12 Athens, GA 13873 33.95 -83.33 Jacksonville, FL 13889 30.50 -81.70 Waycross, GA 13861 31.25 -82.40 Memphis, TN 13893 35.05 -89.98 N. Little Rock, AR 03952 34.83 -92.27 Mobile, AL 13894 30.68 -88.25 Slidell, LA 53813 30.33 -89.82 Montgomery, AL 13895 32.30 -86.40 Centerville, AL 03881 32.90 -87.25 Nashville, TN 13897 36.13 -86.68 Nashville, TN 13897 36.13 -86.68 Shreveport, LA 13957 32.45 -93.82 Longview, TX 03951 32.35 -94.65 Little Rock, AR 13963 34.73 -92.23 N. Little Rock, AR 03952 34.83 -92.27 Wichita Falls, TX 13966 33.98 -98.50 Stephenville, TX 13901 32.22 -98.18 Oklahoma City, OK 13967 35.40 -97.60 Norman, OK 03948 35.23 -97.47 Baton Rouge, LA 13970 30.53 -91.15 Slidell, LA 53813 30.33 -89.82 Dodge City, KS 13985 37.77 -99.97 Dodge City, KS 13985 37.77 -99.97 St. Louis, MO 13994 38.75 -90.37 Peoria, IL 14842 40.67 -89.68 Springfield, MO 13995 38.82 -92.27 Monet, MO 03946 36.87 -93.97 Buffalo, NY 14733 42.93 -78.73 Buffalo, NY 14733 42.93 -78.73 Newark, NJ 14734 41.72 -75.17 Atl antic City, NJ 93755 39.75 -74.67 Albany, NY 14735 42.75 -73.82 Albany, NY 14735 42.75 -73.82 Allentown, PA 14737 40.65 -75.45 Albany, NY 14735 42.75 -73.82 Boston, MA 14739 42.37 -71.02 Chatham, MA 14684 41.67 -69.97Meteorological Data Processing using AERMET July 28, 2006 Rev. 05/12 For HEM-AERMOD



### 3 SURFACE WBAN LAT LON UPPER AIR WBAN LAT LON Hartford, CN 14740 41.93 -72.68 Albany, NY 14735 42.75 -73.80 Burlington, VT 14742 44.47 -73.15 Albany, NY 14735 42.75 -73.82 Portland, ME 14764 43.65 -70.30 Portland, ME 14764 43.65 -70.30 Providence, RI 14765 41.73 -71.43 Chatham, MA 14684 41.67 -69.97 Syracuse, NY 14771 43.12 -76.12 Buffalo, NY 14733 42.93 -78.73 Williamsport, PA 14778 41.27 -77.05 Pittsburgh, PA 94823 40.53 -80.23 Cleveland, OH 14820 41.40 -81.85 Pittsburgh, PA 94823 40.53 -80.23 Columbus, OH 14821 39.98 -82.88 Dayton, OH 13840 39.83 -84.05 Fort Wayne, IN 14827 41.00 -85.20 Dayton, OH 13840 39.83 -84.05 Lansing, MI 14836 42.78 -84.58 Flint, MI 14826 42.97 -83.75 Madison, WI 14837 43.13 -89.33 Green Bay, WI 14898 44.48 -88.13 Erie, PA 14860 42.08 -80.18 Buffalo, NY 14733 42.93 -78.73 Akron, OH 14895 40.92 -81.43 Pittsburgh, PA 94823 40.50 -80.23 Green Bay, WI 14898 44.50 -88.12 Green Bay, WI 14898 44.50 -88.12 Duluth, MN 14913 46.83 -92.22 Int'l Falls, MN 14918 48.57 -93.40 Minneapolis, MN 14922 44.88 -93.23 St. Cloud, MN 14926 45.55 -94.05 Moline, IL 14923 41.45 -90.52 Peoria, IL 14842 40.67 -89.68 Rochester, MN 14925 43.92 -92.50 St. Cloud, MN 14926 45.55 -94.08 Sioux Falls, SD 14944 43.57 -96.73 Huron, SD 14936 44.38 -98.22 Eau Claire, WI 14991 44.87 -91.48 St. Cloud, MN 14926 45.55 -94.08 Lihue, HI 22536 21.98 -159.33 Lihue, HI 22536 21.98 -159.33 Midland, TX 23023 31.95 -102.18 Midland, TX 23023 31.95 -102.18 El Paso, TX 23044 31.82 -106.38 El Paso, TX 23044 31.82 -106.38 Amarillo, TX 23047 35.22 -101.70 Amarillo, TX 23047 35.22 -101.70 Albuquerque, NM 23050 35.03 -106.62 Albuquerque, NM 23050 35.03 -106.62 Denver, CO 23062 39.77 -104.87 Denver, CO 23062 39.77 -104.88 Goodland, KS 23065 39.37 -101.70 Do dge City, KS 13985 37.77 -99.97 Grand Junction, CO 23066 39.12 -108.53 Grand Junction, CO 23066 39.12 -108.53 Tonopah, NV 23153 38.05 -117.08 Ely, NV 23154 39.30 -114.85 Tucson, AZ 23160 32.12 -110.93 Tucson, AZ 23160 32.12 -110.93 Daggett, CA 23161 34.87 -116.78 Desert Rock, NV 03160 36.62 -116.02 Los Angeles, CA 23174 33.93 -118.38 Miramar, CA 03190 32.87 -117.15 Prescott, AZ 23184 34.65 -112.43 Tucson, AZ 23160 32.12 -110.93 San Diego, CA 23188 32.73 -117.17 Miramar, CA 03190 32.87 -117.15 Sacramento, CA 23232 38.52 -121.50 Oakland, CA 23230 37.75 -122.22 San Francisco, CA 23234 37.62 -122.40 Oakland, CA 23230 37.72 -122.22 Bismarck, ND 24011 46.78 -100.75 Bismarck, ND 24011 46.78 -100.75 Cheyenne, WY 24018 41.15 -104.77 Denver, CO 23062 39.77 -104.88 Billings, MT 24033 45.80 -108.55 Gr eat Falls, MT 24143 47.47 -111.38 Casper, WY 24089 42.90 -106.47 Lander, WY 24021 42.82 -108.73 Rapid City, SD 24090 44.05 -103.07 Rapid City, SD 24090 44.05 -103.07 Salt Lake City, UT 24127 40.78 -111.97 Salt Lake City, UT 24127 40.78 -111.97 Great Falls, MT 24143 47.47 -111.38 Great Falls, MT 24143 47.47 -111.38 Spokane, WA 24157 47.62 -117.53 Spokane, WA 24157 47.62 -117.53 Medford, OR 24225 42.37 -122.87 Medford, OR 24225 42.37 -122.87 Portland, OR 24229 45.60 -122.60 Salem, OR 24232 44.90 -123.00 Salem, OR 24232 44.90 -123.00 Salem, OR 24232 44.90 -123.00Meteorological Data Processing using AERMET July 28, 2006 Rev. 05/12 For HEM-AERMOD



### 4 SURFACE WBAN LAT LON UPPER AIR WBAN LAT LON Seattle, WA 24233 47.45 -122.32 Quillayute, WA 94240 47.93 -124.55 Juneau, AK 25309 58.37 -134.58 Annette Is., AK 25308 55.03 -131.57 Cold Bay, AK 25624 55.20 -162.72 Cold Bay, AK 25624 55.20 -162.72 Fairbanks, AK 26411 64.80 -147.88 Fairbanks, AK 26411 64.80 -147.88 Valdez, AK 26442 61.13 -146.35 An chorage, AK 26409 61.22 -149.85 Anchorage, AK 26451 61.20 -150.00 Anchorage, AK 26409 61.22 -149.85 Nome, AK 26617 64.50 -165.43 Nome, AK 26617 64.50 -165.43 Colo. Springs 93037 38.82 -104.72 Denver, CO 23062 39.77 -104.88 Fresno, CA 93193 36.78 -119.72 Oakland, CA 23230 37.72 -122.22 Baltimore, MD 93721 39.18 -76.67 Sterling, VA 93734 38.98 -77.47 Sterling, VA 93738 38.95 -77.45 Sterling, VA 93734 38.98 -77.47 Evansville, IN 93817 38.05 -87.52 Paducah, KY 03816 37.05 -88.77 Indianapolis, IN 93819 39.73 -86.28 Dayton, OH 13840 39.83 -84.05 Lexington, KY 93820 38.03 -84.60 Dayton, OH 13840 39.83 -84.05 Louisville, KY 93821 38.18 -85.73 Dayton, OH 13840 39.83 -84.05 Springfield, IL 93822 39.85 -89.68 Peoria, IL 14842 40.67 -89.68 Pittsburgh, PA 94823 40.53 -80.23 Pittsburgh, PA 94823 40.50 -80.23 Toledo, OH 94830 41.58 -83.80 Flint, MI 14826 42.97 -83.73 Chicago, IL 94846 42.00 -87.93 Peoria, IL 14842 40.67 -89.68 Detroit, MI 94847 42.23 -83.33 Flint, MI 14826 42.97 -83.75



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

FIGURE 1. SURFACE AND UPPER AIR STATIONSMeteorological Data Processing using AERMET July 28, 2006 Rev. 05/12 For HEM-AERMOD



### 5Meteorological Data Processing using AERMET July 28, 2006 Rev. 05/12 For HEM-AERMOD



### 6 MISSING DATA

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

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

TABLE 2

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

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

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

Dayton, OH 13840 Paducah, KY 03826

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

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

Peoria, IL 14842 Omaha, NE 94918 Pittsburgh, PA 94823 Huntington, WV 03860 Salt Lake City, UT 24127 Ely, NV 23154 St. Cloud, MN 14926 Huron, SD 14936Meteorological Data Processing using AERMET July 28, 2006 Rev. 05/12 For HEM-AERMOD



### 7 ANEMOMETER HEIGHTS

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

SURFACE CHARACTERISTICS

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

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

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

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

The surface roughness not only includes runways and areas between runways, but usually terminal buildings and other airport structur es. In addition, in urban environments, offairport structures often are within 3 kilometers of the measurement site. This combination of land covers suggests a valu e of 0.2 – 0.3 meters is appropriate. MACTEC used 0.25 for the roughness length.Meteorological Data Processing using AERMET July 28, 2006 Rev. 05/12 For HEM-AERMOD



### 8 TABLE 3

SURFACE CHARACTERISTICS

MODIFICATIONS TO AERMET CODE

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

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

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

RESULTS

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

ADDENDUM – May 2012 Since development of the original meteorologi cal data set (122 stat ions) discussed above, additional sites have been added for Risk and Technology Review (RTR) analyses. These include meteorological data provided by state and local agencies, as well as additional sites processed by EPA risk staff to better represent dispersion conditions at Albedo 0.15

Bowen Ratio 1.0

Surface Roughness (m) 0.25Meteorological Data Processing using AERMET July 28, 2006 Rev. 05/12 For HEM-AERMOD



### 9 specific facility locations. In each case, data were processed using the latest available version of AERMET and processing approach outlined on the USEPA’s SCRAM website. A listing of these additiona l sites is provided in Table 5.Meteorological Data Processing using AERMET July 28, 2006 Rev. 05/12 For HEM-AERMOD



### 10 TABLE 4

PERCENT CALM AND MISSING BY STATION

WBAN Station Calms % Calm Missing % Missing Total % Total 03816 Paducah, KY 820 9.4% 66 0.75% 886 10.1% 03856 Huntsville, AL 719 8.2% 0 0.00% 719 8.2% 03860 Huntington, WV 340 3.9% 28 0.32% 368 4.2% 03870 Greenville, SC 810 9.2% 20 0.23% 830 9.5% 03927 Fort Worth, TX 508 5.8% 24 0.27% 532 6.1% 03928 Wichita, KS 215 2.5% 0 0.00% 215 2.5% 03937 Lake Charles, LA 616 7.0% 15 0.17% 631 7.2% 03940 Jackson, MS 1228 14.0% 58 0.66% 1286 14.7% 03947 Kansas City, MO 231 2.6% 0 0.00% 231 2.6% 11641 San Juan, PR 670 7.6% 11 0.13% 681 7.8% 12815 Orlando, FL 144 1.6% 0 0.00% 144 1.6% 12816 Gainesville, FL 952 10.9% 11 0.13% 963 11.0% 12842 Tampa, FL 572 6.5% 0 0.00% 572 6.5% 12844 W. Palm Beach, FL 566 6.5% 8 0.09% 574 6.6% 12912 Victoria, TX 320 3.7% 0 0.00% 320 3.7% 12916 New Orleans, LA 721 8.2% 8 0.09% 729 8.3% 12917 Port Arthur, TX 736 8.4% 10 0.11% 746 8.5% 12919 Brownsville, TX 418 4.8% 36 0.41% 454 5.2% 12921 San Antonio, TX 913 10.4% 11 0.13% 924 10.5% 12924 Corpus Christi, TX 371 4.2% 0 0.00% 371 4.2% 12960 Houston, TX 1097 12.5% 13 0.15% 1110 12.7% 13722 Raleigh, NC 810 9.2% 0 0.00% 810 9.2% 13723 Greensboro, NC 542 6.2% 24 0.27% 566 6.5% 13737 Norfolk, VA 598 6.8% 39 0.45% 637 7.3% 13739 Philadelphia, PA 154 1.8% 2 0.02% 156 1.8% 13740 Richmond, VA 605 6.9% 0 0.00% 605 6.9% 13741 Roanoke, VA 1287 14.7% 1 0.01% 1288 14.7% 13748 Wilmington, NC 976 11.1% 0 0.00% 976 11.1% 13781 Wilmington, DE 837 9.6% 0 0.00% 837 9.6% 13865 Meridian, MS 1567 17.9% 56 0.64% 1623 18.5% 13866 Charleston, WV 1257 14.3% 29 0.33% 1286 14.7% 13874 Atlanta, GA 309 3.5% 21 0.24% 330 3.8% 13876 Birmingham, AL 1228 14.0% 15 0.17% 1243 14.2% 13877 Bristol, TN 2541 29.0% 28 0.32% 2569 29.3% 13880 Charleston, SC 487 5.6% 0 0.00% 487 5.6% 13881 Charlotte, NC 985 11.2% 0 0.00% 985 11.2% 13883 Columbia, SC 1340 15.3% 20 0.23% 1360 15.5% 13889 Jacksonville, FL 1134 12.9% 12 0.14% 1146 13.1% 13893 Memphis, TN 809 9.2% 0 0.00% 809 9.2% 13894 Mobile, AL 527 6.0% 8 0.09% 535 6.1% 13895 Montgomery, AL 1027 11.7% 19 0.22% 1046 11.9% 13897 Nashville, TN 87 1.0% 0 0.00% 87 1.0% 13957 Shreveport, LA 1088 12.4% 11 0.13% 1099 12.5%Meteorological Data Processing using AERMET July 28, 2006 Rev. 05/12 For HEM-AERMOD



### 11 WBAN Station Calms % Calm Missing % Missing Total % Total 13963 Little Rock, AR 1090 12.4% 0 0.00% 1090 12.4% 13966 Wichita Falls, TX 622 7.1% 23 0.26% 645 7.4% 13967 Oklahoma City, OK 158 1.8% 0 0.00% 158 1.8% 13970 Baton Rouge, LA 787 9.0% 9 0.10% 796 9.1% 13985 Dodge City, KS 30 0.3% 32 0.37% 62 0.7% 13994 St. Louis, MO 416 4.7% 26 0.30% 442 5.0% 13995 Springfield, MO 212 2.4% 39 0.45% 251 2.9% 14733 Buffalo, NY 181 2.1% 0 0.00% 181 2.1% 14734 Newark, NJ 197 2.2% 0 0.00% 197 2.2% 14735 Albany, NY 1217 13.9% 8 0.09% 1225 14.0% 14737 Allentown, PA 465 5.3% 9 0.10% 474 5.4% 14739 Boston, MA 36 0.4% 38 0.43% 74 0.8% 14740 Hartford, CN 204 2.3% 8 0.09% 212 2.4% 14742 Burlington, VT 155 1.8% 9 0.10% 164 1.9% 14764 Portland, ME 381 4.3% 25 0.29% 406 4.6% 14765 Providence, RI 215 2.5% 39 0.45% 254 2.9% 14771 Syracuse, NY 481 5.5% 0 0.00% 481 5.5% 14778 Williamsport, PA 1403 16.0% 24 0.27% 1427 16.3% 14820 Cleveland, OH 271 3.1% 27 0.31% 298 3.4% 14821 Columbus, OH 734 8.4% 0 0.00% 734 8.4% 14827 Fort Wayne, IN 440 5.0% 0 0.00% 440 5.0% 14836 Lansing, MI 632 7.2% 10 0.11% 642 7.3% 14837 Madison, WI 685 7.8% 30 0.34% 715 8.2% 14860 Erie, PA 286 3.3% 4 0.05% 290 3.3% 14895 Akron, OH 232 2.6% 28 0.32% 260 3.0% 14898 Green Bay, WI 414 4.7% 28 0.32% 442 5.0% 14913 Duluth, MN 336 3.8% 0 0.00% 336 3.8% 14922 Minneapolis, MN 311 3.6% 3 0.03% 314 3.6% 14923 Moline, IL 812 9.3% 24 0.27% 836 9.5% 14925 Rochester, MN 10 0.1% 0 0.00% 10 0.1% 14944 Sioux Falls, SD 492 5.6% 0 0.00% 492 5.6% 14991 Eau Claire, WI 1193 13.6% 0 0.00% 1193 13.6% 22536 Lihue, HI 6 0.1% 0 0.00% 6 0.1% 23023 Midland, TX 207 2.4% 19 0.22% 226 2.6% 23044 El Paso, TX 262 3.0% 0 0.00% 262 3.0% 23047 Amarillo, TX 113 1.3% 6 0.07% 119 1.4% 23050 Albuquerque, NM 606 6.9% 0 0.00% 606 6.9% 23062 Denver, CO 530 6.1% 0 0.00% 530 6.1% 23065 Goodland, KS 176 2.0% 41 0.47% 217 2.5% 23066 Grand Junction, CO 295 3.4% 15 0.17% 310 3.5% 23153 Tonopah, NV 341 3.9% 11 0.13% 352 4.0% 23160 Tucson, AZ 154 1.8% 24 0.27% 178 2.0% 23161 Daggett, CA 616 7.0% 11 0.13% 627 7.2% 23174 Los Angeles, CA 944 10.8% 21 0.24% 965 11.0% 23184 Prescott, AZ 376 4.3% 23 0.26% 399 4.6% 23188 San Diego, CA 473 5.4% 21 0.24% 494 5.6% 23232 Sacramento, CA 1324 15.1% 77 0.88% 1401 16.0%Meteorological Data Processing using AERMET July 28, 2006 Rev. 05/12 For HEM-AERMOD



### 12 WBAN Station Calms % Calm Missing % Missing Total % Total 23234 San Francisco, CA 376 4.3% 77 0.88% 453 5.2% 24011 Bismarck, ND 676 7.7% 22 0.25% 698 8.0% 24018 Cheyenne, WY 110 1.3% 0 0.00% 110 1.3% 24033 Billings, MT 273 3.1% 25 0.29% 298 3.4% 24089 Casper, WY 138 1.6% 37 0.42% 175 2.0% 24090 Rapid City, SD 736 8.4% 33 0.38% 769 8.8% 24127 Salt Lake City, UT 581 6.6% 22 0.25% 603 6.9% 24143 Great Falls, MT 111 1.3% 26 0.30% 137 1.6% 24157 Spokane, WA 537 6.1% 36 0.41% 573 6.5% 24225 Medford, OR 1937 22.1% 0 0.00% 1937 22.1% 24229 Portland, OR 1180 13.5% 0 0.00% 1180 13.5% 24232 Salem, OR 1799 20.5% 0 0.00% 1799 20.5% 24233 Seattle, WA 66 0.8% 14 0.16% 80 0.9% 25309 Juneau, AK 1781 20.3% 6 0.07% 1787 20.4% 25624 Cold Bay, AK 119 1.4% 41 0.47% 160 1.8% 26411 Fairbanks, AK 1393 15.9% 0 0.00% 1393 15.9% 26442 Valdez, AK 661 7.5% 2 0.02% 663 7.6% 26451 Anchorage, AK 724 8.3% 0 0.00% 724 8.3% 26617 Nome, AK 358 4.1% 24 0.27% 382 4.4% 93037 Colo. Springs 195 2.2% 0 0.00% 195 2.2% 93193 Fresno, CA 1578 18.0% 76 0.87% 1654 18.9% 93721 Baltimore, MD 361 4.1% 3 0.03% 364 4.2% 93738 Sterling, VA 995 11.4% 0 0.00% 995 11.4% 93817 Evansville, IN 1434 16.4% 66 0.75% 1500 17.1% 93819 Indianapolis, IN 190 2.2% 0 0.00% 190 2.2% 93820 Lexington, KY 234 2.7% 0 0.00% 234 2.7% 93821 Louisville, KY 385 4.4% 0 0.00% 385 4.4% 93822 Springfield, IL 287 3.3% 25 0.29% 312 3.6% 94823 Pittsburgh, PA 493 5.6% 26 0.30% 519 5.9% 94830 Toledo, OH 898 10.3% 13 0.15% 911 10.4% 94846 Chicago, IL 343 3.9% 38 0.43% 381 4.3% 94847 Detroit, MI 468 5.3% 11 0.13% 479 5.5%Meteorological Data Processing using AERMET July 28, 2006 Rev. 05/12 For HEM-AERMOD



### 13 TABLE 5. ADDITIONAL METEOROLOGICAL STATIONS ADDED FOR RTR ANALYSES

City State Year Fairbanks AK 2001 Anaheim CA 2007
Azusa CA 2007 Banning Airport CA 2007 Burbank CA 2007 Central LA CA 2007
Costa Mesa CA 2007 Crestline CA 2007
Fontana CA 2007 Indio CA 2007
La Habra CA 2007 Lake Elsinore CA 2007
LAX CA 2007 Long Beach CA 2007 Lynnwood CA 2007
Mission Viejo CA 2007 Palm Springs CA 2007
Perris CA 2007
Pico Rivera CA 2007
Pomona CA 2007 Redlands CA 2007 Reseda CA 2007 Riverside CA 2007
San Bernardino CA 2007
Santa Clarita CA 2007
Santa Monica CA 2009 Upland CA 2007
Van Nuys Airport CA 2009
West LA CA 2007 Apalachicola FL 2005
Ft. Myers FL 2005 Gainesville FL 2005 Tallahassee FL 2005 Burlington IA 2004 Cedar Rapids IA 2004
Des Moines IA 2004 Dubuque IA 2004
Mason City IA 2004
Sioux City IA 2004Meteorological Data Processing using AERMET July 28, 2006 Rev. 05/12 For HEM-AERMOD



### 14 City State Year Waterloo IA 2004 Moline IL 2004
Iron Mountain MI 1999 Duluth MN 1999 Red Wing MN 1999 Herculaneum MO 1998 Omaha NE 2004
Sioux Falls SD 2004 Clintonville WI 1999 Eagle River WI 1999
Eau Claire WI 1999
Green Bay WI 1999 Hayward WI 1999
Juneau WI 1999
La Crosse WI 1999 Madison WI 1999
Manitowoc WI 1999 Marshfield WI 1999 Milwaukee WI 1999 Mineral Point WI 1999
Mosinee WI 1999 Oshkosh WI 1999 Rhinelander WI 1999 Sturgeon Bay WI 1999
Wausau WI 1999
Wisconsin Dells WI 1999
Wisconsin Rapids WI 1999 Parkersburg WV 2011

### 1 

## Appendix 3 :

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

### 2 

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

Ted Palma Roy Smith EPA/OAQPS/SBAG

1. Introduction

1.1. The problem

The process of listing hazardous air pollutants (HAPs) provided by the Clean Air Act (CAA, section 112(b)(2)) explicitly includ es acute toxicity as a listing criterion. For this reason, in addition to chronic exposures, EPA considers ac ute exposures in risk-based decision-making for the HAP regulatory program. Estimating acute exposures via dispersion modeling requires input data on hourly meteorological c onditions (available for most ar eas of the US) and short-term emission rates of individual facilities (almost universally absent from the National Emissions Inventory (NEI), the Toxic Release Invent ory (TRI), and state emission databases).

Lacking short-term emission rates, we must estimate peak short-term rates based on annual average rates, which are available. For Ri sk and Technology Review (RTR) rulemakings, we have assumed that the 1-hour emission rate for each facility could exceed the annual average hourly emission rate by as much as tenfold, and fu rther assumed that this tenfold emission spike could coincide with worst-case meteorological conditions and the presen ce of a human receptor at the facility boundary, as a means of screen ing for potentially signifi cant acute exposures.

In a consultation on the “RTR Assessment Plan”, a panel of the EPA’s Science Advisory Board (SAB), several reviewers questioned the appropri ateness of the factor of ten; some even suggested that this tenfold a ssumption may underestimate actual maximum short-term emissions for some facilities, and thereby also unde restimate maximum acute risks. The SAB recommended an analysis of available short-term emissions data for HAP to test this assumption. This analysis responds to that SAB recommendation and attempts to test th e protectiveness of the tenfold assumption using a database of “event emissions” collected from facilities in the Houston-Galveston area, to compare events repres entative of HAP releases to long-term release rates. We welcome comments from the public on the methods used and the conclusions reached by this analysis.

2. Methods

2.1. Texas Commission on Environmental Qu ality event emissions database

The Texas Commission on Environmental Quality (TCEQ) collects emissions data using online reporting required of any facility releasing 100 pounds or more of a listed chemical (primarily ozone-forming VOCs) during a non-r outine event. The TCEQ data are intended to improve the state’s knowledge of how short-term releases affect tropospheric ozone levels in that area. The database we utilized in our analysis was a subset of the TCEQ data covering emission events that

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

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

Although some of the released s ubstances were HAPs, this was incidental to the database’s primary purpose of enhancing the TCEQ’s knowledge of photochemical activity. Thus, more than 80% of the released ma ss was ethene and propene, neither of which are HAPs. The database included release events caused by accidents, equipment fa ilures, maintenance, startup, and shutdown. It also contained facility names, information on amounts of individual compounds released. To provide a basis for co mparing the event releases with “typical” emissions, the UTA staff included total VOC emissions data for each facility for calendar year 2004, obtained from the EPA Toxic Release Inventory (TRI). The database did not contain any records for facilities that did not experien ce any reportable events during this period.

2.2. Data filtering

Because the event release data were intended for modeling short-term releases of ozoneproducing VOCs, the database includes releas es from accidents (which are regulated under section 112(r) of the CAA and are therefore not considered in residual risk assessments) and releases of light hydrocarbon compounds that ar e not HAPs and are much more volatile than most HAPs. This intent of this analysis, on the other hand, was to evaluate short-term releases of HAPs due to normal process variability or sche duled startups, shutdowns, and maintenance, relative to long-term release rates. Because the full emi ssion events database was not representative of likely HAP em issions normally considered under the residual risk program, we filtered the release data as follows in an attempt to improve its representativeness:

1. Hydrocarbons of C5 or less were droppe d, except that all HAPs (including non-VOCs) were retained regardless of molecular structure;

2. Accidental releases were dropped, but all others (including st artup, shutdown, and maintenance) were retained;

3. Only facilities whose long-term VOC releases exceeded 0.068 tons per day (25 tons per year) were retained, to approximate the populat ion of facilities likely to be subject to residual risk standards (i .e., major facilities);

4. A few release records had to be dropped because their facility number s did not link to any facility in the database;

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

2.3. Analysis

### 4 Annual VOC emissions and emission event release data were both converted to lb/hr. In order to conform to our atmospheric dispersion models, which estimate ambient concentrations for periods of 1 hour or more, amounts released duri ng events shorter than 1 hour were assigned to the whole hour. For example, a release of 100 lb in ten minutes was converted to 100 lb/hr. Events longer than 1 hour were converted norm ally, e.g., a release of 100 lb in 120 minutes was converted to 50 lb/hr. The ev ent release rates for individual compounds were summed, yielding a total release rate for each event. This to tal release rate for each event was divided by the annual VOC release rate for the facility to derive the ratio of peak-to-mean emission rate for the
event.

3. Results and Discussion

3.1. Database filtering

The original database contained 505 individual contaminants, including multiple redundancies. These redundancies did not affect this analysis, so we did not re solve them. After filtering out light, non-HAP, VOCs, 317 contaminants remained (Table 1).

The database contained release records for 150 unique facilities. Of these, 48 facilities (Table 2) were major VOC emitters that reported releases of at least one of the contaminants in Table 1.

The database contained 3641 individual release events reported by the original 150 facilities. Of these, 319 events involved a Table 1 contaminant released by a Table 2 facility during startup, shutdown, or maintenance. For evaluating short- term releases for residual risk assessments, these 319 events comprise the most representative subset of the full database.

3.2. Descriptive statistics

For this subset of emission events, ratios of even t release rate to long-te rm release rate varied from 0.00000004 to 74. Distribution statistics appear in Tables 3 and 4. The 99 th 

### percentile ratio was 9 (i.e., an event release rate nine times the long-term average). Only 3 ratios exceeded our default assumption of 10, and of these only one exceeded 11. The full cumulative probability density of the ratios is shown in Figure 1.

Figure 2 shows the relationship between ratio and event duration. As expected, the ratio declined as duration increased. Only 18 events lasted less than 2 hour s, but these events produced the three highest ratios. Figure 3 is a similar ratio vs. duration plot, but with duration as a percentage of total time. Only 35 events exceeded 1% of the to tal period covered by the database. Figure 4 shows the relationship betwee n ratio and total amount released, and suggests that the highest ratios were pr oduced by facilities whose routine VOC emissions were relatively small. Thus, the events themselves also tended to be relatively small in absolute terms.

3.3. Discussion

These results suggest that the te nfold ratio assumption for short-te rm releases is protective, and that the facilities for which it may underestimate event releases may tend to be smaller emitters.

However, this analysis is limited in the followi ng ways by the nature of the database and the filtering that we applied:

### 5 1. The only long-term release data available fr om the database were total VOC emissions for 2004. Ideally, we would have preferred to have routine release rates for each individual contaminant. However, retrievi ng these data from other sources and linking them to this database was not feasible.

2. Removing VOCs that are not representative of HAPs, and comparing the releases against all VOCs, would tend to underestimate the true ra tios. This effect could be quantitatively large.

3. Retaining HAPs that are not VOCs (such as toxic metals), and including them in the total to be compared against all VOCs, would tend to overestimate the true ratios. The size of this effect is not known, but seems li kely to be less than for (2) above.

4. The database contains only facilities that had at least one re lease event during the reporting period. The number of facilities in the statistical popul ation that did not experience an event is not known. The lack of data for these facilities (whose ratios in this analysis would have been zero) would cause the descript ive statistics to be skewed toward an overestimate. The size of this effect is unknown.

Table 1. Event emissions in the Houston-Galveston area. Representative contaminants included in the analysis, selected because they are either HAPs or VOCs with more than 5 carbon atoms. (These data were retrieved directly from t he original database, which included multiple redundancies that did not affect the analysis and were left intact.)
Contaminant HAP CAS SAROAD 2-Methyloctane No 3221-61-2 90008 2-Methylpentane No 107-83-5 43229 2-methylhexane No 591-76-4 43263 2-Methylpentane No 107-83-5 43229 2,2,3-Trimethylpentane No 564-02-3 2,2,4-Trimethylpentane Yes 540-84-1 43250 dimethyl butane No 75-83-2 43291 2,3-Dimethylbutane No 79-29-8 43276 2,3,4-Trimethylpentane No 565-75-3 43252 2,3-Dimethylbutane No 79-29-8 43276 2,4-Dimethylpentane No 108-08-7 43247 2-methylheptane No 592-27-8 43296 2-methylhexane No 591-76-4 43263 2-Methylpentane No 107-83-5 43229 3-Methylhexane No 589-34-4 43295 3-Methylpentane No 96-14-0 43230 3-Methylhexane No 589-34-4 43295 3-Methylpentane No 96-14-0 43230 3-Methylheptane No 589-81-1 43253 3-Methylhexane No 589-34-4 43295 3-Methylpentane No 96-14-0 43230

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

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

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

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

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

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

### 12 Table 1. Event emissions in the Houston-Galveston area. Representative contaminants included in the analysis, selected because they are either HAPs or VOCs with more than 5 carbon atoms. (These data were retrieved directly from t he original database, which included multiple redundancies that did not affect the analysis and were left intact.)
Contaminant HAP CAS SAROAD 1,2-Epoxybutane Yes 106-88-7 Methylamine No 74-89-5 1,1-Dimethylcyclohexane No 590-66-9 1,1-Dimethylcyclopentane No 1638-26-2 2-Methylpentane No 107-83-5 43229 dimethyl butane No 75-83-2 43291 2,3,3-Trimethylpentane No 560-21-4 2,3-Dimethylhexane No 584-94-1 2,3-Dimethylpentane No 565-59-3 2,4-Dimethylhexane No 589-43-5 2,5-Dimethyl-hexane No 592-13-2 2-Butoxy ethanol Yes 111-76-2 43308 2-mercaptoethanol No 60-24-2 Bisphenol A No 80-05-7 straight-run mi ddle distillate No 64741-44-2 4-Vinylcyclohexene No 100-40-3 straight-run mi ddle distillate No 64741-44-2 Allyl alcohol No 107-18-6 xylenes Yes 1330-20-7 45102 Naphthalene Yes 91-20-3 46701 3-Methylethylcyclohexane No
VOC No none Gasoline No 86290-81-5 Butyl ether No 142-96-1 dimethyl butane No 75-83-2
Dodecene No 25378-22-7 Styrene Yes 100-42-5 45220 tetrahydrofuran No 109-99-9 70014
hexane Yes 110-54-3 43231 2-Propanol No 67-63-0 43304 liquified petroleum gas No 68476-85-7 Methyl acetylene propadiene No methyl isobutyl ketone Yes 108-10-1 Methyl n-amyl ketone No 110-43-0 43562 Methylpentane No 43133-95-5 Tert-butyl methyl ether Yes 1634-04-4 43376
Toluene Yes 108-88-3 45202
Mineral oil No 8012-95-1
Gasoline No 86290-81-5 2,2-Dimethylpropane No 463-82-1 43222 n-propylbenzene No 103-65-1

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

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

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

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

EQUISTAR CHEMICALS CHANNELVIEW COMPLEX 275.4 EQUISTAR CHEMICALS CHOCOLATE BAYOU COMPLEX 84.87

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

EXXONMOBIL CHEMICAL MONT BELVIEU PLASTICS PLANT 40.64

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

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

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

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

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

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

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



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

Cumulative probability of event ratios

0 50 100 150 200 250 300 350

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

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

Event ratio vs. duration

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

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



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

Event ratio vs. duration

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

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

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

Event ratio vs. 2004 VOC releases -- by event

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

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

### App e ndix 4 D e tailed Ri s k Mod e l i ng R e sul t sT a b l e 1

- F ac ili ty I d e n t i f i cat i o n I n f o r mat i o n

S ou r ce C ate go r y F ac ili ty N EI ID F ac ili ty N ame Add r ess C i ty State Pho s pha t e F er

t ili z er Produ ct ion and Pho s phoric A c id M anu f a ct uring P H O_ N E I 23655 J. R

. S IM PLOT C O M PA N Y 16777 H O W LA N D R OAD LA THR OP C A
Pho s pha t e F er

t ili z er Produ ct ion and Pho s phoric A c id M anu f a ct uring P H O_ N E I 26584 J R S IM PLOT C O M PA N Y D ON S I D I N G C O M PLEX 1150 W

. H I G H W AY 30 PO C A T ELLO I D
Pho s pha t e F er

t ili z er Produ ct ion and Pho s phoric A c id M anu f a ct uring P H O_ N E I 387 SF P H OSP H A T ES, I N C 515 SO UT H H I G H W AY 430 R O C K SP R I N GS W Y
Pho s pha t e F er

t ili z er Produ ct ion and Pho s phoric A c id M anu f a ct uring P H O_ N E I 45571 P C S P H OSP H A T E C O M PA N Y I NC

. - A UR O R A N C 306 N O RT H A UR O R A N C
Pho s pha t e F er

t ili z er Produ ct ion and Pho s phoric A c id M anu f a ct uring P H O_ N E I 7891 AG R I F OS L L C 2001 J A C KSON R D PASA D E N A T X
Pho s pha t e F er

t ili z er Produ ct ion and Pho s phoric A c id M anu f a ct uring PHO_NE I F L0570005 CF I NDUSTR I ES, I NC

. , PLANT C I TY PHOS 10608 PA U L BUCH M A N H W Y P LAN T C I TY FL
Pho s pha t e F er

t ili z er Produ ct ion and Pho s phoric A c id M anu f a ct uring P H O_ N E I F L0570008 M OSA I C F E RT I L I Z E R

, LLC 8813 H W Y. 41 SO UT H R I VE R V I EW F L Pho s pha t e F er

t ili z er Produ ct ion and Pho s phoric A c id M anu f a ct uring PHO_NE I F L1050052 CF I NDUSTR I ES, I NC. 2501 BONN I E MI NE RD BARTOW F L
Pho s pha t e F er

t ili z er Produ ct ion and Pho s phoric A c id M anu f a ct uring P H O_ N E I F L12286 C A R G I LL F E RT I L I Z E R

, I NC

. 3600 H I G H W AY 60 W EST BA RT OW F L
Pho s pha t e F er

t ili z er Produ ct ion and Pho s phoric A c id M anu f a ct uring P H O_ N E I F L T $4056 W H I T E SP R I N GS AG R I CU L TUR AL CH E MI C ALS ,I N C E OF C R 137, E OF U S 41 W H I T E SP R I N GS F L
Pho s pha t e F er

t ili z er Produ ct ion and Pho s phoric A c id M anu f a ct uring P H O_ N E IM S059305 MI SS I SS I PPI P H OSP H A T ES C O R PO R A T I ON 601 H I G H W AY 611 PAS C AGO U LA M S Pho s pha t e F er

t ili z er Produ ct ion and Pho s phoric A c id M anu f a ct uring P H O_ N E I RF L050053 C A R G I LL F E RT I L I Z E R

, I NC

. 4390 C R 640 W EST BA RT OW F L
Pho s pha t e F er

t ili z er Produ ct ion and Pho s phoric A c id M anu f a ct uring P H O_ N E I RF L050055 IM C P H OSP H A T ES C O M PA N Y 7450 H I G H W AY 630 M U LBE RR Y F L
Pho s pha t e F er

t ili z er Produ ct ion and Pho s phoric A c id M anu f a ct uring P H O_ N E I RF L050059 IM C P H OSP H A T ES C O M PA N Y 3095 H I G H W AY 640 M U LBE RR Y F L
Pho s phoric A c id M anu f

a ct uring PHO_NE I LA0930004 IM C PH O SPHATES CO / UNCLE SAM PLNT H I GH W AY 44 UNCLE SAM LA Pho s pha t e F er

t ili z er Produ ct ion PHO_NE I LA0930005 IM C PH O SPHATES CO / F AUS T I N A PLNT H I GH W AY 44 UNCLE SAM LA Pho s phoric A c id M anu f

a ct uring P H O_ N E I 6147 P C S N I TR OGEN F E RT I L I Z E R

, L

. P ./ GE I S M AR H W YS 3115 & 30 GE I S M AR LACancer MIR Cancer

Incidence Noncancer

Max HI Target

Organ Cancer

MIR Noncancer

Max HI Target

Organ SC % of

Facility-wide Cancer Risk

12057NEIFL0570005 4.6E-07 7.3E-04 1.6E-03 respiratory 5.4E-07 1.2E-01 skeletal 84% 12047NEIFLT$4056 1.8E-08 4.2E-06 7.8E-05 skeletal 1.1E-07 3.6E-02 skeletal 17%
12057NEIFL0570008 1.0E-07 1.8E-04 3.1E-03 skeletal 1.9E-07 2.4E-01 skeletal 54%
12105NEIFL12286 2.2E-08 4.2E-05 6.6E-04 skeletal 6.2E-08 6.4E-02 skeletal 36%
12105NEIRFL050059 6.4E-08 9.5E-05 2.1E-03 skeletal 9.1E-08 6.1E-02 skeletal 70%
16029NEIIDR029000 2.5E-09 2.4E-08 8.9E-04 skeletal 1.4E-08 3.1E-02 skeletal 18%
16077NEI26584 2.0E-07 6.8E-06 1.2E-03 kidney 3.1E-07 2.9E-02 skeletal 66%
22093NEILA0930005 1.2E-07 4.2E-05 5.4E-04 developmental 1.2E-07 5.4E-04 developmental 97%

28059NEIMS059305 4.0E-07 5.9E-05 3.4E-03 kidney 4.8E-07 1.8E-01 skeletal 83%
37013NEI45571 3.3E-08 8.2E-06 3.2E-04 kidney 2.0E-07 3.8E-02 skeletal 17%
56037NEI387 5.6E-09 7.7E-07 1.8E-05 skeletal 2.5E-08 1.0E-02 skeletal 22%

Cancer MIR Cancer

Incidence Noncancer

Max HI Target

Organ Cancer

MIR Noncancer

Max HI Target

Organ SC % of

Facility-wide Cancer Risk 12047NEIFLT$4056 9.5E-08 1.5E-05 3.6E-02 skeletal 1.1E-07 3.6E-02 skeletal 89%
37013NEI45571 8.9E-08 1.9E-05 3.8E-02 skeletal 2.0E-07 3.8E-02 skeletal 44%
28059NEIMS059305 8.3E-08 6.8E-06 1.7E-01 skeletal 4.8E-07 1.8E-01 skeletal 17%
12057NEIFL0570005 8.2E-08 7.4E-05 4.8E-02 skeletal 5.4E-07 1.2E-01 skeletal 15%
16077NEI26584 4.2E-08 9.6E-07 2.5E-02 skeletal 3.1E-07 2.9E-02 skeletal 14%
12105NEIRFL050059 2.8E-08 1.6E-05 5.8E-02 skeletal 9.1E-08 6.1E-02 skeletal 31%
22005NEI6147 2.3E-08 1.1E-05 3.7E-02 skeletal 2.4E-08 2.3E-01 respiratory 94%
56037NEI387 2.0E-08 2.67E-06 1.04E-02 skeletal 2.5E-08 1.0E-02 skeletal 78%
12057NEIFL0570008 1.1E-08 1.3E-05 2.3E-01 skeletal 1.9E-07 2.4E-01 skeletal 6%
12105NEIFL12286 1.0E-08 8.4E-06 6.4E-02 skeletal 6.2E-08 6.4E-02 skeletal 16%
16029NEIIDR029000 6.4E-09 4.9E-08 3.8E-02 skeletal 1.4E-08 3.1E-02 skeletal 46%
22093NEILA0930004 2.9E-09 4.1E-07 8.8E-02 skeletal 1.6E-08 9.0E-02 skeletal 18%

1. BOLD indicates a cancer risk greater than 1 in a million or a noncancer risk greater than 1 2. Facility cancer and non-cancer risks represent MIR at census block locations using Draft 2005 NATA. Table 2 – Maximum Predicted HEM-3 Chronic Risks

Facility NEI ID Phosphate Fertilizer Production Chronic Risk 1 Facility-Wide Chronic Risk 1, 2

Facility NEI ID Phosphoric Acid Manufacturing Chronic Risk 1 Facility-Wide Chronic Risk 1, 2

## Appendix 5

Technical Support Document for TRIM

- Based Multipathway Tiered Screening

Scenario for RTR: Summar y of Approach and Evaluation

## 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 T iered Screening Methodology for RTR



## C ONTENTS

Technical Support Document for the TRIM

- Based Multipathway Tiered Screening Methodology for RTR

................................ ................................ ................................ ........... i

1. Introduction and Background

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

1.1 Tier 1

................................ ................................ ................................ ......................... 2 1.1.1 Chemicals of Concern

................................ ................................ ................ 2 1.1.2 Development of Emission Thresholds

................................ ......................... 4

1.2 Tier 2

................................ ................................ ................................ ......................... 7

1.3 Tier 3

................................ ................................ ................................ ....................... 10

1.4 Site

- Specific Assessment

................................ ................................ ........................ 11

2. Tier 1 Methodology

................................ ................................ ................................ ........... 12

2.1 Introduction

................................ ................................ ................................ .............. 12

2.2 Summary of Approach

................................ ................................ ............................. 12 2.2.1 Overvie w

................................ ................................ ................................ ... 12 2.2.2 Chemicals of Potential Concern

................................ ................................ . 15 2.2.3 Conceptual Exposure Scenario

................................ ................................ . 16 2.2.4 Approach to Development of the Tier 1 Scenario

................................ ....... 18 2.2.5 Fate and Transport Modeling (TRIM.FaTE)

................................ ............... 20 2.2.6 Exposure Modeling and Risk Characterization (MIRC)

.............................. 21 2.2.7 Implementation of Risk

- based Emission Scaling Factors for POM and Dioxin Emissions

................................ ................................ ....... 22

2.3 Description of Environmental Modeling Scenario

................................ ..................... 26 2.3.1 Chemical Properties

................................ ................................ .................. 26 2.3.2 S patial 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 Ex posure and Risk Modeling Scenario

................................ .............. 34 2.4.1 Calculating Concentrations in Farm Food Chain Media

............................. 35 2.4.2 Ingestion Exposure

................................ ................................ .................... 35 2.4.3 Calculating Risk

................................ ................................ ......................... 38 2.4.4 Summary of Tier 1 Assumptions

................................ ................................ 39

2.5 Evaluation of Screening Scenario

................................ ................................ ............ 42 2.5.1 Introduction

................................ ................................ ................................ 42 2.5.2 Cadmium Compounds

................................ ................................ ............... 42 2.5.3 Mercury Compounds

................................ ................................ ................. 44 2.5.4 Dioxins

................................ ................................ ................................ ...... 46 2.5.5 Polycyclic Aromatic Hydrocarbons

................................ ............................. 48 2.5.6 Summary

................................ ................................ ................................ ... 50

3. Tier 2 Methodology

................................ ................................ ................................ ........... 52

3.1 Overview o f Approach

................................ ................................ ............................. 52

3.2 Selection of Site

- Specific Characteristics for the Tier 2 Assessment

........................ 55TRIM

- Based T iered Screening Methodology for RTR

3.3 Estimation of Adjustment Facto rs for Selected Site

- Specific Parameters

................................ ................................ ................................ .............. 55 3.3.1 Selection Values for Variables of Interest

................................ .................. 56 3.3.2 Estimation of Adjustme nt Factors

................................ .............................. 59

3.4 Preparing National Databases of Lake and Meteorological Data

............................. 64 3.4.1 Processing Lake Data for Tier 2 Asse ssment

................................ ............ 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 Ass essment

................................ ................................ ................................ .... 74

4.3 Plume

- rise Assessment

................................ ................................ ........................... 76

4.4 Assessment of Time

- series Meteorology and Effective Release Heights

................................ ................................ ................................ .................... 76

5. References

................................ ................................ ................................ ....................... 78

Attachment A. TRIM.FaTE Inputs

................................ ................................ ......................... A

- 1

Description of Multimedia Ingestion Risk Calculator (MIRC) Attachment B. Used for RTR Exposure and Risk Estimates

................................ ................. B

- 1

Dermal Risk Screening

................................ ................................ ................. C

- 1 Attachment C.

Summary of TRIM.FaTE Parameters Considered for Inclusion in Attachment D. Tier 2 Assessment

................................ ................................ ........................ D

- 1

Analysis of Lake and Sustainable Fish Population

............................... E

- 1 Attachment E. SizeTRIM

- Based T iered Screening Methodology for RTR

Introduction and Background 1 October 2014 

## 1. Introduction and Background

Section 112 of t he Clean Air Act (CAA) directs the U.S. Environmental Protection Agency (EPA) to assess the risk remaining (i.e., residual risk) from emissions of hazardous air pollutants (HAPs) following the implementation of maximum achievable control technology (MACT) standards for emission sources. This risk assessment is a major component of EPA’s Risk and Technology Review (RTR) program . As part of this program , EPA must consider additional emission sta ndards 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 resu lting from exposures to persistent and bioaccumulative HAPs (PB

- HAPs) via non

- inhalation pathways , namely ingestion and dermal exposure

. EPA’s a ssessment 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 resu lt of the same emissions (see Attachment C ). Consequently, a methodology has been developed to evaluate ingestion exposure and risk for PB

- HAPs efficiently in the context of EPA’s RTR program.

T o evaluate ingestion exposures a nd human health risks for RTR on a source category basis

, an iterative approach was developed that enables EPA to confidently screen out PB

- HAP emissions unlikely to pose health risks above levels of concern (i.e., a cancer risk of 1 in 1 million or a nonc ancer hazard of 1.0 ) and to focus additional resources on sources of greater

concern within the category

. To estimate exposure and risk, two models are used : the Fate, Transport, and Ecological Exposure module of EPA’s Total Risk Integrated Methodology ( TRIM.FaTE) to model the fate and transport of pollutants released to the environment and the Multimedia Ingestion Risk Calculator (MIRC) to estimate transfer and uptake into the food chain and exposure to receptors consuming contaminated food products and soil. This approach is divided into three tiers of increasing refinement, as follows.

• Tier 1 of the approach begins by identifying the facility

- level emissions of PB

- HAPs within a source category and comparing them to risk

- based emi ssion thresholds. The risk

- based thresholds are derived using the aforementioned models applied for a hypothetical environmental and exposure scenario , assuming ingestion of locally caught fish, locally grown produce and livestock, and local soil . This hypothetical “screening scenario ” is intended to represent a situation in which the ingestion exposure is unlikely to be exceeded at any actual facility evaluated through the RTR program . The thresholds for Tier 1 are derived by estimating t he emission rate that corresponds to a lifetime cancer risk of 1 in 1 million or a chronic non

- cancer hazard quotient (HQ) of 1 for an individual exposed according to the characteristics associated with the screening scenario

. For a facility, if the emission rate of each PB

- HAP is less than th e 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 T ier 2, the actual location of the facility is used to refine some assumptions associated with the environmental scenario while maintaining the Tier 1 ingestion exposure scenario assumptions . Th e environmental scenario assumptions are refined by incorporat ing 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 thatTRIM

- Based T iered Screening Methodology for RTR

Introduction and Background 2 October 2014 facility based on an understanding of how exposure concentrations estimated for the screening scenario change with meteorology and lake location. PB

- HAP emissions that do not exceed the adjusted threshold are assumed to pose risks below levels of concern and no additional multipathway assessment is required. F acilities having em issions 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 scr eening 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 la ke 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 assessment s , a refined

, site

- specific, multipathway risk assessment can be conducted . Such an assessment would incorporate location

- or facility

- specific characteristics regarding the environment to which PB

- HAPs are emitted, relevant exposure pathways, ingestion r ates 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 described in additional detail. The a ttachments provide a comprehensive record of t he characteristics and methods ass ociated with Tier 1 and Tier 2 assessments

. If a site

- specific assessment is conducted

, a separate report detailing that assessment will be prepared.



### 1.1 Tier 1

The methods used in Tier 1 are intended to enable EPA to evaluate PB

- HAP emissions from multiple f acilities in a source category quickly and efficiently and to remove from consideration those that are unlikely to pose risks above levels of concern , while also minimizing the possibility of EPA’s failing to identify risks that exceed levels of concern

. The hypothetical scenario used to estimate Tier 1 thresholds is designed to be health

- protective in estimating exposures and risks ; specifically, it is intended to avoid underestimating exposures to PB

- HAPS that might be encountered for any location throug hout 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 multi pathway 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

• Polycycl ic organic matter (POM).TRIM

- Based T iered Screening Methodology for RTR

Introduction and Background 3 October 2014 Exhibit 1

. Conceptual Decision Tree for Evaluati ng Non

- Inhalation Exposures for PB

- HAPsTRIM

- Based T iered Screening Methodology for RTR

Introduction and Background 4 October 2014 Based on current emissions , bioaccumulation potential, and toxicity considerations, emissions of these four PB

- HAPs are expected to pose the majority of the non

- inhalation risks to humans from air emissions at sources subject to residual risk provisions of the CAA 1 Thus, although EPA has identified nine other PB

- HAPs that should be evaluated as part of residual ri sk assessments, the methods for multipathway assessment described here encompass only these four at this time

. If emissions of any of the four PB

- HAPs listed above are reported for a facility, the emission rate for that PB

- HAP is compared to the threshol d emissions rate derived for that chemical using the hypothetical TRIM

- based screening scenario. This threshold is the emission rate that, when input to the models used in evaluating multipathway risk for RTR, results in a specified cancer risk or non

- can cer HQ threshold level of concern. For the screening scenario, threshold emission rate s 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 nat ure 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 assesses individual congeners , taking into account differences in both the fate and transport and the toxicity amo ng the various congeners

. The details of the methods for assessing dioxins and POM are provided in Section 2

.

1.1.2 Development of Emission Thresholds

Generally, t he approach used to assess ingestion exposure s and resulting risk s fo r 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 cha in 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 exposed human; and

4. C alculating lifetime cancer risk estimates or chronic non

- cancer HQs for each HAP and corres ponding 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 expos ures 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/).TRIM

- Based T iered Screening Methodology for RTR

Introduction and Background 5 October 2014 1 million or a noncancer HQ of 1 (depending on the PB

- HAP) for each PB

- HAP within the hypothetical environmental scenario, as described in more detail in the following sections.

Exhibit 2

. Overview of 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 data and (2) the environmental and chemical

- specific properties associated with fate and transport through the environment. The hypothetical modeled domain includes a farm homestead and a fishable lake near (i.e., 2 km) an emissions source, which are assumed to be the primary food sources for exposed individuals. The spatial layout and other physical aspects of t he modeled domain configuration are designed to be health

- protective, which results in an ingestion exposure situation that is unlikely to be exceeded at any actual facility evaluated under the RTR program. The environmental and chemical

- specific properti es governing fate and transport of PB

- HAPs are parameterized with either conservative (i.e., health protective) values or central

tendency values. The mix of health protective and central

- tendency assumptions and parameterization is expected to result in a scenario configuration that, on average, is likely to overpredict environmental concentrations of PB

- HAPs in media of interest for this evaluation. T he inclusion of central

- tendency values where warranted is intended to minimize the number of false posit ives

. 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 . The Tier 1 assumptions about these two components of fate and transport modeling are refined with relatively mo re site

- specific data in subsequent tiers. The spatial layout used to develop the threshold emission levels in Tier 1 and other details of the Tier 1 methodology are presented in Section 2

.

3 http://www.epa.gov/ttn/fera/trim_fate.html Chemical Emissions to Air

Cancer Risk Hazard Quotient Chemical fate and transport: Physical environment and aquatic ecosystem Uptake and transfer:

Produce and livestock Human Ingestion Exposure Risk and Hazard EstimationTRIM

- Based T iered Screening Methodology for RTR

Introduction and Background 6 October 2014 1.1.2.2 Modeling Transfer and Uptake

MIRC w as developed to conduct the required calculations involving farm food chain transfer, ingestion exposure, and risk. TRIM.FaTE outputs that are used as inputs to MIRC include :

• PB

- HAP concentrations in air

,

• A ir

- to

- surface deposition rates for PB

- HAPs in bot h 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 t he transfer and uptake of PB

- HAPs through the farm food chain using algo rithms 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 homegrown poultry/eggs

• Ingestion of homegrown pork

• Ingestion of fish

• Ingestion of breast milk (children <1 year ol d; dioxins only ) 4

Chemicals are modeled separately to evaluate the potential for risks, with exposures (in terms of ADD ) for each PB

- HAP summed across all ingestion exposure pathways

. For the screening scenario used in Tiers 1 and 2 , exposure characteris tics were selected that result in a highly health protective estimate of total exposure

. The ingestion rate for each exposure pathway listed above was set (as feasible) equal to an upper percentile value (99 th percentile for fish and 90 th percentile for a ll 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

ex posure for a hypothetical exposure scenario

. F or example, the resulting total food ingestion rate is extremely h igh 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 for key exposure factors).

D ermal absorption of chemicals that are originally airborne is generally relatively m inor 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.TRIM

- Based T iered Screening Methodology for RTR

Introduction and Background 7 October 2014 1.1.2.4 Calculating Lifetime Cancer Risk and Non

- Cancer HQs

Lifetime cancer risks and the potential for chronic non

- cancer effects are estimated using chemical

- specific oral cancer slope factors and oral reference doses. Lifetime 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 screenin g threshold emission rate.

1.1.2.5 Determining Threshold Emission Rates

Tier 1 emission threshold s were calculated by conducting iterative model simulations in TRIM.FaTE and MIRC using the screening scenario described above to determine emission rates for cadmium , mercury, dioxins , and POM that correspond to a cancer risk of 1 in 1 million

or a chronic non

- cancer HQ of 1

. Given the generally health protective nature of the scenario inputs, these thresholds are assumed to be appropriate for screening facilities e mi tting 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 ri sk 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 s creen 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 , howev er, can be determined without intensive effort during the assessment

. The use of some site

- specific characteristics instead of the generic characteristics used in Tier 1 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 protectiveness.

In selecting the scenario characteristics to modify in Tier 2, a balance was struck between th e 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

.TRIM

- Based T iered Screening Methodology for RTR

Introduction and Background 8 October 2014 1.2.1.1 Site Specific Considerations

F or Tier 2, location

- s pecific data on two types of variables are taken into account :

• M eteorological characteristics, including the fraction of time the wind blows toward each farm and lake ( using wind direction), wind speed, precipitation rate, and mixing height ; and

• 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 systematical ly 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 , Tie r 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 TRI M.FaTE results of these simulations (and the subsequent exposure and risk characterization, conducted using MIRC)

, threshold adjustment factors were calculated for each unique combination of the five parameters, for each PB

- HAP. These adjustment factors

r epresent the ratio between the risk metric (i.e., cancer risk or HQ) obtained using the Tier 1 screening scenario and the risk metric obtained from the Tier 2 run. For a given facility , for each PB

- HAP it emits and each nearby lake evaluated , an adjusted Tier 2 emission threshold can be estimated by dividing the Tier 1 emission threshold by the adjustment factor that best corresponds to the meteorological conditions present at the site and the location of the lake (hypothetical farms are assessed for every facility, at a constant distance from the facility)

. The contribution of each exposure media toward each scenario’s Tier 2 risk metric (i.e., the individual contributions of fish ingestion, soil ingestion, beef ingestion, etc. toward the total Tier 2 risk metric) was included in the matrix because the Tier 2 assessment separates chemical exposure from fish ingestion from exposure from farm food chain ingestion.

To facilitate the implementation of this approach without requiring facility

- specific data searc hes 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 t han 800 surface stations

, paired with their closest upper

- air station with available data

, located throughout the United States (data available from the National Oceanic and Atmospheric Administration). The lake database, based on U.S. Geological Survey ( USGS) data and including location and size information, 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 upp er trophic level fish, a minimum lake surface area of 25 acres is recommended. Very large lakes and bays (i.e., those larger than 100,000 acres) are not included because their watersheds are too large and

5 The lake size also was changed for each lake distance allowing for a constant ratio between watershed and erosion area compared with lake area within the TRIM modeling structure.TRIM

- Based T iered Screening Methodology for RTR

Introduction and Background 9 October 2014 their lake dynamics are too complex to realisticall y model in the TRIM.FaTE system. Lakes and bays larger than 100,000 acres include the Great Lakes, the Great Salt Lake, Lake Okeechobee, Lake Pontchartrain, Lake Champlain, Green Bay, and Galveston Bay.

When the Tier 2 screening is conducted, three addit ional processing steps are completed for each facility and PB

- HAP that will be analyzed in Tier 2 before looking up the appropriate adjustment factors. First, a proximity analysis for each lake is conducted, whereby each relevant lake within 50 km of the facility is identified and matched to its respective directional “octant” relative to the facility. For the purposes of Tier 2, a “relevant” lake meets the size and designation criteria discussed in the previous paragraph. Second, the lakes are manually

e valuated 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 sc reening process ). Third, the lakes around the facility that remain after the first two processing steps are ranked in order of highest to lowest PB

- HAP concentrations in fish . The se 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 to ol, each facility is matched with the same meteorology station used in RTR inhalation assessments, and the values for the four relevant meteorological parameters at that station are recorded. The distances from the facility to relevant lakes are computed i n the tool as well. These five parameter values become the set of facility

- specific inputs in Tier 2 . Then, the Tier 2 adjustment factors are determined for each PB

- HAP for the combination of these five variables. As described above, the Tier 1 screening e mission 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 F isher 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 t o satisfy the assumed ingestion rate ( i.e.

, 373 g/d for adults )

. This refined fisher assessment uses the assumption that the biological productivity limitation of each lake is 1 gram of fish (wet weight) per acre of lake; meaning that in order to fulfill t he adult ingestion rate, the fisher will need to fish from 373 total acres of lakes

.
In the refined fisher scenario, a fisher travels to each relevant lake in order of highest to lowest chemical concentration in fish (of a given PB

- HAP) and catches fish u p to the lake’s assumed biological productivity limitation. A maximum travel radius of 50 km relative to the facility is used to maintain a realistic scenario. The final Tier 2 scr e ening result for the fisher can be expressed as the sum of the screening re sult from each lake that is fished ( which is based on the amount of fish ingested for each lake multiplied by the PB

- HAP concentration in fish )

. If the 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 lak e s , the risk reflects a reduced ingestion rate based on the cumulative lake acreage.TRIM

- Based T iered Screening Methodology for RTR

Introduction and Background 10 October 2014 

### 1.3 Tier 3

A Tier 3 screening assessment can be conducted on facilities that do not screen out with the Tier 2 assessment , at the discretion of the risk assessor

. The Tier 3 screening approach consists of three individual assessments that further refine the screening scenario (beyond the refinements in Tier 2) based on additional site

- specific data and evaluations. Because t he Tier 3 assessment introduces additional site

- spec ificity to the screening scenario, it requires a potentially higher level of effort than the Tier 2 assessment, but still a much lower level of effort than the full site

- specific assessment ( discussed in Section 1.4 )

. One of th e 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 evaluating the fishability of the lakes used in Tier 2. The USGS dataset occasionally includes lakes that appear to be misclassified, no longer exist, or are estuarian by nature. These lakes are removed from the Tier 3 a ssessment after evaluat ing the ir validity using aerial imagery and other available data

. These additional data sources are not used in the Tier 2 sc reening. 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 usin g the revised lake database and the Tier 2 methods described in Section 1.2 . If removing a lake(s) causes the originally

fished lakes to sum to less than 373 total acres, then in the rescreening

, the fisher will catch and

consu me fish from an additional lake(s) if available. In this situation, the Tier 3 lake assessment is conducted on the newly added lake(s), and another rescreening is conducted, and so on until no further lakes are removed or added to the assessment

. The Tier 3 lake assessment is more thoroughly described in Section 4.2

.
If

, after the lake assessment , the Tier 3 screening result is still above a level of concern, the risk assessor may choose to conduct a plume

- rise a ssessment. Atmospheric conditions coupled with the physical parameters of the chemical release point can cause the chemical plume to rise substantially beyond the physical release height. This process is not explicitly modeled by TRIM.FaTE but can substant ially reduce ground

- level chemical exposure if the plume frequently rises above the mixing height. This assessment uses a scenario in which the chemical release height varies over time due to hourly meteorological conditions and the parameters associated

w ith 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 chem ical deposition or exposure for that hour. I f this occurs across many hours, it will substantially reduce total PB

- HAP exposure and reduce the screening result. The plume

- rise adjustment factor

— the number of hours when the effective release height remains below the mixing height, divided by the number of total modeled hours

— is multiplied by the Tier 2 screening result, thus lowering the screening result. A more thorough description is described in Section 4.3

.
If the Tier 3 screening result after the lake and plume

- rise assessments still is above a level of concern, the risk assessor may choose to conduct a time

- series assessment. This assessment utilizes hourly effective release heights (computed in the plume

- ris e 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 m eteorology statistics used in Tier 2

. U sing th e se data inTRIM

- Based T iered Screening Methodology for RTR

Introduction and Background 11 October 2014 combination with hourly effective release heights is a more complete evaluation of hourly chemical losses due to plume rise compared with the Tier 3 plume

- rise assessment described above. These time

- varying release height and meteorology files are used in a run of TRIM.FaTE that also uses the facility’s PB

- HAP emissions and the Tier 2 spatial scenario associated with the lake being assessed. The TRIM.FaTE modeling, and subsequent exposure and risk ch aracterization conducted using MIRC, leads directly to a screening

- level cancer risk or HQ (i.e., a revised screening result)

. For simplicity in the software implementation of the Tiers 2 and 3 screening assessments, the result of this Tier 3 time

- series a ssessment is converted to a time

- series adjustment factor

— the revised screening result divided by the screening result after the Tier 3 lake assessment

. T his ratio can then be multiplied by the screening result after the Tier 3 lake assessment, yielding th e revised screening result accounting for the time

- series assessment.



### 1.4 Site

- Specific Assessment

If , based on results of the screening assessments , a risk assessor concludes that there is a reasonable probability that individuals could be adversely affected by the facility emissions, a refined site

- specific multipathway assessment might be performed. Examples of recent refined multipathway assessments include residual risk assessments of a ferroalloys production facility (EPA 201 4a )

, petroleum re finery faci lity (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 u ses 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 M IRC. Important site

- specific data likely would include emission release height and plume buoyancy, hourly meteorology (e.g., wind flow, temperature, mixing height, and precipitation)

, surface compartments based on watershed and terrain data, local farms a nd water bodies, land use, soil properties , erosion and runoff rates

, surface water and sediment properties , water transfer rates , and aquatic ecosystem information

. The outputs from the site

- specific run of TRIM.FaTE (i.e., chemical concentrations in

env ironmental 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 rang e of ingestion rates for each modeled PB

- HAP allows the risk assessor to understand, based on TRIM.FaTE and MIRC, the sources and pathways of possible human health risk from emissions of PB

- HAPs.TRIM

- Based T iered Screening Me thodology for RTR

Tier 1 Methodology 12 October 2014 

## 2. Tier 1 Methodology

2.1 Introduction

As discussed in Section 1 , the U.S. Environmental Protection Agency (EPA) 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 per sistent 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 facility to determine whether a facility passes certain human health risk

- based criteria. Sources that are “screene d out” in the Tier 1 assessment are assumed to pose no risks to human health above levels of concern and and are not considered in further assessments . For sources that do not pass the Tier 1 screen, more refined assessments, up to and including site

- spec ific multipathway assessments, can be conducted as appropriate.

This section describes the technical basis for the first screening

- level tier of EPA’s multipathway human health evaluation of PB

- HAP emissions from RTR sources. Specifically, the scenari os, 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 hypo thetical 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 a ssessed.

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 emitted by a source under consideration are reviewed to determine, first, whether emissions are reported for any of the four PB

- HAPs of concern for non

inhalation pathways. If such emissions are reported, the emission rates are compared to Tier 1 threshold

- screening emission rates that have been derived using the TRIM

- based Tier 1 scenario described in this document (s ee Exhibit 4 for threshold screening emission rates )

.TRIM

- Based T iered Screening Me thodology for RTR

Tier 1 Methodology 13 October 2014 Exhibit 3

. Conceptual Decision Tree for Tier 1 Evaluation of Non

- Inhalation Exposures of PB

- HAPs

Facility screens out

Non

- inhalation risks not likely

, but evaluate on a case

- by

- case basis

Facility screens out ; no concern for multipathway risk ( check emissions of PB

- HAPs other than Cd

, dioxins

, Hg

, POM

, Pb on a case

- by

case basis ) Are any PB

- HAPs emitted ?

Are PB

- HAPs of primary concern for RTR emitted ? ( Cd

, dioxins

, Hg

, POM ) ? Evaluate HAP emissions by facility

YES YES

NO NO NO

Does the emission

rate for any PB

HAP exceed Tier

1 threshold ? Diagram Key

Outcome Process Decision

PointTIER1EVALUATION

Proceed to Tier 2 YESTRIM

- Based T iered Screening Me thodology for RTR

Tier 1 Methodology 14 October 2014 The TRIM

- based multipathway modeling configura tion, 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 des cribed here in , including the set of assumptions and input values associated with a hypothetical watershed and the exposure and risk scenarios evaluated for this watershed

. The Tier 1 scenario is a static configuration , and its primary purpose is as a mode ling 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 s creen); 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 environmental modeling are accompanied by additional resource requirements. Stated another way, a suitable approach minimizes both false negatives and false positives. False negatives (i.e., results that suggest that the risk is acceptable when in fact the actual risk is high) can lead to inappropriate and non

- protective health or environmental policy decisions. False positives (i.e., results that suggest more assessment is required when in fact the actual risk is low) can result in wasted resources by leading to additional, unnecessary analysis. For the evaluation of multipathway human health exposures to PB

- HAPs, th e 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 mi nimal

, 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 screening, in additional tiers of analysis, some of the Tier 1 parameters are reassessed, and if appropriate, are chan ged to more accurately reflect site

- specific characteristics. With each successive tier of the assessment, additional Tier 1 assumptions are evaluated and refined to better reflect site

- specific characteristics of the facility being modeled.

The Tier 1 screening evaluation for RTR compares reported air emission rates of PB

- HAPs (summed by PB

- HAP for each facility) to screening threshold emission rates derived using the Tier 1 scenario. A threshold emission rate is the level that, when input to a risk mo del 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 calcula ted for a cancer risk of 1 in 1 million or an HQ of 1.0 and are presented in Exhibit 4

. 6 Conceptually, a threshold level for the RTR multipathway screening evaluation could be obtained by back

- calculating the emission rate that

r esults 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 fro m 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 value s 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.TRIM

- Based T iered Screening Me thodology for RTR

Tier 1 Methodology 15 October 2014 range of emission rates for each chemical. The estimated screening

- level emission rates are verified by performing model runs using the estimat ed 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 Emissi on 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 equivalents) a 2. 81 E

- 09 Cancer

Cadmium 1.18E

- 02 Non

- c ancer
M ercury (as divalent mercury emissions) 3.16E

- 04 Non

- c an cer

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 thre shold risk quantities in nearly all circumstances

, given the health protective and hypothetical nature of the Tier 1 sc reening configuration

. It is considered very unlikely that the estimated risk at a real site would be greater than the estimated risk fo r 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 proba bility 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. Additio nal conservative assumptions used in the Tier 1 screen are described in Section 2.4.4

.
Tier 1 emission thresholds were developed individually for elemental and divalent mercury. Both were based on the lower of the thresholds a ssociated with multipathway exposures to divalent mercury and methyl mercury. 7 Only speciated emissions of divalent mercury are screened because the sum of elemental mercury emissions across all National Emission Inventory (NEI) facilities is less than th e 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 Offi ce 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 Al though 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 r efers 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). Th e terms are used interchangeably throughout this text.TRIM

- Based T iered Screening Me thodology for RTR

Tier 1 Methodology 16 October 2014 • their presence on three existing EPA lists of persistent, bioaccumulative, and toxic substances, and

• a semiquantitative ranking of toxicity and bioaccumulation potential of the entire list of HAPs.

The list’s development and utility in hazard i dentification for multipathway risk assessment are explained further in Chapter 14 and Appendix D of Volume I of EPA’s Air Toxics Risk Assessment (ATRA) Reference Library ( EPA 2004a )

. Exhibit 5 presents t he 14 chemicals and groups that are PB

- HAPs

.
Exhibit 5 . OAQPS PB

- HAP Compounds

PB

- HAP Compound a Addressed by Screening Scenario? Cadmium compounds Yes
Chlordane No
Chlorinated dibenzodioxins and furans Yes DDE (1,1

- dichloro

- 2,2

- bis(p

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

a Source of list: EPA (2004a).

The screening scenario described in this document is not configured for evaluating the risk potential for all 14 PB

- HAPs on the list

. Currently, the scenario can only be used to estimate exposures and risks quantitatively for 4 of the 14 PB

- HAPs ( as indicated in Exhibit 5 )

. These four PB

- HAPs are the focus of the current scenario because, based on current emissions

, bioaccumulation potential, and toxicity considerations, they are expected to pose the vast majority of the non

- inhalation risks to humans from air emissions at sources subject to residual risk provisions of the Clean Air Act

. 9

2.2.3 Conceptual Exposure Scenario

A conceptual exposure scenario was developed that encompasses the specific exposure routes and pathways of interest for the four PB

- HAPs that are assessed in the Tier 1 assessment

. Exposure routes and pathways describe the movement of air toxics from the point of release to the point where exposure occurs and generally consist of the following elements:

• Release to the environment (i.e., emissions);

9 Potential impacts on human health from non

- inhalation exposures to lead are evaluated for RTR using the National Ambient Air Quality Standard for lead, which takes into account multipathway risks. Non

- inhalation exposures to the other nine PB

- HAPs not addressed by the modeling scenario discussed in this report will be evaluated on an individual facility or source category basis as appropriate

.TRIM

- Based T iered Screening Me thodology for RTR

Tier 1 Methodology 17 October 2014 • A retention medium, or a transport mechanism and subsequent retention medium in cases involving media transfer of chemicals;

• A point of potential human contact with the contaminated medium; and

• An e xposure 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 vario us 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 as sess risks from hazardous waste combustion facilities, EPA identifie d several hypothetical receptor scenarios, noting that these scenarios are considered appropriate for a broad range of situations, rather than to represent any actual scenario. These scen arios 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, Resi dent Child, Fisher

, Fisher Child, Acute Receptor, and Nursing Infant. These receptors are distinguished by their pathways of exposures. EPA further notes in HHRAP that some exposure settings might warrant including additional exposure pathways; such as i ncluding exposure through fish ingestion for the farmer receptor. For the RTR screening scenario, risks are assessed for a single hypothetical receptor. Based on the guidance provided in HHRAP, a health protective exposure scenario was developed whereby the hypothetical receptor receives ingestion exposure via both the farm food chain and the fish ingestion pathways. The exposure scenario for the RTR Tier 1 assessment includes the following ingestion pathways:

• Incidental ingestion of soil,

• Ingestion of h omegrown 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 childre n 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 fo r lipophilic compounds like dioxins and has been shown not to contribute meaningfully for exposures to mercury, cadmium, and POM. See Section 2.4.2.3 below and Section 3.4 of Attachment B for full discussions of infant exposures via breast milk ingestion.TRIM

- Based T iered Screening Me thodology for RTR

Tier 1 Methodology 18 October 2014 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 wate r 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 t hat 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 t he potential for non

- inhalation risks above the levels of concern for PB

HAPs emissions from facilities in the context of residual risk assessment s conducted as part of RTR. The Tier 1 scenario used to derive the threshold emission rates is not intended t o be representative of any particular situation

. Rather, it was developed for the purpose of RTR to portray a hypothetical exposure scenario that will generate emissions screening levels that are health protective for any potential exposure situation that might plausibly be encountered in the United States

. A range of conditions was assessed when conceptualizing and developing the screening scenario. The final configuration was chosen so that for a given individual , any potential long

- term exposure level s for a given geographic region would be reasonably unlikely to exceed those of the Tier 1 configuration

. These criteria were met by constructing a hypothetical scenario that would be health

- protective in key aspects, including spatial orientation, meteor ology, types of exposures, and ingestion rates

. The overall result is a scenario that is unlike ly to occur at any one location but has a high likelihood of representing the upper end of all potential exposures

. This latter aspect accomplishes the goal of striking a balance between health protectiveness and the level of accuracy called for in the ideal screening approach previously discussed

.
The 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 Library ( EPA 2004 a, 2005 a ) ; and other EPA publications

. The scenario described herin is the culmination of

assess ments 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 revi ewers, lessons learned as the scenario is further applied for RTR, variations in EPA’s needs and requirements, and other factors.

2.2.4.1 Modeling Framework

The approach for multipathway risk screening and evaluation for RTR can be divided into four steps :

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 chara cteristics of a given assessment (e.g., to estimate PB

- HAP concentrations in treated drinking water derived from reservoirs).TRIM

- Based T iered Screening Me thodology for RTR

Tier 1 Methodology 19 October 2014 1. Fate an d 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 daily ingestion doses for a hypothetical human receptor; and

4. Calculating lifetime cancer risk estimates or chroni c non

- cancer HQs

, as appropriate

, for each PB

- HAP and comparing these to selected evaluation criteria

.

The relationship among these four processes is shown in Exhibit 6

.

As shown in Exhibit 6 , two models are used to evaluate the four steps outlined above. EPA’s TRIM methodology was conceived as a comprehensive modeling framework for evaluating risks from air toxics, and the TRIM system was designed to address each of the four steps involved in screening ingestion risk. 13 Currently , however, only one component corresponding to the first step included in Exhibit 6

— the fa te and transport module

— is available for application in an ingestion risk assessment

. EPA has completed some development activities for TRIM.Expo

- Ingestion and TRIM.Risk

- Human Health, two additional modules that cover the other three steps. M odeling soft ware , 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 estimati ng 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 QuotientTRIM

- Based T iered Screening Me thodology for RTR

Tier 1 Methodology 20 October 2014 cancer risks and chronic non

- cancer HQs

. This framework is conceptually identical to the ingestion exposure and risk assessments that TRIM is intended to cover.

2.2.4.2 Model Co nfiguration and Parameterization

Th e Tier 1 scenario is intended to reduce the possibility that EPA would underestimate potential multipathway human health risks

. Although the health protective approach likely overestimates risk, EPA determined that this approach is appropriate for the purposes of an initial multipathway screening assessment

. As was done with the preliminary multipathway screening for RTR conducted in 2006 ( EPA 2006) , exposures were modeled for a hypothetical farm homestead and fishable l ake located adjacent to an emissions source

. The hypothetical individual for which exposures were calculated was assumed to derive all potentially contaminated foodstuffs from adjacent locations, and many of the exposure/activity assumptions (e.g., amount of food consumed per day) were selected from the upper ends of representative exposure parameter distributions

. The physical/chemical environment represented in the screening scenario was parameterized with two types of values. One type is typical value s, 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 temp oral aspects of the scenario and the components of the scenario that influence air concentrations and deposition rates (which in turn affect all other exposures) were defined to be health protective. Chemical

- specific and non

- chemical

- specific properties of the environmental media were parameterized with either typical or health protective values; properties having greater uncertainty were assigned a greater level of health protective bias. The spatial layout of the Tier 1 scenario and the meteorological d ata (or a combination of these two factors) are generally more influential than physical/chemical parameters in dictating the screening model outcomes , taking into account the potential range of variation in possible values

. For example, where and how the layout is spatially oriented relative to the dominant wind direction can dramatically affect the concentrations in air, thereby driving estimated concentrations of PB

- HAPs in soil, water, and biota . In contrast, relatively large change s in soil character istics within the range of possible values (e.g., organic carbon content, water content) typically result in relatively small changes in media concentrations.

The mix of health protective and central

- tendency assumptions and parameterization is expected to result in a scenario configuration that, on average, is likely to overpredict environmental concentrations of PB

- HAPs in media of interest for this evaluation

. Given the intended application of this scenario as a screening tool, this health protective bias was deliberate, because of the desire to ensure that risks above levels of potential concern are not overlooked (i.e., to minimize false negatives) . Although 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 mo deling step depicted in the first box in Exhibit 6 is implemented for RTR using t he Fate, Transport, and Ecological Exposure module of the T RIM modeling system (TRIM.FaTE )

. 14 In developing the Tier 1 scenario, Version 3.6.2 of TRI M.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

.TRIM

- Based T iered Screening Me thodology for RTR

Tier 1 Methodology 21 October 2014 model the fate and transport of emitted PB

- HAPs and to estimate concentrations in relevant media

. Additional information about TRIM.FaTE, including support documentation, software, and the TRIM.FaTE public reference library, is availabl e 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 compreh ensive evaluation of the performance of TRIM.FaTE for modeling mercury was documented in Volumes I and II of the T RIM.FaTE Evaluation Report ( EPA 2002b, 2005b). Algorithms specific to the fate and transport of chlorinated dibenzo

- dioxin and

- furan cong eners were added following the addition of those for mercury and PAHs. Documentation of the application of TRIM.FaTE for dioxin emissions is contained in the third volume of the TRIM.FaTE Evaluation Report ( EPA 2004). More recently

, the TRIM.FaTE public reference library was updated to include information on modeling for cadmium

. In general, many of the algorithms and properties included in the public reference library that are used to model mercury ( except for the mercury transformation algorithms) are also applicable to cadmium

. Comprehensive t echnical 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) a re documented in Attachment A . Based on a thorough 2011 evaluation of TRIM.FaTE performance in modeling mercury’s fate, transport, and transformation in the aquatic food web, a zooplankton compartment was added to TRIM.FaTE’s aquatic compartment to increase the resolution and accuracy of the aquatic food web modeling. Parameterization of the TRIM.FaTE scenario used for RTR screening is described in more detail in Section 2.3

.
2.2.6 Exposure Modeling and Risk Characterization (MIRC)

The algorithms included in MIRC that calculate chemical concentrations in farm food chain media and ingestion exposures for hypothetical individuals were obtained from EPA’s Human Health Risk Assessment Protocol for Hazardous W aste 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.FaTE outputs (i.e., chemical concentrations in environmental media and fish) and the ingestion exposure and ris k 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.TRIM

- Based T iered Screening Me thodology for RTR

Tier 1 Methodology 22 October 2014 Exhibit 7

. Overview of Process Carried Out in the Multimedia Ingestion Risk Calculator

2.2.7 Implementation of Risk

- based Emission Scaling Factors for POM and Dioxin Emissions

Two of the four PB

- HAPs for which screening emission thresholds have been developed for RTR

— POM and dioxins

— are chemical groups comprising numerous individual entities. The members of these categories reported in NEI include both specific chemicals and groups containing multiple chemicals. For example, for POM, emissions reported in NEI include various species, such as benz[a]anthracene, 2

- methylnaphthalene, and chrysene, as well as

non

- specific entries, such as “PAH, total.” The constituents included in the POM and dioxin PB

- HAP categories are grouped together not only because they are types of the “same” HAP, but also because members of these groups are assumed to have similar characteristics with respect to toxicity and behavio r 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 fo r 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 app ropriate 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

e missions of that substance. That is, as the emissions increase, so too does the exposure to Farm Food Chain

Concentration

Calculator Ingestion Exposure Calculator Risk and Hazard Calculator TRIM

. FaTE

outputs

Human activity / exposure factors Plant

- and animal

- specific

parameters Chemical

specific uptake / transfer factors Ingestion dose

- response values **FFC media concentrations**  **Average daily doses**  **Cancer risks and hazard quotients** 

Access db

process **Tool output**  Input data Key to symbols :TRIM

- Based T iered Screening Me thodology for RTR

Tier 1 Methodology 23 October 2014 that substance. Furthermore, toxicity is assumed to increase linearly with concentration. Consequently, emissions of one substance (e.g., chrysene) can be scaled proportional to a reference compound (e.g., benzo(a)pyrene or BaP ) by applying weighting factors corresponding to the relative differences in exposure behavior and toxicity. Using the POM group as an example and BaP as the reference compound, this scalin g can be expressed through an equation as follows:

Emiss PAHi:BaP = Emiss PAHi × EEF PAHi:BaP × TEF PAHi :BaP

w here :

Emiss PAHi : BaP = Risk

- weighted emissions of PAH i (weighted according to cancer risk relative to BaP for oral exposures)

Emiss PAHi = Emission r ate of PAH i

EEF PAHi:BaP = Exposure equivalency (weighting) factor accounting for difference in relative oral exposure between PAH i and BaP

TEF PAHi:BaP = Toxicity equivalency (weighting) factor accounting for difference in relative toxicity via oral rout e between PAH i and BaP

In combination, the product of the EEF and TEF for a given substance is considered to be a “risk equivalency factor” for the purposes of RTR evaluations that enables scaling of emissions of a given substance for a given exposure sce nario.

The TEF for each PAH and dioxin species can be calculated on the basis of relative toxicities. Toxicities were not evaluated separately for RTR but are based on analyses conducted by EPA elsewhere. For PAHs, oral toxicity values for individual spe cies 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 Organizati on (van de n Berg et al. 2006). Refer to Section 4 of Attachment B for more information on these values.

The EEFs can be calculated directly for each individual chemical that can be modeled in TR IM.FaTE and MIRC. TRIM.FaTE is configured for 14 POM congener s 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 assigned after evaluating the correlation between chemical properties of the POM or dioxin congener and exposure quantified as lifetime avera ge daily dose. The specific calculations for EEFs and exposure surrogates for each chemical group are discussed in the sections that follow.

2.2.7.1 Calculation of Scaling Factors for POM C ongeners

The calculated EEFs , TEFs, and total REFs for the 14 POM congener s 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 be tween chemical

- specific properties (e.g., K ow and Henry’s law constant, k h ) and intermediate modeled values (e.g., deposition , soil concentration ) and exposure in terms of lifetime average daily dose (LADDs) where the average daily doses (ADDs) for the you ngest 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. ExposureTRIM

- Based T iered Screening Me thodology for RTR

Tier 1 Methodology 24 October 2014 surrogate s were thus identified for each congener by calculating Total Lifetime Average Daily Dose (Age Adjusted) for each based 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 Equi valency Factors Relative to BaP for POM Congeners Currently Evaluated in Tiers 1 and 2 Assessments

Chemical Fully Parameterized in TRIM.FaTE? Exposure Equivalency Fa ctor (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 0.0 7 0.3 Retene n 3.7 0.07 0.3 Dibenzo(a,j)acridine n 0.78 0.2 0.1 Perylene n 1.2 0.07 0.08 Benzo(a)anthracene Y 0.09 0.16 0.01 Chrysene Y 0.25 0.02 0.004
2

- Acetylaminofluorene n 0.005 1.4 0.01 Fluoranthene Y 0.04 0.07 0.003 Acenaphthylene Y 0.04 0.07 0.003
2

- Chloronaphthalene n 0.03 0.07 0.002 Fluorene Y 0.03 0.07 0.002 Acenaphthene Y 0.02 0.07 0.002
1

- Methylnaphthalene n 0.02 0.07 0.001
2

- Methylnaphthalene Y 0.02 0.07 0.001 Carbazole n 0.01 0.003 0.00003
Anthracene n 0.06 0 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 “Polycyclic 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

.TRIM

- Based T iered Screening Me thodology for RTR

Tier 1 Methodology 25 October 2014 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

- inhalation) analyses. Naphthalene is listed individually as a HAP under Section 112(b) of the Clean Air Act. POM also is listed as a HAP under Section 112(b) and is defined as organic compounds with more than one benzene ring and a boiling point greater than or equal to 100° C (see http://www.epa.gov/ttn/atw/orig189.html). Under this definition, naphthalene could be considered as part of the POM listing. However, naphthalene is 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 mod erate 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 believ es 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 congene rs 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 e p or t e d ,” “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 TC DD. This approach could be improved by obtaining information on the speciation of dioxin emissions for each facility or an average speciation profile that could be assumed to apply to all facilities in a source category.TRIM

- Based T iered Screening Me thodology for RTR

Tier 1 Methodology 26 October 2014 Exhibit 10 . Exposure and Toxicity Equivalency Factors Relative to TCDD for Modeled Dioxin Congeners

Chemical Exposure Equivalency Factor (EEF) Toxicity Equivalency Factor (TEF) a Risk Equivalency Factor (REF) PentaCDD, 1,2,3,7,8

- 3.8 1 3.8
TetraCDD, 2,3,7,8

- 1 1 1
Dioxins, Total, w/o Indiv. Isomers Rptd. 1 1 1

Dioxins 1 1 1

2,3,7,8

- TCDD TEQ 1 1 1
HexaCDD, 1,2,3,4,7,8

- 1.6 0.1 0.2
PentaCDF, 2,3,4,7,8

- 0.4 0.3 0.1
HexaCDD, 1,2,3,6,7,8

- 1.0 0.1 0.1
HexaCDF, 1,2,3,7,8,9

- 0.5 0.1 0.05
HexaCDF, 1,2,3,6,7,8

- 0. 5 0.1 0.05
HexaCDD, 1,2,3,7,8,9

- 1.0 0.04 0.04
HexaCDF, 1,2,3,4,7,8

- 0.3 0.1 0.03
HexaCDF, 2,3,4,6,7,8

- 0.2 0.1 0.02
PentaCDF, 1,2,3,7,8

- 0.4 0.03 0.01
TetraCDF, 2,3,7,8

- 0.1 0.1 0.01
HeptaCDD, 1,2,3,4,6,7,8

- 1.0 0.01 0.01
HeptaCDF, 1,2,3,4,6,7,8

- 0.2 0.01 0.002
HeptaCDF, 1,2,3,4,7,8,9

- 0.2 0.01 0.002
OctaCDD, 1,2,3,4,6,7,8,9

- 1.1 0.0003 0.0003
OctaCDF, 1,2,3,4,6,7,8,9

- 0.2 0.0003 0.0001

a Values from Van den Berg et al. (2006), except for 1,2,3,7,8,9

- hexaCDD, which is calculated based on the ra tio 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 lo ng

- term PB

- HAP exposures, and the environmental and chemical

- specific properties were parameterized with either health protective or central

- tendency values

. Information regarding the scenario configuration and important aspects of the parameterization pr ocess, 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 Attachm ent A

.
2.3.1 Chemical Properties

The general c hemical/physical properties that TRIM.FaTE require s , such as Henry’s law constant, molecular weight, and melting point , were obtained from peer

- reviewed and standard reference sources . N umerous other chemical

- speci fic properties are related more specifically toTRIM

- Based T iered Screening Me thodology for RTR

Tier 1 Methodology 27 October 2014 a particular abiotic or biotic compartment type; these properties are discussed generally in the sections that follow and are documented in Attachment A

.
2.3.2 Spatial Layout

For the p urpose 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 presen ted in Exhibit 11

. The source parcel is parameterized as a square with sides of 250 m, which is assumed to be a fair estimation for the si z e of a relatively small

- to

- medium facility at the fence line . With a predominant wind dir ection toward the east, the modeled layout is generally symmetric about an east

- west line and is wedge

- shaped to reflect Gaussian dispersion of the emission plume.

A lateral, downwind distance of 10 km was established for the watershed included in the

scen ario. 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 he ight 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 withi n a 10

- km radius. Extending the modeling layout beyond a 10

- km downwind distance would increase the amount of deposition “captured” by the modeled watershed, but the incremental chemical mass expected to accumulate in the watershed diminishes rapidly with distance. In addition, the impact of this additional deposited mass on ingestion exposures is expected to be negligible. 16 Given these conditions, a downwind length of 10 km was determined to be appropriate for the screening scenario.

Exhibit 11

. TRIM.FaTE Surface Parcel Layout

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 ef fect 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.)TRIM

- Based T iered Screening Me thodology for RTR

Tier 1 Methodology 28 October 2014 The north

- south width of the wedge

- shaped watershed was set based on the observed behavior of chemicals emitted to the ambient air. If meteorological stability is known or can be assumed, the latera l 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 is about 550 m using this estimation. In a Gaussian distribution, about 99.6 percent of the plume spread area is contained within 3σ of the median line. Therefore, the plume σ was set at 3 times 550 m, or approximately 1.75 km from the centerline at a distance of 10 km. The plume width for these conditions is expected to be about twice this distance, or 3.5 km. These dimensions were used t o define the dimensions of the overall air and surface parcel layouts for the screening scenario.

The surface (land and surface water) modeling area was initially divided into five pairs of parcels the areas of which increase with distance from the source, which approximately corresponds to the spatial gradient that is expected in the downwind direction from the source . The second north parcel from the source was divided further into two parts; one of them tilled soil (Parcel N6) to represent agricultural conditions and the other (Parcel N7) untilled to represent pasture.

The depth of the surface soil compartments was set to 1 cm , except for Parcel N6, for which the depth was set to 20 cm to simulate the effect of tillage

. Characteristics of the soil lay ers (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 os e 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 divided 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 through and accumulate in various media compartments

. These properties describe the physical characteristics of the environmental media included in the modeled region, as well as the assumed connections and relationships between media types and modeled spatial components th at in turn affect chemical transport via water runoff, ground infiltration

, deposition of suspended sediments in the water col umn, 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

- spec ific 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 scen ario, 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.pdfTRIM

- Based T iered Screening Me thodology for RTR

Tier 1 Methodology 29 October 2014 [ total precipitation] = [evapotranspiration] + [total runoff]

In th is equation, t otal 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 th e Tier 1 scenario are meant to represent a relatively wet and moderately warm location in the United States (USGS 1987) . 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 outflow of water from the lake is about 18 million m 3 /yr, which translates to a volumetric turnover rate of about 12.2 lake volumes per year.

Other quantitative water body and watershed characteristics TRIM.FaTE uses are listed in Attachment 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 balan ce 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 (remo val of sediment from the water column via outflow) is accounted for in TRIM.FaTE by lake flushing rate and the third term (sediment burial) is the transfer of sediment from the unconsolidated benthic sediment compartment to the consolidated sediment layer.

To maintain the sediment balance, erosion rates were calculated for each surface soil compartment using the universal soil loss equation (USLE, Wischmeier and Smith 1978)

, assuming a relatively high rate of erosion

. The total suspended sediment concent ration isTRIM

- Based T iered Screening Me thodology for RTR

Tier 1 Methodology 30 October 2014 assumed to remain constant in TRIM.FaTE

, and the flush ing rate of the lake (calculated via the water balance approach described above) w as then used to estimate the removal of sediment from the modeling domain via lake water outflow

. The differe nce 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 returne d 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 ve locity (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 ma ss of particles in the sediment is also constant)

.
For the Tier 1 scenario described here, the average sediment delivery rate (i.e., transfer of sediment mass from watershed surface soil to the lake due to erosion ) for the entire watershed was estimated to be about 0.0036 kg/m 2

- day , based on calculations using the USLE. T he HHRAP documentation notes that using the USLE to calculate sediment load to a lake from the surrounding watershed sometimes lead s to overestimates ( EPA 2005a)

. For the Tier 1 scenari o, however, this approach was considered to be appropriate in that health protective assumptions are a goal of the screening scenario. 18 S urface soil compartments adjacent to the lake are linked directly to the lake for the purposes of estimating erosion a nd 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 assume d 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, th e 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 properties used in TRIM.FaTE algorithms include air temperature, mixing height, wind speed and direction, and precipitation rate

. These pro perties, which can vary significantly among geographic locations

, and seasonally and hourly for a single location, greatly influence the chemical concentrations predicted in media of interest

. Because the screening scenario is intended to be generally app licable for any U.S

. location, and to minimize the frequency of false negatives, a health protective configuration was used

. The meteorology of the screening scenario was defined to ensure that (when used in combination with the selected spatial layout) t he 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.TRIM

- Based T iered Screening Me thodology for RTR

Tier 1 Methodology 31 October 2014 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 ba sis). 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

- H APs to changes in these meteorological variables was tested. Lower wind speeds and mixing heights affected concentrations the most. This sensitivity is not unexpected because lower wind speeds should increase pollutant deposition onto the soil and lower mixing heights should reduce the volume through which pollutants disperse. The wind speed used for the screening scenario was 2.8 m/s, the 5 th percentile of the annual averages among 239 stations; by comparison, the mean annual average wind speed is appro ximately 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 Meteoro logical Inputs

Meteorological Property Selected Value Justification

Air temperature Constant at 298 Kelvin Recommended default value listed in HHRAP ( EPA 2005a). Value is similar to the mean daily June temperature in much of the U.S. Southern Plains and Southeast. a Mixing height Constant at 710 m Value is 5 th percentile of annual average mixing heights for 75 U.S. locations, using data obtained from EPA’s SCRAM Web site. b Value is the approximate U.S. median for periods without precipitation, based on data compiled by Holzworth (1972). Value is conservative compared to the 1

- to 2

- km typical mid

- latitude daytime value (Stull 1988).
Wind direction Blows from source parcel into scenario domain (west to east) 3 days per week; during other times does not blow into domain A wind direction that favors the location of interest (for example, a watershed downwind of a source of concern) will tend to result in more emitted mass accumulating in the location of interest. For much of the U.S. mid

- Atlantic and we stern 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 pat tern 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 con tiguous United States (about 50 years of data per station). Value is similar to the annual average wind speeds ofTRIM

- Based T iered Screening Me thodology for RTR

Tier 1 Methodology 32 October 2014 Meteorological Property Selected Value Justification

many areas of the U.S. east coast and west coast. a Precipitation frequency Precipitation occurs 3 days per week; wind direction blows into d omain 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 (H olzworth 1972). These locations include parts of the U.S. Northeast and Northwest. a

Total Precipitation 1.5 m/yr Assumed to represent rainy conditions for the United States. This annual precipitation amount is experienced in parts of the U.S. Deep South and parts of the U.S. northwest coast. a Conditional precipitation rate (rainfall rate when precipitation is occurring) is 9.59 mm/d, which is similar to conditions in many areas along the U.S. east coast and in the Midwest and Plains. a

a National Climati c 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 modeli ng 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 sim ulated as sediment particles). Zooplankton feed on algae in the water column, while benthic invertebrate s, 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 zo oplankton 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 s upport small bottom

- feeding fish (young

- of

- the

- year fish for many species), which in turn are consumed by larger bottom

- feeding fish (e.g., catfish). For TRIM.FaTE to provide reasonable predictions of the distribution of a chemical across biotic and abioti c compartments in aquatic systems, the biomass of the aquatic biotic compartments must represent all biota in the system and the distribution of biomass among the trophic levels and groups must be as realistic as possible

. To support the development of a relatively generic freshwater aquatic ecosystem in which to model bioaccumulation in fish, a literature search, review, and analysis was conducted ( ICF 2005). This research demonstrated that the diversity of sp ecies and food webs across U.S. aquatic ecos ystems 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 accumula te higher levels of contaminants in both sediments and biota than lotic systems (rivers, streams). Also, that initial research (ICF 2005) suggest s that a lake of at least 60 hectares (ha) or 150 acres could support higher trophic level predatory fish, wit h 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 l arge enough to support higher trophic level fish given appropriate conditions (e.g., high productivity given a sufficient nutrient base and average temperature and growing season ).TRIM

- Based T iered Screening Me thodology for RTR

Tier 1 Methodology 33 October 2014 Also, t his size was compatible with the overall size of the defined waters hed in the screening scenario. The fish types, biomass, diet fractions, and body weights recommended for fish compartments for the Tier 1 scenario are listed in Exhibit 13

. B iomass is based on an assumption that the total fish b iomass (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 fis h productivity.

In general, the food web implemented in the Tier 1 scenario is consistent with aquatic food webs that support trophic level 4 fish (to maximize bioaccumulation), and is intended to be generally health protective.

Exhibit 13 . Aquatic Biota Parameters for the TRIM.FaTE Screening Scenario

TRIM.FaTE Compartment Type Organisms Represented by

Compartment Biomass

Diet Average Body Weight (kg) Areal density (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 largemo uth 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 carni vore 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, caddisfli es, 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 inverteb rates.

2.3.6 Using TRIM.FaTE Media Concentrations

The Tier 1 scenario outputs include average PB

- HAP concentrations and deposition rates for each year and for each parcel of the model scenario

. In each surface parcel, deposition rates to the soil are provided as are soil concentrations for the surface, root, and vadose zones and grass or leaf concentrations as appropriate for the plants

. For each air parcel, air concentrations are provided

. For the lake, surface water concentrations and concentrations in the various levels of the aquatic food chain are provided. For the ingestion exposureTRIM

- Based T iered Screening Me thodology for RTR

Tier 1 Methodology 34 October 2014 calculations, some concentrations are used to calculate direct exposure (e.g., soil ingestion)

, and some are used to perform the farm food chain concentration calculations in the various media that humans can ingest

. The locations that determine exposures were selected to be health protective. Decisions regarding which TRIM.FaTE outputs to use in calculating exposures for the Tier 1 scenario assume exposure at locations v ery 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 summarize d in Exhibit 14

.

Exhibit 14 . Spatial Considerations

– TRIM.FaTE Results Selected for Calculating Farm Food Chain Media Concentrations and Receptor Exposures

TRIM.FaTE Output Used in Exposure Calculatio ns Representative Compartment

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

Deposition rates, for uptake by farm produce Deposition to surface soil compartment in surface Parcel N6 (tilled soil) Concentration in surface soil, for incidental ingestion by humans and farm animals Surface soil compartment in surface Parcel N1 (untilled soil, closest to facility)
C oncentration in soil , for uptake by farm produce and animal feed Surface soil compartment in surface Parcel N6 (tilled soil) Concentration in fish consumed by fisher Water column carnivore compartment in lake (50% of fish consumed) and benthic carnivore in lake (50% of fish consumed)

TRIM.FaTE can output instantaneous chemic al concentrations for a user

- specified time step and also can be configured to calculate temporal averages (e.g., annual averages). For the Tier 1 scenario, the model is set up to output results on a daily basis, largely because daily is the smallest time step over which input data change (i.e., wind direction and precipitation rate). Daily concentration results were averaged to obtain annual average concentrations. The default assumption is annual average concentrations for media during the fiftieth yea r 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 ercu ry concentrations do not achieve steady state after 50 years in the modeled screening scenario configuration, the rate of change in mercury concentrations shows a decreasing trend

.


### 2.4 Description of Exposure and Risk Modeling Scenario

This section describes the approach for modeling chemical concentrations in farm food chain (FFC) media (Section 2.4.1 ); estimating human exposures associated with ingestion of FFC media, incidental ingestion of soil, ingestion of fish, and infant c onsumption of breast milk (Section 2.4.2 ); and calculating human health screening risk metrics associated with these exposure pathways (Section 2.4.3 ). All of these calculations are conducted using MIRC. For this multipathway screening evaluation

, partitioning of PB

- HAPs into FFC media is modeled with MIRC

, not as a part of the TRIM.FaTE modeling

. Consequently, processes and inputs related to estimating chemical levels in FFC media are summarized in this section and discussed in detail in Attachment B

.TRIM

- Based T iered Screening Me thodology for RTR

Tier 1 Methodology 35 October 2014 2.4.1 Calculating Concentrations in Farm Food Chain Media

As was shown in Exhibit 7

, MIRC was compiled to calculate concentrations of PB

- HAPs in foods tuffs 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 i n MIRC were obtained from EPA’s Human Health Risk Assessment Protocol for Hazardous Waste Combustion Facilities (HHRAP; EPA 2005a ). These algorithms model the transfer of concentrations of PB

- HAP s in FFC media using biotransfer factors. Environmental med ia 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 conce ntrations 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 ge neral, 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 par ameter 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

, consumpti on 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 t hat 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 f or each PB

- HAP to be determined. Data related to exposure factors and receptor characteristics were obtained primarily from EPA’s Exposure Factors Handbook ( EPA 2011).TRIM

- Based T iered Screening Me thodology for RTR

Tier 1 Methodology 36 October 2014 Exhibit 15 . Summary of Ingestion Exposure Pathways and Route s 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 ru noff 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 t o breast milk

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

Belowground pr oduce N/A Soil Root uptake

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

Dairy (milk) Ingestion of forage Air Air Soil Direct deposition to plant Vapor transfer to plant Root uptake Ingestion of silage
Ingestion of grain Soil Root uptake Ingestion of soil Soil Inges tion 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 uptak e 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 inges tion pathways.

b Process by which HAP enters medium ingested by humans.

c Modeled in TRIM.FaTE.

d The consumption of breast milk exposure scenario is discussed in Section 2.4.2.3

.
For the Tier 1 scenario described here, exposure characteristics that would result in a highly health protective estimate of total exposure were selected. The ingestion rate for each medium was set at high

- end values (equal to the 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 ofTRIM

- Based T iered Screening Me thodology for RTR

Tier 1 Methodology 37 October 2014 total chemical exposure for a hypothetical exposure scenario (for example, note th at 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 ch aracteristics 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 Asses sment 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 di stribution 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

- p repared 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 cancer 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 Attachme nt 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 characteristi cs are presented in Attachment B

. Exposure parameter values were based on data obtained primarily from the Exposure Factors Handbook ( EPA 2011 )

. See Attachment B fo r details.

b Exposure factor inputs are used in calculating ADD estimates for each exposure pathway

. ADD equations for each pathway evaluated in this screening assessment are provided in Attachment B

. c Fraction contaminated represents the fraction of food product that is derived from the environment included in the screening scenario (e.g., produce grown on soil impacted by PB

- HAPs). This parameter is defined separately for each FFC medium; however, for the Tier 1 scenario, all ingested media are assumed to be impacted.

d Cooking loss inputs were included to simulate the amount of a food product that is not ingested due to loss during preparatio n or cooking, or after cooking.

e Because “as consumed,” fish consump tion 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 algo rithms 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 dataTRIM

- Based T iered Screening Me thodology for RTR

Tier 1 Methodology 38 October 2014 from the Exposure Factors Handbook ( EPA 2011 )

. The ingestion exposu re 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 In fant 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 che mical concentrations in the breast milk, which in turn can be estimated based on the mother’s chemical intake.

Reports of bioaccumulation of lipophilic compounds such as polychlorinated biphenyls (PCBs), polychlorinated dibenzofurans (PCDFs), and dioxins are prevalent in the scientific literature. Due to their high lipophilicity, these compounds partition almost exclusively to the milk fat of breast milk rather than the aqueous phase ( EPA 1998). PCBs, PCDFs, and PCDDs are the most documented groups of c ontaminants 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, c admium, 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 parti tion 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 m ilk 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 dioxi ns only. This pathway is included in computing total exposure for developing the screening threshold for dioxins. In the absence of congener

- specific data, dioxin congeners were assumed to manifest the same tendency to accumulate in breast milk as 2,3,7,8

- TCDD

.
2.4.3 Calculating Risk

MIRC was used to calculate excess lifetime cancer risk and non

- cancer hazard ( expressed as the hazard quotient or HQ) using the calculated ADDs and ingestion dose

- response values

. Chemical dose

- response data include cancer slope factors ( 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

. Equations used to estimate cancer risk and non

- cancer hazard also are provided in Attachment B

.TRIM

- Based T iered Screening Me thodology for RTR

Tier 1 Methodology 39 October 2014 Exhibit 17 . Dose

- response Values for PB

- HAPs Addressed by the Screening Scenario

PB

- HAP CSF ([mg/kg

- day] - 1 ) Source RfD (mg/kg

- day) Source

**Inorganics**
Cadmium 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] pyren e 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 Office of Research and Development

a For consistency with the overall approach for dose

- response assessment of PAHs, the CSF listed in IRIS for benzo[a]pyrene ([7.3 mg/kg

- day] - 1 ) was adjusted due to its mutagenic mode of action as discussed below (see also Attachment B )

. Estimated individual cancer risks for the PAHs included in the screening scenario were adjusted upward to account for the mutagenic cancer potency of these compounds during childhood, as specified by EPA in sup plemental 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

weigh ted 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 s creening 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 dicta tes soil and air concentrations and deposition rates used to calculate chemical levels in farm produce.
Lake location 375 m from source; generally downwind Health Protective Location dictates where impacted fish population is located.TRIM

- Based T iered Screening Me thodology for RTR

Tier 1 Methodology 40 October 2014 Characteristic Value Neutral or Health Protective? Comments on Assumptions

Surface soil proper ties Typical values or national averages Neutral Based on existing EPA documentation and other references. Size of farm parcel About 4 ha Health Protective Relatively small parcel size results in higher chemical concentration.
Size of lake 47 ha; about 3 m average depth Health Protective Lake is large enough to support an aquatic ecosystem with higher trophic

- level fish, but is relatively small and shallow (thus increasing surface area

- to

- volume ratio). **Meteorological Inputs** Total precipitation 1.5 m/yr Health Protective Intended to represent rainy U.S

. location; set to highest state

- wide average for the contiguous United States

. Precipitation frequency (with respect to impacted farm/lake) 2/3 of total precipitation fall on farm/lake and watershed Healt h Protective Most of total precipitation occurs when the farm/lake are downwind of the source.

Wind direction Farm/lake are downwind 40% of the time Health Protective Farm/lake located in the predominantly downwind direction

. Temporal dominance of wind d irection 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/watershe d. 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 Stat es ); 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.
Sur face 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 yea r Neutral 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 pre cipitation is assumed to flow into or through lake.TRIM

- Based T iered Screening Me thodology for RTR

Tier 1 Methodology 41 October 2014 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 f ish) 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/d etritivore fish favor higher concentrations of bioaccumulative chemicals and result in higher concentrations in consumed fish

. Linear food

- chain maximizes concentration of bioaccumulative chemicals in higher trophic

- level fish. **Parameters for Estimating Concentrations in Farm Food Chain Media**  Fraction of plants and soil ingested by farm animals that is contaminated 1.0 (all food and soil from contaminated areas) Health Protective Assumes livestock feed sources (including grains and silage) are derived fr om 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.

Biot ransfer 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 (f or 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** Inges tion 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 Protect ive 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 wer e adjustedTRIM

- Based T iered Screening Me thodology for RTR

Tier 1 Methodology 42 October 2014 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 m eals from local farm products.

Body weight Mean of national distribution Neutral Note that this does not affect the body

weight

- normalized rates for produce and animal products

. **Other Chemical**

**- Specific Characteristics** 
General chemical properties used in fate and transport modeling (Henry’s law, K ow , etc.) Varies Neutral Obtained from peer

- reviewed sources; intended to be representative of typical behavior and characteristics.

"General" physical properties (plant matter density, aquatic life biomass, alg al 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 subject ed 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 ( Section 2.5.4 ), and POM (Section 2.5.5 ). The scenario evaluations were focused primarily on assessing the behavior of these HAP categories in the environment, the accumulation of these chemicals in ingestible food products, and the predominant pathways of human exposure.

2.5.2 Cadmium Compounds

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

- fired boilers

, other urban and industrial facilities

, phosphate fertilizer manufacturing facilities

, road dust

, and municipal sewage sludge incinerators (ATSDR 2008).

2.5.2.1 Behavior in the Environment

Once emitted into the environment, airborne cadmium particl es 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 dependsTRIM

- Based T iered Screening Me thodology for RTR

Tier 1 Methodology 43 October 2014 strongly on soil pH, clay content, and avail ability 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 2005). 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).

Ca dmium 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 susp ended compounds. Cadmium that remains in the water column is expected to exist primarily in the dissolved state where it is available for uptake by aquatic organisms.

Freshwater fish accumulate cadmium primarily through direct uptake of the dissolved form through the gills and secondarily through the diet, which plays a variable role in total cadmium uptake (Reinfelder et al. 1998; Chen et al. 200 0; Saiki et al. 1995). Although some biomagnification of cadmium has been reported for aquatic food chains in saltwater systems, biomagnification in freshwater systems appears to be present only at lower trophic levels (Chen et al. 2000) and in narrowly defined niches (e.g., plankton/macroinvertebrate food chains; Croteau et al. 2005). Biomagnification factors (B MFs) 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, th e partitioning behavior modeled in TRIM.FaTE was consistent with the behavior of cadmium expected in the natural environment.

2.5.2.2 Concentrations in Ingestible Products

Most non

- inhalation exposure to cadmium outside of occupational settings is through dietary intake

. Available data indicate that cadmium accumulates in plants, aquatic organisms, and terrestrial animals, offering multiple ingestion exposure pathways (ATSDR 2008) . A ctual cadmium levels in ingestible products , however, varies based on type of foo d, 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 co ncentrated 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 scr eening 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 high er 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 particu lar 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 exhibit , fish ingestion is the dominant exposure pathway across all age categories, accounting for nearly 100 percent of the ADD for all groups. The combined contribution from all other exposure pathways accounts for less than 0 .7 percent of the total AD D for all age groups. Most of the additional exposure was fromTRIM

- Based T iered Screening Me thodology for RTR

Tier 1 Methodology 44 October 2014 ingestion of fruits and vegetables. The highest ADD corresponds to children aged 1

– 2 years ; thus, the exposure corresponding to this group was used to determine the emission threshold for cad mium. 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 Hazard 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 productio n facilities ; other urban and industrial facilities; and medical and municipal waste incinerators ( ATSDR 1999) . These facilities can emit a mixture of elemental and divalent mercury , mostly in the gaseous phase, with some divalent forms in particle

- bound phases ( EPA 1997 )

.
2.5.3.1 Behavior in the Environment

Once emitted into the environment, mercury undergoes changes in form and species as it moves through environmental media. Elemental mercury is the most prevalent species of mercury in the atmosphere. Due t o 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 th e surface near the emission source via wet or dry deposition where it appears to adsorb tightly to soil particles ( EPA 1997) or dissolved organic carbon. Divalent mercury in soil also can be methylated by microbes or reduced to elemental mercury and revol atilized back into the atmosphere. Most divalent mercury from atmospheric deposition willTRIM

- Based T iered Screening Me thodology for RTR

Tier 1 Methodology 45 October 2014 remain in the soil profile, however, in the form of inorganic compounds bound to soil organic matter. Although this complexing behavior with organic matter signific antly 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, d ivalent 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 t o 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 (ATSD R 1999). Low pH favors the methylation of mercury in the water column, typically performed by sulfur

- reducing bacteria in anaerobic conditions . Methyl mercury is typically of greatest concern because it readily bio accumulate 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 produc ed 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 organ isms, 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 an d fruits (ATSDR 1999; EPA 1997). Cattle demethylate mercury in the rumen and, therefore, store very little of the mercury they ingest by foraging or consuming silage or grain. Thus, mercury content in meat and cow’s milk is low (ATSDR 1999). Concentrati ons 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. R elative to divalent mercury

, methyl mercury concentrations in fish were very high (approximately 95 percent of total mercury).TRIM

- Based T iered Screening Me thodology for RTR

Tier 1 Methodology 46 October 2014 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 do minant 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 ( ATS DR 1998)

. The five stationary s ource 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, secondar y aluminum smelting, and biological incineration

.TRIM

- Based T iered Screening Me thodology for RTR

Tier 1 Methodology 47 October 2014 2.5.4.1 Behavior in the Environment

Dioxins emitted to the atmosphere can be transported long distances in vapor form or bound to particulates, depositing in soils and water bodies in otherwise pristine locations far from the source. Although airborne dioxins are susceptible to wet and dry deposition, most dioxins emitted to the atmosphere through incineration/combustion processes are not deposited close to the source (ATSDR 1998).

In soil, dioxins strongly adsor b 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 particle s the only significant mechanism for transport to water bodies.

The dry deposition of dioxins from the atmosphere to water bodies is another important transport process. Because of the hydrophobic nature of dioxins, most dioxins entering the water column are expected to adsorb to suspended organic particles or partition to bed sediment, which appears to be the primary environmental sink for this chemical group ( EPA 2004c). Although dioxins bound to aquatic sediment primarily become buried in the sediment compartment, some resuspension and remobilization of congeners can occur if sediments are disturbed by benthic organisms (ATSDR 1998).

Bioaccumulation factors (BAFs) in fish are high as a result of the lipophilic nature of chlorinated dioxins. 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 env ironment 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 ap pears 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 beha vior 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 mult iple 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 a nthropogenic 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 c ontaminated 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 screeningTRIM

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Tier 1 Methodology 48 October 2014 scenario was at least one order of magnitude greater than concentrations in the other compartments. Among the compartments with the lowest concentrations were fruits and vegetables, which do not readily accumulate 2,3,7,8

- TCDD.

Ingestion of breast milk during infancy and fish ingestion contribute to over 97 percent of lifetime dioxin exposure for 2,3,7,8

- TCDD in the screening scenario. Daily intakes of 2,3,7,8

- TCDD from cow’s milk, produce, and fish ha ve 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, mig ht have very different exposure profiles in which fish, meat, and dairy drive congener exposure (ATSDR 1998). Given the subsistence diet modeled in the RTR screening scenario, the high exposure from consumption of fish is appropriate within the context of this assessment

.
2.5.4.3 Lifetime Average Daily Dose (LADD)

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 Exhib it 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 ch ain 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 are also likely attributed to differences in the congener

- specific half

- life in abiotic media and the degree to which the congener is metabolized in biotic media.

2.5.5 Polycyclic Aromatic Hydrocarbons

PAHs can enter the atmosphere as a result of a variety of combustion processes, both natural and anthropogenic. S tationary emission sourc es 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 burnin g, other processes such as power generation; incineration; coal tar, coke, and asphalt production; and petroleum catalytic cracking are also major contributors ( ATSDR 1995)

.TRIM

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Tier 1 Methodology 49 October 2014 Exhibit 21

. Estimate d Contributions of Modeled Food T ypes to Dioxin Ingestion Exposures

2.5.5.1 Behavior in the Environment

PAHs emitted to the atmosphere can travel long distances in vapo r form or attached to particl es, or they can deposit relatively close to an emission source via wet or dry deposition onto wat er, 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 (A TSDR 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 carb on 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

s ediment 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 contam inated food (e.g., aquatic insects, other benthic invertegrates, smaller fish)

, biomagnification generally does not occur because many organisms can rapidly metabolize PAHs (ATSDR 1995). As a result, concentrations of PAHs have generally been observed to decrease with increasing trophic levels (ATSDR 1995). Due to this ability to metabolize PAHs, BAFs in fish are not expected to be especially high. Sediment

- dwelling organisms can experience increasedTRIM

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Tier 1 Methodology 50 October 2014 exposure to PAHs through association (e.g., direct upt ake, 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 valu es 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 troph ic 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 m eat 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 highe r than 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 measure ments and available data that are skewed toward concentrations in highly contaminated products. Considering these mitigating factors, the RTR screening scenario output concentrations are within the range of plausible values for PAHs in ingestible products

.
2.5.5.3 Lifetime Average Daily Dose

T he contribution s of ingestion exposure pathways to the LADD (and thus life time cancer risk) for various PAH s are presented i n Exhibit 22

. As shown

, the variability in the driving exposure pathways a cross 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 envi ronmental 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 exposu re 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 TRIM.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.TRIM

- Based T iered Screening Me thodology for RTR

Tier 1 Methodology 51 October 2014 Exhibit 22

. Estimated Contributions of Modeled Food Types to PAH Ingestion Exposure sTRIM

- Based Tiered Screening Methodolo gy for RTR

Tier 2 Methodology 52 October 2014 

## 3. Tier 2 Met hodology

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 negat ives 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 b e refined to determine whether the facility is actually expected to pose a risk above levels of concern

. One way to refine the risk estimates is to conduct a site

- specific assessment where the Tier 1 model values and layout are replaced with site

- specific values and layouts wherever possible. However, this approach would not be feasible given the resource intensive nature of a site

specific assessment and the number of facilities that might not screen out of the Tier 1 assessment in some source categories.

As an intermediate approach, we instead refine our Tier 1 screening estimates to Tier 2 screening estimates. This is done by replacing some of the more health

- protective assumptions in a Tier 1 screen with more site

- appropriate values. Specifically, for Tier 2, the following parameter values are varied from their Tier 1 values:

• Meteorological characteristics, including the fraction of time the wind blows in the direction of each farm and lake ( using wind direction), the wind speed, the precipitation rate , and the mixing height; and

• 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 bas ed 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 assumpt ion 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 var iables 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

- facil ity 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 f ish ingestion rates might vary on a site

- specific level using the refined fisher scenario based on the availability of lakes to fish

. Tier 2 screening assessments are performed for those facilities that do not screen out during the Tier 1 assessment. The overall implementation of Tier 2 is shown in Exhibit 23

. The starting point (shown in green) is the ratio of the facility emission rate for the PB

- HAP of concern to the Tier 1 threshold for that PB

- HAP . Next, t he facility

- specific estimates of the Tier 2 meteorological and lake

- location parameters listed above must be gathered for each facility (shown in red). Then, the associated TRIM.FaTE and MIRC risk estimates must be calculated (shown in orange). Because of the volume of faci lities that need to be evaluated in Tier 2, the implementation focuses on estimating refined risk using pre

- calculated databases (discussedTRIM

- Based Tiered Screening Methodolo gy for RTR

Tier 2 Methodology 53 October 2014 below) rather than gathering the input data and performing TRIM.FaTE and MIRC modeling speartely for each facility.

Exhibit 23

. Basic Process for Implementing Tier 2

First, databases of the relevant U.S. meteorological and lake data were created that can be accessed during a Tier 2 evaluation (shown in red in the figure). These meteorologic al 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 wit h their closest upper

- air stations located throughout the United States. These surface data cover year 2011 and are the sameAERMOD

- ready data used by EPA OAQPS for RTR inhalation modeling

. As discussed below , in most cases the 2011 precipitation data were not used, and instead

, the 30

- year average annual precipitation data for each station

were used. The database of lakes, available from ESRI ® and based on U.S. Geological Survey data, includes information on the location, size, use or type designation, and name (if available) of all lakes in the United States. To focus on lakes that can support angling of upper trophic level fish, lakes used for disposal, evaporation, or treatment were excluded, and only lakes greater than 25 acres in area are included. Lak es 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 o r 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 combin ation 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 FactorsTRIM

- Based Tiered Screening Methodolo gy for RTR

Tier 2 Methodology 54 October 2014 (and the subsequent exposure and ri sk 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 exposu re 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 factor s. 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 fac ility, 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 curr ently 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 facto rs were irrespective of chemical (specific only to the facility). In implementing the Tier 2 process, a risk equivalency approach was used to convert speciated emissions of PAHs and dioxins into BaP and TCDD equivalents, respectively, similar to the Tier 1 screening approach.

Finally, to implement the Tier 2 screen, a Microsoft® Access TM 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 dir ection, 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 faci lity

- specific parameters. The threshold adjustment factor corresponding to this set of site

- specific data is then estimated using the matrix of adjustment factors and the wind direction adjustment

. Wind direction values are used as

- is with no rounding or binning. If one of the four facility matrix variables (wind speed, precipitation rate, mixing height, or lake location) is between two of the computed levels available for that variable in the simulation matrix, the more health

- protective of the two levels is selected (i.e., the level that resulted in the smaller adjustment to the Tier 1 exposure). The Tier 1 screening emission threshold for a PB

- HAP is then divided by the appropriate adjustment factors to obtain an updated Tier 2 emission threshold for th at 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

.TRIM

- Based Tiered Screening Methodolo gy for RTR

Tier 2 Methodology 55 October 2014 

### 3.2 Selection of Site

- Specific Characteristics for the Tier 2 Assessment

The screening scen ario 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 gene rally 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 cha racteristics 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 pla usible 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 unce rtainty associated with the parameter high or low ?

• How complicated or time

- consuming is the incorporation of a given parameter (e.g., wind speed) into the screening scenario set

- up ?

Attachment D provides an exhibit showing all the TRIM.FaTE variables considered for the Tier 2 assessment . These variables were evaluated qualitatively using the criteria above to determine whether the variable was of high, medium, or low priority. The following five “high priority” variables were selected for implementation in the current Tier 2 assessment :

• Wind direction (the percent of time the wind blows toward the lake and farm),

• Wind speed,

• Precipitation,

• Mixing height, and

• Location of fishable 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 Facto rs for Selected Site

- Specific Parameters

The purpose of including site

- specific detail for a facility evaluated in Tier 2 is to develop a more realistic estimate of risk associated with facility emissions. This purpose is achieved within the assessment by generating revised emission thresholds of potential concern specific to a given PB

- HAP on a facility

- by

- facility basis. However, instead of performing full

- fledged, site

- specific model runs for each facility that does not “screen out” in Tier 1, a set of generally applicable threshold adjustment factors for each PB

- HAP was developed based on a set of model runs.

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.TRIM

- Based Tiered Screening Methodolo gy for RTR

Tier 2 Methodology 56 October 2014 For each PB

- HAP, these runs corresponded to unique combinations of values for wind speed, precipitation rate, mixing height, and lake location ( wind direction is assessed separately). These adjustment factors were based on a set of runs in which the values for these parameters were varied systematically. The wind direction adjustment factor is the ratio of the frequency that winds blow toward th e lake and farm in the Tier 1 screening scenario (43 percent) and the frequency that winds blow toward the facility’s farm and lake in Tier 2 , with a separate wind direction adjustment factor for each directional octant (i.e., separate factors for the farm and lakes in the northern octant, the farm and lakes in the northeastern octant, etc.) . The appropriate adjustment factors are then applied to the Tier 1 threshold for that PB

- HAP when evaluating a facility in Tier 2.

The analyses conducted to select th e 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 par ameter 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 asse ssment

, 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 freq uencies, 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 i nterest 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 be low.

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) increases

, 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 es timate 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 pat terns 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 pr esent 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 relation ship 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 thr eshold 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 t he time on average).TRIM

- Based Tiered Screening Methodolo gy for RTR

Tier 2 Methodology 57 October 2014 Wind Speed : Although the impact of wind speed on non

- inhalation risks also is likely to depend on configurational parameters such as the location of farms and lakes, in general it is reasonable to assume that higher wind speeds lead t o 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, correspon ding 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 sta tions 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 modele d cases, increasing the wind speed while maintaining constant lake location, wind direction, mixing height, and precipitation led to Tier 2 estimations of high

- end risk or hazard that were smaller than that of Tier 1. To ensure that the approach is health

- protective, a facility was assigned the lower end value of the bin into which it was placed. Facilities with wind speeds less than 2.8 m/s were assumed to have a wind speed of 2.8 m/s.

Precipitation : Higher levels of precipitation over the modeled domain are expected to increase

non

- inhalation risks by increasing particulate and gaseous wet deposition near

- field to the

source. The screening scenario used in Tier 1 assessment assumed an annual precipitation rate of 1,500 mm/year. This level of annual prec ipitation 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) pre cipitation in t he N ational Climatic Data Center’s (NCDC) 30

- year ( 1981

– 2010 ) data from U

. S

. stations

. 23 To estimate adjustment factors i n the Tier 2 assessment

, model simulations were conducted with the parameter value set to three lower values (i.e., 1,18 7 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 mini mum 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 precipitatio n 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 speed s 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.htmlTRIM

- Based Tiered Screening Methodolo gy for RTR

Tier 2 Methodology 58 October 2014

Exhibit 24

. Lake Distance and Meteorological Parameter Values for which Adjus tment 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 the value is equal to the value used in Tier 1.

b Wind direction is not shown here because its effect on modeled exposure and risk in TRIM.FaTE is linear.

Mixing Height: Greater mixing heights can dilute the concentration of pollutants in air, resulting in lower deposition and other transfer rates from air to surfaces and consequently also lower ingestion exposures. The Tier 1 screening assessment assumed a mixing height of 710 meters. This v alue is the 5 th percentile (i.e., lower) of annual average mixi ng 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 di fferent 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 maintainin g 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 wh ich 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 farth er 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.htmTRIM

- Based Tiered Screening Methodolo gy for RTR

Tier 2 Methodology 59 October 2014 the fish consumption pathway, which is an important pathway of exposure for several c hemicals (for example, in the case of methyl mercury, it is by far the predominant exposure pathway). For the scenario modeled in Tier 1, the center of the lake was situated approximately 2 km from the source. To estimate lake location adjustment factors for use in Tier 2, we completed a series of model runs in which the lake was located 5 km, 10 km, 20 km, and 40 km from the source, as well as runs with no lake (values also shown in Exhibit 24 ). Accordingly, the bins used to cl assify 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 placed. A facility with a lake closer than 2 km was assumed to have a lake at 2 km.

In resituating the lake at these alternative locations, we maintained ratios consistent with those included in the Tier 1 screening scenario for (1) lake area to total la nd area in the modeled domain, (2) runoff watershed area to lake area, and (3) erosion watershed area to lake area. Exhibit 25 provides a diagram of the TRIM.FaTE layout in each of the alternate lake location simulations except no

- lake. We used a “thin” lake shape (i.e., downwind width much smaller than the cross

- wind length) that minimized the potential effect of declining deposition with distance from stack that might affect lakes that are long in the downwind direction. By

con trolling 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 o f 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 r equirements associated with reconfiguring the TRIM.FaTE spatial layout). The changes in lake size between these four runs are not expected to have a substantial independent effect on exposure and risk because the effect of increased lake size is offset by greater total deposition and runoff . Furthermore, the lake depth was not changed, which might be as important a parameter as lake surface area in determining the chemical concentrations in the water column and sediment

. As noted above

, we set up the

con figurations 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 a pplied 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 actu al 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. Alth ough 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. Thi s suggests that the ratio of total estimated exposures (and consequently risks) obtained for the screening scenario and any alternative configuration could be used as an adjustment factor to scale emissions for that specific alternative configuration. The risk in the alternative configuration following such a scaling of emissions would be equal to the risk in the screening scenario (which in Tier 1 was set a t 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)

.TRIM

- Based Tiered Screening Methodolo gy for RTR

Tier 2 Methodology 60 October 2014 Exhibit 25 . Layouts for Tier 2 TRIM.FaTE Simulations Using Alternate Distances Betwe en the Facility and the Fishable Lake a

a The no

- lake scenario is not shown.TRIM

- Based Tiered Screening Methodolo gy fo r RTR

Tier 2 Methodology 61 October 2014 To account for potential interactions between the chosen Tier 2 variables, matrix adjustment factors were estimated by performing TRIM.FaTE runs for each unique combinati on 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 a ssessment 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 e ach exposure media toward each scenario’s Tier 2 risk metric (i.e., the individual contributions of fish ingestion, soil ingestion, beef ingestion, etc. toward the total Tier 2 risk metric) was included in the matrix because the Tier 2 assessment separates chemical exposure from fish ingestion from exposure from farm food chain ingestion.

The adjustment factor for wind direction is handled separately. Based on TRIM.FaTE test runs, the fraction of the time the wind blows in the direction of the farm and l ake 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 adjus tment 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 Incorpor ation of the Risk Equivalency Approach

The adoption of a risk

- equivalency approach to convert speciated emissions of PAHs and dioxins to BaP and TCDD equivalents, respectively, in Tier 1 required the development of risk equivalency factors (REFs) for each reported species in these groups. The REFs for PAHs and dioxins represent the ratio of the risk posed by a particular species to the risk posed by BaP and TCDD, respectively, at equivalent emissions rates in a given scenario.

The REFs can be represented a s 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 ex posure 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 par ticular species to the CSF for BaP and TCDD, respectively, and are the same in Tier 1 and in Tier 2.

The EEFs depend on the TRIM.FaTE configuration, including layout and meteorlogical input values. For example, the exposure profile (i.e., how the different ingestion pathways contribute to total exposure and risk) is different for each PAH. For a PAH where fish is a dominant driver of risk, moving the lake will have a large effect on the overall risk; however, if produce is the dominant driver of risk, movin g 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, other s 15 based on K ow - based regression estimates) and the dioxin species currentlyTRIM

- Based Tiered Screening Methodolo gy fo r RTR

Tier 2 Methodology 62 October 2014 evaluated (most based on direct TRIM.FaTE modeling, and a small number assumed to behave like TCDD).

The following mathematical formulas demonstrate how the Tier 2 adjustment f actors 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 emi ssion 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 SPECIES = 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 TIE R 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

Tier 2 Adjustment Factor PAH SPECIES = 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 thresho ld for a particular PAH species, the adjustment factor must include the ratio of the BaP Tier 1 and 2 thresholds (as is true for cadmium, mercury, and TCDD, as discussed above) and the ratio of the EEFs for the particular PAH species in Tier 2 and Tier 1. This additional EEF factor is needed to account for the fact that the EEFs are different for each Tier 2 TRIM.FaTE configuration.TRIM

- Based Tiered Screening Methodolo gy fo r RTR

Tier 2 Methodology 63 October 2014 Finally, the ratio of total BaP equivalents contributed by all PAH species at a given facility to the BaP Tier 2 threshold 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 equival ents 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 productivi ty 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 i s 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 subst antially 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 ord er of high est 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 acreag e is smaller than 373 acres, the n 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 ) Scaled T 2 Fis h risk = T 2 Fis h risk × � Lake SA ( acs )
373 acs �

Equation 1: Scaled fish risk for each lake 1 to n

- 1TRIM

- Based Tiered Screening Methodolo gy fo r RTR

Tier 2 Methodology 64 October 2014 ( 2 ) Scaled T 2 risk = T 2 Fis h risk × � 373 acs

− ∑ ( SAs of Previously Vis ited Lakes ( acs ) )
373 acs �

Equation 2: Scaled fish risk for nth lake

The risk associated with each lake visited is then summed together for a final risk (equation 3).

( 3 ) Total Scaled T 2 Fis h risk = ( 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 ) To t al Scaled T 2 Fis h risk = � ( Eq

. 1 )
Equation 4: Total fish risk if total acreage of lakes does not reach 373 acres



### 3.4 Preparing National Databases of Lake and Meteorological Data

To facilitate the effective application of the Tier 2 screening procedures, databases were prepared containing national

- scale information about lakes (locations and sizes) and meteorological data at available surface stations (including wind direction, wind speed, precipitation, and estimated mixing height). The deve lopment 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 a s “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 pro cessing 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 asse ssment 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 Da ta 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).TRIM

- Based Tiered Screening Methodolo gy fo r RTR

Tier 2 Methodology 65 October 2014 with the assumed exposure scenario. To estimate the relationship between high fish consumption rates, harvest rates

, and lake size, the following 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 (WC Cs) 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 ar e 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 enviro nment. 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 ma ximum 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 s ize 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 103 g/day (with half that from the WCC and half from BC). However, to be health protective and t o ensure t hat small lakes that might be more highly contaminated than estimated by the TRIM.FaTE screens were not eliminated, we selected 25 acres as the “cutoff” for the minimum size for an actual lake nearTRIM

- Based Tiered Screening Methodolo gy fo r RTR

Tier 2 Methodology 66 October 2014 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, th e 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 appro ximately 2 kg), instead of several years for fish hatched in the lake.

To place an upper bound on the radial distance from the source up to which lake

- derived risks need to be assessed, we took into account the limitations of TRIM.FaTE. We limited t he lake analysis to an outward radial bound of 50 km from the stack. For facilities with no lakes within 50 km, lake

- derived risk is assumed 0

.TRIM

- Based Tiered Screening Methodolo gy for RTR

Tier 2 Methodology 67 October 2014 Exhibit 26

. Maximum Fish I ngestion R ate (g/day) A ssociated with S ustainable F ishin g 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 83 117 166 25 0 333 499 666 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 at le ast 500 TL4 adult fish present; w hite 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.TRIM

- Based Tiered Screening Methodolo gy for RTR

Tier 2 Methodology 68 October 2014 3.4.2 Processing Meteorological Data for Tier 2 Assessment

In addition to the lake database, a database of U.S. surface stations with complete dat a 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 Adviso ry 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 NAT A (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 onsi te 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 th ough 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; EP A 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 stati ons for use in multipathway screening assessments, EPA started with the same U.S. meteorology dataset used in RTR inhalation assessments. RTR inhalation assessments use data from 824 ASOS (Automated Surface Observing System) stations that record hourly and sub

- hourly measurements. These data represent the year 2011, and the surface stations are paired with their closest, regularly

- reporting upper

- air stations. This number of stations is far greater than the 350 and 158 stations

, respectively

, used in the 19 96 NATA report (EPA 2001b) and the 2009 RTR report (EPA 2009)

. The 2011 precipitation measurements reflect 2011 weather conditions, and, like any other year, some areas of the country experienced rainfall that was significantly less than normal, and some

a reas 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 d ata 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 proximit y 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 densit y of the 2005 NATA facilities. That is, the density tends to be greatest in the Great Lakes region, along the East and West Coasts, and in the Southern Plains, and tends to be lowest in the Rockies (except Colorado) and Northern Plains

.TRIM

- Based Tiered Screening Methodolo gy for RTR

Tier 2 Methodology 69 October 2014 3.4.2.3 Data Processing

To facilitate application of the meteorological data to the Tier 2 assessment , EPA gathered wind information in directional octants that could be linked to the direction of the relevant lake s (see Introduction and Section 3.5 ). EPA divided the periphery aroun d 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 degr ees SW: >202.5 to 247.5 degrees W: >247.5 to 292.5 degrees NW: >292.5 to 337.5 degreesTRIM

- Based Tiered Screening Methodolo gy for RTR

Tier 2 Methodology 70 October 201 4 Exhibit 27

. The Locations of Meteorological Stations and Point Source Facilities a

a The 2005 NATA was the most recent, comprehensive, f inalized dataset of nationwide point source emitters of hazardous air pollutants, and it is used here only for illustrative purposes. The 2005 NATA used a meteorological dataset different from the one used in this report.TRIM

- Based Tiered Screening Methodology for RTR

Tier 2 Methodology 71 October 2014 A software program was develop ed to calculate the following statistics for each of these stations:

• Number of hourly observations,

• Number of hours with calm winds or missing winds,

• Percentage of time the wind blows into each octant (after excluding missing wind hours),

• Median wind speed blowing into each octant

,

• Median mixing 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 ava ilable).

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 va lues are more health protective, we selected the median statistic for all stations.



### 3.5 Implementation of Tier 2 Assessment

The Tier 2 screening assessment is conducted using a Microsoft® AccessTM tool that was developed specifically for this purpose. Similar t o 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 sc reening 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 leve ls. 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 dispo sal or treatment facilities, retention ponds, etc). In addition, each facility is matched to the same meteorology station used in RTR inhalation assessments, which is usually the nearest meteorology station ; when the meteorology station match is not known from the RTR inhalation assessment, the tool conducts a proximity analysis. The tool allows the user to review matching lakes and exclude ones that are not considered suitable for modeling (e.g., based on names indicating industrial, waste, or treatment pu rposes, as discussed in Section 3.4.1 .) Any excluded lakes are recorded by the tool so that they are omitted from subsequent screening assessments.

26 30

- year average annual precipitation was obtained from the National Climatic Data Center (NCDC). http://www.ncdc.noaa.gov/oa/climate/normals/usnormals.html

.TRIM

- Based Tiered Screening Methodology for RTR

Tier 2 Methodology 72 October 2014 Per facility and PB

- HAP, the tool evaluates the screening result at each lake a nd hypothetical farm. For each lake, the initial calculations produce screening results consistent with the full screening scenario ingestion rate (i.e., 373 g/day for adults, regardless of assumed lake fish productivity), and then additional calculations adjust the screening results for the lakes based on the initial screening results and areas of the lakes (see Section 3.3.2.3 ). Each modeling scenario consists of a farm and a lake (or, in some cases, no lake )

. Alt hough the dis tance 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 facility. T he tool presents the final Tier 2 screening results for the fisher and farm er separately, including results specific to individual farm food chain media, for each chemical, and then summed together for each PB

- HAP group.

The tool also can incorporate the results of Tier 3 assessments. Since the tool allows the user to manually exclude lakes, if any lakes are removed from the assessment in Tier 3, those lakes can be removed in the tool and the Tier 2 screening can be repeated with the revised lake database. If Tier 3 plume

- rise or time

- series assessments are conducted, the resulting Tier 3 adjustment factors can be entered into the tool and it will update the Tier 2 screening results to produce Tier 3 screening results.

The tool outputs several results tabl es, including an intermediate table that provides information on each lake and farm, including the fish ingestion and screening

- level risk associated with each lake . The final screening results for each facility and PB

- HAP group are also output. A summary table identifies the facilities exceeding the Tier 2 emission screening threshold of each PB

- HAP group (separately for the fisher and farmer) and the largest screening results. All intermediate and final results tables present the Tier 1 and Tier 2 screeni ng 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 T ool 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” mea ns “screening value”, which is the ratio of emissions to emission screening threshold.TRIM

- Based Tiered Screening Methodology for RTR

Tier 2 Methodology 73 October 2014 Exhibit 29

. Example of the Facility Output Table from Tier 2 Tool a, b

a The screen shot examples shown in this section are for actual faci lities 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 show n. The headers “Stationary Fisher” and “Traveling Fisher” are shorthand references to the initial fisher scenario (i.e., catches and consume s 373 g/d per lake, regardless of lake area) and the refined fisher scenario (i.e., assumes a 1 g/acre/d lake produc tivity), 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 Asse ssment, Plume Rise Adj Factors, and Time Series Adj Factors would not be shown

.TRIM

- Based Tiered Screening Methodology for RTR

Tier 3 Methodology 74 October 2014 

## 4. Tier 3 Methodology

4.1 Overview of Approach

The Tier 3 screening assessment is an additional assessment that can be conducted , at the discretion of the risk assessor, on facilities that do not screen out with the Tier 2 assessment. The Tier 3 screening approach consists of three individual assessments that further refine the screening scenario (beyond the refinements in Tier 2) based on additional site

- specific data and evaluations. The assessments introduce additional site

- specificity to the screening scenario, requiring a potentially higher level of effort than the Tier 2 assessment, but still a much lower level of effort than the full site

- specific assessment. One of the Tier 3 as sessments (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 adjustme nt 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 asses sment 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 conduct ed (beyond the Tier 2 methods) is to assess t he 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

- view imagery and internet searches can be used to quickly ascertain if an assessed lake actually exists, if it is likely not fished (e.g., appears swampy or covered in algae), if it is likely used for industrial or waste disposal/treatment purposes, and/or if i t 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 d ataset 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 qua lified, 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 fis h 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 that w ere 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 assessme nt is conducted again (using the Tier 2 methods) with the revised lake dataset. Although this additional lake assessment is conducted based on the Tier 2 screening results of a particular set of facilities, lakes removed during that assessment could affect the screening results of other facilities in theTRIM

- Based Tiered Screening Methodology for RTR

Tier 3 Methodology 75 October 2014 source category beyond that original set. This can happen if an assessed facility is within 50 km of another assessed facility, whereby removing a lake could affect the screening results of both facilities. It should be noted, however, that s creening results across different facilities are not summed

.
Exhibit 31 . Example of Lake Removed from Screening

— Likely Evaporated or Drained

Note: Aerial imagery from ESRI World Imagery (2014 ).

Exhibit 32 . Example of Lake Removed from Screening

— Likely an Industrial Lake

Note: Aerial imagery from ESRI World Imagery (2014). This map was created during the assessment of a specific source category, thus the reference to “the assessment in this report” in the text box.TRIM

- Based Tiered Screening Methodology for RTR

Tier 3 Methodology 76 October 2014 

### 4.3 Plume

- rise Assessment

In Tiers 1 and 2, all chemicals are emitted inside the mixing layer and are available for ground

level exposure. In reality, the physical height of an emission source, in combinati on 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 upp er

- air layer) is unavailable for ground

- level exposure (i.e., the upper

- air layer functions as a chemical sink). Many emission sources in RTR are physically taller than the mixing height during some hours, and hot exit gas temperatures (i.e., buoyancy) and /or high exit gas velocities (i.e., momentum) can further elevate the chemical plume.

The Tier 3 methodology utilizes methods summarized by Seinfeld and Pandis (1998) to estimate how often a facility’s emissions reach the upper

- air sink, which decreases av ailability 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 atmospher ic 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.

F or 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 che mical 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 heig ht 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 screening re sults 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 ave rage 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 scr eening 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 facilit y 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 facil ity’s matching hourly meteorology data, the facility’s emission rates, and the fish ingestion rates, chemical fate and transport is modeled in TRIM.FaTE and corresponding screening

- level risks are calculated using MIRC. This results in a screening

- level ri sk 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 ingested fi sh caught from the lake; summing those products across all modeled lakes results in the screening

- level fisher risk (i.e., fisher screening result ) associated with the evaluated PB

-TRIM

- Based Tiered Screening Methodology for RTR

Tier 3 Methodology 77 October 2014 HAP emitted by the facility. The time

- series

- adjusted Tier 3 screening resu lt s are compared to the Tier 2 screening result s calculated previously.TRIM

- Based Tiered Screening Methodology for RTR

Tier 3 Methodology 78 October 2014 

## 5. References

ATSDR (Agency for Toxic Substances and Disease Registry). 1995. Toxicological profile for polycyclic aromatic hydrocarbons. Atlanta, GA: U.S. Department of Health and Hum an Services, Public Health Service.

ATSDR. 1998. Toxicological profile for chlorinated dibenzo

- p

- dioxins. Atlanta, GA: U.S. Department of Health and Human Services, Public Health Service.

ATSDR. 1999. Toxicological profile for Mercury. Atlanta, GA: U.S. De partment of Health and Human Services, Public Health Service.

ATSDR. 2008. Toxicological profile for Cadmium. Atlanta, GA: U.S. Department of Health and Human Services, Public Health Service.

Burger, J. 2002. Daily consumption of wild fish and game: Exposu res of high end recreationists. International Journal of Environmental Health Research 12:343

- 354.

Cal/EPA (California Environmental Protection Agency) Office of Environmental Health Hazard Assessment (OEHHA). 2000. Air Toxics Hot Spots Program Risk Assess ment Guidelines; Part IV, Exposure Assessment and Stochastic Analysis Technical Support Document. Section 6, Dermal Exposure Assessment. September. Available at: http://www.oehha.ca.gov/air /hot_spots/pdf/chap6.pdf

. Chen, C.Y., R.S. Stemberger, B. Klaue, J.D. Blum, P.C. Pickhardt, and C.L. Folt. 2000. Accumulation of heavy metals in food web components across a gradient of lakes. Limnology and Oceanography 45(7): 1525

- 1536.

Croteau, M., S.N. Luoma, and A.R. Stewart. 2005. Trophic transfer of metals along freshwater food webs: Evidence of cadmium biomagnification in nature. Limnology and Oceanography 50(5):1511

- 1519.

EPA 1997. Mercury Study Report to Congress. Volume III: Fate and Transport of Mercury in the Environment. EPA

- 452/R

- 97

- 005. Office of Air Quality Planning and Standards and Office of Research and Development. December.

EPA 1998. Methodology for Assessing Health Risks Associated with Multiple Pathways of Exposure to Combustor Emiss ions. National Center for Environmental Assessment. Cincinnati, OH. EPA

- 600/6

- 90/003. December.

EPA 1999. Short Sheet: IEUBK Model Soil/Dust Ingestion Rates. EPA

- 540

- F

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- 007; OSWER

9285.7

- 33. Washington, D.C.: Office of Solid Waste and Emergency Response ; December. Available at: http://www.epa.gov/superfund/lead/products/ssircolo.pdf

.
EPA 2001a. NATA

– Evaluating the National

- scale Air Toxics Assessment, 1996 DATA

– An SAB Advisory. U .S EPA Science Advisory Board. EPA

- SAB

- EC

- ADV

- 02

- 001. 12/2001.

EPA 2001b. National

- scale Air Toxics Assessment for 1996, Draft for EPA Science Advisory Board Review. EPA Office of Air Quality Planning and Standards. EPA

- 453/R

- 01

- 003. 01/18/2001.

EPA 2002a. Total Risk Integrated Methodology: TRIM.FaTE Technical Support Document. Volume II: Description of Chemical Transport and Transformation Algorithms. EPA

- 453/R

-TRIM

- Based Tiered Screening Methodology for RTR

Tier 3 Methodology 79 October 2014 02

- 011b. Office of Air Quality Planning and Standards: Research Triangle Park, NC. September.

EP A 2002b. Evaluation of TRIM.FaTE, Volume I: Approach and Initial Findings. EPA

- 453/R

- 02

- 0012. Office of Air Quality and Planning Standards: Research Triangle Park, NC. September.

EPA 2003. Multimedia, Multipathway, and Multireceptor Risk Assessment (3MRA) Modeling System, Volume II: Site

- based, Regional, and National Data. SAB Review Draft. EP

- 530/D

03

- 001b. Office of Research and Development, Athens, GA, and Research Triangle Park, NC, and Office of Solid Waste, Washington, DC. July. Available at: http://www.epa.gov/epaoswer/hazwaste/id/hwirwste/risk03.htm

. EPA 2004a. Air Toxics Risk Assessment Reference Library; Volume 1

– Technical Resource Document, Part III, Human Health Risk As sessment: Multipathway Chapter 14, Overview and Getting Started: Planning and Scoping the Multipathway Risk Assessment. Office of Air Quality Planning and Standards, Research Triangle Park, NC. April. Available at: http://www.epa.gov/ttn/fera/data/risk/vol_1/chapter_14.pdf

. EPA 2004b. Evaluation of TRIM.FaTE Volume III: Model Comparison Focusing on Dioxin Test Case. EPA

- 453/R

- 04

- 002. Available at: http://www.epa.gov/ttn/fera/data/tr im/eval_rept_vol3_2005.pdf

EPA 2004c. Exposure and Human Health Reassessment of 2,3,7,8

- Tetrachlorodibenzo

- p

Dioxin (TCDD) and Related Compounds. Volume 2: Properties, Environmental Levels, and Background Exposures. Dioxin Reassessment, NAS Review Draft. U.S. Environmental Protection Agency, Washington, D.C., EPA/600/P

- 00/001Cb. Available at: http://www.epa.gov/ncea/pdfs/dioxin/nas

- review/

EPA 2005a. Human Health Risk Assessment Protocol for H azardous Waste Combustion Facilities (including the Hazardous Waste Companion Database of chemical

- specific parameter values). U.S. Environmental Protection Agency, Office of Solid Waste and Emergency Response, Washington, DC. EPA

- 530

- R

- 05

- 006. September.

EPA 2005b. Evaluation of TRIM.FaTE, Volume II: Model Comparison Focusing on Mercury Test Case. EPA

- 453/R

- 05

- 002. Office of Air Quality and Planning Standards: Research Triangle Park, NC. July.

EPA 2005c. Supplemental Guidance for Assessing Susceptibility from Early

- Life Exposure to Carcinogens. EPA

- 630/R

- 03

- 003F. Risk Assessment Forum: Washington, DC. March.

EPA 2006. Risk and Technology Review (RTR) Assessment Plan. Office of Air and Radiation. November 20, 2006. Available at: http://www.epa.gov/sab/panels/consul_risk_and_tech_assessment_plan.htm

.
EPA 2007. Prioritized Chronic Dose

- Response Values for Screening Risk Assessments (Table 1). Office of Air Quality Planning and Standards; June 12, 2007. Available at: http://www.epa.gov/ttn/atw/toxsource/summary.html

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

– MACT I Petroleum Refining Sources and Portland Cement Manufacturing. EPA Office of Air Quality Planning and Standards. EPA

- 452/R

- 09

- 006. 06/2009.TRIM

- Based Tiered Screening Methodology for RTR

Tier 3 Methodology 80 October 2014 EPA 2010a Development of a Relative Potency Factor (RPF) Approac h for Polycyclic Aromatic Hydrocarbon (PAH) Mixtures (External Review Draft). Washington, DC, EPA/635/R

- 08/012A February. Available at: http://cfpub.epa.gov/ncea/iris_drafts /recordisplay.cfm?deid=194584

EPA 2010 Review of EPA’s draft entitled, “Risk and Technology Review (RTR) Risk Assessment Methodologies: For Review by the EPA’s Science Advisory Board with Case Studies

– MACT I Petroleum Refining Sources and Portland Ceme nt Manufacturing.” EPA Science Advisory Board. EPA

- SAB

- 10

- 007. 05/07/2010.

EPA 2011a Exposure Factors Handbook: 2011 edition. National Center for Environmental Assessment, Washington, DC; EPA/600/R

- 09/052F. Available from the National Technical Information Service, Springfield, VA, and online at http://www.epa.gov/ncea/efh.

EPA 2011b Human Health Multipathway Residual Risk Assessment for the Secondary Lead Smelting Source Category. Draft Report. Prepared by ICF International for EPA Risk and Exposure Assess ment Group. 02/15/2011.

EPA 2011c Technical Support Document: Case Study Analyses of Potential Local

- scale Human Health Risks Associated with Mercury Emissions from Electric Utility Steam

- generating Units. Draft. Prepared by ICF International for EPA Offic e of Air Quality Planning and Standards. 02/17/2011.

EPA 2014a Technical Support Document: Human Health Multipathway Residual Risk Assessment for the Ferroalloys Production Source Category. Draft. Prepared by ICF International for EPA Office of Air Quality Planning and Standards. 02/21/2014.

EPA 2014b Technical Support Document: Human Health Multipathway Residual Risk Assessment for the Petroleum Refineries Sector. Draft. Prepared by ICF International for EPA Office of Air Quality Planning and Standards. 01 /31/2014.

ESRI (Environmental Systems Research Institute). 2014. World Imagery. Accessed June 03, 2014. Available at: http://www.arcgis.com/home/item.html?id=10df2279f9684e4a9f6a7f08febac2a9.

Hazardous Substances Data Bank (HSDB). 2005. Bethesda, MD: Natio nal Library of Medicine, U.S. [Last Revision Date 06/23/2005]. Cadmium Compounds; Hazardous Substances Databank Number: 6922. Available at: http://toxnet.nlm.nih.gov/cgi

- bin/sis/htmlgen?HSDB

Holzworth, G.C. 1972. Mixing Heights, Wind Speeds, and Potential for Urban Air Pollution Throughout the Contiguous United States,” AP

- 101, January 1972, U.S. Environmental Protection Agency, Office of Air Programs, Research Triangle Park, North Carolina.

ICF (ICF International). 2005. Memorandum: TRIM.FaTE Screening Sce nario: Aquatic Food Web Analysis; submitted to Deirdre Murphy and Terri Hollingsworth, U.S. EPA, from Margaret McVey and Rebecca Kauffman, ICF Consulting. October 18.

Mason R.P., J. Laporte, and S. Andres. 2000. Factors controlling the bioaccumulation of

m ercury, methylmercury, arsenic, selenium, and cadmium by freshwater invertebrates and fish. Archives for Environmental Contamination and Toxicology 38(3):283

- 97.

McKone, T.E., A. Bodnar, and E. Hertwich. 2001. Development and evaluation of state

- specific l andscape data sets for multimedia source

- to

- dose models. University of California at Berkeley. Supported by U.S. Environmental Protection Agency (Sustainable TechnologyTRIM

- Based Tiered Screening Methodology for RTR

Tier 3 Methodology 81 October 2014 Division, National Risk Management Research Laboratory) and Environmental Defense Fund. July. LBNL

- 43722.

Morton, F.I. 1986. Practical estimates of lake evaporation. Journal of Climate and Applied Meteorology 25:371

- 387.

NCDC (National Climatic Data Center). 1995. Hourly United States Weather Observations (HUSWO) 1990

- 1995.

National Climatic Data Center. (2007). Cimate Maps of the United States (CLIMAPS). Available online at http://cdo.ncdc.noaa.gov/cgi

- bin/climaps/climaps.pl

National Climatic Data Center (NCDC). (2012) Quali ty Controlled Local Climatological Data. Available online at http://cdo.ncdc.noaa.gov/qclcd/QCLCD?prior=N

Reinfelder, J.R., N.S. Fisher, S.N. Luoma, J.W. Nichols, and W.

- X. Wang. 1998. Trace elem ent trophic transfer in aquatic organisms: A critique of the kinetic model approach. The Science of the Total Environment 219: 117

- 135.

Saiki, M.K., D.T. Castleberry, T.W. May, B.A. Martin, and F.N. Bullard. 1995. Copper, cadmium, and zinc concentrations i n aquatic food chains from the upper Sacramento River (California) and selected tributaries. Arch. Environ. Contam. Toxicol. 29:484

- 491.

Seinfeld, J.H., and S.N. Pandis. 1998. Atmospheric Chemistry and Physics: From Air Pollution to Climate Change, Wiley

- I nterscience, New York, pp. 931

– 933.

Stull, R.B. 1988. An Introduction to Boundary Layer Meteorology. Kluwer Academic Publishers, The Netherlands. 452 pp.

Turner, D.B. 1970. Workbook of Atmospheric Dispersion Estimates. PHS Publication No. 999

- AP

- 26. U.S. D epartment of Health, Education, and Welfare, National Air Pollution Control Administration, Cincinnati, Ohio.

USGS (U.S. Geological Survey). (2012) National Hydrography Dataset. Available online at http://nhd.usgs.gov/

. USGS (U.S. Geological Survey). 1987. National Water Summary 1987

– Hydrologic Events and Water Supply and Use. USGS Water

- Supply Paper 2350. J.E. Carr, E.B. Chase, R.W. Paulson, and D.W. Moody, Compilers.

Van den Berg, M., L.S. Birnbaum, M. Denison, M. DeVito, W. Farlans, M. Feeley, H. Fiedler, H. Hakansson, A. Hanberg, L.. Haws, M. Rose, S. Safe, D. Schrenk, C. Tohyama, A. Tritscher, J. Tuomisto, M. Tysklind, N. Walker, and R.E. Peterson. 2006. The 2005 World Health Organization reevaluation of human and mammalian toxic equivalency factors for dioxins and dioxin

- like compounds. Toxicol Sci. 93(2): 223

- 41.

Wischmeier, W.H., and D. Smith. 1978. Predicting Rainfall Erosion Losses: A Guide to Conservation Planning. USDA

- ARS Agriculture Handbook No. 537, Wa shington, D.C. 58 pp.TRIM

- Based Tiered Screening Methodology for RTR

[This page intentionally left blank.]TRIM

- Based Tiered Screening Methodology for RTR

Attachment A i October 2014 TRIM.FaTE Inputs 

## TRIM .FaTE Inputs Attachment A.[This page intentionally left blank.]TRIM

- Based Tiered Screening Methodology for RTR

Attachment A iii October 2014 TRIM.FaTE Inputs Exhibits, Attachment A

Exhibit A

- 1. TRIM.FaTE Simulation Parameters fo r 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 Scree ning 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 C ompartments in the TRIM.FaTE Screening Scenario

................................ ............................... A

- 25

Exhibit A

- 19. Dioxin Chemical

- Specific Parameters for Abiotic Compartments in the TRIM.FaTE Screening Scenario

................................ ............................... A

- 28

Exhibit A

- 20. Cadmium Chemical

- Specific Parameters for Plant Compartments in the TRIM.FaTE Screening Scenario

................................ ............................ A

- 32

Exhibit A

- 21. Mercury Chemical

- Specific Pa rameters 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

- 35TRIM

- Based Tiered Screening Methodology for RTR

Attachment A iv October 2014 TRIM.FaTE Inputs Exhibit A

- 24. Cadmium Chemical

- Specific Parameters for Aquatic Species in the TRIM.FaTE Screening Scenario

................................ ................................ ..... A

- 37

Exhibit A

- 25. Mercury Chemical

- Specific Parameters for Aquatic Species in the TRIM.FaTE Screening Scenario

................................ ................................ ..... A

- 39

Exhibit A

- 26. PAH Chemical

- Specific Parameters for Aquatic Species in the TRIM.FaTE Screening Scenario

................................ ................................ ..... A

- 40

Exhibit A

- 27. Dioxin Chemi cal

- Specific Parameters for Aquatic Species in the TRIM.FaTE Screening Scenario

................................ ................................ ..... A

- 43TRIM

- Based Tiered Screening Methodology for RTR

Attachment A A

- 1 October 2014 TRIM.FaTE Inputs This attachment provides tables of the modeling inputs for the TRIM.FaTE screening scenario. Exhibit A

- 1 present s 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

- 2 7 present paramet ers 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 Ref erence 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. Outp ut time step a hr 4 Selected value.

a Output time step is set in TRIM.FaTE using the scenario properties "simulationStepsPerOutputStep" and "simulationTimeStep."

.TRIM

- Based Tiered Screening Methodology for RTR

Attachment A A

- 2 October 2014 TRIM.FaTE Inputs Exhibit A

- 2

. Meteorological Inputs for the TRIM.FaTE Screening Scenario

Parameter Name Units Value Used Reference **Meteorological Inputs** Air temperature degrees K 298 USEPA 2005. Horizontal wind speed m/sec 2.8 5th percentile annual average value for contiguous 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 degrees clockwise from N (blowing from) 3

- days

- on 4

- days

- off On is defined as time during which wind is blowing into the model domain. A conservative estimate of time during which wind should blow into the modeling domain was determined by evaluating HUSWO; it w as 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 Isl and 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 Holzwo rth, 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 unitles s

-- Value not used in current dynamic runs (would need to be reevaluated if steady

state runs are needed). isDay_SteadyState_forOther unitless

--TRIM

- Based Tiered Screening Methodology for RTR

Attachment A A

- 3 October 2014 TRIM.FaTE Inputs Exhibit A

- 3

. Air Para meters 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 orga nic matter on particulates unitless 0.2 Harner and Bidleman 1998TRIM

- Based Tiered Screening Methodology for RTR

Attachment A A

- 4 October 2014 TRIM.FaTE Inputs Exhibit A

- 4

. Soil and Groundwater Parameters for the TRIM.FaTE Screening Scenario

Parameter Name Units V alue 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 laye r thickness above surface soil m 0.005 Thibodeaux 1996; McKone et al. 2001 (Table 3). Density of soil solids (dry weight) kg[soil]/m 3 [soil] 2600 Default in McKone et al. 2001 (Table 3)

. Thickness

- untilled a m 0.01 McKone et al. 2001 (p. 30).
Thickness

- tilled a m 0.20 USEPA 2005.
Erosion fraction unitless varies b See 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 Ass umption ; 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[compartm ent] 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 McKone e t al. 2001 (Table 3).
Fraction s and unitless 0.25 Assumption

.TRIM

- Based Tiered Screening Methodology for RTR

Attachment A A

- 5 October 2014 TRIM.FaTE Inputs Exhibit A

- 4

. Soil and Groundwater Parameters for the TRIM.FaTE Screening Scenario

Parameter Name Units Value Used Reference **Root Zone Soil Comp artment 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 velocity o f water (percolation) m/day 8.22E

- 04 Assumed as 0.2 times average precipitation for New England in McKone et al. 2001. Density of soil solids (dry weight) kg[soil]/m 3 [soil] 2,600 Default in McKone et al. 2001 (Table 3).
Fraction s and unitless 0.35 Assump tion

. 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 (Tabl e 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 space ]/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.TRIM

- Based Tiered Screening Methodology for RTR

Attachment A A

- 6 October 2014 TRIM.FaTE Inputs Exhibit A

- 5

. Runoff Assumptions for the TRIM.FaTE S creening 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 SurfSoil_ S1 SW_Pond 1.0 SurfSoil_Source 0.0

SurfSoil_N1 0.0 sink 0.0 SurfSoil_N6 SW_Pond 1.0 SurfSoil_N1 0.0 SurfSoil_N7 0.0 sink 0.0 SurfSoil_N7 SW_Pond 1.0 SurfSoil_N6 0.0 SurfSoil_N3 0.0 sink 0.0 SurfSoil_N3 SW_Pond 1.0 SurfSoil_N7 0.0 S urfSoil_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.TRIM

- Based Tiered Screening Methodology for RTR

Attachment A A

- 7 October 2014 TRIM.FaTE In puts 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 Supportin g 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 17.5 5 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 fores t 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 empirical slope coefficient (always 0.125).TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment A A

- 8 October 2014 TRIM.FaTE Inputs Exhibit A

- 7

. Terrestrial Plant Placement for the TRIM.FaTE Screening Scenario

Surface Soil Volume Element Surface Soil Depth (m) Coniferous Forest Grasses/ Herbs None

Source 0.01 x

N1 0.01 x N6 0.20 (tilled) x N7 0.01 x N3 0.01 x N4 0.01 x N5 0.01 x S1 0.01 x S4 0.01 x S5 0.01 xTRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment A A

- 9 October 2014 TRIM.FaTE Inputs Exhibit A

- 8

. Terrestrial Plant Parameters for the TRIM.FaTE Screening Scenario

Parameter Name Units 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 lea f 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

.

Corre ction 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 Assumed 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 ele ments, Muller and Prohl 1993. 3.00E

- 04 1E

- 04 to 6E

- 04 for different crops and elements, Muller and Prohl 1993.

Length of leaf m 0.01 Assumption

. 0.05 Assumption

. Lipid content kg/kg wet weight 0.00224 European beech, Riederer 1995. 0.00224 European beech , Riederer 1995. Litter fall rate 1/day 0.0021 value assumes 1st

- order relationship and that 99% of leaves fall over 6 years 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 ye ars 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 foliage. 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

-TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment A A

- 10 October 2014 TRIM.FaTE Inputs Exhibit A

- 8

. Terrestrial Plant Parameters for the TRIM.FaTE Screening Scenar io

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 distribution for atmospheric particles measured on an adhesive surface, Coe and Lindberg 1987. 1.00E

- 09 Based on particle density and size distribution for atmospheric particles measured on an adhesive surface, Coe and Lindberg 1987. **Root Compartment Type**

**– Nonwoody O**  **nly** 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 r oot 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 unitl ess 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 flow

r ate 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 Ass umption

. 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).TRIM

- Base d Tiered Screening Methodolo gy for RTR

Attachment A A

- 11 October 2014 TRIM.FaTE Inputs Exhibit A

- 9

. Surfa ce Water Parameters for the TRIM.FaTE Screening Scenario

Parameter Name Units Value Used Reference Algae carbon content (fraction) unitless 0.465 APHA 1995.

Algae density in water column g[algae]/L[water] 0.0025 Millard et al. 1996 as cited in ICF 2005. Algae growth rate 1/day 0.7 Hudson et al. 1994 as cited in Mason et al. 1995b

. Algae radius um 2.5 Mason et al. 1995b. Algae water content (fraction) unitless 0.9 APHA 1995. Average algae cell density (per vol ume cell, not water) g[algae]/m 3 [algae] 1,0 00,000 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 base d on relationship between drainage basin and lake area size.
Dimensionless viscous sublayer thickness unitless 4 Ambrose et al. 1995.

Drag coefficient for water body unitless 0.0011 Ambrose et al. 1995.

Flush rate 1/year 12.17 Calculated based on pond d imensions 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 suspended 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."TRIM

- Base d Tiered Screening Methodolo gy for RTR

Attachment A A

- 12 October 2014 TRIM.FaTE Inputs Exhibit A

- 10

. Sediment Parameters for the TRIM.FaTE Screening Scenario

Parameter Name Units Value Used Reference Depth a m 0.05 McKone et al. 2001 (Table 3).
Fraction s and unitless 0.25 Assumption

. Organic carbon fraction unitless 0.02 McKo ne 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

. S ediment resuspension velocity m/day 6.69E

- 05 Calculated from water balance model.

a Set using the volume element properties named "top" and "bottom."TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment A A

- 13 October 2014 TRIM.FaTE Inputs Exhibit A

- 11

. Aquati c 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

.TRIM

- Based Tiered Scree ning Methodolo gy for RTR

Attachment A A

- 14 October 2014 TRIM.FaTE Inputs Exhibit A

- 12

. Cadmium Chemical

- Specific Parameters for the TRIM.FaTE Screening Scenario

Parameter 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 199 9 (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 part ition 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.TRIM

- Based Tiered Scree ning Methodolo gy for RTR

Attachment A A

- 15 October 2014 TRIM.FaTE Inputs Exhibit A

- 13

. Mercury Chemical

- Specific Parameters for the TRIM.FaTE Screening Scenario

Parameter Name Units Value Reference Hg(0) b Hg(2) b MHg b

CAS number unitless 7439

- 97

- 6 14302

- 87

- 5 22967

- 92

- 6

- Diffusion coe fficient 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 5.50E+0 2 443 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 al. 1 995.

a All parameters in this table are TRIM.FaTE chemical properties.

b On this and all following tables, Hg(0) = elemental mercury, Hg(2) = divalent mercury, and MHg = methyl mercury.TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment A A

- 16 October 2014 TRIM.FaTE Inputs Exhibit A

- 14

. PAH Chemical

- Specific Parameters for the TRIM.FaTE Screening Scenario

Parameter Name Units Value 2Methyl 712DMB Acenaphthene Acenaphthylene BaA BaP BbF BghiP
CAS number unitless 91

- 57

- 6 57

- 97

- 6 83

- 32

- 9 208

- 96

- 8 56

- 55

- 3 50

- 32

- 8 205

- 99

- 2 191

- 24

- 2
Diffusion coefficient in pure air m 2 /day 0.451 0.691 0.009 0.388 0.441 0.372 0.009 0.190

Diffusion coefficient in pure water m 2 /day 6.70E

- 05 6.91E

- 05 8.64E

- 05 6.03E

- 05 7.78E

- 05 7.78E

- 05 8.64E

- 05 4.54E

- 05

Henry's Law constant Pa

- m 3 /mol 50.56 0.20 18.50 12.70 1.22 0.07 0.05 0.03

Melting point degrees K 307.75 396.65 366.15 365.65 433 452 441 550.15 Molecular weight g/mol 142.20 256.35 154.21 152.20 228.29 252.32 252.32 276.34
Octanol

- water partition coefficient (K ow ) L[water]/L[oc tanol] 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 p ure 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 6TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment A A

- 17 October 2014 TRIM.FaTE Inputs Exhibit A

- 1 4. PAH Chemical

- Specific Parameter s 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, Ace naphthylene, 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 cons tant Pa

- m 3 /mol USEPA 2005. Exceptions include USEPA 2003 (2

- Methylnaphthalene) HSDB 2001a (7,12

- Dimethylbenz(a)anthracene), HSDB 2001b (Acenaphthylene), and HSDB 2001c (Benzo(g,h,i)perylene) Melting point degrees K Budavari 1996. Exceptions include USEPA 2003 (2

- Methylnaphthalene), HSDB 2001a (7,12

- Dimethylbenz(a)anthracene), HSDB 2001b (Acenaphthylene), HSDB 2001c (Benzo(g,h,i)perylene), and USEPA 2005 (Acenaphthene, Fluoranthene, and Fluorene) Molecular weight g/mol Budavari 1996. Exceptions include USE PA 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[octa nol] Hansch et al. 1995. Exceptions include Passivirta et al. 1999 (Acenaphthylene, Benzo(k)fluoranthene, and Indeno(1,2,3

- cd)pyrene), and Sangster 1993 (Benzo(b)fluoranthene)TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment A A

- 18 October 2014 TRIM.FaTE Inputs Exhibit A

- 15

. Dioxin Chemical

- Specific Parameters for the TRIM.FaTE Screening Scenario

Parameter Name Units 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

- 05 6. 91E

- 05 6.91E

- 05 6.91E

- 05 6.91E

- 05 6.91E

- 05

Henry's Law constant Pa

- m 3 /mol 0.684 0.19 1.22 1.43 1.42 1.08 1.45 Melting 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

- wat er 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 6.9 1E

- 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+07TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment A A

- 19 October 2014 TRIM.FaTE Inputs 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 EPA 2 005

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 200 0a (1,2,3,6,7,8

- HxCDD, 1,2,3,6,7,8

- HxCDF, 1,2,3,7,8,9

- HxCDD, 1,2,3,7,8,9

- HxCDF, and 2,3,4,6,7,8

- HxCDF), and Sijm et al. 1989 as cited in USEPA 2000b (1,2,3,7,8

- PeCDD)TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment A A

- 20 October 2014 TRIM.FaTE Inputs Exhibit A

- 16

. Cadmium Chemical

- Specific Parameters for Abiotic Compartments in the TRIM.FaTE Screening Scenario

Parameter Name Units Value Reference

**Air Compartment Type** Particle dry deposition velocity m/day 260 Calculated from 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 = no, E lse = 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 w ater L[water]/g[algae wet wt] 1.87 McGeer et al. 2003.TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment A A

- 21 October 2014 TRIM.FaTE Inputs Exhibit A

- 17

. Mercury Chemical

- Specific Parameters for Abiotic Compartments in the TRIM.FaTE Screening Scenario

Par ameter 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 Assumpti on

. 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 cha racteristic 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)

- estimate 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 pote ntial 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.
Oxidati on 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 fores ted region (Lindberg 1996; Carpi and Lindberg 1997).TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment A A

- 22 October 2014 TRIM.FaTE Inputs Exhibit A

- 17 . Mercury Chemical

- Specific Parameters for Abiotic Compartments in the TRIM.FaTE Screening Scenario

Parameter Name Units Value Reference Hg(0) Hg(2) MHg Root Zone Soil Compartment Ty pe 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 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 r ate 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). **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 repor ted 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).TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment A A

- 23 October 2014 TRIM.FaTE Inputs Exhibit A

- 17 . Mercury Chemical

- Specific Parameters for Abiotic Compartments in the TRIM.FaTE Screening Scenario

Parameter Name Units Value Reference Hg(0) Hg(2) MHg **Groundwater Compartment Type**
Soil

- water partition coefficient L[water]/kg[soil wet wt] 1,000 58,000 7,000 USEPA 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 condi tions. Methylation rate 1/day 0 0.001 0 Range reported in Porvari and Verta 1995 is 2E

- 4 to 1E

- 3 /day; value is average maximum potential methylation rate constant under anaerobic conditions.
Oxidation rate 1/day 1.00E

- 08 0 0 Small default nonzero value (0 assumed in USEPA 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 rat e 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[water] /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 rat e 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 great er than 17m, up to 3.5/day (Xiao et al. 1995; Vandal et al. 1995; Mason et al. 1995a; Amyot et al. 1997).TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment A A

- 24 October 2014 TRIM.FaTE Inputs Exhibit A

- 17 . Mercury Chemical

- Specific Parameters for Abiotic Compartments in the TRIM.FaTE Screening Scenario

Parameter Name Units Value Referenc e 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. Methyl ation 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.TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment A A

- 25 October 2014 TRIM.FaTE Inputs Exhibit A

- 18

. PAH Chemical

- Specific Parameters for Abiotic Compartments in the TRIM.FaTE Screening 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 200 000 **Surface Soil Compartment Type** User input characteristic depth ( Boolean ) 0 = No, Else = Yes 0 0 0 0 0 0 0 0 0

Half

- life day 18 24 56 66.5 680 530 610 415 2140 **Root Zone Soil Compartment Type** User input characteristic depth ( Boolean ) 0 = No, Else = Yes 0 0 0 0 0 0 0 0 0

Half

- life day 18 24 56 66.5 680 530 610 415 2140 **Vadose Zone Soil Compartment Type** User input characteristic depth ( Boolean ) 0 = No, Else = Yes 0 0 0 0 0 0 0 0 0

Half

- life day 36 48 112 133 1360 1060 1220 830 4280
**Groundwater Com partment 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 2290TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment A A

- 26 October 2014 TRIM.FaTE Inputs Exhibit A

- 1 8. PAH Chemical

- Specific Parameters for Abiotic Compartments in the TRIM.FaTE Screening Scenario

Parameter Name Units Value Refer ence 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. Ex ceptions include ATSDR 2005 (2

- Methylnaphthalene), USEPA 1998 (7,12

- Dimethylbenz(a)anthracene, Benzo(g,h,i)perylene, and Fluoranthene) / average of range, HSDB 2001d (Acenaphthene), HSDB 2001b (Acenaphthylene), and Spero et al. 2000 (Fluorene)

. Washout r a tio 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. Exce ptions 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 Comp artment Type**  User input characteristic depth ( Boolean ) 0 = No, Else = Yes 0 0 0 0 0 Assumption.

Half

- life day 1000 940 275 33 730 Howard et al. 1991 / upper bound measured or estimated value. Exceptions include ATSDR 2005 (2

- Methylnaphthalene), USEPA 19 98 (7,12

- Dimethylbenz(a)anthracene, Benzo(g,h,i)perylene, and Fluoranthene) / average of range, HSDB 2001d (Acenaphthene), HSDB 2001b (Acenaphthylene), and HSDB 2001e (Fluorene)

. **Vadose Zone Soil Compartment Type** User input characteristic depth ( Boolean ) 0 = No, Else = Yes 0 0 0 0 0 Assumption.TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment A A

- 27 October 2014 TRIM.FaTE Inputs 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 Compartm ent Type**  **, continued**
Half

- life day 2000 1880 550 66 1460 Howard et al. 1991 / upper bound measured or estimated value. Exceptions include ATSDR 2005 (2

- Methylnaphthalene), USEPA 1998 (7,12

- Dimethylbenz(a)anthracene, Benzo(g,h,i)perylene, and Fluoranthene) / twice average of range, HSDB 2001d (Acenaphthene) / multiplied by 2, HSDB 2001b (Acenaphthylene) / multiplied by 2, and HSDB 2001e (Fluorene) / multiplied by 2

. **Groundwater Compartment Type**
Half

- life day 2000 1880 550 66 1460 Howard et al. 1991 / uppe r 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

.

Ha lf

- life day 1.626 97.8 160 8.5 750 Howard et al. 1991 / upper bound measured or estimated value. Exceptions include HSDB 2005 (2

- Methylnaphthalene), HSDB 2001a (7

- 12 Dimethylbenz(a)anthracene), HSDB 2001d (Acenaphthene), HSDB 2001b (Acenaphthylene), and H SDB 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. assig ned for sediment, except for BbF and IcdP, which were not i n Table 2.3 of Mackay et. al.TRIM

- Based Tiered Screening Methodolo gy for RTR

At tachment A A

- 28 October 2014 TRIM.FaTE Inputs Exhibit A

- 19

. Dioxin Chemical

- Specific Parameters for Abiotic Compartments in the TRIM.FaTE Screening Scenario

Parameter Name Units 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 500 500 500 500 500 500

H alf

- 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 = Yes 0 0 0 0 0 0

Halflife day 3650 3650 3650 3650 3650 3650 **Root Zone Soil Compartment Type** Input characteristic depth m 0.08 0.08 0.08 0.08 0.08 0.08 Use input characteristic depth 0 = No, Else = Yes 0 0 0 0 0 0

Half

- life day 3650 3650 3650 3650 3650 3650 **Vadose Zone Soil Compartment Type** Input characteristic depth m 0.08 0.08 0.08 0.08 0.08 0.08 Use input characteristic depth ( Boolean ) 0 = No, Else = Yes 0 0 0 0 0 0

Half

- life day 1008 1008 1008 1008 1008 1008 **Groundwater Compartment Type**
Half

- life da y 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**
Hal f

- life day 1095 1095 1095 1095 1095 1095TRIM

- Based Tiered Screening Methodolo gy for RTR

At tachment A A

- 29 October 2014 TRIM.FaTE Inputs 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 characteri stic depth m 0.08 0.08 0.08 0.08 0.08 0.08 Use input characteristic depth ( Boolean ) 0 = No, Else = Yes 0 0 0 0 0 0

Half

- life day 3650 3650 3650 3650 3650 3650 **Root Zone Soil Compartment Type** Input characteristic depth m 0.08 0.08 0.08 0.08 0.08 0.08
U se input characteristic depth 0 = No, Else = Yes 0 0 0 0 0 0

Half

- life day 3650 3650 3650 3650 3650 3650 **Vadose Zone Soil Compartment Type** Input characteristic depth m 0.08 0.08 0.08 0.08 0.08 0.08 Use input characteristic depth ( Boolean ) 0 = No, Else = Yes 0 0 0 0 0 0

Half

- life day 1008 1008 1008 1008 1008 1008 **Groundwater Compartment Type**
Half

- life day 1008 1008 1008 1008 1008 1008 **Surface Water Compartment Type** Ratio Of Conc In Algae To Conc Dissolved In Water (g[chem]/g[algae])/ (g[chem]/L[wate r]) 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 1095TRIM

- Based Tiered Screening Methodolo gy for RTR

At tachment A A

- 30 October 2014 TRIM.FaTE Inputs Exhibit A

- 1 9. Dioxin Chemical

- Specific Parameters for Abiotic Compartments in the TRIM.FaTE Screening Sc enario

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 10000 14000 18000 19000 **Surface Soil Compartment Type** Input characteristic depth m 0.08 0.08 0.08 0.08 0.08 Use input characteristic depth ( Boolean ) 0 = No, Else = Yes 0 0 0 0 0

Half

- life day 3650 3650 3650 3650 3650 **Root Zone Soil Compartment Type** Input c haracteristic 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 charact eristic depth ( Boolean ) 0 = No, Else = Yes 0 0 0 0 0

Half

- life day 1008 1008 1008 1008 1008 **Groundwater Compartment Type**
Half

- life day 1008 1008 1008 1008 1008 **Surface Water Compartment Type** Ratio Of Conc In Algae To Conc Dissolved In Water (g[chem]/g [algae])/ (g[chem]/L[water]) 1.75 4.26 1.39 1.76 0.71

Half

- life day 0.19 0.58 0.19 2.7 0.18 **Sediment Compartment Type**
Half

- life day 1095 1095 1095 1095 1095TRIM

- Based Tiered Screening Methodolo gy for RTR

At tachment A A

- 31 October 2014 TRIM.FaTE Inputs 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 Com partment 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 Zon e 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

. **Vadose Z one 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 aero bic 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.
**Surf ace 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 bas ed on Adriaens and Grbic

- Galic 1992,1993 and Adriaens et al. 1995 as cited in USEPA 2000 b

.TRIM

- Based Tiered Screening Methodolo gy for RTR

At tachment A A

- 32 October 2014 TRIM.FaTE Inputs Exhibit A

- 20

. Cadmium C hemical

- Specific Parameters for Plant Compartments in t he TRIM.FaTE Screening Scenario

Parameter Name Units Value Reference

**Leaf Compartment Type** Transfer factor to leaf particle 1/day 0.002 Assumption

. **Particle on Leaf Compartment Type**
Transfer factor to leaf 1/day 0.200 Assumption

. **Root Compartment Type** 

**- Grasses and 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 va rious 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 Tsiros et al. 1999.

a Roots and stems are not modeled for deciduous or coniferous forest in the current version of TRIM.FaTE.TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment A A

- 33 October 2014 TRIM.FaTE Inputs Exhibit A

- 21

. Mercury Chemical

- Specific Parameters f or 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 particle to leaf. Demethylation rate 1/day N/A N/A 0.03 Calculated from Bache et al. 1973. Methylation rate 1/day 0 0 0 Assumed from Gay 1975, Bache et al. 1973. Oxidation rate 1/day 1.0E+06 0 0 A ssumed to be nearly instantaneous Reduction rate 1/day 0 0 0 Assump tion

. **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 As sumption

. **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. 19 98, 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 A ssumption

. 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. 1998. Demethylation rate 1/day N/A N/A 0.03 Calculated from Bache et al. 1973. Methylation rate 1/day 0 0 0 Assumption

. Oxidation rate 1/day 0 0 0 Assumption

. Reduction rate 1/day 0 0 0 Assumption

.

a Roots and stems are not modeled for deciduous or coniferous forest in the current version of TRIM.FaTE.TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment A A

- 34 October 2014 TRIM.FaTE Inputs Exhibit A

- 22

. PAH Chemical

- Specific Parameters for Plant Compartments in the TRIM.FaTE Screening Scenario

Parameter Name Units Value

2Methyl 712DMB Acenaph

thene Acenaph

thylene BaA BaP BbF BghiP BkF

**Leaf Compartment Type**
Transfer factor to leaf particle 1/day 1.00E

- 04 1.00E

- 04 1.00E

- 04 1.00E

- 04 1.00E

- 04 1.00E

- 04 1.00E

- 04 1.00E

- 04 1.00E

- 04

Half

- life day 3.50 3.50 3.50 3.50 3.50 3.50 3.50 3.50 3.50 **Particle on Leaf Compartment Type**
Transfer factor to leaf 1/day 1.00E

- 04 1.00E

- 04 1.00E

- 04 1.00E

- 04 1.00E

- 04 1.00E

- 04 1.00E

- 04 1.00E

- 04 1.00E

- 04

Half

- life day 2.31 2.31 2.31 2.31 1.84 2.31 3.56 2.31 17.80 **Root Compartment Type**

**- Grasses and 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.TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment A A

- 35 October 2014 TRIM.FaTE Inputs Exhibit A

- 23

. PAH Chemical

- Specifi c Parameters for Plant Compartments in the TRIM.FaTE Screening Scenario

Parameter Name Units Value
Reference Chr DahA Fluoran

thene Fluorene IcdP

**Leaf Compartment Type**
Transfer factor to leaf particle 1/day 1.00E

- 04 1.00E

- 04 1.00E

- 04 1.00E

- 04 1.00E

- 04 Assumption

.

Half

- life day 3.50 3.50 3.50 3.50 3.50 Edwards 1988 ( calculated from metabolic rate constant )

. **Particle on Leaf Compartment Type**
Transfer factor to leaf 1/day 1.00E

- 04 1.00E

- 04 1.00E

- 04 1.00E

- 04 1.00E

- 04 Assumption

.

Half

- life day 4.12 17 .80 2.31 2.31 17.80 Calculated as 2 times the measured photolysis half

- life from Mackay et al. 1992. Exceptions include values that have been set equal to Benzo(a)pyrene (2

- Methylnaphthalene; 7,12

- Dimethylbenz(a)anthracene; Acenaphthene; Acenaphthylene; B enzo(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 0.95 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 T RIM.FaTE.TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment A A

- 36 October 2014 TRIM.FaTE Inputs 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 Calculated a s 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/d ay 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**

**- Gra**  **sses 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 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.

a Roots and stems are not modeled for deciduous or coniferous forest in the current version of TRIM.FaTE.TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment A A

- 37 October 2014 TRIM.FaTE Inputs Exhibit A

- 24

. Cadmium Chemical

- Specific Parameters for Aquatic Species in the TRIM.FaTE Screening Scenario

Parameter Name Units Value Reference

**Zooplankton Compartment Type**
Ab sorption 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 Typ e**  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 Assu mption. **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.
Elimin ation 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 on Ya n 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.TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment A A

- 38 October 2014 TRIM.FaTE Inputs Exhibit A

- 24 . Cadmium Chemical

- Specific Parameters for Aquat ic 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 unitless 0.1 Assumption based on Yan and Wang 2002. Absorption rate constant unitless 1.23 Calculated based on body weight from regression in Hendriks and Heikens 200 1.
Elimination rate constant unitless 1.73E

- 02 Assumption.
**Water**

**- column Carnivore Compartment T**  **ype** 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

- 02 As sumptionTRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment A A

- 39 October 2014 TRIM.FaTE Inputs Exhibit A

- 25

. Mercury Chemical

- Specific Parameters for Aquatic Species in the TRIM.FaTE Screening Scenario

Parameter Name Units Value Reference Hg(0) Hg(2) M Hg **Zooplankton Compartment Type**
Assimilation Efficiency From Algae unitless 0.2 0.015 0.5 Environment Canada 2002.
Half

- life day 1.0E+09 1.0E+09 1.0E+09 Assumption

. How Much Faster Hg Elimination Is Than For MHg unitless 3 3 1 Assumption

.

Methylation rate 1/day 0 0 0 Assumption

. Oxidation rate 1/day 0 1.0E+06 0 Assumption

. Reduction rate 1/day 0 0 0 Assumption

. **Benthic Invertebrate Compartment Type** Alpha of equilibrium for sediment partitioning unitless 0.95 0.95 0.95 Selected valu e (i.e., proport ion 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 equilib rium 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 Willi ams 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 efficien cy from plankton unitless 0.06 0.06 0.5 Williams et al. 2010.

a Screening scenario includes: Benthic Omnivore, Benthic Carnivore, Water

- column Herbivore, Water

- column Omnivore, and Water

- column Carnivore.TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment A A

- 40 October 2014 TRIM.FaTE Inputs Exhibit A

- 26

. PAH Chemical

- Specific Parameters for Aquatic Species in the TRIM.FaTE Screening Scenario

Parameter Name Units Value

2Methyl 712DMB Acenaph

thene Acenaph

thylene BaA BaP BbF BghiP BkF

**Zooplankton Compa rtment 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/d ay 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, and W ater

- column Carnivore.TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment A A

- 41 October 2014 TRIM.FaTE Inputs 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 rate

c onstant 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)

. Eli mination 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 723 5 7235 7235 7235 7235 Stehly et al. 1990

.

Half

- life day 0.495 17 0.722 0.722 17 Moermond et al. 2007

.TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment A A

- 42 October 2014 TRIM.FaTE Inputs Exhibit A

- 26 . PAH Chemical

- Specific Parameters for Aquatic Species in the TRIM.FaTE Screening Scenario

Parameter Name Units Value Reference Chr DahA 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

- Methylnaphthalene and Acenaphthene) and Niimi and Palazzo 1986 (Acenaphthylene, Fluoranthene, and Fluorene)

. Half

- life day 0.533 2 0.165 0.2 2 Moermond et al. 2007

.

a Screening scenario includes: Benthic Omnivore, Benthic Carnivore, Water

- column Herbivore, Water

- column Omn ivore, and Water

- column Carnivore.TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment A A

- 43 October 2014 TRIM.FaTE Inputs Exhibit A

- 27 . Dioxin Chemical

- Specific Parameters for Aquatic Species in the TRIM.FaTE Screening 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]

- day 86 40 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 lph a 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 70TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment A A

- 44 October 2014 TRIM.FaTE Inputs 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 unitless 0 .31 0.31 0.42 0.42 0.31 0.42 0.41 0.51
Elimination Rate Constant 1/day 0.0099 0.0413 0.0819 0.2316 0.0192 0.4331 0.2268 1.0375
Half

- life day 7E+06 7E+06 7E+06 7E+06 7E+08 7E+08 7E+06 7E+08 **Benthic Invertebrate Compartment**
Clearance Constant unitless 0 0 0 0 0 0 0 0 Sediment Partitioning Partition Coefficient kg/kg 0.015 0.067 0.098 0.024 0.072 0.17 0.205 0.056

Sediment Partitioning Alpha of Equilibrium unitless 0.95 0.95 0.95 0.95 0.95 0.95 0.95 0.95

Sediment Partitioning Time to Reach Alpha of Equil ibrium 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 fficie ncy 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 70TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment A A

- 45 October 2014 TRIM.FaTE Inputs Exhibit A

- 2 7. Dioxin Chemical

- Specific Parameters for Aquati c 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 Morrison 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 between va lues 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

. **Benthic I nvertebrate 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

. Sediment 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 Comp artmentsa**  Assimilation Efficiency from Food unitless Morrison et al. 1999. Exceptions include Niimi and Oliver 1996 (1,2,3,4,6,7,8,9

- OCDD, 1,2,3,4,6,7,8,9

- OCDF), Van den Berg et al. 1994 (1,2,3,4,6,7,8

- HpCDD), Berntssen et al. 2007 ( 1,2,3,4,6,7,8

- HpCDF), and 1,2,3,4,7,8,9

- HpCDF set conservatively as approximate linear interpolation between values for 1,2,3,4,7,8

- HxCDD and 1,2,3,4,6,7,8

- HpCDD / 1,2,3,4,6,7,8

- HpCDF (i.e., 0.3 to 0.1

– 0.2). Chemical Uptake Rate Via Gill L[water]/kg[fish wet wt]

- day Muir et al. 1985. Exception is Opperhuizen et al. 1986 (1,2,3,7,8,9

- HxCDF, 1,2,3,7,8

- PeCDD, 1,2,3,7,8

- PeCDF, 2,3,4,7,8

- PeCDF, 2,3,7,8

- TCDD, 2,3,7,8

- TCDF)

. Half

- life day Berntssen et al. 2007

.

a Screening scenario includes: Benthic Omnivore, Benthic Carnivore, Wat er

- 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 c olumn omnivore, benthic carnivore, and water column carnivore; 0.37 used for water column herbivore.TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment A A

- 46 October 2014 TRIM.FaTE Inputs 

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Attachment A A

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. H .G

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, G. R o s e n l und

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. E

n v i r o n me n tal T o

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c o l o g y a n d Ch e m istr y 26 ( 1 ) : 1 3

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. 1 8 97 / 06

- 1 2 2 r

. 1

.
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. d o i: 1 0

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m a t e rials a n d me t a b o lic t ra n s f o r m a t i o n

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c on c ent rat ion s in La ke O nt ario a qu atic b i o ta. Canadian Journal of Fisheries and Aquatic Sciences 56(8 ) :1389

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– 1400 (1999).TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment A A

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. T. Tiss ue

. 1980 . Ca dm i u m io n t h e sou t he rn b asin o f L a k e Mi c h i g a n

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mo l e c u lar s tr u ct u re a n d w at e r c h e m i s tr y

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e n e ri ). E n v i r o nm e n tal T o

xi

c o l o g y 5 : 49

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d

a (E ph e m e r o p t e ra) as
a b i o l o g ical m o d e l in a qu a tic e c o t o xic o l o g y

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s e d i me n t. E n v ir

o n me n t al P o ll u t i o n 6 9 : 51

- 67.TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment A A

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. M

., H

. W e ve r, P

. J

. d e Vri e s, a n d A

. O pp e r h u i z e n

. 1 9 89

. Octan

- 1

- o l / w a t e r p ar t i ti o n
c oe ff i cie n ts o f p o l y c h l o ri n a t e d d i b e nz o

- p

- d i o xi n s a n d d i b e nz o f u ra n s: e x p e r i me n t al v al u e s d e t e r m i n e d with a s tirri n g m e t h o d

. Ch e m o s ph e r e 1 9 ( 1

- 6 ) : 2 6 3

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, S. L ., a n d R .A

. H i te s. 1 9 94

. I m p o r t a n c e o f v e g e tat i o n in r e m ov i n g p o l y c y clic ar o m a tic h y d r o car b o n s f r o m t h e a t m o s ph e r e

. N at

u r

e 3 7 0 : 49

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, B. De V i t o , a n d L

. T h e o d o r e

. [ e d s. ]

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, N Y: CRC P r e ss, p

. 1 0 7 2

.
S t e h l y , G

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. L a nd r u m , M

. G. H e n r y , a n d C. K l e mm

. 1 9 90

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o n me n t al T o

xi

c o l o g y a n d Ch em i s tr y 9 : 1 67

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T h i b o d e a u x, L .J

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, N Y: J o h n Wi l e y a n d S o n s, I n c.

T ho m a nn , R .V

. 19 89 . B i o a c c u m u lat i o n mo d e l o f o r g a n i c

- c h em i cal d istri bu t i o n in a qu atic f o o d

- c h a i n s. E

n v ir

o n me n t al S cie n c e a n d T e c h n o l o g y 2 3 ( 6 ) : 6 9 9

- 707.

Tra pp , S

. 1 9 9 5

. M o d e l f o r up take o f x e n o b i o tics i n to p l a n ts. I n : Tra pp , S . a n d J . C. M c F arla n e , e d s. P la n t c o n t a m i n at i o n : M o d e li n g a n d si m u lat i o n o f o r g a n ic c h em ical p r o c e s s e s. B o ca R a t o n , F L : L e wis P ub lis h e rs. pp

. 1 07

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Tr ud e l, M

., a n d J. B. Ra s m u s s e n

. 1 9 9 7

. M o d e li n g t h e e l i m i n at i o n o f m e rc u ry b y fi s h

. E n v ir

o n m e n tal S ci e n ce a n d T e c h n o l o gy 3 1 : 1 7 16

- 1 7 22.

Tsir o s, I

. X

. ; R.B. A m b r o s e , a n d A . C h r o n o p o u l o u

- S e r eli. 1 9 99

. A ir

- v e g etat i o n p ar t i t i o n i n g o f t o xi c
c h em i c als in e n v ir o n me n t a l si m u lat i o n mo d e li ng . Gl o b al N E S T: T h e I n t ernational J o u r n al 1 ( 3 ) : 1 77

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US E P A ( U.S. Environmental Protection Agency ). 2 0 07

. D r aft risk asses s m e n t f o r t h e S ie m e n s Wa t er T e c hn o l o g i e s C o r p

. car b o n r e act i v ati o n facili t y P ark e r , A ri z o n a. App e nd ix F . C h e m i cal

- ph y sical p ara me t e rs f o r c o m p o und s n o t in U S E P A' s HH R A P

. A v aila b le at: h tt p : / / w ww

. e p a .g o v / re g i on 9/ w ast e / si e m e n s / pd f / Ri s k A s ses s m e n t / s i e m e n s

- ris k assess App x F.pd f.

U S E P A

. 2 0 05

. H u m an H e a l th Risk A ss e s s m e n t P r o t o c o l f o r H a z ar d o u s Wa s te C o m bu s t i o n F aciliti e s. C om p a n i o n D ata b as e

. A v aila b le a t: h tt p : / / w ww

. e p a .g o v / r e g i on 6 / 6 pd / rcr a _c / p r o t o c o l / p r o t o c o l .h t m.
USEPA 2003. Toxicological Review of 2

- Methylnaphthalene. Available online at: http://www.epa.gov/iris/toxreviews/1006tr.pdf

.
USEPA

. 2 0 00 a. E st i m a ti o n P r o g ram I n t e rfa c e ( E P I) s u i t e . Offi c e o f Po ll u t i o n P r e ve n t i o n a n d T o xics ( O P P T). A v aila b le a t: h tt p : / / w ww

. e p a .g o v/ o pp t / ex p o s u r e / d o c s / e p is u ite d l

. h t m

.
USEPA

. 2 0 00 b

. D raft e x p o s u r e a n d hu m an h e alth r e a s ses s m e n t o f 2

, 3

, 7

, 8

- t e t r ac h l o r o d i b e nzo

- p

- d i o xin T C DD ) a n d r e la te d c o m p o und s, V o l u m e 3 : P r o p e rt i e s, e n v ir o n me n tal l e ve ls, a n d b ack g r o un d

e x p o s u r e s, C h a p t e r 2 : P h y s i cal a n d c h e m i cal p r o p e r ties a n d fate a n d App e nd ix A . E P A / 6 0 0 / P

0 0 / 0 0 1 B c. A v aila b le at: h tt p : / / w ww

. e p a .g o v / n c e a /pd fs / d i o xi n / p art 1 / vo l u m e3 / c h ap

- 2 .pd f.

USEPA

. 1 9 99

. S c r ee n i n g le v el e c o l o g ical r i sk asses s me n t p r o t o c o l f o r h a z ar d o u s wa s te c o m b u sti o n f acil i ties. P e e r R e v i e w D ra f t, N o v em b e r. A v ail a b le at: h tt p : / / w ww

. e p a .g o v /e p a o sw e r / h a z was t e / c o m bu s t / e c o risk .h t mTRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment A A

- 53 October 2014 TRIM.FaTE Inputs USEPA

. 1 9 98

. C h em ical fate h alf

- li v es f o r T o xics Re l ease I n v e n t o ry (T RI) c h em i cals. Ju l y

. S RC T R 9 8

- 0 08

.
USEPA

. 1 9 97 a. Supp l eme n tal b a ck g r o un d d o c um e n t; N o ng r o und w a t e r p at h way risk as s es s m e n t ; P et r o le u m pr o c e ss w aste listing d e t e r m i n at i o n

. App e nd ix B. C o m p o un d s p e cif i c i npu t v al u e s. A v aila b le a t : h tt p : / / w ww

. e p a .g o v/ o s w /h a z ar d / was t e t y p e s / was t e i d / p e t r o r e f / a pp e ndb.pd f.

USEPA

. 1 9 97 b

. U .S . E n v ir o n m e n tal P r o t ect i o n Ag e n c y

. M erc u r y st ud y r e p o rt to c o ng r e ss. V o l um e III: F a te a n d tra n s po rt o f m e r c u ry in t h e e n v i r on me n t. Office o f A ir Q u ality P la nn i n g a n d S ta nd ar d s a n d Office o f Re s earch a n d D e v e l o pm e n t.

V an d e n B e r g , M

., J

. D e J o n gh

, H

. Po i g e r, a n d J. R. Ol s on

. 1 9 94

. T h e t o x i c o ki n e tics a n d me t a b o li s m o f p o l y c h l o ri n at e d d i b e n z o

- p

- d i o xi n s ( P C DD s) a n d d i b e nz o f u ra n s ( P C D F s ) a n d t h e ir r e le v a n c e f o r t o x i cit y

. CRC Critical R e v i e w s in T o

x

i c o l o g y 2 4 ( 1 ) :1

- 74.

V a nd al, G

. M

. , W .F

. F it zg e r a l d

, K

. R. R o lf hu s, a n d C .H. L a m b o r g

. 1 9 95

. M o d e li n g t h e e l e me n tal m e rc u ry
c y cle in P al l e t t e L ak e , Wis c o n si n , U SA

. W at

e r, A ir, a n d S o il Po ll u t i o n 8 0 : 7 89

- 7 9 8.

V u l yk h

, N ., a n d V. Sh ata l ov

. 2 0 01 . I n ve sti g a t i o n o f D i o x i n / Fu ran C o m po siti o n in E m issi o n s a n d in E n v ir o n me n t al M e d ia: S e l e cti o n o f C o ng e n e rs f o r M od e li ng . T e c hn ical N o te 6 / 20 01

. M e t e o r o l o g ical S y n t h e si z i n g C e n t e r

- E a st.

Wa ng , X

. , a n d W. X

. Wa ng

. 2 0 06 . B i o a c c u m u lat i o n a n d tra n sf e r o f b e nz o ( a ) p y r e n e in a s i m p lified

m ari n e f oo d c h ai n

. M ari n e E c o l o gy P r o g r ess S erie s 3 1 2 : 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) bioaccumula tion from water and food by the benthic amphipod Leptocherius plumulosus

. Environmental Toxicology and Chemistry 29(8):1755

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Wil m e r, C., a n d M

. F ric k e r. 1 9 96

. S t o m ata. S e c o n d e d

. N e w Y o r k

, N Y: C h a p m an a n d H all. p

. 1 2 1

.
WI D N R (Wi s c o n sin De p ar t me n t o f N at u ral R e s o u r c e s) 2 0 07

. A G u i d e to U nd e rs t a nd i n g t h e H y d r o l o g ic C o nd iti o n o f Wis c o n si n ’s L a ke Sup e r i o r W a t e rs h e d s. Wisc o n sin De p a r t me n t o f N at u ral R e s o u rc e s ( D N R).

X ia o , Z .F.

, D

. S t r o m b e r g , a n d O. L i ndq v ist. 1 9 9 5 . I n fl u e n ce o f hu m ic s ub sta n c e s o n ph o t o l y s is o f d i v al e n t me rc u r y in a qu e o u s s o l u t i on

. Wat e r, A ir, a n d S o il P o ll u ti o n 8 0 : 7 89

- 7 9 8.

Ya n , X ; and Wa ng , W X

. 2 0 0 2

. Ex p o s u re a n d po t e n tial f oo d c h ain tra n sf e r f ac t o r o f C d

, S e a n d Zn in

m ari n e fish L u tja nu s ar g e n ti m a c u l a t u s

. Marine Ecology Progress Series 2 3 8 : 1 7 3

- 1 86

.
Z h a ng , Q

. , L

. Ya ng , a n d W

. X . Wa ng

. 2 0 1 1 . B i o acc u m u l a ti o n a n d t r o ph ic t ra n sf e r o f d i o xi n s in m ari n e
c o p e p o d s a n d fis h

. E n v ir

o n me n tal P o ll u t i o n 1 5 9 ( 1 2 ) : 3 3 9 0

- 3 3 9 7

.[ This page intentionally left blank. ]TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B i Oct ober 2014 Description of MIRC 

## Description of Multimedia Ingestion Risk Calculator Attachment B. (MIRC) Used for RTR Exposure and Risk Estimates[ This page intentionally left blank. ]TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B iii October 2014 Description of MIRC 

## CONTENTS, ATTACHMENT B

1. Introduction

................................ ................................ ................................ ..................... B

- 1

1.1. Purpose and Overview

................................ ................................ .......................... B

- 1

1.2. Scope of MIRC

................................ ................................ ................................ ...... B

- 1

1.3. Use in EPA’s Air Toxics Program

................................ ................................ ........... B

- 2

1.4. MIRC Highlights

................................ ................................ ................................ ..... B

- 2

1.5. Organization of This Attachment

................................ ................................ ............ B

- 3

2. MIRC Overview

................................ ................................ ................................ ............... B

- 3

2.1. Software

................................ ................................ ................................ ................ B

- 4

2.2. Exposure Pathways

................................ ................................ ............................... B

- 6

2.3. Receptor Groups

................................ ................................ ................................ ... B

- 7

3. Exposure Algorithms

................................ ................................ ................................ ....... B

- 9

3.1. Farm Food Chain Algorithms

................................ ................................ ................. B

- 9 3.1.1. Estimating Chemical Concentrations in Produce

................................ ... B

- 10 3.1.2. Estimating Chemical Concentrations in Animal Products

....................... B

- 16

3.2. Chemical Intake Calculations for Adults and Non

- Infant Children

........................ B

- 19 3.2.1. Chemical Intake from Soil Ingestion

................................ ....................... B

- 21 3.2.2. Chemical Intake from Fish Ingestion

................................ ...................... B

- 21 3.2.3. Chemical Intake from Fruit Ingestion

................................ ..................... B

- 23 3.2.4. Chemical Intake from Vegetab le Ingestion

................................ ............. B

- 23 3.2.5. Chemical Intake from Animal Product Ingestion

................................ .... B

- 25 3.2.6. Chemical Intake from Drinking Water Ingestion

................................ ..... B

- 27

3.3. Total Chemical Intake

................................ ................................ .......................... B

- 27

3.4. Chemical Intake Calculations for Nursing Infants

................................ ................. B

- 28 3.4.1. Infant Average Daily Absorbed Dose

................................ ..................... B

- 29 3.4.2. Chemical Concentration in Breast Milk Fat

................................ ............ B

- 30 3.4.3. Chemical Concentration in Aqueous Phase of Breast Milk

.................... B

- 33 3.4.4. Alternative Model for Infant Intake of Methyl Mercury

............................ B

- 35

4. Dose

- Response Values Used for Assessment

................................ .............................. B

- 36

4.1. Cadmium

................................ ................................ ................................ ............. B

- 39

4.2. Dioxins (2,3,7,8

- TCDD)

................................ ................................ ....................... B

- 39

4.3. Mercury

................................ ................................ ................................ ............... B

- 40

4.4. Polycyclic Organic Matter

................................ ................................ .................... B

- 40

5. Risk Estimation

................................ ................................ ................................ ............. B

- 42

5.1. Cancer Risks

................................ ................................ ................................ ....... B

- 42

5.2. Non

- cancer Hazard Quotients

................................ ................................ ............. B

- 44 5.2.1. Hazard Quotients for Chemicals with a Chronic RfD

.............................. B

- 45TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B iv October 2014 Description of MIRC 5.2.2. Hazard Quotients for Chemicals with RfD Based on Developmental Effects

................................ ................................ .......... B

- 45 5.2.3. Hazard Index for Chemicals with RfDs

................................ .................. B

- 45

6. Model Input Options

................................ ................................ ................................ ...... B

- 46

6.1. Environmental Concentrations

................................ ................................ ............. B

- 47

6.2

. Farm

- Food

- Chain Parameter Values

................................ ................................ ... B

- 47 6.2.1. List of Farm

- Food

- Chain (FFC) Parameters

................................ .......... B

- 47 6.2.2. Produce Parameter Values

................................ ................................ .... B

- 49 6.2.3. Animal Product Parameter Values

................................ ......................... B

- 62

6.3. Adult and Non

- Infant Exposure Parameter Values

................................ ............... B

- 64 6.3.1. Body Weights

................................ ................................ ........................ B

- 65 6.3.2. Water Ingestion Rates

................................ ................................ ........... B

- 66 6.3.3. Local Food Ingestion Rates

................................ ................................ ... B

- 66 6.3.4. Local Fish Ingestion Rates

................................ ................................ .... B

- 70 6.3.5. Soil Ingestion Rates

................................ ................................ ............... B

- 77 6.3.6. Total Food Ingestion Rates

................................ ................................ .... B

- 77

6.4. Other Exposure Factor Values

................................ ................................ ............. B

- 78 6.4.1. Exposure Frequency

................................ ................................ ............. B

- 78 6.4.2. Fraction Contaminated

................................ ................................ .......... B

- 79 6.4.3. Preparation and Cooking Losses

................................ ........................... B

- 79 6.4.4. Food Preparation/Cooking Ad justment Factor (FPCAF) for Fish B

- 81

6.5. Breast

- Milk Infant Exposure Pathway Parameter Values

................................ ..... B

- 82 6.5.1. Receptor

- specific Paramete rs

................................ ................................ B

- 82 6.5.2. Chemical

- Specific Parameter Values

................................ ..................... B

- 86

7. Summary of MIRC Default Exposure Parameter Settings

................................ ............. B

- 89

7.1. Default Ingestion Rates

................................ ................................ ....................... B

- 89 7.1.1. Fish Ingestion Rates

................................ ................................ .............. B

- 90 7.1.2. Farm Food Chain In gestion

................................ ................................ ... B

- 90

7.2. Default Screening

- Level Population

- Specific Parameter Values

.......................... B

- 92

7.3. Default Chemical

- Specific Parameter Values fo r 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

- 96TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B v October 2014 Description of MIRC 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 Abov eground Produce

............................ B

- 10

Exhibit B

- 5. Chemical Transfer Pathways for Animal Products

................................ .............. B

- 16

Exhibit B

- 6. Oral Dose

- response Values Used to C alculate 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 Foo ds

................................ ................................ ................................ .... B

- 48

Exhibit B

- 10. Chemical

- Specific Inputs for Produce Parameters for Chemicals Included in MIRC

................................ ................................ ............................ B

- 49

Exhibit B

- 11. Chemical

- Spe cific Inputs by Plant Type for Chemicals in MIRC

...................... B

- 51

Exhibit B

- 12. Non

- Chemical

- Specific Produce Inputs

................................ ........................... B

- 61

Exhibit B

- 13. Animal P roduct Chemical

- specific Inputs for Chemicals Included in MIRC

................................ ................................ ................................ .............. B

- 62

Exhibit B

- 14. Soil and Plant Ingestion Rates for Animals

................................ ...................... B

- 64

Ex hibit 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 Ingestion Rates for Children and Adults ( IR PC,y )

................................ .............. B

- 75

Exhibit 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 Capita To tal Food Intake for Children and Adults

................................ ................................ ......................... B

- 78TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B vi October 2014 Description of MIRC Exhibit B

- 25. Fraction Weight Losses from Preparation of Various Foods

............................ B

- 80

Exhibit B

- 2 6. Scenario

- and Receptor

- Specific Input Parameter Values Used to Estimate Infant Exposures via Breast Milk

................................ ...................... B

- 83

Exhibit B

- 27. Average Body Weight for Infants

................................ ................................ ..... B

- 83

Exhibit B

- 28. Time

- weighted Average Body Weight for Mothers

................................ .......... B

- 84

Exhibit B

- 29. Infant Breast Milk Intake Rates

................................ ................................ ....... B

- 86

Exhibit B

- 30. Chemical

- specific Input Parameter Values for Breast Milk Exposure Pathway

................................ ................................ .......................... B

- 87

Exhibit B

- 31. Farm Food Category Ingestion Rates for Health Protective Screening Sc enario for Farming Households

................................ .................. B

- 91

Exhibit B

- 32. Mean Body Weight Estimates for Adults and Children a

................................ ... B

- 92

Exhibit B

- 33. Chemical

- Spe cific 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

- 94TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B vii October 2014 Description of MIRC Equations, Att achment B

Equation B

- 1. Chemical Concentration in Aboveground Produce

................................ .......... B

- 11 Equation B

- 2. Chemical Concentration in Aboveground Produce D ue to Root Uptake

................................ ................................ ................................ ............ B

- 11 Equation B

- 3. Chemical Concentration in Aboveground Produce Due to Deposition of Particle

- phase Chemical

................................ ............................ B

- 12

E quation 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 from D ry

- to Wet

- Weight Basis

................................ ................................ ................ B

- 13 Equation B

- 6. Chemical Concentration in Belowground Produce: Nonionic Organic Chemicals

................................ ................................ .......................... B

- 14 Equation B

- 7. Chemical Concentration in Belowground Produce: Inorganic Chemicals

................................ ................................ ................................ ....... B

- 15 Equation B

- 8. Conversion of Belowground Produce Chemical Concentration from Dry

- to Wet

- Weight Basis

................................ ................................ ................ B

- 15 Equation B

- 9. Chemical Concentration in Beef, Pork, or Total Dairy

................................ ..... B

- 16 Equation B

- 10. Chemical Concentration in Poultry or Eggs

................................ ................... B

- 17 Equation B

- 11. Incidental Ingestion of Chemical in Soil by Livestock

................................ .... B

- 17 Equation B

- 12. Ingestion of Chemical in Feed by Livestock

................................ .................. B

- 18 Equation B

- 13. Chemical Concentration in Livestock Feed (All Aboveground)

...................... B

- 18 Equation B

- 14. Chemical Concentration in Livestock Feed Due to Root Uptake

.................. B

- 19 Equation B

- 15. Average Daily Dose for Specified Age Group and Food Type

...................... B

- 19 Equation B

- 16. Chemical Intake from So il Ingestion

................................ ............................. B

- 21 Equation B

- 17. Chemical Intake from Fish Ingestion

................................ ............................ B

- 22 Equation B

- 18. Consumption

- weighted Chemical Concentration in Fish

.............................. B

- 22 Equation B

- 19. Chemical Intake from Consumption of Exposed Fruits

................................ . B

- 23 Equation B

- 20. Chemical Intake from Consumption of Protected Fruits

............................... B

- 23 Equation B

- 21. Chemical Intake from Exposed Vegetables

................................ .................. B

- 24 Equation B

- 22. Chemical Intake from Protected Vegetables

................................ ................ B

- 24 Equation B

- 23. Chemical Intake from Root Vegetables

................................ ........................ B

- 24 Equation B

- 24. Chemical Intake from Ingestion of Beef

................................ ....................... B

- 25 Equation B

- 25. Chemical Intake from Dairy Ingestion

................................ .......................... B

- 25 Equation B

- 26. Chemical Intake from Pork Ingestion

................................ ........................... B

- 25 Equation B

- 27. Chemical Intake from Poultry Ingestion

................................ ........................ B

- 26 Equation B

- 28. Chemical Intake from Egg Ingestion

................................ ............................ B

- 26 Equation B

- 29. Chemical Intake from Drinking Water Ingestion

................................ ........... B

- 27TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B viii October 2014 Description of MIRC Equation B

- 30.

................................ ................................ ................................ ...................... B

- 27 Equation B

- 31.

................................ ................................ ................................ ...................... B

- 27 Equation B

- 32.

................................ ................................ ................................ ...................... B

- 27 Equation B

- 33.

................................ ................................ ................................ ...................... B

- 28 Equation B

- 34.

................................ ................................ ................................ ...................... B

- 28 Equation B

- 35.

................................ ................................ ................................ ...................... B

- 28 Equation B

- 36. Lifetime Average Daily Dose (LADD)

................................ ........................... B

- 28 Equation B

- 37 . Average Daily Dose of Chemical to the Nursing Infant

................................ . B

- 29 Equation B

- 38. Chemical Concentration in Breast Milk Fat

................................ .................. B

- 30 Equation B

- 39. Daily Maternal Absorbed Intake

................................ ................................ ... B

- 31 Equation B

- 40. Biological Elimination Rate Constant for Chemicals for 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 Hydr ophilic Chemicals

.................. B

- 35 Equation B

- 45. Calculation of Infant Average Daily Absorbed Dose of Methyl Mercury

................................ ................................ ................................ ........... B

- 36 Equation B

- 46. Calcul ation 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 Equa tion B

- 52.

................................ ................................ ................................ ...................... B

- 44 Equation B

- 53.

................................ ................................ ................................ ...................... B

- 44 Equation B

- 54. Hazard Quotient for Chemicals with a Chronic RfD

................................ ...... B

- 45 Equation B

- 55. Hazard Index Calculation

................................ ................................ ............. B

- 46 Equation B

- 56. Calculation of Age

- Group

- Specific and Food

- Specific Ingestion Rates

................................ ................................ ................................ .............. B

- 70 Equation B

- 57. Calculation of Alternative Age

- Group

- Specific Fish Ingestion Rates

................................ ................................ ................................ .............. B

- 73TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 1 October 2014 Description of MIRC 

## 1. Introduction

1.1. Purpose and Overview
This document provides a detailed description of the Multimedia Inges tion 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 inge stion of produce or animal products, fish, and water in the vicinity of a source of chemical emissions to air. The user can evaluate either generalized (e.g., health protective default) or more site

- specific scenarios using the same tool. MIRC includes a database of exposure parameter values, offering the user the option of selecting mean, median, and upper percentile values for many parameters, data permitting. Generally health protective default values were assigned to each parameter in the tool and th e 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 hom e

- 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 concentration s, along with user

- input chemical concentrations for fish and drinking water, to estimate chemical intake rates, as average daily doses (ADDs), for adults, children, and nursing infants. Users can obtain chemical input concentrations and deposition rates from measurements at an actual site or from a transport and fate model, such as TRIM.FaTE, as is done for RTR risk assessment.

For a specified set of chemical concentrations and MIRC parameter options, MIRC calculates ADDs separately for adults, four age groups of children, and infants to reflect differences in food ingestion rates and diet at different lifestages. MIRC estimates age

- specific hazard quotients (HQs) as the ratio of age

- specific ADDs to the reference dose (RfD) for a chemical. The most ap propriate 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 mil k 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, man y with several options, used by these equations

. The MIRC database includes values for the relevant physiochemical properties and toxicity reference values for more than 500 chemicals, including approximately 60 inorganics taken primarily from a database developed for HHRAP (EPA 2005a). Although designed for OAQPS ’ RTR assessments for sources of hazardous air pollutants (HAPs), the tool is flexible in its design and can be used to assess risks in many other contexts where soil and air concentrations are p redicted 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.TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 2 October 2014 Description of MIRC grown in an area of chemical deposition. Vegetables and fruits in such areas can become contaminated directly by deposition of the airborne chemical to foliage, fruits, and vegetables or indirectly by root uptake of the chemical deposited to soils. Livestock can be exposed to the PB chemicals via ingestion of contaminated forage and incidental ingestion of contaminated soils.

For PB chemicals, evaluation of the inhalation pathway for air pollutants may reveal only a portion of the risk to individuals in such populations. Household s 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, ther efore, 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 exposure pa thways, 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 Integ rated 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 d ry 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 bioaccumul ation 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 vario us 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, ingesti on 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 Highl ights Although designed to assist EPA OAQPS in its RTR assessments, MIRC is a stand

- alone software application that can be used in other contexts. A user can supply either measured or estimated chemical concentrations for soil, air, water, and fish, and a lso can provide air deposition rates likely for the location(s) of interest based on local meteorology. The user can accept the default values for many exposure parameters and screen for small possibilities of risk, or the user can select other options or overwrite parameter values to tailor the estimates to a specific scenario or location.

28 General information about the TRIM system is available at http://www.epa.gov/ttn/fera/trim_gen.html

.TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 3 October 2014 Description of MIRC MIRC complies with EPA’s latest guidelines for exposure and risk assessment, including HHRAP ; the Agency’s 2005 Guidelines for Carcinogen Risk Assessment (Cancer Guid elines)

, Supplemental Guidance for Assessing Susceptibility from Early

- Life Exposure to Carcinogens (Supplemental Guidance), and Guidance on Selecting Age Groups for Monitoring and Assessing Childhood Exposures to Environmental Contaminants ( EPA 2005b, c,d) ; and its 2008 Child

- Specific Exposure Factors Handbook ( EPA 2008a)

. In particular, MIRC provides several important capabilities:

• When provided air and soil concentrations, the MIRC software package allows rapid calculation of screening

- level exposures an d 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 oth er 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, MI RC estimates a lifetime ADD using the three lifestages and potency adjustment factors recommended in EPA’s 2005 Cancer Guidelines and Supplemental Guidance.

• The data for exposure parameters in the tool have been updated to include the latest recommended va lues 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. S ection 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 use d 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, a nd Section 8 provides the references.

Note that the default parameterization described in Section 7 was used to estimate Tier 1 screening threshold emission rates of PB

- HAPs from RTR facilities. T hese 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 waterTRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 4 October 2014 Description of MIRC consumption, incidental soil ingestion, fish ingestion, and ingestion of ten types of farm food chain (FFC) product s : exposed fruits, protected fruits, exposed vegetables, protected vegetables, root vegetables, beef, total dairy, pork, poultry, and eggs. The tool also includes a breast milk ingestion and risk module for nursing infants

. For fruits and vegetables, the terms “exposed” and “protected” refer to whether the edible portion of the plant is exposed to the atmosphere

. The remainder of this overview consists of three sections. The first (Section 2.1 of this attachment ) provides a n 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 env ironmental concentrations of a chemical estimated for air, soil, drinking water, and fish tissue.

• Internal chemical transfer and exposure algorithms and database of options for FFC algorithm parameter value, chemical

- specific inputs, and exposure factors.

• Tabulated outputs of calculated chemical concentrations in the various farm food products (e.g., fruits, vegetables, beef, eggs) and ADDs for those foods and for water and fish ingestion for each receptor category.

• Output tables with estimated cancer ris ks and non

- cancer hazard estimates associated with total ingestion exposure to each chemical for each receptor category.

Exhibit B

- 1 provides a flowchart displaying the types of required and optional inputs and the general flow of calculations carried out by the tool.TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 5 October 2014 Description of MIRC Exhibit B

- 1

. Overview of MIRC Software Application for Performing Farm

- Food

- Chain Ingestion Exposure and Risk Calculations

User Selects Receptor Characteristics
From Options or Over

- write Body Weight User Specifies Environmental Concentrations for Chemical of 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 RisksTRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 6 October 2014 Description of MIRC A f orm 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 of va rious 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 deposi tion rates for particle

- phase chemical,

• Drinking water concentration,

• Chemical concentration in surface soils (two locations; can be tilled and untilled),

• Chemical concentration in root

- zone soils (two locations; can be tilled and untilled), and

• Chemical c oncentrations 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 d atabase) 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 o f 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 acros s 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 individu al is exposed via all of the pathways specified (e.g, fruits and vegetables, animal products, soil, etc.). The tool therefore is useful in estimating risk to the maximally exposed individuals in a risk assessment. To evaluate other receptor populations, the user must specify the each exposure scenario separately.



### 2.2. Exposure Pathways

MIRC estimates t he concentrations of chemicals i n FFC food categories grown in an area of airborne chemical deposition using algorithms and parameter values provided in HHRAP (E PA 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).TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 7 October 2014 Description of MIRC Exhibit B

- 2

. Transfer Pathways for Modeled Farm Food Chain (FFC) Media

Farm Food Media Chemical Transfer Pathways Exposed fruit and vegetables • Direct deposition from air of particle

- bound chemical

• Air

- to

- plant transfer of vapor phase chemical

• Root uptake from soil Protected fruit and vegetables (including root vegetables) • Root uptake from soil

Beef and total dairy (including milk) • 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, and g rain 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 concentratio n 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

concen tration 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 o ther fresh plant material that has been stored and fermented), and feed grain products grown on the farm (e.g., corn, soybeans). All three animal feed products are assumed to accumulate chemical via root uptake from the soil. Forage and silage also can a ccumulate 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 chickens consume only grain. The incidental ingestion of the chemical in soils during grazing or consumption of foods placed on the ground is estimated using empirical soil ingestion v alues . For secondary animal products (dairy products and eggs), chemical concen trations 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 preference s, and other factors

. It is important, therefore, to evaluate the contribution of exposures during childhood to total lifetime risk using appropriate exposure factor values

.TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 8 October 2014 Description of MIRC EPA’s HHRAP ( Chapter 4, EPA 2005a) recommends assessing exposures for children and adults separately, but considers all non

- infant children in one category. Specifically, HHRAP recommends eight categories of receptor: farmer, child farmer, resident, child resident, fisher, child fisher, acute receptor, and nursing infant . Over time , different EPA programs have used different child age groupings to evaluate body weights, ingestion rates, and other parameter values needed to estimate chemical exposures and risks to children.

To improve the match between age groups used to estimate v alues across exposure parameters, i n 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 years

. These age groupings correspond to different developmental stages and reflect different food ingestion rates per unit body weight, with the highest ingestion rates occurring for the youngest, most rapidly growing, age groups.

For purposes of RTR assessment using MIRC, the selection of age categories is limited by the categories for which most of the FFC food ingestion rates have bee n calculated. In Chapter 13 of both its Exposure Factors Handbook (EFH; EPA 2011a) and its Child

- Specific Exposure Factors Han dbook ( 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 becau se infants are unlikely to consume those foods . They are more likely to be nursing or to be fed formula and other commercial baby

- food products.

Although the age groupings used to estimate FFC ingestion rates do not match precisely the groupings that EP A 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) rema ins 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 products 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 < 12 ye ars) ; 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 eff ects, 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

.TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 9 October 2014 Description of MIRC For assessment of cancer risks from early

- life exposure, EPA recognizes that infants and children may be more sensitive to a carcinogenic chemical than adults, with cancers appearing earlier in life or with lower doses experienced during childhood ( EPA 2005c, d)

. Thus, the “potency” of a carcinogen might be higher for infants and children than for ad ults

. 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 life stages 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 m utagenic 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:

• ch ildren under the age of 2 years (i.e., 0 to < 2 years); and

• children from 2 through 15 years (i.e., 2 to < 16 years).

Different age groupings are needed for the assessment of risks from carcinogenic chemicals with a mutagenic MOA and other carcinogens wi th 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 ch emical 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 chemical intake calculations are described in Section 3.3

. Finally, the sets of algorithms used to estimate chemical intake via consumption of breast milk by nursing infants are described in Section 3.4

. A s 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 che mical concentrations in produce and animal products are described in S ections 3.1.1 and 3.1.2 of this attachment , respectively. D iscussion s of the parameter value options and the va lues 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 differen ce 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 emissio n 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 multip athway exposures for RTR, TRIM.FaTE is used to estimate air concentrations, air

- to

- surface deposition rates, and soil concentrations, and these outputs are used in the FFC algorithms.TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 10 October 2014 Description of MIRC 

### 3.1.1. Estimating Chemical Concentrations in Produce
Produce (vegetables and fr uits) can become contaminat ed 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: abov eground 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 Med ia Chemical Transfer Pathways Aboveground Produce Exposed fruits and vegetables Direct deposition from air of particle

- bound chemical Air

- to

- plant transfer of vapor phase chemical Root uptake from soil
Protected fruits and vegetables Root uptake from s oil

Belowground Produce Root vegetables Root uptake from soil



### 3.1.1.1. Aboveground Produce
For abovegrou nd 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 )

. Th e 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 concentrati on 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 ProduceTRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 11 October 2014 Description of MIRC concentration in the plant to the average chemical concentration in the soil at the root

- zone depth in the produce

- growing area ( Cs root

- zone_produce )

. The edible portions of aboveground protected produce are not subject to contamination via particle deposition ( Pd ) or vapor transfer ( Pv ). Therefore, root uptake of chemicals is the primary mechanism t hrough 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 protected, o n 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) = Chemical concentration in edible portion of aboveground produce type i, exposed or protected, due to root uptake from soil at the root

- zone depth of the produce growing area (mg/kg produce DW)

Pv (i) = Chemical concentration in edible portion of aboveground produc e type i due to air

- to

- plant transfer (μg/g [or mg/kg] produce DW); for protected aboveground produce, Pv equals zero

Equation B

- 2

. Chemical Concentration in Aboveground Produce Due to Root Uptake

where:

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 p roduce type i

, exposed or protected (g soil DW / g produce DW) ) ( ) ( ) ( ) ( P i i i DW produce AG i DW produce AG Pv Pd r C + + =

− − − −

) ( ce zone_produ

- root ) ( P i DW produce AG i DW produce AG Br Cs r

− − − − × =TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 12 October 2014 Description of MIRC Equation B

- 3

. Chemical Concentration in Aboveground Produce Due to Deposition of Particle

- phase Chemical

where:

Pd (i) = Chemical concentration in aboveground produce type i on a dry

- weight (DW) basis due to particle deposition (mg/kg produce DW); set equal to zero for protected aboveground produce
Drdp = Average annual dry deposition of particle

- phase chemical (g/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 port ion 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 ed ible 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/se c, of chemical from the source and the proportion of the chemical that remains in, or partitions to, the particle

- phase in the air

. Also in HHRAP, the dry and wet particle phase deposition rates, Dydp and Dywp, respectively, are normalized to the emission rate and are expressed in units of sec/m 2

- yr

.
With MIRC, the user inputs both the dry and wet particle

- phase deposition rates, Drdp and Drwp , respectively, in units of g/m 2

- yr for a specific location relative to an emissions source

. Those deposition ra tes 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

- part icle

- 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

i i i Tp i kp i i 

### kp Yp e Rp Drwp Fw Drdp Pd

× − × × × + ×

= −

a i AG i AG i 

### VG Bv Fv Ca Pv ρ ) ( ) ( ) ( 

### × × ×

=TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 13 October 2014 Description of MIRC w here:

Pv (i) = Concentration of chemical in edible portion of aboveground produce ty pe 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 sou rce 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) b efore 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 CTRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 14 October 2014 Description of MIRC 

### 3.1.1.2. Belowground Produce
The equations by which chemical concentrations are estimated in belowground produce are different for nonionic organic chemicals than for inorganic chemicals and ionic organic chemicals.



### 3.1.1.2.1. Nonionic Organic Chemicals
For belowground 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 c hemical 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 possib le 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 concentratio ns 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 a n estimate of the chemical’s K ow from empirically UCF Kds VG RCF Cs rootveg produce zone root

× × ×

= −

_ WW

- produce

- BG CTRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 15 October 2014 Description of MIRC derived regression models. Those models are shown in HHRAP Appendix A

- 2, Equations A

- 2

- 14 and A

- 2

- 15 ( RCF ) and in Equations A

- 29 and A

- 2

- 10 ( Kds ). The RCF and Kds values so 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 inorga nic 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 (D W) 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 below ground 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 abovegroun d 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 zone root -DW BG-produce VG Br Cs C × ×

= − − −



###      

− × =

− − − − 

### 100 ) 100 ( BG DW produce BG WW produce BG 

### MAF C CTRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 16 October 2014 Description of MIRC 

### 3.1.2. Estimating Chemical Concentrations in Animal Products
Chemical concentrations in animal products are estimated based on the amount of chemical consumed by each animal group m through each plant feed type i ( 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 a ll 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 a ccumulate 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 m i Intake Ch m Intake Ch m m mammal 

### Plant Soil MF Ba C

1 )

, ( ) ( ) ( ) (TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 17 October 2014 Description of MIRC 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 i ntake/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 con sumption 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 fo od 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 

# ( ) )

, ( ) ( ) ( ) ( m i Intake Ch m Intake Ch m m poultry Plant Soil Ba C

− − + × =

Bs Cs Qs Soil livestock s m Intake Ch × × =

− − ) (TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 18 October 2014 Description of MIRC 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 calcu lated 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 che mical 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 m i m i m i Intake Ch C Qp F Plant × × =

−

(i) (i) feed(i) feed(i) 

### Pv Pd Pr C + + =TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 19 October 2014 Description of MIRC 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 a rea 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 us ed 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 ingesti on 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 zone root i feed Br Cs r × =

−

               

× × × ×

= days 365 (y)

(y) (y) (y) (i) (y,i) (i) (y,i) EF
AT BW ED FC IR C ADDTRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 20 October 2014 Description of MIRC 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 di scussed 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 t hese variables drop out of Equation B

- 15

. For each chemical included in a screening scenario, total average daily exposure for age group y ( ADD (y) ) is estimated as the sum of chemical intake from all ingestion pathways combined: Note that the last exposure pathway is limited to infants

.

• Incidental soil ingestion;

• Ingestion of fish;

• Ingestion of homegrown fruits (exposed and protected);

• Ingestion of homegrown vegetables (exposed, protected, and root);

• Ingestion of animal products from home

- raised animals:

– Milk and other dairy products from cows,TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 21 October 2014 Description of MIRC – Beef products,

– Pork products, and

– Poultry and eggs;

• Ingestion of drinking water from specified source; and

• Ingestion of breast milk by infants.

The algorithms for the 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) SoilTRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 22 October 2014 Description of MIRC Equation B

- 17

. Chemical Intake from Fish Ingestion

Equation B

- 18

. Consumption

- weighted Chemical Concentration in Fish



### 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 concentr ation 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 fi sh (L1 Fish and L2 Fish in Equation B

- 17 )

. 

## ( ) ( ) days 365 g kg 001 0
2 1 1 1        

×        

× × ×

×

− ×

− = EF
BW FC

. IR C
L L ADD
(y) Fish Fish(y) Fish

Fish Fish Fish(y)TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 23 October 2014 Description of MIRC 

### 3.2.3. Chemical Intake from Fruit Ingestion
Average daily doses of a chemical from homegrown exposed fruits are calculated separately for exposed and protected fruits ( 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 2 1 1 1 EF FC

. IR C L L ADD ExpFruit ) ExpFruit(y ExpFruit ExpFruit ExpFruit ) ExpFruit(y



## ( )        

×        

× × × ×

− = days 365 g kg 001 0 1 1 EF FC

. IR C L ADD ProFruit ) ProFruit(y ProFruit ProFruit ) ProFruit(yTRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 24 October 2014 Description of MIRC 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 1 1 EF FC

. IR C L ADD ExpVeg ExpVeg(y) ExpVeg ExpVeg ExpVeg(y)



# ( )        

×        

× × × ×

− = days 365 g kg 001 0 1 1 EF FC

. IR C L ADD ProVeg ProVeg(y) ProVeg ProVeg ProVeg(y)



# ( ) ( )        

×        

× × × ×

− ×

− = days 365 g kg 001 0 2 1 1 1 EF FC

. IR C L L ADD RootVeg RootVeg(y) RootVeg RootVeg RootVeg RootVeg(y)TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 25 October 2014 Description of MIRC 

### 3.2.5. Chemical Intake from Animal Product Ingestion
Calculations of chemical intake from the consumption of farm animals and related food products are provided below 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 2 1 1 1 EF FC

. IR C L 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 2 1 1 1 EF FC

. IR C L L ADD Pork Pork(y) Pork Pork Pork Pork(y)TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 26 October 2014 Description of MIRC 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; av eraged 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 o btained 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 e xposure 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 2 1 1 1 EF FC

. IR C L L ADD Poultry Poultry(y) Poultry Poultry Poultry Poultry(y)

       

×        

× × × = days 365 g kg 001 0 EF FC

. IR C ADD Egg Egg(y) Egg Egg(y)TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 27 October 2014 Description of MIRC 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 )

, 2 1 ( ) 2 1 ( 

# ∑

= −

− 

### = n

i i 

### ADD ADD 1 )

, 5 3 ( ) 5 3 (TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 28 October 2014 Description of MIRC Equation B

- 33

. Equation B

- 34

. Equation B

- 35

.

where i represents the i th food type or ingestion medium and n equals the total number of food types or ingestion media, and ADD parameters are defined below:

ADD (<1) = 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 aver age 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 )

, 19 12 ( ) 19 12 ( 

# ∑

= 

### = 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 LADDTRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 29 October 2014 Description of MIRC 

### 3.4.1. Infant Average Daily Absorbed Dose The average daily dose of chemical absorbed by the infant ( 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 chemi cal 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 ). 

## ( ) ( ) 

# ( ) 

# [ ]

AT BW ED AE IR f C f C DAI

inf inf milk mbm aqueous mbm milkfat inf 

### × × × ×

− × + ×

= 1TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 30 October 2014 Description of MIRC 

### 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) approac h 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 m aternal 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 o f 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 assumpti on 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) 

# ( )        

−        

− − × + × × ×

= − − bf elac fat pn elim t k

elac fat

elim t k

bf elac fat elac fat

elim

fm elim f

mat milkfat e k k e t k k k
f k f DAI C

_ 1 1 1

_ _ _TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 31 October 2014 Description of MIRC Equation B

- 38 relies on the daily mate rnal 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 attac hment

, 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 × =TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 32 October 2014 Description of MIRC Equation B

- 35

, used to calculate ADD (adult), is based on many medium

- specific ingestion rates that are normalized to body weight . The adult body weights to 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 in takes 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 c hemical 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 lact ating 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 f f IR k k

× × × + =

_TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 33 October 2014 Description of MIRC 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 weig ht 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 elac aq bm pl mat aqueous 

### f k Pc f DAI C

× × ×

=

_TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 34 October 2014 Description of MIRC 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 va lue 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 bp bl pl 

### f Pc f f f f

− + ×

= 1TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 35 October 2014 Description of MIRC 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 fract ion 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 t o 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 chemica ls 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 elac aq k k =

_TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 36 October 2014 Description of MIRC fetus, including a PBTK dynamic compartmental model for gestational transfer of MeHg in humans developed by Gearhart et al . (1995, 1996), and reparameterized by Clewell et al

. (1999).

We did find, however, that Byczkowski and Lipscomb (2001) had added a lactat ional 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 . 19 76) 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 M eHg 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 mat ernal 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 (I RIS); 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 =TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 37 October 2014 Description of MIRC Exhibit B

- 6 . Oral Dose

- response Values Used to Calculate RTR Screening Threshold Emission Rates for PB

- HAP 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 availableTRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 38 October 2014 Description of MIRC Exhibit B

- 6 . Oral Dose

- response Values Used to Calculate RTR Screening Threshold Emission Rates for PB

- HAP 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 availableTRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 39 October 2014 Description of MIRC Exhibit B

- 6 . Oral Dose

- response Values Used to Calculate RTR Screening Threshold Emission Rates for PB

- HAP 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 equival ency 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 av ailable 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 av ailable 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 f irst 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 compar ison 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 concent rations on the basis of toxicity. For risk assessment of dioxins for RTR (with one exception), the World Health Organization (WHO) 2005 TEFs presented in Exhibit B

- 7 were used to derive the CSFs (shown in Exhibit B

- 6 ) for dioxin congeners without available EPA dose response values. The one exception is 1,2,3,7,8,9

- Hexachlorodibenzo

- p

- dioxin , whose TEF was based on data from IRIS.TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 40 October 2014 Description of MIRC Exhibit B

- 7 . WHO 2005 Toxic Equivalency Factors (TEFs) for Dioxin Congeners

Dioxin Congener CAS No. WHO 2005 Toxic Equivalency 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 f or 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 associ ated 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 memberTRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 41 October 2014 Description of MIRC POM species reported in NEI, not just the species currently evaluated in this assessment ) and the corresponding CSFs using this methodology are presented in Exhibit B

- 8

. These group CSFs are used only when OAQPS has not, for the purposes of RTR, identified a CSF specific to the individual chemical.

Exhibit B

- 8 . Oral Dose

- response Values for Polycyclic Organic Matter (POM) Groups a

Individual POM or POM Group CAS No. C ancer S lope F actor 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)py rene 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 247 Methylchrysene 248 Methylanthracene 26914181
Benzofluoranthenes 56832736
9

- Methylbenz(a)anthracene 779022
1

- Methylphenanthrene 832699 Acenaphthene 83329
Phenanthrene 85018
Fluorene 86737
2

- Methylnaphthalene 91576
2

- Chloronaphthalene 91 587 **POM Group 73002** 7,12

- Dimethylbenz(a)anthracene 57976 1000 **POM Group 74002** Dibenzo(a,i)pyrene 189559 100 Dibenzo(a,h)pyrene 189640TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 42 October 2014 Description of MIRC 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 actor b (mg/kg

- day) - 1

**POM Group 75002**
3

- Methylcholanthrene 56495

10 Dibenzo(a,e)pyrene 192654
5

- Methylchrysene 3697243
Benzo ( a ) pyr ene 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)anthr acene 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 fa ctors to POM groups was the same as that used in the 1999 National Air Toxics Assessment (EPA 1999). A complete description of the methodology is available at: http://www.epa.gov /ttn/atw/nata1999/99pdfs/pomapproachjan.pdf

.


## 5. Risk Estimation
For persistent and bioaccumulative hazardous air pollutants (PB

- HAPs), risks from inhalation of a chemical directly from air generally will be negligible compared with risks from ingestion of the chemical from foodstuffs grown in an area subject to air deposition of the chemical. Risk characterization for carcinogens with a linear mode of action at low doses is described in Section 5.1 of this attachment . Risk cha racterization 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 e xcess 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 wi th a low

- dose linear mode of action , the estimated ELCR is calculated as the product of the lifetime average daily dose (LADD) and the

cancer slope factor (CSF) :TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 43 October 2014 Description of MIRC 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 pathways 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 diff erences 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 consi ders 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 the CSF to protect children. To date, the only mode of action (MOA) for carcinogenesis for which EPA has adequate data to develop a reasonable quantitative default approach is mutagenesis ( EPA 2005 b, c) . For carcinogens with a mutagenic MOA for cancer, EPA c oncluded 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 2 005c). 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 rate s, 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 ca ncer risk for each of the three lifestages separately and then adding the risks for i = 1 to 6 age groups. 

## ( ) ( )

3 5 6 2 yr 1 3 yr 1 10 10

5 3 2 1

= = × + ×

= = <

)

- ( )

- ( 1) (

ADAF . ADAF ADAF 

## ( ) ( )

1 2 8 yrs 4 1 yrs 4 3 3

19 12 11 6

= = × + ×

= =

(adult) )

- ( )

- (

ADAF ADAF ADAFTRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 44 October 2014 Description of MIRC Equations B

- 47 to B

- 53 . Lifetime Cancer Ri sk: 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) Equat ion B

- 51

. Risk (12

– 19) = ADD (12

– 19) × 2 × CSF × (8 yr/70 yr) Equation B

- 52

. Risk (adult) = ADD (adult) × 1 × CSF × (50 yr/70 yr)
Equation B

- 53

. **ELCR**

In other words, Equation B

- 53 indicates that the total excess lifetime cancer risk ( ELCR) equals the sum of the age

- group

- specific risks estimated by Equation B

- 47 through Equation B

- 52

, where:

Risk (<1) = R isk from chemical ingestion in first year of life

Risk (1

-

2) = Risk from chemical ingestion fro m 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) = Averag e 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 ) (TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 45 October 2014 Description of MIRC 

### 5.2.1. Hazard Quotients for Chemicals with a Chronic RfD For chemicals with a chronic RfD, MIRC calculates an HQ for each age group separately using Equation B

- 54 to indicate th e 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 threshol d effects and are not additive across age groups.

Equation B

- 54

. Hazard Quotient for Chemicals with a Chronic RfD

where:

HQ = Hazard quotient for chemical (unitless)

ADD = Average daily ingested dose of chemical (mg/kg

- day) from all food types and ingested media for the age group

RfD = Chronic oral reference dose for chemical (mg/kg

- day)



### 5.2.2. Hazard Quotients for Chemicals with RfD Based on Developmental Effects

For chemicals for which the toxicity reference value is an RfD based on developmental effects

, a shorter exposure duration ( ED ) and averaging time ( AT ) may be required

. For this type of chemical (e.g., methylmercury, 2,3,7,8

- TCDD), the appropriate ED/AT and sensitive l ifestage 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

Wh en 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 assumptio ns 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 highe st HQs to develop an appropriate approach to assessing their potential joint action. 

### RfD ADD HQ =TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 46 October 2014 Description of MIRC Equation B

- 55

. Hazard Index Calculation

HI = HQ 1 + HQ 2 ... HQ n

where:

HI = Hazard index (u nitless)

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 dif ferent 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 chem ical 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 Supple mentary 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 t o 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 cu rrently evaluated using the TRIM

- based RTR screening scenario. The database included with MIRC contains chemical

- specific parameter values for a large number of chemicals (more than 500), because all of the chemical

- specific input data compiled by EPA for use in HHRAP were uploaded into MIRC. However, only chemicals that are PB

- HAPs evaluated for RTR are discussed in this document (see Sections 1.1.1 and 2.2.2 ), and the HHRAP inputs provided for oth er chemicals have not been reviewed or verified. The data presented in this chapter were reviewed and used to develop the set of modeling defaults used to calculate screening threshold emission rates for RTR. Note that the default values used to estimate RTR screening thresholds, and the justification for selecting a specific value from the data sets described in this chapter, are discussed in Chapter 7.TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 47 October 2014 Description of MIRC 

### 6.1. Environmental Concentrations

As noted in Section 2 of this attachment

, MIRC is intended to estimate exposures and risks to self

- sufficient farming and fishing families from ingestion of FFC media in an area of airborne chemical deposition

. The tool analyzes one exposure scenario at a time (e.g., adult farmer exposed to dioxi n 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 si ngle 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 g ame fish (i.e., fish in TL 3 and TL 4) (in mg/kg wet weight);

• concentrations in drinking water (in g/L); and

• four chemical concentrations in soil (in μg/g dry weight), one each for:

– surface soil in produce growing area,

– surface soil where livestock feed,

– root

- zone soil in produce growing area, and

– root

- zone soil in livestock feed growing area.

The MIRC software is configured to estimate ingestion exposures via drinking water for a specified chemical concentration in the drinking water source (e.g., ground water 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 dev eloped 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 concent rations 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 w ritten 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 fa rms: 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 above 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.,TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 48 October 2014 Description of MIRC soil

- to

- plant transfer coe fficients) 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

- recommend ed parameter values are the default values in MIRC; however, t hese and other inputs in MIRC can be revised as needed. describes the parameters that are included in the algorithms used to estimate chemical concentrations in the farm food categories. The parameter names and symbols are referenced in this section for plants/produce and animal products

.

Exhibit B

- 9

. MIRC Parameters Used to Estimate Chemical Concentrations in Farm Foods

Parameter Description Units
**Plants/Produce**
Br AG

- produce

DW(i) Chemical

- specific plant/soil chemical bioconcentration factor for edible portion of aboveground produce type i, exposed or protected Unitless ( g soil DW / g produce DW )

Bv AG(i) Chemical

- specific 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 adheres 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 animal forage and silage yr - 1

MAF (i) Moistu re 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

RCF Chemical

- specific r oot concentration fa ctor 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 exposur e 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 chem ical 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 the diffusive transfer of chemicals from th e 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 UnitlessTRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 49 October 2014 Description of MIRC Exhibit B

- 9

. MIRC Parameters Used to Estimate Chemical Concentrations in Farm Foods

Parameter Description Units
Ba (b eef ) Chemical

- specific b iotransfer factor for chemical in diet of cow to chemical in beef on a fresh

- wet (FW; e quivalent 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 ) Biotransfer factor in poultry day/kg FW tissue
B a (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 Source: EPA 2005 a DW = dry weight; FW = fre sh weight; WW = wet weight



### 6.2.2. Produce Parameter Values

Exhibit B

- 10 and Exhibit B

- 11 provide the chemical

- specific input values that are the current defaults for produce FFC food types in MIRC. Exhibit B

- 12 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 parameter values were obtained from HHRAP

. O ptions 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 Fraction of Wet Deposition ( **Fw** ) (unitless) a Root Concentration Factor ( **RCF** ) (belowground) (L/kg) b Soil

- Water Partition Coefficient ( **Kds** ) (L/kg) c Chemical Air

- to

Plant Biotransfer Factor ( **Bv AG(i)**  ) (unitless) d **Inorganics** Cadmium compounds 0.6 NA 7.5E+01 NA e Mercury (elemental) 0.6 NA 1.0E+03 0 f

Merc uric chloride 0.6 NA 5.8E+04 1.8E+03 Methyl mercury 0.6 NA 7.0E+03 0 f

**PAHs**
2

- Methylnaphthalene 0.6 2.2E+02 5.0E+01 1.4E+00 7,12

- Dimethylbenz(a)anthrace

ne 0.6 6.8E+03 4.0E+03 4.2E+04

Acenaphthene 0.6 2.4E+02 3.9E+01 4.6E+00 Acenaphthylene 0.6 2.8E+02 6.8E+01 8.1E+00 Benz(a)anthracene 0.6 6.7E+03 2.9E+03 6.8E+03 Benzo(a)pyrene 0.6 9.2E+03 7.8E+03 1.7E+05TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 50 October 2014 Description of MIRC Exhibit B

- 10

. Chemical

- Specific Inputs for Produce Parameters for Chemicals Included in MIRC

Chemical Fraction of Wet Deposition ( **Fw** ) (unitless) a Root Concentration Factor ( **RCF** ) (belowground) (L/kg) b Soil

- Water Partition Coefficient ( **Kds** ) (L/kg) c Chemical Air

- 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+04TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 51 October 2014 Description of MIRC Exhibit B

- 10

. Chemical

- Specific Inputs for Produce Parameters for Chemicals Included in MIRC

Chemical Fraction of Wet Deposition ( **Fw** ) (unitless) a Root Concentration Factor ( **RCF** ) (belowground) (L/kg) b Soil

- Water Partition Coefficient ( **Kds** ) (L/kg) c Chemical Air

- 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 radionu clides were deposited onto pasture grass via simulated rain. Note that the values developed experimentally for pasture grass may not accurately represent all aboveground produce

- specific values. Also note that values based on the behavior of insoluble pa rticles tagged with radionuclides may not accurately represent the behavior of organic compounds under site

- specific conditions.

b For nonionic organic chemicals, as described in HHRAP (EPA 2005a), Appendix A (available at http://www.epa.gov/osw/hazard/ts d/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 com pound 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 mercur ic 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 conc luded 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 me rcury, 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

- Belowgrou nd 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. Fru it 1.3E

- 01

- - Prot. Veg. 1.3E

- 01

- - Root 6.4E

- 02 1.0E+00

- Silage 3.6E

- 01

- 5.0E

- 01TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 52 October 2014 Description of MIRC Exhibit B

- 11 . Chemical

- Specific Inputs by Plant Type for Chemicals in MIRC

Compound Name Plant Part Plant

- Soil Bio

Concentration Factor ( **Br** AG

- produce

- DW(i) ) (unitless) a Empirical Correction Factor

- Belowgrou nd 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

- S ilage

- - 5.0E

- 01

Mercuric chloride Exp. Fruit 1.5E

- 02

- 1.0E+00 Exp. Veg. 1.5E

- 02

- 1.0E+00 Forage 0.0E+00

- 1.0E+00
Grain 9.3E

- 03

- - Prot. Fruit 1.5E

- 02

- - Prot. Veg. 1.5E

- 02

- - Root 3.6E

- 02 1.0E+00

- Silage 0.0E+00

- 5.0E

- 01

Methyl me rcury 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 2.3 E

- 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

- 01TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 53 October 2014 Description of MIRC Exhibit B

- 11 . Chemical

- Specific Inputs by Plant Type for Chemicals in MIRC

Compound Name Plant Part Plant

- Soil Bio

Concentration Factor ( **Br** AG

- produce

- DW(i) ) (unitless) a Empirical Correction Factor

- Belowgrou nd 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
F orage 2.1E

- 01

- 1.0E+00
Grain 2.1E

- 01

- - Prot. Fruit 2.1E

- 01

- - Prot. Veg. 2.1E

- 01

- - Root 6.2E+00 1.0E+00

- Silage 2.1E

- 01

- 5.0E

- 01

Acenaphthylene Exp. Fruit 1.9E

- 01

- 1.0E

- 02 Exp. Veg. 1.9E

- 01

- 1.0E

- 02 Forage 1.9E

- 01

- 1.0E+00
Grain 1.9E

- 01

- - Prot. Fruit 1.9E

- 01

- - Prot. Veg. 1.9E

- 01

- - Root 4.1E+00 1.0E

- 02

- Silage 1.9E

- 01

- 5.0E

- 01

Benz(a)anthracene Exp. Fruit 1.7E

- 02

- 1.0E

- 02 Exp. Veg. 1.7E

- 02

- 1.0E

- 02 Forage 1.7E

- 02

- 1.0E+00
Grain 1.7E

- 02

- - Prot. Fruit 1 .7E

- 02

- - Prot. Veg. 1.7E

- 02

- - Root 2.3E+00 1.0E

- 02

- Silage 1.7E

- 02

- 5.0E

- 01

Benzo(a)pyrene Exp. Fruit 1.4E

- 02

- 1.0E

- 02 Exp. Veg. 1.4E

- 02

- 1.0E

- 02 Forage 1.4E

- 02

- 1.0E+00
Grain 1.4E

- 02

- - Prot. Fruit 1.4E

- 02

- - Prot. Veg. 1.4E

- 02

- - Root 1.2E+00 1.0E

- 02

- Silage 1.4E

- 02

- 5.0E

- 01TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 54 October 2014 Description of MIRC Exhibit B

- 11 . Chemical

- Specific Inputs by Plant Type for Chemicals in MIRC

Compound Name Plant Part Plant

- Soil Bio

Concentration Factor ( **Br** AG

- produce

- DW(i) ) (unitless) a Empirical Correction Factor

- Belowgrou nd 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

- - Prot. Veg. 1.8E

- 02

- - Root 1.7E+00 1.0E

- 02

- Silage 1.8E

- 02

- 5.0E

- 01

Benzo(g,h,i)perylene Exp. Fruit 5.7E

- 03

- 1.0E

- 02 Exp. Veg. 5.7E

- 03

- 1.0E

- 02 Forage 5.7E

- 03

- 1.0E+00
Grain 5.7E

- 03

- - Prot. Fruit 5.7E

- 03

- - Prot. Veg. 5.7E

- 03

- - Root 1.1E+00 1.0E

- 02

- Silage 5.7E

- 03

- 5 .0E

- 01

Benzo(k)fluoranthene Exp. Fruit 1.4E

- 02

- 1.0E

- 02 Exp. Veg. 1.4E

- 02

- 1.0E

- 02 Forage 1.4E

- 02

- 1.0E+00
Grain 1.4E

- 02

- - Prot. Fruit 1.4E

- 02

- - Prot. Veg. 1.4E

- 02

- - Root 1.6E+00 1.0E

- 02

- Silage 1.4E

- 02

- 5.0E

- 01

Chrysene Exp. Fru it 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

- 01TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 55 October 2014 Description of MIRC Exhibit B

- 11 . Chemical

- Specific Inputs by Plant Type for Chemicals in MIRC

Compound Name Plant Part Plant

- Soil Bio

Concentration Factor ( **Br** AG

- produce

- DW(i) ) (unitless) a Empirical Correction Factor

- Belowgrou nd 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
E xp. Veg. 6.8E

- 03

- 1.0E

- 02 Forage 6.8E

- 03

- 1.0E+00
Grain 6.8E

- 03

- - Prot. Fruit 6.8E

- 03

- - Prot. Veg. 6.8E

- 03

- - Root 1.6E+00 1.0E

- 02

- Silage 6.8E

- 03

- 5.0E

- 01

Fluoranthene Exp. Fruit 4.0E

- 02

- 1.0E

- 02 Exp. Veg. 4.0E

- 02

- 1.0E

- 02
Fora ge 4.0E

- 02

- 1.0E+00
Grain 4.0E

- 02

- - Prot. Fruit 4.0E

- 02

- - Prot. Veg. 4.0E

- 02

- - Root 5.6E+00 1.0E

- 02

- Silage 4.0E

- 02

- 5.0E

- 01

Fluorene Exp. Fruit 1.5E

- 01

- 1.0E

- 02 Exp. Veg. 1.5E

- 01

- 1.0E

- 02 Forage 1.5E

- 01

- 1.0E+00
Grain 1.5E

- 01

- - Prot. Fruit 1.5E

- 01

- - Prot. Veg. 1.5E

- 01

- - Root 6.2E+00 1.0E

- 02

- Silage 1.5E

- 01

- 5.0E

- 01

Indeno(1,2,3

- cd)pyrene Exp. Fruit 5.1E

- 03

- 1.0E

- 02 Exp. Veg. 5.1E

- 03

- 1.0E

- 02 Forage 5.1E

- 03

- 1.0E+00
Grain 5.1E

- 03

- - Prot. Fruit 5.1E

- 03

- - Prot. Veg. 5.1E

- 03

- - Root 1.1E+00 1.0E

- 02

- Silage 5.1E

- 03

- 5.0E

- 01TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 56 October 2014 Description of MIRC Exhibit B

- 11 . Chemical

- Specific Inputs by Plant Type for Chemicals in MIRC

Compound Name Plant Part Plant

- Soil Bio

Concentration Factor ( **Br** AG

- produce

- DW(i) ) (unitless) a Empirical Correction Factor

- Belowgrou nd 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

- - Pr ot. Veg. 7.1E

- 04

- - Root 6.1E

- 01 1.0E

- 02

- Silage 7.1E

- 04

- 5.0E

- 01

OctaCDF, 1,2,3,4,6,7,8,9

- Exp. Fruit 9.2E

- 04

- 1.0E

- 02 Exp. Veg. 9.2E

- 04

- 1.0E

- 02 Forage 9.2E

- 04

- 1.0E+00
Grain 9.2E

- 04

- - Prot. Fruit 9.2E

- 04

- - Prot. Veg. 9.2E

- 04

- - Root 6.8E

- 01 1.0E

- 02

- Silage 9.2E

- 04

- 5.0E

- 01

HeptaCDD, 1,2,3,4,6,7,8

- Exp. Fruit 9.2E

- 04

- 1.0E

- 02 Exp. Veg. 9.2E

- 04

- 1.0E

- 02 Forage 9.2E

- 04

- 1.0E+00
Grain 9.2E

- 04

- - Prot. Fruit 9.2E

- 04

- - Prot. Veg. 9.2E

- 04

- - Root 6.8E

- 01 1.0E

- 02

- Silage 9.2E

- 04

- 5.0E

- 01

HeptaCDF, 1,2,3,4,6,7,8

- Exp. Fruit 2.0E

- 03

- 1.0E

- 02 Exp. Veg. 2.0E

- 03

- 1.0E

- 02 Forage 2.0E

- 03

- 1.0E+00
Grain 2.0E

- 03

- - Prot. Fruit 2.0E

- 03

- - Prot. Veg. 2.0E

- 03

- - Root 9.4E

- 01 1.0E

- 02

- Silage 2.0E

- 03

- 5.0E

- 01TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 57 October 2014 Description of MIRC Exhibit B

- 11 . Chemical

- Specific Inputs by Plant Type for Chemicals in MIRC

Compound Name Plant Part Plant

- Soil Bio

Concentration Factor ( **Br** AG

- produce

- DW(i) ) (unitless) a Empirical Correction Factor

- Belowgrou nd 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

- 02

- Silage 4.0E

- 03

- 5.0E

- 01

HexaCDD, 1,2,3,4,7,8

- Exp. Fruit 1.2E

- 03

- 1.0E

- 02 Exp. Veg. 1.2E

- 03

- 1.0E

- 02 Forage 1.2E

- 03

- 1.0E+00
Grain 1.2E

- 03

- - Prot. Fruit 1.2E

- 03

- - Prot. Veg. 1.2E

- 03

- - Root 7.6E

- 01 1.0E

- 02

- Silage 1.2E

- 03

- 5.0E

- 01

HexaCDF, 1,2,3,4,7,8

- Exp. Fruit 3.5E

- 03

- 1.0E

- 02 Exp. Veg. 3.5E

- 03

- 1.0E

- 02 Forage 3.5E

- 03

- 1.0E+00
Grain 3.5E

- 03

- - Prot. Fruit 3.5E

- 03

- - Prot. Veg. 3.5E

- 03

- - Root 1.2E+00 1.0E

- 02

- Silage 3.5E

- 03

- 5.0E

- 01

HexaCDD, 1,2,3,6,7,8

- Exp. Fruit 7.0E

- 04

- 1.0E

- 02 Exp. Veg. 7.0E

- 04

- 1.0E

- 02 Forage 7.0E

- 04

- 1.0E+00
Grain 7.0E

- 04

- - Prot. Fruit 7.0E

- 04

- - Prot. Veg. 7.0E

- 04

- - Root 6.1E

- 01 1.0E

- 02

- Silage 7.0E

- 04

- 5.0E

- 01TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 58 October 2014 Description of MIRC Exhibit B

- 11 . Chemical

- Specific Inputs by Plant Type for Chemicals in MIRC

Compound Name Plant Part Plant

- Soil Bio

Concentration Factor ( **Br** AG

- produce

- DW(i) ) (unitless) a Empirical Correction Factor

- Belowgrou nd 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 Forage 1.6E

- 03

- 1.0E+00
Grain 1.6E

- 03

- - Prot. Fruit 1.6E

- 03

- - Prot. Veg. 1.6E

- 03

- - Root 8.5E

- 01 1.0E

- 02

- Silage 1.6E

- 03

- 5.0E

- 01

HexaCDF, 2,3,4,6,7,8

- Exp. Fruit 1.0E

- 03

- 1.0E

- 02 Exp. Veg. 1.0E

- 03

- 1.0E

- 02 Forage 1.0E

- 03

- 1.0E+00
Grain 1.0E

- 03

- - Prot. Fruit 1.0E

- 03

- - Prot. Veg. 1.0E

- 03

- - Root 7.1E

- 01 1.0E

- 02

- Silage 1.0E

- 03

- 5.0E

- 01TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 59 October 2014 Description of MIRC Exhibit B

- 11 . Chemical

- Specific Inputs by Plant Type for Chemicals in MIRC

Compound Name Plant Part Plant

- Soil Bio

Concentration Factor ( **Br** AG

- produce

- DW(i) ) (unitless) a Empirical Correction Factor

- Belowgrou nd 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. Fruit 2.4E

- 03

- - Prot. Veg. 2.4E

- 03

- - Root 1.0E+00 1.0E

- 02

- Silage 2.4E

- 03

- 5.0E

- 01

PentaCDF, 1,2,3,7,8

- Exp. Fruit 4.6E

- 03

- 1.0E

- 02 Exp. Veg. 4.6E

- 03

- 1.0E

- 02 Forage 4.6E

- 03

- 1.0E+00
Grain 4.6E

- 03

- - Prot. Fruit 4.6E

- 03

- - Prot. Veg. 4.6E

- 03

- - Root 1.3E+00 1.0E

- 02

- Silage 4.6E

- 03

- 5.0E

- 01

PentaCDF, 2,3,4,7,8

- Exp. Fruit 6.8E

- 03

- 1.0E

- 02 Exp. Veg. 6.8E

- 03

- 1.0E

- 02 Forage 6.8E

- 03

- 1.0E+00
Grain 6.8E

- 03

- - Prot. Fruit 6.8E

- 03

- - Prot. Veg. 6.8E

- 03

- - Root 1.5E+00 1.0E

- 02

- Silage 6.8E

- 03

- 5.0E

- 01

TetraCDD, 2,3,7,8

- Exp. Fruit 4.5E

- 03

- 1.0E

- 02 Exp. Veg. 4.5E

- 03

- 1.0E

- 02 Forage 4.5E

- 03

- 1.0E+00
Grain 4.5E

- 03

- - Prot. Fruit 4.5E

- 03

- - Prot. Veg. 4.5E

- 03

- - Root 1.3E+00 1.0E

- 02

- Silage 4.5E

- 03

- 5.0E

- 01TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 60 October 2014 Description of MIRC Exhibit B

- 11 . Chemical

- Specific Inputs by Plant Type for Chemicals in MIRC

Compound Name Plant Part Plant

- Soil Bio

Concentration Factor ( **Br** AG

- produce

- DW(i) ) (unitless) a Empirical Correction Factor

- Belowgrou nd 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 discussed in HHRAP ( EPA 2005a), the Br AG

-

produ ce

- 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 bas is were obtained from Travis and Arms (1988)

. For cadmium, Br values were derived from uptake slope factors provided in EPA 1992

. Uptake slope is the ratio of contaminant concentration in dry weight plant tissue to the mass of contaminant applied per hec tare 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 represents an empirical correction factor that reduces produce concentration

. Because of the protective outer skin, size, and shape of bulky produce, transfer of lipophilic chemicals ( i.e., log K ow greater than 4) to the center of the produce is not likely

. In addition, typical preparation techn iques, 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 discu ssed 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 cooki ng, 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. I n 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 (potentially underestimating t he concentration of P B

- 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 sila ge, this value may under

- or overestimate the actual value

.TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 61 October 2014 Description of MIRC Exhibit B

- 12

. Non

- Chemical

- Specific Produce Inputs

Plant Part Interception Fraction

( **Rp (i)**  ) (unitless) a Pla nt Surface

Loss Coefficient ( **k**

**p (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 0.16 0.8 92 Grain (animal feed) NA NA NA NA 90
Source: EPA 2005a. NA = not applicable.

a Baes et al

. (1984) used an empirical relationship developed by Chamberlain (1970) to identify a correlation between initial Rp values and pasture grass productivity (standing crop biomass [ Yp ]) to calculate Rp values for exposed vegetables, exposed fruits, forage, and silage

. Two key uncertainties are associated with using these values for Rp : (1) Chamberlain’s (1970) empirical relationship developed for pasture grass may not accurately represent abovegroun d 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

- specifi c mixes of forage or silage consumed by livestock.

b The term kp is a measure of the amount of chemical that is lost to natural physical processes (e.g., wind, water) over time

. The HHRAP

- recommended value of 18 yr - 1 (also recommended by EPA 1994a and 1998 ) represents the midpoint of a range of values reported by Miller and Hoffman (1983)

. There are two key uncertainties associated with using these values for kp : (1) The recommended equation for calculating kp includes a health protective bias in that it d oes 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 us ing 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 c onstant based on the average period between successive hay harvests

. Belcher and Travis (1989) estimated this period at 60 days

. Tp is calculated as 60 days ÷ 365 days/year = 0.16 years

. For forage, the average of the average period between successive h ay 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 usin g 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 concentrations (which are lower owing to the dilution by water compared with dry

- weight concentrations)

. Values obtained from Chapter 10 of EPA's 2003 SAB Review materials for 3MRA Modeling System, Volume II, “Farm Food Chain and Terrestrial Food Web Data” ( EPA 2003a), which references EPA 1997c

. Note that the value for grain used as animal feed is based on corn and soybeans, not seed grains such as barley, oats, or wheat.TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 62 October 2014 Description of MIRC 

### 6.2.3. Animal Product Parameter Values
MIRC also requires chemical

- specific inputs for many of the animal product algorithms. The relevant values are shown in Exhibit B

- 13 for the chemicals included in MIRC to date. The HHRAP algorithms require additional inputs for the animal products calculations that are not specific to PB

- HAPs , but are specific to the animal and animal product type. The soil and plant ingestion rates recommended in HHRAP for beef cattle, dairy cattle, swine, and chicken are provided in Exhibit B

- 14

.

Exhibit B

- 13

. Animal Product Chemical

- specific Inputs for Chemicals Included in MIRC

Compound Name Soil Bio

Availability Factor ( **Bs** ) (unitless) Biotransfer Factors (Ba m ) (day/kg FW tissue) a and Metabolism Factors (MF) ( unitless) b

Mamm al 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 NA
Mercuric chloride 1 1.1E

- 04 1.4E

- 06 3. 4E

- 05 1 2.4E

- 02 2.4E

- 02 NA Methyl mercury 1 1.2E

- 03 1.7E

- 05 5.1E

- 06 1 3.6E

- 03 3.6E

- 03 NA
**PAHs**
2

- Methylnaphthalene 1 2.4E

- 02 5.0E

- 03 2.9E

- 02 0.01 1.0E

- 02 1.7E

- 02 NA 7,12

- Dimethylbenz(a)anthra

cene 1 3.9E

- 02 8.3E

- 03 4.8E

- 02 0.01 1.7E

- 02 2.9E

- 02 NA

Acenap hthene 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 N A 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 5.8 E

- 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 NATRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 63 October 2014 Description of MIRC Exhibit B

- 13

. Animal Product Chemical

- specific Inputs for Chemicals Included in MIRC

Compound Name Soil Bio

Availability Factor ( **Bs** ) (unitless) Biotransfer Factors (Ba m ) (day/kg FW tissue) a and Metabolism Factors (MF) ( unitless) b

Mamm al 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

Hex aCDF, 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 otherwi se 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 contents: milk

- 4%; beef

- 19%; pork

- 23 %; poultry

- 14%; and eggs

- 8%.

b As discussed in HHRAP (EPA 2005a), EPA (1995c) recommends using a metabolism factor (MF) to account for metabolism of PAHs by mammals to offset the amount of bioaccumulation suggested by biotransfer factors. EPA has recomm ended an MF of 0.01 for bis(2

- ethylhexyl)phthalate (BEHP) and 1.0 for all other chemicals (EPA 1995d). For MIRC, an MF of 0.01 is also used to calculate concentrations of PAHs in food products from mammalian species based on the work of Hofelt et al. (200 1). This factor takes into account the P450

- mediated metabolism of PAHs in mammals; applying this factor in our approach reduced the concentrations of chemicals in beef, pork, and dairy by two orders of magnitude.TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 64 October 2014 Description of MIRC Exhibit B

- 14

. Soil and Plant Ingestion Rates for Animals

Animal Soil Ingestion Rate

– **Qs (m)**  (kg/day) a Plant Part Consumed by Animal Plant Ingestion Rate

– **Qp (I,m)**  (kg/day)

Beef cattle b 0.5 Silage 2.5
For age 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 (1997) and EPA (1994 b ) recommended a soil in gestion rate for subsistence beef cattle of 0.5 kg/day based on Fries (1994) and NAS (1987)

. As discussed in HHRAP, Fries (1994) reported soil ingestion to be 4 percent of the total dry matter intake

. NAS (1987) cited an average beef cattle weight of 590 kg, and a daily dry matter intake rate (non

- lactating cows) of 2 percent of body weight

. This results in a daily dry matter intake rate of 11.8 kg DW/day and a daily soil ingestion rate of about 0.5 kg/day

. Dairy cattle : NC DEHNR (1997 ) and EPA (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 w eight of 630 kg and a daily dry matter intake rate (non

- lactating cows) of 3.2 percent of body weight

. This resulted in a daily dry matter intake rate of 20 kg/day DW, and a daily soil ingestion rate of approximately 0.4 kg/day

. Uncertainties associated with Qs include the lack of current empirical data to support soil ingestion rates for 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, estimat ed 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. Chick en : HHRAP (EPA 2005a) assumes that chickens consume 10 percent of their total diet (which is approximately 0.2 kg/day grain) as soil, a percentage that is consistent with the study from Stephens et al

. (1995)

. Uncertainties include the lack of current

em pirical data to support soil ingestion rates for chicken and the assumption of uniform contamination of soil ingested by chic ken.

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 unc ertainty 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 anima ls 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/day for a swine, referencing N AS (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 di et, which varies from location to location

. e Chickens consume grain provided by the farmer. The daily quantity of grain feed consumed by chicken is assumed to be 0.2 kg/day (Ensminger ( 1980 ) , Fries ( 1982 ) , and NAS ( 1987)

. Uncertainties associated with this variable include the variability of actual grain ingestion rates from site to site

. In addition, assuming uniform contamination of plant materials consumed by chicken introduces some uncertainty.



### 6.3. Adult and Non

- Infant Exposure Parameter Values

The e xposure 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 MIRC (see Section 2.3 above for MIRC age groups), time

weighted average values were estimated for the MIRC age groups from the available data

.TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 65 October 2014 Description of MIRC In MIRC, ingestion rates for h ome

- p roduce d farm food items are included for exposed fruit, protected fruit, exposed vegetable s, 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 values 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 p er 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 ) opti ons 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 screening assessment, EPA uses the mean BW for each age group. The BWs currently in the MIRC database are listed in Exhibit B

- 15

. In general, BW values for the five children’s age groups were calculated from the summary data provided in Table 8

- 3 of EPA’s 2008 CSEFH . For purposes of comparison, alternative BW values 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 Exhibit B

- 15 , but are not included in MIRC. The means calculated using the two methods for children ages 12 through 19 years were essentially identical at 64 kg

. The other percentile values for this age group using the two methods differed by approximately 10 percent or less.

Exhibit B

- 15

. Mean and Percentile Body Weight Estimates for Adults and Children

Lifestage

(years) Duration (years) Body Weight (kg)
Mean 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 2 008 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 siz es 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 esti mate was c alculated as the average of the 8 single

- year age groups from 12 to 13 years through 19 to 20 years

. Values for the other percentiles were estimated using Portier et al., 2007

. g Each BW represents a time

- weighted average of body weights for ag e 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 years . Those values are provided for compa rison purposes only and are not included in MIRC.TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 66 October 2014 Description of MIRC 

### 6.3.2. Water Ingestion Rates MIRC also includes the option of calculating chemical ingestion via drinking water obtained from surface

- water sources or from wells (i.e., from groundwater) in the contaminated area

. Users have the option in MIRC to set drinking water ingestion rates to zero or to revise the drinking water ingestion rates in MIRC to better reflect site

- specific water uses

. The 2008 CSEFH recommends values for drinking water ingestion rates for child ren based on a study reported by Kahn and Stralka (2008)

. Table 3

- 4 of the CSEFH provides per capita estimates of community water ingestion rates by age categories

. Community water ingestion includes both direct and indirect ingestion of water from the t ap. 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, presented in Exhib it 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 3 16 778 999 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 months, and 6 to <12 months from Table 3

- 4 of the 2008 CSEFH

. c Each IR represents a time

- weighted average of ingestion rates for age groups 1 to <2 years and 2 to <3 years from Table 3

- 4 of the 2008 CSEFH.

d Each IR represents the ingestion rate for age group 3 to <6 years from Table 3

- 4 of the 2 008 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 r epresents 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 throug h 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 (ages 20+), direct plus indirect water ingest ion.



### 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 andTRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 67 October 2014 Description of MIRC percentile values are from EPA’s anal ysis 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 mea ns 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 opt ions for a household to self

- identify in one or more of five categories: as a household that gardens, raises animals, hunts, fishes, or farms. As of September 2008, that survey was the most recent NFCS available ( EPA 2008a, CSEFH), and we are not aware of any that might be more recent. 29

For the adult age group in MIRC, data were compiled on food

- specific IRs separately for two types of households as indicated in the “Response to Questionnaire” ( EPA 2011a , Chapter 13): (1) households that farm (F) and (2) households that garden or raise animals (HG for homegrown)

. This division reflects EPA’s data analysis

. EPA tabulated IRs for fruits and vegetables separately for households that farm and households that garden . Similarly, EPA tabulated IRs for animals and animal products for households that farm and for households that raise animals

. Thus, the first type of household, F, represents farmers who may both grow crops and raise animals and who are likely to consume more homegrown/raised foods than the

seco nd 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

- spe cific ingestion rates are based on the amount of each food type that households that farm (F) or households that garden and raise animals (HG) produced and brought into their homes for consumption and the number of persons consuming the food. EPA averaged the actual consumption rate for home

- grown foods over the 1

- week survey period.

The default food

- specific ingestion rates in MIRC for adults are those for farming households (F) in Exhibit B

- 17 . The user can specify use of the non

- farming household (HG) ingestion rates if they are more appropriate for the user’s exposure scenario.

Exhibit B

- 17

. Summary of Age

- Group Specific Food Ingestion Rates for Farm Food Items

Product Child (age in yr) Adult (20

– 70

yrs) <1 1

– 2 3

– 5 6

– 11 12

– 19
**Mean ingestion rates (**  **g/kg**

**- day**  **)**
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 distingui sh between exposed and protected fruits and vegetables when recommending food ingestion rates based on the same data set for the same age categories

. EPA’s 1997 analysis for its EFH therefore remains the most appropriate data source for use in MIRC.TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 68 October 2014 Description of MIRC Exhibit B

- 17

. Summary of Age

- Group Specific Food Ingestion Rates for Farm Food Items

Product Child (age in yr) Adult (20

– 70

yrs) <1 1

– 2 3

– 5 6

– 11 12

– 19

Protected Fruit a N/A 16.6 12.4 8.50 2.96 5.19 Protected Vegetable a N/A 2.46 1.30 1.10 0.78 0.862
Root Ve getable 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 Vegetabl e 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 3.88 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 1 0.4 4.73 5.59 3.32 3.37TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 69 October 2014 Description of MIRC Exhibit B

- 17

. Summary of Age

- Group Specific Food Ingestion Rates for Farm Food Items

Product Child (age in yr) Adult (20

– 70

yrs) <1 1

– 2 3

– 5 6

– 11 12

– 19
Water (mL/day) c N/A 903 999 1499 2163 3087
**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 CSFI I.

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 200 3

– 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 assess ments and chemical threshold calculations

.
For children, EPA estimated food

- specific IRs for four age categories ( EPA 2011a ): 1

– 2 years, 3

– 5 years, 6

– 11 years, and 12

– 19 years

. Sample sizes were insufficient to distinguish IRs for children in different types of households; hence, for children, a single IR value represents both F and HG households for a given food type and age category ( Exhibit B

- 17 )

. For some food types and age categories, there were insufficient data for EPA to provide consumer

- only intake rates (i.e., data set for the subpopulation consisted of fewer than 20 observations ). 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 include d in Chapter 13 of the 2011 EFH , that is IR age_ group _x , were derived using the following information:

• Mean or percentile

- specific consumer

- only intake of the farm food item, as brought into the home, for the total NFCS survey population (from EFH Chapter 13)

– IRCO_total;

• Mean or percentile

- specific per capita intake of the food type from all sources, as consumed, for the specific child age group, from Chapter 3 of the CSFII Analysis of Food Intake Distributions (EPA 2003c)

– IRPC, age_group_x; and

• Mean or percentile

- specific per capita intake of the farm food item for the total CSFII survey population (from Chapter 3 of EPA 2003c)

– IRPC_total.TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 70 October 2014 Description of MIRC T he ratio of IR PC, age_ group _x to IR PC_tot al from the CSFII data shows the consumption rate of a particular food type by a specific age group relative to the consumption rate for that food type for the population as a whole

. The ratio of IR CO, age

_ group

_ x to IR CO_total , that is the consumption rate of a particular food type by a specific age group (consumers only) relative to the consumption rate for that food type for the NFCS survey population as a whole (consumers only), should be approximately the sa me

. 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 child age group X

IR CO_total = Mean or pe rcentile

- 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 specifi c 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

- only ) indicates the intake rates are based o n the entire population rather than the subset of the population that ingests the particular food category (i.e., consumers). Here, the use of per capita ingestion rates are recommended by the HHRAP methodology because no consumer

- only percentile

- specific intakes are provided for the different age groups.

The above calculation implicitly assumes that the distribution of the consumption rate for a food type for a specific age group (consumers only) has the same shape as the distribution of the consumption r ate 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 t he shapes of the two distributions differ in the upper percentiles (or “tails”) of the distributions.

In the instances where the above calculations were used to fill data gaps in the above exhibit, only the dairy child

- specific age group intake estimates are not strictly increasing with increasing percentile. The distributions likely track better (and thus the above assumption of equal ratios is more reasonable) for lower percentiles, with deviations occurring due to outlier ingestion rates based on only a few respondents in the tails of the distributions. The MIRC defaults use the 90 th percentile ingestion estimates, which are likely more reliable than the 95 th or 99 th percentile estimates in this particular calculation.



### 6.3.4. Local Fish Ingestion Rates

6.3.4.1. Screen ing 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 

### ×

=TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 71 October 2014 Description of MIRC Various Home Produced Food Items) (EPA 2011a)

, includes family

- c aught fish ingestion rates by age category. There are several disadvantages, however, to using that data source to estimate fish ingestion rates. First, due to inadequate sample sizes, EPA did not report fish ingestion rates for children less than 6 year s of age. Second, the NFCS data were collected more than two decades ago. Third, the reported fish ingestion rates are for ages 6 to 11 and 12 to 19 and are based on 29 and 21 individuals in each age category, respectively (EPA 2011a, Table 13

- 20). Fina lly, the ingestion rates from NFCS data are based on total weight of fish as brought into the home, and do not include losses from preparation of the fish (i.e., removal of inedible parts and, possibly, the skin). Estimates of preparation losses for fish intended to apply to the NFCS fish ingestion rate data are very uncertain and are based on a wide variety of freshwater, estuarine, and marine fish, and squid (EPA 2011a, Table 13

- 69). Additionally, when considering the multipathway screening methodology, it is important that potential health effects to those individuals who are most likely to have the greatest PB

- HAP exposure are not underestimated and, therefore, ingestion rates that are reflective of subsistence fisher ingestion rates are desired. There fore, 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 Burger (2002). This rate is based upon ingesti on 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 speci es 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 fisher s of child bearing age are located throughout the United States. Finally, we note that using a high

- end subsistence fish ingestion rate is consistent with section 112 of the CAA, which focuses on risks associated with maximally exposed individuals.

Becaus e 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 ingesti on rate, reflective of subsistence fisher ingestion rates, and based on adequate sample sizes. To satisfy these requirements, data on child ingestion rates from EPA’s Estimated Per Capita Fish Consumption in the United States (EPA 2002) were selected for use. Specifically, the estimated 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 part of the 1994

– 96 and 1998 USDA Continuin g Survey of Intakes by Individuals (CSFII; USDA 2000). Values reflect “as prepared” ingestion rates for which cooking and preparation losses ( L1 and L2 ) did not need to be considered. “Total” fish as reported in this source represent s consumption of finf ish 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 e stimate ingestion rates for this age group,TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 72 October 2014 Description of MIRC EPA used the ingestion rate for the 3

– 5 year age category scaled downward by the ratio of the mean body weight of the 1

– 2 year age group to the mean body weight of the 3

– 5 year age group. Because MIRC uses a 3

– 5 year age category, no adjustment was needed for CSFII data from that age category. For the 6

– 11 and 12

– 19 age categories, time

- weighted average ingestion rates were calculated based on the CSFII ingestion rates. Exh ibit B

- 18 provides the fish ingestion rates used in the screening assessment

.

Exhibit B

- 18

. Fish I ngestion R ates Used in Screening Assessment

Ingestion Rates (g/day)
Infants

<1 yr Child 1

– 2 yrs Child 3

– 5 yrs Child 6

– 11 yrs Child 12

– 19 yrs Adult 20

– 70 yrs
NA 107.7 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 represents the consumer

- only fish ingestion rate for ages 3 to 5 from EPA (2002) (Section 4 .2.1.1 Table 5 [freshwater/estuarine habitat]), scaled down by the ratio of the mean Child 1

- 2 body weight to the mean Child 3

- 5 body weight

. b This value represents the consumer

- only fish ingestion rate for ages 3 to 5 from EPA (2002), Section 4 .2.1.1 Table 5 (freshwater/estuarine habitat) rounded to the nearest full number

. c These values represent time

- weighted average consumer

- only fish ingestion rate s based on ingestion rates from EPA (2002), Section 4 .2.1.1 T able 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, commerc ial 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 fi sh ingestion rates for most populations in the United States; however, it also might underestimate locally caught fish ingestion rates for some populations (e.g., Native Americans, Asian and Pacific Island communities, rural African American communities). Because consumption of locally caught fish varies substantially from region to region in the United States and from one population or ethnic group to the next, users of MIRC are encouraged to use more locally relevant data whenever available.

For childr en, 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 2002, 2008a) . Those rates include individua ls 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 rate s estimated from the two

- day CSFII recall period are zero for some child age groups

. Although not presented in CSEFH Tab le 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 percentile fish ingestion rate s that are zero resu lt 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 dail y ingestion of other types of food products (e.g., dairy, produce, meat)

. Use of zero for fish ingestion rate s, however, is not useful in MIRC

. As a result, an alternative method was used to estimate fish ingestion rate s for children and adults that coul d provide reasonable, non

- zero values for all age groups and percentiles

.TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 73 October 2014 Description of MIRC The alternative, age

- group

- specific fish ingestion rate s were derived using values for each age group, y :

• Mean or other appropriate percentile 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 freshwater/estuarine fish, FPC, y , calculated as

co nsumer

- 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 capita fish ingestion rate for age group y (g/day)

IR CO,y = Consumer

- only fish ingestion rates for age group y (g/day) ( EPA 2002, Section 5.2.1.1, Table 5, for freshwater/estuarine habitat)

F

PC,y = Fraction of the population consuming freshwater/estuarine fish, calculated as

consumer

- only sample size / total U.S

. population sample size for age group y (unitless) (2008 CSEFH, EPA 2002)

In the above, per capita (as opposed to consumer

- only ) indicates the intake rates are based on the entire population rather than the subset of the population tha t 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 ingesti on 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 ingesti 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 dat a 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 marin e), 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 scr eening 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 × =TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 74 October 2014 Description of MIRC As noted in Section 6.4.3 of this at tachment , 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 l oss factors L1 and L2 in Equation B

- 15 . Suggested values are presented in Section 6.4.3

.

Exhibit B

- 19

. Daily Me an 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 ag e do not consume fish.

* Indicates that the sample size does not meet minimum reporting requirements as described in EPA 2002

. Owing to the small sample sizes, these upper percentiles values are highly uncertain.

a Per capita fish ingestion (FI) rates for c hildren 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 provi de 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 h abitat]), scaled down by the ratio of the mean Child 1

- 2 body weight to the mean Child 3

- 5 body weight

. c These values represent the consumer

- only fish ingestion rate for ages 3 to 5 from EPA (2002), Section 5.2.1.1 Table 5 (freshwater/estuarine habitat)

. Sample size = 442.

d These values represent the consumer

- only fish ingestion rate for ages 6 to 10 from EPA (2002), Section 5.2.1.1 Table 5 (freshwater/estuarine habitat)

. Sample size = 147.

e These values represent the time

- weighted average per capita fi sh 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

- onl y fish ingestion rate for individuals 18 years and older from EPA (2002), Section 5.2.1.1 Table 4 (freshwater/estuarine habitat)

. Sample size = 1,633.TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 75 October 2014 Description of MIRC

Exhibit B

- 20

. Fraction of Population Consuming Freshwater/Estuarine Fish on a Single Day ( **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 calculat ed using the ages 3 to 5 sample size for consumers only divided by the sample size for the U.S. population divided by 2 to represent the proportion consuming fish on a single day (the consumers

- only group includes individuals who consumed fish on at least one of two survey days) to match the one

- day ingestion rate.

b As in footnote a, the value was calculated using the ages 6 to 10 sample size for consumers only divided by the sample size f or U.S. population divided by 2.

c The value was calculated by summing the ages 11 to 15 and 16 to 17 sample sizes for consumers only and dividing by both by the sum of the sample sizes for U.S. population and by a factor of 2.

d The value was calculated using the ages 18 and older sample size for consumers only divided by th e 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

Child <1 NA NA NA NA NA
Child 1

- 2 a 1.37 0.79 3.24 4.41 6.98
Child 3

- 5 b 2.03 1.16 4.79 6.51 10.3
Child 6

- 11 c 2.71 1.25 6.90 10.9 17.0
Child 12

- 19 d 3.90 2.13 8.95 10.4 20.9
Adult e 6.90 4.03 16.99 23.73 43.02

Sources: EPA 2002, 2008a N A = not applicable; it is assumed that children < 1 year of age do not consume fish.

a Values were c alculated as (consumer

- only IR for Child 1

- 2) x (fraction of population consuming fish for Child 3

- 5).

b Values were c alculated as (consumer

- only IR for Child 3

- 5) x (fraction of population consuming fish for Child 3

- 5 ).

c Values were c alculated as (consumer

- only IR for Child 6

- 11) x (fraction of population consuming fish for Child 6

- 11).

d Values were c alculated 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).TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 76 October 2014 Description of MIRC MIRC also includes values for the mean and the 90th percentile fish ingestion rates for recreational fisher s, black and female recreat ional 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 tha n 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, Laotian, and Vietnamese populations were deri ved 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 S hilling 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 Hispanic, Laotian, and Vietnamese fishers w ere 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 Populatio ns 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 Vietnamese 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. White, L. Lippert, and M. Lubell. 2010. Co ntaminated fish consumption in California’s Central Valley Delta. Environmental Research 110:334

- 344.

Applications to date of MIRC have used whole fish concentrations estimated by TRIM.FaTE

. The proportion of lipid in TL3 and TL4 fish in TRIM.FaTE is as sumed to be 5.7 percent (by weight) for the whole fish, based on information provided by Thomann (1989)

. The lipid content of the part(s) of the fish normally consumed is likely to be less than 5.7 percent. For example, EPA estimated a consumption

- weight ed 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 fatty tissues of fish, much of the higher concentration tissues might be stripped from the fish during preparation (e.g., belly fat, viscera which includes fat in liver, etc

. , fat under skin)

. The degree to which the concentration of chemical in a fillet is less than the average total concentrati on in the whole fish is chemical specific

. Assuming the chemical concentration in the fillet is the same as in the whole fish may result in a health protective bias for highly lipophilic chemicals . For persons who prefer to consume fillets with the skin on and do not discard belly fat

, assuming the same concentration of chemical in the fish consumed as in the whole fish also is protective.TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 77 October 2014 Description of MIRC 

### 6.3.5. Soil Ingestion Rates
Adult gardeners and farmers may incidentally ingest soils from gardening activities, and gardeni ng 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 fo r childr en 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 E xhibit 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 g arden 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 Ing estion 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 m ean 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 high

- 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 calculated in Stanek et al. 1997 ; used to r epresent 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 probabilistic module develop ed for MIRC

. Th e total food ingestion rates presented in Exhibit B

- 24 will be used to normalize or to truncate the sum of food

- specific ingestion rates to reasonable values

. This procedure is particularly important when chemical intake from mul tiple 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 f or one or any of the other food categories.TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 78 October 2014 Description of MIRC Exhibit B

- 24 . Daily Mean and Percentile **Per Capita** Total Food Intake for Children and Adults

Lifestage (years) Percent of G roup Consuming Food Mean 50th 90 th 95 th 99 th

**Total Food Intake (g/day, as consumed)**
Child < 1 a 67.0%

- 99.7% h 322 270 599 779 1152
Child 1

- 2 b 100% 1,032 996 1537 1703 2143
Child 3

- 5 c 100% 1,066 1,020 1,548 1,746 2,168
Child 6

- 11 d 100% 1,118 1,052 1,6 42 1,825 2,218
Child 12

- 19 e 100% 1,197 1,093 1,872 2,231 2,975
Adult f 100% 1,100 1,034 1,738 2,002 2,736 **Total Food Intake (g/kg**

**- day, as consumed)** 
Child < 1 a 67.0%

- 99.7% h 39 34 72 95 147
Child 1

- 2 b 100% 82 79 125 144 177
Child 3

- 5 c 100% 61 57 91 1 02 132
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 t o <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 o b tained from Table 14

- 3 of the 2008 CSEFH (age group 3 to <6 years, N=4,112).

d These values were o btained from Table 14

- 3 of the 2008 CSEFH (age group 6 to <11 years, N=1,553) . These values r epresents

a health protective (i.e., slightly low) estimate for a ges 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 v alues include 11

- year

- olds and individuals through age 20, which contributes to uncertainty in the estimates.

f These values represent a time

- weighted average for age groups 20 to 39 years (N=2,950) and 40 to 69 years (N=4,818) from Table 5B of the 2005 EPA analysis of CSFII

. g These values represent a time

- weighted average for age groups 20 to 39 years (N=2,950) and 40 to 69 years (N=4,818) from Table 5A of the 2005 EPA analysis of CSFII

. h Percents consuming foods from Table 14

- 3 of the 2008 CSEFH includ e: 67.0% (birth to <1 month); 74.7% (1 to <3 months); 93.7% (3 to <6 months); and 99.7% (6 to <12 months)

. Infants under the age of 1 that consume breast milk are classified as “non

consumers” of food.



### 6.4. Other Exposure Factor Values
The other exposure pa rameters 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 foo d 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 a nd all potential receptors. This assumption is consistent with the food ingestion rates used in MIRC (i.e., daily intake rates equivalent to annual totals divided by 365 days) and does not imply that residents necessarily consume home

- produced food produc ts every day of the year

.TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 79 October 2014 Description of MIRC If the user wishes to evaluate daily intake rates based on shorter averaging times, the user can overwrite both the food

- specific ingestion rates and the EF for each home

- grown food product. Users of MIRC might want to specify a lower EF values for various food types where residents obtain some of their diet from commercial sources and where consumption of home grown produce is seasonal.



### 6.4.2. Fraction Contaminated

The fraction contaminated (FC) represents the fraction of each food pr oduct consumed that is contaminated by the chemical at a level consistent with environmental concentrations in the area of concern (e.g., area with maximum deposition rates). Obviously, the most health protective assumption is that all food products co nsumed (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 p roduced 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 in to 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 loss es (e.g., non

- consumption of bones, draining cooking liquid [e.g., spinach]). MIRC includes mea n values for these three types of preparation and c ooking loss es for exposed fruit, protected fruit, exposed vegetables, protected vegetables, root vegetables, beef, pork, poultry, and fish . Different types of losses apply to different types of foods. Th erefore, the losses can be represented by only two parameters, L1 and L2 , the definitions of which vary according to the food type as explained in the endnotes in Exhibit B

- 25 . All preparation and cooking loss parameter values we re estimated as specified in the exhibit’s endnotes from data presented in Chapter 13 of the EPA’s 1997 and 2011 EFH (EPA 1997a and 2011a).TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 80 October 2014 Description of MIRC Exhibit B

- 25

. Fraction Weight Lo sses 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
Prote cted 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 , includes losses from draining cooked forms

. Fo r 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, fa t, or other ingredients

. For meats , includes dripping and vol atile losses during cooking.

b For fruits , includes losses from removal of skin or peel, core or pit, stems or caps, seeds and defects; may also include losses from removal of drained liquids from canned or frozen forms

. For vegetables , includes losses fro m 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 set equal to the v alue for oranges (Table 1 3

- 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 ep resent average s of means for all root vegetables with availa ble 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 match a survey of the whole weight of fish br ought 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 water evaporation, which might leave the ch emical in the food (chemical not lost) , and pouring the cooking liquid down the drain (chemical lost) or using the liquid to create a sauce (chemical not lost) . In addition, the concentration of chemical might be highest in t he skin, which often is discar ded , 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 and lima beans), which increases the range of L1 values across different vegetables.

Note that the default L1 and L2 values for fish are set to zero

. That is because the data source for the fish ingestion rates is not the USDA’s 1987 to 1988 NFCS (USDA 1993, 1994 a ) as reported in EPA’s EFH , whi ch 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 f ish 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 consum ing the fish), the user should also set the L1 and L2 parameter to non

- zero values.TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 81 October 2014 Description of MIRC 

### 6.4.4. Food P reparation/ C ookin g 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 uncooked fish concentration to estimate

a co ncentration 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 Freshwat er 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 lev els per unit fish (as consumed) because MeHg concentrates in the muscle

, while preparation involves removal primarily of non

- muscle elements of the fish. The value is based on a study by Morgan et al. (1997).



### 6.4.4.2. Cadmium
Similar to mercury, cadmium will bind to muscle and will be retained during the cooking process. As such, the same FPCAF of 1.5 that is used for mercury is assumed for cadmium.



### 6.4.4.3. Dioxin
Dioxins are lipophilic and have been demonstrated to be lost during cooking. Based on a literature review, an FPCAF of 0.7 to is applied to account for these losses during the cooking process. A brief summary of supporting literature follows.

• Schecter et al. (1998) found that the mass of PCDD and PCDF in fresh catfish fillet (skin on) decreased by about 50 percent per serving portion during cooking. Given the simultaneous losses of moisture/fats during broiling of the catfish, the PCDDs and PCDFs concentrations decreased by 33 percent (i.e., multiply uncooked concentration in fresh fish by a factor of 0.66 = 0.70 t o 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 trou t 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 u ncooked 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 percen t for lake trout. Comparing losses of TCDD for fillets cooked with the skin on versus fillets that were both skinned and cooked, Zabik and Zabik (1995) found reductions in TCDD concentrations of approximately 43 percent for Chinook Salmon cooked with the s kin on and 57 percent for c hinook s almon cooked withTRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 82 October 2014 Description of MIRC the skin off. They found a 37 percent reduction of TCDD concentration for carp fillets cooked with the skin on and 54 percent reduction if the skin was removed.

The three studies listed above indicate th at the 0.7 factor is not likely to overestimate loss of PCDD/PCDFs from fish during cooking (pan frying, broiling, grilling). Reductions in TCDD concentrations could be much higher with skin removal and trimming of fat.



### 6.4.4.4. PAHs

While it is reasonable to assum e 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 ack nowledges these competing forces, but does not provide information to disentangle the gain and loss mechanisms. As such, a neutral approach was taken, which is to assume an adjustment factor of 1.0 (i.e., no adjustment) for PAHs.



### 6.5. Breast

- Milk Infant Exposu re 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 empir ically 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 eq uation 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 parameter values that can be estimated when emp irical values are not available, see the equation description in Section 3.4 of this attachment

.TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 83 October 2014 Description of MIRC Exhibit B

- 26 . Scenario

- and Receptor

- Specific Input Parameter Values Used to Estimate Infant Exposures via Breast 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 du ration 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

- 36
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 mo ther’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 feeding (days) 365

t pn Duration of maternal ch emical 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 ex posure scenario considered for this age group, both AT and ED equal the duration of the nursing period, and they therefore cancel each other out in the infant average daily dose equation

. Infant body weight ( BW **inf**

)

. The user selects a value for BW inf , the time

- weighted average body weight of the infant over the entire duration of breast feeding, based on the age at which the infant stops breast feeding. For example, if the infant breast feeds for one year, the user should select the body weight for an infant th at is averaged from birth to the first birthday

. Similarly, if an infant breast feeds for 6 months, the user should select the body weight for an infant that is averaged from birth to six months . Because the default breast feeding duration ( 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 Exposure 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

. Av erage 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 11.0TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 84 October 2014 Description of MIRC Exhibit B

- 27

. Av erage 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 ave rage 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. T he maternal body weight is needed to calculate the biological elimination constant for the lipophilic chemical in lactating women ( k fat_elac ). MIRC assumes that the mother will be pregnant for 9 months (i.e., 0.75 year) and will be lactating for 1 year. The MIRC default maternal body weight also assumes that the mother has been exposed for 10 years total. For 8.25 years, she is not pregnant or lactating, for 0.75 year she is pregnant, and for 1 year she is lactating. The MIRC default BW mat of 66 kg is b ased 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 both men and women. For those ingestion rat es, 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) reported that plasma volume accounts for approxima tely 60 percent of the total blood volume in non

- lactating human females (EPA 1998). Harrison (1967) and Ueland (1976) reported plasma volumes between 63 to 70 percent in postpartum women (EPA 1998). The default value in MIRC of 65 percent ( 0.65 )TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 85 October 2014 Description of MIRC is the value recommended by EPA in its Methodology for Assessing Health Risks Associated with Multiple Pathways of Exposure to Combustor Emissions (MPE, EPA 1998).

Fraction of mother’s body weight that is fat ( f f **m** )

. A limitation of using a steady

- state, instead of a dynamic partitioning, model for lactational transfer of chemicals is that several key parameters change over the course of exposure. For example, Equation B

- 38 , used to estimate the concentration of a lipophilic chemical in breast milk fat, assumes that the mother’s body fat will remain constant over the entire duration of breast feeding (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

- p

dioxins show a correlation between percent body fat and the elimination rate of dioxins, with longer half

- lives for dioxins in individuals with a higher proportion of fat in their bodies (ATSDR 1998). In the context of a screening model, however, EPA recommends a default value for the fraction of a mother’s body comprised of fat of 0.3 based on data and discussions presented by Smith (1987) and Sullivan 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 esta blish 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 constant fraction of breast milk is fat, even though there is evidence that indicates variation in the fat content of breast milk throughout lactation (Sim and McNeil 1992). Different studies suggest a fat content of breast milk in humans of between 1 and 5 percent (Jensen 1987, Schecter et al

. 1994 , Hong et al

. 1994, McLachlan 1993, Bates et al

. 1994, NAS 1991, Butte et al

. 1984, Maxwell and Burmaster 1993, EPA 2011a, Smith 1987, Sullivan et al . 1991). The MIRC default value for f mbm of 0.04 (i.e., 4 percent) is the value EPA recommended for MPE (E PA 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 vo lume 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 recommende d time

- weighted average and upper percentile milk ingestion rates for infants that nurse for six and for twelve months (EPA 2011a, Table 15

- 3). To estimate an “average” value, EPA first estimated study

- sample

- size weighted average values for 1 through 12 months of age and then developed time

- weighted average milk ingestion rates from those (EPA 2011a). EPA estimated an upper percentile (upper bound) value as the mean plus two standard deviations. MIRC converts the ingestion rates measured volumetrically (mL/day) to mass

- based estimates (kg/day) assuming the density of human milk to be 1.03 g/mL (reported by NAS 1991 and recommended by EPA 2011a). The resulting values are shown in the first two rows of Exhibit B

- 29 . The MIRC scr eening

- 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 categories from the CSEFH (EPA 2008a, Table 15

- 1). The values demonstrate that although infants grow substantially from birth to one year of age, the “upper bound” estimates of their milk ingestion rates are very close to 1 liter per day at all stages of development in the first year.TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 86 October 2014 Description of MIRC Exhibit B

- 29

. Infant Breast Milk Intake Rates

Age Category Average (mL/d) Average (kg/d) “Upper Bound” (mL/d) “Upper Bound” (kg/d) Reference

1 to 6 months 742 0.764 1,033 1.064 EPA 2011a †

0 to < 12 months 688 0.709 980 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 620 0.639 1,000 1.03 EPA 2008 a †

a MIRC defau lt ; † Based on review of multiple studies ; †† Based on a single study

Duration of breast feeding ( t **bf**

)

. This parameter is equal to the infant’s exposure duration ( ED ) and the infant’s averaging time ( AT ). In its MPE Methodology, EPA asserts a health pr otective 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 ex posure 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 chemica l with a long biological half

- life (e.g., many years). The factor is needed for applications of the model to scenarios with a brief exposure duration (e.g., beginning a few months prior to the start of nursing ) relative to the chemical half

- life. As the duration of an exposure scenario increases to 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 of 8 years and a nursing period of 1 year. If exposure of the mother starts at the beginning of nursing, using Equation B

- 38 without the 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 predictio n 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 dioxins , or 10 years

. Only 3

, 285 d ays 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 lip ophilic chemicals (i.e., dioxins), for which lactational transfer is assumed to occur via milk fat, and inorganic chemicals, for which the transfer is assumed to occur via the aqueous phase of breast milk (i.e., mercury). All dioxin congeners were assumed to manifest identical values as TCDD in regard to breast milk

- related parameters.TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 87 October 2014 Description of MIRC Exhibit B

- 30

. Chemical

- specific Input Parameter Values for Breast Milk Exposure Pathway

Parameter and Description 2,3,7,8

- TCDD MeHg

AE inf Infant absorption efficiency of the chemical by the oral route of exposure (i.e., fraction of ingested chemical that is absorbed by the infant; unitless) 1.0 (default) 1.0 (default)

AE mat Maternal absorp tion efficiency of the chemical by the oral route of exposure (i.e., fraction of ingested chemical that is absorbed by the mother; unitless) 1.0 (default) 1.0 (default)

f bl Fraction of steady

- state total body burden of hydrophilic chemical in mother that is in the mother’s whole blood compartment (unitless) NA 0.059 (Kershaw et al

. 1980) a

f f Fraction of steady

- state lipophilic chemical body burden in mother that is stored in body fat (unitless) ≥0.90 (ATSDR 1992) NA

f pl Fraction of steady

- state total hyd rophilic 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 tota l 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 eliminati on 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 assump tion) 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 deter mined 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 h alf

- 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 the lipid phase of breast milk is equal to th e 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 br east 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 mothe r 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 m atrices (e.g., solid foods) may not be relevant (EPA 2001a). If chemical

- specific data are not available for adults or infants, a health protective default value for AE inf for a screening level assessment is 1 .0, which assumes 100 percent absorption (EPA 1998).TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 88 October 2014 Description of MIRC 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 o f 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 h igh 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 MeHg is 1.0, as described in the previous para graph.

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. This study used a single

- dose and thus may not be appropriate for a chronic exposure analysis.

Fraction of total maternal chemical body burden that is in body fat ( f **f** )

. Based on ATSDR’s Toxicological Profile for Selected PCBs (ATSDR 1992) and Sullivan et al . (1991), EPA concluded that the “fraction o f 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 dio xins 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 body that is circulating in the blood plasma . Values for f pl may be available for some chemicals in the scientific literature. No value for this parameter for methyl mercury has been identified from the literature at this time. A value can be calculated using Equation 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 t o 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 h ealth 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 lactatin g 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 fo r MeHg of 50 days with a range of 42

- 70 days . The MIRC default for MeHg is set to the longer average half

- life of 50 days.

Chemical elimination rate constant for lactating women

– aqueous ( k **aq_elac** )

. The parameter k

aq_elac is equal to k elim plus the loss rate for the chemical in the aqueous phase of breast

- milk during lactation. EPA has yet to propose a term for the additional elimination of a chemical in the aqueous phase of milk from breast feeding. In the absence of empirical values, a reasonable ass umption 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
.TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 89 October 2014 Description of MIRC 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

life assuming first

- order kinetics as shown i n 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 value for k elim is estimated to be 0.014 per day

. Rate constant for total elimination of lipophilic chemicals by lactating women ( k **fat_elac** ). Although values for this parameter might be found in the scientific literature for some chemicals, in MIRC, k

fat_elac for dioxins is calculated from Equation 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 between maternal blood plasma and aqueous ph ase 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 b ased on Hollins et al . (1975) study of cats expos ed 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 emission rates used for screening emissions of P B

- HAPs from sources included in RTR risk assessments. These values are the default, or initial setting, for parameter values in MIRC as described in Section 6 of this attachment . This section summarizes the default parameter values used to calculate screening thresholds.

This chapter is organized to present the chemical

- and scenario

- specific inputs to MIRC by data type. The screening

- level analysis uses 90 th percentile ingestion rates for soil, breast milk, and farm food ite ms and 99 th percentile ingestion rates for fish (presented in Section 7.1 ) and population

- specific characteristic assumptions (presented in Section 7.2 ), that are generally health protective in natu re. Screening thresholds were derived for five RTR chemical species: benzo(a)pyrene, cadmium, mercuric chloride, methyl mercury, and 2,3,7,8

- TCDD; Section 7.3 presents chemical

- specific parameter inputs for these five chemical s. 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 Ra tes 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 medium. In general, these values were obtained from the 2011 Exposure Factors Handbook or the 2008 Child

- Specific Exposure Factors Handbook (see Exhibit B

- 31 ). Fish ingestion rates are also available from these sources; however, as described in Section 6.3.4 , these sources were not used to obtain fish ingestion rates.TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 90 October 2014 Description of MIRC 

### 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 fish for high

- end recreationalists (white, bla ck 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), 172 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 percenti le 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, rather than per

- captita rates, which include b oth 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 direct ly correspond to the age groups in the EPA (2002) report. As described in Section 6.3.4 , these adjustments convert the available age

specific data on fish ingestion rates to the age

- specific values needed for MIRC.

For the sc reening

- level fish ingestion exposure scenario, the consumer evaluated is an individual who regularly consumes a large amount of fish that he or she has caught locally over the course of a 70

- year lifetime. Modeled exposures are intended to encompass thos e 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 pres ented 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 d ata 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 r ates strike s 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 conserva t 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 States. Using a high

- end subsistence fish ingestio n 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 specifi ed by the user (i.e., fraction of food from contaminated area = 1.0).

For estimates of screening threshold emission rates for PB

- HAPS, environmental concentrations and air deposition rates were estimated using TRIM.FaTE for the area of maximal deposition in the vicinity of a hypothetical facility, and thus represent risks estimated for a maximally exposed individual/farm/family.TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 91 October 2014 Description of MIRC Exhibit B

- 31 also includes a sum of the 90th percentile ingestion rates for homegrow n food categories and 99th percentile fish ingestion to show the implied total food ingestion rate associated with setting multiple food

- type

- specific ingestion rates at upper percentiles. Because these upper percentile values for each farm food category are likely to reflect different individuals, it is likely that addition of multiple upper percentile intake values will exceed the total food ingestion rates expected for the general population. This sum is shown on the third row from the bottom (Total Fo od : 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 by Individuals (CSFII) (USDA 2000) data sets, as analyzed by EPA (EPA 2005e). The total ingestion rate for the farming households (third row from bottom) takes into account the cooking losses typical of each food category to provide a better comparison with the 90 th percentile individual total food i ngestion rates from CSFII (which are based on consumption of prepared foods). The final row of Exhibit B

- 31 shows the likely magnitude of the overestimates by age category by presenting the ratio of the two preceding rows. The v alues in this row demonstrate the potential for overestimating intake by using upper percentile values for all food groups. This bias may be considered when evaluating the results estimated by MIRC.

Exhibit B

- 31 . Farm Food Category Ingestion Rates for Health Protective Screening Scenario for Farming Households

Product Screening

- Level Consumer Ingestion Rate Units Infants

<1 yr Child 1

– 2 yrs Child 3

– 5 yrs Child 6

– 11 yr s 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 Vegeta ble 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/dayTRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 92 October 2014 Description of MIRC Exhibit B

- 31 . Farm Food Category Ingestion Rates for Health Protective Screening Scenario for Farming Households

Product Screening

- Level Consumer Ingestion Rate Units Infants

<1 yr Child 1

– 2 yrs Child 3

– 5 yrs Child 6

– 11 yr s Child 12

– 19 yrs Adult 20

– 70 yrs **Total Food Ingestion Rates for Comparison Only (not i n 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 unav ailable 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 a re 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 t h 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 Estimated Per Capita Fish Consumption in the U nited States (2002)

— Section 4. 2 .1.1 Table 5 (for child age categories) adjusted and scaled. Values reflect “as prepared” ingestion rates.

g The fish ingestion rate for children aged 3

– 5 years was obtained directly from Section 4.2.1.1, Table 5 in the EPA (2002) report (value presented is rounded) ; for these children, the RTR age

- group range matches the EPA (2002) age category. Fish ingestion rates for children less than 3 years old, however, were not provided. Therefore, f or children aged 1

– 2 years, the f ish 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 r ates were calculated using t he 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 e stimate 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

cook ing 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

- Level Population

- Specific Parameter Values

The screening

- level values for body weights (BWs) for the RTR screening threshold analysis, which serve as the default values in MIRC, are mean values and are presented in Exhibit B

- 32

. As stated in Section 6 of this attachment

, EPA recommends using the mean BW for each age group when using upper percentile values for medium ingestion rates. Use of the mean body weights introduces no bias toward over

- or underestimating risk.

Exhibit B

- 32

. Mean Body Weight Estimates for Adults and 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 18.6TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 93 October 2014 Description of MIRC 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

- weighted average of BWs for age groups 6 to <1 1 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 f rom 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 Anal ysis

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 metabolism factors ( MF ), correction factors for b elowground 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 possible overestimate of the transfer of chemicals f rom the outside to the inside of bulky tubers or roots (based on carrots and potatoes) 0.01 1 1 0.01 0.01 unitless

Fw Fraction of wet deposition that adheres to plant surfaces; 0.2 for anions, 0.6 for cations and most organics 0.6 0.6 0.6 0.6 0.6 unitlessTRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 94 October 2014 Description of MIRC 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 and root produce 9,180 0 0 0 40,002 L soil pore water/kg root WW

Kds Chemical

- specific soil/water partition coefficient 7,750 75 58,000 7,000 31,126 L soil pore water/kg soil DW

a Values presented in this exhibit are also presented in previous exhibits; however exact values used in the assessment are presented here, rather than values restricted by significant figures. In addition, only values for those chemicals that are specifically used in the screening assessment are provided here.

Only single estimates were developed for each of these parameter s 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

- type

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 maximal air deposition of chemical. Input valu es were summarized in Section 6.5 of this attachment

.


### 7.4.1. Dioxins
For dioxins, chemical intake via breast milk by nursing infants was estimated using the model presented in EPA’s Methodology for Assessing Health Risks Associated with Multiple Pathways of Exposure to Combustor Emissions (MPE, EPA 199 8). The assumption that lactational transfer of dioxins to the infant occurs via the lipid

- phase of milk appears reasonable. TheTRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 95 October 2014 Description of MIRC following screening

- level assumptions used in that mod el should bias the results toward health

protective estimates of risks.

• Duration of nursing is a full year and no other foods or liquids are consumed by the infant; a more “typical” value would be six months.

• Absorption efficiency of dioxin in food or milk by mother and infant are 100 percent; this assumption might overestimate absorption but probably by no more than 15 percent (see Section 6.5.2 ).

• The fat content of human milk is assumed to be 4 percent, a value toward the high end of the reported range of values (1 to 5 percent).

• The maternal chemical intake is estimated using upper percentile ingestion rates for the different homegrown foods (see discussion for Exhibit B

- 31 ); this assumption might

ove restimate 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 ATSD R (1998), then actual exposures of the infant may be less than estimated.

There also are parameter values and model assumptions for the lipid

- phase breast

- milk pathway for which possible bias is unknown.

• The accuracy of the model is unknown; it has not bee n verified or validated with empirical data.

• Using a half

- life of 10 years for dioxins may over

- or under

- estimate risks.

Finally, there is one assumption that might possibly introduce some bias toward underestimating risks. The model results are sensitiv e 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 duratio n of exposure of the mother.



### 7.4.2. Methyl Mercury
For MeHg, empirical data from a single human study (Fujita and Takabatake 1977) was used in conjunction with a physiologically based pharmacokinetic (PBPK) model of lactational transfer of MeHg developed and part ially 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

.TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 96 October 2014 Description of MIRC 

## 8. References

Altman, P.L., and D.S. Dittmer, Eds. 1964. Biology Data Book. Volume 1. Bethesda, MD. 263

- 264 (As cited in EPA 1998).

Amin

- Zaki, L., S. Elhassani, M.A. Majeed, T.W. Clarkson, R.A. Doherty, M.R. Greenwood, and T. Giovanoli

- Jakubczak. 1976. Perinatal methylmercury poisoning in Iraq. Am. J. Diseases in Children 130: 1070

- 1076 (As cited in Byczkowski and Lipscomb 2001).

ATSDR (Agency for Toxic Substances and Disease Registry) . 1992. Toxicological profile for

s elected PCBs (Arochlor

- 1260, 1254, 124 8, 1242, 1232, 1221, and 1016). Atlanta, GA: U.S

. Department of Health and Human Services, Public Health Service (As cited in EPA 1 998 )

.
ATSDR

. 1998

. Toxicological profile for chlorinated dibenzo

- p

- dioxins

. Atlanta, GA: U.S

. Department of Health and Human Services, Public Health Service.

Bacci E., M. Cerejeira, C. Gaggi, G. Chemello, D. Calamari, and M. Vighi. 1992. Chlorinated dioxin s: Volatilization from soils and b i oconcentration in plant leaves. Bull. Environ. Contam. Toxicol. 48: 401

- 408.

Baes, C.F., R.D. Sharp, A.L. Sjoreen, and R.W. Shor. 1984. Review and analysis of parameters and assessing transport of environmentally released radionuclides through a griculture. ORNL

- 5786. Oak Ridge National Laboratory. Oak Ridge, Tennessee. September.

Bates, M.N., D.S. Hannah, S.J. Buckland, J.A. Taucher, and T. van Mannen. 1994. Chlorinated organic contaminants in breast milk of New Zealand wo men . Environmental Health Perspectives 102(Supplement 1): 211

- 217.

Belcher , G.D., and C.C. Travis. 1989. Modeling s upport for the RURA and municipal waste combustion projects: Final report on sensitivity and uncertainty analysis for the terrestrial food ch ain m odel

. Interagency Agreement No. 1824

- A020

- A1, Office of Risk Analysis, Health and Safety Research Division, Oak Ridge National Laboratory. Oak Ridge, Tennessee. October.

Boone, F.W., Y.C. Ng, and J.M. Palm. 1981. Te rrestrial p athway s of radionuclide p articulates. Health Physics 41:735

- 747.

Briggs, G.G., R.H. B romilow, and A.A. Evans. 1982. Relationships b etween lipophilicity and r oot

u ptake and translocation of non

- ionized chemicals by barley. Pesticide Science 13: 495

- 504 (As cited in EPA 2005a, Appen dix A

- 2).

Burger J. 2002 . Daily consumption of wild fish and game: Exposures of high end recreationalists. International Journal of Environmental Health Research 12 : 343

- 354

. Butte, N.F., C. Garza, E.O. Smith, and B.L. Nichols. 1984. Human milk intake and g rowth in exclusively breast

- fed infants. The Journal of Pediatrics 104(2):187

- 195.

Byczkowski, J.Z., and J.C. Lipscomb. 2001. Physiologically based pharmacokinetic modeling of the lactational transfer of methylmercury. Risk Analysis 21(5): 869

- 882.

Chamber lain, A.C. 1970. Interception and retention of r adio active aerosols by vegetation. Atmospheric Environment 4: 57

- 78.TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 97 October 2014 Description of MIRC Clewell, H.J, J.M. Gearhart, R.P. Gentry, T.R Covington, C.B. VanLandingham, C.B. Crump, and A.M. Shipp. 1999. Evaluation of the uncertaint y in an oral reference dose for methylmercury due to interindividual variability in pharmacokinetics. Risk Analysis 19: 547

- 558 (As cited in Byczkowski and Lipscomb 2001).

Conley, C.L. 1974. The blood. Medical Physiology 13 ( 2 ). V.B. Moutcasle, Ed. 1997. Th e C.V. Mosby Company, St. Louis, MO. 1027

- 1046 (As cited in EPA 1998).

Ensminger, M.E. 1980. Poultry Science

. Interstate Printers and Publishers, Inc. Danville, Illinois.

Fries, G. F. 1982. Potential polychlorinated biphenyl residues in animal products fr om application of contaminated sewage sludge to land. J. Environ. Qual. 11:14.

Fri es, G.F. 1994. Personal c ommuni cation b etween G.F. Fries, U.S. Department of Agriculture, and Glenn Rice and Jennifer Windholtz, U.S. Environmental Protection Agency, Offi ce of Research and Development. Agricultural Research Service. March.

Fujita, M., and E. Takabatake. 1977. Mercury levels in human maternal and neonatal blood, hair and milk. Bull. Environ. Contam. Toxicol. 18(2): 205

- 209.

Gearhart, J.M., H.J. Clewell III, K. S. Crump, A.M. Shipp, and A. Silvers. 1995. Pharmacokinetic dose estimates of mercury in children and dose

- response curves of performance tests in a large epidemiological study. Water, Air, Soil Pollut. 80: 49

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Gearhart, J., T. Covington, and H. Clewell III. 1996. Application of a physiologically based pharmacokinetic model for MeHg in a dose reconstruction of the Iraqi accidental exposures. Paper presented at the Fourth International Conference on Mercury as a Global Pollutant, Congress Cen tre, Hamburg, Germany; August (As cited in Byczkowski and Lipscomb 2001).

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USDA. 1994a. F ood Consumption and Dietary Levels of H ouseholds in the United States, 1987

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USDA . 1994b. Vegetables 1993 Summary . National Agricultural Statistics Service, Agricultural Statistics Board. Washington, D.C. Vg 1

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USDA . 1994c. Noncitrus Fruits and Nuts 1993 Summary . National Agricultural Statistics Service, Agricultural Statistics Board, Washington, D.C. Fr Nt 1

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USDA

. 2000. 1994

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EPA

. 1994c. Estimating Exposure to Dioxin

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.
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. O ffice of Research and Development

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. 1995d. Waste Technologies Industries Screening Human Health Risk Assessment (SHHRA): Evaluation of Potential Risk from Exposure to Routine Operat ing Emissions

. Volume V. External Review Draft. U.S. EPA Region 5, Chicago, Illinois.TRIM

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- 96/018, April 1996.

EPA

. 1997a. Exp osure Factor s Handbook. Volumes I, II, and III. Office of Research and Development, Washington, D.C. EPA

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.
EPA

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.
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.
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.
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.
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.
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.
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. Office of Water, Office of Science and Technology, Washington, D.C. EPA

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.
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.
EPA

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– An Update. Office of Water, Office of Science and Technology, Washington, D.C. EPA

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-TRIM

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Attachment B B

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- 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

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EPA

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05

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.
EPA

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.
EP A

. 2005c. Guidelines for Carcinogen Risk Assessment. Risk Assessment Forum, Washington, DC. EPA/630/P

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.
EPA

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.
EPA

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.
EPA

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.
EPA

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EPA

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- Specific Exposure Factors Handbook. Office of Research and Development, Washington, D

. C

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.
EPA

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– 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=194584TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B B

- 103 October 2014 Description of MIRC EPA

. 2011a

. Exposure Factors Handbook: 2011 Edition

. Office of Research and Development, Washington, D.C. EPA/600/R

- 090/052F. September

. Available at: http://cfpub.epa.gov/ncea/risk/recordisplay.cfm?deid=236252

.
EPA

. 2011b

. Revised Technical Support Document: National

- Scale Assessment of Mercury Risk to Populations with High Consumption of Self

- caught Freshwater Fish; In Support o f 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. Far lans, 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 Organiza tion reevaluation of human and m ammalian to xic 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 Environ mental Contamination and Toxicology. 55:264

- 269.[This page intentionally left blank.]TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment C i October 2014 Dermal Risk Screening 

## Dermal Risk Screening Attachment C.[ This page intentionally left blank. ]TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment C iii October 2014 Dermal Risk Screening 

## CONTENTS
, ATTACHMENT C

1. Hazard Identification and Dose Response Assessment

................................ .................. C

- 1

2. Dermal Exposure Estimation

................................ ................................ ........................... C

- 2

2.1. Equations for Estimating Dermal Exposure

................................ ............................ C

- 2

2.2. Exposure Factors and Assumptions

................................ ................................ ...... C

- 3

2.3. Receptor

- Specific Parameters

................................ ................................ ............... C

- 4

2.4. Scenario

- Specific Parameters

................................ ................................ ............... C

- 4

2.5. Chemical

- Specific Parameters

................................ ................................ ............... C

- 5

3. Screening

- Level Cancer Risks and Non

- Cancer Hazard Quotients

................................ . C

- 6

3.1. Dermal Cancer Risk

................................ ................................ ............................... C

- 6

3.2. Dermal Hazard Quotient

................................ ................................ ........................ C

- 7

4. Dermal Screening Results

................................ ................................ .............................. C

- 7

5. References

................................ ................................ ................................ ....................... 10TRIM

- Based T iered Screening Methodology for RTR

Attachment C iv October 2014 Dermal Risk Screening 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 Body Surface Area Assumed to be Exposed to Chemi cals

................................ ................................ ................................ ......... C

- 4

Exhibit C

- 3. Scenario

- Specific Exposure Values for Water and Soil Contact

.......................... C

- 5

Exhibit C

- 4. Chemical

- Specific Dermal Exposur e 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

- 9TRIM

- Based Tiered Screening Methodolo gy for R TR

Attachment C C

- 1 October 2014 Dermal Risk Screening Non

- inhalation exposure to PB

- HAPs can occur by way of the dermal pathway through contact with PB

- HAP

- contaminated soil and water. H owever, dermal absorption of chemicals that are originally airborne is generall y a relatively minor pathway of exposure compared to other exposure pathwa ys ( EPA 2006, Cal/EP A 2000) . This section demonstrates that for the conservative tiered screening scenario developed for RTR multipathway evaluation, the dermal exposure route is no t a significant risk pathway when compared to the ingestion pathway. In general, the RTR multipathway assessment followed the protocol for evaluating a reasonable maximum exposure as described in EPA’s Risk Assessment Guidance for Superfund (RAGS), Volume I: Human Health Evaluation Model, Part E, Supplemental Guidance for Dermal Risk Assessment ( EPA 2004)

. 

## 1. Hazard Identification and Dose Response Assessment
To assess the potential contribution of dermal exposure to non

- inhalation exposure, w e evaluated th e 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 has not developed carcinogenic potency slope f actors ( 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 extrapo lation 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) ora l 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 based on the oral reference dose (RFD O ) and th e 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 chemical absorbed in gastrointestinal tract (un itless) GI o ABS 

### ABS CSF CSF =

GI o ABS 

### ABS RfD RfD × =TRIM

- Based Tiered Screening Methodolo gy for R TR

Attachment C C

- 2 October 2014 Dermal Risk Screening The GI absorptions for 2,3,7,8

- TCDD and all polycyclic aromatic hydrocarbons (PAHs) (which includes benzo[a]pyrene) were estimated to be greater than 50 percent. Therefore, as shown in Exhibit C

- 1 , no adjustments to the available oral CSFs were required. For cadmium and divalent mercury, adjustments were made based on absorption data provided in RAGS Part E, Exhibit 4

- 1. The RfDs for dermal exposure to cadmium and divalent mercury are also shown in Exhibit C

- 1

.
Exhibit C

- 1 . Cancer Slope Factors and Reference Doses Based on Absorbed Dose

PB

- HAP F raction of C ontaminant A bsorbed in GI T ract ( ABS GI ) (unitless) A bsor bed 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 requir ed 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 risk s resulting from absorption of the chemical through the skin from contact with contaminated water and soil w ere evaluated for the RTR screening scenario. Individuals were assumed to be exposed on a fraction of their bodies (i.e., their head, forearms, hands, lower legs, and feet) to contaminated soil from the TRIM.FaTE surface soil parcel with the highest concentration (N1) on a daily basis. For the water evaluation, individuals were assumed to b e exposed to contaminated surface water with the same PB

- HAP concentration as the TRIM.FaTE screening scenario lake over their entire bodies on a daily basis.



### 2.1. Equations for Estimating Dermal Exposure

The general equation used to estimate dermal absorbed d ose (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

.

wh ere:

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) 

### AT BW SA EF ED EV DA DAD event



### × × × × ×

=TRIM

- Based Tiered Screening Methodolo gy for R TR

Attachment C C

- 3 October 2014 Dermal Risk Screening EF = Exposure frequency (days/year)

SA = Ski n 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 estimated to be the total dose absorbed throug h 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 Chemical s:

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 

### t 6 K FA 2 C DA × × × × × =

event p w event t K C DA × × =

CF ABS AF C DA s event × × × =TRIM

- Based Tiered Screening Methodolo gy for R TR

Attachment C C

- 4 October 2014 Dermal Risk Screening



### 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.TRIM

- Based Tiered Screening Methodolo gy for R TR

Attachment C C

- 5 October 2014 Dermal Risk Screening Exhibit C

- 3 . Scenario

- Specific Exposure Values for Water and Soil Contact

Exposure Parameter Receptor Value Source
**Water Contact** Event Duration (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 w ater 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 s oil 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.TRIM

- Based Tiered Screening Methodolo gy for R TR

Attachment C C

- 6 October 2014 Dermal Risk Screening Exhibit C

- 4 . Chemical

- Specific Dermal Exposure Values for Water and Soil Contact

PB

- HAP Cadmium Divalent 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 d ermal 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 (7 0 years).TRIM

- Based Tiered Screening Methodolo gy for R TR

Attachment C C

- 7 October 2014 Dermal Risk Screening



### 3.2. Dermal Hazard Quotient
Dermal hazard quotient (HQ) was estimated as the ratio of age

- specific 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 H Q accounts for exposures that a n 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 f or children less than 1 year of age) or less. This is approximately 1 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.TRIM

- Based Tiered Screening Methodolo gy for R TR

Attachment C C

- 8 October 2014 Dermal Risk Screening Exhibit C

- 5 . Summary of Dermal Non

- Cancer Hazards

0 0.0000005 0.000001 0.0000015 0.000002 0.0000025 0.000003 0.0000035 0.000004 0.0000045

Child <1 Child 1

- 2 Child 3

- 5 Child 6

- 11 Child 12

- 19 Adult 20

- 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

TotalTRIM

- Based Tiered Screening Methodolo gy for R TR

Attachment C C

- 9 October 2014 Dermal Risk Screening Exhibit C

- 6 . Summary of Dermal Cancer Risks

PB

- HAP Dermal Lifetime Cancer Risk Magnitude of 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 na ture 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.TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment C 10 October 2014 Dermal Risk Screening 

## 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. Avail able 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

.TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment D i October 2014 RIM.FaTE Parameters 

## Summary of TRIM.FaTE Parameters Considered for Attachment D. Inclusion in Tier 2 Assessment[This page intentionally left b lank.]TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment B , Addendum 1 iii October 2014 Tier 2 TRIM.FaTE P arameters Exhibits, Addendum 1

Exhibit Add B1

- 1. TRIM.FaTE Parameters Considered for Inclusion in Tier 2 Analysis

................................ ................................ ................................ ............ D

- 1[This page intentionally left blank.]TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment D D

- 1 October 2014 Tier 2 TRIM.FaTE Parameters 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 chemica l 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. HighTRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment D D

- 2 October 2014 Tier 2 TRIM.FaTE Parameters 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. HighTRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment D D

- 3 October 2014 Tier 2 TRIM.FaTE Parameters 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. HighTRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment D D

- 4 October 2014 Tier 2 TRIM.FaTE Parameters 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 previou s 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 a s 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 f arming now or in the future. MediumTRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment D D

- 5 October 2014 Tier 2 TRIM.FaTE Parameters 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 require s 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. MediumTRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment D D

- 6 October 2014 Tier 2 TRIM.FaTE Parameters 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. MediumTRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment D D

- 7 October 2014 Tier 2 TRIM.FaTE Parameters 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[This page intentionally left blank.]TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment E i October 2014 Lake Size Analysis 

## Analysis of Lake Size and Attachment E. Sustainable Fish Population[This page intentionally left blank.]TRIM

- Based Ti ered Screening Methodolo gy for RTR

Attachment E iii October 2014 Lake Size Analysis 

## CONTENTS , ADDENDUM 2

1. Introduction

................................ ................................ ................................ ..................... E

- 1

2. Angler Behavior

................................ ................................ ................................ .............. E

- 1

3. Fish Biology

................................ ................................ ................................ .................... E

- 2

3.1. Lake Productivity

................................ ................................ ................................ ... E

- 2

3.2. Proportion of Fish Biomass by Trophic Level

................................ ......................... E

- 4

3.3. Minimum Viable Population Size

................................ ................................ ............ E

- 5

4. Summary of Assumptions for the Lake Size Analysis

................................ ...................... E

- 6

5. Equations Used to Determine Lake Fish Populations

................................ ...................... E

- 6

6. References

................................ ................................ ................................ ..................... E

- 8TRIM

- Based Ti ered Screening Methodolo gy for RTR

Attachment E iv October 2014 Lake Size Analysis [This page intentionally left blank.]TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment E E

- 1 October 2014 Lake Size Analysis 

## 1. Introduction
Identifying the smal lest 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 thres hold 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 fis h 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 assumptio n 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 estima te, and models to predict sustainable harvests of different fisheries are numerous and complex. Species

- specific parameters of key to suchTRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment E E

- 2 October 2014 Lake Size Analysis models include fecundity with age and size; survivorship of eggs, fry, and juveniles to sexual maturity (recruitment ); natural predation pressures; and temporal variation in food availability. For the purpose of this analysis, simplifying assumptions are required. In the analysis by Håkanson and Boulion (2004), which included a survey of 122 lakes, the authors noted 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 p onds 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 supporti ng 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 3 1 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 cat egories 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 bi omass 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 regres sion model based on samples from 21 lakes ranging in sur face area from 0.1

– 25,000 hectares (~0.25

– 62,000 acres) and located between 0° and 56° N latitude andTRIM

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Attachment E E

- 3 October 2014 Lake Size Analysis 121° E to 122° W longitude. Their linear regression relating TP to total fish standing biomass ( B) had a correlation coefficient ( r 2 ) of 0.84 :

B = 0.792 + 0 .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 concent rations.

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 1 0 (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 faci lities 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 incl uded 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 minimumTRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment E E

- 4 October 2014 Lake Size Analysis and maximum total fish biomass densities were 3.2 and 133.2 g ww/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 hig h 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 Tro phic 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 l akes, 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 b iomass) 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 ) com prise 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 Håka nson and Boulion (2004). They designed their model to predict productivity and standing crop of prey and predatory fishes in lakes of northern Europe. The authors acknowledged that fish feeding patterns are complicated by the fact that fish change their feeding preferences as they age. Some fish species consume zooplankton or benthic invertebrates in their first year, and switch to small fish and then to larger fish as they mature and grow in size

.TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment E E

- 5 October 2014 Lake Size Analysis 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 t otal 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 s ustainable 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 d uration (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 variatio n (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 ha s 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). S everal 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 extensiveTRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment E E

- 6 October 2014 Lake Size Analysis population viability analyses in the United States has been conducted on the spotted owl (Boyce, 1993)

. Given the number and complexity of factors that influence MVP, however, including the definitions of time horizon (e.g., 100 years) and probability of survival ( e.g., 95 percent ), population biologists caution against using a “rule of thumb ” for MVP 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 d ata 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 stan ding 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

.

WCC SB = Total SB x Fraction WCC ( Equation 1)

where:

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

.

No

. WCC = Lake Size x WCC SB x CF

B W a ( Equation 2 )

where:TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment E E

- 7 October 2014 Lake Size Analysis No. WCC = Total number of WCC fish in Lake X

Lake Size = Size of Lake X ( acres )

WCC SB = Standing biomass of WCC fish ( g ww/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

.

Productivity WCC = Lake Size x WCC SB x CF 1

CF 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

.

Max Sustain IR ( BC + WCC ) = 2 x Productivity WCC x FF x HF x CF 1

CF 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 )TRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment E E

- 8 October 2014 Lake Size Analysis 

## 6. References
Akçakaya, H.R., Burgman, M.A., and Ginzburg, L.R. 1999. Applied Population Ecology: Principles and Computer Exercises using RAMAS EcoLab. Sunderland, MA: Sinauer Associates, Inc. Publishers.

Bachmann, RW; Jones, BL; Fox, DD; Hoyer, M; Bull, LA; Canfield, DE. 1996. Relations between trophic state indicators and fish in Florida (USA) lakes. Canadian Journal of Fisheries and Aquatic Sciences. 53:842

- 855.

Barnthouse, L .W., Munns, W.R. Jr., and Sorensen, M.T. 2008. Population

- Level Ecological Risk Assessment. Boca Raton, FL: CRC Taylor & Francis Group; Pnesacola, FL: Society of Environmental Toxicology and Chemistry (SETAC).

Boyce, M.S. 1993. Population viability analysi s: 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 preparedTRIM

- Based Tiered Screening Methodolo gy for RTR

Attachment E E

- 9 October 2014 Lake Size Analysis by the National Reservoir Research Program, U.S. Fish and Wildlife Service, for the U.S. Army Corps of Engineers, Waterways Experiment Station, Vicksburg, 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 Theo ry 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.[This page intentionally left blank.]

### Appendix 6

Dispersion Model Receptor Revisions and Additions

### Dispersion Model Receptor Revisions and Additions for the Phosphoric Acid Manufacturing and Phosphate Fertilizer Production Source Categories

To estimate ambient concentrations for evaluating long

- term exposures , t he HEM

- 3
model uses the geographic centroids of census blocks ( currently utilizing the 20 10 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 abo ut 40 people or 10

- 15 households. However, in cases where a block centroid is located on industrial property , or where a census block is large and the centroid less likely to be representative of the block’s
residential locations , the block centroid may not be an appropriate 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 ae rial images of the facility to determine whether the block
centroid was likely located on facility property. The selection of the 300

- meter distance reflects a compromise between too few and too many blocks identified as being potentially on facility

prope rty. Distances smaller than 300 meters would identify only block centroids near the emission sources and could exclude some block centroids that are still within facility boundaries, particularly for large facilities. Distances significantly larger than 300 meters would identify many block centroids that are outside facility boundaries, particularly for small facilities. Where we confirmed a block centroid on facility property, we moved the block centroid to a location that best represents the residenti al locations in the block

.

In addition , census block centroids for blocks with large areas may not be representative
of residential locations. Risk estimates based on such centroids can be understated if there are residences nearer to a facility than the centroid, and overstated if the residences are farther from the facility than the centroid. To avoid understating the maximum individual risk associated with a facility, in some cases we 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 located 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 spread out over the block), we add ed 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.

### Revised Census Block Centroi d Locations and Additional Receptors

for P hosphate Fertilizer Production and P hosphoric Acid Manufacturing

Centroid Revisions

A dditional Receptors

### 1



# A ppendix 7

– Environmental Risk Screen

### Table 1

– PB

- HAP Benchmarks Included in the Environmental Risk Screen

EcoHAP Assessment Endpoint Benchmark Effects Level Benchmark Value Benchmark Source Cadmium Fish

- Avian Piscivores NOAEL (merganser) 0.7 (mg/kg BW/day) d Cadmium Fish

- Avian Piscivores LOAEL (merganser) 1 (mg/kg BW/day) d Cadmium Fish

- Mammalian Piscivores NOAEL (mink) 0.742 (mg/kg BW/day) e Cadmium Fish

- Mammalian Piscivores LOAEL (mink) 7.42 (mg/kg BW/day) e Cadmium Sediment Community No

- effect Level 0.6 (mg/kg dry wt) b Cadmium Sediment Community Threshold Level 1.2 (mg/kg dry wt) a Cadmium Sediment Community Probable

- effect Level 3.5 (mg/kg dry wt) b Cadmium Surface Soil

- Dist. 1

- 312 m Threshold

- Mammalian Insectivores (shrew) 0.36 (mg/kg dry wt) a

Cadmium Surface Soil

- Dist. 1

- 312 m Threshold

- Avian Ground Insectivores (woodcock) 0.77 (mg/kg dry wt) a

Cadmium Surface Soil

- Dist. 1

- 312 m Threshold Level

- Plant Community 32 (mg/kg dry wt) a Cadmium Surface Soil

- Dist. 1

- 312 m Threshold Level

- Invertebrate Community 140 (mg/kg dry wt) a Cadmium Surface Soil

- Dist. 2

- 850 m Threshold

- Mammalian Insectivores (shrew) 0.36 (mg/kg dry wt) a

Cadmium Surface Soil

- Dist. 2

- 850 m Threshold

- Avian Ground Insectivores (woodcock) 0.77 (mg/kg dry wt) a

Cadmium Surface Soil

- Dist. 2

- 850 m Threshold Level

- Plant Community 32 (mg/kg dry wt) a Cadmium Surface Soil

- Dist. 2

- 850 m Threshold Level

- Invertebrate Community 140 (mg/kg dry wt) a Cadmium Surface Soil

- Dist. 3

- 1,500 m Threshold

- Mammalian Insectivores (shrew) 0.36 (mg/kg dry wt) a

Cadmium Surface Soil

- Dist. 3

- 1,500 m Threshold

- Avian Ground Insectivores (woodcock) 0.77 (mg/kg dry wt) a

Cadmium Surface Soil

- Dist. 3

- 1,500 m Threshold Level

- Plant Community 32 (mg/kg dry wt) a Cadmium Surface Soil

- Dist. 3

- 1,500 m Threshold Level

- Invertebrate Community 140 (mg/kg dry wt) a Cadmium Surface Soil

- Dist. 4

- 3,500 m Threshold

- Mammalian I nsectivores (shrew) 0.36 (mg/kg dry wt) a

Cadmium Surface Soil

- Dist. 4

- 3,500 m Threshold

- Avian Ground Insectivores (woodcock) 0.77 (mg/kg dry wt) a

Cadmium Surface Soil

- Dist. 4

- 3,500 m Threshold Level

- Plant Community 32 (mg/kg dry wt) a Cadmium Surface Soil

- Dist. 4

- 3,500 m Threshold Level

- Invertebrate Community 140 (mg/kg dry wt) a Cadmium Surface Soil

- Dist. 5

- 7,500 m Threshold

- Mammalian Insectivores (shrew) 0.36 (mg/kg dry wt) a

Cadmium Surface Soil

- Dist. 5

- 7,500 m Threshold

- Avian Ground Insectivores (woodcock) 0.77 (mg/kg dry wt) a

Cadmium Surface Soil

- Dist. 5

- 7,500 m Threshold Level

- Plant Community 32 (mg/kg dry wt) a Cadmium Surface Soil

- Dist. 5

- 7,500 m Threshold Level

- Invertebrate Community 140 (mg/kg dry wt) a C admium Water

- column Community Threshold Level 0.25 (ug/L) c Cadmium Water

- column Community Frank

- effect Level 2 (ug/L) c Dioxin Fish

- Avian Piscivores NOAEL (merganser) 0.0000014 (mg/kg BW/day) f Dioxin Fish

- Avian Piscivores LOAEL (merganser) 0.000014 (mg/kg BW/day) f

Dioxin Fish

- Mammalian Piscivores NOAEL (mink) 0.000000771 (mg/kg BW/day) f

### Table 1

– PB

- HAP Benchmarks Included in the Environmental Risk Screen

EcoHAP Assessment Endpoint Benchmark Effects Level Benchmark Value Benchmark Source Dioxin Fish

- Mammalian Piscivores LOAEL (mink) 0.00000771 (mg/kg BW/day) f Dioxin Sediment Community Threshold Level 0.00000116 (mg/kg dry wt) g Dioxin Surface Soil

- Dist. 1

- 312 m Threshold

- Mammalian Insectivores (shrew) 0.0000002 (mg/kg dry wt) h Dioxin Surface Soil

- Dist. 2

- 850 m Threshold

- Mammalian Insectivores (shrew) 0.0000002 (mg/kg dry wt) h Dioxin Surface Soil

- Dist. 3

- 1,500 m Threshold

- Mammalian Insectivores (shrew) 0.0000002 (mg/kg dry wt) h Dioxin Surface Soil

- Dist. 4

- 3,500 m Threshold

- Mammalian Insectivores (shrew) 0.0000002 (mg/kg dry wt) h Dioxin Surface Soil

- Dist. 5

- 7,500 m T hreshold

- Mammalian Insectivores (shrew) 0.0000002 (mg/kg dry wt) h Dioxin Water

- column Community Threshold Level 0.000012 (ug/L) i Dioxin Water

- column Community Frank

- effect Level 0.1 (ug/L) i Mercuric Chloride Sediment Community Threshold Level 0.15 (mg/kg dry wt) a Mercuric Chloride Sediment Community Probable

- effect Level 0.7 (mg/kg dry wt) j Mercuric Chloride Surface Soil

- Dist. 1

- 312 m Threshold Level

- Plant Community 0.3 (mg/kg dry wt) k Mercuric Chloride Surface Soil

- Dist. 1

- 312 m Threshold Level

- Invertebrate Community 0.1 (mg/kg dry wt) k Mercuric Chloride Surface Soil

- Dist. 2

- 850 m Threshold Level

- Plant Community 0.3 (mg/kg dry wt) k Mercuric Chloride Surface Soil

- Dist. 2

- 850 m Threshold Level

- Invertebrate Community 0.1 (mg/kg dry wt) k Mercuric Chloride Surface Soil

- Dist. 3

- 1,500 m Threshold Level

- Plant Community 0.3 (mg/kg dry wt) k Mercuric Chloride Surface Soil

- Dist. 3

- 1,500 m Threshold Level

- Invertebrate Community 0.1 (mg/kg dry wt) k Mercuric Chloride Surface Soil

- Dist. 4

- 3,500 m Threshold Level

- Plant Community 0.3 (mg/kg dry wt) k Mercuric Chloride Surface Soil

- Dist. 4

- 3,500 m Threshold Level

- Invertebrate Community 0.1 (mg/kg dry wt) k Mercuric Chloride Surface Soil

- Dist. 5

- 7,500 m Threshold Level

- Plant Community 0.3 (mg/kg dry wt) k Mercuric Chloride Surface Soil

- Dist. 5

- 7,500 m Threshold Level

- Invertebrate Community 0.1 (mg/kg dry wt) k Mercuric Chloride Water

- column Community Threshold Level 0.77 (ug/L) c Mercuric Chloride Water

- column Community Frank

- effect Level 1.4 (ug/L) c Mercury (methyl) Fish

- Avian Piscivores NOAEL (merganser) 0.013 (mg/kg BW/day) f Mercury (methyl) Fish

- Avian Piscivores LOAEL (merganser) 0.078 (mg/kg BW/day) f Mercury (methyl) Fish

- Mammalian Piscivores NOAEL (mink) 0.0247 (mg/kg BW/day) e Mercury (methyl) Fish

- Mammalian Piscivores LOAEL (mink) 0.123 (mg/kg BW/day) e Mercury (methyl) Sediment Community Threshold Level 0.2 (mg/kg dry wt) l Mercury (methyl) Sediment Community Probable

- effect Level 1 (mg/kg dry wt) l Mercury (methyl) Surface Soil

- Dist. 1

- 312 m Threshold Level

- Invertebrate Community 0.67 (mg/kg dry wt) I Mercury (methyl) Surface Soil

- Dist. 2

- 850 m Threshold Level

- Invertebrate Community 0.67 (mg/kg dry wt) i Mercury (methyl) Surface Soil

- Dist. 3

- 1,500 m Threshold Level

- Invertebrate Community 0.67 (mg/kg dry wt) i Mercury (methyl) Surface Soil

- Dist. 4

- 3,500 m Threshold Level

- Invertebrate Community 0.67 (mg/kg dry wt) i Mercury (methyl) Surface Soil

- Dist. 5

- 7,500 m Threshold Level

- Invertebrate Community 0.67 (mg/kg dry wt) i Mercury (methyl) Water

- column Community Threshold Level 0.0028 (ug/L) c Mercury (methyl) Water

- column Community Frank

- effect Level 0.099 (ug/L) c PAH Fish

- Mammalian Piscivores NOAEL (mink) 0.417 (mg/kg BW/day) e PAH Fish

- Mammalian Piscivores LOAEL (mink) 4.17 (mg/kg BW/day) e

### Table 1

– PB

- HAP Benchmarks Included in the Environmental Risk Screen

EcoHAP Assessment Endpoint Benchmark Effects Level Benchmark Value Benchmark Source PAH Sediment Community No

- effect Level 0.032 (mg/kg dry wt) b PAH Sediment Community Threshold Level 0.15 (mg/kg dry wt) l, h PAH Sediment Community Probable

- effect Level 1.45 (mg/kg dry wt) l PAH Surface Soil

- Dist. 1

- 312 m Threshold

- Mammalian Insectivores (shrew) 1.52 (mg/kg dry wt) h
PAH Surface Soil

- Dist. 2

- 850 m Threshold

- Mammalian Insectivores (shrew) 1.52 (mg/kg dry wt) h PAH Surface Soil

- Dist. 3

- 1,500 m Threshold

- Mammalian Insectivores (shrew) 1.52 (mg/kg dry wt) h
PAH Surface Soil

- Dist. 4

- 3,500 m Threshold

- Mammalian Insectivores (shrew) 1.52 (mg/kg dry wt) h PAH Surface Soil

- Dist. 5

- 7,500 m Threshold

- Mammalian Insectivores (shrew) 1.52 (mg/kg dry wt) h
PAH Water

- column Community Threshold Level 0.014 (ug/L) a, h, m PAH Water

- column Community Frank

- effect Level 0.24 (ug/L) a Lead Ambient Air NAAQS Secondary Standard 0.15 ug/m 3 n

Benchmark Sources :

a. U.S. EPA OSWER
b. Environment Canada
c. U.S. EPA OW d. CA DTSC HERD (2009) e. Sample, et. al, 1996
f. U.S. EPA 1995 g. U.S. EPA Region 3, 4, and 5. h. U.S. EPA Region 5 i. U.S. EPA Region 4 j. Florida DEP, NOAA k. ORNL and EPA R egion 4 and 6 l. MacDonald, et. al. 2000 m. U.S. EPA Region 6 n. NAAQS for Lead, Secondary S tandard

### 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 L OEL 0.4

### Table 3

– Hydrofluoric Acid Ecological Screening Data for Each Phosphoric Acid Manufacturing Facility

NA

– Not applicable, this is a facility level metric. 1

– If the average concentration of all off site data points in the modeling domain does not exceed the benchmark then “No” will appear in the column. If the a verage concentration of all off site data points in the modeling domain exceeds the benchmark, then the number of times the concentration exc eeds the benchmark will appear in the column. 2

- The percent of the off site modeled area that exceeded the benchmark. A value of 0% indicates that none of the individual modeled data points exceeded the benchmark. 3

– T he average HF concentration for a ll off site points that exceeded the benchmark divided by the benchmark. 4

- T he highest off

site HF concentration for a single point at each facility divided by the benchmark. Facility NEI ID Canadian LOEL of 0.4 ug/m 3 Washington State LOEL of 0.5 ug/m 3

Does Average Facility Concentration Exceed Benchmark? 1 % of Area Modeled Exceeding 2 Does Average Facility

Concentration Exceed Benchmark? 1 % of Area Modeled Exceeding 2

570008 No 2% No 2% 4056 No 6% No 3% 45571 No 3% No 2% 93004 No <1% No <1% 50059 No 1% No 1% 12286 No 1% No 1% 59305 No <1% No <1% 570005 No <1% No <1% 6147 No 0% No 0% 26584 No 0% No 0% 387 No 0% No 0% Category NA 1% NA 1%

### Appendix 8

Acute Noncancer Risk Analysis for Phosphate Fertilizer and Phosphoric Acid Manufacturing

### Acute Modeling Approach

Initial acute screening risk calculations were performed with the HEM

- 3 model. HEM

- 3 estimates acute (1

- hour) impacts at both polar and census block receptors , refer
to Table 1 . It is assumed for this short period of time that an exposed individual could be located at any off

- site location. The lack of readily available detailed property boundary information for many of the facilities evaluated made it difficult to determine whether receptors were on

- or off

- site. In the abse nce 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 o verestimating 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, re gardless of whether those maximum concentrations occurred during the same hour. In other words, HEM

- 3 assumes that the maximum impact from each source at each receptor occurs at the same time. In actuality, maximum impacts from different sources may occur at different times.

This appendix includes the results of refined acute assessments performed for facilities that exceeded short

- term health benchmarks using the HEM

- 3 approach , refer to Table 2 . The refinements can address both areas of conservatism described above. The simplest refinement is to plot the HEM

- 3 polar grid results on aerial photographs of the facilities; This allows the assessment of off

- site locations that may be accessible to the public ( e.g., roadways and public buildings.). In ca ses where this refinement results in lower acute risks no further refinement was conducted. However, if acute risk still remain at or near levels of concern a further refinement is conducted. This second refinement includes remodeling with the AERMOD mo del directly (instead of using HEM) to estimate maximum 1

- hour concentrations at each receptor by pairing individual source impacts in time and space. The AERMOD results are then plotted on aerial photographs as noted above to determine off

- site impacts. Modeling with AERMOD also allows the estimation of the frequency of higher impacts at or above the health levels of concern. The attached f igures present the estimated hazard quotient values (the modeled 1

- hour concentration of a pollutant divided by it s short

- term health benchmark) at the polar receptors near the facilities. Each set of figures is prefaced with a description of the maximum off

- site hazard quotient value and where that value occurs. Refer to below for a brief summary;

• NEI4 5571 ; maximum offsite acute HQ (REL) 

### for hydrofluoric acid of 2; east of facility along Pamlico Sound (waterbody)

• NEI LA0930004; maximum offsite acute HQ (REL) 

### for hydrofluoric acid of 2

, south east of the facilityREL AEGL1 AEGL2 ERPG1 ERPG2
22093NEILA0930005 Elemental Gaseous Mercury 4.2E-01 0.0E+00 1.5E-04 0.0E+00 1.2E-04
12057NEIFL0570008 Hydrofluoric acid 2.9E-01 8.5E-02 3.5E-03 4.4E-02 4.4E-03
12105NEIRFL050059 Hydrofluoric acid 1.6E-01 4.5E-02 1.9E-03 2.3E-02 2.3E-03
28059NEIMS059305 Arsenic compounds 1.2E-01 0.0E+00 0.0E+00 0.0E+00 0.0E+00
16029NEIIDR029000 Hydrofluoric acid 1.1E-01 3.3E-02 1.4E-03 1.7E-02 1.7E-03
12105NEIFL12286 Hydrofluoric acid 7.6E-02 2.2E-02 9.1E-04 1.1E-02 1.1E-03
28059NEIMS059305 Hydrofluoric acid 5.7E-02 1.7E-02 6.8E-04 8.6E-03 8.6E-04
22093NEILA0930005 Arsenic compounds 4.2E-02 0.0E+00 0.0E+00 0.0E+00 0.0E+00
37013NEI45571 Hydrofluoric acid 3.4E-02 9.9E-03 4.1E-04 5.1E-03 5.1E-04
12057NEIFL0570008 Arsenic compounds 2.5E-02 0.0E+00 0.0E+00 0.0E+00 0.0E+00
12057NEIFL0570005 Arsenic compounds 2.3E-02 0.0E+00 0.0E+00 0.0E+00 0.0E+00
12057NEIFL0570005 Hydrofluoric acid 2.2E-02 6.4E-03 2.6E-04 3.3E-03 3.3E-04 12047NEIFLT$4056 Hydrofluoric acid 2.1E-02 6.3E-03 2.6E-04 3.2E-03 3.2E-04
12057NEIFL0570005 Elemental Gaseous Mercury 2.0E-02 0.0E+00 6.9E-06 0.0E+00 5.9E-06
12105NEIRFL050059 Arsenic compounds 1.2E-02 0.0E+00 0.0E+00 0.0E+00 0.0E+00 16077NEI26584 Hydrofluoric acid 1.2E-02 3.5E-03 1.4E-04 1.8E-03 1.8E-04 16077NEI26584 Arsenic compounds 1.1E-02 0.0E+00 0.0E+00 0.0E+00 0.0E+00 12105NEIFL12286 Arsenic compounds 6.5E-03 0.0E+00 0.0E+00 0.0E+00 0.0E+00 12047NEIFLT$4056 Arsenic compounds 5.0E-03 0.0E+00 0.0E+00 0.0E+00 0.0E+00 37013NEI45571 Arsenic compounds 2.8E-03 0.0E+00 0.0E+00 0.0E+00 0.0E+00 37013NEI45571 Elemental Gaseous Mercury 2.5E-03 0.0E+00 8.9E-07 0.0E+00 7.5E-07 56037NEI387 Hydrofluoric acid 2.2E-03 6.5E-04 2.7E-05 3.3E-04 3.3E-05 22093NEILA0930005 Hydrofluoric acid 2.0E-03 5.9E-04 2.4E-05 3.0E-04 3.0E-05 12047NEIFLT$4056 Elemental Gaseous Mercury 1.7E-03 0.0E+00 5.9E-07 0.0E+00 5.0E-07 56037NEI387 Arsenic compounds 1.6E-03 0.0E+00 0.0E+00 0.0E+00 0.0E+00 16077NEI26584 Elemental Gaseous Mercury 1.5E-03 0.0E+00 5.4E-07 0.0E+00 4.6E-07 28059NEIMS059305 Elemental Gaseous Mercury 1.3E-03 0.0E+00 4.7E-07 0.0E+00 4.0E-07 12057NEIFL0570008 Elemental Gaseous Mercury 1.3E-03 0.0E+00 4.5E-07 0.0E+00 3.9E-07 12105NEIRFL050059 Elemental Gaseous Mercury 1.3E-03 0.0E+00 4.4E-07 0.0E+00 3.8E-07 12105NEIFL12286 Elemental Gaseous Mercury 3.3E-04 0.0E+00 1.2E-07 0.0E+00 1.0E-07 16029NEIIDR029000 Arsenic compounds 2.1E-04 0.0E+00 0.0E+00 0.0E+00 0.0E+00 56037NEI387 Elemental Gaseous Mercury 1.4E-04 0.0E+00 4.8E-08 0.0E+00 4.1E-08 16029NEIIDR029000 Elemental Gaseous Mercury 8.8E-05 0.0E+00 3.1E-08 0.0E+00 2.6E-08 12047NEIFLT$4056 Beryllium compounds 0.0E+00 0.0E+00 0.0E+00 0.0E+00 1.3E-04 12057NEIFL0570005 Beryllium compounds 0.0E+00 0.0E+00 0.0E+00 0.0E+00 9.8E-05 Table 1 – Maximum Predicted Acute Risks (HEM-3)

Pollutant Maximum Hazard Quotient 1 Facility NEI ID Phosphate Fertilizer Production Screen with Emissions Multiplier 10(x)

1 of 5REL AEGL1 AEGL2 ERPG1 ERPG2 Table 1 – Maximum Predicted Acute Risks (HEM-3)

Pollutant Maximum Hazard Quotient 1 Facility NEI ID Phosphate Fertilizer Production Screen with Emissions Multiplier 10(x)

12057NEIFL0570008 Beryllium compounds 0.0E+00 0.0E+00 0.0E+00 0.0E+00 9.4E-06 12105NEIFL12286 Beryllium compounds 0.0E+00 0.0E+00 0.0E+00 0.0E+00 2.5E-06 12105NEIRFL050059 Beryllium compounds 0.0E+00 0.0E+00 0.0E+00 0.0E+00 4.4E-06 16029NEIIDR029000 Beryllium compounds 0.0E+00 0.0E+00 0.0E+00 0.0E+00 2.7E-07 16077NEI26584 Beryllium compounds 0.0E+00 0.0E+00 0.0E+00 0.0E+00 9.2E-06 22093NEILA0930005 Beryllium compounds 0.0E+00 0.0E+00 0.0E+00 0.0E+00 4.7E-05 28059NEIMS059305 Beryllium compounds 0.0E+00 0.0E+00 0.0E+00 0.0E+00 3.1E-05 37013NEI45571 Beryllium compounds 0.0E+00 0.0E+00 0.0E+00 0.0E+00 3.5E-06 56037NEI387 Beryllium compounds 0.0E+00 0.0E+00 0.0E+00 0.0E+00 2.7E-06 12047NEIFLT$4056 Gaseous Divalent Mercury 0.0E+00 0.0E+00 0.0E+00 0.0E+00 0.0E+00 12057NEIFL0570005 Gaseous Divalent Mercury 0.0E+00 0.0E+00 0.0E+00 0.0E+00 0.0E+00 12057NEIFL0570008 Gaseous Divalent Mercury 0.0E+00 0.0E+00 0.0E+00 0.0E+00 0.0E+00 12105NEIFL12286 Gaseous Divalent Mercury 0.0E+00 0.0E+00 0.0E+00 0.0E+00 0.0E+00 12105NEIRFL050059 Gaseous Divalent Mercury 0.0E+00 0.0E+00 0.0E+00 0.0E+00 0.0E+00 16029NEIIDR029000 Gaseous Divalent Mercury 0.0E+00 0.0E+00 0.0E+00 0.0E+00 0.0E+00 16077NEI26584 Gaseous Divalent Mercury 0.0E+00 0.0E+00 0.0E+00 0.0E+00 0.0E+00 22093NEILA0930005 Gaseous Divalent Mercury 0.0E+00 0.0E+00 0.0E+00 0.0E+00 0.0E+00 28059NEIMS059305 Gaseous Divalent Mercury 0.0E+00 0.0E+00 0.0E+00 0.0E+00 0.0E+00 37013NEI45571 Gaseous Divalent Mercury 0.0E+00 0.0E+00 0.0E+00 0.0E+00 0.0E+00 56037NEI387 Gaseous Divalent Mercury 0.0E+00 0.0E+00 0.0E+00 0.0E+00 0.0E+00

Note: BOLD indicates acute risks greater than 1

REL AEGL1 AEGL2 ERPG1 ERPG2
37013NEI45571 Hydrofluoric acid 2 2.43E+00 7.10E-01 2.91E-02 3.64E-01 3.64E-02
22093NEILA0930004 Hydrofluoric acid 2 2.20E+00 6.44E-01 2.64E-02 3.30E-01 3.30E-02
12105NEIRFL050059 Hydrofluoric acid 2 2.20E+00 6.43E-01 2.64E-02 3.29E-01 3.29E-02
16029NEIIDR029000 Hydrofluoric acid 2 1.75E+00 5.14E-01 2.11E-02 2.63E-01 2.63E-02
12057NEIFL0570008 Hydrofluoric acid 2 1.42E+00 4.16E-01 1.71E-02 2.13E-01 2.13E-02
12105NEIFL12286 Hydrofluoric acid 2 1.10E+00 3.21E-01 1.32E-02 1.65E-01 1.65E-02 28059NEIMS059305 Hydrofluoric acid 2 1.06E+00 3.10E-01 1.27E-02 1.59E-01 1.59E-02
12057NEIFL0570005 Hydrofluoric acid 2 9.73E-01 2.85E-01 1.17E-02 1.46E-01 1.46E-02 12047NEIFLT$4056 Hydrofluoric acid 2 6.00E-01 1.76E-01 7.20E-03 9.00E-02 9.00E-03 Phosporic Acid Manufacturing

Facility NEI ID Pollutant Maximum Hazard Quotient 1 1 Some maximum acute impacts may be at onsite locations.

2 of 5REL AEGL1 AEGL2 ERPG1 ERPG2 Table 1 – Maximum Predicted Acute Risks (HEM-3)

Pollutant Maximum Hazard Quotient 1 Facility NEI ID Phosphate Fertilizer Production Screen with Emissions Multiplier 10(x)

37013NEI45571 Elemental Gaseous Mercury 4.43E-01 0.00E+00 1.56E-04 0.00E+00 1.33E-04
22005NEI6147 Hydrofluoric acid 2 4.03E-01 1.18E-01 4.84E-03 6.05E-02 6.05E-03 12047NEIFLT$4056 Arsenic compounds 2.09E-01 0.00E+00 0.00E+00 0.00E+00 0.00E+00 12105NEIFL12286 Arsenic compounds 1.92E-01 0.00E+00 0.00E+00 0.00E+00 0.00E+00
16077NEI26584 Hydrofluoric acid 2, 3 1.84E-01 5.37E-02 2.20E-03 2.75E-02 2.75E-03
56037NEI387 Hydrofluoric acid 2 7.28E-02 2.13E-02 8.74E-04 1.09E-02 1.09E-03 28059NEIMS059305 Arsenic compounds 6.65E-02 0.00E+00 0.00E+00 0.00E+00 0.00E+00 12105NEIRFL050059 Arsenic compounds 4.86E-02 0.00E+00 0.00E+00 0.00E+00 0.00E+00 37013NEI45571 Hydrochloric acid 1.94E-02 1.51E-02 1.23E-03 9.05E-03 1.36E-03 12057NEIFL0570008 Arsenic compounds 8.92E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00 37013NEI45571 Arsenic compounds 7.58E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00 16077NEI26584 Arsenic compounds 7.58E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00 12057NEIFL0570005 Arsenic compounds 6.99E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00 12047NEIFLT$4056 Elemental Gaseous Mercury 6.08E-03 0.00E+00 2.15E-06 0.00E+00 1.82E-06 37013NEI45571 Acrolein 3.93E-03 1.43E-04 4.28E-05 8.94E-05 2.89E-05 16029NEIIDR029000 Arsenic compounds 3.93E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00 16077NEI26584 Elemental Gaseous Mercury 2.58E-03 0.00E+00 9.10E-07 0.00E+00 7.74E-07 12057NEIFL0570005 Elemental Gaseous Mercury 2.02E-03 0.00E+00 7.14E-07 0.00E+00 6.07E-07 12105NEIFL12286 Elemental Gaseous Mercury 1.95E-03 0.00E+00 6.88E-07 0.00E+00 5.85E-07 56037NEI387 Arsenic compounds 1.88E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00 22093NEILA0930004 Arsenic compounds 1.80E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00 22005NEI6147 Arsenic compounds 1.20E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00 12105NEIRFL050059 Elemental Gaseous Mercury 1.05E-03 0.00E+00 3.71E-07 0.00E+00 3.16E-07 28059NEIMS059305 Elemental Gaseous Mercury 1.02E-03 0.00E+00 3.60E-07 0.00E+00 3.06E-07 37013NEI45571 Formaldehyde 9.99E-04 5.00E-05 3.23E-06 4.58E-05 4.58E-06 22093NEILA0930004 Elemental Gaseous Mercury 4.28E-04 0.00E+00 1.51E-07 0.00E+00 1.28E-07 12057NEIFL0570008 Elemental Gaseous Mercury 3.97E-04 0.00E+00 1.40E-07 0.00E+00 1.19E-07 16029NEIIDR029000 Elemental Gaseous Mercury 2.73E-04 0.00E+00 9.64E-08 0.00E+00 8.19E-08 22005NEI6147 Elemental Gaseous Mercury 1.28E-04 0.00E+00 4.52E-08 0.00E+00 3.84E-08 37013NEI45571 Benzyl chloride 9.90E-05 0.00E+00 0.00E+00 4.57E-06 4.57E-07 56037NEI387 Elemental Gaseous Mercury 9.81E-05 0.00E+00 3.46E-08 0.00E+00 2.94E-08 37013NEI45571 Acetaldehyde 4.12E-05 2.39E-07 3.95E-08 1.07E-06 5.37E-08 37013NEI45571 Benzene 3.41E-05 2.61E-07 1.71E-08 2.77E-07 9.24E-08 37013NEI45571 Chloroform 1.34E-05 0.00E+00 6.46E-09 0.00E+00 8.35E-09 37013NEI45571 Methyl bromide 1.39E-06 0.00E+00 6.63E-09 0.00E+00 2.86E-08 37013NEI45571 Carbon disulfide 7.13E-07 1.11E-07 8.84E-09 1.43E-06 2.76E-08 37013NEI45571 Methylene chloride 7.03E-07 1.43E-08 5.18E-09 9.84E-09 3.78E-09

3 of 5REL AEGL1 AEGL2 ERPG1 ERPG2 Table 1 – Maximum Predicted Acute Risks (HEM-3)

Pollutant Maximum Hazard Quotient 1 Facility NEI ID Phosphate Fertilizer Production Screen with Emissions Multiplier 10(x)

37013NEI45571 Toluene 4.58E-07 2.26E-08 3.77E-09 8.92E-08 1.54E-08 37013NEI45571 Phenol 9.37E-08 9.37E-09 6.11E-09 1.43E-08 2.86E-09 37013NEI45571 Xylenes (mixed) 5.71E-08 2.25E-09 3.14E-10 0.00E+00 0.00E+00 37013NEI45571 Tetrachloroethene 5.06E-08 4.21E-09 6.32E-10 1.49E-09 7.22E-10 37013NEI45571 Styrene 4.04E-08 9.98E-09 1.54E-09 4.04E-09 7.71E-10 37013NEI45571 o-Xylene 7.03E-09 0.00E+00 0.00E+00 0.00E+00 0.00E+00 12047NEIFLT$4056 Beryllium compounds 0.00E+00 0.00E+00 0.00E+00 0.00E+00 4.42E-05 12057NEIFL0570005 Beryllium compounds 0.00E+00 0.00E+00 0.00E+00 0.00E+00 1.87E-05 12057NEIFL0570008 Beryllium compounds 0.00E+00 0.00E+00 0.00E+00 0.00E+00 1.88E-06 12105NEIFL12286 Beryllium compounds 0.00E+00 0.00E+00 0.00E+00 0.00E+00 2.02E-05 12105NEIRFL050059 Beryllium compounds 0.00E+00 0.00E+00 0.00E+00 0.00E+00 6.83E-06 16029NEIIDR029000 Beryllium compounds 0.00E+00 0.00E+00 0.00E+00 0.00E+00 1.79E-06 16077NEI26584 Beryllium compounds 0.00E+00 0.00E+00 0.00E+00 0.00E+00 9.56E-06 22005NEI6147 Beryllium compounds 0.00E+00 0.00E+00 0.00E+00 0.00E+00 4.88E-07 22093NEILA0930004 Beryllium compounds 0.00E+00 0.00E+00 0.00E+00 0.00E+00 2.59E-06 28059NEIMS059305 Beryllium compounds 0.00E+00 0.00E+00 0.00E+00 0.00E+00 2.73E-06 37013NEI45571 Beryllium compounds 0.00E+00 0.00E+00 0.00E+00 0.00E+00 8.21E-06 56037NEI387 Beryllium compounds 0.00E+00 0.00E+00 0.00E+00 0.00E+00 3.21E-06 37013NEI45571 Chlorobenzene 0.00E+00 1.62E-08 1.08E-09 0.00E+00 0.00E+00 37013NEI45571 Cumene 0.00E+00 7.20E-10 1.20E-10 0.00E+00 0.00E+00 37013NEI45571 Dimethyl sulfate 0.00E+00 1.36E-05 2.63E-06 0.00E+00 0.00E+00 37013NEI45571 Ethyl benzene 0.00E+00 2.34E-08 6.84E-10 0.00E+00 0.00E+00 37013NEI45571 Ethylene dibromide 0.00E+00 3.13E-10 2.26E-10 0.00E+00 0.00E+00 37013NEI45571 Ethylene dichloride 0.00E+00 0.00E+00 0.00E+00 6.78E-09 1.67E-09 37013NEI45571 Methyl chloride 0.00E+00 0.00E+00 9.47E-09 0.00E+00 2.17E-08 37013NEI45571 Methyl hydrazine 0.00E+00 0.00E+00 1.80E-06 0.00E+00 0.00E+00 37013NEI45571 Methyl methacrylate 0.00E+00 9.72E-09 1.39E-09 0.00E+00 0.00E+00 37013NEI45571 Methyl tert-butyl ether 0.00E+00 6.60E-09 5.66E-10 0.00E+00 0.00E+00 37013NEI45571 n-Hexane 0.00E+00 0.00E+00 1.89E-10 0.00E+00 0.00E+00 37013NEI45571 Propionaldehyde 0.00E+00 1.17E-07 2.08E-08 0.00E+00 0.00E+00 37013NEI45571 Vinyl acetate 0.00E+00 1.07E-08 4.09E-10 1.43E-08 9.92E-10

Note: BOLD indicates acute risks greater than 1

2 An acute hourly multiplier of 1 was used vs 10 1 Some maximum acute impacts may be at onsite locations.

4 of 5REL AEGL1 AEGL2 ERPG1 ERPG2 Table 1 – Maximum Predicted Acute Risks (HEM-3)

Pollutant Maximum Hazard Quotient 1 Facility NEI ID Phosphate Fertilizer Production Screen with Emissions Multiplier 10(x)

3 An acute hourly multiplier of 2 was used for hydrofluoric acid emissions vs 10 based upon site specific data, refer to memorandum entitled, “Emissions Data Used in Residual Risk Modeling: Phosphoric Acid and Phosphate Fertilizer Production Source Categories” available in the docket for this action.

5 of 537013NEI45571 Hydrofluoric acid REL 2.4 1.6 Max off-site HQ with emission specific multipliers

22093NEILA0930004 Hydrofluoric acid REL 2.2 2.2 Max off-site HQ with emission specific multipliers

12105NEIRFL050059 Hydrofluoric acid REL 2.2 0.6 Max off-site HQ with emission specific multipliers

16029NEIIDR029000 Hydrofluoric acid REL 1.8 0.6 Max off-site HQ with emission specific multipliers Phosphoric Acid Manufacturing

1 Indicates modeling technique used to refined estimates Table 2: Maximum Predicted Acute Risks Greater than 1 (Refined Approach)

Facility NEI ID Pollutant Criteria HEM-3 (Screening) Refined

Results Refined Modeling Approach 1

# Phosphoric Acid Manufacturing (Refined Acute Risk Assessment)

• Figure 1

– NEI4571; maximum offsite acute HQ (REL) 

# for hydrofluoric acid of 2; east of facility

• Figure 2

– NEILA0930004 maximum offsite acute 

# HQ (REL) 

# for hydrofluoric acid of 2; southeast of facility

# Copyrighted page(s) removed. Copyrighted material is not available in Regulations.gov since it may not be reproduced without consent of the copyright holder.

Removed pages can be viewed at the USEPA Docket Center, Public Reading Room.

Address: 1301 Constitution Ave, NW Room 3334 Washington, DC 20004

Telephone: 202-566-1744

Email: docket-customerservice@epa.gov

### Appendix 9

Technical Support Document Human Health Multipathway Residual Risk Screening Assessment for the Phosphate Fertilizer Production and Phosphoric Acid Manufacturing Source Categories

## Technical Support Document: Human Health Multipathway Residual Risk Screening Assessment for the Phosphate Fertilizers Production and Phosphoric Acid Manufacturing Source Categor ies

REVISED DRAFT
July 7

, 201 4

Prepared For:

U.S. Environmental Protection Agency Office of Air Quality Planning and Standards Research Triangle Park, NC 27711

Under Contract No. EP

- 12

- 010, Work Assignment 2

- 20

Prepared By:

ICF International 2635 Meridian Pkwy. Suite 200 Durham, NC 27713[This page intentionally left blank.]Human Health Multipathway Residual Risk Screening Assessment for the Phosphate Fertilizers Pr

oduction and Phosphoric Acid Manufacturing Source Categories



## CONTENTS

Tables

................................ ................................ ................................ ................................ ........ iii

Figures

................................ ................................ ................................ ................................ ....... iv

1. Introduction

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

2. Tier 1 Emission Screening Assessment

................................ ................................ ............. 2

2.1 Tier 1 Emission Screening Results for these Source Categories

............................... 3

3. Tier 2 Emission Screening Assessment

................................ ................................ ............. 4

3.1 Adjustment Factors Applied to Tier 1 Screening Values

................................ ............ 4

3.2 Calculating Refined Tier 2 Fisher Screening Values

................................ .................. 5

3.3 Finalizing Tier 2 Farmer Screening Values

................................ ................................ 7

3.4 Tier 2 Screening Results for these Source Categories

................................ ............... 7

4. Tier 3 Emission Screening Assessment

................................ ................................ ............. 9

4.1 Lake Assessment

................................ ................................ ................................ ...... 9

4.2 Tier 3 Lake Assessments Results for these Source Categories

............................... 11

4.3 Plume

- rise Assessment

................................ ................................ ........................... 12

4.4 Tier 3 Plume

- rise Assessment Results for these Source Categories

....................... 13

4.5 Assessment of Time

- series Meteorology and Effective Release Heights

................................ ................................ ................................ .................... 14

4.6 Tier 3 Time

- series Assessment Results for these Source Categories

...................... 14

5. Compariso n to Tier 1 Screening Results for this Source Category

................................ .... 16

6. References

................................ ................................ ................................ ....................... 18



## TABLES
Table 2

- 1. Tier 1 Emission Screening Results for these Source Categories

............................... 3

Table 3

- 1. Tier 2 Screening Results for these Source Categories

................................ .............. 8

Table 4

- 1. Results of Tier 3 Lake Assessments for Mercury Emissions

................................ .... 11

Table 4

- 2. Results of Tier 3 Plume

- rise Assessments for Mercury Emissions

........................... 13

Table 4

- 3. Tier 3 SVs Before and After Additional Lake and Plume

- rise Assessments

................................ ................................ ................................ ...... 14

Table 4

- 4. Tier 3 Fisher SVs Before and After Time

- series Assessments

................................ . 16

Table 5

- 1. Comparison of Tier 1 and Tier 3 Screening Results for these Source Categories

................................ ................................ ................................ .......... 18

Draft iii July 2014Human Health Multipathway Residual Risk Screening Assessment for the Phosphate Fertilizers Pr

oduction and Phosphoric Acid Manufacturing Source Categories



## FIGURES Figure 4

- 1. Example of Lake Removed from Screening

— Likely Evaporated or Drained

................................ ................................ ................................ ............... 10 Figure 4

- 2. Example of Lake Removed from Screening

— Likely an Industrial Lake

................... 10 Figure 4

- 3. Lakes Assessed for NEIMS059305 (Phosphate Fertilizers Production Source Category)

................................ ................................ ................................ 12 Figure 4

- 4. The Modeling Spatial Scenario Used for the Unnamed Lake and for “Crane Lakes”

................................ ................................ ................................ ..... 15 Figure 4

- 5. The Modeling Spatial Scenario Used for “Black Creek Cooling Pond”

..................... 16

Draft iv July 2014Human Health Multipathway Residual Risk Screening Assessment for the Phosphate Fertilizers Production and Phosphoric Acid Manufacturing Source Categories



## 1. Introduction

This report provides a discussion on a screening

- level assessment of human health risks, via the ingestion pathway, from exposure to persistent and bioaccumulative hazardous air pollutants (PB

- HAPs) emitted to the air fr om phosphate fertilizers production facilities and phosphoric acid manufacturing facilit ies

. While this report contains a discussion o n some of the screening methods , a more detailed discussion on the development, methods, and implementation of the screeni ng assessment can be found in Appendix 5 of the Risk Report. 1 Where appropriate, this discussion provides references to particular sections of Appendix 5 to the Risk Report, where further details on the subject matter can be found.

Cadmium, dioxins, mercur y, and polycyclic aromatic hydrocarbons (PAHs) were the evaluated PB

- H AP groups , 2 selected from a larger list of PB

- HAPs based in part on ranking their toxicity

weighted air emissions across all source categories. The evaluated exposure scenarios were a fisher scenario (i.e., consumption of fish from a lake) and a farmer scenario (i.e., consumption of farm food chain [FFC] media such as animal products, fruits and vegetables, and incidental ingestion of soil). EPA evaluates t he two scenarios together (i.e ., the sum of fisher and farmer exposures and risks) in the first tier of the assessment, and EPA evaluates them separately in the second and third tier s

. EPA designed the screening scenarios to be health

- protective in estimating exposures and risks. Speci fically, they are intended to avoid underestimating ingestion exposure and risk due to the air emissions of these PB

- HAPs from facilit ies nationwide evaluated in the Risk and Technology Review (RTR) program

.
EPA uses the TRIM.FaTE 3 model to evaluate the en vironmental transport, transfer, and fate of PB

- HAP air emissions. The MIRC 4 program then calculates FFC chemical uptake along with human exposure

, human health cancer risk s , and human health non

- cancer hazard quotients (HQs) resulting from ingestion of fi sh and FFC media . For screening assessments of multipathway risk across the RTR program, EPA used these models with fate and transport parameter values, idealized spatial layouts, and generalized meteorology that overall create ingestion exposure situation s that are unlikely to be exceeded at any actual facility evaluated in the RTR program. ICF (hereafter “we”) conduct ed these model runs to identify threshold emission rates (hereafter “thresholds”) that correspond to levels of concern for cancer risks and HQs

. The levels of concern are incremental, lifetime, human cancer risks of 1

- in

- one million ( for PAHs and dioxins) and chronic

, non

- cancer HQs of 1 ( for cadmium and mercury)

.
For the iterative (“tiered”) screening

- level ingestion assessment described in this report, we assessed the potential for ingestion risks above levels of concern in the phosphate fertilizers production source category and the phosphoric acid manufacturing source categor y by comparing PB

- HAP emissions from faci lities with in the source categor ies to the thresholds. We

use the term “screening values ” ( SV s) to refer to the ratio of a facility ’s emissions of a PB

- HAP

1 Appendix 5 to the Risk Report is the Technical Support Document for the TRIM

- Based Multipathway T iered Screening Methodology for RTR

. 2 The phrase “PB

- HAP group” is used to distinguish the individual PB

- HAP chemicals and congeners from the overall family (grouping) of those chemicals. For example, EPA’s ingestion risk methods in its Risk and Technology Review (RTR) program evaluate emissio ns of 17 individual congeners of chlorinated dibenzo

- p

- dioxins and chlorinated dibenzofurans in the “dioxin” PB

- HAP group.

3 TRIM.FaTE is the Fate, Transport, and Ecological Exposure module of the Total Risk Integrated Methodology modeling system. Addition al TRIM.FaTE information can be found at: http://www2.epa.gov/fera/total

- risk

- integrated

methodology

- trim

- trimfate

. 4 MIRC is the Multimedia Ingestion Risk Calculator.

Draft 1 July 2014Human Health Multipathway Residual Risk Screening Assessment for the Phosphate Fertilizers Production and Phosphoric Acid Manufacturing Source Categories

to that PB

- HAP’s threshold. When rounded to one significant figure, an SV of 1 or smaller indicates that the emissions did not exceed the threshold and are unlikely to pose health risks above levels of concern (i.e., the emissions “screened out”). An SV of 2 or greater indicates that the emissions exceeded the threshold and potentially pose health risks above levels of concern.

The phosphate fertilizers production source category contains 1 1 facilities , and the phosphoric acid manufacturing source category contains 12 facilities. As described in this report, most emissions of the evaluated PB

- HAPs screened out in the Tier 1 assessment. For the phosphoric acid manufacturing source category, one facility’s cadmium and mercury emissions exceeded the ir respective Tier 1 thresholds but screened out in the Tier 2 as sessment. For the phosphate fertilizers production source category, one facility’s mercury emissions exceeded the Tier 1 threshold , but the emissions screened out in Tier 3 , which included additional assessments of its emissions and corresponding meteorolo gy and lake data.

In Sections 2

, 3 , and 4

, we briefly describe the Tier s 1, 2 , and 3 screening methods and discuss the results of those screening assessments. We provide in Section 5 a summary comparison of the Tier 1 screening results with the screening results after the Tiers 2 and 3 assessments.



## 2. Tier 1 Emission Screening Assessment

T he thresholds developed for Tier 1 are intended to apply to any U.S. facility; that is, EPA designed them to be applicable to every facility regardless of the facility’s charac teristics and surrounding environment and the population exposed in its vicinity

. T he approach to develop the thresholds 5 assume s ingestion exposure situations that are unlikely to be exceeded at any actual facility evaluated in the RTR program. This health

- protectiveness is achieved by several methods, including (but not limited to): (1) an idealized spatial layout where the evalu ated lake and farming area are within 2 to 3 km of the emission source and

, thus

, receive high levels of direct deposition; (2) a watershed design where the lake receives substantial indirect chemical deposition through large magnitudes of runoff and erosi on; (3) a meteorology scenario that enhances air concentrations and chemical deposition ; and, (4) relatively high ingestion rates representing subsistence

- level consumption of fish and FFC media

.
Each evaluated PB

- HAP group has a single Tier 1 threshold

, as shown in Table 2

- 1 (column e )

. For Tier 1, EPA normalize s the emissions of all dioxin congeners to 2,3,7,8

- t etrachlorodibenzo

p

- d ioxin

- equivalent emissions

, and EPA normalize s the emissions of all PAH congeners to benzo(a)pyrene

- equivalent emissions

. 6 T hese normalization factors (i.e., exposure equivalency factors [EEFs]) are based on oral toxicity factors and factors related to the congeners’ environmental fate and transport characteristics as analyzed in the T RIM.FaTE model. 7 For mercury, EPA established the threshold for emissions of divalent mercury, although the threshold is based on hazards associated with ingestion of methyl mercury from contaminated fish (with relatively minimal contribution from ingestion of divalent mercury from FFC media)

. 8

5 See Appendix 5 of the Risk Report (Sections 1 and 2, and Attachment A ) for additional information.

6 We sometimes use 2,3,7,8

- TCDD and BaP this report as shorthand terms for 2,3,7,8

- t etrachlorodibenzo

- p

- d ioxin and benzo(a)pyrene, respectively.

7 See Append ix 5 to the Risk Report ( Section A.2.7 of Attachment A ) for additional information on Tier 1 screening methods.

8 TRIM models mercury transformation in the environment, and exposure in the screening scenario associated with ingestion of fish contaminated with methyl mercury (related to emissions of divalent mercury and transformation to methyl mercury) results in much larger hazards than exposure associated with ingestion of fish or FFC contaminated with divalent mercury. (See Section A.2 and A.5.3 in Appendix 5 to the Risk Report.)

Draft 2 July 2014Human Health Multipathway Residual Risk Screening Assessment for the Phosphate Fertilizers Production and Phosphoric Acid Manufacturing Source Categories



### 2.1 Tier 1 Emission Screening Results for th ese Source Categor ies

As shown in Table 2

- 1

, emissi ons of dioxins and PAHs (emitted by one facility in the phosphoric acid manufacturing source category) screened out because the emissions were approximately a factor of 5 below the respective thresholds.

In the phosphate fertilizers production source categ ory, cadmium emissions screened out. Mercury emissions exceeded the threshold by approximately a factor of 20 at one (out of 11) mercury

- emitting facilities. This facility was NEIMS059305 , a Mississippi Phosph ates Corp. facility in Pascagoula, MS

.
In the phosphoric acid manufacturing source category, cadmium and mercury emissions exceeded their respective thresholds by approximately factors of 2 and 7, respectively, at one (out of 12) facilities emitting cadmium and mercury. This facility was NEI45571, a P CS Phosphates Co. facility in Aurora, NC. PAH and dioxin emissions screened out. When PAH and dioxin SVs w e re summed together per facility (i.e., calculating the total PAH+dioxin screening

level potential cancer risk), the highest total “cancer SV” w a s 0.4 a nd screen e d out (not shown in table).

Table 2

- 1

. Tier 1 Emission Screening Results for th ese Source Categor ies

Source Cat. PB

- HAP Group ( a ) Num. Facil. Exceeding Threshold ( b ) Largest

SV [( d )/( e )] (c) Facil . with Largest SV (d) Emiss. at Facil. with

Largest SV (TPY) a ( e ) Emiss. Screening

Thresh.

(TPY) Phosphate Fertilizers Production Mercury b (non

- cancer) 1 2 0 NEIMS059305 ( Mississippi Phosphates Corp. ; Pascagoula, MS ) 6.74E

- 03 3.16E

- 04

Cadmium (non

- cancer) 0 0.9 NEIMS059305 ( Mississippi Phosphates Corp. ; Pascagoula, MS ) 1. 11 E

- 0 2 1.18E

- 02

PAHs as BaP (cancer) N/E N/E N/E N/E 2.58E

- 03

Dioxins as 2,3,7,8

- TCDD (cancer) N/E N/E N/E N/E 2.81E

- 09

Phosphoric Acid Manufacturing Mercury b (non

- cancer) 1 7 NEI 45571 ( PCS Phosphate Co. Inc.

- Aurora ; Aurora, NC ) 2.23E

- 03 3.16E

- 04

Cadmium (non

- cancer) 1 2 NEI 45571 ( PCS Phosphate Co. Inc.

- Aurora ; Aurora, NC ) 2.13E

- 02 1.18E

- 02

PAHs as BaP (cancer) 0 0.2 NEI 45571 ( PCS Phosphate Co. Inc.

- Aurora ; Aurora, NC ) 5.54E

- 04 2.58E

- 03

Dioxins as 2,3,7,8

- TCDD (cancer) 0 0.2 NEI 45571 ( PCS Phosphate Co. Inc.

- Aurora ; Aurora, NC ) 5.63E

- 10 2.81E

- 09

Draft 3 July 2014Human Health Multipathway Residual Risk Screening Assessment for the Phosphate Fertilizers Production and Phosphoric Acid Manufacturing Source Categories

Source Cat. PB

- HAP Group ( a ) Num. Facil. Exceeding Threshold ( b ) Largest

SV [( d )/( e )] (c) Facil . with Largest SV (d) Emiss. at Facil. with

Largest SV (TPY) a ( e ) Emiss. Screening

Thresh.

(TPY)

N otes : N /E = not emitted. TPY = tons per year. SV s rounded to one significant figure. Cell shadings indicate records corresponding to SV s of 2 or greater (i.e., the emissions exceeded the threshold )

. a We normalized P AH and dioxin emissions in this column to BaP and 2,3,7,8

- TCDD, respectively, for oral toxicity and Tier 1 modeled environmental fate and transport.

b The emission screening threshold for mercury applies to emissions of divalent mercury, although EPA derived it based on hazards associated with ingestion of fish contaminated with methyl mercury (i.e., emissions of divalent mercury, which transforms to me thyl mercury within the environment

, accumulates in fish tissue , and is consumed by the fisher ). The contribution from hazards associated ingestion of divalent mercury from contaminated FFC or fish media is relatively minimal.



## 3. Tier 2 Emission Screening Assessment

The generic s ite characteristics used in Tier 1 can differ from those actually present at many facilities. In Tier 2

, EPA takes into account some site

- specific characteristics

— specifically,

m eteorologica l conditions and locations of potentially fishable lakes

— to apply adjustments to the Tier 1 SV s , creating Tier 2 SV s

. 9

For a facility undergoing Tier 2 screening , EPA partitions the 50

- km area around the facility into eight “wedges” or “octants” centered on the facility and compartmentalizing areas north of the facility, northeast, east, and so on. As described in Section 3.1

, the lake locations and some meteorological conditions related to a fac ility can vary by octant in Tier 2. Though t he distance from facility to modeled farm remains fixed for each octant, EPA adjusts the location of the modeled lake based on actual lake loc ations. This affects chemical concentrations in some FFC media due to modeled erosion and runoff patterns that are directed mostly toward the lake. Therefore, for every relevant PB

- HAP emitted by a facility, EPA calculates the fisher SV of each lake, using the distance from facility to lake and the meteorology conditions of the octant the lake is in; and, since lake location can affect the farm SV

, EPA calculates a corresponding farm er SV for each lake scenario.

Based on the Tier 2 SV s and other considerations with respect to the source category being assessed, EPA might conduct a Tier 3 assessment on some facilities, leading to adjustments to the Tier 2 SV s for those facilities. The results we present in Section 3.4 are prior to th is Tier 3 assessment ; we discuss the Tier 3 assessment and its effect s on SV s in Section 4

.


### 3.1 Adjustment Factors Applied to Tier 1 Screening Values

In developing the adjustment factors used in Tier 2

, we conducted hundreds of TRIM.FaTE and MIRC model runs corresponding to ranges of meteorological characteristics expected to be found at most U.S. locations, and corresponding to lake locations ranging from 2 km to 40 km away from the facility. As in Tier 1, the model runs provided emission rates corresponding to risk and hazard levels of concern. Because each modeled scenario has diffe rent environmental fate and transport characteristics, each scenario also has different EEFs to relate PAH and dioxin congeners to BaP and 2,3,7,8

- TC DD, respectively. Unlike in Tier 1

, EPA calculates EEFs and thresholds separately in Tier 2 for the fisher and farmer scenarios. T he Tier 2 adjustment factors are the r atios of the Tier 2 EEFs and thresholds to those of Tier 1

. EPA applies the Tier 2

9 See Appendix 5 to the Risk Report (Sections 1 and 3, and Attachment B) for additional information on Tier 2 screening methods.

Draft 4 July 2014Human Health Multipathway Residual Risk Screening Assessment for the Phosphate Fertilizers Production and Phosphoric Acid Manufacturing Source Categories

adjustment factors to Tier 1 SV s in order to calculate Tier 2 fisher SV s and Tier 2 farmer SV s

. We discuss below an additional adjustment factor based on wind direction.

In order to match an assessed facility in Tier 2 with its appropriate meteorology

- and lake

related Tier 2 adjustment factors, EPA matches the facility with the same me teorology station to which it is matched in the RTR inhalation risk assessment (i.e., typ ically the closest station), and EPA also identifies qualifying lakes around the facility. Some of a facility’s meteorology patterns vary by octant, and qualifying lakes (discussed below) can be sc attered around multiple octants and at multiple distances from the facility. Therefore, for each lake in each octant around a facility, EPA matches its combination of meteorology data and lake distance to its appropriate scenario in the Tier 2 model runs a nd, thus, to the appropriate Tier 2 adjustment factors.

EPA bases t he meteorology data used in Tier 2 on the same dataset used in RTR inhalation risk assessments, consisting of more than 800 meteorology stations nationwide. W e processed hourly data from the meteorology stations to derive typical or total station

- specific values of wind speed, wind direction, mixing height, and precipitation. For each station, we then matched the combination of these values with the most representative meteorological scena rio used in the Tier 2 model runs . T he matching method is health

- protective (i.e., wind speeds and mixing heights match to their closest, smaller value in the Tier 2 model runs , and a precipitation value to its closest, larger value). We also calculated th e frequency of winds blowing toward each of the octants around each station

. EPA compares the frequency of winds blowing into an octant to the frequency used in Tier 1, creating a Tier 2 wind frequency adjustment factor.

EPA analyzes all qualifying lakes w ithin 50 km of the facility. A qualifying lake (1) is at least 25 acres in surface area but no larger than 100,000 acres; 10 (2) is classified by USGS as a lake or reservoir but not used for disposal, treatment, or evaporator purposes; (3) does not have a

na me suggesting it is not intended for public use (e.g., “Gavin Fly Ash Impoundment”); and, (4) has not been removed in previous source category assessments based on these and other inspection methods . EPA assigns eac h lake to its respective octant around th e assessed facility and to the closest lake distance value used in the Tier 2 model runs , where the matching method is health

- protective (i.e., a lake’s distance matche s to its closest, smaller value in the Tier 2 model runs ). In cases where there are no q ualifying lakes around the facility, there is no exposure or risk to the fisher

.
T he Tier 2 fisher and farmer SV s calculated at this point in the assessment are “initial SV s”; this is because subsequent steps in Tier 2 refine the fisher SV s (see Section 3.2 ) and select one of the potentially many farmer SV s (see Section 3.3 )

.


### 3.2 Calculating Refined Tier 2 Fisher Screening Values

The subsistence fisher in Tier 1 consumes fish from one lake at the subsistence ingestion rates, regardless of the lake’s ability to support a fish population that allows for that level of

consum ption a cross many years. In Tier 2, EPA considers the lake’s capacity to accommodate the assumed fish ingestion rate s. For any particular lake, t he fisher in Tier 2 catches and

consumes fish from that lake at an amount proportional to the area of the lake , which serves as an approximation of the lake’s biological productivity

. 11 In order to consume fish at the ingestion rate used in Tier 2, the fisher might need to fish multiple lakes (and be exposed to several different chemical concentration levels in fish)

.

10 D iscussed fur ther in Attachment B of Appendix 5 to the Risk Report

. 11 See Appendix 5 to the Risk Report (Attachment B) for additional information on potential lake productivity.

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Th e initial Tier 2 fisher SV s and the Tier 1 fisher SV s use a fixed set of fish ingestion rates covering a range of ages. The highe s t fish ingestion rate among the age groups is 373 g fish/d for adults , which for simplicity EPA uses to estimate a lake’s capac ity to support subsistence fishing

. Smaller lakes are unlikely to support this level of fish consumption over time (i.e., the lake would be “fished out”)

, al though the fisher could catch and consume that amount of fish collectively from multiple small lake s. In Tier 2

, EPA assumes that a fisher can catch and

consume no more than 1 g fish/d/acre lake without impacting the lake’s long

- term sustainability

. In order to reach the assumed ingestion rate, EPA assumes that t he fisher catches and consumes fish from one or more lakes until he is able to fish a total of 373 acres of lake and

consume a total of 373 g fish/d (i.e., the screening scenario adult fish ingestion rate ) or until there are no more qualifying lakes to f ish to reach 373 total acres.

EPA uses the initial Tier 2 fisher SV s calculated for all qualifying lakes around a facility , along with the acreage values of those lakes, to identify which lake(s) the fisher catches and consumes fish from and the amount of fish caught from each lake

. Using this info rmation, EPA refines the Tier 2 fisher SV based on a scenario assuming consumpti on of fish from multiple lakes. EPA conduct s t he steps below for each PB

- HAP group emitted by a facility.

1. Identify the lake with the highest chemical concentration in fish 12 for a given PB

- HAP group

, according to the initial Tier 2 fisher SV s

. 2. If the lake with the highest concentration is at least 373 acres in area , then the fisher consumes fish from only that lake

, at the subsistence

- level ingestion rates (the adult rate being 3 73 g fish/d)

. That is, the refined Tier 2 fisher SV equals the initial Tier 2 fisher SV

. 3. If the lake with the highest concentration is smaller than 373 acres, then the fisher catches only the amount of fish consistent with the assumption of 1 g fish/d/acr e lake. That is,

mu ltiply t he initial Tier 2 fisher SV for that lake by [its acreage]÷[373 acres]; put another way, multiply the SV by [the daily mass of fish consumed from the lake]÷[373 g fish/d]

. Apply the following logic to subsequent lakes.

a. T he fisher catches and consumes fish from one or more additional lakes, in order of highest to lowest concentration

. If multiple lakes have the same concentration

, then he fishe s them in order of largest to smallest acreage. He stops fishing when h e fishes lak es totaling 373 acres or when there are no more qualifying lakes to fish to reach 373 total acres

. S cale e ach additional lake’s initial Tier 2 fisher SV by acreage (i.e., by fish consumption rate) in the same way as the first fish ed lake (see above)

. b. If th e cumulative area of all qualifying lakes is smaller than 373 acres (i.e., if all lakes together cannot support a 373 g fish/d adult ingestion rate, assuming 1 g fish/d/acre lake ) , then the fisher does no t consume the full 373 g fish/d.

c. If the cumulative area of all qualifying lakes is larger than 373 acres, the fisher consumes only the amount of fish from the final lake necessary to reach 373 g fish/day. That is, scale the initial Tier 2 fisher SV for that lake by [ the remaining acreage necessary to reach 373 acres , after fishing the prior lake(s )] ÷[ 373 acres].

12 In the screening assessments, for a given ingestion rate, SVs are directly and proportional ly related to chemical concentrations in the exposure media. For simplicity and to more clearly relate the potential health impacts of consuming a lake’s fish to the chemical concentrations in those fish, EPA discusses fish concentrations in describing the methods of calculating the refined Tier 2 angler SVs.

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d. The refined Tier 2 fisher SV ( reflecting the refined fisher methods ) is the sum of the adjusted Tier 2 fisher SV s of all fish ed lakes as calculated above

.


### 3.3 Finalizing Tier 2 Farmer Screening Values

For a given PB

- HAP group emitted by a facility, EPA identifies the largest Tier 2 farmer SV from all the relevant meteorology and lake

- distance scenarios. This helps to avoid underestimating exposure and risk.



### 3.4 Tier 2 Screening Results for th ese Source Categor ies

In this section, we discuss the Tier 2 screening results. The results in this section use the refined Tier 2 fisher SV s (discussed in Section 3.2 ) and the largest Tier 2 farm er SV s (discussed in Section 3.3 ).

As shown in Table 3

- 1

, all emissions screened out except for mercury emissions from NEIMS059305 (phosphate fertilizers production source category ). The fisher SV at that facility was 3, while the farmer SV was 0.002. Mercury and cadmium emissions from NEI45571 (phosphoric acid production source category ), which exceeded their respective Tier 1 thresholds by factors of 7 and 2, screened out in Tier 2

— its mercury and cadmium Tier 2 fisher SV s were 1 and 0.3, respectively, and the Tier 2 farmer SV s were 0.0007 and 0.02, respectively.

When PAH and dioxin SVs w e re summed together per facility in the phosphoric acid manufacturing source category (i.e., calculating the total PAH+dioxin screening

- level potential

canc er risk), the highest total “cancer SVs” screen ed out (SV = 0.04 for fisher, 0.07 for farmer; not shown in table).

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Table 3

- 1 . Tier 2 Screening Results for th ese Source Categor ies

Facil. Type PB

- HAP Group Num. Facil. Exceeding

Fisher Threshold Num. Facil. Exceeding

Farmer Threshold Num. Facil. Exceeding Threshold , **Either Fisher** 

**or Farmer** , Both Fisher and Farmer Largest Fisher

SV Largest Farmer

SV Facil. with Largest Fisher SV Facil. with Largest Farmer SV Phosphate Fertilizers Production Mercury a (non

- cancer) 1 1 1 0 3 0.002 NEIMS059305 ( Mississippi Phosphates Corp. ; Pascagoula, MS ) NEIMS059305 ( Mississippi Phosphates Corp. ; Pascagoula, MS )
Cadmium (non

- cancer) 0 0 0 0 0.1 0.008 NEIMS059305 ( Mississippi Phosphates Corp. ; Pascagoula, MS ) NEIMS059305 ( Mississippi Phosphates Corp. ; Pascagoula, MS )
PAHs as BaP (cancer) N/E N/E N/E N/E N/E N/E N/E

Dioxins as 2,3,7,8

- TCDD (cancer) N/E N/E N/E N/E N/E N/E N/E

Phosphoric Acid Manufacturing Mercury a (non

- cancer) 0 0 0 0 1 0.0007 NEI 45571 ( PCS Phosphate Co. Inc.

- Aurora ; Aurora, NC ) NEI 45571 ( PCS Phosphate Co. Inc.

- Aurora ; Aurora, NC )
Cadmium (non

- cancer) 0 0 0 0 0.3 0.02 NEI 45571 ( PCS Phosphate Co. Inc.

- Aurora ; Aurora, NC ) NEI 45571 ( PCS Phosphate Co. Inc.

- Aurora ; Aurora, NC )
PAHs as BaP (cancer) 0 0 0 0 0.01 0.06 NEI 45571 ( PCS Phosphate Co. Inc.

- Aurora ; Aurora, NC ) NEI 45571 ( PCS Phosphate Co. Inc.

- Aurora ; Aurora, NC )
Dioxins as 2,3,7,8

- TCDD (cancer) 0 0 0 0 0.03 0.01 NEI 45571 ( PCS Phosphate Co. Inc.

- Aurora ; Aurora, NC ) NEI 45571 ( PCS Phosphate Co. Inc.

- Aurora ; Aurora, NC )

Notes: N/E = not emitted. TPY = tons per year. SV s rounded to one significant figure. Cell shadings indicate records corresponding to SV s of 2 or greater (i.e., the emissions exceeded the threshold )

. Emissio ns and screening threshol ds not shown for space purposes.

a The emission screening threshold for mercury applies to emissions of divalent mercury, although EPA derived it based on hazards associated with ingestion of fish contaminated with methyl mercury (i.e., emissions of divalent mercury, which transforms to methyl mercury within the environment, accumulates i n fish tissue, and is consumed by the fisher ). The contributio n from hazards associated ingestion of divalent mercury from contaminated FFC or fish media is relatively minimal.

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## 4. Tier 3 Emission S creening Assessment

Based on the Tier 2 SV s and other considerations with respect to the source category being assessed, EPA may conduct Tier 3 assessments on some facilities. We discuss in this section the Tier 3 assessment conducted for mercury hazards for NEIMS059305 in the phosphate fertilizers production source category, whose Tier 2 fisher SV for mercury was 3

.


### 4.1 Lake Assessment

Other than verifying a f acility’s emission rates , one of the simplest assessments EPA can conduct (beyond the Tier 2 methods) is assessing the existence

, the potential purpose

, the

access ibility and fishability , and the suitability of lakes for the models and methods used in Tier 2

. The full USGS dataset of lakes and reservoirs nationwide that EPA uses in Tier 2 does not contain information on lake accessibility or fishability . In addition, it occasionally identifies a lake that no longer exist s (e.g., it has evaporated or been dr ained ) or it uses a classification that might not accurately reflect the lake’s purpose or type. EPA uses aerial and street

- view imagery and internet searches to quickly ascertain if an assessed lake actually exists, if it is probably not fished (e.g., app ears s wampy or covered in algae), if it is likely used for industrial or waste disposal/treatment purposes, and/or if it is adjacent to or connected to a river or saltwater body (estuaries

, rivers

, and saltwater areas are not ideal for the assessment models and methods)

. For example, the blue outline in Figure 4

- 1 identifies a lake from the USGS dataset that

or iginally qualified for Tier 2 based on the information provided in that dataset, but aerial imagery shows it is likely evaporated or drained

. Similarly, the outline in Figure 4

- 2 identifies a lake that originally qualified, but aerial imagery shows that it is directly adjacent to an industrial facility and likely used only for on

- site industrial purposes.

For facilities undergoing Tier 3 screening

, EPA evaluates all l akes from which the fish catches and consumes fish according to the refined Tier 2 fisher methods discussed in Section 3.2 . EPA permanently removes

, from the screening assessment, all lakes meeting the criteria discussed in the previous paragraph s , so that they are no longer used in any screening assessment. If EPA removes a lake from the facility’s assessment , it might be necessary for the hypothetical fisher to catch and consume fish from additional lake(s) in ord er to fish a total of 373 acres. I n this case, EPA evaluates additional lakes that were not fished

, using the methods discussed in Section 3.2 , in descending order of initial Tier 2 fisher SV and lake area

. If EPA removes any lakes from the screening , EPA conduct s the screening again (using the Tier 2 methods) with the revised lake dataset. Alt hough EPA conduct s this additional lake assessment based on the Tier 2 SV s of a particular set of facilities, lakes removed during that assessment could affect the SV s of other facilities in the source category beyond that original set . This can happen if an assessed facility is within 50 km of another assessed facility, whereby removing a lake could affect the SV s of both facilities. EPA does not sum SV s across facilities.

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Figure 4

- 1 . Ex ample of Lake Removed from Screening

— Likely Evaporated or Drained

Note: Aerial imagery from ESRI World Imagery (2014).

Figure 4

- 2 . Example of Lake Removed from Screening

— Likely an Industrial Lake

Note: Aerial imagery from ESRI World Imagery (2014).

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### 4.2 Tier 3 Lake Assessments Results for these Source Categories

The information in Table 4

- 1 is related to the lakes evaluated in the screening of NEIMS059305 in the phosphate fertilizers production source category. The table includes one lake we removed during the assessment (see gr ay shaded row). For lakes remaining in the screening after the lake assessment, the table contains information on how much of the subsistence fisher ’s daily fish consumption came from each lake, and how much of the corresponding fisher SV was attributable to each lake.

Table 4

- 1 . Results of Tier 3 Lake Assessments for Mercury Emissions

Facil. NEI ID Lake Name Lake Area (acres) Selection Order Percent of Ingested

Fish Coming

From This Lake Percent of Tier 3 Fisher SV Attributed

To This

Lake Reason for Removal NEIMS059305 (phosphate fertilizers production source category) Name Not Provided 49 1 9% 16%

-- Crane Lakes 82 2 10% 14 %

-- Black Creek Cooling Pond a 59 3 81% 70 %

--
Presley Pond 30 RMV

- 1

-- -- Evaporated or drained
Notes: The lakes shown in this table, and their corresponding selection order and percentages of ingestion and SV , are related only to the mercury emissions from th is facility

. For o ther PB

- HAP groups emitted by th is facility, the f isher might fish a different set of lakes and/or with a different selection order

. “RMV” indicates that we removed the lake during Tier 3 . The number beside the “RMV” designation was for map labeling purposes (see the figure in this subsection). The percentages of ingestion and SV reflect the assessment after we removed this lake.

a Black Creek Cooling Pond appears to have been created by a dam and, despite its name, we found no reliable information suggesting it is restricted access, unfishable, or industrial. Figure 4

- 3 is an aerial image delineating the lakes in Table 4

- 1 . The labels of the lakes in the map correspond to the dat a in the “Selection Order” column of the table. Lakes labeled in the map with negative numbers (e.g.,

- 1,

- 2, and so on) are qualifying lakes that the fisher did not fish because the fish ingestion rate was already met by fishing the first set of lakes (i. e., by fishing those labeled 1, 2, and so on). The map is focused on the fished and removed lakes, so it does not show all unfished lakes. Table 4

- 1

, for space purposes, does not contain information on these unfished lakes. NEIMS059305 is close to the Gulf of Mexico and has many water bodies of varying types within 50 km; the red outlines and text in the map call attention to water bodies within the map extent that did not qualify for the Tier 2 because they appeared to be estuarine.

As discussed in Section 3.4 , this facility’s mercury emissions ex ceeded the Tier 2 thresholds for fisher s by approximately a factor of 3. Removing one lake from this facility’s screening did not effectively decrease its Tier 3 fisher SV , so we conducted a plume

- rise assessment on the mercury emi ssions, as discussed in S ection s 4.3 and 4.4

.

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Figure 4

- 3 . Lakes Assessed for NEIMS059 305

(Phosphate Fertilizers Production Source Category)

Notes: Aerial imagery from ESRI (2014). Lake labels correspond to the

“ Selection Order” column in Table 4

- 1

. The “lake” shown in Figure 4

- 1 corresponds to “RMV

- 1” here.



### 4.3 Plume

- r ise 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 caus e some of the chemical plume to reach areas above the mixing layer. In TRIM.FaTE modeling, the chemical mass deposited above the mixing layer (i.e., the model’s upper

- air layer) is unavailable for ground

- level exposure (i.e., the upper

- air layer functions as a chemical sink). Many emission sources in RTR are physically taller than the mixing height during some hours, and hot exit gas temperatures (i.e., buoyancy) and/or high exit gas velocities (i.e., momentum) can further elevate the chemical plume.

EPA us es 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 req uire use of the facility’s

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corresponding meteorology data (e.g., air temperature and vertical temperature gradient, wind speed, and atmospheric stability ), the mass of the PB

- HAP emitted from each source, the physical characteristics of the source s (i.e., release height, inside diameter at the release point, and exit gas temperature and velocity), and an estimate of the size of the facility

. For each relevant emission source, EPA estimates the hourly effective release height (i.e., sum of actual release height and plume rise) and compares it to the hourly mixing height to determine the mass of chemical remaining in the mixing layer

. EPA compares the mass of chemical remaining in the mixing layer , summed across the sources, to the total emitted mas s of the chemical . This results in an adjustment factor expressing the fraction of emissions remaining in the mixing layer after accounting for release height and plume rise. EPA applies t his final plume

- rise adjustment factor to the Tier 2 SV s

.


### 4.4 Tier 3 Plu me - r ise Assessment Results for th ese Source Categor ies

T he Tier 3 l ake assessment did not effectively lower the Tier 3 fisher SV for mercury emissions from NEIMS059305 (phosphate fertilizers production source category; see Section 4.2 ). W e also conduct ed a plume

- rise assessment on the emissions

. Table 4

- 2 contains information on the mercury emissions and physical characteristics of each unique emission source

, along with the fraction of mercury emitted from each source that remained in the mix ing layer (i.e., is not deposited into the upper

- air sink )

. We show in the last column t he total fraction of emissions from all sources together that remained in the mixing layer. We calculated this value by weighting the value in the previous colum n (frac tion remaining in the mixing layer, by source) by the fraction of mercury emissions coming from each source (i.e., the third column) , and summing those weighted values. To account for these plume

- rise data, we scaled the Tier 2 fisher and farmer SV s by the fractions shown in the last column.

Table 4

- 2

. Results of Tier 3 Plume

- r ise Assessments for Mercury Emissions

Facil. NEI ID Source Num. Fraction of Emiss. From This Source Phys ical Characteristics of Emiss. Source Fraction of Emiss. Remaining in Mixing Layer

Height

(m) Exit Gas Temp

.

(K) Inside Diameter (m) Exit Gas Vel

. (m/s) By Source Facility Total NEIMS059305 (phosphate fertilizers production source category) 1 0.0121 24.7 350 1.8 13.1 0.806 0.7679

2 0.98 79 48.8 355 1.8 12.5 0.7675

Table 4

- 3 contains the previously calculated Tier 2 SV s for mercury (reflecting the assessment described in Section 3 ), along with the SV s after the Tier 3 lake and plume

- r ise assessments, for NEIMS059305 (phosphate fertilizers production s ource category)

.
While t he Tier 3 lake assessments alone did not ef fectively decrease the SV s , the combined effect of the lake and plume

- rise assessments was a decrease in the SV s from 3 to 2 for the fisher scenario, and from 0.002 to 0.001 for the farmer scenario. The mercury emissions still exceeded the threshold for th e fisher scenario. These assessment s did not impact SV s at any other facilities in the phosphate fertilizers production or phosphoric acid manufacturing source categories

. We discuss in Section s 4.5 and 4.6 an additional assessment that we conducted on the emissions using time

- series meteorology a nd release

- height data.

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Table 4

- 3

. Tier 3 SV s Before and After Additional Lake and Plume

- ri se Assessment s

Facil. NEI ID Facil. Name Facil. City Facil. State Tier 2 and Tier 3 Mercury SV a

Before Tier 3 Assessments After Tier 3 Lake Assessments After Tier 3 Lake AND Plume

- r ise Assessments

Fisher Farmer Fisher Farmer Fisher Farmer NEIMS059305 (phosphate fertilizers production

source category) Mississippi Phosphates Corp. Pascagoula MS 3 0.002 3 0.002 2 0.001

a SV s rounded to one significant figure. Cell shadings indicate SV s of 2 or greater (i.e., the emissions exceeded the threshold ).


### 4.5 Assessment of Time

- series Meteorology and Effective Release Heights

As discussed in Section 3

, EPA bases the T ier 2 SV s on the average meteorological conditions prevailing at the facility being assessed. The use of time

- series meteorology data, which capt ures 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 par ameters and by improving the method of accounting for mass adv ected into the modeling domain.

EPA also bases Tier 2 SV s on a constant emission release height that is always within the mixing layer. Including hourly effective release heights (discussed in Section s 4.3 and 4.4 ) along with hourly meteorology data further increas es the accuracy of the estimates of potential risk.

For a facility undergoing a time

- series assessment, EPA develops a time series of hourly effective release heights for sources emitting the assessed PB

- HAP, and EPA identifies the Tier 2 spatial scenario that best matches each lake from which the hypothetical fisher catches and consumes fish. Using this data along with the facility’s matching hourly meteorology data, the facility’s emission rates, and the subsistence ingestion rates, EPA models chemical fa te and transport in TRIM.FaTE and calculates the corresponding screening

- level risk s using MIRC. This modeling results in a screening

- level risk value for each lake, corresponding to ingestion of fish exposed to the evaluated PB

- HAP. If the farmer scenario is specifically being assessed, the modeling will also result in a screening

- level risk value for each farm. EPA multiplies the screening

- level risk associated with each lake by the percent of daily ingested fish caught from the lake (see Sections 3.2 and 4.2 ) ; summing those products across all modeled lakes results in the sc reening

- level fisher risk (i.e., fisher SV ) associated with the evaluated PB

- HAP emitted by the facility. The time

- series

- adjusted Tier 3 SV s are compared to the Tier 2 SV s calculated previously.



### 4.6 Tier 3 Time

- series Assessment Results for these Source Categories

In order to account for the potential effects on exposure and risk modeling from using time

series meteorology and effective release heights in TRIM.FaTE, we ran TRIM.FaTE three times, corresponding to each of the three assesse d lakes for the mercury assessment for NEIMS059305 ( phosphate fertili zers production source category )

. We used the “lake at 10 km” spatial scenario, shown in Figure 4

- 4 , to model concentrations in the unnamed lake and Crane Lakes (see Table 4

- 1 ). In reality, these two lakes were respectively 12 and 19 km from the facility. We used the “lake at 20 km” spatial scenario, shown in Figure 4

- 5 , to model

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concentrations in Black Creek Cooling Pond (see Table 4

- 1 ), which in reality was 23 km from the facility.

Figure 4

- 4

. The Modeling Spatial Scenario Used for the Unnamed Lake and for “Crane Lakes”

Notes: Labels = surface parcel names for modeling

. Shadings = ground cover type for modeling. Arrows within scenario = direction of runoff and erosion.

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Figure 4

- 5 . The Modeling Spatial Scenario Used for “Black Creek Cool ing Pond”

Notes: Labels = surface parcel names for modeling. Shadings = ground cover type for modeling. Arrows within scenario = direction of runoff and erosion.

Table 4

- 4 contains the Tier 3 fisher SV s for mercury emissions from NEIMS059305 (phosphate fertilizers production source category), both before and after the time

- series assessment s . Both SV s reflect the Tier 3 lake assessments (see Section 4.2 ) but not the plume

- rise assessments (see Section 4.4 ) ; this is bec ause the time

- series assessment includes chemical loss due to plume rise into the upper

- air sink . Beside th e previous results, the table contains the fisher SV after the time

- series assessment s. We did not assess farmer SV s with these time

- series runs. The time

- series assessments decrease d the fisher SV to below levels of concern (i.e., the emissions screened out)

.
Table 4

- 4 . Tier 3 Fisher SV s Before and After Time

- series Assessments

Facil. NEI ID Facil. Name Facil. City Facil. State Tier 3 Mercury Fisher SV a Before Tier 3 Time

series Assessment s After Tier 3 Time

series Assessment s NEIMS059305 (phosphate fertilizers production source category) Mississippi Phosphates Corp. Pascagoula MS 3 1

a SV s rounded to one significant figure. Cell shadings indicate SV s of 2 or greater (i.e., the emissions exceeded the threshold ). We only assessed the fisher scenario during the time

- series assessments.



## 5. Comparison to Tier 1 Screening Results for this Source Category

In Table 5

- 1

, we present a high

- level comparison between the Tier 1 SVs and Tier 3 SV s

. The Tier 3 SVs reflect the Tier 3 screening of mercury emissions from one facility ; otherwise, the

Draft 16 July 2014Human Health Multipathway Residual Risk Screening Assessment for the Phosphate Fertilizers Production and Phosphoric Acid Manufacturing Source Categories

Tier 3 SVs are unchanged from the Tier 2 screening. The remainder of this section provides an overview of the results for each of the four PB

- HAP s

.

• Mercury:

– Emissions at one facility in each source category exceeded the Tier 1 threshold but screened out in Tier 3

.

– The largest Tier 3 fisher and farmer SV s were respectively approximately 20 times and 20,000 times smal ler than the largest Tier 1 SV (which combined fish

- and FFC

related exposure).

– When the largest Tier 3 fisher S V wa s combined with the largest Tier 3 farmer SV (to directly compare to the largest Tier 1 SV ), the combined Tier 3 SV wa s approximately 20 times smaller (not shown in the table) . T his is because nearly all mercury exposure occurs through fish ingestion.

• Cadmium:

– Emissions at one phosphoric acid manufacturing facility exceeded the Tier 1 threshold but screened out in Tier 3

.

– The largest Tier 3 fisher and farmer SV s were respectively approximately nine times and 100 times smaller than the largest Tier 1 S V (which combined fish

- and FFC

related exposure).

– When the largest Tier 3 fisher SV was combined with the largest Tier 3 farmer SV (to directly compare to the largest Tier 1 SV ), the combined Tier 3 SV was approximately three times smaller than the Tier 1 SV (not shown in the table) . T his is because nearly all cadmium exposure occurs through fish ingestion.

• PAHs :

– All emissions screened out.

– The largest Tier 3 fisher and farmer SV s were respectively approximately 2 0 times and three times smaller than the largest Tier 1 SV (which combined fish

- and FFC

related exposure)

.

– When the largest Tier 3 fisher SV was combined with the largest Tier 3 farmer SV (to directly compare to the largest Tier 1 SV ), the combined Tier 3 SV was approximately three times smaller than the largest Tier 1 SV (not shown in the table)

.

• D ioxins :

– All emissions screened out.

– The largest Tier 3 fisher and farmer SV s were respectively approximately seven times and 20 times smaller than the largest Tier 1 SV (which combined fish

- and FFC

related exposure).

– When the largest Tier 3 fisher SV was combined with the largest Tier 3 farmer SV (to directly compare to the largest Tier 1 SV ), the combined Tier 3 SV was approximately five times smaller than the large st Tier 1 SV (not shown in the table).

• PAHs + Dioxins:

– The facility

- level sum of PAH and dioxin SVs screen ed out in all screening assessments (not shown in table).

Draft 17 July 2014Human Health Multipathway Residual Risk Screening Assessment for the Phosphate Fertilizers Production and Phosphoric Acid Manufacturing Source Categories

Table 5

- 1 . Comparison of Tier 1 and Tier 3 S creening Results for th ese Source Categor ies

Source Cat. PB

- HAP Group Num. Facil. Exceeding Threshold Largest SV Tier 1, Combined Fisher and Farmer Tier 3 , Fisher Tier 3 , Farmer Tier 1, Combined Fisher and Farmer Tier 3 , Fisher Tier 3 , Farmer Phosphate Fertilizers Production Mercury a (non

- cancer) 1 0 0 20 1 0.001

Cadmium (non

- cancer) 0 0 0 0.9 0.1 0.008

PAHs as BaP (cancer) N/E N/E N/E N/E N/E N/E

Dioxins as 2,3,7,8

- TCDD (cancer) N/E N/E N/E N/E N/E N/E

Phosphoric Acid Manufacturing Mercury a (non

- cancer) 1 N/E N/E 7 1 0.0007

Cadmium (non

- cancer) 1 N/E N/E 2 0.3 0.02

PAHs as BaP (cancer) 0 0 0 0.2 0.01 0.06

Dioxins as 2,3,7,8

- TCDD (cancer) 0 0 0 0.2 0.03 0.01

Notes: N/E = not emitted. SV s rounded to one significant figure. Cell shadings indicate records corresponding to SV s of 2 or greater (i.e., the emissions exceeded the Tier 1 threshold ). Emissions and screening thresholds not shown for space pu rposes.

a The emission screening threshold for mercury applies to emissions of divalent mercury, although EPA derived it based on hazards associated with ingestion of fish contaminated with methyl mercury (i.e., emissions of divalent mercury, which transf or ms to methyl mercury within the environment, accumulates in fish tissue, and is consumed by the fisher ). The contribution from hazards associated ingestion of divalent mercury from contaminated FFC or fish media is relatively minimal.



## 6. References

ESRI (Environmental Systems Research Institute). 201 4 . World Imagery. Accessed June 12

, 2014 . Available at: http://www.arcgis.com/home/item.html?id=10df2279f9684e4a9f6a7f08 febac2a9

. Seinfeld, J.H.

, and S.N. Pandis

. 1998

. Atmospheric Chemistry and Physics: From Air Pollution to Climate Change, Wiley

- Interscience, New York, pp. 931

– 933.

Draft 18 July 2014